General practice activity in Australia 2009–10
BEACH
Bettering the Evaluation And Care of Health 
Helena Britt, Graeme C Miller, Jan Charles, Joan Henderson, Clare Bayram, Ying Pan, Lisa Valenti, Christopher Harrison, Julie O’Halloran, Salma Fahridin
December 2010
The Australian Institute of Health and Welfare is Australia’s national
health and welfare statistics and information agency. The Institute’s mission is
 better information and statistics for better health and wellbeing.
© Australian Institute of Health and Welfare 2010

This work is copyright. Apart from any use as permitted under the Copyright Act 1968, no part may be reproduced without prior written permission from the Australian Institute of Health and Welfare. Requests and enquiries concerning reproduction and rights should be directed to the Head of the Communications, Media and Marketing Unit, Australian Institute of Health and Welfare, GPO Box 570, Canberra ACT 2601.
This publication is part of the Australian Institute of Health and Welfare’s General practice series, from the Australian General Practice Statistics and Classification Centre, a collaborating unit of the Family Medicine Research Centre of the University of Sydney and the Australian Institute of Health and Welfare. A complete list of the Institute’s publications is available from the Institute’s website <www.aihw.gov.au>. 

ISSN 1442-3022
ISBN 978-1-74249-079-3
Suggested citation 
Britt H, Miller GC, Charles J, Henderson J, Bayram C, Pan Y, Valenti L, Harrison C, O’Halloran J & Fahridin S 2010. General practice activity in Australia 2009–10. General practice series no. 27. 
Cat. no. GEP 27. Canberra: AIHW.
Keywords
Australia, delivery of health care/statistics and numerical data, family practice/statistics and numerical data, health care surveys/methods. 
Companion publication
Britt H, Miller GC, Charles J, Henderson J, Bayram C, Valenti L, Pan Y, Harrison C, O’Halloran J, Fahridin S & Chambers T 2010. General practice activity in Australia 2000–01 to 2009–10: 10 year data tables. General practice series no. 28. Cat. no. GEP 28. Canberra: AIHW.
Australian Institute of Health and Welfare 
Board Chair
Hon. Peter Collins, AM, QC
Director 
Penny Allbon

Any enquiries about or comments on this publication should be directed to:
The Australian General Practice Statistics and Classification Centre
Sydney School of Public Health, University of Sydney
Level 7, 16–18 Wentworth Street, Parramatta NSW 2150
Phone: +61 2 9845 8151; Fax: +61 2 9845 8155; Email: gpstats@fmrc.org.au

Published by the Australian Institute of Health and Welfare
Printed by Union Offset Printers 
Please note that there is the potential for minor revisions of data in this report.
Please check the online version at <www.aihw.gov.au> for any amendments.

Foreword
This report, General practice activity in Australia 2009–10, and its companion document General practice activity in Australia 2000–01 to 2009–10: 10 year data tables continue their strong and unique contribution to our understanding of the clinical, diagnostic and treatment mix seen within the Australian general practice sector. Details of approximately 100,000 consultations from about 1,000 different general practitioners (GPs) across all states and territories each year, provide a critical ‘snap shot’ of general practice’s contribution to Australian health care delivery. This is not only of pivotal importance for Australian consumers, clinicians, and planners, but allows credible comparison for international stakeholders within a sector growing in policy interest. In Australia, accessible BEACH data has become an integral part of the healthcare reporting ‘woodwork’, yet in many other countries there is little information about what primary care physicians do for the community and what role they play in overall health care provision within their country. 
These latest reports carry much of interest for all such groups in understanding the contribution of the Australian general practice sector currently, and within the future reform agenda. In 2009–10, 83% of the Australian population consulted a GP at least once and together used approximately 116.8 million general practice services paid by Medicare. The growing focus on teamwork within the practice is demonstrated by the increasing number of encounters involving a practice nurse—the proportion more than doubled from 4.2% in 2005–06 to 9.0% in 2008–09. 
Opportunity for at-risk lifestyle intervention within general practice is clearly considerable. One of the BEACH SAND studies shows that 60% of the 31,932 sampled adults and 28% of 3,183 sampled children were overweight or obese, an estimated 15% of the adults were daily smokers, and 27% reported drinking at-risk levels of alcohol. General practices have excellent access to ‘at-risk’ Australians—building their potential to successfully support effective national interventions should be a priority.
General practice continues to grow in use of computers for clinical purposes with almost two-thirds (64%) BEACH GPs using electronic records exclusively, 85% producing prescriptions electronically, and 72% receiving pathology results on line. Over the past 10 years, the trend to larger practices also continued.
Patients are bringing more issues for attention to the encounter, and GPs are managing more problems at patient encounters, including more newly diagnosed problems, and more chronic problems. This is hardly surprising considering our ageing population, our rising population-based visit rate to GPs, and the recent emphasis on primary prevention and early diagnosis of disease. 
These results confirm the impression that GP’s consultations are becoming increasingly complex, as the population ages and prevalence of co-morbidity increases. Government policy may have contributed to some of this change. The introduction of programs such as Beyond Blue and of Medicare items for the management of diabetes, have allowed general practice much more scope in the management of depression and diabetes over the decade. General practice continues to be the first point of contact for chronic disease management for an increasing number of Australians.
Prescribing rates reported here indicate that on average 10 fewer scripts were written for every 100 problems managed in 2009–10 than 10 years earlier. At the same time, prescribing rates of several drug groups increased significantly, including drugs used to treat high blood pressure and high cholesterol, and blood thinning agents like aspirin, warfarin and clopidogrel, used increasingly in the prevention of coronary and cerebrovascular disease.
Over the decade, orders for pathology tests increased by 43%, and imaging requests by 21%. Some of this is explained by the growing involvement of general practice in best-practice chronic disease management, and likely to be related to changes in policy regarding diabetes care payments, older people’s annual heath checks, the 45–49 year old health check, and chronic disease risk screening.
 The data in this report are invaluable in describing a sector with profound population reach, significant opportunities for broad-based preventive medicine, a growing focus on chronic disease management and multiple co-morbidity, and an increased reliance on prescribing and pathology review within such treatment paradigms. From the profession’s perspective, BEACH offers us the opportunity to regularly reassess our service delivery role using independent, highly reliable data. 
Our health care system is paused at a time of significant reform and re-moulding. The information in this report will be of far-reaching significance for policy makers (allowing evidence-based policy development) and the community (building an understanding of the contribution general practice makes to their health care). This is critical if we are to frame an effective, capable and expanding primary care sector in the years ahead

Claire Jackson MBBS, MD, MPH, CertHEcon, GradCert Management, FRACGP, GAICD
President
Royal Australian College of General Practitioners
Professor in General Practice and Primary Health Care
The School of Medicine 
University of Queensland.

Contents
Acknowledgments	ix
Abbreviations	x
Symbols	xi
Summary	xii
1	Introduction	16
1.1	Background	16
1.2	The BEACH program	17
Aims		17
Current status of BEACH	17
The strengths of the BEACH program	18
1.3	Issues when using BEACH data with other national data	19
The Pharmaceutical Benefits Scheme	19
Medicare Benefits Schedule	20
Pathology data from the MBS	21
Imaging data from the MBS	21
The National Health Survey	22
1.4	Access to BEACH data	22
Public domain	23
Participating organisations	23
External purchasers of reports	23
2	Methods	24
2.1	Sampling methods	24
2.2	Recruitment methods	24
2.3	Data elements	25
2.4	The BEACH relational database	26
2.5	Supplementary Analysis of Nominated Data	27
2.6	Statistical methods	27
2.7	Classification of data	28
Presentation of data classified in ICPC-2	29
Classification of pharmaceuticals	30
2.8	Quality assurance	32
2.9	Validity and reliability	32
2.10	Extrapolated national estimates	33
Limitations of extrapolations	35

3	The sample	36
3.1	Response rate	36
3.2	Representativeness of the GP sample	37
3.3	Weighting the data	40
3.4	Representativeness of the final encounter sample	41
3.5	The weighted data set	42
4	The participating GPs	43
4.1	Characteristics of the GP participants	43
4.2	Computer use at GP practices	47
4.3	Changes in characteristics of the GPs over the decade 2000–01 to 2009–10	48
5	The encounters	49
5.1	Content of the encounters	49
5.2	Encounter type	50
5.3	Consultation length	55
5.4	Changes in the encounters over the decade 2000–01 to 2009–10	55
6	The patients	56
6.1	Age–sex distribution of patients at encounter	56
6.2	Other patient characteristics	56
6.3	Patient reasons for encounter	59
Number of reasons for encounter	59
Reasons for encounter by ICPC-2 chapter	59
Distribution of RFEs by ICPC-2 component	62
Most frequent patient reasons for encounter	62
6.4	Changes in patients and reasons for encounter over the past decade (2000–01 to 2009–10)	64
7	Problems managed	65
7.1	Number of problems managed at encounter	65
7.2	Problems managed by ICPC-2 chapter	66
7.3	Problems managed by ICPC-2 component	69
7.4	Most frequently managed problems	70
7.5	Most common new problems	72
7.6	Most frequently managed chronic problems	73
7.7	Work-related problems managed	75
7.8	Management of back problems in 2009–10	76
7.9	Changes in problems managed over the decade 2000–01 to 2009–10	78
8	Overview of management	80
8.1	Changes in management over the decade 2000–01 to 2009–10	83
9	Medications	84
9.1	Source of medications	84
9.2	Prescribed medications	85
Number of repeats	85
Age–sex-specific rates of prescribed medications	85
Types of medications prescribed	86
Most frequently prescribed medications	91
9.3	Medications supplied by GPs	93
9.4	Medications advised for over-the-counter purchase	94
9.5	Antibiotics prescribed or supplied in 2009–10	96
9.6	Systemic antibiotic prescribing, 1998–99 to 2009–10	97
Summary	100
9.7	Changes in medications over the decade 2000–01 to 2009–10	102
10	Other treatments	103
10.1	Number of other treatments	103
10.2	Clinical treatments	104
Most frequent clinical treatments	104
Problems managed with clinical treatments	105
10.3	Procedural treatments	106
Most frequent procedures	107
Problems managed with a procedural treatment	108
10.4	Changes in other treatments over the decade 2000–01 to 2009–10	109
Clinical treatments	109
Procedural treatments	109
11	Referrals and admissions	110
11.1	Number of referrals and admissions	110
11.2	Most frequent referrals	111
11.3	Problems most often referred	112
11.4	Changes in referrals over the decade 2000–01 to 2009–10	114
12	Investigations	115
12.1	Number of investigations	115
12.2	Pathology ordering	116
Nature of pathology orders at encounter	116
Problems for which pathology tests were ordered	117
12.3	Imaging ordering	118
Nature of imaging orders at encounter	118
Problems for which imaging tests were ordered	120
12.4	Other investigations	121
12.5	Computerised tomography scans of the lumbar and lumbosacral spine (CT-LS)	122
12.6	Changes in investigations over the decade 2000–01 to 2009–10	123
13	Practice nurse activity	125
13.1	Practice nurse Medicare claims and practice nurse activity	126
Distribution of practice nurse item numbers claimed at encounters	126
Treatments provided by practice nurses	128
13.2	Problems managed with practice nurse involvement	130
13.3	Discussion	131
13.4	Changes in practice nurse activity, 2005–06 to 2009–10	132
14	Patient risk factors	133
14.1	Body mass index	133
Method	134
Results	134
14.2	Smoking (patients aged 18 years and over)	136
Method	136
Results	137
14.3	Alcohol consumption (patients aged 18 years and over)	138
Method	139
Results	139
14.4	Risk factor profile of adult patients	140
14.5	Changes in patient risk factors over the decade 2000–01 to 2009–10	143
15	SAND abstracts and research tools	144
References	169
Glossary	175
Appendices	178
Appendix 1: Example of a 2009–10 recording form	178
Appendix 2: GP characteristics questionnaire, 2009–10	178
Appendix 3: Dissemination of results from the BEACH program	178
Appendix 4: Code groups from ICPC-2 and ICPC-2 PLUS	178
Appendix 5: Chronic code groups from ICPC-2 and ICPC-2 PLUS	178
List of tables	179
List of figures	181

Acknowledgments
The Australian General Practice Statistics and Classification Centre wishes to thank the 988 general practitioners who participated in BEACH between April 2009 and March 2010. This report would not have been possible without their valued cooperation and effort in providing the data.
We thank the following organisations for their financial support and their contribution to the ongoing development of the BEACH program in 2009–10. 
•	Australian Government Department of Health and Ageing
•	AstraZeneca Pty Ltd (Australia) 
•	Janssen-Cilag Pty Ltd 
•	Merck, Sharp and Dohme (Australia) Pty Ltd 
•	Pfizer Australia 
•	Abbott Australasia Pty Ltd
•	Sanofi-Aventis Australia Pty Ltd
•	Wyeth Australia Pty Ltd
•	Novartis Pharmaceuticals Australia Pty Ltd
•	GlaxoSmithKline Australia Pty Ltd.
Some financial support for the program was also provided by:
•	Australian Government Department of Veterans’ Affairs.
We acknowledge the support of the Royal Australian College of General Practitioners, the Australian Medical Association, the Australian General Practice Network, the Australian College of Rural and Remote Medicine, and the Consumers Health Forum, as well as the contribution of their representatives to the BEACH Advisory Board.
The research team is grateful to Clare Bayram for her coordination and editing of this report, for the IT support of Timothy Chambers and the administrative support of Denise Barratt and Gervaise Woods, and for the valuable contribution of the general practitioner recruitment staff (Errol Henderson, Jan Fitzgerald and David Went) and data entry staff. We recognise the contribution of past members of the BEACH team. We appreciate the cooperation of the Australian Government Department of Health and Ageing in regularly supplying general practitioner random samples and national Medicare statistics. 
Ethics approval for this study was obtained from the Human Ethics Committee of the University of Sydney and the Ethics Committee of the Australian Institute of Health and Welfare.

Abbreviations
ABS	Australian Bureau of Statistics	
ACE	angiotensin-converting enzymes 	
ACRRM	Australian College of Rural and Remote Medicine	
AGPSCC	Australian General Practice Statistics and Classification Centre	
AHP	allied health professional	
AIHW	Australian Institute of Health and Welfare	
ATC	Anatomical Therapeutic Chemical (classification)	
BEACH	Bettering the Evaluation And Care of Health	
BMI	body mass index	
BP	blood pressure	
CAPS	Coding Atlas for Pharmaceutical Substances	
CI	confidence interval (in this report 95% CI is used)	
COPD	chronic obstructive pulmonary disease	
CT	computerised tomography	
CT-LS	computerised tomography scans of the lumbar and lumbosacral spine	
DoHA	Australian Government Department of Health and Ageing	
DVA	Australian Government Department of Veterans’ Affairs	
FBC	full blood count	
FMRC	Family Medicine Research Centre	
ESR	erythrocyte sedimentation rate	
EUC	electrolytes, urea and creatinine	
FRACGP	Fellow of the Royal Australian College of General Practitioners	
FTE	full-time equivalent	
GP	general practitioner	
HbA1c	haemoglobin, type A1c	
HDL	high-density lipoprotein	
HIV	human immunodeficiency virus	
ICPC	International Classification of Primary Care	
ICPC-2	International Classification of Primary Care—Version 2	
ICPC-2 PLUS	a terminology classified according to ICPC-2	
INR	international normalised ratio	
LABA	long-acting beta-agonist	
LCL	lower confidence limit	
LDL	low-density lipoprotein	
MBS	Medicare Benefits Schedule	
M,C&S	microscopy, culture and sensitivity	
MRI	magnetic resonance imaging	
NDSHS	National Drug Strategy Household Survey	
NESB	non-English-speaking background	
NHMRC	National Health and Medical Research Council	
NHS	National Health Survey	
OTC	over-the-counter (medications advised for over-the-counter purchase)	
PBS	Pharmaceutical Benefits Scheme	
RACGP	Royal Australian College of General Practitioners	
RFE	reason for encounter	
RRMA	Rural, Remote and Metropolitan Area classification	
SAND	Supplementary Analysis of Nominated Data	
SAS	Statistical Analysis System	
UCL	upper confidence limit	
URTI	upper respiratory tract infection 	
WHO	World Health Organization	
Wonca	World Organization of Family Doctors	
Symbols
—	not applicable	
<	less than	
>	more than	
NEC	not elsewhere classified	
n	number	
NOS	not otherwise specified	

Summary
This report describes clinical activity at, or associated with, general practitioner (GP) encounters, from April 2009 to March 2010 inclusive. It summarises results from the 12th year of the Bettering the Evaluation And Care of Health (BEACH) program, using a sample of 98,800 patient encounters with 988 GPs. After weighting for GP Medicare Benefits Schedule (MBS) claims activity and variations in GP characteristics of the final sample compared with the sample frame, 101,349 encounters were analysed in this report.
BEACH is a continuous cross-sectional national study of general practice activity that began in April 1998. Every year approximately 1,000 randomly selected GPs participate. Each GP records details of 100 consecutive patient encounters on structured paper recording forms, and provides information about themselves and their practice. The age–sex distribution of patients at the weighted encounters has excellent precision with that of patients at all Medicare GP‑claimed encounters.
Smaller studies are done in subsamples of encounters. Results for patient body mass index, smoking status and alcohol consumption are reported and abstracts are provided with results of other substudies finalised in 2009–10. 
A web-based summary report of data from the past 10 years of BEACH highlighting major changes over that time, General practice activity in Australia 2000–01 to 2009–10: 10 year data tables, is available at <www.aihw.gov.au/publications/index.cfm/subject/19>. 
The general practitioners
Of the 988 GP participants in 2009–10:
•	44% were female; 35% were aged 55 years and over and 7% were aged less than 35 years
•	71% had graduated in Australia
•	11% averaged less than 21 hours per week in direct clinical patient care, 56% 21–40 hours and 33% more than 40 hours per week
•	54% were Fellows of the Royal Australian College of General Practitioners
•	24% conducted some consultations in a language other than English 
•	98% used a computer for some clinical purpose(s); 85% produced prescriptions electronically, and 72% were receiving pathology results on line; almost two-thirds (64%) reported having paperless medical records
•	91% worked in an accredited practice and 65% in a teaching practice 
•	41% worked in a practice with 5–9 individual GPs—the most common practice size— and 29% in a practice with 5–9 full‑time equivalent GPs 
•	79% worked in a practice that employed one or more practice nurses
•	49% had a pathology collection centre, 44% a psychologist, and 29% a physiotherapist, located within 50 metres of the practice
•	45% worked in a practice providing its own/cooperative after-hours patient care.
The encounters
•	Direct encounters (patient was seen by the GP) accounted for 98% of all encounters, and the vast majority (97%) of direct encounters were claimable through either Medicare or the Department of Veterans’ Affairs (DVA). 
•	Surgery consultations accounted for 93% of all MBS/DVA‑claimable GP encounters, standard consultations being most common (82% of GP consultations). Home, residential aged care and hospital visits were few. 
•	About 1% of encounters were claimable as GP mental health care items, and 1% as chronic disease management items. Health assessment and case conference were rare. 
•	The measured mean length of MBS/DVA-claimable encounters was 15.3 minutes, and the median length was 14.0 minutes. 
Who goes to see the GP? 
•	Patients aged less than 25 years accounted for 21% of encounters, those aged 
25–44 years for 23%, 45–64 years for 28%, and 65 years and over for 28%. 
•	Females accounted for 57% of encounters. 
•	At 8% of encounters the patient was new to the practice.
•	Almost half the encounters were with patients who held either a Commonwealth concession card (46%) or a Repatriation health card (3%).
•	At just over 1% of encounters the patient identified as an Aboriginal and/or Torres Strait Islander person, and at 9% the patient was from a non-English-speaking background.
•	For every 100 encounters, patients gave 155 reasons for encounter (RFEs): 65 symptoms/
complaints; 31 diagnoses/diseases; and 59 requests for services (such as, prescription).
•	The most common RFEs were requests for a check-up, prescriptions, and test results. 
What problems do GPs manage at patient encounters? 
On average, GPs managed 153 problems per 100 encounters, the number increasing with patient age, and being higher for females than males. 
•	Problems managed most often were respiratory (22 per 100 encounters), of a general or unspecified nature (19), musculoskeletal (17) and cardiovascular (17).
•	The most common individual problems managed were hypertension (9 per 100 encounters), immunisation/vaccination (7), check‑ups (7), upper respiratory tract infection (6), depression (4), arthritis (4) and diabetes (4). 
•	New problems (39% of all problems) were managed at a rate of 59 per 100 encounters, the most frequently managed being upper respiratory tract infection (URTI), immunisation/vaccination and acute bronchitis. 
•	Chronic conditions made up 35% of all problems managed, the most common being non‑gestational hypertension (17% of chronic conditions), depressive disorder (8%), chronic arthritis (7%), non-gestational diabetes (7%), and lipid disorders (6%).
•	Work-related problems (1.6% of all problems) were managed at a rate of 2.5 per 100 encounters, with more than half of these related to the musculoskeletal system.
An example of the relationship between a problem managed and other data fields is given for GP management of back problems in 2009–10.
Management actions
For an ‘average’ 100 encounters, GPs recorded 107 medications (83 prescribed), 35 clinical treatments, 18 procedures, 8 referrals to specialists, 4 referrals to allied health services, and ordered 45 pathology tests/batteries and 10 imaging tests.
Medications 
•	For every 100 problems managed, on average, 54 medications were prescribed, 9 were supplied by the GP and 6 were advised for over-the-counter purchase.
•	No prescription was given for 57% of all problems managed, one was given for 35%, two for 6%, and more than two for 2%. 
•	Medications most often prescribed were: the antibiotics amoxycillin (4% of all prescriptions), cephalexin (3%) and amoxycillin with potassium clavulanate (2%); the analgesics paracetamol (3%) and paracetamol/codeine (2%); and the lipid modifying agent atorvastatin (2%).
•	Of the 64,718 prescriptions (77% of all prescriptions) where number of repeats was recorded, the GP specified no repeats for 34%, and five repeats for 36%.
•	Medications were supplied by the GP at a rate of 14 per 100 encounters, with vaccines accounting for almost two-thirds of these medications.
•	Medications were advised for over-the-counter purchase at a rate of 10 per 100 encounters; the most common were paracetamol and ibuprofen. 
The pattern of GP prescribing of systemic antibiotics for the common cold (also known as URTI), other respiratory infections and other problems is presented as an example of pharmaco‑epidemiological analysis of BEACH data. 
Other treatments
There were 53 other treatments given by the GP per 100 encounters, or 34 per 100 problems managed, two-thirds being clinical treatments (35 per 100 encounters), and one-third being procedures (18 per 100).
Clinical treatments: The most frequently recorded clinical treatments were: general advice and education (4 per 100 problems); counselling about the problem (3 per 100); and advice and education about treatment (3 per 100).
Procedural treatments: The most frequently recorded procedures were excisions (2 per 100 problems), local injections (2 per 100), dressings (2 per 100) and incisions (1 per 100).
Referrals and admissions 
There were 13 referrals made per 100 encounters, or 9 per 100 problems managed. Two‑thirds (64%) of the referrals were to specialists, 29% to allied health services and less than 5% to hospitals or emergency departments.
•	Referrals to specialists (6 per 100 problems managed) were most often to surgeons (10%), orthopaedic surgeons (9%) ophthalmologists (8%) and dermatologists (8%).
•	Referrals to allied health services (3 per 100 problems managed) were most often to physiotherapists (29%), psychologists (20%), podiatrists (9%) and dentists (8%).
Tests and investigations
Pathology tests ordered: Tests were ordered at a rate of 45 per 100 encounters or 29 per 100 problems. Chemistry tests (17 tests/batteries per 100 problems) accounted for 58% of all orders. The most common individual tests were: full blood count, lipids, electrolytes, urea and creatinine; liver function; and glucose/glucose tolerance.
Imaging ordered: Imaging was ordered at a rate of 10 per 100 encounters and 6 per 100 problems managed. Diagnostic radiology accounted for 47% of these, and ultrasound 38%.
An example of the relationship of tests and investigations to other data elements is provided for computerised tomography scans of the lumbar and lumbosacral spine. 
Practice nurse activity 
•	Practice nurses were involved in 9% of encounters and 6% of all problems managed. 
•	The majority of practice nurse activities were procedural (93%), and these procedures represented 40% of all procedures recorded. Clinical treatments accounted for 7% of practice nurse activity, but only 2% of all recorded clinical treatments. 
•	The most common procedures done by practice nurses were injections (48% of their recorded procedures), dressings (15%), incisions (7%) and check-ups (7%). 
•	For only 46% of encounters involving practice nurses was a practice nurse item number recorded as claimable, the most common item being for immunisation (75%). 
Patient risk factors
Overweight and obesity in adults (18 years and over): In 31,932 sampled adults, 26% were obese and 34% overweight. After adjusting for age–sex attendance rates, prevalence in the attending adult population was 25% obese, 34% overweight, 2% underweight, and 38% normal.
Overweight and obesity in children (2–17 years): Of 3,183 sampled children, 28% were overweight (18%) or obese (10%). There was no difference in prevalence among male (28%) and female children (27%).
Smoking status (adults 18 years and over): Of 32,744 adult patients, 15% (18% of men and 13% of women) were daily smokers. After adjusting for age–sex attendance rates, an estimated 18% of the population attending general practice were daily smokers.
Alcohol consumption (adults 18 years and over): Of 31,771 adult patients 27% (32% of men and 23% of women) reported drinking at-risk levels of alcohol. After adjusting for attendance rates, prevalence of at-risk drinking among the adult population attending general practice was 30%.

1	Introduction
This publication is the 12th annual report and the 27th book in the series from the BEACH (Bettering the Evaluation And Care of Health) program, a continuous national study of general practice activity in Australia. It provides the annual results for the period April 2009 to March 2010 inclusive, using details of 98,800 encounters between general practitioners (GPs) and patients (almost a 0.1% sample of all general practice encounters) from a random sample of 988 practising GPs across the country. In parallel with the release of this report, a summary of results from the most recent 10 years of the BEACH program is published on the web in a report called General practice activity in Australia 2000–00 to 2009–10: 10 year data tables, at <www.aihw.gov.au/publications/index.cfm/subject/19> (AIHW catalogue number 
GEP 28).1 
The BEACH program is conducted by the Australian General Practice Statistics and Classification Centre (AGPSCC). The AGPSCC is a collaborating unit of the Family Medicine Research Centre (FMRC) at the University of Sydney and the Australian Institute of Health and Welfare (AIHW). BEACH is currently supported financially by government instrumentalities and private industry (see ‘Acknowledgments’).
The BEACH program is the only continuous randomised study of general practice activity in the world, and the only national program that provides direct linkage of management actions (such as prescriptions, referrals, investigations) to the problem under management. It began in April 1998, and the BEACH database now includes information for almost 1.2 million encounters from 11,873 participants representing about 8,384 individual GPs, almost half the sample frame from which the GP samples are drawn.
1.1	Background
In December 2009, the population of Australia was estimated to be 22.16 million people.2 
Health expenditure during the 2007–08 financial year was $103.6 billion (accounting for 9.1% of gross domestic product. The Australian Government contribution made up 43.2%, the contributions for state/territory and local governments 25.5%, with the remainder (31.1%) being paid by the non-government sector.3
GPs are usually the first port of call in the Australian health care system. Payment for GP visits is largely on a fee-for-service system, there being no patient lists or registration. People are free to see multiple practitioners and visit multiple practices of their choice. There is a universal medical insurance scheme (managed by Medicare Australia), which covers all or most of a person’s costs for a GP visit. 
In 2007 in Australia, there were 20,134 practising primary care practitioners (vocationally recognised GPs and other medical practitioners), making up 19,999 full-time equivalents (based on a 45 hour week), or 99 per 100,000 people.3 
In 2009–10, about 83% of the Australian population claimed at least one GP service from Medicare (personal communication, Department of Health and Ageing (DoHA), June 2010). From March 2009 to April 2010, about 116.8 million general practice services (excluding practice nurse items) were paid through Medicare, at an average of about 5.3 GP visits per head of population4, or 6.4 visits per person who visited at least once. This equates to about 320,000 services per day, or more than 2.2 million per week. 
From April 2009 to March 2010, the 116.8 million GP service items claimed accounted for $4.85 billion, and more than 7 million practice nurse services accounted for a further $83 million. Together these services accounted for 32% of total Australian Government expenditure on medical and allied health services.5 We estimate that in 2009–10 an additional 5.5 million (see Chapter 4) GP services were paid for by other funders (such as the Department of Veterans’ Affairs, workers compensation, state/territory governments) or for which no charge was made by the GP. 
While Medicare statistics provide information about frequencies and costs of visits claimed from Medicare for GP services, they cannot tell us about the content of these visits. The BEACH program fills this gap.
1.2	The BEACH program
In summary, the BEACH program is a continuous national study of general practice activity in Australia. It uses details of about 100,000 encounters between GPs and patients from a random sample of approximately 1,000 recognised practising GPs from across the country. The BEACH methods are described in Chapter 2 of this report.
A random sample of GPs who claimed at least 375 general practice Medicare items of service in the previous 3 months is regularly drawn from Medicare Australia data by DoHA. GPs are approached by letter and followed up by telephone recruitment. Each participating GP completes details for 100 consecutive GP–patient encounters on structured paper encounter forms (Appendix 1). They also provide information about themselves and their major practice (Appendix 2). 
Aims
The three main aims of the BEACH program are to:
•	provide a reliable and valid data collection process for general practice that is responsive to the ever-changing needs of information users, and provides insight into the evolving character of GP–patient encounters in Australia
•	establish an ongoing database of GP–patient encounter information
•	assess patient risk factors and health states, and the relationship these factors have with health service activity.
Current status of BEACH
BEACH began in April 1998 and is now in its 13th year. The database for the past 10 data years includes data for 984,200 GP–patient encounters from 9,842 participating GPs. Each year the AGPSCC publishes an annual report of BEACH results through the AIHW. This current publication reports results from the previous BEACH data year (that is, April 2009 to March 2010) on a national basis to provide an overview of general practice activity.
A companion publication General practice activity in Australia 2000–01 to 2009–10: 10 year data tables1, provides summaries of changes measured in the most frequent events over the decade. 
The strengths of the BEACH program
•	BEACH is the only national study of general practice activity in the world that is continuous, relying on a random ever-changing sample of GPs, and directly linking management actions to the morbidity under management. 
•	The sheer size of the GP sample (1,000 per year) and the relatively small cluster of encounters around each GP provide more reliable estimates than a smaller number of GPs with large clusters of patients and/or encounters.6 Our access to a regular random sample of recognised GPs in active practice, through DoHA, ensures that the GP sample is drawn from a very reliable sample frame of currently active GPs.
•	There are sufficient details about the characteristics of all GPs in the sample frame to test the representativeness of the final sample, and to apply post-stratification weighting to correct for any under-representation or over-representation in the sample compared with the original sample frame. The ever-changing nature of the sample (where each GP can participate only once per triennium) ensures reliable representation of what is happening in general practice across the country. The sampling methods ensure that new entrants to the profession are available for selection because the sample frame is based on the most recent Medicare Australia data. 
•	Where data collection programs use a fixed set of GPs over a long period, they are measuring what that group is doing at any one time, or how that group has changed over time, and there may well be a ‘training effect’ inherent in longer-term participation in such programs. Such measures cannot be generalised to the whole of general practice. Further, where GPs in the groups have a particular characteristic in common (for example, all belong to a professional organisation to which not all GPs belong; all use a selected software system which is not used by all GPs), the group is biased and cannot represent all GPs.
•	Each GP records for a set number of encounters (100), but there is wide variance among them in the number of patient consultations they conduct in any one year. DoHA therefore provides an individual count of activity level (that is, number of A1 Medicare item numbers claimed in the previous period) for all randomly sampled GPs, allowing us to give a weighting to each GP’s set of encounters commensurate with his or her contribution to total general practice encounters. This ensures that the final encounters represent encounters with all GPs.
•	The structured paper encounter form leads the GP through each step in the encounter, encouraging entry of data for each element (see Appendix 1), with instructions and an example of a completed form. In contrast, systems such as electronic health records rely on the GP to complete fields of interest without guidance.
•	The activities described in BEACH include all patient encounters, not just those covered by Medicare.
•	The medication data include all prescriptions, rather than being limited to those prescribed medications covered by the Pharmaceutical Benefits Scheme (PBS) (as are PBS data). 
•	BEACH is the only source of information on medications supplied directly to the patient by the GP, and about the medications GPs advise for over-the-counter (OTC) purchase, the patients to whom they provide such advice and the problems managed in this way. 
•	The inclusion of other (non-pharmacological) treatments such as clinical counselling and procedural treatments provides a broader view of the interventions used by GPs in the care of their patients than other data sources. 
•	The link from all management actions (for example, prescribing, ordering tests) to the problem under management provides a measure of the ‘quality’ of care rather than just a count of the number of times an action has occurred (for example, how often a specific drug has been prescribed).
•	The use of an internationally standard well-structured classification system (ICPC-2)7 designed specifically for general practice, together with the use of an extended vocabulary of terms which facilitates reliable classification of the data by trained secondary coders, removes the guesswork often applied in word searches of available records (in free text format) and in classification of a concept. 
•	The use of the World Health Organization’s (WHO) Anatomical Therapeutic Chemical Classification for pharmaceuticals at the generic level ensures reporting of medications data is in terms of the international standard.
•	The analytical techniques applied to the BEACH data ensure that the clustering inherent in the sampling methods is dealt with. Results are reported with 95% confidence intervals. Users are therefore aware of how reliable any estimate might be.
•	Reliability of the methods is demonstrated by the consistency of results over time where change is not expected, and by the measurement of change when it might be expected. 
1.3	Issues when using BEACH data with other national data
Users of the BEACH data might wish to integrate information from multiple national data sources, as this can provide a more comprehensive picture of the health and health care of the Australian community. It is therefore important that readers are aware of how the BEACH data differ from those drawn from others. This section summarises differences between BEACH and other national sources of data about general practice in Australia.
The Pharmaceutical Benefits Scheme 
Prescribed medications paid for under the Pharmaceutical Benefits Scheme (PBS) are recorded by Medicare Australia. The PBS data:
•	count the prescription each time it crosses the pharmacist’s counter (so that one prescription written by the GP with five repeats in BEACH would be counted by the PBS six times if the patient filled all repeats)
•	count only those prescribed medications subsidised by the PBS and costing more than the minimum subsidy (and therefore covered by the PBS for all patients), or medications prescribed for those holding a Commonwealth concession card or for those who have reached the safety net threshold 
•	will change with each change in the PBS copayment level for non-Commonwealth concession cardholders—when the copayment level increases, those medications 
that then fall under the new level will no longer be counted in the PBS for 
non-Commonwealth concession cardholders8
•	have no record of the problem being managed (with the exception of authority prescriptions, which require an indication and account for a small proportion of PBS data), and the morbidity cannot be reliably assumed on the basis of the prescription type.9
In BEACH:
•	total medications include those prescribed (whether covered by the PBS for all or some patients), those supplied to the patient directly by the GP, and those advised for OTC purchase
•	each prescription recorded reflects the GP’s intent that the patient receives the prescribed medication, and the specified number of repeats; the prescription, irrespective of the number of repeats ordered, is counted only once 
•	the medication is directly linked to the problem being managed by the GP
•	there is no information on the number of patients who do not present their prescription to be filled (this also applies to the PBS).
These differences have a major impact on the numbers of prescriptions counted and also affect their distribution. For example, the majority of broad spectrum antibiotics such as amoxycillin fall under the PBS minimum subsidy level and would not be counted in the PBS data, except where patients received the medication under the PBS because they are Commonwealth concession cardholders or had reached the annual safety net threshold.8
Medicare Benefits Schedule
Consultations with GPs that are paid for in part or in full under the Medicare Benefits Schedule (MBS) are recorded by Medicare Australia.
•	Publicly available MBS claims data do not include data about patients and encounters funded through the Department of Veterans’ Affairs (DVA). 
•	The MBS data include GP services that have been billed to Medicare. BEACH includes all consultations, irrespective of whether a charge is made or who pays for them. 
•	The MBS data reflect the item number charged to Medicare for a service and some patient demographics, but hold no information about the content of the consultation.
•	In 2009–10, BEACH participants were limited to recording three Medicare item numbers for each encounter. In contrast, MBS data include all Medicare item numbers claimed. In the BEACH data set this may result in a lower number of ‘other’ Medicare items than would be counted in the Medicare data. 
•	In activities of relatively low frequency with a skewed distribution across individual GPs, the relative frequency of the event in the BEACH data may not reflect that reported in the MBS data. For example, a study of early uptake of some enhanced primary care items by GPs demonstrated in 2002 that almost half the enhanced primary care items claimed through the MBS came from about 6% of active GPs.10 Where activity is so skewed across the practising population, a national random sample will provide an underestimate of activity because the sample reflects the population rather than the minority.
•	One of the advantages of BEACH over the MBS is also the relative consistency over time of the data collection form. BEACH is relatively resilient to changes in MBS payment policies, such as the inclusion or removal of items from the MBS. 
Pathology data from the MBS
Pathology tests undertaken by pathologists that are charged to Medicare are recorded by Medicare Australia. However, these Medicare data are not comparable with BEACH data.
•	MBS pathology data reflect pathology orders made by specialists and GPs. About 70% of the volume of MBS pathology data are generated by GP orders.11
•	Each pathology company can respond differently to a specific test order label recorded by the GP. So the tests completed by a pathologist in response to a GP order for a full blood count may differ between companies.
•	The pathology companies can charge through the MBS only for the three most expensive items undertaken, even when more were actually done. This is called ‘coning’ and is part of DoHA pathology payment system. This means that the tests recorded in the MBS include only those charged for, not all those that were done. Coning applies only to GP pathology orders, not to those generated by specialists.
•	This means that the MBS pathology data reflect those tests billed to the MBS after interpretation of the order by the pathologist, and after selection of the three most expensive items. 
•	Pathology MBS items contain pathology tests that have been grouped on the basis of cost (for example, ‘any two of the following… tests’). Therefore an MBS item often does not give a clear picture of the precise tests performed.
In BEACH, the pathology data:
•	include details of pathology tests ordered by the participating GPs; however, the GP is limited to the recording of five tests or battery of tests at each encounter, and as the number of tests/batteries ordered on any single occasion is increasing,12 an increasing number of additional tests ordered will be lost
•	reflect the terms used by GPs in their orders to pathologists, and for reporting purposes these have been grouped by the MBS pathology groups for comparability. 
The distributions of the two data sets will therefore differ, reflecting on the one hand the GP order and on the other the MBS-billed services from the pathologist.
Pathology ordering by GPs is described in Chapter 12 of this report. Those interested in pathology test ordering by GPs should also view the following publications:
•	‘GP pathology ordering’ chapter in General practice in Australia, health priorities and policies 1998 to 200812
•	Evidence-practice gap in GP pathology test ordering: A comparison of BEACH pathology data and recommended testing13
•	Changes in pathology ordering by general practitioners in Australia 1998–200114 
Imaging data from the MBS
Some of the issues discussed regarding pathology data also apply to imaging data. Although coning (see above) is not an issue for imaging, radiologists can decide whether the test ordered by the GP is the most suitable and whether to undertake other tests of their choosing. The MBS data therefore reflect the tests that are actually undertaken by the radiologist, whereas the BEACH data reflect those ordered by the GP. 
Those interested in GP imaging ordering should view Chapter 12 of this report, and the publication Imaging orders by general practitioners in Australia 1999–0015, available at <www.fmrc.org.au/publications/>.
The National Health Survey
The National Health Survey, conducted by the Australian Bureau of Statistics, provides estimates of population prevalence of specific diseases, and a measure of the problems taken to the GP by people in the 2 weeks before the survey.
•	Prevalence estimates are based on self-reported morbidity from a representative sample of the Australian population, using a structured interview to elicit health-related information from participants.16 
•	Community surveys such as the National Health Survey have the advantage of accessing people who do not go to a GP as well as those who do. They can therefore provide an estimate of population prevalence of disease and point estimates of incidence.
•	Self-report has been demonstrated to be susceptible to misclassification because of a lack of clinical corroboration of diagnoses.17
Management rates of health problems in general practice represent GP workload for a health problem. BEACH can be used to estimate the period incidence of diagnosed disease presenting in general practice through the number of new cases of that disease. The management rates of individual health problems and management actions can be extrapolated to national management rates. 
The general practice patient population sits between the more clinical hospital-based population and the general population18,19, with about 83% of Australians visiting a GP at least once in 2009–10 (personal communication DoHA, June 2010). Disease management rates are a product of both the prevalence of the disease/health problem in the population, and the frequency with which a patient visits a GP for the treatment of that problem. Those who are older and/or have more chronic disease are therefore likely to visit more often, and have a greater chance of being sampled in the encounter data. 
There has been a substudy of disease prevalence among patients seen in general practice (using the Supplementary Analysis of Nominated Data method, see Section 2.4). Those interested in disease prevalence should refer to the following papers: Estimating prevalence of common chronic morbidities in Australia20, and Prevalence and patterns of multimorbidity in Australia.21 
1.4	Access to BEACH data
Different bundles of BEACH data are available to the general public, to BEACH‑participating organisations, and to other organisations and researchers.
Public domain
This annual publication provides a comprehensive view of general practice activity in Australia. The BEACH program has generated many papers on a wide variety of topics in journals and professional magazines. Appendix 3 lists all published material from BEACH, available at <www.aihw.gov.au/publications/index.cfm/subject/19>.
Since April 1998, a section at the bottom of each encounter form has been used to investigate aspects of patient health or health care delivery not covered by general practice consultation‑based information. These additional substudies are referred to as SAND (Supplementary Analysis of Nominated Data). The SAND methods are described in Section 2.5. Abstracts of results and the research tools used in all SAND substudies from April 1998 to March 2010 have been published. Those from:
•	April 1998 to March 1999 were published in Measures of health and health care delivery in general practice in Australia22
•	April 1999 to July 2006 were published in Patient-based substudies from BEACH: abstracts and research tools 1999–200623
•	August 2006 to March 2007 were published in General practice activity in Australia
 2006–0724
•	April 2007 to January 2008 were published in General practice activity in Australia 
2007–0825
•	April 2008 to March 2009 were published in General practice activity in Australia 2008–0926
•	April 2009 to March 2010 are included in Chapter 15 of this report.
Abstracts of results for all SAND substudies are also available on the FMRC website <www.fmrc.org.au/publications/SAND_abstracts.htm>, where you can search the abstracts by topic.
Participating organisations
Organisations providing funding for the BEACH program receive summary reports of the encounter data quarterly, and standard reports or specifically designed analyses about their subjects of interest. Participating organisations also have direct access to straightforward analyses on any selected problem, medication, pathology or imaging test through an interactive web server. All data made available to participating organisations have been further ‘de-identified’. Patients’ encounter data are not identifiable even from the original forms, but are further stripped of date of birth (replaced with age in years and months) and postcode of residence (replaced with state and area type). GP characteristics data are provided only in the form of grouped output (for example, GPs aged less than 35 years) to any external organisation.
External purchasers of reports
Non-contributing organisations may purchase standard reports or other ad hoc analyses. Charges are outlined at: <www.fmrc.org.au/purchase.htm#1>. The AGPSCC should be contacted for specific quotations. Contact details are provided at the front of this publication.
Analysis of the BEACH data is a complex task. The AGPSCC has designed standard reports that cover most aspects of a subject under investigation. Examples of a problem-based standard report (subject: ischaemic heart disease in patients aged 45 years and over), a group report (subject: female patients aged 15–24 years) and a pharmacological-based standard report (subject: allopurinol) for a single year’s data are available at <www.fmrc.org.au/purchase.htm>.
Individual data analyses can be done where the specific research question is not adequately answered through standard reports. 
2	Methods 
In summary:
•	each year, BEACH involves a random sample of about 1,000 GPs
•	each GP records details about 100 doctor–patient encounters of all types
•	the GP sample is a rolling (ever-changing) sample, with approximately 20 GPs participating in any 1 week, 50 weeks a year
•	each GP can be selected only once per quality assurance (QA) triennium (that is, once every 3 years)
•	the encounter information is recorded by the GPs on structured paper encounter forms (Appendix 1)
•	GP participants also complete a questionnaire about themselves and their practice (Appendix 2).
2.1	Sampling methods
The source population includes all vocationally registered GPs and all general practice registrars who claimed a minimum of 375 general practice A1 Medicare items in the most recently available 3-month Medicare data period (which equates to 1,500 A1 Medicare claims a year). This ensures inclusion of the majority of part-time GPs, while excluding those who are not in private practice but claim for a few consultations a year.
The Primary and Ambulatory Care Division of the DoHA updates the sample frame from the Medicare records quarterly, leaving out of the sample frame any GPs already randomly sampled in the current triennium, and draws a new sample from those currently in the sample frame. This ensures the timely addition of new entries to the profession, and timely exclusion of those GPs who have stopped practising, or have already participated or been approached in the current triennium.
2.2	Recruitment methods
The randomly selected GPs are approached by letter, posted to the address provided by DoHA.
•	Over the following 10 days, the telephone numbers generated from the Medicare data are checked using the electronic white and yellow pages. This is necessary because many of the telephone numbers provided from the Medicare data are incorrect.
•	The GPs are then telephoned in the order they were approached and, referring to the approach letter, asked whether they will participate.
•	This initial telephone contact with the practice often indicates that the selected GP has moved elsewhere, but is still in practice. Where new address and/or telephone number can be obtained, these GPs are followed up at their new address.
•	GPs who agree to participate are set an agreed recording date several weeks ahead.
•	A research pack is sent to each participant before the planned start date.
•	Each GP receives a telephone reminder early in the agreed recording period—this also provides the GP with an opportunity to ask questions about the recording process.
•	GPs can use a ‘freecall’ (1800) number to ring the research team with any questions during their recording period.
•	Non-returns are followed up by regular telephone calls for 3 months.
•	Participating GPs earn clinical audit points towards their QA requirements through the Royal Australian College of General Practitioners (RACGP). As part of this QA process, each receives an analysis of his or her results compared with those of nine other de-identified GPs who recorded at approximately the same time. Comparisons with the national average and with targets relating to the National Health Priority Areas are also provided. In addition, GPs receive some educational material related to the identification and management of patients who smoke or consume alcohol at hazardous levels. Additional points can be earned if the participant chooses to do a follow-up audit of smoking and alcohol consumption among a sample of patients about 6 months later.
2.3	Data elements
BEACH includes three interrelated data collections: GP characteristics, encounter data and patient health status. An example of the form used to collect the encounter data and the data on patient health status is included in Appendix 1. The GP characteristics questionnaire is provided in Appendix 2. The GP characteristic and encounter data collected are summarised below. Patient health status data re described in Section 2.5.
GP profile form (Appendix 2)
•	GP characteristics: age and sex, years in general practice, number of direct patient care hours worked per week, country of graduation, postgraduate general practice training status, Fellow of the RACGP status, Fellow of the Australian College of Rural and Remote Medicine status, usual bulk-billing behaviour, use of computers at work, work undertaken in other clinical settings.
•	Practice characteristics: postcode and GP Division of major practice, number of individual, and number of full‑time equivalent GPs working in the practice, number of individual and number of full-time equivalent practice nurses working in the practice, usual after-hours care arrangements, whether the practice is accredited, whether it is a teaching practice.
Encounter recording form (Appendix 1)
•	Encounter data: date of consultation, type of consultation (direct/indirect) (tick box options), up to three MBS/DVA item numbers (where applicable), and other payment source (where applicable) (tick boxes).
•	Patient data: date of birth, sex and postcode of residence. Tick boxes (yes/no options) are provided for Commonwealth concession cardholder, holder of a Repatriation health card (from DVA), non-English-speaking background (patient self-report—a language other than English is the primary language at home), Aboriginal person (self‑identification), and Torres Strait Islander person (self-identification). Space is provided for up to three patient reasons for encounter (RFEs) (see ‘Glossary’).
•	The problems managed at encounter (at least one and up to four). Tick boxes are provided to denote the status of each problem as new or continuing for the patient and whether the problem is considered by the GP to be work-related.
•	Management of each problem, including:
–	medications prescribed, supplied by the GP and advised for over-the-counter purchase including brand name, form (where required), strength, regimen, status (whether new or continuing medication for this problem for this patient) and number of repeats
–	other treatments provided for each problem, including counselling, advice and education, and procedures undertaken, and whether the recorded other treatment was provided by practice nurse (tick box)
–	new referrals to medical specialists, allied health professionals, and an emergency department, and hospital admissions
–	investigations, including pathology tests, imaging and other investigations ordered at the encounter.
Patient health status 
Described in Section 2.5.
2.4	The BEACH relational database
The BEACH relational database is described diagrammatically in Figure 2.1. Note that:
•	all variables can be directly related to GP and patient characteristics, and to the encounter
•	RFEs have only an indirect relationship with problems managed, as a patient may describe one RFE (such as ‘repeat prescriptions’) that is related to multiple problems managed, or several RFEs (such as ‘runny nose’ and ‘cough’) that relate to a single problem (such as upper respiratory tract infection) managed at the encounter (see Section 6.3)
•	all types of management are directly related to the problem being managed.

Figure 2.1: The BEACH relational database
Diagram
Note: FTE—full-time equivalent; FRACGP—Fellow of the Royal Australian College of General practitioners; ACRRM—Fellow of the Australian College of Rural and Remote Medicine; DVA—Department of Veterans’ Affairs; NESB—non-English-speaking background; SAND—Supplementary Analysis of Nominated Data.

2.5	Supplementary Analysis of Nominated Data 
A section at the bottom of each recording form investigates aspects of patient health or health care delivery in general practice not covered by the consultation-based data. These additional substudies are referred to as SAND, Supplementary Analysis of Nominated Data.
•	The year-long data period is divided into 10 blocks, each of 5 weeks, with three substudies per block. The research team aims to include data from about 100 GPs in each block so each SAND topic includes about 3,000 records. Some topics are repeated to increase sample size.
•	Each GP’s pack of 100 forms is made up of 40 forms that ask for the start and finish times of the encounter, and include questions about patient risk factors: patient height and weight (used to calculate body mass index, BMI), alcohol intake and smoking status (patient self-report). The methods and results of topics in the SAND substudies for alcohol consumption, smoking status and BMI are reported in Chapter 14. The start and finish times collected on these encounters are used to calculate the length of consultation. The length of consultation for Medicare-claimable encounters is reported in Section 5.3.
•	The remaining 60 forms in each pack are divided into two blocks of 30. Different questions are asked of the patient in each block and these vary throughout the year.
•	The order of SAND sections is rotated in the GP recording pack, so that 40 patient risk factor forms may appear first, second or third in the pad. Rotation of ordering ensures there was no order effect on the quality of the information collected.
Abstracts of results and the research tools used in all SAND substudies from April 1998 to March 2010 have been published. Those:
•	from April 1998 to March 1999 were published in Measures of health and health care delivery in general practice in Australia22
•	from April 1999 to July 2006 were published in Patient-based substudies from BEACH: abstracts and research tools 1999–200623
•	since August 2006 have been published, in each of the general practice activity annual reports24-26
•	conducted in the 2009–10 BEACH year are provided in Chapter 15 of this publication.
Abstracts of results for all SAND substudies are also available on the FMRC’s website <www.fmrc.org.au/publications/SAND_abstracts.htm>.
2.6	Statistical methods
The analysis of the 2009–10 BEACH data was conducted with Statistical Analysis System (SAS) version 9.1.327, and the encounter is the primary unit of inference. Proportions are used only when describing the distribution of an event that can arise only once at a consultation (for example, patient or GP age and sex), or to describe the distribution of events within a class of events (for example, problem A as a percentage of total problems). Rates per 100 encounters are used when an event can occur more than once at the consultation (for example, RFEs, problems managed or medications).
Rates per 100 problems are also used when a management event can occur more than once per problem managed. In general, the results present the number of observations (n), the rate per 100 encounters, and (in the case of management actions) the rate per 100 problems managed, and the 95% confidence interval.
BEACH is a single stage cluster sample study design, each 100 encounters forming a cluster around each GP participant. In cluster samples, variance needs to be adjusted to account for the correlation between observations within clusters. Procedures in SAS version 9.1.3 were used to calculate the intracluster correlation, and adjust the confidence intervals accordingly.27 
Post-stratification weighting of encounter data adjusts for: any difference in the age–sex distribution of the participating GPs and those GPs in the sample frame from which the samples were drawn; and for the varying activity level of each GP (measured by number of claims each has made in the previous 12 months from Medicare Australia) (see Chapter 3).
2.7	Classification of data
The following data elements are classified according to the International Classification of Primary Care—Version 2 (ICPC-2), a product of the World Organization of Family Doctors (Wonca)7:
•	patient reasons for encounter (RFEs)
•	problems managed
•	clinical treatments (for example, counselling, advice)
•	procedural treatments
•	referrals
•	investigations ordered (including pathology, imaging and other investigations).
The ICPC-2 is used in more than 45 countries as the standard for data classification in primary care. It is accepted by the World Health Organization (WHO) in the WHO Family of International Classifications28, and is the declared national standard in Australia for reporting of health data from general practice and patient self-reported health information.29
The ICPC-2 has a biaxial structure, with 17 chapters on one axis (each with an alphabetic code) and seven components on the other (numeric codes) (Figure 2.2). Chapters are based on body systems, with additional chapters for psychological and social problems. Component 1 includes symptoms and complaints. Component 7 covers diagnoses—it can also be expanded to provide data about infections, injuries, neoplasms, congenital anomalies and ‘other’ diagnoses. 
Component 2 (diagnostic, screening and prevention) is often applied in describing the problem managed (for example, check-up, immunisation). Components 3 to 6 cover other processes of care, including referrals, other (non-pharmacological) treatments and orders for pathology and imaging. The components are standard and independent throughout all chapters. The updated component groupings of ICPC-2 codes, released by the Wonca International Classification Committee in 200430 have been used in this report.
The ICPC-2 is an excellent epidemiological tool. The diagnostic and symptom rubrics have been selected for inclusion on the basis of their relative frequency in primary care settings, or because of their relative importance in describing the health of the community. ICPC has about 1,370 rubrics and these are sufficient for meaningful analyses. However, reliability of data entry, using ICPC-2 alone, requires a thorough knowledge of the classification for correct classification of a concept to be ensured.
In 1995, recognising a need for a coding and classification system for general practice electronic health records, the FMRC (then the Family Medicine Research Unit) developed an extended clinical terminology classified according to the ICPC, now called ICPC-2 PLUS.31 This is an interface terminology, developed from all the terms used by GPs in studies such as the Australian Morbidity and Treatment Survey 1990–91 (113,468 encounters)32, A comparison of country and metropolitan general practice 1990–91 (51,277 encounters)33, the Morbidity and Therapeutic Index 1992–1998 (a clinical audit tool that was available to GPs) (approximately 400,000 encounters), and BEACH 1998–2010 (about 1.2 million encounters), that together make up about 2.7 million encounter records, involving more than 4 million free text descriptions of problems managed and a further 4 million for patient reasons for encounter. These terms are classified according to ICPC-2 to ensure international standards for reporting. Readers interested in seeing how coding works can download the ICPC-2 PLUS Demonstrator at <www.fmrc.org.au/icpc2plus/demonstrator.htm>.
When the free-text data are received from the GPs, trained secondary coders (who are undergraduate students, code the data in more specific terms using ICPC-2 PLUS. This ensures high coder reliability and automatic classification of the concept, and provides the ability to ‘ungroup’ such ICPC-2 rubrics as ‘other diseases of the circulatory system’ and select a specific disease from the terms within it. 
Figure 2.2: The structure of the International Classification of Primary Care—Version 2 (ICPC-2)
Chart.
Presentation of data classified in ICPC-2
Statistical reporting is almost always at the level of the ICPC-2 classification (for example, acute otitis media/myringitis is ICPC-2 code H71). However, there are some exceptions where data are grouped either above the ICPC-2 level or across the ICPC-2 level. These grouped morbidity, pathology and imaging codes are defined in Appendix 4, and chronic morbidity groups are provided in Appendix 5. Appendices 4 and 5 are available at <www.aihw.gov.au/publications/index.cfm/subject/19>.
Reporting morbidity with groups of ICPC-2 codes
When recording problems managed, GPs may not always be very specific. For example, in recording the management of hypertension, they may simply record the problem as ‘hypertension’. In ICPC-2, ‘hypertension, unspecified’ is classified as ‘uncomplicated hypertension’ (code K86). There is another code for ‘complicated hypertension’ (K87). In some cases the GP may simply have failed to specify that the patient had hypertension with complications. The research team therefore feels that for national data reporting, it is more reliable to group the codes K86 and K87 and label this ‘Hypertension*’—the asterisk indicating that multiple ICPC-2 codes (as in this example) or ICPC-2 PLUS codes (see below) are included. Appendix 4 lists the codes included in these groups. 
Reporting morbidity with groups of ICPC-2 PLUS codes
In other cases, a concept can be classified within (but be only part of) multiple ICPC-2 codes. For example, osteoarthritis is classified in ICPC-2 in multiple broader codes according to site, such as L92—shoulder syndrome (includes bursitis, frozen shoulder, osteoarthritis of shoulder, rotator cuff syndrome). When reporting osteoarthritis in this publication, all the more specific osteoarthritis ICPC-2 PLUS terms classified within all the appropriate ICPC‑2 codes are grouped. This group is labelled ‘Osteoarthritis*’—the asterisk again indicating multiple codes, but in this case they are PLUS codes rather than ICPC-2 codes. Appendix 4 lists the codes included in these groups.
Reporting chronic morbidity
Chronic conditions are medical conditions characterised by a combination of the following characteristics: duration that has lasted or is expected to last 6 months or more, a pattern of recurrence or deterioration, a poor prognosis, and consequences or sequelae that affect an individual’s quality of life. 
To identify chronic conditions, a chronic condition list34 classified according to ICPC-2 was applied to the BEACH data set. In general reporting, both chronic and non-chronic conditions (for example, diabetes and gestational diabetes) may have been grouped together when reporting (for example, diabetes—all*). When reporting chronic morbidity, only problems regarded as chronic have been included in the analysis. Where the group used for the chronic analysis differs from that used in other analyses in this report, they are marked with a double asterisk. Codes included in the chronic groups are provided in Appendix 5.
Reporting pathology and imaging test orders
All the pathology and imaging tests are coded very specifically in ICPC-2 PLUS, but ICPC-2 classifies pathology and imaging tests very broadly (for example, a test of cardiac enzymes is classified in K34—Blood test associated with the cardiovascular system; a CT scan of the lumbar spine is classified as L41—Diagnostic radiology/imaging of the musculoskeletal system). In Australia, the MBS classifies pathology and imaging tests in groups that are relatively well recognised. The team therefore regrouped all pathology and imaging ICPC-2 PLUS codes into MBS standard groups. This allows comparison of data between data sources. The groups are marked with an asterisk, and inclusions are provided in Appendix 4.
Classification of pharmaceuticals
Pharmaceuticals that are prescribed, provided by the GP or advised for over-the-counter purchase are coded and classified according to an in-house classification, the Coding Atlas for Pharmaceutical Substances (CAPS).
This is a hierarchical structure that facilitates analysis of data at a variety of levels, such as medication class, medication group, generic composition and brand name.
Strength and regimen are independent fields that, when combined with the CAPS code, give an opportunity to derive the prescribed daily dose for any prescribed medication or group of medications.
CAPS is mapped to the Anatomical Therapeutic Chemical (ATC)35 classification, which is the Australian standard for classifying medications at the generic level.29
The ATC has a hierarchical structure with five levels. For example:
•	Level 1: C—Cardiovascular system
•	Level 2: C10—Serum lipid reducing agents
•	Level 3: C10A—Cholesterol and triglyceride reducers
•	Level 4: C10AA—HMG CoA reductase inhibitors
•	Level 5: C10AA01—Simvastatin (the generic drug).
Use of the pharmaceutical classifications in reporting
For pharmaceutical data, there is the choice of reporting in terms of the CAPS coding scheme or the ATC. They each have advantages in different circumstances.
In the CAPS system, a new drug enters at the product and generic level, and is immediately allocated a generic code. Therefore, the CAPS classification uses a bottom-up approach.
In the ATC, a new generic may initially enter the classification at any level (1 to 5), not always at the generic level. Reclassification to lower ATC levels may occur later. Therefore, the ATC uses a top-down approach.
When analysing medications across time, a generic medication that is initially classified to a higher ATC level will not be identifiable in that data period and may result in under‑enumeration of that drug during earlier data collection periods.
•	When reporting the 2009–10 annual results for pharmaceutical data, the CAPS database is used in tables of the ‘most frequent medications’ (tables 9.2 to 9.4).
•	When reporting the annual results for pharmaceuticals in terms of the ATC hierarchy (Table 9.1), ATC levels 1, 3, and 5 are used. The reader should be aware that the results reported at the generic level (Level 5) may differ slightly from those reported in the ‘most frequent medication’ tables for the reasons described above.

2.8	Quality assurance
All morbidity and therapeutic data elements were secondarily coded by staff entering key words or word fragments, and selecting the required term or label from a pick list. This was then automatically coded and classified by the computer. 
The quality assurance program to ensure reliability of data entry includes ongoing development of computer-aided error checks (‘locks’) at the data entry stage, and a physical check of samples of data entered versus those on the original recording form. Further logical data checks are conducted through SAS regularly.
2.9	Validity and reliability
A discussion of the reliability and validity of the BEACH program has been published elsewhere.36 This section touches on some aspects of reliability and validity of active data collection from general practice that should be considered by the reader. 
In the development of a database such as BEACH, data gathering moves through specific stages: GP sample selection, cluster sampling around each GP, GP data recording, secondary coding and data entry. At each stage the data can be invalidated by the application of inappropriate methods. The methods adopted to ensure maximum reliability of coding and data entry have been described above. The statistical techniques adopted to ensure valid analysis and reporting of recorded data are described in Section 2.6. Previous work has demonstrated the extent to which a random sample of GPs recording information about a cluster of patients represents all GPs and all patients attending GPs.37 Other studies have reported the degree to which GP-reported patient RFEs and problems managed accurately reflect those recalled by the patient38, and the reliability of secondary coding of RFEs39 and problems managed.32 The validity of ICPC as a tool with which to classify the data has also been investigated in earlier work.40
However, the question of the extent to which the GP-recorded data are a reliable and valid reflection of the content of the encounter must also be considered. In many primary care consultations, a clear pathophysiological diagnosis is not reached. Bentsen41 and Barsky42 suggest that a firm and clear diagnosis is not apparent in about half of GPs’ consultations, and others suggest the proportion may be even greater.43 Further, studies of general ambulatory medical practice have shown that a large number of patients presenting to a primary care practitioner are without a serious physical disorder.44,45 As a result, it is often necessary for a practitioner to record a problem in terms of symptoms, signs, patient concerns, or the service that is requested, such as immunisation. For this reason, this report refers to patient ‘problems’ rather than ‘diagnoses’.
A number of studies have demonstrated wide variance in the way a GP perceives the patient’s RFE and the manner in which the GP describes the problem under management. In a direct observational study of consultations via a one-way mirror, Bentsen demonstrated differences in the way practitioners labelled problems, and suggested that clinical experience may be an important influence on the identification of problems within the consultation.41 Two other factors that might affect GPs’ descriptions of patient RFEs have been identified: although individuals may select the same stimuli, some label each stimulus separately, whereas others cluster them under one label; and individuals differ in the number of stimuli they select (selective perception).46
The extent to which therapeutic decisions may influence the diagnostic label selected has also been discussed. Howie47 and Anderson44 argue that, while it is assumed that the diagnostic process used in general practice is one of symptom  diagnosis  management, the therapeutic method may well be selected on the basis of the symptom, and the diagnostic label chosen last. They suggest that the selection of the diagnostic label is therefore influenced by the management decision already made.
Anderson has also pointed out that the therapeutic decision may be influenced by fashion, and, in turn, this affects the selection of the problem label. He gives the example of a rise in the occurrence of neurotic depression in parallel with a decrease in the use of menopause as a diagnosis in the United Kingdom, and suggests this may be the result of a change in the preferred treatment from oestrogen therapy to antidepressants.44 This should be remembered when considering the changes in general practice described in this report.
Alderson contends that to many practitioners ‘diagnostic accuracy is only important to the extent that it will assist them in helping the patient’. He further suggests that if major symptoms are readily treatable, some practitioners may feel no need to define the problem in diagnostic terms.48 Crombie stated that in the second and third national morbidity surveys in the United Kingdom there was ‘enormous variability in the rates at which doctors perceive and record illnesses’. He concluded that the probable cause arose from the different ways in which GPs gave priority in their perceptions and recording of certain morbidities while discounting or ignoring others. He was unable to account statistically for this variation by the effect of geography, age, sex or class differences in the practice populations.49 Differences in the way male and female GPs label problems also appear to be independent of such influences.50
These problems are inherent in the nature of general practice. Knottnerus argues that the GP is confronted with a fundamentally different pattern of problems from the specialist, the GP often having to draw up general diagnostic hypotheses related to probability, severity and consequences.51 Anderson suggests that morbidity statistics from family practice should therefore be seen as ‘a reflection of the physician’s diagnostic opinions about the problems that patients bring to them rather than an unarguable statement of the problems managed’.44 In any case, doctors base their actions on problems as they perceive them.
While these findings regarding limitations in the reliability and validity of practitioner‑recorded morbidity should be kept in mind, they apply equally to data drawn from medical records, whether paper or electronic, as they do to active data collection methods.52,53 There is as yet no more reliable method of gaining detailed data about morbidity and its management in general practice. Further, irrespective of the differences between individual GPs in their labelling of the problems, morbidity data collected by GPs in active data collection methods have been shown to provide a reliable overview of the morbidity managed in general practice.54
2.10	Extrapolated national estimates
Extrapolations can be used to estimate the number of GP encounters in Australia involving a selected event at a single time point, or to estimate the total national effect of changes. 
In this report, extrapolations using data from a single time point estimate the number of GP encounters in Australia in 2009–10 that involve a selected event. The method of extrapolation described below can be applied to a single time point. 
A section in each chapter highlights major changes that have occurred over the decade 
2000–01 to 2009–10. Extrapolations used in these sections estimate the national change across total GP Medicare services from 2000–01 to 2009–10. These sections refer to data published in General practice activity in Australia 2000–01 to 2009–10: 10 year data tables.1 Where the results demonstrate a significant change over time, the estimated national change across total GP Medicare services from 2000–01 to 2009–10 can be calculated using the method described below. 
•	The national estimates are calculated by dividing the rate per 100 encounters of the selected event for 2000–01 by 100, and then multiplying by the total number of general practitioner services claimed through Medicare in that year (rounded to the nearest 100,000, see Table 2.1) to give the estimated annual number of events in 2000–01. The process is then repeated for 2009–10. The difference between the two estimates gives the estimated national change in the rate of encounters for that event over the period of interest. Estimates are rounded to the nearest 100,000 if more than a million and to the nearest 10,000 if below a million.
•	This is expressed as the estimated increase or decrease over the study period (between 2000–01 and 2009–10), in the number of general practice contacts for that event; for example, an increase or decrease in the number of GP management contacts with problem X occurring in Australia in 2009–10 when compared with 2000–01.
Table 2.1 provides the total number of general practice professional service items claimed from Medicare in each financial year from 2000–01 to 2009–10. In this report, extrapolations are calculated using the number of GP Medicare items claimed rounded to the nearest 100,000. The rounded number is also provided in Table 2.1. 
Table 2.1: Number of general practice professional services claimed from Medicare Australia each financial year, 2000–01 to 2009–10 (‘000)
	2000–01	2001–02	2002–03	2003–04	2004–05	2005–06	2006–07	2007–08	2008–09	2009–10(a)	
Number of GP 
MBS items 	100,645	99,921	96,919	96,330	98,180	101,095	103,433	109,518	113,045	116,832	
Rounded number of GP MBS items	100,600	99,900	96,900	96,300	98,200	101,100	103,400	109,500	113,000	116,800	
(a)	Medicare data for the 2009–10 year included data from the April 2009 to March 2010 quarters because the 2009–10 financial year data were not available at the time of preparation of this report.
Source: Medicare statistics, Table B1C: Medicare: Number of services (‘000) by quarter of processing by broad type of service; Table B1A: Medicare: Number of services (‘000) by financial year of processing by broad type of service. Available at <www.health.gov.au/internet/main/publishing.nsf/Content/medstat-mar10-tables-ba>.
Examples of extrapolation
Example A: Change in the number of problems managed by GPs nationally 
There was a significant increase in the number of problems managed at encounter, from 144.5 per 100 encounters in 2000–01 to 153.3 in 2009–10 (see Table 7.2 in General practice activity in Australia 2000–01 to 2009–10: 10 year data tables1): 
•	(144.5/100) x 100.6 million = 145.4 million problems managed nationally in 2000–01, and (153.3/100) x 116.8 million = 179.1 million problems managed nationally in 2009–10. 
This suggests there were 33.7 million (179.1 million minus 145.4 million) more problems managed at GP encounters in Australia in 2009–10 than in 2000–01. 
Example B: Change in the number of medication prescriptions by GPs nationally
As demonstrated in Table 2.1 there has been a 16% increase in the number of GP service items claimed from Medicare per year, from 100.6 million in 2000–01 to 116.8 million in 2009–10.
This increase means that even where there has been a decrease in the rate of a management action per 100 encounters, the overall result may be an increase in the number of those actions. An excellent example of this effect is apparent in the prescribed medications in BEACH.
There was a significant decrease in the number of medications prescribed at encounter, from 92.3 per 100 encounters in 2000–01 to 83.4 per 100 in 2009–10 (see Table 9.1b in General practice activity in Australia 2000–01 to 2009–10: 10 year data tables1): 
•	(92.3/100) x 100.6 million = 92.9 million GP prescriptions nationally in 2000–01, and 
(83.4/100) x 116.8 million = 97.4 million prescriptions nationally in 2009–10. 
This suggests there were 4.5 million (97.4 million minus 92.9 million) more prescriptions written by GP in Australia in 2009–10 than in 2000–01. 
This result of an increase in total prescriptions rather than a decrease (that might have been expected from the decreasing prescription rate) is due to the 16% increase in the total number of GP consultations over the period. 
Limitations of extrapolations
The extrapolations to the total encounters occurring nationally in any 1 year are only an estimate. It may provide:
•	an underestimate of the true ‘GP workload’ of a condition/treatment because the extrapolations are made to GP Medicare items claimed, not to the total number of GP encounters per year—an additional 5% or so of BEACH encounters annually include encounters paid by sources other than Medicare, such as DVA, state governments, workers compensation insurance, and employers.
•	an underestimate of activities of relatively low frequency with a skewed distribution across individual GPs. For example, a study of early uptake of some enhanced primary care items by GPs demonstrated that almost half the enhanced primary care items claimed through the MBS came from about 6% of active GPs.10 Where activity is so skewed across the practising population, a national random sample will provide an underestimate of activity because the sample reflects the population rather than the minority.
•	an overestimate of the management rate of a group of conditions (for example, cardiovascular disease) because there is a chance that more than one problem of this type will be managed at a single encounter. In the extrapolations, two cardiovascular problems managed at one encounter will be counted as two encounters.
Further, the base numbers used in the extrapolations are rounded to the nearest 100,000, and extrapolation estimates are rounded to the nearest 100,000 if more than a million and to the nearest 10,000 if below a million. However, the rounding has been applied to all years, so the effect on measures of change will be very small. Therefore, the extrapolation still provides an indication of the size of the effect of measured change nationally.
3	The sample
This chapter describes the GP sample and sampling methods used in the BEACH program. The methods are only summarised in this chapter. A more detailed explanation of the BEACH methods are described in Chapter 2.
A summary of the annual BEACH samples are reported for each year from 2000–01 to 
2009–10 in the companion report General practice activity in Australia 2000–01 to 2009–10: 
10 year data tables.1
3.1	Response rate
A random sample of GPs who claimed at least 375 general practice Medicare items of service in the previous 3 months is regularly drawn from Medicare claims data by the Primary and Ambulatory Care Division of DoHA (see Chapter 2).
Contact was attempted with 4,355 GPs—13.8% could not be contacted. More than one-third of these had moved, retired or died, and were untraceable (Table 3.1), although the majority were those with whom contact could not be established after five calls. It is notable that of GPs approached who were aged less than 35 years, 26.6% were no longer at that practice and could not be traced (Table 3.1). These would largely be registrars moving through practices during training. In contrast, 12.6% of GPs aged 35 years and over were not traceable (results not shown).
The final participating sample consisted of 988 practitioners, representing 26.3% of those who were contacted and available, and 22.7% of those with whom contact was attempted (Table 3.1).
Table 3.1: Recruitment and participation rates
Type of contact	Number	Per cent of 
approached 
(n = 4,355)	Per cent of contacts established 
(n = 3,755)	
Letter sent and phone contact attempted	4,355	100.0	—	
No contact 	600	13.8	—	
	No phone number	39	0.9	—	
	Moved/retired/deceased	235	5.4	—	
	Unavailable (overseas, maternity leave, etc)	43	1.0	—	
	No contact after five calls	283	6.5	—	
Telephone contact established	3,755	86.2	100.0	
	Declined to participate	2,509	57.6	66.8	
	Agreed but withdrew	258	5.9	6.9	
	Agreed and completed	988	22.7	26.3	

3.2	Representativeness of the GP sample
Whenever possible, the study group of GPs should be compared with the population from which the GPs were drawn to identify and, if necessary, adjust for any sample bias that may affect the findings of the study. Differences between the final GP sample and the sample frame are provided below. Weightings generated as a result of these comparisons and applied to the data are described in Section 3.3 
Statistical comparisons, using the chi-square statistic (2) (significant at the 5% level), were made between BEACH participants, and all recognised GPs in the sample frame during the study period (Table 3.2). The GP characteristics data for BEACH participants were drawn from the GP profile questionnaire. DoHA provided the data for all GPs in the sample frame, drawn from Medicare claims data.
Table 3.2 demonstrates that there were significant differences in GP characteristics between the final sample of BEACH participants and all GPs in the sample frame, in terms of sex, age, and state; male GPs, those aged 55 years and over, and those practising in Victoria and the Northern Territory were under-represented in BEACH, whereas female GPs, those aged 
45–54 years, and GPs practising in New South Wales and Queensland were over‑represented. Distribution by place of graduation and across Rural, Remote and Metropolitan Area classes did not significantly differ from that of the total sample frame.
However, the BEACH participants in terms of age and location were more closely representative of the sample provided by DoHA than of the sample frame (Table 3.3). The random sampling process has, in this instance, produced a sample with greater proportions of GPs aged 35–44 years and 45–54 years, and in New South Wales and Queensland, and smaller proportions in those aged 55 years and over, and Victoria. While this may provide some explanation for the differences between BEACH participants and the sample frame in some of the age groups and state distributions, the over‑representation of female GPs in this BEACH sample reflects a more positive response from female GPs.

Table 3.2: Comparison of BEACH participants and all active recognised GPs in Australia
(the sample frame)
Variable	BEACH(a)(b)	Australia(a)(c)	
	Number	Per cent of GPs (n = 988)	Number	Per cent of GPs (n = 18,981) 	
Sex (2 = 17.0, p < 0.001)		
	Males	557	56.4	11,938	62.9	
	Females	431	43.6	7,043	37.1	
Age (2 = 10.9, p = 0.012)		
	< 35 years	70	7.1	1,304	6.9	
	35–44 years	210	21.4	3,899	20.5	
	45–54 years	360	36.7	6,205	32.7	
	55+ years	342	34.8	7,573	39.9	
	Missing	6	—	0	—	
Place of graduation (2 = 2.76, p = 0.097)		
	Australia	697	70.6	12,925	68.1	
	Overseas	290	29.4	6,056	31.9	
	Missing	1	—	0	—	
State (2 = 49.5, p < 0.001)		
	New South Wales	367	37.1	6,320	33.3	
	Victoria	180	18.2	4,775	25.2	
	Queensland	238	24.1	3,625	19.1	
	South Australia	60	6.1	1,607	8.5	
	Western Australia	83	8.4	1,738	9.2	
	Tasmania	39	3.9	503	2.7	
	Australian Capital Territory	18	1.8	292	1.5	
	Northern Territory	3	0.3	121	0.6	
RRMA (2 = 9.7, p = 0.14)		—	
	Capital	616	62.3	12,556	66.2	
	Other metropolitan	84	8.5	1,468	7.7	
	Large rural	72	7.3	1,187	6.3	
	Small rural	70	7.1	1,301	6.9	
	Other rural	131	13.3	2,163	11.4	
	Remote centre	4	0.4	146	0.8	
	Other remote	11	1.1	160	0.8	
(a)	Missing data removed.
(b)	Data drawn from the BEACH GP profile completed by each participating GP.
(c)	All GPs who claimed at least 375 MBS GP consultation services during the most recent 3-month Medicare Australia data period. Data provided by the Primary Care Division of the Department of Health and Ageing.
Note: RRMA—Rural, Remote and Metropolitan Area classification.

Table 3.3: Comparison of all active recognised GPs in Australia (the sample frame), GPs in the sample from Medicare claims data (drawn by DoHA), and BEACH participants 2009–10
	Sample frame 
(all Australia)(a)	Sample from Medicare claims data(b)	BEACH participants 	
Variable	Number	Per cent 
of GPs	Number	Per cent 
of GPs	Number	Per cent 
of GPs	
Sex (missing)	(0)		(1)		(0)		
	Males	11,938	62.9	2,650	60.9	557	56.4	
	Females	7,043	37.1	1,704	39.1	431	43.6	
Age (missing)	(0)		(0)		(6)		
	< 35 years	1,304	6.9	380	8.7	70	7.1	
	35–44 years	3,899	20.5	1,047	24.0	210	21.4	
	45–54 years	6,205	32.7	1,504	34.5	360	36.7	
	55+ years	7,573	39.9	1,424	32.7	342	34.8	
State (missing)	(0)		(3)		(0)		
	New South Wales	6,320	33.3	1,523	35.0	367	37.1	
	Victoria	4,775	25.2	931	21.4	180	18.2	
	Queensland	3,625	19.1	931	21.4	238	24.1	
	South Australia	1,607	8.5	341	7.8	60	6.1	
	Western Australia	1,738	9.2	403	9.3	83	8.4	
	Tasmania	503	2.7	114	2.6	39	3.9	
	Australian Capital Territory	292	1.5	86	2.0	18	1.8	
	Northern Territory	121	0.6	23	0.5	3	0.3	
Total	18,981	100.0	4,355	100.0	988	100.0	
(a)	Sample frame—all recognised (see ‘Glossary’) GPs in Australia who claimed at least 375 general practice service items in the previous quarter (from Medicare claims data).
(b)	Random sample of GPs from the sample frame, drawn from Medicare claims data and supplied by DoHA to approach for BEACH participation.
Data on the number of MBS general practice A1 (and if applicable A2) service items claimed in the previous quarter were also provided by DoHA for each GP in the samples drawn, but not for GPs in the sample frame. These data were used to determine the ‘activity level’ of each GP. There were significant differences between the proportions of BEACH participants and non-participants in the services groups. A greater proportion of participants than non‑participants were in the 375–750 services and 750–1,500 services groups, showing a greater proportion of participants who claimed fewer items compared with GPs who did not participate (Table 3.4). This result may be an influence of the over-representation of females in the sample frame, a considerable proportion of whom work part-time.
There was a significant difference (p < 0.0001) in the mean number of consultation items claimed by participants (1,179.0 claims for the quarter) compared with the GPs who declined to participate (1,279.5 for the quarter) (Table 3.4). Comparisons of the median number of claims for each group showed a difference of fewer than seven consultations per week (6.7), and a difference of 7.7 consultations per week in the mean number. BEACH may offer an avenue for fulfilling RACGP Clinical Audit requirements to part-time GPs who may not be as able to take up other avenues. It is possible that the time required to participate in BEACH may be a greater issue for ‘busier’ GPs. It cannot be assumed, however, that a GP seeing 20 patients per day 3 days per week is any less ‘busy’ than a GP seeing 20 patients per day 5 days per week.
As in previous years, only A1 (and if applicable A2) items of service were included for this comparison, as these were the only items available from the sample provided from DoHA throughout the previous year. This will change in future years to include all GP service items used in other comparison tables.
Table 3.4: Activity level of participating and non-participating GPs
Variable	Participants(a) 
(n = 988)	Non-participants(a) 
(n = 2,767)	
	Number of GPs	Per cent	Number of GPs	Per cent	
Activity (2 = 15.4, p = 0.0005)		
	375–750 services in previous quarter	284	28.7	701	25.3	
	750–1,500 services in previous quarter	462	46.8	1,208	43.7	
	> 1,500 services in previous quarter	242	24.5	858	31.0	
	Number of claims		Number of claims		
Mean activity level (t = 4.17, p < 0.0001)	1,179.0	—	1,279.5	—	
Median activity level	1,023.0	—	1,110.0	—	
Standard deviation	634.97	—	693.04	—	
(a)	Missing data removed.
3.3	Weighting the data
Age–sex weights 
As described in Section 3.2, female GPs and those in the 45–54 age group were over‑represented, and those aged 55 years and over were under-represented among BEACH participants for 2009–10. To achieve comparable estimates and precision, GP age–sex weights were applied to the data sets in post-stratification weighting. 
Activity weights 
In BEACH, each GP provides details of 100 consecutive encounters. There is considerable variation among GPs in the number of services each provides in a given year. Encounters were therefore assigned an additional weight that was directly proportional to the activity level of the recording GP. GP activity level was measured as the number of MBS general practice A1 (and if applicable A2) service items claimed by the GP in the previous 12 months (data supplied by DoHA).
Total weights 
The final weighted estimates were calculated by multiplying raw rates by the GP age–sex weight and the GP sampling fraction of services in the previous 12 months. Table 3.5 shows the precision ratio calculated before and after weighting the data.

3.4	Representativeness of the final encounter sample
BEACH aims to gain a representative sample of GP–patient encounters. To assess the representativeness of the final weighted sample of encounters, the age–sex distribution of patients at weighted BEACH encounters with GP consultation service items claimed was compared with that of patients at all encounters claimed as MBS GP consultation service items in the 2009–10 study period (data provided by DoHA). 
As shown in Table 3.5, there is an excellent fit of the MBS and BEACH unweighted age–sex distribution with that of the MBS claims distribution, with no age–sex category varying by more than 20% (maximum variance 16% in males <1 year) from the population distribution. The range of raw precision ratios (0.84–1.15) indicates that the BEACH sample of encounters is a good representation of Australian GP–patient encounters. After weighting, the precision ratios improved slightly in some aspects, and all were within the 0.89–1.10 range.
The age–sex distribution of patients at BEACH encounters and for MBS GP consultation service item claims is shown graphically for all patients in Figure 3.1, for males in Figure 3.2, and for females in Figure 3.3.
Table 3.5: Age–sex distribution of patients at BEACH and MBS GP consultation service items
Sex/age	BEACH–raw(a)	BEACH–weighted(b)	Australia(c)	Precision ratios	
	Number	Per cent 
(n = 81,002)	Number	Per cent
(n = 83,108)	Per cent	Raw(a)	Weighted(c)	
Male		
	< 1 year	1,063	1.3	1,028	1.2	1.1	0.84	0.89	
	1–4 years	2,284	2.8	2,304	2.8	2.8	0.99	1.01	
	5–14 years	2,396	3.0	2,557	3.1	3.4	1.15	1.10	
	15–24 years	2,350	2.9	2,646	3.2	3.3	1.14	1.04	
	25–44 years	6,271	7.7	7,004	8.4	8.7	1.12	1.03	
	45–64 years	9,004	11.1	10,337	12.4	11.8	1.06	0.95	
	65–74 years	4,268	5.3	4,983	6.0	5.8	1.1	0.97	
	75+ years	3,964	4.9	4,510	5.4	5.6	1.15	1.03	
Female		
	< 1 year	869	1.1	830	1.0	1.0	0.93	1.00	
	1–4 years	1,987	2.5	1,935	2.3	2.4	0.98	1.03	
	5–14 years	2,411	3.0	2,455	3.0	3.2	1.07	1.08	
	15–24 years	4,960	6.1	4,699	5.7	5.7	0.93	1.01	
	25–44 years	12,953	16.0	12,028	14.5	14.5	0.91	1.00	
	45–64 years	13,853	17.1	13,357	16.1	15.6	0.91	0.97	
	65–74 years	5,796	7.2	5,898	7.1	6.7	0.94	0.94	
	75+ years	6,573	8.1	6,536	7.9	8.4	1.04	1.07	
(a)	Unweighted, GP consultation Medicare service items only, excluding encounters with patients who hold a DVA Repatriation health card.
(b)	Calculated from BEACH weighted data, excluding encounters with patients who hold a DVA Repatriation health card.
(c)	MBS claims data provided by the Primary Care Division of the Department of Health and Ageing.
Note: GP consultation Medicare services—see ‘Glossary’. Only encounters with a valid age and sex are included in the comparison.

Figure 3.1: Age distribution of all patients at BEACH and MBS GP consultation services 2009–10
Stacked line char showing, for BEACH GP and MBS GP consultation service items, per cent on y-axis and age group (years) on x-axis.
Figure 3.2: Age distribution of male patients at BEACH and MBS GP consultation services
2009–10
Stacked line char showing, for BEACH GP and MBS GP consultation service items, per cent on y-axis and age group (years) on x-axis.
Figure 3.3: Age distribution of female patients at BEACH and MBS GP consultation services
2009–10
Stacked line char showing, for BEACH GP and MBS GP consultation service items, per cent on y-axis and age group (years) on x-axis.
3.5	The weighted data set
The final unweighted data set from the 12th year of collection contained encounters, reasons for encounters, problems and management/treatments. The apparent number of encounters, reasons for encounter and number of medications all increased after weighting, and the number of problems managed, other treatments, referrals, imaging and pathology all decreased after weighting. Raw and weighted totals for each data element are shown in Table 3.6. The weighted data set is used for all analyses in the remainder of this report.
Table 3.6: The BEACH data set, 2009–10
Variable	Raw	Weighted	
General practitioners	988	988.4	
Encounters	98,800	101,349.0	
Reasons for encounter	154,199	157,070.6	
Problems managed	155,889	155,372.6	
Medications	103,232	108,000.6	
Other treatments(a)	54,817	53,242.9	
Referrals	14,281	13,481.4	
Pathology	49,564	45,594.3	
Imaging	9,943	9,876.8	
Other investigations	849	753.2	
(a)	Other treatments excludes injections for immunisations/vaccinations (raw n = 6,008, weighted 
n = 5,917) (see Chapter 10).

4	The participating GPs
This chapter reports data collected between April 2009 and March 2010 about the participating GPs and their practices from the 12th year of the BEACH program. Data on GP and practice characteristics are reported for each year from 2000–01 to 2009–10 in the 10-year summary report General practice activity in Australia 2000–01 to 2009–10: 10 year data tables.1
4.1	Characteristics of the GP participants
All participants returned a GP profile questionnaire, although some were incomplete. The results are provided in Table 4.1. Of the 988 participants:
•	56% were male, and 35% were aged 55 years and over
•	more than half had been in general practice for more than 20 years
•	71% had graduated in Australia
•	33% spent more than 40 hours each week on direct patient care services
•	24% conducted some consultations in a language other than English
•	More than 50% were Fellows of the RACGP, and 7% were Fellows of the Australian College of Rural and Remote Medicine (ACRRM)
•	25% bulk-billed Medicare for all patients and 74% bulk-billed for selected patients; only 1% did not bulk bill Medicare for any patient consultations
•	54% had provided care in a residential aged care facility in the previous month
•	69% practised in Major cities (classified using the Australian Standard Geographical Classification)
•	39% were in practices of fewer than five individual GPs, and 19% were in practices of more than 10 individual GPs
•	Two-thirds (64%) were in practices of fewer than five full-time equivalent GPs
•	79% of the GPs worked in a practice that employed practice nursing staff—for almost two-thirds of these, (59.4%) the practice employed less than two full-time equivalents 
(35–45 hours per week) 
•	91% worked in an accredited practice
•	almost half had a pathology laboratory or collection centre co-located at the practice
•	45% worked in a practice that provided their own or cooperative after-hours care, and 53% in a practice that used a deputising service for after-hours patient care (multiple responses allowed)
•	65% worked in a teaching practice for undergraduates, junior doctors, registrars, or all three.
Those interested in the clinical activity of overseas-trained doctors will find more information in Bayram et al. (2007) Clinical activity of overseas-trained doctors practising in general practice in Australia.55 
Readers interested in the effects of GP age on clinical practice will find more information in Charles et al. (2006) The independent effect of age of general practitioner on clinical practice.56 
Table 4.1: Characteristics of participating GPs and their practices
GP characteristic	Number(a)	Per cent of GPs(a)
(n = 988)	
Sex (missing = 0)		
	Male	557	56.4	
	Female	431	43.6	
Age (missing = 6)		
	< 35 years	70	7.1	
	35–44 years	210	21.4	
	45–54 years	360	36.7	
	55+ years	342	34.8	
Years in general practice (missing = 7)		
	< 2 years	11	1.1	
	2–5 years	87	8.9	
	6–10 years	121	12.3	
	11–19 years	229	23.3	
	20+ years	533	54.3	
Place of graduation (missing = 1)		
	Australia	697	70.6	
	Asia	87	9.8	
	United Kingdom	87	8.8	
	Africa	51	5.2	
	Europe	20	2.0	
	New Zealand	19	1.9	
	Other	16	1.6	
Direct patient care hours (worked) per week (missing = 15)		
	≤ 10 hours	3	0.3	
	11–20 hours	100	10.3	
	21–40 hours	547	56.2	
	41–60 hours	300	30.8	
	61+ hours	23	2.4	
Consult in languages other than English (missing = 3)		
	< 25% of consultations	182	18.5	
	25–50% of consultations	35	3.6	
	> 50% of consultations	18	1.8	
Currently in general practice training program (missing = 6)	35	3.6	
Fellow of RACGP (missing = 4)	526	53.5	
Fellow of ACRRM (missing = 26)	71	7.4	
(continued)

Table 4.1 (continued): Characteristics of participating GPs and their practices
GP characteristic	Number(a)	Per cent of GPs(a)
 (n = 988)	
Bulk-billing(b) (missing = 4)		
	All patients	245	24.9	
	Some patients	728	74.0	
	No patients	11	1.1	
Patient care provided in previous month(b) 		
	In a residential aged care facility (missing = 5)	532	54.1	
	As a salaried/sessional hospital medical officer (missing = 20)	117	12.1	

Practice location by RRMA (missing = 0)		
	Capital	616	62.4	
	Other metropolitan	84	8.5	
	Large rural	72	7.3	
	Small rural	70	7.1	
	Other rural	131	13.3	
	Remote central	4	0.4	
	Other remote, offshore	11	1.1	
Practice location by ASGC remoteness structure (missing = 0)		
	Major cities	684	69.2	
	Inner regional	200	20.2	
	Outer regional	90	9.1	
	Remote	11	1.1	
	Very remote	3	0.3	
Size of practice—number of individual GPs (missing = 11)		
	Solo	90	9.2	
	2–4 	293	30.0	
	5–9 	404	41.4	
	10–14	132	13.5	
	15+ 	58	5.9	
Size of practice—full-time equivalent GPs (missing = 51)		
	1.0–1.99	142	15.2	
	2.0–2.99	153	16.3	
	3.0–3.99	153	16.3	
	4.0–4.99	152	16.2	
	5.0–9.99	270	28.8	
	10.0–14.99	52	5.6	
	15+	15	1.6	
(continued)

Table 4.1 (continued): Characteristics of participating GPs and their practices
GP characteristic	Number(a)	Per cent of GPs(a)
 (n = 988)	
Practice nurse at major practice address (missing = 11)	772	79.0	
Number of individual practice nurses (missing = 20)		
	1	194	 20.0	
	2 	206	 21.3	
	3	160	 16.5	
	4–5 	151	15.6	
	6+	52	5.4	
Number of full-time equivalent practice nurses (missing = 61; 
unspecified = 30)(d)		
	< 1(d)	88	9.8	
	1.0–1.99 	323	36.0	
	2.0–2.99	175	19.5	
	3.0–3.99	62	6.9	
	4.0+	44	4.9	
Accredited practice (missing = 8)	892	91.0	
Co-located services(c) (missing = 3)		
	Pathology laboratory/collection centre	479	48.6	
	Psychologist	433	44.0	
	Physiotherapist	286	29.0	
	Specialist	209	21.2	
	Imaging	147	14.9	
After-hours arrangements(b) (missing = 2)		
	Practice does own and/or cooperative with other practices	447	45.3	
		Practice does its own	287	29.1	
		Cooperative with other practices	175	17.8	
	Deputising service	524	53.1	
	Other arrangement	80	8.1	

Major practice a teaching practice(b) (missing = 2)		
	Not a teaching practice	349	35.4	
	Yes—for undergraduates	549	55.7	
	Yes—for junior doctors	77	7.8	
	Yes—for registrars	354	35.9	
(a)	Missing data removed.
(b)	Multiple responses allowed.
(c)	Services located/available on the same premises, in the same building or within 50 metres, available on a daily or regular basis.
(d)	30 GPs answered ’2’, ‘3’, ‘4’, ‘5’ or ‘6’ to individual practice nurse but ‘0’ to FTE - these are tabulated as ‘unspecified’ and not included in the denominator as numerators could not be determined; 36 GPs answered ‘1’ to individuals but ‘0’ to FTE – these were included in the ‘<1’ FTE group.
Note: RRMA—Rural, Remote and Metropolitan Areas classification; ASGC—Australian Standard Geographical Classification; 
RACGP—Royal Australian College of General Practitioners; ACRRM—Australian College of Rural and Remote Medicine.

4.2	Computer use at GP practices
As computers are increasingly being used by GPs in their clinical activity, the GP profile questionnaire was redesigned in 2008–09 so that more comprehensive information could be collected about the uses to which computers are put in a general practice clinical environment (see Appendix 2). In particular, more specific information was collected about pathology test ordering and receipt of results, and whether the medical records used were paper only, a mix of paper and electronic medical records, or whether the practice was completely paperless in this regard.
Table 4.2 shows the proportion of individual participating GPs who used computers for each of nine listed activities.
•	Only 2.2% of GPs did not use a computer at all for clinical purposes.
•	Computers were used mainly for prescribing, receiving pathology results electronically and for internet use.
•	84.6% of GPs were producing prescriptions electronically.
•	71.9% were receiving pathology results online, half were producing and printing pathology orders, and 54% were ordering pathology electronically.
•	Almost two-thirds (64.2%) had electronic medical records exclusively (that is, were paperless).
•	Almost one-third (30.2%) reported maintaining a hybrid record where some patient information is kept electronically and some on paper records (for the same patients).
Table 4.2: Computer applications available/used at major practice address
Computer use	Number	Per cent of GPs 
(n = 988)(a)	
Not at all	22	2.2	
Internet/email only	14	1.4	
Prescribing	835	84.6	
Internet	763	77.3	
Email	35	3.5	
Pathology ordering (online)	176	17.8	
Produce/print pathology orders only	533	54.0	
Pathology results receipt (on line)	710	71.9	
Medical records—complete (paperless)	634	64.2	
Partial/hybrid records	298	30.2	
Paper records only	20	2.0	
Imaging/other tests	528	53.5	
Missing	1	—	
(a)	Missing data removed.
Further information about reported individual GP use of computers at the practice can be found in Henderson et al. (2006) Extent and utilisation of computerisation in Australian general practice.57 Those interested in the effect of computerisation on quality of care in general practice will find more detailed information in Henderson (2007) The effect of computerisation on the quality of care in Australian general practice.58
4.3	Changes in characteristics of the GPs over the decade 2000–01 to 2009–10
Changes over the decade 2000–01 to 2009–10 are described in detail in the accompanying report General practice activity in Australia 2000–01 to 2009–10: 10 year data tables.1 Briefly, the major changes noted were:
•	the proportion of GP participants who were female increased over time
•	the proportion of GPs who were younger than 44 years decreased, whereas the proportion aged 45 years or more increased over the decade
•	reflecting the increase in the age of GP participants, the proportion who had worked in general practice for more than 20 years also increased significantly over time
•	the proportion of GPs in solo practice and smaller practices decreased significantly, and the proportion of GPs in practices with five or more practitioners steadily increased
•	the proportion of participants holding the Fellowship of the RACGP increased over the decade
•	fewer practices are providing after-hours care on their own, or in cooperation with other practices, than a decade earlier.

5	The encounters
This chapter describes the content and type of encounters recorded in the 2009–10 BEACH year. Data about the encounters are also reported for each year from 2000–01 to 2009–10 in the 10-year report General practice activity in Australia 2000–01 to 2009–10: 10 year data tables.1 
5.1	Content of the encounters
In 2009–10, details of 101,349 encounters (weighted data) were available for 988 GPs. The content of these encounters is summarised in Table 5.1. Reasons for encounter (RFEs) and problems managed are expressed as rates per 100 encounters. Each management action is presented in terms of both a rate per 100 encounters and a rate per 100 problems managed, with 95% confidence limits.
•	On average, patients gave 155 RFEs, and GPs managed about 153 problems per 100 encounters.
•	Chronic problems accounted for 35% of all problems managed, being managed at a rate of 54 chronic problems per 100 encounters.
•	New problems accounted for 39% of all problems, being managed at a rate of 59 per 100 encounters.
•	Work-related problems were managed at a rate of 2.5 per 100 encounters.
•	Medications were the most common treatment choice, at a rate of 70 per 100 problems managed. Most of these medications were prescribed (rather than supplied or advised), at a rate of 54 per 100 problems managed.
•	Clinical treatments (such as advice and counselling) were provided at a rate of 23 per 100 problems, and procedures undertaken at a rate of 11 per 100 problems.
•	For every 100 problems managed there were 9 referrals for care to other providers, most often to medical specialists (6 referrals per 100 problems), and less often to allied health professionals (3 referrals per 100 problems).
•	GPs ordered 29 pathology tests/batteries of tests and 6 imaging tests in the management of every 100 problems (Table 5.1).

Table 5.1: Summary of morbidity and management
Variable	Number	Rate per 100 encounters (n = 101,349)	95% LCL	95% UCL	Rate per 100 problems 
(n = 155,373)	95% LCL	95% UCL	
General practitioners	988	—	—	—	—	—	—	
Encounters	101,349	—	—	—	—	—	—	
Reasons for encounter	157,071	155.0	153.1	156.8	—	—	—	
Problems managed	155,373	153.3	151.1	155.5	—	—	—	
	New problems	59,851	59.1	57.6	60.5	38.5	37.6	39.5	
	Chronic problems	54,866	54.1	52.2	56.1	35.5	34.3	36.3	
	Work-related	2,529	2.5	2.3	2.7	1.6	1.5	1.8	
Medications	108,001	106.6	103.6	109.5	69.5	67.9	71.1	
	Prescribed	84,540	83.4	80.6	86.2	54.4	52.8	56.0	
	GP-supplied	13,829	13.6	12.7	14.6	8.9	8.3	9.5	
	Advised OTC	9,632	9.5	8.7	10.3	6.2	5.7	6.7	
Other treatments	53,243	52.5	49.8	55.3	34.3	32.6	36.0	
	Clinical*	35,484	35.0	32.6	37.4	22.8	21.3	24.3	
	Procedural*	17,759	17.5	16.5	18.6	11.4	10.8	12.1	
Referrals	13,481	13.3	12.8	13.8	8.7	8.4	9.0	
	Medical specialist*	8,562	8.4	8.1	8.8	5.5	5.3	5.7	
	Allied health services*	3,971	3.9	3.7	4.2	2.6	2.4	2.7	
	Hospital*	362	0.4	0.3	0.4	0.2	0.2	0.3	
	Emergency department*	202	0.2	0.2	0.2	0.1	0.1	0.2	
	Other medical services*	80	0.1	0.1	0.1	0.1	0.0	0.1	
	Other referrals*	304	0.3	0.2	0.4	0.2	0.2	0.2	
Pathology	45,594	45.0	43.1	46.9	29.3	28.2	30.4	
Imaging	9,877	9.8	9.3	10.1	6.4	6.1	6.6	
Other investigations	753	0.7	0.7	0.8	0.5	0.4	0.5	
*	Includes multiple ICPC-2 or ICPC-2 PLUS codes (see Appendix 4, <www.aihw.gov.au/publications/index.cfm/subject/19>).
Note: LCL—lower confidence limit; UCL—upper confidence limit; OTC—over-the-counter.
5.2	Encounter type
During the first 7 years of the BEACH program, where one (or more) MBS/DVA item number was claimable for the encounter, GP participants were instructed to record only one item number. Where multiple item numbers (for example, an A1 item such as ‘standard surgery consultation’ and a procedural item number) were claimable for an encounter, GPs were instructed to record the lower of the item numbers (usually an A1 item number).
From the 2005–06 BEACH data year, changes to the BEACH form were made to capture practice nurse activity associated with the GP–patient consultations. One of these changes was to allow GPs to record multiple (up to three) Medicare item numbers per encounter.
For comparability with earlier years, in tables 5.3, 5.4 and 5.5 only one item number per Medicare/DVA-claimable encounter has been counted. Selection of one item number was undertaken on a priority basis: consultation item numbers override incentive item numbers, which override procedural item numbers, which override other Medicare item numbers. Table 5.6 provides a breakdown of all item numbers recorded by the GPs. Chapter 13 gives a more specific description for each of the practice nurse Medicare item numbers recorded.
Table 5.2 provides an overview of the MBS/DVA item numbers recorded in BEACH in 
2009–10. At least one MBS/DVA item number was recorded at 89,307 encounters. A single item number was recorded at three-quarters of BEACH encounters said to be claimable from the MBS/DVA (76.2%).
Table 5.2: Overview of MBS items recorded
Variable	Number	Per cent of MBS/DVA encounters
(n = 89,307)	
Encounters at which one MBS item was recorded	68,007	76.2	
Encounters at which two MBS items were recorded	19,531	21.9	
Encounters at which three MBS items were recorded	1,769	2.0	
Total encounters at which at least one item was recorded	89,307	100.0	

Table 5.3 shows the breakdown of MBS or DVA items of service according to whether the MBS item associated with the encounter was a GP or practice nurse item. Direct encounters are defined as those where the patient was physically seen by the GP. At indirect encounters, the patient was not physically seen by the GP. Of the 89,307 MBS/DVA items of service recorded (counting only one item number per encounter), 94.9% of encounters related to GP items of service. 
Practice nurse item numbers were recorded at 0.2% of encounters not accompanied by a GP item of service. This figure is not indicative of all practice nurse item numbers recorded. See Chapter 13 for more information about practice nurse activity. 
Table 5.3 Breakdown of MBS/DVA items of service according to provider (counting one item number per encounter)
Type of encounter	Number	Per cent of encounters(a) (n = 93,862)	95% LCL	95% UCL	
MBS/DVA GP item of service 	89,113	94.9	94.5	95.4	
MBS/DVA practice nurse item of service (no related GP item)	194	0.2	0.1	0.3	
Direct encounters 	94	0.1	0.1	0.1	
Indirect encounters 	84	0.1	0.0	0.1	
Unspecified as direct or indirect	15	0.0	0.0	0.0	
MBS/DVA item of service (all encounters)(b)	89,307	95.1	94.7	95.6	
(a)	Missing data removed from analysis (n = 7,487).
(b)	Includes direct encounters at which either a GP or a practice nurse item was recorded.
Note: LCL—lower confidence limit; UCL—upper confidence limit; MBS—Medicare Benefits Schedule; DVA—Department of Veterans’ Affairs.

Table 5.4 reports the breakdown of encounter type (by payment source), counting a single Medicare item number per encounter (where applicable). 
•	Direct encounters (patient was seen by the GP) accounted for 98.4% of all encounters.
•	Indirect encounters (where the patient was not seen by the GP) accounted for 1.6% of all encounters.
•	Direct encounters where the GP indicated that no charge was made occurred infrequently, accounting for 0.5% of encounters.
•	The vast majority of all direct encounters (95.0%) were claimable either through Medicare or the DVA.
•	Encounters payable through workers compensation accounted for 2% of encounters.
•	Encounters payable through other sources (including hospital-paid encounters) accounted for 0.9% of encounters.
Table 5.4: Type of encounter at which a source of payment was recorded for the encounter (counting one item number per encounter)
Type of encounter	Number	Per cent of encounters(a)
(n = 93,862)	95% LCL	95% UCL	Per cent of direct encounters
(n = 92,352)	
Indirect encounters(b)	1,495	1.6	1.3	1.9	—	
Direct encounters	92,352	98.4	98.1	98.7	100.0	
	MBS/DVA items of service (direct encounters only)(c)	89,201	95.0	94.6	95.5	96.6	
	Workers compensation	1,843	2.0	1.8	2.1	2.0	
	Other paid (hospital, state, etc)	821	0.9	0.6	1.1	0.9	
	No charge	486	0.5	0.4	0.7	0.5	
Practice nurse only items (unspecified as direct or indirect)	15	0.0	0.0	0.0	—	
Total	93,862	100.0	—	—	—	
(a)	Missing data removed from analysis (n = 7,487).
(b)	Five encounters involving chronic disease management or case conference items were recorded as indirect encounters.
(c)	Includes direct encounters at which either a GP or a practice nurse item (or both) was recorded.
Note: LCL—lower confidence limit; UCL—upper confidence limit; MBS—Medicare Benefits Schedule; DVA—Department of Veterans’ Affairs.

Table 5.5 provides a summary of the MBS items recorded in BEACH, counting one item number per encounter. This provides comparable data about item numbers recorded to those reported in previous years. 
•	Standard surgery consultations accounted for 82% of MBS/DVA‑claimable GP consultations, and for 78% of all encounters for which a payment source was recorded. 
•	9% of MBS/DVA claimable encounters were long or prolonged surgery consultations.
•	Home visits, residential aged care consultations and hospital consultations were also all relatively rare. 
•	About 1% of encounters were claimable as GP mental health care items. Chronic disease management items, health assessments and case conference items were not recorded often. 
Table 5.5: Summary of GP only MBS/DVA items recorded (counting one item number per encounter)
MBS/DVA item	Number	Rate per 100 encounters(a)
(n = 93,862)	95% LCL	95% UCL	Per cent of Medicare-paid 
GP items 
(n = 89,113)	
Short surgery consultations	1,987	2.1	1.8	2.4	2.2	
Standard surgery consultations	73,075	77.9	76.6	79.1	82.0	
Long surgery consultations	7,359	7.8	7.3	8.4	8.3	
Prolonged surgery consultations	419	0.5	0.4	0.5	0.5	
Home visits	604	0.6	0.5	0.8	0.7	
Hospital	254	0.3	0.2	0.4	0.3	
Residential aged care facility	1,128	1.2	0.9	1.5	1.3	
Health assessments	324	0.4	0.3	0.4	0.4	
Chronic disease management items	863	0.9	0.8	1.0	1.0	
Case conferences	13	0.0	0.0	0.0	0.0	
GP mental health care	1,107	1.2	1.0	1.3	1.2	
Incentive payments	150	0.2	0.1	0.2	0.2	
Other items	1,829	2.0	1.2	2.7	2.1	
Surgical operations	338	0.4	0.3	0.4	0.4	
Therapeutic procedures	464	0.5	0.4	0.6	0.5	
Acupuncture	88	0.1	0.0	0.2	0.1	
Other items	939	1.0	0.2	1.8	1.1	
Total MBS/DVA items of service (GPs only)	89,113	94.9	94.5	95.4	100.0	
(a)	Encounters with missing payment source were removed from analysis (n = 7,487). Denominator used for analysis = 93,862.
Note: LCL—lower confidence limit; UCL—upper confidence limit; MBS—Medicare Benefits Schedule; DVA—Department of Veterans’ Affairs.

Table 5.6 provides the distribution of all Medicare item numbers recorded across Medicare item number groups. Overall, there were 112,374 MBS item numbers recorded in BEACH in 2009–10. At encounters where at least one MBS item was recorded an average of 1.3 items were written.
Surgery consultations (including short, standard, long and prolonged) were the most commonly recorded type of item number, at 93% of the encounters where at least one item was recorded. They accounted for 74% of all MBS items recorded in BEACH.
The second most commonly recorded were items for bulk-billed incentive payments, which accounted for 14% of all items recorded. Items for hospital, residential aged care and home visits together accounted for 1.8% of all MBS items. Practice nurse items (including items for practice nurses conducting health assessments) accounted for 3.7% of all MBS items, and were recorded at 4.7% of encounters at which at least one MBS item was recorded. For a more detailed breakdown of practice nurse item numbers, and related data on practice nurse activity, refer to Chapter 13.
Table 5.6: Medicare item number distribution across item number groups
Items/encounters	All MBS items(a)	At least one item recorded(b)	
	Number	Per cent	Number	Per cent	95% LCL	95% UCL	
Surgery consultations	82,840	73.7	82,840	92.8	91.8	93.7	
Hospital, residential aged care and home visits	1,987	1.8	1,987	2.2	1.8	2.7	
Health assessments	406	0.4	405	0.5	0.4	0.5	
Chronic disease management items (including case conferences)	1,615	1.4	1,089	1.2	1.1	1.4	
Incentive payments	168	0.1	168	0.2	0.1	0.2	
Acupuncture	97	0.1	97	0.1	0.0	0.2	
Bulk-billed incentive payment	15,902	14.2	15,898	17.8	15.8	19.8	
Practice nurse services—health assessments	14	0.0	14	0.0	0.0	0.0	
Practice nurse services—other	4,202	3.7	4,158	4.7	4.1	5.2	
Allied health items	1	0.0	1	0.0	0.0	0.0	
Diagnostic procedures and investigations	587	0.5	570	0.6	0.5	0.7	
Therapeutic procedures	578	0.5	568	0.6	0.5	0.8	
Surgical operations	1,428	1.3	1,384	1.6	1.4	1.7	
Diagnostic imaging services	10	0.0	10	0.0	0.0	0.0	
Pathology services	230	0.2	230	0.3	0.2	0.3	
GP mental health care items	1,274	1.1	1,272	1.4	1.3	1.6	
Other items	1,035	0.9	1,033	1.2	0.3	2.0	
Total items	112,374	100.0	—	—	—	—	
(a)	Up to three MBS items could be recorded at each encounter. 
(b)	Identifies encounters where at least one item from a MBS group was recorded.
Note: LCL—lower confidence limit; UCL—upper confidence limit; MBS—Medicare Benefits Schedule.

5.3	Consultation length
In a subsample of 33,613 BEACH encounters containing start and finish times for all MBS/DVA-claimable encounters, the mean length of consultation in 2009–10 was 15.3 minutes (95% CI: 15.0–15.5). The median length was 14.0 minutes (results not tabled).
For A1 MBS/DVA-claimable encounters, the mean length of consultation in 2009–10 was 15.0 minutes (95% CI: 14.7–15.2), and the median length was 13.0 minutes (results not tabled). Methods describing the substudy from which data on consultation length are collected are described in Section 2.5.
The determinants of consultation length have been investigated by Britt et al. (2004) in Determinants of GP billing in Australia: content and time59 and Britt et al. (2005) in Determinants of consultation length in Australian general practice.60 
5.4	Changes in the encounters over the decade 2000–01 to 2009–10
An overview of changes in general practice encounters over the decade to 2009–10 can be found in the companion report General practice activity in Australia 2000–01 to 2009–10: 10 year data tables.1 The major changes between 2000–01 and 2009–10 are summarised below.
•	There was a significant increase in the average number of problems managed at encounter, from 145 per 100 encounters in 2000–01 to 153 in 2009–10 and this was reflected in increases in the number of new problems and the number of chronic problems managed per 100 encounters.
•	The number of work-related problems managed decreased over the decade from 3.3 to 2.5 per 100 encounters. 
•	The number of medications prescribed significantly decreased, while the number supplied direct to the patient by the GP significantly increased 
•	The number of procedures undertaken per 100 encounters rose by almost 50% from 12.2 to 17.5 per 100 encounters. 
•	There was an increased rate of referrals, which was due to increases in both referrals to specialists and to allied health services 
•	Pathology test/battery order rates increased by more than 50%, while the increase in imaging test orders was significant but smaller than that in pathology ordering.
Of the encounters claimable from Medicare/DVA:
•	short surgery consultations as a proportion of all Medicare/DVA claimed consultations varied considerably over the study period, more than doubling from their low of 1.0% in 2004–05 to 2.2% in 2009–10 
•	the proportion designated residential aged care visits, chronic disease management items, or health assessments all increased significantly 
•	home visits halved over the decade from 1.5% of these MBS/DVA claimable encounters to less than 1%.

6	The patients
This chapter reports data collected between April 2009 and March 2010 about the characteristics of patients and their reasons for encounter from the 12th year of the BEACH program. Data on patient characteristics and reasons for encounter are reported for each year from 2000–01 to 2009–10 in the 10 year summary report General practice activity in Australia 2000–01 to 2009–10: 10 year data tables.1
6.1	Age–sex distribution of patients at encounter
The age–sex distribution of patients at the 101,349 encounters is shown in Figure 6.1. Females accounted for the greater proportion of encounters (56.9%) (Table 6.1). This was reflected across all age groups except for children aged less than 15 years (Figure 6.1).
Patients aged less than 25 years accounted for 21.1% of encounters; those aged 25–44 years for 22.9%; those aged 45–64 years accounted for 28.2% and those aged 65 years and over for 27.8% of encounters (Table 6.1).
Figure 6.1: Age–sex distribution of patients at encounter
Vertical bar chart showing, for male and female, per cnt on y-axis and age group (years) on x-axis.
Note: Missing data removed. The distributions will not agree perfectly with those in Table 6.1 because of missing data in either age or 
sex fields.
The relationship between patient age, patient general practice attendance rates and the age distribution of the Australian population is reported in General practice activity in Australia, health priorities and policies 1998 to 2008.12 
6.2	Other patient characteristics
In earlier years of BEACH, for patient variables other than age and sex in Table 6.1, the encounter form included only a ‘yes’ option for GPs to indicate whether the variable applied to the patient. The absence of a ‘no’ option prevented any differentiation between a ‘no’ response and no answer at all (that is, ‘missing’ data). From 2001, the encounter form was redesigned to include both ‘yes’ and ‘no’ response options, to allow identification of the proportion of ‘missing’ data.
For comparison purposes, the reporting of these characteristics with the missing data counted as ‘no’ responses continued, as footnoted in the relevant table in previous reports. As 9 years of data are now available, these variables will be reported with the missing responses removed. In the companion report General practice activity in Australia 2000–01 to 2009–10: 10 year data tables1, the results for the 10 years to 2009–10 have been reanalysed and are presented with the ‘missing’ data removed. The proportion of ‘missing’ responses was small enough to have not changed the outcome for any variable, but it is important to give as comprehensive and clear a picture as possible of the data, and this change allows for reporting of these variables consistently with all others variables reported. 
Table 6.1 provides a view of other characteristics of the patients. In summary:
•	the patient was new to the practice at 7.7% of encounters
•	nearly half the encounters were either with patients who held a Commonwealth concession card (45.9%) or a Repatriation health card (2.9%)
•	at 9.0% of encounters the patient was from a non-English-speaking background
•	at 1.3% of encounters the patient identified themselves as an Aboriginal or Torres Strait Islander person.

Table 6.1: Characteristics of the patients at encounters
Patient characteristics	Number	Per cent of encounters (n = 101,349)	95% LCL	95% 
UCL	
Sex (missing)(a)	931	—	—	—	
	Males	43,317	43.1	42.3	43.9	
	Females	57,100	56.9	56.1	57.7	
Age group (missing)(a)	781	—	—	—	
	< 1 year	2,080	2.1	1.9	2.3	
	1–4 years	4,760	4.7	4.5	5.0	
	5–14 years	5,707	5.7	5.4	6.0	
	15–24 years	8,657	8.6	8.2	9.0	
	25–44 years	23,000	22.9	22.1	23.6	
	45–64 years	28,386	28.2	27.7	28.8	
	65–74 years	12,768	12.7	12.2	13.2	
	75+ years	15,209	15.1	14.3	16.0	
New patient to practice (missing)(a)	1,307	—	—	—	
	New patient to practice	7,710	7.7	7.1	8.3	
	Patient seen previously	92,332	92.3	91.7	92.9	
Commonwealth concession card status (missing)(a)	8,046	—	—	—	
	Has a Commonwealth concession card	42,790	45.9	44.3	47.4	
	No Commonwealth concession card	50,513	54.1	52.6	55.7	
Repatriation health card status (missing)(a)	9,496	—	—	—	
	Has a repatriation health card	2,705	2.9	2.7	3.2	
	No repatriation health card	89,148	97.1	96.8	97.3	
Language status (missing)(a)	9,528	—	—	—	
	Non-English-speaking background	8,230	9.0	7.3	10.6	
	English-speaking background	83,591	91.0	89.4	92.7	
Indigenous status (missing)(a)	9,499	—	—	—	
	Aboriginal and/or Torres Strait Islander	1,166	1.3	1.0	1.6	
	Non-Indigenous	90,684	98.7	98.4	99.0	
(a)	Missing data removed.
Note: LCL—lower confidence limit; UCL—upper confidence limit.

6.3	Patient reasons for encounter
International interest in reasons for encounter (RFEs) has been developing over the past three decades. RFEs reflect the patient’s demand for care and can provide an indication of service use patterns, which may benefit from intervention on a population level.61 
RFEs are those concerns and expectations that patients bring to the GP. Participating GPs were asked to record at least one and up to three patient RFEs in words as close as possible to those used by the patient, before the diagnostic or management process had begun. These reflect the patient’s view of their reasons for consulting the GP. RFEs can be expressed in terms of one or more symptoms (for example, ‘itchy eyes’, ‘chest pain’), in diagnostic terms (for example, ‘about my diabetes’, ‘for my hypertension’), a request for a service (‘I need more scripts’, ‘I want a referral’), an expressed fear of disease or a need for a check-up.
Patient RFEs can have a one-to-one, one-to-many, many-to-one and many-to-many relationship to problems managed. That is, the patient may describe a single RFE that relates to a single problem managed at the encounter, one RFE that relates to multiple problems, multiple RFEs that relate to a single problem managed, or multiple RFEs that relate to multiple problems managed at the encounter.
Number of reasons for encounter
There were 157,071 RFEs recorded at 101,349 encounters in 2009–10. At 57.7% of encounters only one RFE was recorded, at 29.7% of encounters two RFEs were recorded and at 12.6% of encounters three RFEs were recorded (Table 6.2). Patients presented on average with 155.0 RFEs per 100 encounters, or about 1.5 RFEs per encounter (Table 6.3).
Table 6.2: Number of patient reasons for encounter
Number of RFEs at encounter	Number of encounters
(n = 101,349)	Per cent of
encounters	95%
LCL	95%
UCL	
One RFE	58,439	57.7	56.5	58.9	
Two RFEs	30,099	29.7	29.0	30.4	
Three RFEs	12,811	12.6	11.9	13.4	
Total	101,349	100.0	—	—	
Note: RFEs—reasons for encounter; LCL—lower confidence limit; UCL—upper confidence limit.
Reasons for encounter by ICPC-2 chapter
The distribution of patient RFEs by ICPC-2 chapter and the most common RFEs within each chapter are presented in Table 6.3. Each chapter and individual RFE is expressed as a percentage of all RFEs and as a rate per 100 encounters with 95% confidence limits. 
RFEs of a general and unspecified nature were presented at a rate of 42.7 per 100 encounters, with requests for prescriptions and test results most frequently recorded. RFEs related to the respiratory system arose at a rate of 22.8 per 100 encounters, while those related to the musculoskeletal system were recorded at a rate of 15.4 per 100 encounters, and those relating to skin at a rate of 14.8 per 100 encounters (Table 6.3). 
Table 6.3: Distribution of patient reasons for encounter, by ICPC-2 chapter and most frequent individual reasons for encounter within chapter
Reasons for encounter	Number	Per cent of
total RFEs(a)
(n = 157,071)	Rate per 100
encounters(b)
(n = 101,349)	95% 
LCL	95% 
UCL	
General and unspecified	43,291	27.6	42.7	41.5	43.9	
	Prescription—NOS	8,759	5.6	8.6	8.1	9.2	
	General check-up*	4,508	2.9	4.4	4.2	4.7	
	Results tests/procedures NOS	7,085	4.5	7.0	6.6	7.4	
	Immunisation/vaccination NOS	3,277	2.1	3.2	2.9	3.5	
	Fever	2,261	1.4	2.2	2.0	2.5	
	Administrative procedure NOS	2,095	1.3	2.1	1.9	2.2	
	Weakness/tiredness 	1,398	0.9	1.4	1.3	1.5	
	Observation/health education/advice/diet NOS	1,202	0.8	1.2	1.0	1.3	
	Chest pain NOS	1,013	0.6	1.0	0.9	1.1	
	Blood test NOS	1,010	0.6	1.0	0.9	1.1	
	Other referrals NEC 	989	0.6	1.0	0.9	1.1	
	Other reason for encounter NEC 	926	0.6	0.9	0.8	1.0	
	Follow-up encounter unspecified 	857	0.5	0.8	0.7	0.9	
	Trauma/injury NOS	855	0.5	0.8	0.8	0.9	
	Clarify/discuss patient RFE/demand NOS	745	0.5	0.7	0.6	0.8	
Respiratory	23,138	14.7	22.8	21.9	23.8	
	Cough	6,954	4.4	6.9	6.4	7.3	
	Immunisation/vaccination—respiratory	3,093	2.0	3.1	2.7	3.4	
	Throat symptom/complaint	2,980	1.9	2.9	2.7	3.2	
	Upper respiratory tract infection	2,243	1.4	2.2	1.9	2.5	
	Sneezing/nasal congestion	1,572	1.0	1.6	1.3	1.8	
	Asthma	792	0.5	0.8	0.7	0.9	
	Shortness of breath/dyspnoea	743	0.5	0.7	0.7	0.8	
Musculoskeletal	15,632	10.0	15.4	14.7	16.2	
	Back complaint*	3,142	2.0	3.1	2.9	3.3	
	Knee symptom/complaint	1,381	0.9	1.4	1.2	1.5	
	Shoulder symptom/complaint	1,157	0.7	1.1	1.0	1.3	
	Foot/toe symptom/complaint	1,071	0.7	1.1	1.0	1.1	
	Leg/thigh symptom/complaint	943	0.6	0.9	0.8	1.0	
	Neck symptom/complaint	839	0.5	0.8	0.7	1.0	
	Injury musculoskeletal NOS	768	0.5	0.8	0.7	0.8	
Skin	14,997	9.6	14.8	14.3	15.3	
	Rash*	2,435	1.6	2.4	2.2	2.6	
	Skin symptom/complaint, other	1,625	1.0	1.6	1.5	1.7	
	Skin check-up*	1,294	0.8	1.3	1.0	1.5	
	Swelling*	988	0.6	1.0	0.9	1.1	
	Laceration/cut	763	0.5	0.8	0.7	0.8	
(continued)
Table 6.3 (continued): Distribution of patient reasons for encounter, by ICPC-2 chapter and most frequent individual reasons for encounter within chapter
Reasons for encounter	Number	Per cent of
total RFEs(a)
(n = 157,071)	Rate per 100
encounters(b)
(n = 101,349)	95% 
LCL	95% 
UCL	
Cardiovascular	10,157	6.5	10.0	9.5	10.5	
	Cardiac check-up*	4,468	2.8	4.4	4.1	4.7	
	Hypertension*	2,060	1.3	2.0	1.8	2.3	
Digestive	9,935	6.3	9.8	9.5	10.1	
	Abdominal pain*	1,620	1.0	1.6	1.5	1.7	
	Diarrhoea	1,261	0.8	1.2	1.1	1.4	
	Vomiting	916	0.6	0.9	0.8	1.0	
Psychological	8,572	5.5	8.5	8.0	8.9	
	Depression*	2,189	1.4	2.2	2.0	2.3	
	Anxiety*	1,109	0.7	1.1	1.0	1.2	
	Sleep disturbance	1,086	0.7	1.1	1.0	1.2	
Endocrine and metabolic	6,169	3.9	6.1	5.8	6.4	
	Diabetes (non-gestational)*	1,168	0.7	1.2	1.0	1.3	
	Prescription—endocrine/metabolic	903	0.6	0.9	0.8	1.0	
Female genital system	4,807	3.1	4.7	4.4	5.1	
	Female genital check-up/Pap smear*	1,776	1.1	1.8	1.6	1.9	
Neurological	4,424	2.8	4.4	4.1	4.6	
	Headache	1,515	1.0	1.5	1.4	1.6	
	Vertigo/dizziness	975	0.6	1.0	0.9	1.0	
Ear	3,630	2.3	3.6	3.4	3.8	
	Ear pain	1,274	0.8	1.3	1.2	1.4	
Pregnancy and family planning	3,478	2.2	3.4	3.2	3.7	
	Pre/post natal check-up*	896	0.6	0.9	0.7	1.0	
Urology	2,672	1.7	2.6	2.5	2.8	
Eye	2,370	1.5	2.3	2.2	2.5	
Blood	1,385	0.9	1.4	1.2	1.5	
Male genital system	1,237	0.8	1.2	1.1	1.4	
Social	1,175	0.8	1.2	1.1	1.3	
Total RFEs	157,071	100.0	155.0	153.1	156.8	
(a)	Only individual RFEs accounting for >= 0.5% of total RFEs are included.
(b)	Figures do not total 100, as more than one RFE can be recorded at each encounter.
*	Includes multiple ICPC-2 or ICPC-2 PLUS codes (see Appendix 4, <www.aihw.gov.au/publications/index.cfm/subject/19>).
Note: RFEs—reasons for encounter; LCL—lower confidence limit; UCL—upper confidence limit; NOS—not otherwise specified; 
NEC—not elsewhere classified.

Distribution of RFEs by ICPC-2 component
The distribution of patient RFEs by ICPC-2 component is presented in Table 6.4, expressed as a percentage of all RFEs and as a rate per 100 encounters with 95% confidence limits. 
Table 6.4 uses the updated component groupings of ICPC-2 codes, released by the Wonca International Classification Committee in 2004.30 The ‘diagnosis, disease’ group has also been expanded to provide data about infections, injuries, neoplasms, congenital anomalies and ‘other’ diagnoses. These component groupings are not comparable with those published in previous years. The updated component groupings have been applied to previous years data and are reported in General practice activity in Australia 2000–01 to 2009–10: 10 year data tables.1
Nearly half (42.0%) of patient RFEs were expressed in terms of symptoms or complaints (for example, ‘tired’, ‘fever’). RFEs were described in diagnostic terms for 17.4% of RFEs (for example, ‘about my diabetes’, ‘for my depression’). The remaining 40.6% of RFEs were described in terms of processes of care, such as requests for a health check, to renew scripts, to get a referral, to find out test results or to get a medical certificate.
Table 6.4: Distribution of RFEs by ICPC-2 component
ICPC-2 component(a)	Number	Per cent of 
total RFEs
(n = 157,071)	Rate per 100
encounters(b)
(n = 101,349)	95% 
LCL	95% 
UCL	
Symptoms and complaints	65,909	42.0	65.0	63.1	67.0	
Diagnosis, diseases	31,150	19.8	30.7	29.1	32.4	
	Infections	8,147	5.2	8.0	7.5	8.6	
	Injuries	4,704	3.0	4.6	4.4	4.9	
	Neoplasms	1,105	0.7	1.1	1.0	1.2	
	Congenital anomalies	266	0.2	0.3	0.2	0.3	
	Other diagnoses	16,927	10.8	16.7	15.6	17.8	
Diagnostic and preventive procedures	27,325	17.4	27.0	26.0	27.9	
Medications, treatments and therapeutics	14,285	9.1	14.1	13.4	14.8	
Results	8,227	5.2	8.1	7.7	8.6	
Referrals and other RFEs	7,714	4.9	7.6	7.2	8.1	
Administrative	2,461	1.6	2.4	2.2	2.6	
Total RFEs	157,071	100.0	155.0	153.1	156.8	
(a)	This table uses the updated component groupings of ICPC-2 codes released by the Wonca International Classification Committee in 2004. These groupings are not comparable with those reported in previous years. Readers interested in changes should refer to General practice activity in Australia 2000–01 to 2009–10: 10 year data tables.1
(b)	Figures do not total 100, as more than one RFE can be recorded at each encounter.
Note: RFEs—reasons for encounter; LCL—lower confidence limit; UCL—upper confidence limit.
Most frequent patient reasons for encounter
The 30 most commonly recorded RFEs, listed in order of frequency in Table 6.5, accounted for more than half of all RFEs. In this analysis the specific ICPC-2 chapter to which an across‑chapter RFE belongs is disregarded, so that, for example, ‘check-up—all’ includes all check-ups from all body systems, irrespective of whether the type was specified.
Of the top 30 most common RFEs, the majority were descriptive of symptoms such as cough, throat complaint, back complaint and rash. However, four of the top five RFEs reflected requests for a process of care (that is, requests for check-up, prescription, test result and immunisation), and together accounted for a quarter of all RFEs (25.9%) (Table 6.5).
Table 6.5: Most frequent patient reasons for encounter
Patient reason for encounter	Number	Per cent of 
total RFEs
(n = 157,071)	Rate per 100 encounters(a)
(n = 101,349)	95% 
LCL	95% 
UCL	
Check-up—all*	14,103	9.0	13.9	13.3	14.5	
Prescription—all*	11,757	7.5	11.6	11.0	12.2	
Test results*	8,227	5.2	8.1	7.7	8.6	
Cough	6,954	4.4	6.9	6.4	7.3	
Immunisation/vaccination—all*	6,542	4.2	6.5	5.9	7.0	
Back complaint*	3,142	2.0	3.1	2.9	3.3	
Throat symptom/complaint	2,980	1.9	2.9	2.7	3.2	
Rash*	2,435	1.6	2.4	2.2	2.6	
Fever	2,261	1.4	2.2	2.0	2.5	
Upper respiratory tract infection	2,243	1.4	2.2	1.9	2.5	
Depression*	2,189	1.4	2.2	2.0	2.3	
Administrative procedure NOS	2,095	1.3	2.1	1.9	2.2	
Hypertension*	2,060	1.3	2.0	1.8	2.3	
Skin symptom/complaint, other	1,625	1.0	1.6	1.5	1.7	
Abdominal pain*	1,620	1.0	1.6	1.5	1.7	
Sneezing/nasal congestion	1,572	1.0	1.6	1.3	1.8	
Headache	1,515	1.0	1.5	1.4	1.6	
Weakness/tiredness 	1,398	0.9	1.4	1.3	1.5	
Knee symptom/complaint	1,381	0.9	1.4	1.2	1.5	
Ear pain/earache	1,274	0.8	1.3	1.2	1.4	
Diarrhoea	1,261	0.8	1.2	1.1	1.4	
Observation/health education/advice NOS	1,202	0.8	1.2	1.0	1.3	
Diabetes—all*	1,171	0.7	1.2	1.0	1.3	
Shoulder symptom/complaint	1,157	0.7	1.1	1.0	1.3	
Anxiety*	1,109	0.7	1.1	1.0	1.2	
Sleep disturbance	1,086	0.7	1.1	1.0	1.2	
Foot/toe symptom/complaint	1,071	0.7	1.1	1.0	1.1	
Chest pain NOS	1,013	0.6	1.0	0.9	1.1	
Blood test NOS	1,010	0.6	1.0	0.9	1.1	
Other referrals NEC 	989	0.6	1.0	0.9	1.1	
Subtotal	88,447	56.3	—	—	—	
Total RFEs	157,071	100.0	155.0	153.1	156.8	
(a)	Figures do not total 100, as more than one RFE can be recorded at each encounter. Also, only the most frequent RFEs are included.
*	Includes multiple ICPC-2 or ICPC-2 PLUS codes (see Appendix 4, <www.aihw.gov.au/publications/index.cfm/subject/19>).
Note: RFEs—reasons for encounter; LCL—lower confidence limit; UCL—upper confidence limit; NOS—not otherwise specified; NEC—not elsewhere classified.
6.4	Changes in patients and reasons for encounter over the past decade (2000–01 to 2009–10) 
An overview of changes in referrals over the decade to 2009–10 can be found in Chapter 11 of the companion report General practice activity in Australia 2000–01 to 2009–10: 10 year data tables.1 
Major changes identified between 2000–01 and 2009–10 are summarised below. 
The proportion of encounters with younger patients significantly decreased in all age groups, except for those aged less than 1 year. Counteracting this, the proportion with patients aged 45 year and over increased in all age groups (45–64 years, 65–74 years and 75 years and over). This increase was largely due to increased attendance by those aged 75 years and over (from 11.3% in 2000–01 to 15.1% in 2009–10). 
Extrapolation suggests that, combined with the increasing overall attendance rate, this changing patient distribution resulted nationally in an increase of about 190,000 encounters with younger patients aged less than 45 years, and a national increase of about 16 million encounters with patients aged 45 years and over in 2009–10 compared with 2000–01.
There was a significant increase in the number of RFEs per 100 encounters across the decade, from 151.0 in 2000–01 to 155.0 in 2009–10. Fewer patients were giving single RFEs and more were giving two RFEs. This increase in RFEs is probably related to the increasing proportion of encounters with older people, who are more likely to visit for multiple chronic disease management. 
There was a significant decrease in the rate of RFEs described as symptoms and complaints, and increases in rates of patient presentations for check-ups, medications, referrals, tests, test results and administrative procedures. The increase in patients’ requests for tests and test results ties in with the increased use of pathology testing over the decade (discussed in Chapter 12). 

7	Problems managed
A ‘problem managed’ is a formal statement of the provider’s understanding of a health problem presented by the patient, family or community, and can be described in terms of a disease, symptom or complaint, social problem or ill-defined condition managed at the encounter. As GPs were instructed to record each problem at the most specific level possible from the information available, the problem managed may at times be limited to the level of a presenting symptom.
At each patient encounter, up to four problems could be recorded by the GP. A minimum of one problem was compulsory. The status of each problem to the patient—new (first presentation to a medical practitioner) or old (follow-up of previous problem)—was also indicated. The concept of a principal diagnosis, which is often used in hospital statistics, is not adopted in studies of general practice where multiple problem management is the norm rather than the exception. Further, the range of problems managed at the encounter often crosses multiple body systems and may include undiagnosed symptoms, psychosocial problems or chronic disease, which makes the designation of a principal diagnosis difficult. Thus, the order in which the problems were recorded by the GP is not significant. All problems managed in general practice are included in this section, including those that involved management by a practice nurse at the recorded encounter. Problems that included management by a practice nurse are reported specifically in Chapter 13.
There are two ways to describe the relative frequency of problems managed: as a percentage of all problems managed in the study, or as a rate at which problems are managed per 100 encounters. Where groups of problems are reported (for example, cardiovascular problems) it must be remembered that more than one of that type of problem (such as hypertension and heart failure) may have been managed at a single encounter. In considering these results, the reader must be mindful that although a rate per 100 encounters for a single ungrouped problem (for example, asthma, 2.4 per 100 encounters) can be regarded as equivalent to ‘asthma is managed at 2.4% of encounters’, such a statement cannot be made for grouped concepts (ICPC-2 chapters and those marked with asterisks in the tables).
Data on problems managed in Australian general practice from the BEACH study are reported for each year from 2000–01 to 2009–10 in the 10-year summary report General practice activity in Australia 2000–01 to 2009–10: 10 year data tables.1 
7.1	Number of problems managed at encounter
There were 155,373 problems managed, at a rate of 153.3 per 100 encounters in 2009–10 (Table 5.1). Table 7.1 shows the number of problems managed at each encounter. Only one problem was managed at more than 60% of encounters, two problems were managed at 25% of encounters, and almost 10% involved the management of three problems. The management of four problems at an encounter was less common (3% of encounters).

Table 7.1: Number of problems managed at an encounter
Number of problems managed at encounter	Number of encounters	Per cent	95% LCL	95% UCL	
One problem	63,065	62.2	60.9	63.5	
Two problems	25,744	25.4	24.7	26.1	
Three problems	9,340	9.2	8.7	9.7	
Four problems	3,200	3.2	2.8	3.5	
Total	101,349	100.0	—	—	
Note: LCL—lower confidence limit; UCL—upper confidence limit.
Figure 7.1 shows the age–sex-specific rates of problems managed. The number of problems managed at encounter increased steadily with the age of the patient. 
Significantly more problems were managed overall at encounters with female patients (156.2 per 100 encounters, 95% CI: 153.9–158.6) than at those with male patients (149.7 per 100 encounters, 95% CI: 147.3–152.1) (results not tabled). Figure 7.1 demonstrates that this difference was particularly evident in the 15–24 year age group.
 Figure 7.1: Age–sex-specific rates of problems managed per 100 encounters with 95% CI
Stacked line chart showing, for males and females, rate per 100 encounters on y-axisa nd age group (years) on x-axis.

7.2	Problems managed by ICPC-2 chapter
The frequency and the distribution of problems managed, by ICPC-2 chapter, are presented in Table 7.2. Rates per 100 encounters and the proportion of total problems are provided at the ICPC-2 chapter level, and for frequent individual problems within each chapter. Only those individual problems accounting for at least 0.5% of all problems managed are listed in the table, in decreasing order of frequency.
The most common problems managed were:
•	those classified to the respiratory system (22.2 per 100 encounters)—in particular upper respiratory tract infection, respiratory immunisations, acute bronchitis and asthma
•	problems of a general and unspecified nature (19.4 per 100 encounters)—such as check-ups, immunisations and prescriptions
•	musculoskeletal problems (16.8 per 100 encounters)—particularly arthritis and back complaints 
•	cardiovascular problems (16.7 per 100 encounters)—such as hypertension and atrial fibrillation
•	skin problems (16.5 per 100 encounters)—such as contact dermatitis and malignant skin neoplasms (Table 7.2).

Table 7.2: Distribution of problems managed, by ICPC-2 chapter and most frequent individual problems within chapter 
Problem managed 	Number	Per cent total problems(a) (n = 155,373)	Rate per 100 encounters(b) (n = 101,349)	95% 
LCL	95% 
UCL	
Respiratory	22,449	14.5	22.2	21.4	22.9	
	Upper respiratory tract infection	6,081	3.9	6.0	5.5	6.4	
	Immunisation/vaccination—respiratory	4,199	2.7	4.1	3.7	4.6	
	Acute bronchitis/bronchiolitis	2,467	1.6	2.4	2.2	2.6	
	Asthma	2,110	1.4	2.1	1.9	2.3	
	Sinusitis 	1,365	0.9	1.3	1.2	1.5	
	Tonsillitis*	895	0.6	0.9	0.8	1.0	
	Chronic obstructive pulmonary disease	841	0.5	0.8	0.7	0.9	
General and unspecified	19,649	12.7	19.4	18.6	20.2	
	General check-up*	3,013	1.9	3.0	2.7	3.2	
	Immunisation/vaccination—general	2,926	1.9	2.9	2.7	3.1	
	Prescription NOS	1,658	1.1	1.6	1.4	1.9	
	Results tests/procedures NOS	1,456	0.9	1.4	1.3	1.6	
	Viral disease, other/NOS	1,128	0.7	1.1	1.0	1.3	
	Abnormal results/investigations NOS	904	0.6	0.9	0.8	1.0	
	Administrative procedures NOS	895	0.6	0.9	0.8	1.0	
	Observation/health education/advice NOS	750	0.5	0.7	0.6	0.9	
(continued)

Table 7.2 (continued): Distribution of problems managed, by ICPC-2 chapter and most frequent individual problems within chapter
Problem managed 	Number	Per cent total problems(a) (n = 155,373)	Rate per 100 encounters(b) (n = 101,349)	95% 
LCL	95% 
UCL	
Musculoskeletal	17,057	11.0	16.8	16.1	17.6	
	Arthritis—all*	3,997	2.6	3.9	3.6	4.3	
		Osteoarthritis*	2,945	1.9	2.9	2.6	3.2	
	Back complaint*	2,755	1.8	2.7	2.5	2.9	
	Sprain/strain*	1,469	0.9	1.4	1.3	1.6	
	Bursitis/tendonitis/synovitis NOS	1,154	0.7	1.1	1.0	1.2	
	Osteoporosis	883	0.6	0.9	0.8	1.0	
	Fracture*	877	0.6	0.9	0.8	0.9	
	Injury musculoskeletal NOS	797	0.5	0.8	0.7	0.9	
Cardiovascular	16,897	10.9	16.7	16.0	17.4	
	Hypertension*	9,192	5.9	9.1	8.6	9.6	
	Atrial fibrillation/flutter	1,184	0.8	1.2	1.1	1.3	
	Ischaemic heart disease*	1,173	0.8	1.2	1.0	1.3	
	Cardiac check-up*	991	0.6	1.0	0.8	1.1	
Skin	16,756	10.8	16.5	15.9	17.1	
	Contact dermatitis	1,642	1.1	1.6	1.5	1.7	
	Malignant neoplasm skin	1,285	0.8	1.3	1.1	1.4	
	Solar keratosis/sunburn	1,269	0.8	1.3	1.1	1.4	
	Laceration/cut	853	0.5	0.8	0.8	0.9	
	Skin disease, other	840	0.5	0.8	0.7	0.9	
Endocrine and metabolic	12,819	8.3	12.7	12.1	13.2	
	Diabetes—non-gestational*	3,731	2.4	3.7	3.5	3.9	
	Lipid disorders	3,526	2.3	3.5	3.2	3.7	
	Vitamin/nutritional deficiency	1,173	0.8	1.2	1.0	1.3	
Psychological	12,285	7.9	12.1	11.6	12.7	
	Depression*	4,329	2.8	4.3	4.0	4.5	
	Anxiety*	1,800	1.2	1.8	1.6	1.9	
	Sleep disturbance	1,476	1.0	1.5	1.3	1.6	
	Tobacco abuse	773	0.5	0.8	0.7	0.8	
Digestive	10,815	7.0	10.7	10.3	11	
	Gastroenteritis*	1,453	0.9	1.4	1.3	1.6	
	Oesophageal disease	2,548	1.6	2.5	2.3	2.7	
Female genital system	5,535	3.6	5.5	5.1	5.8	
	Female genital check-up/Pap smear*	1,729	1.1	1.7	1.5	1.9	
	Menopausal symptom/complaint	748	0.5	0.7	0.7	0.8	
(continued)

Table 7.2 (continued): Distribution of problems managed, by ICPC-2 chapter and most frequent individual problems within chapter
Problem managed 	Number	Per cent total problems(a) (n = 155,373)	Rate per 100 encounters(b) (n = 101,349)	95% 
LCL	95% 
UCL	
Pregnancy and family planning	3,890	2.5	3.8	3.6	4.1	
	Pregnancy*	1,467	0.9	1.4	1.3	1.6	
	Oral contraception*	1,090	0.7	1.1	1.0	1.2	
Ear	3,733	2.4	3.7	3.5	3.8	
	Acute otitis media/myringitis	1,021	0.7	1.0	0.9	1.1	
	Excessive ear wax	771	0.5	0.8	0.7	0.8	
Neurological	3,506	2.3	3.5	3.3	3.6	
Urology	3,266	2.1	3.2	3.1	3.4	
	Urinary tract infection*	1,780	1.1	1.8	1.6	1.9	
Eye	2,501	1.6	2.5	2.3	2.6	
Male genital system	1,899	1.2	1.9	1.7	2.0	
Blood	1,520	1.0	1.5	1.4	1.6	
Social	796	0.5	0.8	0.7	0.9	
Total problems	155,373	100.0	153.3	151.1	155.5	
(a)	Figures do not total 100, as more than one problem can be recorded at each encounter.
(b)	Only those individual problems accounting for ≥ 0.5% of total problems are included.
*	Includes multiple ICPC-2 or ICPC-2 PLUS codes (see Appendix 4, <www.aihw.gov.au/publications/index.cfm/subject/19>).
Note: LCL—lower confidence limit; UCL—upper confidence limit; NOS—not otherwise specified.
7.3	Problems managed by ICPC-2 component
Problems managed in general practice may also be examined using the components of the ICPC-2 classification to provide a more thorough understanding of the types of problems managed during general practice encounters. Table 7.3 lists the distribution of problems managed by ICPC-2 component. It uses the updated component groupings of ICPC-2 codes, released by the Wonca International Classification Committee in 2004.30 The ‘diagnosis, disease’ group has also been expanded to provide data about infections, injuries, neoplasms, congenital anomalies and ‘other’ diagnoses. These component groupings are not comparable with those published in previous years. The updated component groupings have been applied to previous years data and are reported in General practice activity in Australia 
2000–01 to 2009–10: 10 year data tables.1
In the BEACH program, participating GPs are instructed to record the problem being managed at the encounter at the highest diagnostic level possible using the currently available evidence. As such, two-thirds of problems were expressed as diagnoses or diseases (66.6%), with the majority of other problems described as symptoms or complaints (17.4%), or as diagnostic or preventive procedures (11.0%) such as check-ups. However, in some situations, rather than providing clinical details about the problem under management, a ‘process’ was recorded. That is, the problem was described in such terms as a ‘prescription’, ‘test result’, as a referral or as an administrative procedure.

Table 7.3: Distribution of problems managed, by ICPC-2 component
ICPC-2 component(a)	Number	Per cent of
total problems (n = 155,373)	Rate per 100
encounters(b)
(n = 101,349)	95%
 LCL	95%
 UCL	
Diagnosis, diseases	103,542	66.6	102.2	100.3	104.1	
	Infections	25,302	16.3	25.0	24.2	25.7	
	Injuries	6,999	4.5	6.9	6.6	7.2	
	Neoplasms	4,731	3.0	4.7	4.3	5.0	
	Congenital anomalies	687	0.4	0.7	0.6	0.7	
	Other diagnoses	65,822	42.4	65.0	63.0	66.9	
Symptoms and complaints	27,103	17.4	26.7	25.9	27.5	
Diagnostic and preventive procedures	17,111	11.0	16.9	16.0	17.7	
Medications, treatments and therapeutics	3,493	2.3	3.5	3.1	3.8	
Results	1,798	1.2	1.8	1.6	2.0	
Referrals and other RFEs	1,284	0.8	1.3	1.1	1.4	
Administrative	1,041	0.7	1.0	0.9	1.1	
Total problems 	155,373	100.0	153.3	151.1	155.5	
(a)	This table uses the updated component groupings of ICPC-2 codes released by the Wonca International Classification Committee in 2004. These groupings are not comparable with those reported in previous years. Readers interested in changes should refer to General practice activity in Australia 2000–01 to 2009–10: 10 year data tables.1
(b)	Figures do not total 100, as more than one problem can be managed at each encounter.
Note: LCL—lower confidence limit; UCL—upper confidence limit; RFE—reason for encounter. 
7.4	Most frequently managed problems
Table 7.4 shows the most frequently managed individual problems in general practice, in decreasing order of frequency. These 30 problems accounted for more than half of all problems managed.
In this analysis, the specific chapter to which ‘across chapter concepts’ (for example, check‑ups, immunisation/vaccination and prescriptions) apply is ignored, and the concept is grouped with all similar concepts regardless of body system. For example, immunisation/vaccination includes vaccinations for influenza, childhood diseases, and hepatitis.
The most common problems managed were hypertension (9.1 per 100 encounters), immunisation/vaccination (7.3 per 100), check‑ups (6.6 per 100), upper respiratory tract infection (URTI) (6.0 per 100), and depression (4.3 per 100) (Table 7.4). 
The far right-hand column in Table 7.4 lists the percentage of each problem that was new to the patient. The problem is considered new if it is a new problem to the patient or a new episode of a recurrent problem, and the patient has not been treated for that problem by any medical practitioner before. This can provide a measure of general practice incidence. For example, only 5.4% of all contacts with diabetes were new diagnoses. In contrast, more than three-quarters of URTI problems were new to the patient, suggesting that the majority of people attend the GP for URTI only once per episode. 

Table 7.4: Most frequently managed problems
Problem managed	Number	Per cent of
total problems (n = 155,373)	Rate per 100
encounters(a) (n = 101,349)	95%
LCL	95%
UCL	Per cent
 new problems(b)	
Hypertension*	9,192	5.9	9.1	8.6	9.6	5.7	
Immunisation/vaccination—all*	7,354	4.7	7.3	6.7	7.8	59.4	
Check-up—all*	6,730	4.3	6.6	6.3	7.0	42.5	
Upper respiratory tract infection	6,081	3.9	6.0	5.5	6.4	77.3	
Depression*	4,329	2.8	4.3	4.0	4.5	15.6	
Arthritis—all*	3,997	2.6	3.9	3.6	4.3	17.6	
Diabetes—all*	3,747	2.4	3.7	3.5	3.9	5.4	
Lipid disorders	3,526	2.3	3.5	3.2	3.7	12.5	
Back complaint*	2,755	1.8	2.7	2.5	2.9	24.5	
Oesophageal disease	2,548	1.6	2.5	2.3	2.7	16.2	
Acute bronchitis/bronchiolitis	2,467	1.6	2.4	2.2	2.6	71.1	
Prescription—all*	2,337	1.5	2.3	2.0	2.6	4.7	
Asthma	2,110	1.4	2.1	1.9	2.3	17.1	
Anxiety*	1,800	1.2	1.8	1.6	1.9	20.6	
Test results*	1,798	1.2	1.8	1.6	2.0	30.1	
Urinary tract infection*	1,780	1.1	1.8	1.6	1.9	62.1	
Contact dermatitis	1,642	1.1	1.6	1.5	1.7	46.2	
Sleep disturbance	1,476	1.0	1.5	1.3	1.6	21.7	
Sprain/strain*	1,469	0.9	1.4	1.3	1.6	62.0	
Pregnancy*	1,467	0.9	1.4	1.3	1.6	38.2	
Gastroenteritis*	1,453	0.9	1.4	1.3	1.6	76.0	
Sinusitis acute/chronic	1,365	0.9	1.3	1.2	1.5	63.6	
Malignant neoplasm skin	1,285	0.8	1.3	1.1	1.4	54.5	
Solar keratosis/sunburn	1,269	0.8	1.3	1.1	1.4	48.7	
Atrial fibrillation/flutter	1,184	0.8	1.2	1.1	1.3	6.6	
Ischaemic heart disease*	1,173	0.8	1.2	1.0	1.3	8.4	
Vitamin/nutritional deficiency	1,173	0.8	1.2	1.0	1.3	33.0	
Bursitis/tendonitis/synovitis NOS	1,154	0.7	1.1	1.0	1.2	57.3	
Viral disease, other/NOS	1,128	0.7	1.1	1.0	1.3	77.7	
Abnormal test results*	1,091	0.7	1.1	1.0	1.2	45.2	
Subtotal 	80,880	52.1	—	—	—	—	
Total problems	155,373	100.0	153.3	151.1	155.5	38.5	
(a)	Figures do not total 100, as more than one problem can be recorded at each encounter. Also, only more frequently managed problems are included.
(b)	The proportion of problems of this type that were new problems (the first presentation of a problem, including the first presentations of a recurrence of a previously resolved problem, but excluding the presentation of a problem first assessed by another provider).
*	Includes multiple ICPC-2 or ICPC-2 PLUS codes (see Appendix 4, <www.aihw.gov.au/publications/index.cfm/subject/19>).
Note: LCL—lower confidence limit; UCL—upper confidence limit; NOS—not otherwise specified.
7.5	Most common new problems
For each problem managed, participating GPs are asked to indicate whether the problem under management is a new problem for the patient. The problem is considered new if it is a new problem to the patient or a new episode of a recurrent problem, and the patient has not been treated for that problem by any medical practitioner before. Table 7.5 lists the most common new problems managed in general practice, in decreasing order of frequency. Overall, 59,851 problems (38.5% of all problems) were specified as being new, being managed at a rate of 59.1 per 100 encounters.
The most common new problems managed were largely acute and included upper respiratory tract infections (4.6 per 100 encounters), immunisations/vaccinations (4.3), acute bronchitis (1.7), general check-ups (1.5) and urinary tract infection (1.1) (Table 7.5).
The far right-hand column of this table shows the new cases of this problem as a proportion of total contacts with this problem. This provides an idea of the incidence of each problem. For example, the 675 new cases of depression represented only 16% of all GP contacts with diagnosed depression, suggesting that by far the majority of contacts for depression were for ongoing management. In contrast, three out of four gastroenteritis cases were first consultations to a medical practitioner for this episode of gastroenteritis, the balance (24%) being follow-up consultations for this episode of this problem. This indicates that most patients only require one visit to a GP for the management of an episode of gastroenteritis.
Table 7.5: Most frequently managed new problems
New problem managed	Number	Per cent of total
 new problems (n = 59,851)	Rate per 100
 encounters(a)
(n = 101,349)	95%
 LCL	95% UCL	Per cent 
of this problem(b)	
Upper respiratory tract infection	4,700	7.9	4.6	4.3	5.0	77.3	
Immunisation/vaccination—all*	4,368	7.3	4.3	3.9	4.7	59.4	
Acute bronchitis/bronchiolitis	1,754	2.9	1.7	1.6	1.9	71.1	
General check-up*	1,503	2.5	1.5	1.3	1.6	49.9	
Urinary tract infection*	1,105	1.8	1.1	1.0	1.2	62.1	
Gastroenteritis*	1,105	1.8	1.1	1.0	1.2	76.0	
Sprain/strain*	911	1.5	0.9	0.8	1.0	62.0	
Viral disease, other/NOS	876	1.5	0.9	0.7	1.0	77.7	
Sinusitis acute/chronic 	869	1.5	0.9	0.8	0.9	63.6	
Female genital check-up/Pap smear*	778	1.3	0.8	0.7	0.9	45.0	
Contact dermatitis 	760	1.3	0.7	0.7	0.8	46.2	
Acute otitis media/myringitis	719	1.2	0.7	0.6	0.8	70.4	
Malignant neoplasm skin	700	1.2	0.7	0.6	0.8	54.5	
Depression*	675	1.1	0.7	0.6	0.7	15.6	
Back complaint*	674	1.1	0.7	0.6	0.7	24.5	
Bursitis/tendonitis/synovitis NOS	662	1.1	0.7	0.6	0.7	57.3	
Tonsillitis*	658	1.1	0.6	0.6	0.7	73.5	
Solar keratosis/sunburn	618	1.0	0.6	0.5	0.7	48.7	
Pregnancy*	560	0.9	0.6	0.5	0.6	38.2	
(continued)
Table 7.5 (continued): Most frequently managed new problems
New problem managed	Number	Per cent of total
 new problems (n = 59,851)	Rate per 100
 encounters(a)
(n = 101,349)	95%
 LCL	95% UCL	Per cent 
of this problem(b)	
Conjunctivitis, infectious	552	0.9	0.5	0.5	0.6	79.0	
Test results*	542	0.9	0.5	0.4	0.6	30.1	
Osteoarthritis*	528	0.9	0.5	0.4	0.6	17.9	
Hypertension*	521	0.9	0.5	0.4	0.6	5.7	
Influenza	495	0.8	0.5	0.3	0.6	80.3	
Abnormal test results*	493	0.8	0.5	0.4	0.5	45.2	
Skin disease, other	446	0.7	0.4	0.4	0.5	53.1	
Observation/health education/
advice NOS	445	0.7	0.4	0.3	0.5	59.3	
Excessive ear wax	444	0.7	0.4	0.4	0.5	57.5	
Lipid disorders*	441	0.7	0.4	0.4	0.5	12.5	
Skin infection, post traumatic	416	0.7	0.4	0.3	0.5	65.5	
Subtotal	29,318	49.0	—	—	—	—	
Total new problems	59,851	100.0	59.1	57.6	60.5	—	
(a)	Figures do not total 100, as more than one new problem can be recorded at each encounter. Also, only the most frequently managed new problems are included.
(b)	The proportion of total contacts with this problem that were accounted for by new problems.
*	Includes multiple ICPC-2 or ICPC-2 PLUS codes (see Appendix 4, <www.aihw.gov.au/publications/index.cfm/subject/19>).
Note: LCL—lower confidence limit; UCL—upper confidence limit; NOS—not otherwise specified.
7.6	Most frequently managed chronic problems
To identify chronic conditions, a list classified according to ICPC-2, based on work undertaken by O’Halloran et al. in 200434 and regularly updated by O’Halloran (see grouper G84 <www.fmrc.org.au/icpc2plus/demonstrator.htm>), was applied to the BEACH data set. More than one-third (35.3%) of the problems managed in general practice were chronic. At least one chronic problem was managed at 40.7% of encounters (95% CI: 39.7–41.8), and chronic problems were managed at an average rate of 54.1 per 100 encounters.
In other parts of this chapter, both chronic and non-chronic conditions (for example, diabetes and gestational diabetes) may have been grouped together when reporting (for example, diabetes—all*, Table 7.4). In this section, only problems regarded as chronic have been included in the analysis. For this reason, the condition labels and figures in this analysis may differ from those in Table 7.4. Where the group used for the chronic analysis differs from that used in other analyses in this report, they are marked with a double asterisk. Codes included in the chronic group can be found in Appendix 5.
Table 7.6 shows the most frequently managed chronic problems in decreasing order of frequency. These 30 chronic problems together accounted for 79.9% of all chronic problems managed, and for 28.2% of all problems managed. The top six chronic problems made up almost half of all chronic problems managed: non-gestational hypertension (16.7% of chronic conditions), depressive disorder (7.8%), chronic arthritis (7.3%), non-gestational diabetes (6.8%), lipid disorders (6.4%), and oesophageal disease (4.6%) (Table 7.6).
Table 7.6: Most frequently managed chronic problems
Chronic problem managed	Number	Per cent of total
chronic problems (n = 54,866)	Rate per 100
 encounters(a)
(n = 101,349)	95%
 LCL	95%
UCL	
Hypertension (non-gestational)**	9,182	16.7	9.1	8.6	9.5	
Depressive disorder**	4,304	7.8	4.2	4.0	4.5	
Chronic arthritis**	3,985	7.3	3.9	3.6	4.3	
Diabetes (non-gestational)**	3,731	6.8	3.7	3.5	3.9	
Lipid disorders**	3,526	6.4	3.5	3.2	3.7	
Oesophageal disease	2,548	4.6	2.5	2.3	2.7	
Asthma	2,110	3.8	2.1	1.9	2.3	
Malignant neoplasm of skin	1,285	2.3	1.3	1.1	1.4	
Atrial fibrillation/flutter	1,184	2.2	1.2	1.1	1.3	
Ischaemic heart disease**	1,173	2.1	1.2	1.0	1.3	
Back syndrome with radiating pain**	1,010	1.8	1.0	0.9	1.1	
Osteoporosis	883	1.6	0.9	0.8	1.0	
Chronic obstructive pulmonary disease	841	1.5	0.8	0.7	0.9	
Hypothyroidism/myxoedema	675	1.2	0.7	0.6	0.7	
Chronic skin ulcer	621	1.1	0.6	0.5	0.7	
Obesity (BMI > 30)	619	1.1	0.6	0.5	0.7	
Heart failure	572	1.0	0.6	0.5	0.6	
Migraine	563	1.0	0.6	0.5	0.6	
Gout	553	1.0	0.5	0.5	0.6	
Shoulder syndrome (excluding arthritis)**	540	1.0	0.5	0.4	0.6	
Dementia (including senile, Alzheimer’s)	475	0.9	0.5	0.4	0.6	
Anxiety disorder**	460	0.8	0.5	0.4	0.5	
Schizophrenia	435	0.8	0.4	0.4	0.5	
Chronic acne**	431	0.8	0.4	0.4	0.5	
Chronic back pain**	387	0.7	0.4	0.3	0.4	
Malignant neoplasm prostate	370	0.7	0.4	0.3	0.4	
Chronic alcohol abuse	359	0.7	0.4	0.3	0.4	
Chronic pain NOS	350	0.6	0.3	0.3	0.4	
Vertiginous syndrome	337	0.6	0.3	0.3	0.4	
Epilepsy	309	0.6	0.3	0.3	0.3	
Subtotal	43,818	79.9	—	—	—	
Total problems	54,866	100.0	54.1	52.2	56.1	
(a)	Figures do not total 100, as more than one chronic problem can be recorded at each encounter. Also, only the most frequently managed chronic problems are included.
**	Includes multiple ICPC-2 or ICPC-2 PLUS codes and indicates that this group differs from that used for analysis in other sections of this chapter, as only chronic conditions have been included in this analysis (see Appendix 5 for codes included in analysis of chronic conditions <www.aihw.gov.au/publications/index.cfm/subject/19>).
Note: LCL—lower confidence limit; UCL—upper confidence limit; BMI—body mass index.
7.7	Work-related problems managed
The work-related status of a problem under management is determined by the GP, and is defined as any problem that is likely (in the GP’s view) to have resulted from work-related activity or workplace exposure, or a pre-existing condition that has been significantly exacerbated by work activity or workplace exposure. Work-related problems accounted for 1.6% of problems and were managed at a rate of 2.5 per 100 general practice encounters in 2009–10 (Table 7.7).
The most common group of work-related problems were musculoskeletal problems, accounting for 57.1% of work-related problems and managed at a rate of 1.4 per 100 general practice encounters. Almost 1 in 10 musculoskeletal problems managed in general practice were work related. The most common musculoskeletal work-related problems were back complaint (16.2% of work-related problems), sprain and strain (10.5%), unspecified musculoskeletal injury (7.9%) and fracture (3.3%).
Work-related psychological problems accounted for 10.9% of total work-related problems, and were managed at a rate of 0.3 per 100 encounters. The most common were depression (4.4% of work-related problems), acute stress reaction (2.2%), anxiety (1.9%) and 
post-traumatic stress disorder (1.8%). Psychological work-related problems accounted for only 2.2% of total psychological problems managed in general practice. 
Check-ups and vaccinations related to the patient’s work accounted for 4.3% of work-related problems and were performed at a rate of 0.1 per 100 encounters. The majority of these were check-ups classified in the General and Unspecified chapter of ICPC-2, including pre‑employment and employment check-ups.
Other work-related problems accounted for 27.7% of work‑related problems, and included skin injuries not elsewhere classified (3.9%) and lacerations (2.8%).
Although back complaint was the most commonly managed individual work-related problem (accounting for 16.2% of work-related problems), it accounted for only 14.9% of the management of all back complaints. In contrast, post-traumatic stress disorder accounted for 1.8% of work-related problems, but represented 31.3% of all post-traumatic stress disorder problems managed (Table 7.7).

Table 7.7: Work-related problems, by type and most frequently managed individual problems
Work-related problem managed	Number	Per cent of total
work-related problems (n = 2,529)	Rate per 100
 encounters
(n = 101,349)	95%
 LCL	95%
UCL	Per cent of this problem(a) 	
Musculoskeletal problems	1,443	57.1	1.4	1.3	1.6	8.5	
	Back complaint*	410	16.2	0.4	0.3	0.5	14.9	
	Sprain/strain*	265	10.5	0.3	0.2	0.3	18.1	
	Injury musculoskeletal NOS	200	7.9	0.2	0.2	0.2	25.1	
	Fracture*	84	3.3	0.1	0.1	0.1	9.5	
	Shoulder syndrome	72	2.9	0.1	0	0.1	13.3	
	Bursitis/tendonitis/synovitis NOS	63	2.5	0.1	0	0.1	5.5	
	Acute internal knee damage	58	2.3	0.1	0	0.1	25.2	
Psychological problems	275	10.9	0.3	0.2	0.3	2.2	
	Depression*	112	4.4	0.1	0.1	0.1	2.6	
	Acute stress reaction	55	2.2	0.1	0	0.1	9.3	
	Anxiety	47	1.9	0	0	0.1	2.6	
	Post traumatic stress disorder	45	1.8	0	0	0.1	31.3	
Check-up—all* and Immunisation/vaccination—all*	109	4.3	0.1	0.1	0.1	0.9	
	General check-up*	80	3.2	0.1	0.1	0.1	2.7	
Other work-related problems	701	27.7	0.7	0.6	0.8	0.6	
	Injury skin, other	97	3.9	0.1	0.1	0.1	16.3	
	Laceration/cut	70	2.8	0.1	0	0.1	8.2	
Total work-related problems	2,529	100.0	2.5	2.3	2.7	—	
(a)	The proportion of total contacts with this problem that were accounted for by work-related problems.
*	Includes multiple ICPC-2 or ICPC-2 PLUS codes (see Appendix 4, <www.aihw.gov.au/publications/index.cfm/subject/19>).
Note: LCL—lower confidence limit; UCL—upper confidence limit; NOS—not otherwise specified. Only the most frequent individual work-related problems accounting for > 1.5% of total work-related problems are reported.
7.8	Management of back problems in 2009–10
This section uses the example of management of back problems to demonstrate how BEACH data pertaining to a selected problem can be analysed and viewed. In this section back problems is defined as: back symptom/complaint, low back symptom/complaint, back syndrome without radiating pain and back syndrome with radiating pain (ICPC-2 codes L02, L03, L84 and L86 respectively).
Back problems are commonly managed in general practice, with 3,379 recorded contacts with the problem, a management rate of 3.3 per 100 encounters with patients in 2009–10 (Figure 7.2). This represents about 3.9 million encounters at which a back problem was managed in general practice across Australia in that year.

Patient age
Patients aged 45–64 years were most likely to have back problems managed (4.7 per 100 encounters), followed by patients aged 25–44 years (3.9) and those aged 65–74 years (3.8).
Reasons for encounter 
The most common reasons for encounter given by patients were back problem (66.1 per 100 back encounters), need for a prescription (15.4) or test result (8.0), and leg and thigh symptoms (4.5). 
Other problems managed
Hypertension was the comorbidity most often managed with back problem (7.4 per 100 back problem encounters), followed by depression (4.9), immunisation/vaccination (3.5), oesophageal disease (3.1), and diabetes (2.7). 
Medications
Medications were prescribed significantly more often in the management of back problems (75.3 per 100 problems, 95% CI: 70.0–80.6) than average for all problems (54.4) in the 2009–10 BEACH year. 
The medications most often prescribed for back problems were paracetamol/codeine (12.2 per 100 back problems), oxycodone (11.7), paracetamol (8.9), tramadol (8.2), and meloxicam (4.0).
Other treatments
Other treatments were provided at a rate of 36.2 per 100 back problems. Two-thirds of these treatments were clinical treatments (22.3 per 100 back problems), of which general advice and education (4.9), counselling about the back problem (4.3), and the provision of medical certificates (3.0) were the most common. 
Procedural treatments accounted for one-third of all other treatments provided for back problems (13.9 per 100 problems), of which physical medicine and rehabilitation was the most common (7.8 per 100).
Referrals
Referrals for back problems were provided at a rate of 15.4 per 100. Referrals to allied health services (9.6 per 100 back problems), mostly to a physiotherapist, were significantly more common than referrals to medical specialists (5.2).

Figure 7.2: Management of back problems in general practice, 2009–10
Diagram.
(a)	Back problem includes the ICPC-2 rubrics: L02, L03, L84 and L86.
(b)	Age and sex-specific rate per 100 encounters in each age/sex group. 
(c)	Expressed as a rate per 100 encounters at which back problems were managed.
(d)	Expressed as a rate per 100 back problems managed.
*	Includes multiple ICPC-2 or ICPC-2 PLUS codes (see Appendix 4).
Note:	NOS—not otherwise specified; proced—procedure; NEC—not elsewhere classified; ESR—erythrocyte sedimentation rate; 
EUC—electrolytes, urea and creatinine.

Imaging
Imaging was ordered at a rate of 16.7 per 100 back problems. The most common imaging ordered were lumbar CT scan (3.0 per 100), lumbosacral x-ray (2.8), lumbosacral CT scan (2.0) and lumbar x-ray (1.7).
The ordering of lumbar CT scans is investigated in more detail in Section 12.5.
Pathology
Pathology was ordered at a rate of 7.4 tests/batteries per 100 back problems. The most common were full blood count (1.6), erythrocyte sedimentation rate (0.9) and electrolytes, urea and creatinine tests (0.6).
7.9	Changes in problems managed over the decade 2000–01 to 2009–10
Data about the problems managed in general practice from each of the past 10 years of the BEACH study, 2000–01 to 2009–10 are reported in the companion report General practice activity in Australia 2000–01 to 2009–10: 10 year data tables.1
Major changes that have occurred over the decade are summarised below.
•	There was a significant increase in the number of problems managed at encounter, from 144.5 per 100 encounters in 2000–01 to 153.3 in 2009–10. This suggests an additional 33.7 million problems were managed at GP encounters in Australia in 2009–10 than in 
2000–01. This was reflected in significant increases in the management rate of new problems (47.4 rising to 59.1 per 100 encounters), and chronic conditions (48.2 rising to 54.1 per 100 encounters) over the decade.
•	Changes in the most common individual problems managed in general practice are summarised below.
–	The management rate of immunisation/vaccination increased from 4.6 per 100 encounters in 2000–01 to 7.3 per 100 in 2009–10, about 3.9 million more occasions nationally in 2009–10 than in 2000–01.
–	The management rate of URTI decreased significantly from 6.9 per 100 encounters in 2000–01 to 6.0 in 2009–10. However, the large increase in the number of GP encounters provided in Australia (100.6 million in 2000–01 compared with 116.8 million in 2009–10) outweighed this decrease, resulting in a national increase of about 70,000 GP consultations for URTI in 2009–10 compared with 2000–01. 
–	The management rate of depression increased from 3.7 per 100 encounters in 
2000–01 to 4.3 in 2009–10, an estimated national increase of 1.3 million occasions of depression management in 2009–10 compared with 2000–01. 
–	The management rate of diabetes increased significantly from 2.8 per 100 encounters in 2000–01 to 3.7 in 2009–10, suggesting about 1.5 million more occasions where diabetes was managed in 2009–10 than in 2000–01.
–	Management of lipid disorders increased significantly from 2.9 per 100 encounters in 2000–01 to 3.5 in 2009–10, resulting in about 1.2 million more encounters nationally for lipid disorders in 2009–10 than in 2000–01. 
8	Overview of management
The BEACH survey form allows GPs to record several aspects of patient management for each problem managed at each encounter. Pharmaceutical management is recorded in detail. Other modes of treatment, including clinical treatments (for example, counselling) and procedures, recorded briefly in the GP’s own words, are also related to a single problem. Provision is made on the form for referrals and hospital admissions, and for pathology and imaging test orders, to be related to a single or multiple problems (see Appendix 1).
A summary of management at general practice encounters from 2000–01 to 2009–10 is reported for each year in the 10-year report General practice activity in Australia 2000–01 to 2009–10: 10 year data tables.1 
At the 101,349 encounters, GPs undertook 230,949 management activities in total. The most common management form was medication, either prescribed, GP-supplied, or advised for over-the-counter purchase. ‘Other treatments’ were the second most common management activity, with clinical treatments more frequent than procedural treatments (Table 8.1).
For an ‘average’ 100 GP–patient encounters, GPs provided 83 prescriptions, and 35 clinical treatments, undertook 18 procedures, made 8 referrals to medical specialists and 4 to allied health services, and placed 45 pathology test orders and 10 imaging test orders.
Table 8.1: Summary of management
Management type	Number	Rate per 100 encounters (n = 101,349)	95% LCL	95% UCL	Rate per 100 problems 
(n = 155,373)	95% LCL	95% UCL	
Medications	108,001	106.6	103.6	109.5	69.5	67.9	71.1	
	Prescribed	84,540	83.4	80.6	86.2	54.4	52.8	56.0	
	GP-supplied	13,829	13.6	12.7	14.6	8.9	8.3	9.5	
	Advised OTC	9,632	9.5	8.7	10.3	6.2	5.7	6.7	
Other treatments	53,243	52.5	49.8	55.3	34.3	32.6	36.0	
	Clinical*	35,484	35.0	32.6	37.4	22.8	21.3	24.3	
	Procedural*	17,759	17.5	16.5	18.6	11.4	10.8	12.1	
Referrals	13,481	13.3	12.8	13.8	8.7	8.4	9.0	
	Medical specialist*	8,562	8.4	8.1	8.8	5.5	5.3	5.7	
	Allied health services*	3,971	3.9	3.7	4.2	2.6	2.4	2.7	
	Hospital*	362	0.4	0.3	0.4	0.2	0.2	0.3	
	Emergency department*	202	0.2	0.2	0.2	0.1	0.1	0.2	
	Other medical services*	80	0.1	0.1	0.1	0.1	0.0	0.1	
	Other referrals*	304	0.3	0.2	0.4	0.2	0.2	0.2	
Pathology	45,594	45.0	43.1	46.9	29.3	28.2	30.4	
Imaging	9,877	9.8	9.3	10.1	6.4	6.1	6.6	
Other investigations(a)	753	0.7	0.7	0.8	0.5	0.4	0.5	
Total management activities	230,949	227.8	—	—	148.7	—	—	
(a)	Other investigations reported here include only those ordered by the GP. Other investigations in Chapter 12 include those ordered by the GP and those done by the GP or practice staff.
*	Includes multiple ICPC-2 or ICPC-2 PLUS codes (see Appendix 4, <www.aihw.gov.au/publications/index.cfm/subject/19>).
Note: LCL—lower confidence limit; UCL—upper confidence limit; OTC—over-the-counter.
Another perspective emerges in analysing the number of encounters or problems for which at least one form of management was recorded by the GP (Table 8.2). At least one management action was recorded at 91.3% of encounters and for 85.8% of problems managed.
•	At least one medication or other treatment was given for nearly three-quarters (72.8%) of the problems managed.
•	At least one medication (most commonly prescribed) was prescribed, supplied or advised for more than half (54.2%) of the problems managed.
•	At least one other treatment (most commonly clinical) was provided for nearly one-third (30.3%) of problems managed.
•	At least one referral (most commonly to a medical specialist) was made for 8.7% of problems managed.
•	At least one investigation (most commonly pathology) was requested for 18.1% of problems managed (Table 8.2).
Table 8.2: Encounters and problems for which management was recorded
Management type	Number of encounters	Per cent of total encounters(a)
(n = 101,349)	Number of problems	Per cent of total problems(a)
(n = 155,373)	
At least one management type	92,543	91.3	133,241	85.8	
	At least one medication or other treatment	82,711	81.6	113,157	72.8	
		At least one medication 	65,452	64.6	84,135	54.2	
		At least one prescription	53,078	52.4	67,088	43.2	
		At least one GP-supplied	10,680	10.5	11,173	7.2	
		At least one OTC advised	8,373	8.3	8,633	5.6	
		At least one other treatment	40,800	40.3	47,133	30.3	
		At least one clinical treatment	28,027	27.7	31,938	20.6	
		At least one procedural treatment	15,868	15.7	16,577	10.7	
	At least one referral	12,554	12.4	13,476	8.7	
		At least one referral to a specialist	8,203	8.1	8,691	5.6	
		At least one referral to allied health	3,765	3.7	3,977	2.6	
		At least one referral to hospital	362	0.4	381	0.2	
		At least one referral to emergency 	department	202	0.2	205	0.1	
		At least one referral to other medical services	80	0.1	90	0.1	
		At least one referral NOS	304	0.3	318	0.2	
	At least one investigation	24,500	24.2	28,124	18.1	
		At least one pathology order	17,982	17.7	20,571	13.2	
		At least one imaging order	8,625	8.5	8,912	5.7	
		At least one other investigation(b)	717	0.7	737	0.5	
(a)	Figures will not total 100, as multiple events may occur in one encounter or in the management of one problem at encounter.
(b)	Other investigations reported here only include those ordered by the GP. Other investigations in Chapter 12 include those ordered by the GP and those done by the GP or practice staff.
Note: OTC—over-the-counter; NOS—not otherwise specified.
The combinations of management types related to each problem were investigated. The majority of treatments occurred either as a single component or in combination with one other component. Management was provided:
•	as a single component for almost two-thirds (61.9%) of the problems managed
•	as a double component for 19.8% of problems managed
•	rarely with more than two components (results not tabled).
Table 8.3 lists the most common management combinations. Medication alone was the most common management, followed by a clinical treatment alone, and the combination of a medication and a clinical treatment. When a problem was referred to another health professional it was most likely that no other treatments were given for the problem at the encounter. This situation also applied to pathology testing.
Table 8.3: Most common management combinations
1+ medication	1+ clinical treatment	1+ procedural 
treatment	1+ referral	1+ imaging
order	1+ pathology
order	Per cent of total problems 
(n = 155,373)	Per cent 
of total encounters
 (n = 101,349)	
No recorded management	14.2	8.7	
1+ management recorded	85.8	91.3	
tick						36.5	30.6	
	tick					9.4	6.7	
tick	tick					6.5	10.5	
					tick	4.9	2.9	
			tick			4.4	3.3	
		tick				4.4	3.7	
tick					tick	3.0	4.6	
tick		tick				2.9	4.6	
				tick		2.3	1.8	
		tick			tick	1.4	1.3	
tick			tick			1.3	2.8	
	tick				tick	1.1	1.2	
tick				tick		1.1	1.8	
	tick		tick			1.1	1.1	
tick	tick				tick	0.6	1.7	
				tick 	tick	0.5	0.6	
tick	tick		tick			0.4	1.2	
tick		tick			tick	0.3	1.1	
	tick	tick				0.3	0.7	
			tick		tick	0.3	0.5	
	tick			tick		0.3	0.4	
Note: 1+—at least one specified management type.
8.1	Changes in management over the decade
2000–01 to 2009–10
Changes over the decade 2000–01 to 2009–10 are described in detail in the accompanying report General practice activity in Australia 2000–01 to 2009–10: 10 year data tables.1 In that report, changes over time are measured as change in the management of problems (that is, as a rate per 100 problems). This reflects change in how GPs are managing problems and accounts for the significant increase in the number of problems managed per encounter over the decade (see Section 7.9).
The major changes over the 10 years to 2009–10 are summarised below.
•	There was a significant decrease in the proportion of problems managed for which one or more medications were prescribed (from 51.2% to 43.2%), and in the total number of medications prescribed (from 63.9 per 100 problems managed to 54.4). 
•	There was a significant increase in the proportion of problems for which the GP supplied medication direct to the patients (from 3.8% to 7.2% of problems managed), and an increase in the total number of medications supplied in this manner (4.8 medications to 8.9 per 100 problems managed).
•	One or more procedures were undertaken in the management of a significantly greater proportion of problems managed in 2009–10 (10.7%) than in 2000–01 (8.0%). So that the rate of procedures undertaken by GPs increased from 8.4 to 11.4 per 100 problems managed over the decade. 
•	The likelihood of patients being referred for the problem being managed increased significantly (7.2% of the problems managed in 2000–01 and 8.7% in 2009–10 being referred), particularly to specialists (from 5.1% in 2000–01 to 5.6% in 2009–10), and to allied health practitioners (from 1.6% to 2.6%). There was a marginal decrease in the proportion of problems for which the patient was referred to hospital, from 0.4% to 0.2% over the 10 years. 
•	There was an increase in the likelihood of the GP ordering at least one investigation for the problems managed, 14.9% of problems being sent for investigation in 2000–01 and 18.1% in 2009–10. In 2000–01, at least one pathology test was ordered was 10.6% of problem managed, and at least one imaging test was ordered was 5.2%. By 2009–10 these proportions had significantly increased to 13.2% and 5.7% of problems, respectively. 

9	Medications
GPs could record up to four medications for each of four problems—a maximum of 16 medications per encounter. Each medication could be recorded as prescribed (the default), supplied by the GP, or recommended for over-the-counter (OTC) purchase.
•	GPs were asked to:
–	record the generic or brand name, the strength, regimen and number of repeats ordered for each medication
–	designate this as a new or continued medication for this patient for this problem.
•	Generic or brand names were entered into the database in the form recorded by the GP.
•	Medications were coded using the Coding Atlas of Pharmaceutical Substances (CAPS) system (developed by the FMRC) which is able to capture details of products at the brand and generic level. Every medication in the CAPS coding system is mapped to the international Anatomical Therapeutic Chemical (ATC) classification.62
•	The reporting of results at drug group, subgroup and generic level uses ATC levels 1, 3 and 5. The most frequently prescribed, supplied or advised individual medications are reported at the CAPS generic level, the equivalent of ATC Level 5, because ATC does not include many over-the-counter medications that arise in BEACH. Further, some ATC level 5 labels are not specific enough for clarity.
Data on medications are reported for each year from 2000–01 to 2009–10 in the 10-year summary report General practice activity in Australia 2000–01 to 2009–10: 10 year data tables.1
Readers interested in adverse drug events will find more detailed information from the BEACH program in Miller et al. (2006) Adverse drug events in general practice patients in Australia.63
9.1	Source of medications
As reported in Chapter 8, a total of 108,001 medications were recorded, at rates of 107 per 100 encounters and 70 per 100 problems managed.
•	Almost four out of five of all medications (78.3%) were prescribed.
•	One in eight (12.8%) medications was supplied to the patient by the GP.
•	There were 8.9% of medications recommended by the GP for OTC purchase.
When these results are extrapolated to the 116.8 million general practice Medicare-claimed encounters in Australia in 2009–10, GPs in Australia:
•	prescribed medications more than 97.4 million times
•	supplied 15.9 million medications directly to the patient
•	recommended medications for OTC purchase 11.1 million times.

9.2	Prescribed medications
There were 84,540 prescriptions recorded, at rates of 83 per 100 encounters and 54 per 100 problems managed (Table 8.1). GPs recorded 80.5% of prescribed medications by brand (proprietary) name and 19.5% by their generic (non-proprietary) name (results not tabled).
On a per problem basis:
•	no prescription was given for 56.8% of all problems managed
•	one prescription was given for 34.7% of problems managed
•	two prescriptions were given for 6.3% of problems managed
•	three or four prescriptions were given for 2.1% of problems managed (Figure 9.1). 
Figure 9.1: Number of medications prescribed per problem
Vertical bar chart showing per cent of problems on y-axis and number of medications prescribes on x-axis.

Number of repeats
For 64,718 prescriptions (76.6% of all prescriptions) the GPs recorded ‘number of repeats’. The distribution of the specified number of repeats (from nil to more than five) is provided in Figure 9.2. For 34.2% of these prescriptions, the GP specified that no repeats had been prescribed, and for 35.8% five repeats were ordered. The latter proportion reflects the PBS provision of 1 month’s supply and five repeats for many medications used for chronic conditions such as hypertension. The ordering of one repeat was also quite common (15.9%).
Figure 9.2: Number of repeats ordered per prescription
Vertical bar chart showing per cent of prescriptions on y-axis and number of repeats ordered on x-axis.
Age–sex-specific rates of prescribed medications
Age–sex-specific analysis found similar prescription rates per 100 encounters for males and females (84.3 and 82.9 respectively). It also showed the well-described tendency for the number of prescriptions written at each encounter to rise with the advancing age of the patient, with the rate of 57 per 100 encounters with patients aged less than 25 years almost doubling to 108 per 100 encounters for patients aged 65 years and over (results not tabled).
Figure 9.3, however, demonstrates that this age-based increase lessens if the prescription rate is considered in terms of the number of problems being managed in each age group. This suggests that a substantial part of the increase in prescription rate for older patients is due to the increased number of health problems they have managed at an encounter. The remaining increase in prescription rate associated with patient age is a reflection of the problems under management, which are more likely to be chronic at encounters with older patients. 
Figure 9.3: Age–sex-specific prescription rates per 100 problems managed
Stacked line chart showing, for male and female, rate per 100 problems on y-axis and age group (years) on x-axis.
Types of medications prescribed
Table 9.1 shows the distribution of prescribed medications using the WHO ATC classification.62 This allows comparison with other data sources such as those produced by Medicare Australia for PBS data. The table lists medications in frequency order within ATC levels 1, 3 and 5. Prescriptions are presented as a percentage of total prescriptions, as a rate per 100 encounters, and as a rate per 100 problems managed, with 95% confidence intervals. 
The high number of opioids shown in this table (compared with BEACH data published previously) is due to a re-classification of some medications. It was decided to recode codeine combinations which contained 30 mg of codeine as opioids in the ATC Index, whereas in the past they were coded as ‘other analgesics and antipyretics’. In the ATC classification, either grouping would be correct. The decision was taken to place high-dose codeine products in the opioid group in accordance with MIMS grouping64 and following the Poisons Regulations of the Therapeutic Goods Administration65, which stipulates that high-dose codeine combinations are Schedule 4 (prescription only) medications. However, a few combination analgesics containing less than 30mg of codeine but classified as Schedule 4 may be missed because there are other criteria which form part of the scheduling criteria for prescription only codeine. One of these is pack-size, which is not recorded in BEACH.
Similarly, all aspirin (acetylsalicylic acid) was previously classified in the analgesic group of neurological medications. This year, the coding of aspirin has been split depending on dosage. Low-dose (100 mg) plain aspirin has been reclassified as an anti-thrombotic medication in the blood medications group, while higher doses and combinations with other analgesic/antipyretics remain in the neurological group.
If readers are making comparisons with previous BEACH publications, they should note that this change has caused the opioid and anti-thrombotic groups to increase, and ‘other analgesics and antipyretics’ to decrease. In the companion report to this current publication, General practice activity in Australia 2000–01 to 2009–10: 10 year data tables1, medications have been re-analysed to incorporate the adjustment for all 10 years.
Table 9.1: Distribution of prescribed medications, by ATC levels 1, 3 and 5
ATC 
Level 1	ATC Level 3	ATC Level 5	Number	Per cent of prescribed medications
(n = 84,540)	Rate per 100 encounters(a)
(n = 101,349)
95% CI	Rate per 
100 problems
(n = 155,373)
95% CI	
Nervous system 	18,245	21.6	18.0
(17.1–18.9)	11.7
(11.2–12.3)	
	Opioids	5,173	6.1	5.1
(4.8–5.4)	3.3
(3.1–3.5)	
		Codeine, combinations excluding psycholeptics	1,667	2.0	1.7
(1.5–1.8)	1.1
(1.0–1.2)	
		Oxycodone	1,325	1.6	1.3
(1.2–1.4)	0.9
(0.8–0.9)	
		Tramadol	878	1.0	0.9
(0.8–1.0)	0.6
(0.5–0.6)	
	Antidepressants		3,854	4.6	3.8
(3.5–4.1)	2.5
(2.3–2.6)	
		Sertraline	651	0.8	0.6
(0.6–0.7)	0.4
(0.4–0.5)	
		Venlafaxine	516	0.6	0.5
(0.5–0.6)	0.3
(0.3–0.4)	
	Other analgesics and antipyretics	2,956	3.5	2.9
(2.6–3.3)	1.9
(1.7–2.1)	
		Paracetamol [plain]	2,720	3.2	2.7
(2.3–3.0)	1.8
(1.5–2.0)	
	Anxiolytics		1,800	2.1	1.8
(1.6–1.9)	1.2
(1.1–1.3)	
		Diazepam	1,003	1.2	1.1
(0.9–1.1)	0.6
(0.6–0.7)	
		Oxazepam	541	0.6	0.5
(0.5–0.6)	0.4
(0.3–0.4)	
	Hypnotics and sedatives	1,489	1.8	1.5
(1.3–1.6)	1.0
(0.9–1.0)	
		Temazepam	1,059	1.3	1.0
(0.9–1.2)	0.7
(0.6–0.8)	
	Antipsychotics		1,096	1.3	1.1
(1.1–1.2)	0.7
(0.6–0.8)	
	Drugs used in addictive disorders	719	0.9	0.7
(0.6–0.8)	0.5
(0.4–0.5)	
	Antiepileptics		678	0.8	0.7
(0.6–0.7)	0.4
(0.4–0.5)	
Cardiovascular system	16,702	19.8	16.5
(15.6–17.4)	10.8
(10.2–11.3)	
	Lipid modifying agents, plain	3,696	4.4	3.7
(3.4–3.9)	2.4
(2.2–2.5)	
		Atorvastatin	1,616	1.9	1.6
(1.5–1.7)	1.0
(1.0–1.1)	
		Rosuvastatin	804	1.0	0.8
(0.7–0.9)	0.5
(0.5–0.6)	
		Simvastatin	784	0.9	0.8
(0.7–0.9)	0.5
(0.5–0.6)	
(continued)

Table 9.1 (continued): Distribution of prescribed medications, by ATC levels 1, 3 and 5
ATC 
Level 1	ATC Level 3	ATC Level 5	Number	Per cent of prescribed medications
(n = 84,540)	Rate per 100 encounters(a)
(n = 101,349)
95% CI	Rate per 
100 problems
(n = 155,373)
95% CI	
	ACE inhibitors, plain 	2,381	2.8	2.4
(2.2–2.5)	1.5
(1.4–1.6)	
		Perindopril	1,186	1.4	1.2
(1.1–1.3)	0.8
(0.7–0.8)	
		Ramipril	716	0.9	0.7
(0.6–0.8)	0.5
(0.4–0.5)	
	Angiotensin II antagonists, plain 	2,364	2.8	2.3
(2.2–2.5)	1.5
(1.4–1.6)	
		Irbesartan	976	1.2	1.0
(0.9–1.1)	0.6
(0.6–0.7)	
		Candesartan	716	0.9	0.7
(0.6–0.8)	0.5
(0.4–0.5)	
		Telmisartan	536	0.6	0.5
(0.4–0.6)	0.4
(0.3–0.4)	
	Beta blocking agents 	1,667	2.0	1.6
(1.5–1.8)	1.1
(1.0–1.2)	
		Atenolol	813	1.0	0.8
(0.7–0.9)	0.5
(0.5–0.6)	
	Selective calcium channel blockers with mainly vascular effects 	1,601	1.9	1.6
(1.4–1.7)	1.0
(0.9–1.1)	
		Amlodipine	698	0.8	0.7
(0.6–0.8)	0.4
(0.4–0.5)	
	Angiotensin II antagonists, combinations 	1,227	1.5	1.2
(1.1–1.3)	0.8
(0.7–0.9)	
		Irbesartan and diuretics	706	0.8	0.7
(0.6–0.8)	0.5
(0.4–0.5)	
	ACE inhibitors, combinations 	628	0.7	0.6
(0.5–0.7)	0.4
(0.4–0.5)	
	High-ceiling diuretics 	580	0.7	0.6
(0.5–0.6)	0.4
(0.3–0.4)	
		Furosemide	578	0.7	0.6
(0.5–0.6)	0.4
(0.3–0.4)	
Anti-infectives for systemic use	16,470	19.5	16.3
(15.6–16.9)	10.6
(10.1–11.1)	
	Beta-lactam antibacterials, penicillins 	5,889	7.0	5.8
(5.5–6.1)	3.8
(3.6–4.0)	
		Amoxycillin	3,271	3.9	3.2
(3.0–3.5)	2.1
(1.9–2.3)	
		Amoxycillin and enzyme inhibitor	1,667	2.0	1.6
(1.5–1.8)	1.1
(1.0–1.2)	
	Other beta-lactam antibacterials 	3,302	3.9	3.3
(3.1–3.5)	2.1
(2.0–2.3)	
		Cefalexin	2,671	3.2	2.6
(2.5–2.8)	1.7
(1.6–1.8)	
		Cefaclor	539	0.6	0.5
(0.4–0.6)	0.3
(0.3–0.4)	
(continued)

Table 9.1 (continued): Distribution of prescribed medications, by ATC levels 1, 3 and 5
ATC 
Level 1	ATC Level 3	ATC Level 5	Number	Per cent of prescribed medications
(n = 84,540)	Rate per 100 encounters(a)
(n = 101,349)
95% CI	Rate per 
100 problems
(n = 155,373)
95% CI	
	Macrolides, lincosamides and streptogramins 	2,720	3.2	2.7
(2.5–2.9)	1.8
(1.6–1.9)	
		Roxithromycin	1,333	1.6	1.3
(1.2–1.5)	0.9
(0.8–1.0)	
		Erythromycin	687	0.8	0.7
(0.6–0.8)	0.4
(0.4–0.5)	
	Viral vaccines		971	1.2	1.0
(0.8–1.1)	0.6
(0.5–0.8)	
		Influenza vaccine	596	0.7	0.6
(0.4–0.7)	0.4
(0.3–0.5)	
	Tetracyclines		757	0.9	0.8
(0.7–0.8)	0.5
(0.4–0.5)	
		Doxycycline	655	0.8	0.7
(0.6–0.7)	0.4
(0.4–0.5)	
	Sulfonamides and trimethoprim 	665	0.8	0.7
(0.6–0.7)	0.4
(0.4–0.5)	
Alimentary tract and metabolism 	8,304	9.8	8.2
(7.7–8.7)	5.3
(5.0–5.6)	
	Drugs for peptic ulcer and gastro-oesophageal reflux 	3,209	3.8	3.2
(2.9–3.4)	2.1
(1.9–2.2)	
		Esomeprazole	1,287	1.5	1.3
(1.1–1.4)	0.8
(0.8–0.9)	
		Pantoprazole	715	0.9	0.7
(0.6–0.8)	0.5
(0.4–0.5)	
	Blood glucose lowering drugs, excluding insulins 	2,175	2.6	2.2
(1.9–2.4)	1.4
(1.3–1.6)	
		Metformin	1,301	1.5	1.3
(1.2–1.4)	0.8
(0.8–0.9)	
		Gliclazide	535	0.6	0.5
(0.4–0.6)	0.3
(0.3–0.4)	
	Propulsives		644	0.8	0.6
(0.6–0.7)	0.4
(0.4–0.5)	
		Metoclopramide	578	0.7	0.6
(0.5–0.6)	0.4
(0.3–0.4)	
Respiratory system 	5,329	6.3	5.3
(4.8–5.7)	3.4
(3.2–3.7)	
	Adrenergics, inhalants 	2,843	3.4	2.8
(2.5–3.1)	1.8
(1.7–2.0)	
		Salbutamol	1,388	1.6	1.4
(1.2–1.5)	0.9
(0.8–1.0)	
		Salmeterol and other drugs for obstructive airways disease	789	0.9	0.8
(0.7–0.9)	0.5
(0.5–0.6)	
		Formoterol and other drugs for obstructive airways disease	537	0.6	0.5
(0.7–0.9)	0.5
(0.5–0.6)	
	Decongestants and other nasal preparations for topical use	917	1.1	0.9
(0.8–1.0)	0.6
(0.5–0.7)	
	Other drugs for obstructive airway diseases, inhalants 	809	1.0	0.8
(0.4–0.6)	0.4
(0.3–0.4)	
(continued)
Table 9.1 (continued): Distribution of prescribed medications, by ATC levels 1, 3 and 5
ATC 
Level 1	ATC Level 3	ATC Level 5	Number	Per cent of prescribed medications
(n = 84,540)	Rate per 100 encounters(a)
(n = 101,349)
95% CI	Rate per 
100 problems
(n = 155,373)
95% CI	
Musculoskeletal system	4,309	5.1	4.3
(3.9–4.6)	2.8
(2.6–3.0)	
	Anti-inflammatory and antirheumatic products, 
non-steroid 	3,204	3.8	3.2
(2.9–3.4)	2.1
(1.9–2.2)	
		Meloxicam	877	1.0	0.9
(0.8–1.0)	0.6
(0.5–0.6)	
		Diclofenac	766	0.9	0.8
(0.6–0.9)	0.5
(0.4–0.6)	
		Celecoxib	533	0.6	0.5
(0.4–0.6)	0.3
(0.3–0.4)	
	Drugs affecting bone structure and mineralisation 	544	0.6	0.5
(0.5–0.6)	0.4
(0.3–0.4)	
Dermatologicals	3,662	4.3	3.6
(3.4–3.8)	2.4
(2.2–2.5)	
	Corticosteroids, plain 	2,184	2.6	2.2
(2.0–2.3)	1.4
(1.3–1.5)	
		Betamethasone	711	0.8	0.7
(0.6–0.8)	0.5
(0.4–0.5)	
		Mometasone	601	0.7	0.6
(0.5–0.7)	0.4
(0.3–0.4)	
Genitourinary system and sex hormones	3,100	3.7	3.1
(2.9–3.2)	2.0
(1.9–2.1)	
	Hormonal contraceptives for systemic use 	1,330	1.6	1.3
(1.2–1.4)	0.9
(0.8–0.9)	
		Levonorgestrel and oestrogen	753	0.9	0.7
(0.7–0.8)	0.5
(0.4–0.5)	
	Oestrogens		539	0.6	0.5
(0.5–0.6)	0.4
(0.3–0.4)	
Blood and blood forming organs	2,968	3.5	2.9
(2.7–3.2)	1.9
(1.8–2.1)	
	Antithrombotic agents 	2,272	2.7	2.2
(2.1–2.4)	1.5
(1.4–1.6)	
		Warfarin	1,166	1.4	1.2
(1.0–1.3)	0.8
(0.7–0.8)	
		Acetylsalicylic acid [antithrombotic]	533	0.6	0.5
(0.5–0.6)	0.3
(0.3–0.4)	
Sensory organs	2,381	2.8	2.4
(2.2–2.5)	1.5
(1.4–1.6)	
	Anti-infectives 	989	1.2	1.0
(0.9–1.1)	0.6
(0.6–0.7)	
		Chloramphenicol	905	1.1	0.9
(0.8–1.0)	0.6
(0.5–0.6)	
	Corticosteroids and anti-infectives in combination 	600	0.7	0.6
(0.5–0.7)	0.4
(0.3–0.4)	
Systemic hormonal preparations, excluding sex hormones	2,166	2.6	2.1
(2.0–2.3)	1.4
(1.3–1.5)	
	Corticosteroids for systemic use, plain 	1,354	1.6	1.3
(1.2–1.5)	0.9
(0.8–1.0)	
		Prednisolone	778	0.9	0.8
(0.7–0.9)	0.5
(0.4–0.6)	
(continued)
Table 9.1 (continued): Distribution of prescribed medications, by ATC levels 1, 3 and 5
ATC 
Level 1	ATC Level 3	ATC Level 5	Number	Per cent of prescribed medications
(n = 84,540)	Rate per 100 encounters(a)
(n = 101,349)
95% CI	Rate per 
100 problems
(n = 155,373)
95% CI	
	Thyroid preparations 	631	0.8	0.6
(0.6–0.7)	0.4
(0.4–0.5)	
		Levothyroxine sodium	628	0.7	0.6
(0.6–0.7)	0.4
(0.4–0.4)	
Antineoplastic and immunomodulating agents	435	0.5	0.4
(0.4–0.5)	0.3
(0.2–0.3)	
Various	302	0.4	0.3
(0.2–0.4)	0.2
(0.1–0.3)	
Antiparasitic products, insecticides and repellent	167	0.2	0.2
(0.1–0.2)	0.1
(0.1–0.1)	
Total prescribed medications	84,540	100.0	83.4
(80.6–86.2)	54.4
(52.8–56.0)	
(a)	Column will not add to 100, as multiple prescriptions could be written at each encounter and only common Level 3 and Level 5 drugs are included. 
Note: ATC—Anatomical Therapeutic Chemical classification; CI—confidence interval; ACE—angiotensin-converting enzyme.
Most frequently prescribed medications
The most frequently prescribed individual medications are reported at the CAPS generic level (ATC Level 5 equivalent) in Table 9.2. Together these 30 medications made up 43.3% of all prescribed medications. 
Table 9.2: Most frequently prescribed medications 
Generic medication	Number	Per cent of prescribed medications
(n = 84,540)	Rate per 100
 encounters(a)
(n = 101,349)
95% CI	Rate per 
100 problems
(n = 155,373)
95% CI	
Amoxycillin	3,271	3.9	3.2
(3.0–3.5)	2.1
(1.9–2.3)	
Paracetamol plain	2,720	3.2	2.7
(2.3–3.0)	1.8
(1.5–2.0)	
Cephalexin	2,671	3.2	2.6
(2.5–2.8)	1.7
(1.6–1.8)	
Paracetamol/codeine [all]	1,712	2.0	1.7
(1.5–1.8)	1.1
(1.0–1.2)	
Amoxycillin/potassium clavulanate	1,667	2.0	1.6
(1.5–1.8)	1.1
(1.0–1.2)	
Atorvastatin	1,616	1.9	1.6
(1.5–1.7)	1.0
(1.0–1.1)	
Salbutamol	1,421	1.7	1.4
(1.2–1.6)	0.9
(0.8–1.0)	
Roxithromycin	1,333	1.6	1.3
(1.2–1.5)	0.9
(0.8–1.0)	
Oxycodone	1,325	1.6	1.3
(1.2–1.5)	0.9
(0.8–0.9)	
Metformin	1,301	1.5	1.3
(1.2–1.4)	0.8
(0.8–0.9)	
(continued)
Table 9.2 (continued): Most frequently prescribed medications
Generic medication	Number	Per cent of prescribed medications
(n = 84,540)	Rate per 100
 encounters(a)
(n = 101,349)
95% CI	Rate per 
100 problems
(n = 155,373)
95% CI	
Esomeprazole	1,287	1.5	1.3
(1.2–1.4)	0.8
(0.8–0.9)	
Perindopril	1,186	1.4	1.2
(1.1–1.3)	0.8
(0.7–0.8)	
Warfarin sodium	1,166	1.4	1.2
(1.0–1.3)	0.8
(0.7–0.8)	
Temazepam	1,059	1.3	1.0
(0.9–1.2)	0.7
(0.6–0.8)	
Diazepam	1,003	1.2	1.0
(0.9–1.1)	0.7
(0.6–0.7)	
Irbesartan	976	1.2	1.0
(0.9–1.1)	0.6
(0.6–0.7)	
Chloramphenicol eye	905	1.1	0.9
(0.8–1.0)	0.6
(0.5–0.6)	
Tramadol	878	1.0	0.9
(0.8–1.0)	0.6
(0.5–0.6)	
Meloxicam	877	1.0	0.9
(0.8–1.0)	0.6
(0.5–0.6)	
Atenolol	813	1.0	0.8
(0.7–0.9)	0.5
(0.5–0.6)	
Rosuvastatin	804	1.0	0.8
(0.7–0.9)	0.5
(0.5–0.6)	
Fluticasone/salmeterol	789	0.9	0.8
(0.7–0.9)	0.5
(0.5–0.6)	
Simvastatin	784	0.9	0.8
(0.7–0.9)	0.5
(0.5–0.6)	
Levonorgestrel/ethinyloestradiol	753	0.9	0.7
(0.7–0.8)	0.5
(0.4–0.5)	
Ramipril	716	0.9	0.7
(0.6–0.8)	0.5
(0.4–0.5)	
Candesartan cilexetil	715	0.9	0.7
(0.6–0.8)	0.5
(0.4–0.5)	
Pantoprazole	715	0.9	0.7
(0.6–0.8)	0.5
(0.4–0.5)	
Betamethasone topical	711	0.8	0.7
(0.6–0.8)	0.5
(0.4–0.5)	
Irbesartan/hydrochlorothiazide	706	0.8	0.7
(0.6–0.8)	0.5
(0.4–0.5)	
Amlodipine	689	0.8	0.7
(0.6–0.8)	0.4
(0.4–0.5)	
Subtotal	36,567	43.3	—	—	
Total prescribed medications	84,540	100.0	83.4
(80.6–86.2)	54.4
(52.8–56.0)	
(a)	Column will not add to 100, as multiple prescriptions could be written at each encounter, and only the most frequently prescribed medications are included in this table. 
Note: CI—confidence interval.

9.3	Medications supplied by GPs
GPs supplied their patients with 13,829 medications in this study, at a rate of 13.6 medications supplied per 100 encounters. At least one medication was supplied at 10.5% of encounters for 7.2% of problems. Table 9.3 shows the most commonly supplied medications at the CAPS generic level (ATC Level 5 equivalent), with vaccines accounting for almost two-thirds of this group. 
Table 9.3: Medications most frequently supplied by GPs
Generic medication	Number	Per cent of 
GP-supplied medications
(n = 84,540)	Rate per 100
 encounters(a)
(n = 101,349)
95% CI	Rate per 
100 problems
(n = 155,373)
95% CI	
Influenza virus vaccine	4,198	30.4	4.1
(3.7–4.6)	2.7
(2.4–3.0)	
Pneumococcal vaccine	698	5.1	0.7
(0.6–0.8)	0.5
(0.4–0.5)	
Triple antigen (diphtheria/pertussis/tetanus)	404	2.9	0.4
(0.3–0.5)	0.3
(0.2–0.3)	
Mumps/measles/rubella vaccine	389	2.8	0.4
(0.3–0.4)	0.3
(0.2–0.3)	
Vitamin B12 (Cobalamin)	375	2.7	0.4
(0.3–0.4)	0.3
(0.2–0.3)	
Diptheria/pertussis/tetanus/
hepatitis B/polio/Hib vaccine	316	2.3	0.3
(0.3–0.4)	0.2
(0.2–0.2)	
Polio vaccine oral sabin/injection	271	2.0	0.3
(0.2–0.3)	0.2
(0.1–0.2)	
Haemophilus B vaccine	257	1.9	0.3
(0.2–0.3)	0.2
(0.1–0.2)	
Human papillomavirus vaccine	247	1.8	0.2
(0.2–0.3)	0.2
(0.1–0.2)	
Rotavirus vaccine	203	1.5	0.2
(0.2–0.2)	0.1
(0.1–0.2)	
Chickenpox (varicella zoster)	193	1.4	0.2
(0.2–0.2)	0.1
(0.1–0.2)	
ADT/CDT (diphtheria/tetanus) vaccine	192	1.4	0.2
(0.2–0.2)	0.1
(0.1–0.2)	
Diphtheria/pertussis/tetanus/hepatitis B vaccine	172	1.2	0.2
(0.1–0.2)	0.1
(0.1–0.1)	
Meningitis vaccine	170	1.2	0.2
(0.1–0.2)	0.1
(0.1–0.1)	
Meloxicam	149	1.1	0.2
(0.1–0.2)	0.1
(0.1–0.1)	
Betamethasone systemic	134	1.0	0.1
(0.1–0.2)	0.2
(0.1–0.2)	
Allergen treatment	132	1.0	0.1
(0.1–0.2)	0.1
(0.1–0.1)	
Diphtheria/pertussis/tetanus/polio vaccine	131	0.9	0.1
(0.1–0.2)	0.2
(0.1–0.2)	
Hepatitis B vaccine	125	0.9	0.1
(0.1–0.2)	0.1
(0.1–0.1)	
Lignocaine with/without adrenaline injection	119	0.9	0.1
(0.0–0.2)	0.1
(0.0–0.2)	
(continued)
Table 9.3 (continued): Medications most frequently supplied by GPs
Generic medication	Number	Per cent of 
GP-supplied medications
(n = 84,540)	Rate per 100
 encounters(a)
(n = 101,349)
95% CI	Rate per 
100 problems
(n = 155,373)
95% CI	
Immunisation unspecified	118	0.9	0.1
(0.1–0.2)	0.1
(0.0–0.1)	
Metoclopramide	117	0.9	0.1
(0.1–0.2)	0.1
(0.1–0.1)	
Hepatitis A vaccine	107	0.8	0.1
(0.1–0.1)	0.1
(0.1–0.1)	
Local anaesthetic injection	103	0.8	0.1
(0.1–0.1)	0.1
(0.0–0.1)	
Esomeprazole	98	0.7	0.1
(0.1–0.1)	0.1
(0.1–0.1)	
Typhoid vaccine (Salmonella typhi)	94	0.7	0.1
(0.1–0.1)	0.1
(0.0–0.1)	
Hepatitis A and B vaccine	91	0.7	0.1
(0.1–0.1)	0.1
(0.0–0.1)	
Budesonide/Eformoterol	89	0.7	0.1
(0.1–0.1)	0.1
(0.0–0.1)	
Hepatitis A/Typhoid vaccine (Salmonella typhi)	86	0.6	0.1
(0.1–0.1)	0.1
(0.0–0.1)	
Salbutamol	85	0.6	0.1
(0.1–0.1)	0.1
(0.0–0.1)	
Subtotal	9,861	71.3	—	—	
Total supplied medications	13,829	100.0	13.6
(12.7–14.6)	8.9
(8.3–9.5)	
(a)	Column will not add to 100, as multiple medications could be given at each encounter, and only the medications most frequently supplied by GPs are included.
Note: CI—confidence interval.
9.4	Medications advised for over-the-counter purchase
The GPs recorded 9,632 medications as recommended for OTC purchase, at rates of 9.5 per 100 encounters and 6.2 per 100 problems managed. At least one OTC medication was recorded as advised at 8.3% of encounters and for 5.6% of problems. Table 9.4 shows the top 30 advised medications at the CAPS generic level (ATC Level 5 equivalent). A wide range of medications were recorded in this group, the most common being paracetamol, which accounted for 26.0% of these medications.
The re-classification of aspirin described in section 9.2 on prescribed medications, also has an impact on the rate of OTC-advised aspirin, which has decreased compared with earlier published BEACH data. Only the higher-dose analgesic aspirin appears in this table. The frequency of OTC-advised low-dose aspirin for anti-thrombotic purposes was too low for inclusion.

Table 9.4: Most frequently advised over-the-counter medications
Generic medication	Number	Per cent of OTC medications
(n = 84,540)	Rate per 100
 encounters(a)
(n = 101,349) 95% CI	Rate per 
100 problems (n = 155,373) 95% CI	
Paracetamol	2,502	26.0	2.5
(2.2–2.8)	1.6
(1.4–1.8)	
Ibuprofen	620	6.4	0.6
(0.5–0.7)	0.4
(0.3–0.5)	
Sodium chloride topical nasal	234	2.4	0.2
(0.2–0.3)	0.2
(0.1–0.2)	
Loratadine	170	1.8	0.2
(0.1–0.2)	0.1
(0.1–0.1)	
Diclofenac topical	166	1.7	0.2
(0.1–0.2)	0.1
(0.1–0.1)	
Sodium/potassium/citric/glucose	144	1.5	0.1
(0.1–0.2)	0.1
(0.1–0.1)	
Saline bath/solution/gargle	136	1.4	0.1
(0.1–0.2)	0.1
(0.1–0.1)	
Clotrimazole topical	124	1.3	0.1
(0.1–0.2)	0.1
(0.1–0.1)	
Bromhexine	123	1.3	0.1
(0.1–0.2)	0.1
(0.0–0.1)	
Ergocalciferol 	122	1.3	0.1
(0.1–0.2)	0.1
(0.1–0.1)	
Hydrocortisone/clotrimazole	108	1.1	0.1
(0.1–0.1)	0.1
(0.1–0.1)	
Cetirizine	108	1.1	0.1
(0.1–0.1)	0.1
(0.1–0.1)	
Fexofenadine	105	1.1	0.1
(0.1–0.1)	0.1
(0.1–0.1)	
Paracetamol/codeine	100	1.0	0.1
(0.1–0.1)	0.1
(0.0–0.1)	
Sorbolene/glycerol/cetomacrogol	96	1.0	0.1
(0.1–0.1)	0.1
(0.1–0.1)	
Hydrocortisone topical	90	0.9	0.1
(0.1–0.1)	0.1
(0.0–0.1)	
Folic acid	88	0.9	0.1
(0.1–0.1)	0.1
(0.0–0.1)	
Mouthwash/gargle other	81	0.8	0.1
(0.0–0.1)	0.1
(0.0–0.1)	
Cold and Flu medication NEC	78	0.8	0.1
(0.0–0.1)	0.1 
(0.0–0.1)	
Clotrimazole vaginal	76	0.8	0.1
(0.1–0.1)	0.1
(0.0–0.1)	
Cinchocaine/hydrocortisone	74	0.8	0.1
(0.1–0.1)	0.1
(0.0–0.1)	
Hyoscine butylbromide	73	0.8	0.1
(0.1–0.1)	0.1
(0.0–0.1)	
Simple analgesics NEC	72	0.8	0.1
(0.1–0.1)	0.1
(0.0–0.1)	
Ferrous sulfate/sodium ascorbate	71	0.7	0.1
(0.1–0.1)	0.1
(0.0–0.1)	
Fish oil	69	0.7	0.1
(0.1–0.1)	0.0
(0.0–0.1)	
 (continued)
Table 9.4 (continued): Most frequently advised over-the-counter medications
Generic medication	Number	Per cent of OTC medications
(n = 84,540)	Rate per 100
 encounters(a)
(n = 101,349) 95% CI	Rate per 
100 problems
(n = 155,373) 95% CI	
Calcium carbonate/vitamin D	68	0.7	0.1
(0.1–0.1)	0.1
(0.0–0.1)	
Loperamide	68	0.7	0.1
(0.0–0.1)	0.0
(0.0–0.1)	
Cream/ointment/lotion NEC	64	0.7	0.1
(0.0–0.1)	0.0
(0.0–0.1)	
Aspirin [analgesic]	61	0.6	0.1
(0.0–0.1)	0.0
(0.0–0.1)	
Sodium bicarbonate/citrate/tartaric acid	61	0.6	0.1
(0.0–0.1)	0.0
(0.0–0.1)	
Subtotal	5,952	61.8	—	—	
Total advised medications	9,632	100.0	9.5
(8.7–10.3)	6.2
(5.7–6.7)	
(a)	Column will not add to 100, as multiple medications could be given at each encounter and only the medications most frequently advised for over-the-counter purchase are included.
Note: OTC—over-the-counter medication; CI—confidence interval; NEC—not elsewhere classified.
9.5	Antibiotics prescribed or supplied in 2009–10 
The relationships between patients, their reasons for encounter and the problems managed with an antibiotic are presented in Figure 9.4. Medications from the Antibacterials for Systemic Use ATC group (J01) were prescribed or supplied by GPs at a rate of 14.3 per 100 encounters, and 9.3 per 100 problems managed. For every 100 problems managed with a systemic antibiotic, 103 antibiotics were prescribed or supplied (100 antibiotics prescribed and 3 supplied). 
Patient age and sex
Patients aged 1–14 years were most likely to be prescribed or supplied an antibiotic, at a rate of 25.4 per 100 encounters, followed by patients aged 15–24 years (19.7 per 100 encounters). Infants aged less than 1 year had the lowest rate (8.8). The sex-specific antibiotic rates were similar for males (15.3 per 100 encounters) and females (13.5). 
Reasons for encounter 
The reason for encounter most often given by patients at encounters where an antibiotic was prescribed or supplied was cough (25.0 per 100 antibiotic encounters). The second most common reason was throat symptom/complaint (11.4 per 100 antibiotic encounters).
Problems managed with an antibiotic
As would be expected with the high proportion of acute problems managed with an antibiotic, there was a high proportion of new problems managed at the encounters (70.9%) (results not shown). More than half (51.7%) of problems managed with an antibiotic were of a respiratory nature, with acute upper respiratory infections and bronchitis each accounting for 14.0% of problems managed with an antibiotic. Skin conditions such as post-traumatic infections, boils and cellulitis accounted for another 16.8%, while urological problems, mainly urinary tract infections, made up 10.0% of problems managed with an antibiotic. 
Individual antibiotics prescribed or supplied
The most frequently prescribed/supplied antibiotic was amoxycillin, which accounted for 22.9% of all antibiotics recorded. The second most common was cephalexin, accounting for 18.8%. The combination product amoxicillin/potassium clavulanate was also frequently prescribed/supplied, accounting for 11.6% of antibiotics, as was roxithromycin (9.3% of all antibiotics recorded).
Section 9.6 gives a more in-depth investigation of changes in antibiotic prescribing for acute upper respiratory infection (the common cold) and other conditions including sinusitis, tonsillitis and otitis media.
Figure 9.4: Systemic antibiotics prescribed or supplied in general practice, 2009–10
Diagram
(a)	Includes medications from the Antibacterials for Systemic Use ATC group (J01)
(b)	Expressed as a per cent of problems managed with an antibiotic.
(c)	Expressed as a rate per 100 encounters at which an antibiotic was prescribed or supplied.
(d)	Age and sex-specific rate per 100 encounters in each age and sex group.
*	Includes multiple ICPC-2 or ICPC-2 PLUS codes (see Appendix 4).

9.6	Systemic antibiotic prescribing, 1998–99 to 2009–10 
This section examines changes in the prescribing of systemic antibiotics by GPs from 1998–99 to 2009–10. ‘Antibiotics’ in this text refer to all oral/systemic antibiotic prescriptions recorded in BEACH. 
Over the past decade there has been a concerted effort by government agencies and other organisations to encourage decreased use of antibiotics in treatment of upper respiratory infections. This was one of the objectives of the Quality Use of Medicines initiative of the National Medicines Policy 200066, which drew on resources from several partners, such as the National Prescribing Service (NPS) and the Pharmaceutical Benefits Advisory Committee. In 2000, the NPS launched the first of its annual ‘common colds community campaign’67 which aimed to reduce inappropriate use of antibiotics for the common cold (known in general practice as acute upper respiratory tract infection, or acute URTI). Public advertising campaigns and therapeutic guidelines for health professionals were used to inform and advise. The Therapeutic Guidelines state that the benefits of antibiotic therapy for a range of respiratory conditions are more limited than previously thought and, consequently, routine use of antibiotics in these conditions should be avoided to limit potential adverse effects and to reduce bacterial resistance in individuals and in the community68. 
Figure 9.5 shows the prescribing rate of antibiotics for all problems (not just respiratory) from 1998–99 to 2009–10. The GP prescribing rate, at its highest in 1998–99 (at 119 per 1,000 problems managed), significantly decreased in 1999–00 to 110 per 1,000 problems managed. This was followed by a slower decline until 2003–04 when it reached 98 per 1,000. Since then, there has been no regular trend from year to year. However, the 2009–10 prescribing rate is significantly lower (at 93.4 per 1,000 problems managed) than it was at the beginning of the study period, though it is not lower than it was in 2002–04.
The prescribing rate per head of population followed a similar pattern, decreasing from 94.3 antibiotic prescriptions per 100 people, to 75.8 per 100 people per year (Figure 9.5).

Figure 9.5: Systemic antibiotic medications prescribed in general practice, 1998–99 to 2009–10, 95% CI
Stacked line chart showing, for rate per 1,000 problems and rate per 100 people, rate on y-axis and beach years on x-axis.
This measured decrease does not necessarily mean a decrease in the use of antibiotics for acute URTI. Figure 9.6 shows the proportion of total antibiotics that were prescribed in 
2009–10 for: acute URTIs (the common cold); ‘other upper respiratory tract infections’ (including: streptococcal throat, acute/chronic sinusitis, acute tonsillitis, acute laryngitis/tracheitis, influenza and epiglottitis); all other respiratory conditions; otitis media; skin problems; urological problems; and all other problems (see Appendix 4, Table A4.2 for ICPC-2 code inclusions). 
Only 14.0% of antibiotics prescribed by GPs were for management of acute URTI, a further 16.0% were for ‘other upper respiratory tract infections’, and 21.0% for other respiratory conditions—together accounting for only half the antibiotics prescribed (Figure 9.6). Even if the campaigns to reduce systemic antibiotic use for acute URTI were successful, they would have little impact on the overall use of antibiotics since antibiotics for acute URTI only account for 14% of the total prescribed. Therefore the changes demonstrated in Figure 9.5 reflect broader changes in antibiotic prescribing.
Figure 9.6: Distribution of problems for which systemic antibiotics were prescribed in 2009–10
Pie chart diagram.

Figure 9.7 shows the proportion of acute URTI, the proportion of ‘other upper respiratory infections’ and the proportion of otitis media problems managed for which antibiotics were prescribed, from 1998–99 to 2009–10. 
•	The proportion of acute URTI problems for which the GP prescribed an antibiotic decreased significantly from 42.0% in 1998–99 to 33.0% in 2001–02, then increased to peak at 40.6% in 2004–05. Another peak in 2008–09 (39.0%) was followed by a significant decrease to 32.4% in 2009–10. 
•	The proportion of ‘other upper respiratory infections’ problems managed with antibiotics significantly decreased from 71.6% in 1998–99 to 64.9% in 1999–2000 but then rose to peak in 2004–05 at 73.6%. The proportion decreased between 2008–09 (72.0%) and 2009–10 (62.6%).
•	A total of 78% of otitis media contacts were managed with antibiotics in 1998–99. This proportion increased in 2001–02 (to 83.4%) and remained significantly higher than the 1999–00 levels for all years until 2009–10.
If the public education campaigns were successful, one would assume they would have an effect of lowering presentation rates of acute URTI, with patients opting not to see the GP. 
If this were true, it may mean that the infections being presented on average would be more severe. It is possible that the prescribing of antibiotics may to some degree be dependent on the severity of the infection being presented. Therefore the management rate of these conditions over the years was investigated to see whether there had been a change in the number of presentations of the selected problems over time (Figure 9.8).
•	The management rate of acute URTI began to decrease in 2000–01 from its highest rate (7.2 per 100 encounters) in 1999–00 to the significantly lower rate of 5.5 per 100 encounters in 2003–04. The management rate of acute URTI has not changed significantly since then.
•	The management rate of ‘other upper respiratory tract infections’ also peaked in 
1999–00 (11.6 per 100 encounters), then declined significantly to 8.6 per 100 encounters by 2004–05. However, the management rate significantly increased to 9.7 per 100 in 
2005–06, and has since remained stable. In 2009–10, it was lower than the 1999–00 peak rate but higher than the lowest rate in 2004–05.
•	The management rate of otitis media steadily decreased from the highest point of 1.8 per 100 encounters in 1998–99 to the significantly lower rate of 1.0 per 100 encounters in 2009–10.
It can therefore be concluded that from the introduction of the ‘common cold campaign’67 in 2000 to about 2003–04 there was a reduction in not only the management rate of acute URTI, but also in the management rate of ‘other upper respiratory tract infections’ and otitis media. However, the management rate did not change in the second half of the decade.
Figure 9.7: Proportion of problems that have a systemic antibiotic prescription recorded, 1998–99 to 2009–10, 95% CI
Stacked line chart showing for acute URTI, Otitis media and other URTIs, per cent on y-axis and BEACH years on x-axis.

Figure 9.8: Management rates of systemic antibiotic related problems, 1998–99 to 2009–10, 95% CI
Stacked line chart showing for acute URTI, Otitis media and other URTIs, rate per 100 encounters on y-axis and BEACH years on x-axis.

Figure 9.9 shows the national estimated annual number of antibiotics prescribed (not counting repeats if given) by GPs for each problem managed (often referred to as ‘indication’) for the antibiotic. This calculation considers: 
•	the number of GP–patients encounters nationally (see Section 2.10) +
•	the rate at which the problem was managed +
•	the rate at which systemic antibiotics were prescribed for the problem in each year.
The number of antibiotics prescribed by GPs for acute URTI decreased between 1998–99 and 2003–04 by just over 40% (about 1.25 million fewer antibiotic prescriptions in 2003–04). This decrease resulted from a significantly lower prescription rate (Figure 9.7) combined with a significantly lower management rate (Figure 9.8) and a decrease in the national number of GP–patient encounters over this period (Section 2.10). However, between 2003–04 and 
2009–10 the number of antibiotics prescribed by GPs for acute URTI increased by about 35% (about 600,000 more prescriptions in 2009–10 than in 2003–04). This increase was primarily due to the increase in the number of GP–patient encounters over this period with little change in the proportion of acute URTIs for which an antibiotic prescription was given, or in the management rate of URTI problems.
The number of antibiotics prescribed for ‘other upper respiratory tract infections’ followed a similar pattern to that for acute URTIs. The main difference, however, was that for ‘other upper respiratory tract infections’ the initial decrease in the number of systemic antibiotics prescribed was not due to any change in GP management style (as shown in Figure 9.7), but resulted from a large drop in its management rate (Figure 9.8) combined with the decrease in total GP–patient encounters nationally (that is, decreasing attendance rate).
In contrast to the respiratory problems, the number of antibiotics prescribed for otitis media stayed relatively stable after 2003–04. This was due to the continued decrease in its management (Figure 9.8) countering the increase in overall GP–patient encounters, with no solid trend of change in GP prescribing behaviour.
Figure 9.9 shows that more than 40% of all antibiotics prescribed in Australian general practice were for problems other than respiratory problems or otitis media. In 2002–03 there were about 900,000 fewer antibiotic prescriptions written for the management of these problems than there were in 1998–99. However, in 2009–10, there were an estimated 1.8 million more antibiotic prescriptions given by GPs for these non–respiratory problems than in 2002–03. This increase is larger than all the increases for respiratory problems combined. 

Figure 9.9: National estimated number of annual systemic antibiotic prescriptions by GPs, by problem managed
(millions), 1998–99 to 2009–10, 95% CI
Stacked line chart showing for acute URTI, Otitis media, other respiratory, other problems and other URTIs, number (millions) on y-axis and BEACH years on x-axis.

Summary
The results of this study suggest that prescriptions for antibiotics (for all conditions) per head of population decreased sharply (by 27%) between 1998–99 and 2003–04, agreeing with an earlier report by the NPS.67 However, since then there has been little change. Together, the common cold (14%) and other upper respiratory tract infections (16%) accounted for 30% of all antibiotic prescriptions in 2009–10. 
These results demonstrate that two major factors influence the total number of antibiotics prescribed by GPs for upper respiratory infections. These are: 
•	the management decision of the GP whether or not to prescribe an antibiotic for URTI
•	the number of times URTI is managed by GPs (which in turn is influenced by the incidence of infection in the community, the proportion of those with a respiratory infection who decide to visit the GP, the number of visits they make per episode and the total number of GP–patient encounters).
The decrease in antibiotic prescribing for URTI between 1998–99 and 2003–04 appears due to:
•	a decrease in the management rate of acute URTI per 100 GP–patient encounters—this may have been influenced by community education programs and GP advice about management of URTI in previous episodes
•	a decrease in GP prescribing of systemic antibiotics in the management of acute URTI, which may have been affected by GP education programs such as that undertaken by NPS
•	a decrease in the number of visits per head of population nationally.
In contrast, the decrease in systemic antibiotic prescribing for ‘other URTIs’ and otitis media over the same period was due to a lower management rate of both conditions combined with a lower national attendance rate, and not from a decrease in GPs prescribing antibiotics for these conditions.
Since 2003–04, there has been little change in either the prescribing rate of antibiotics in the management of acute URTI and of ‘other URTIs,’ or in the management rate of these conditions. There has been no definite trend for either an increased or decreased prescribing rate of antibiotics for these conditions. However, because there has been an increase in number of GP attendances since that time, there has been an increase in the number of antibiotics prescribed for both conditions.
These results also highlight the importance of using data that link the prescription to the problem being managed. Making inferences about GP behaviour in managing URTI from the number of antibiotics claimed through the PBS by health care card holders69,70 can be misleading since URTI only accounts for a fraction of antibiotics prescribed. The greatest proportion of the national increase in prescribed antibiotics in the 5 years to 2009–10 has been for management of non-respiratory problems, and this increase does not reflect a change in the way GPs manage URTIs. 

9.7	Changes in medications over the decade
2000–01 to 2009–10 
Data on medications are reported for each year from 2000–01 to 2009–10 in Chapter 9 of the web-based companion report entitled General practice activity in Australia 2000–01 to 2009–10: 10 year data tables.1 In that report, changes over time are measured as change in the management of problems (that is, as a rate per 100 problems). This reflects change in how GPs are managing problems, and accounts for the significant increase in the number of problems managed per encounter over the decade to 2009–10 (see Section 7.9).
The rate at which medications were prescribed fell from 63.9 per 100 problems managed in 2000–01 to 54.4 per 100 in 2009–10. This significant decrease means that an average of 9.5 fewer prescriptions were written for every 100 problems managed in 2009–10 than 10 years earlier. However, in 2009–10 there were 16.2 million (16%) more encounters claimed through Medicare than there were in 2000–01. As a result, the extrapolated national effect of this change is 4.5 million more prescriptions given by GPs in 2009–10 than in 2000–01. If, instead of decreasing, the prescribing rate had remained static over the decade, the increase in number of GP encounters combined with the increase in the number of problems managed per encounter would have resulted in 21.5 million more prescriptions in 
2009–10 than in 2000–01.
Among the prescribed drug groups that decreased were psycholeptics, drugs for obstructive airways disease and systemic anti-inflammatory medications. At the same time, prescribing rates of several drug groups increased significantly, including agents acting on the rennin‑angiotensin system, serum lipid-reducing agents, psychoanaleptics, drugs for acid‑related digestive disorders, and anti-thrombotic agents. 
At the individual generic level, temazepam, levonorgestrel/ethinyloestradiol, cefaclor and celecoxib were among the medications for which significant decreases in prescribing rates occurred over time. On the other hand, significant increases were found in the prescribing rates of many medications. Among them were atorvastatin, oxycodone, perindopril, esomeprazole, and tramadol.
Other changes that occurred over the 10-year period were a steady rise in the proportion of prescriptions for which five repeats were recorded, and an increase in the rate of medications supplied by GPs (mainly vaccines). Rates of medications recommended by GPs for over‑the‑counter purchases showed no significant change either in total rates or in the individual medications advised. 

10	Other treatments
The BEACH survey form allows GPs to record up to two other (non-pharmacological) treatments for each problem managed at the encounter. Other treatments include all clinical and procedural treatments provided. These groups are defined in Appendix 4. Routine clinical measurements or observations, such as measurements of blood pressure and physical examinations, were not included if undertaken by the GP, but were included if undertaken by the practice nurse.
The GPs were also asked to indicate whether the treatment was done by a practice nurse (tick box). In this chapter all ‘other treatments’ are reported, irrespective of whether they were done by the GP or by the practice nurse. That is, the non-pharmacological management provided in general practice patient encounters is described, rather than management provided specifically by the GP. Treatments provided by the practice nurse are reported separately in Chapter 13.
Data on other treatments are reported for each year from 2000–01 to 2009–10 in the 10-year summary report General practice activity in Australia 2000–01 to 2009–10: 10 year data tables.1
10.1	Number of other treatments
Other treatments were frequently provided for the management of patient problems. 
In 2009–10, a total of 53,243 other treatments were recorded, at a rate of 52.5 per 100 encounters. Two-thirds of these were clinical treatments. For every 100 problems managed, one in five was managed with a clinical treatment (22.8 per 100 problems) (Table 10.1). 
Table 10.1: Summary of other treatments
Variable	Number	Rate per 100 encounters
(n = 101,349)	95% LCL	95% UCL	Rate per 100 problems
(n = 155,373)	95% LCL	95% UCL	
At least one other treatment	40,800	40.3	38.5	42.0	—	—	—	
Other treatments	53,243	52.5	49.8	55.3	34.3	32.6	36.0	
	Clinical treatments	35,484	35.0	32.6	37.4	22.8	21.3	24.3	
	Procedural treatments	17,759	17.5	16.5	18.6	11.4	10.8	12.1	
Note: LCL—lower confidence limit; UCL—upper confidence limit.
Table 10.2 shows the relationship between other treatments and pharmacological treatments given to patients. 
•	In nearly two-thirds (61.6%) of the problems that were managed with an ‘other treatment’, no concurrent pharmacological treatment was provided.
•	A clinical treatment was provided in the management of 20.6% of problems. For nearly two-thirds (60.5%) of these problems, no medication was provided. 
•	A procedure was undertaken in the management of 10.7% of problems, with no pharmacological management given for 63.0% of these problems.

Table 10.2: Relationship between other treatments and pharmacological treatments
Co-management of problems with other treatments	Number of problems 	Per cent 
within class	Per cent of 
problems
(n = 155,373)	95% LCL	95% UCL	
At least one other treatment 	47,133	100.0	30.3	29.0	31.7	
	Without pharmacological treatment	29,022	61.6	18.7	17.9	19.5	
At least one clinical treatment 	31,938	100.0	20.6	19.3	21.8	
	Without pharmacological treatment	19,314	60.5	12.4	11.8	13.1	
At least one procedural treatment	16,577	100.0	10.7	10.1	11.3	
	Without pharmacological treatment 	10,444	63.0	6.7	6.2	7.2	
Note: LCL—lower confidence limit; UCL—upper confidence limit.
10.2	Clinical treatments
Clinical treatments include general and specific advice, counselling or education, family planning, and administrative processes. During 2009–10, there were 35,484 clinical treatments recorded, at a rate of 35.0 per 100 encounters, or 22.8 per 100 problems managed (Table 10.1).
Most frequent clinical treatments
Table 10.3 lists the most common clinical treatments provided. Each treatment is expressed as a percentage of all other treatments, as a rate per 100 encounters with 95% confidence limits and as a rate per 100 problems with 95% confidence limits.
General advice and education was the most frequently recorded clinical treatment, at a rate of 6.2 per 100 encounters. Counselling about the problem under management was provided at a rate of 4.3 per 100 encounters, and advice and education about a patient’s treatment was provided at a rate of 3.9 per 100 encounters. Psychological counselling was provided at a rate of 3.4 per 100 encounters, and advice and education about medication was given at a rate of 2.4 per 100 encounters (Table 10.3).
Several clinical treatments related to preventive activities done. The most common preventive activity was counselling about nutrition and weight (3.7 per 100 encounters). Several other groups could also be considered preventive, including counselling/advice for exercise, smoking, prevention, lifestyle and alcohol. Together, these preventive treatments accounted for 20.5% of all clinical treatments, provided at a rate of 7.1 per 100 encounters (Table 10.3).

Table 10.3: Most frequent clinical treatments
Clinical treatment	Number	Per cent of other treatments
(n = 53,243)	Rate per 100 encounters (n = 101,349)	95% LCL	95% UCL	Rate per 100 problems (n = 155,373)	95% LCL	95% UCL	
Advice/education*	6,319	11.9	6.2	5.3	7.1	4.1	3.5	4.6	
Counselling—problem*	4,406	8.3	4.3	3.7	5.0	2.8	2.4	3.2	
Advice/education—treatment*	3,981	7.5	3.9	3.3	4.5	2.6	2.2	3.0	
Counsel/advice—nutrition/weight*	3,767	7.1	3.7	3.4	4.1	2.4	2.2	2.7	
Counselling—psychological*	3,492	6.6	3.4	3.2	3.7	2.2	2.1	2.4	
Advice/education—medication*	2,439	4.6	2.4	2.2	2.6	1.6	1.4	1.7	
Other administrative/document*	2,083	3.9	2.1	1.9	2.3	1.3	1.2	1.5	
Reassurance, support	1,411	2.7	1.4	1.1	1.7	0.9	0.7	1.1	
Sickness certificate*	1,400	2.6	1.4	1.2	1.6	0.9	0.8	1.0	
Counsel/advice—exercise*	1,252	2.4	1.2	1.0	1.4	0.8	0.7	0.9	
Counsel/advice—smoking*	738	1.4	0.7	0.6	0.8	0.5	0.4	0.5	
Counsel/advice—prevention*	588	1.1	0.6	0.5	0.7	0.4	0.3	0.5	
Counsel/advice—life style*	545	1.0	0.5	0.4	0.7	0.4	0.3	0.4	
Counsel/advice—alcohol*	399	0.7	0.4	0.3	0.5	0.3	0.2	0.3	
Counsel/advice—pregnancy*	345	0.6	0.3	0.3	0.4	0.2	0.2	0.3	
Counsel/advice—health/body*	342	0.6	0.3	0.3	0.4	0.2	0.2	0.3	
Observe/wait*	330	0.6	0.3	0.2	0.4	0.2	0.1	0.3	
Family planning*	311	0.6	0.3	0.3	0.4	0.2	0.2	0.2	
Subtotal 	34,150	64.1	—	—	—	—	—	—	
Total clinical treatments 	35,484	66.6	35.0	32.6	37.4	22.8	21.3	24.3	
*	Includes multiple ICPC-2 or ICPC-2 PLUS codes (see Appendix 4, <www.aihw.gov.au/publications/index.cfm/subject/19>).
Note: LCL—lower confidence limit; UCL—upper confidence limit. Includes the most common clinical treatments—those accounting for > 0.5% of all other treatments. 

Problems managed with clinical treatments
Table 10.4 lists the top 10 problems managed with a clinical treatment. It also shows the extent to which clinical treatments were used for that problem, and the relationship between the use of a clinical treatment and a medication for individual problems. Clinical treatments were provided in the management of 31,938 problems (20.6% of all problems).
•	Depression and URTI were the problems most often managed with a clinical treatment, each at a rate of 1.9 per 100 encounters. 
•	Almost half the contacts with depression involving management with a clinical treatment did not result in a medication being prescribed/advised/supplied (48.7%) at that encounter.
•	Almost 32% of URTI contacts involved a clinical treatment, with 53.1% of these being managed without medication at that encounter.
•	More than 1 in 10 (10.6%) hypertension contacts resulted in a clinical treatment, with 44.2% of these being managed without medication at that encounter.
•	A clinical treatment was used at one-quarter (25.1%) of contacts with lipid disorders, and 66.2% of these did not involve medication at that encounter.
Table 10.4: The 10 most common problems managed with a clinical treatment
Problem managed	Number	Per cent of problems
with clinical
 treatment	Rate per 100
encounters(a)
(n = 101,349)	95%
LCL	95%
UCL	Per cent
 of this
problem(b)	Per cent of treated
 problems no medications(c)	
Upper respiratory tract infection	1,931	6.0	1.9	1.6	2.2	31.8	53.1	
Depression*	1,925	6.0	1.9	1.7	2.1	44.5	48.7	
Hypertension*	976	3.1	1.0	0.8	1.1	10.6	44.2	
Diabetes*	968	3.0	1.0	0.9	1.1	25.8	59.9	
Lipid disorders	884	2.8	0.9	0.8	1.0	25.1	66.2	
Anxiety*	804	2.5	0.8	0.7	0.9	44.7	65.0	
Gastroenteritis*	604	1.9	0.6	0.5	0.7	41.5	56.4	
Back complaint*	540	1.7	0.5	0.5	0.6	19.6	46.3	
Test results*	497	1.6	0.5	0.4	0.6	27.6	90.8	
Tobacco abuse	476	1.5	0.5	0.4	0.5	61.6	48.0	
Subtotal 	9,605	30.1	—	—	—	—	—	
Total problems with clinical treatments	31,938	100.0	31.5	29.5	33.5	—	—	
(a)	Rate of provision of clinical treatment for selected problem per 100 total encounters.
(b)	Percentage of contacts with this problem that generated at least one clinical treatment.
(c)	The numerator is the number of cases of this problem that generated at least one clinical treatment but generated no medications. 
The denominator is the total number of contacts for this problem that generated at least one clinical treatment (with or without medications).
*	Includes multiple ICPC-2 or ICPC-2 PLUS codes (see Appendix 4, <www.aihw.gov.au/publications/index.cfm/subject/19>).
Note: LCL—lower confidence limit; UCL—upper confidence limit.
10.3	Procedural treatments
Procedural treatments included therapeutic actions and diagnostic procedures undertaken at the encounter. Injections for immunisations/vaccinations are not counted here as procedures, as these have already been reported as medications (see Chapter 9). There were 17,759 procedural treatments provided in these general practice encounters during 2009–10, at a rate of 17.5 per 100 encounters (Table 10.1).

Most frequent procedures
Table 10.5 lists the most common procedural treatments provided. Each treatment is expressed as a percentage of all other treatments, and as a rate per 100 encounters with 95% confidence limits. These results only report investigations actually undertaken at the encounter. They do not include investigations that were ordered by the GP to be performed by an external provider. A summary of all investigations (both undertaken and ordered) is provided in Table 12.6.
The most frequently recorded group of procedures in 2009–10 were excisions, at a rate of 3.0 per 100 encounters, and accounting for 5.7% of all other treatments. Other procedural treatments frequently recorded included local injections (2.5 per 100 encounters), dressings (2.4 per 100 encounters) and incisions (1.4 per 100 encounters) (Table 10.5).
Table 10.5: Most frequent procedural treatments
Procedural treatment	Number	Per cent 
of other
treatments
(n = 53,243)	Rate per 100 encounters
(n = 101,349)	95%
 LCL	95%
 UCL	Rate per 100 problems
(n = 155,373)	95%
 LCL	95%
 UCL	
Excision/removal tissue/biopsy/
destruction/debridement/cauterisation*	3,024	5.7	3.0	2.7	3.2	1.9	1.8	2.1	
Local injection/infiltration*(a)	2,544	4.8	2.5	2.3	2.7	1.6	1.5	1.8	
Dressing/pressure/compression/
tamponade*	2,439	4.6	2.4	2.2	2.6	1.6	1.4	1.7	
Incision/drainage/flushing/aspiration/
removal body fluid*	1,402	2.6	1.4	1.2	1.5	0.9	0.8	1.0	
Physical medicine/rehabilitation*	1,260	2.4	1.2	1.0	1.5	0.8	0.7	1.0	
Pap smear*	1,046	2.0	1.0	0.9	1.2	0.7	0.6	0.8	
Other therapeutic procedures/surgery NEC*	987	1.9	1.0	0.4	1.6	0.6	0.2	1.0	
Repair/fixation—suture/cast/prosthetic device (apply/remove)*	915	1.7	0.9	0.8	1.0	0.6	0.5	0.6	
Check-up—practice nurse*	683	1.3	0.7	0.3	1.0	0.4	0.2	0.7	
Electrical tracings*	594	1.1	0.6	0.5	0.7	0.4	0.3	0.4	
INR test	560	1.1	0.6	0.4	0.7	0.4	0.3	0.4	
Other preventive procedures/high risk medication*	549	1.0	0.5	0.5	0.6	0.4	0.3	0.4	
Physical function test*	542	1.0	0.5	0.4	0.6	0.3	0.3	0.4	
Urine test*	287	0.5	0.3	0.2	0.3	0.2	0.1	0.2	
Other diagnostic procedures*	267	0.5	0.3	0.2	0.3	0.2	0.1	0.2	
Subtotal 	17,100	32.1	—	—	—	—	—	—	
Total procedural treatments 	17,759	33.4	17.5	16.5	18.6	11.4	10.8	12.1	
(a)	Excludes all local injection/infiltrations performed for immunisations.
*	Includes multiple ICPC-2 or ICPC-2 PLUS codes (see Appendix 4, <www.aihw.gov.au/publications/index.cfm/subject/19>).
Note: LCL—lower confidence limit; UCL—upper confidence limit; NEC—not elsewhere classified; INR—international normalised ratio. Only the most common procedural treatments—those accounting for > 0.5% of all other treatments—were tabled. 

Problems managed with a procedural treatment
Table 10.6 lists the top 10 problems managed with a procedural treatment. It also shows the proportion of contacts with each problem that was managed with a procedure, and the proportion of problems managed with a procedure without medication given concurrently.
•	A total of 16,577 problems (10.7% of all problems) involved a procedural treatment in their management.
•	The top 10 problems accounted for 34.4% of all problems for which a procedure was used.
•	Female genital check-ups/Pap smears were the most common problem managed with a procedure (0.9 per 100 encounters), with a procedure undertaken at more than half (53.9%) of all contacts.
•	Nearly four-fifths (79.7%) of contacts for lacerations and cuts were treated with a procedure. Of these, 77.4% were not concurrently given a medication for this problem at the encounter.
Table 10.6: The 10 most common problems managed with a procedural treatment
Problem managed	Number	Per cent of problems with procedure 	Rate per 100 encounters(a)
(n = 101,349)	95% LCL	95% UCL	Per cent of this problem(b)	Per cent of treated problems no medications(c)	
Female genital check-up/
Pap smear*	931	5.6	0.9	0.8	1.0	53.9	97.9	
Solar keratosis/sunburn	842	5.1	0.8	0.7	0.9	66.4	97.9	
Laceration/cut	679	4.1	0.7	0.6	0.7	79.7	77.4	
Excessive ear wax	575	3.5	0.6	0.5	0.6	74.6	92.2	
Malignant neoplasm of skin	571	3.4	0.6	0.5	0.7	44.5	94.0	
Chronic ulcer skin (including varicose ulcer)	484	2.9	0.5	0.4	0.6	77.9	78.6	
General check-up*	480	2.9	0.5	0.4	0.6	15.9	71.4	
Warts	449	2.7	0.4	0.4	0.5	75.9	95.2	
Sprain/strain*	354	2.1	0.3	0.2	0.5	24.1	40.8	
Back complaint*	338	2.0	0.3	0.2	0.5	12.3	55.8	
Subtotal 	5,704	34.4	—	—	—	—	—	
Total problems with procedural treatments	16,577	100.0	16.4	15.4	17.3	—	—	
(a)	Rate of provision of procedural treatment for selected problem per 100 total encounters.
(b)	Percentage of contacts with this problem that generated at least one procedural treatment.
(c)	The numerator is the number of cases of this problem that generated at least one procedural treatment but generated no medications. The denominator is the total number of contacts (for this problem) that generated at least one procedural treatment (with or without medications).
*	Includes multiple ICPC-2 or ICPC-2 PLUS codes (see Appendix 4, <www.aihw.gov.au/publications/index.cfm/subject/19>).
Note: LCL—lower confidence limit; UCL—upper confidence limit.
10.4	Changes in other treatments over the decade 2000–01 to 2009–10
An overview of changes in other treatments provided in general practice over the decade can be found in Chapter 10 of the companion report General practice activity in Australia 2000–01 to 2009–10: 10 year data tables.1 In that report, changes over time are measured as change in the management of problems (that is, as a rate per 100 problems). This reflects change in how GPs are managing problems and accounts for the significant increase in the number of problems managed per encounter over the decade (see Section 7.9).
Clinical treatments
In summary, there was a significant decrease in the rate of clinical treatments provided for every 100 problems managed between 2000–01 and 2009–10. However, this decrease was not linear over the study period—the rate increased between 2000–01 and 2004–05 (from 25.8 clinical treatments per 100 problems managed to 27.0 per 100), but then decreased to a low of 19.9 per 100 in 2006–07 and has gradually been increasing since. By 2009–10 the rate had still not reached the level of 2000–01. 
The overall decrease was reflected in the rates of advice and education about treatment, counselling and advice about nutrition and weight, and counselling and advice about exercise. 
In light of policy changes (including the establishment of beyondblue in 200071, introduction of the Better Outcomes in Mental Health Care program in 200172, and the Better access to psychiatrists, psychologists and general practitioners through the MBS initiative in 200673), two results were of particular interest: 
•	there was no change in the rate of GP provision of psychological counselling 
•	the rate at which other administration/documentation work was recorded increased between 2000–01 and 2009–10. 
There was little change in the rates of clinical treatments provided for any of the problems most often managed with clinical treatments.
Procedural treatments
There was a significant increase in the number of procedures performed between 2000–01 (8.4 per 100 problems managed) and 2009–10 (11.4 per 100). In particular, there were significantly more local injections given, more procedures from the group dressing/
pressure/compression/tamponade, more INR tests, and more other preventive procedures.
11	Referrals and admissions
A referral is defined as the process by which the responsibility for part or all of the care of a patient is temporarily transferred to another health care provider. Only new referrals arising at the encounter were included (that is, continuations were not recorded). For each encounter, GPs could record up to two referrals. These included referrals to medical specialists, allied health professionals, hospitals for admission, emergency departments or other medical services. Referrals to hospital outpatient clinics and to other GPs were classified as referrals to other medical services.
Data on referrals and admissions are reported for each year from 2000–01 to 2009–10 in the 10-year summary report General practice activity in Australia 2000–01 to 2009–10: 10 year data tables.1
11.1	Number of referrals and admissions
Table 11.1 provides a summary of referrals and admissions, and the rates per 100 encounters and per 100 problems for which referrals were provided. The patient was given at least one referral at 12.4% of all encounters, and for 8.7% of all problems managed. There were a total of 13,481 referrals made at a rate of 13.3 per 100 encounters. The most frequent referrals were to medical specialists (8.4 per 100 encounters, 5.5 per 100 problems managed), followed by referrals to allied health services (3.9 per 100 encounters, 2.6 per 100 problems). Very few patients were referred to hospitals, to the hospital emergency department or to other medical services.
Table 11.1: Summary of referrals and admissions
Variable	Number	Rate per 100 encounters
(n = 101,349)	95%
 LCL	95% UCL	Rate per 100 problems
(n = 155,373)	95% LCL	95% UCL	
At least one referral(a)	12,554	12.4	11.9	12.9	8.7	8.4	9.0	
Referrals	13,481	13.3	12.8	13.8	8.7	8.4	9.0	
	Medical specialist	8,562	8.4	8.1	8.8	5.5	5.3	5.7	
	Allied health service	3,974	3.9	3.7	4.2	2.6	2.4	2.7	
	Hospital	362	0.4	0.3	0.4	0.2	0.2	0.3	
	Emergency department	202	0.2	0.2	0.2	0.1	0.1	0.2	
	Other medical services	80	0.1	0.1	0.1	0.1	0.0	0.1	
	Other referrals	301	0.3	0.2	0.4	0.2	0.1	0.2	
(a)	Rate per 100 problems for at least one referral is calculated using a numerator of number of individual problems with a referral (n = 13,477).
Note: LCL—lower confidence limit; UCL—upper confidence limit.

11.2	Most frequent referrals
Table 11.2 shows the medical specialists and allied health service groups to whom GPs most often referred patients. The most common specialist referrals were to surgeons (9.7% of specialist referrals), orthopaedic surgeons (9.2%), ophthalmologists (8.1%), and dermatologists (7.7%). 
Almost one-third (28.9%) of referrals to allied health services were to physiotherapists, one‑fifth were to psychologists (20.1%), one-tenth to podiatrists or chiropodists (9.4%), and 7.6% to dentists.
Table 11.2: Most frequent referrals, by type
Professional/organisation	Number	Per cent of AHP and specialist referrals	Per cent of referral group	Rate per 100 encounters
(n = 101,349)	95% LCL	95% UCL	Rate per 100 problems
(n = 155,373)	95% LCL	95% UCL	
Medical specialist 	8,562	68.3	100.0	8.4	8.1	8.8	5.5	5.3	5.7	
	Surgeon 	827	6.6	9.7	0.8	0.7	0.9	0.5	0.5	0.6	
	Orthopaedic surgeon 	789	6.3	9.2	0.8	0.7	0.8	0.5	0.5	0.6	
	Ophthalmologist 	696	5.6	8.1	0.7	0.6	0.8	0.4	0.4	0.5	
	Dermatologist	655	5.2	7.7	0.6	0.6	0.7	0.4	0.4	0.5	
	Gastroenterologist	574	4.6	6.7	0.6	0.5	0.6	0.4	0.3	0.4	
	Cardiologist 	555	4.4	6.5	0.5	0.5	0.6	0.4	0.3	0.4	
	Ear, nose and throat 	521	4.2	6.1	0.5	0.5	0.6	0.3	0.3	0.4	
	Gynaecologist	505	4.0	5.9	0.5	0.4	0.6	0.3	0.3	0.4	
	Urologist 	321	2.6	3.8	0.3	0.3	0.4	0.2	0.2	0.2	
	Neurologist	254	2.0	3.0	0.3	0.2	0.3	0.2	0.1	0.2	
	Subtotal: top 10 medical 
	specialist referrals 	5,698	45.5	66.6	—	—	—	—	—	—	
Allied health and other professionals 	3,974	31.7	100.0	3.9	3.7	4.2	2.6	2.4	2.7	
	Physiotherapy 	1,148	9.2	28.9	1.1	1.0	1.3	0.7	0.7	0.8	
	Psychologist 	798	6.4	20.1	0.8	0.7	0.9	0.5	0.5	0.6	
	Podiatrist/chiropodist	373	3.0	9.4	0.4	0.3	0.4	0.2	0.2	0.3	
	Dentist 	302	2.4	7.6	0.3	0.2	0.3	0.2	0.2	0.2	
	Dietitian/nutritionist	258	2.1	6.5	0.3	0.2	0.3	0.2	0.1	0.2	
	Optometrist 	98	0.8	2.5	0.1	0.1	0.1	0.1	0.1	0.1	
	Audiologist/acoustic testing	96	0.8	2.4	0.1	0.1	0.1	0.1	0.0	0.1	
	Diabetes education 	76	0.6	1.9	0.1	0.1	0.1	0.0	0.0	0.1	
	Counsellor 	57	0.5	1.4	0.1	0.0	0.1	0.0	0.0	0.0	
	Drug and alcohol	56	0.4	1.4	0.1	0.0	0.1	0.0	0.0	0.1	
	Subtotal: top 10 allied 
	health referrals	3,262	26.0	82.1	—	—	—	—	—	—	
Subtotal: all referrals listed	8,960	71.5	—	—	—	—	—	—	—	
Total allied health and specialist referrals	12,536	100.0	—	12.4	11.8	12.9	8.1	7.8	8.4	
Note: AHP—allied health professionals; LCL—lower confidence limit; UCL—upper confidence limit.
11.3	Problems most often referred
Each referral may have been provided for the management of multiple problems, and multiple referrals may have been used in the management of a single problem. There are more problem–referral links than referrals. Table 11.3 shows the most common problems referred to medical specialists, in decreasing frequency order of problem–referral combinations.
The 8,562 specialist referrals were provided in the management of 8,783 problems. The 10 problems most commonly referred to a specialist accounted for 18.9% of all 
problem–referral links. Those most often referred were pregnancy (3.1% of problem–referral links), malignant skin neoplasm (2.7%), osteoarthritis (2.3%) and diabetes (1.9%) (Table 11.3).
Table 11.3 also shows the rate of referral per 100 contacts for each problem. Pregnancy was the problem most likely to result in a referral to a specialist (18.6 per 100 pregnancy problems referred), followed by malignant skin neoplasm and abnormal test results.
Table 11.3: The 10 problems most frequently referred to a medical specialist
Problem managed	Number	Per cent of problem–referral links 	Rate per 100 encounters
(n = 101,349)	95% LCL	95% UCL	Rate per 100 contacts of this problem(a)	
Pregnancy*	273	3.1	0.3	0.2	0.3	18.6	
Malignant skin neoplasm	234	2.7	0.2	0.2	0.3	18.2	
Osteoarthritis*	199	2.3	0.2	0.2	0.2	6.7	
Diabetes—all*	170	1.9	0.2	0.1	0.2	4.5	
Back complaint*	151	1.7	0.1	0.1	0.2	5.5	
Sleep disturbance	145	1.6	0.1	0.1	0.2	9.8	
Ischaemic heart disease*	125	1.4	0.1	0.1	0.1	10.7	
Depression*	121	1.4	0.1	0.1	0.1	2.8	
Oesophageal disease	120	1.4	0.1	0.1	0.1	4.7	
Abnormal test results*	117	1.3	0.1	0.1	0.1	10.8	
Subtotal: top 10 problems referred to a medical specialist	1,656	18.9	—	—	—	—	
Total problems referred to medical specialist 	8,783	100.0	8.7	8.3	9.1	—	
(a)	The rate of referrals to medical specialists per 100 contacts with the problem.
*	Includes multiple ICPC-2 or ICPC-2 PLUS codes (see Appendix 4, <www.aihw.gov.au/publications/index.cfm/subject/19>).
Note: LCL—lower confidence limit; UCL—upper confidence limit.
The 3,974 referrals to an allied health professional or service were provided in the management of 4,140 problems. The 10 most common of these accounted for 46.6% of all problem–referral links. Depression was the problem most frequently referred to an allied health service (12.1% of problem–referral links). However, the problem most likely to result in a referral to an allied health service was teeth/gum disease, with more than one in four contacts resulting in referral (Table 11.4).
The 362 referrals to a hospital were provided in the management of 381 problems. The 10 problems most frequently referred to hospital are shown in Table 11.5. Pregnancy was the most common. However, appendicitis was the problem most likely to be referred.
Table 11.4: The 10 problems most frequently referred to allied health services
Problem managed	Number	Per cent of problem–referral links 	Rate per 100 encounters
(n = 101,349)	95% LCL	95% UCL	Rate per 100 contacts of this problem(a)	
Depression*	500	12.1	0.5	0.4	0.5	11.5	
Diabetes—all* 	281	6.8	0.3	0.2	0.3	7.5	
Back complaint*	256	6.2	0.3	0.2	0.3	9.3	
Anxiety* 	195	4.7	0.2	0.2	0.2	10.8	
Sprain/strain*	193	4.7	0.2	0.1	0.3	13.1	
Teeth/gum disease	147	3.6	0.1	0.1	0.2	28.3	
Osteoarthritis*	127	3.1	0.1	0.1	0.2	4.3	
Bursitis/tendonitis/synovitis NOS 	98	2.4	0.1	0.1	0.1	8.5	
Shoulder syndrome	71	1.7	0.1	0.0	0.1	13.1	
Administrative procedure NOS	63	1.5	0.1	0.0	0.1	7.1	
Subtotal: top 10 problems referred to AHS	1,931	46.6	—	—	—	—	
Total problems referred to AHS 	4,140	100.0	4.1	3.8	4.4	—	
(a)	The rate of referrals to allied health services per 100 contacts with the problem.
*	Includes multiple ICPC-2 or ICPC-2 PLUS codes (see Appendix 4, <www.aihw.gov.au/publications/index.cfm/subject/19>).
Note: LCL—lower confidence limit; UCL—upper confidence limit; NOS—not otherwise specified; AHS—allied health service.
Table 11.5: The 10 problems most frequently referred to hospital
Problem managed	Number	Per cent of problem–referral links 	Rate per 100 encounters
(n = 101,349)	95% LCL	95% UCL	Rate per 100 contacts of this problem(a)	
Pregnancy* 	23	6.0	0.02	0.01	0.04	1.6	
Fracture*	20	5.2	0.02	0.01	0.03	2.2	
Pneumonia	14	3.6	0.01	0.01	0.02	3.8	
Gastroenteritis*	10	2.5	0.01	0.00	0.02	0.7	
Chronic obstructive pulmonary disease	9	2.4	0.01	0.00	0.02	1.1	
Urinary tract infection*	9	2.2	0.01	0.00	0.02	0.5	
Depression*	8	2.1	0.01	0.00	0.02	0.2	
Appendicitis	8	2.0	0.01	0.00	0.01	34.3	
Acute bronchitis/bronchiolitis	8	2.0	0.01	0.00	0.01	0.3	
Ischaemic heart disease*	8	2.0	0.01	0.00	0.01	0.6	
Subtotal: top 10 problems referred for admission	114	29.9	—	—	—	—	
Total problems referred to hospital	381	100.0	0.38	0.32	0.44	—	
(a)	The rate of referrals to hospital per 100 contacts with the problem.
*	Includes multiple ICPC-2 or ICPC-2 PLUS codes (see Appendix 4, <www.aihw.gov.au/publications/index.cfm/subject/19>).
Note: LCL—lower confidence limit; UCL—upper confidence limit.
The 202 referrals to an emergency department were provided in the management of 205 problems. The 10 problems most frequently referred to an emergency department are shown in Table 11.6. Pneumonia was the most common. However, appendicitis was the problem most likely to be referred.
Table 11.6: The 10 problems most frequently referred to an emergency department
Problem managed	Number	Per cent of problem–referral links 	Rate per 100 encounters
(n = 101,349)	95% LCL	95% UCL	Rate per 100 contacts of this problem(a)	
Pneumonia	10	5.1	0.01	0.00	0.02	2.9	
Ischaemic heart disease*	10	5.0	0.01	0.00	0.02	0.9	
Abortion, spontaneous	7	3.6	0.01	0.00	0.01	7.7	
Fracture*	7	3.4	0.01	0.00	0.01	0.8	
Chest pain NOS	6	3.1	0.01	0.00	0.01	1.8	
Appendicitis	6	3.0	0.01	0.00	0.01	27.7	
Abdominal pain*	6	2.9	0.01	0.00	0.01	1.0	
Stroke/cerebrovascular accident	6	2.9	0.01	0.00	0.01	3.8	
Complication of medical treatment	6	2.7	0.01	0.00	0.01	1.8	
Cholecystitis/cholelithiasis	5	2.7	0.01	0.00	0.01	2.7	
Subtotal: top 10 problems referred to emergency department	71	34.5	—	—	—	—	
Total problems referred to emergency department	205	100.0	0.20	0.16	0.24	—	
(a)	The rate of referrals to an emergency department per 100 contacts with the problem.
*	Includes multiple ICPC-2 or ICPC-2 PLUS codes (see Appendix 4, <www.aihw.gov.au/publications/index.cfm/subject/19>).
Note: LCL—lower confidence limit; UCL—upper confidence limit; NOS—not otherwise specified.
11.4	Changes in referrals over the decade 2000–01 to 2009–10
An overview of changes in referrals over the decade can be found in Chapter 11 of the companion report General practice activity in Australia 2000–01 to 2009–10: 10 year data tables.1 In that report, changes over time are measured as change in the management of problems (that is, as a rate per 100 problems). This reflects change in how GPs are managing problems, and accounts for the significant increase in the number of problems managed per encounter over the decade (see Section 7.9). 
In summary, over the 10 years there was a significant increase in the proportion of problems that were referred to other health providers: in 2000–01 at least one referral was made in the management of 7.2% of problems and this increased to 8.7% of problems managed in 
2009–10. Referrals to medical specialists rose marginally from 5.1 to 5.5 per 100 problems managed, reflected in marginal increases in referrals to cardiologists and gastroenterologists. However, referrals to allied health services rose far more, from 1.6 to 2.6 per 100 problems managed (a 63% increase). This was reflected in significant increases in referral rates to psychologists, podiatrist or chiropodists, dentists, and marginal increases in the rate of referral to dietitians or nutritionists, and physiotherapists per 100 problems.
There were no changes over the decade in the rate of referrals to hospitals and emergency departments per 100 problems managed.
12	Investigations
The GPs participating in the study were asked to record (in free text) any pathology, imaging or other tests ordered or undertaken at the encounter, and to nominate the patient problem(s) associated with each test order placed. This allows the linkage of test orders to a single problem or multiple problems. Up to five orders for pathology, and two for imaging and other tests could be recorded at each encounter. A single test may have been ordered for the management of multiple problems, and multiple tests may have been used in the management of a single problem.
A pathology test order may be for a single test (for example, Pap smear, HbA1c) or for a battery of tests (for example, lipids, full blood count). Where a battery of tests was ordered, the battery name was recorded rather than each individual test. GPs also recorded the body site for any imaging ordered (for example, x-ray chest, CT head).
Data on investigations are reported for each year from 2000–01 to 2009–10 in the 10-year summary report General practice activity in Australia 2000–01 to 2009–10: 10 year data tables.1
12.1	Number of investigations
Table 12.1 shows the number of encounters and problems at which a pathology or imaging test was ordered. There were no tests recorded at a large majority (76.2%) of encounters.
At least one pathology test order was recorded at 17.7% of encounters (for 13.2% of problems managed), and at least one imaging test was ordered at 8.5% of encounters (for 5.7% of problems managed).
Table 12.1: Number of encounters and problems for which pathology or imaging was ordered
Pathology/imaging test ordered	Number of encounters 	Per cent of encounters (n = 101,349)	95% LCL	95% UCL	Number of problems	Per cent of problems (n = 155,373)	95% LCL	95% UCL	
Pathology and imaging ordered	2,478	2.4	2.3	2.6	1,819	1.2	1.1	1.3	
Pathology only ordered	15,504	15.3	14.8	15.8	18,751	12.1	11.7	12.5	
Imaging only ordered	6,148	6.1	5.8	6.3	7,093	4.6	4.4	4.7	
No pathology or imaging tests ordered	77,220	76.2	75.5	76.9	127,709	82.2	81.7	82.7	
At least one pathology 
ordered	17,982	17.7	17.1	18.3	20,571	13.2	12.8	13.7	
At least one imaging 
ordered	8,625	8.5	8.2	8.9	8,912	5.7	5.5	6.0	
At least one other investigation ordered	717	0.7	0.6	0.8	737	0.5	0.4	0.5	
At least one other investigation performed in the practice	1,342	1.3	1.2	1.5	1,359	0.9	0.8	1.0	
At least one other investigation ordered or performed	2,009	2.0	1.8	2.1	2,050	1.3	1.2	1.4	
Note: LCL—lower confidence limit; UCL—upper confidence limit.

12.2	Pathology ordering
A report on changes in pathology ordering by GPs from 1998 to 2001 was produced as an AIHW–University of Sydney book in 2003.14 A review of GP pathology orders in the National Health Priority Areas and other selected problems between 2000 and 2008 is reported in the AGPSCC publication General practice in Australia, health priorities and policies 1998 to 2008.12 A report Evidence-practice gap in pathology test ordering: a comparison of BEACH pathology data and recommended testing was produced by the FMRC for the Australian Government Quality Use of Pathology Program in June 2009.13 Readers may wish to consider those reports in conjunction with the information presented below. 
Nature of pathology orders at encounter
The GPs recorded 45,594 orders for pathology tests/batteries of tests, at a rate of 45.0 per 100 encounters or 29.3 per 100 problems.
The distribution of pathology tests by MBS group, and the most common tests within each group are presented in Table 12.2. Each group and individual test is expressed as a percentage of all pathology tests, as a percentage of the group, as a rate per 100 encounters and as a rate per 100 problems with 95% confidence limits.
The pathology tests recorded were grouped according to the categories set out in 
Appendix 4. The main pathology groups reflect those used by Medicare Australia.74 
Test orders classed as chemistry accounted for more than half of all pathology test orders, the most common being lipids, for which there were 4.3 orders per 100 encounters and 2.8 per 100 problems, electrolytes, urea and creatinine (3.2; 2.1), liver function (3.1; 2.0), and thyroid function tests (2.4 per 100 encounters; 1.6 per 100 problems). The most frequently ordered individual test was full blood count at 6.3 orders per 100 encounters.
Table 12.2: Distribution of pathology orders across MBS pathology groups and most frequent individual test orders within group
Pathology test ordered	Number	Per cent 
of all pathology 	Per cent 
of group	Rate per 100 encounters
(n = 101,349)	95% LCL	95% UCL	Rate per 100 problems
(n = 155,373)	95% LCL	95% UCL	
Chemistry* 	26,249	57.6	100.0	25.9	24.6	27.2	16.9	16.1	17.6	
	Lipids*	4,355	9.6	16.6	4.3	4.0	4.6	2.8	2.6	3.0	
	Electrolytes, urea and creatinine*	3,262	7.2	12.4	3.2	3.0	3.5	2.1	1.9	2.3	
	Liver function*	3,113	6.8	11.9	3.1	2.8	3.3	2.0	1.8	2.2	
	Thyroid function*	2,431	5.3	9.3	2.4	2.2	2.6	1.6	1.5	1.7	
	Glucose/glucose tolerance*	2,332	5.1	8.9	2.3	2.1	2.5	1.5	1.4	1.6	
	Multibiochemical analysis*	2,212	4.9	8.4	2.2	1.9	2.5	1.4	1.2	1.6	
	Ferritin*	1,388	3.1	5.3	1.4	1.2	1.5	0.9	0.8	1.0	
	Chemistry; other*	1,387	3.0	5.3	1.4	1.2	1.5	0.9	0.8	1.0	
	HbA1c*	1,183	2.6	4.5	1.2	1.1	1.3	0.8	0.7	0.8	
	Prostate specific antigen*	1,028	2.3	3.9	1.0	0.9	1.1	0.7	0.6	0.7	
	Hormone assay*	757	1.7	2.9	0.8	0.7	0.8	0.5	0.4	0.5	
	C reactive protein	748	1.6	2.8	0.7	0.7	0.8	0.5	0.4	0.5	
(continued)
Table 12.2 (continued): Distribution of pathology orders across MBS pathology groups and most frequent individual test orders within group
Pathology test ordered	Number	Per cent 
of all pathology 	Per cent 
of group	Rate per 100 encounters
(n = 101,349)	95% LCL	95% UCL	Rate per 100 problems
(n = 155,373)	95% LCL	95% UCL	
Haematology* 	8,382	18.4	100.0	8.3	7.8	8.7	5.4	5.1	5.7	
	Full blood count* 	6,352	13.9	75.8	6.3	5.9	6.6	4.1	3.9	4.3	
	Erythrocyte sedimentation rate 	1,082	2.4	12.9	1.1	1.0	1.2	0.7	0.6	0.8	
	Coagulation* 	736	1.6	8.8	0.7	0.6	0.8	0.5	0.4	0.5	
Microbiology* 	6,350	13.9	100.0	6.3	5.9	6.6	4.1	3.9	4.3	
	Urine M,C&S*	1,929	4.2	30.4	1.9	1.8	2.0	1.2	1.2	1.3	
	Microbiology; other*	1,022	2.2	16.1	1.0	0.9	1.1	0.7	0.6	0.7	
	Hepatitis serology*	488	1.1	7.7	0.5	0.4	0.6	0.3	0.3	0.4	
	Faeces M,C&S*	398	0.9	6.3	0.4	0.3	0.4	0.3	0.2	0.3	
	Chlamydia*	313	0.7	4.9	0.3	0.3	0.4	0.2	0.2	0.2	
	Vaginal swab M,C&S*	298	0.7	4.7	0.3	0.2	0.3	0.2	0.2	0.2	
	Venereal disease*	294	0.6	4.6	0.3	0.2	0.4	0.2	0.1	0.2	
	HIV*	266	0.6	4.2	0.3	0.2	0.3	0.2	0.1	0.2	
Cytology* 	1,713	3.8	100.0	1.7	1.5	1.9	1.1	1.0	1.2	
	Pap smear* 	1,671	3.7	97.5	1.7	1.5	1.8	1.1	1.0	1.2	
Other NEC* 	774	1.7	100.0	0.8	0.6	0.9	0.5	0.4	0.6	
	Blood test 	369	0.8	47.7	0.4	0.3	0.5	0.2	0.2	0.3	
	Other test NEC	253	0.6	33.7	0.3	0.2	0.3	0.2	0.1	0.2	
Tissue pathology* 	770	1.7	100.0	0.8	0.7	0.9	0.5	0.4	0.6	
	Histology; skin	615	1.4	80.0	0.6	0.5	0.7	0.4	0.3	0.5	
Immunology* 	923	2.0	100.0	0.9	0.8	1.0	0.6	0.5	0.7	
	Immunology, other*	479	1.1	52.0	0.5	0.4	0.5	0.3	0.3	0.3	
	Anti-nuclear antibodies	185	0.4	20.0	0.2	0.2	0.2	0.1	0.1	0.1	
	Rheumatoid factor	181	0.4	19.6	0.2	0.1	0.2	0.1	0.1	0.1	
Simple basic tests* 	193	0.4	100.0	0.2	0.2	0.2	0.1	0.1	0.1	
Infertility/pregnancy*	263	0.6	100.0	0.3	0.2	0.3	0.2	0.1	0.2	
Total pathology tests 	45,594	100.0	— 	45.0	43.1	46.9	29.3	28.2	30.4	
*	Includes multiple ICPC-2 and ICPC-2 PLUS codes (see Appendix 4, <www.aihw.gov.au/publications/index.cfm/subject/19>).
Note: LCL—lower confidence limit; UCL—upper confidence limit; M,C&S—microscopy, culture and sensitivity; HIV— human immunodeficiency virus; NEC—not elsewhere classified.
Problems for which pathology tests were ordered
Table 12.3 describes the most common problems for which pathology was ordered, in decreasing frequency order of problem–pathology combinations. Diabetes, hypertension, general check-ups and lipid disorders were the most common problems for which pathology tests were ordered. The two right-hand columns show the proportion of each problem that resulted in a pathology order, and the rate of pathology tests/batteries of tests per 100 specified problems when at least one test is ordered. For example, 31.8% of contacts with diabetes resulted in pathology orders, and when pathology was ordered for diabetes, 287 tests/batteries of tests were ordered per 100 diabetes contacts that resulted in a pathology test order. In contrast, only 11.6% of contacts with hypertension problems resulted in a pathology test, but the resulting test orders accounted for almost as many tests (6.4%) as did diabetes.
Table 12.3: The 10 problems for which pathology was most frequently ordered
Problem managed	Number of problems	Number of problem–pathology combinations(a)	Per cent of problem–pathology combinations(a)	Per cent of problems with test(b)	Rate of path orders per 100 problems with pathology(c)	
Diabetes—all*	3,747	3,424	7.3	31.8	287.0	
Hypertension*	9,192	3,030	6.4	11.6	284.7	
General check-up*	3,014	2,808	5.9	27.8	335.8	
Lipid disorders*	3,526	2,169	4.6	28.4	216.8	
Weakness/tiredness 	659	1,696	3.6	66.8	385.3	
Female genital check-up/Pap smear*	1,729	1,648	3.5	77.7	122.6	
Urinary tract infection*	1,780	1,081	2.3	53.2	114.2	
Pregnancy*	1,467	1,037	2.2	33.4	211.7	
Abnormal test results*	1,091	969	2.1	49.7	178.7	
Blood test NOS	290	911	1.9	87.2	360.5	
Subtotal	26,495	18,773	39.8	—	—	
Total problems	155,373	47,208	100.0	13.2	229.5	
(a)	A test was counted more than once if it was ordered for the management of more than one problem at an encounter. There were 45,594 pathology test orders and 47,208 problem–pathology combinations.
(b)	The percentage of total contacts with the problem that generated at least one order for pathology.
(c)	The rate of pathology orders placed per 100 contacts with that problem generating at least one order for pathology.
*	Includes multiple ICPC-2 and ICPC-2 PLUS codes (see Appendix 4, <www.aihw.gov.au/publications/index.cfm/subject/19>).
Note: NOS—not otherwise specified.
12.3	Imaging ordering
Readers wanting a more detailed study of imaging orders should consult the comprehensive report on imaging orders by GPs in Australia in 1999–00, written by the General Practice Statistics and Classification Unit using BEACH data, and published by the AIHW and the University of Sydney in 2001.15
Nature of imaging orders at encounter
There were 9,877 imaging test orders recorded, at a rate of 9.8 per 100 encounters and 6.4 per 100 problems managed. 
The distribution of imaging tests by MBS group, and the most common tests within each group are presented in Table 12.4. Each group and individual test is expressed as a percentage of all imaging tests, as a percentage of the group, as a rate per 100 encounters, and as a rate per 100 problems with 95% confidence limits. Diagnostic radiology accounted for almost half (47.0%) of all imaging test orders, and ultrasound accounted for 37.5%.

Table 12.4: The most frequent imaging tests ordered, by MBS group
Imaging test ordered	Number	Per cent of all imaging 	 Per cent of group	Rate per 100 encounters
(n = 101,349)	95% LCL	95% UCL	Rate per 100 problems
(n = 155,373)	95% LCL	95% UCL	
Diagnostic radiology*	4,637	47.0	100.0	4.6	4.3	4.8	3.0	2.8	3.1	
	X-ray; chest	1,064	10.8	23.0	1.1	0.9	1.2	0.7	0.6	0.8	
	X-ray; knee	485	4.9	10.4	0.5	0.4	0.5	0.3	0.3	0.4	
	Mammography; female	316	3.2	6.8	0.3	0.3	0.4	0.2	0.2	0.2	
	X-ray; foot/feet 	253	2.6	5.5	0.3	0.2	0.3	0.2	0.1	0.2	
	X-ray; shoulder	250	2.5	5.4	0.3	0.2	0.3	0.2	0.1	0.2	
	Test; densitometry	242	2.5	5.2	0.2	0.2	0.3	0.2	0.1	0.2	
	X-ray; hip	233	2.4	5.0	0.2	0.2	0.3	0.2	0.1	0.2	
	X-ray; ankle	199	2.0	4.3	0.2	0.2	0.2	0.1	0.1	0.2	
	X-ray; spine; lumbosacral	138	1.4	3.0	0.1	0.1	0.2	0.1	0.1	0.1	
	X-ray; wrist 	135	1.4	2.9	0.1	0.1	0.2	0.1	0.1	0.1	
	X-ray; hand	135	1.4	2.9	0.1	0.1	0.2	0.1	0.1	0.1	
	X-ray; spine; lumbar 	118	1.2	2.5	0.1	0.1	0.1	0.1	0.1	0.1	
	X-ray; finger(s)/thumb 	108	1.1	2.3	0.1	0.1	0.1	0.1	0.1	0.1	
	X-ray; abdomen	88	0.9	1.9	0.1	0.1	0.1	0.1	0.0	0.1	
	X-ray; spine; cervical	86	0.9	1.9	0.1	0.1	0.1	0.1	0.0	0.1	
	X-ray; elbow	78	0.8	1.7	0.1	0.1	0.1	0.1	0.0	0.1	
Ultrasound*	3,702	37.5	100.0	3.7	3.5	3.8	2.4	2.3	2.5	
	Ultrasound; pelvis	578	5.9	15.6	0.6	0.5	0.6	0.4	0.3	0.4	
	Ultrasound; shoulder	413	4.2	11.2	0.4	0.4	0.5	0.3	0.2	0.3	
	Ultrasound; abdomen	350	3.5	9.5	0.4	0.3	0.4	0.2	0.2	0.3	
	Ultrasound; breast; female	321	3.3	8.7	0.3	0.3	0.4	0.2	0.2	0.2	
	Ultrasound; obstetric	286	2.9	7.7	0.3	0.2	0.3	0.2	0.2	0.2	
	Echocardiography	174	1.8	4.7	0.2	0.1	0.2	0.1	0.1	0.1	
	Ultrasound; kidney 	145	1.5	3.9	0.1	0.1	0.2	0.1	0.1	0.1	
	Test; Doppler	122	1.2	3.3	0.1	0.1	0.1	0.1	0.1	0.1	
	Ultrasound; abdomen upper 	98	1.0	2.6	0.1	0.1	0.1	0.1	0.1	0.1	
	Ultrasound; leg	91	0.9	2.5	0.1	0.1	0.1	0.1	0.0	0.1	
	Ultrasound scrotum	90	0.9	2.4	0.1	0.1	0.1	0.1	0.0	0.1	
	Ultrasound; thyroid	89	0.9	2.4	0.1	0.1	0.1	0.1	0.0	0.1	
	Test; Doppler carotid	77	0.8	2.1	0.1	0.1	0.1	0.1	0.0	0.1	
	Ultrasound; hip	74	0.7	2.0	0.1	0.1	0.1	0.1	0.0	0.1	
Computerised tomography*	1,270	12.9	100.0	1.3	1.1	1.4	0.8	0.7	0.9	
	CT scan; brain	204	2.1	16.1	0.2	0.2	0.2	0.1	0.1	0.2	
	CT scan; abdomen 	183	1.9	14.4	0.2	0.1	0.2	0.1	0.1	0.1	
	CT scan; spine; lumbar	145	1.5	11.4	0.1	0.1	0.2	0.1	0.1	0.1	
	CT scan; sinus 	100	1.0	7.9	0.1	0.1	0.1	0.1	0.1	0.1	
(continued)
Table 12.4 (continued): The most frequent imaging tests ordered, by MBS group
Imaging test ordered	Number	 Per cent of all imaging 	 Per cent of group	Rate per 100 encounters
(n = 101,349)	95% LCL	95% UCL	Rate per 100 problems
(n = 155,373)	95% LCL	95% UCL	
	CT scan; head	91	0.9	7.2	0.1	0.1	0.1	0.1	0.1	0.1	
	CT scan; chest	88	0.9	6.9	0.1	0.1	0.1	0.1	0.0	0.1	
	CT scan; spine; lumbosacral	86	0.9	6.7	0.1	0.1	0.1	0.1	0.0	0.1	
Magnetic resonance imaging	142	1.4	100.0	0.1	0.1	0.2	0.1	0.1	0.1	
Nuclear medicine imaging*	127	1.3	100.0	0.1	0.1	0.2	0.1	0.1	0.1	
Total imaging tests	9,877	100.0	—	9.8	9.3	10.1	6.4	6.1	6.6	
*	Includes multiple ICPC-2 and ICPC-2 PLUS codes (see Appendix 4, <www.aihw.gov.au/publications/index.cfm/subject/19>).
Note: LCL—lower confidence limit; UCL—upper confidence limit; CT—computerised tomography.
Problems for which imaging tests were ordered
Table 12.5 lists the most common problems for which imaging was ordered, in decreasing frequency order of problem–imaging combinations. The most common problem was back complaint, accounting for 5.0% of orders, followed by osteoarthritis (4.7%), and sprain/strain (4.0%). The two right-hand columns show the proportion of each problem that resulted in an imaging test, and the rate of imaging tests per 100 specified problems when at least one test was ordered. For example, 33.6% of contacts with fractures resulted in an imaging test, and 107.1 tests were ordered per 100 fracture contacts when at least one test was ordered.
Table 12.5: The 10 problems for which an imaging test was most frequently ordered
Problem managed	Number of problems	Number of 
problem–imaging
 combinations(a)	Per cent of problem–imaging combinations	Per cent 
of problems with test(b)	Rate of imaging orders per 100 problems with imaging(c)	
Back complaint*	2,755	494	5.0	15.5	115.4	
Osteoarthritis*	2,945	468	4.7	14.3	111.0	
Sprain/strain*	1,469	400	4.0	22.9	119.1	
Pregnancy*	1,467	368	3.7	24.7	101.3	
Bursitis/tendonitis/synovitis NOS	1,154	318	3.2	23.4	117.5	
Fracture*	877	315	3.2	33.6	107.1	
Injury musculoskeletal NOS	797	274	2.7	30.7	111.9	
Abdominal pain*	614	256	2.6	37.0	112.7	
Shoulder syndrome	540	236	2.4	31.0	140.4	
Breast lump/mass (female)	162	157	1.6	67.6	143.3	
Subtotal	12,780	3,286	33.1	—	—	
Total problems	155,373	9,981	100.0	5.7	112.0	
(a)	A test was counted more than once if it was ordered for the management of more than one problem at an encounter. There were 9,877 imaging test orders and 9,961 problem–imaging combinations.
(b)	The percentage of total contacts with the problem that generated at least one order for imaging.
(c)	The rate of imaging orders placed per 100 contacts with that problem generating at least one order for imaging.
*	Includes multiple ICPC-2 and ICPC-2 PLUS codes (see Appendix 4, <www.aihw.gov.au/publications/index.cfm/subject/19>).
Note: NOS—not otherwise specified.
12.4	Other investigations
Other investigations include diagnostic procedures ordered by the GP at the encounter or undertaken by the GP or practice staff. GPs ordered 753 other investigations during the study year, and GPs or practice staff undertook 1,429 other investigations. This means there were 2,182 total other investigations either ordered or undertaken in the practice (Table 12.6).
The first half of Table 12.6 lists the other investigations ordered by GPs. The second half lists the other investigations undertaken in the practice by GPs or practice staff. Each investigation is expressed as a percentage of total other investigations ordered or undertaken, as a rate per 100 encounters, and as a rate per 100 problems, each with 95% confidence limits. 
Table 12.6: Other investigations ordered by GPs or performed in the practice
Investigation ordered	Number	Per cent of ordered investigations	Rate per 100 encounters
(n = 101,349)	95%
LCL	95%
UCL	Rate per 100 problems
(n = 155,373)	95% LCL	95% UCL	
Electrical tracings*	395	52.5	0.39	0.33	0.45	0.25	0.22	0.29	
Diagnostic endoscopy*	191	25.3	0.19	0.14	0.24	0.12	0.09	0.15	
Physical function test* 	140	18.6	0.14	0.11	0.17	0.09	0.07	0.11	
Other diagnostic procedures*	28	3.7	0.03	0.01	0.04	0.02	0.01	0.03	
Total other investigations ordered	753	100.0	0.74	0.66	0.83	0.48	0.43	0.54	
Investigation undertaken in the practice	Number	Per cent of undertaken investigations	Rate per 100 encounters
(n = 101,349)	95%
LCL	95%
UCL	Rate per 100 problems
(n = 155,373)	95% LCL	95% UCL	
Electrical tracings*	594	41.6	0.59	0.51	0.66	0.38	0.33	0.43	
Physical function test*	542	37.9	0.53	0.44	0.63	0.35	0.29	0.41	
Other diagnostic procedures*	230	16.1	0.23	0.17	0.28	0.15	0.11	0.18	
Diagnostic endoscopy*	64	4.4	0.06	0.04	0.09	0.04	0.02	0.06	
Total other investigations undertaken	1,429	100.0	1.41	1.25	1.57	0.92	0.82	1.02	
Total other investigations ordered or undertaken in the practice	2,182	100.0	2.15	1.97	2.34	1.4	1.29	1.52	
*	Includes multiple ICPC-2 or ICPC-2 PLUS codes (see Appendix 4, <www.aihw.gov.au/publications/index.cfm/subject/19>).
Note: LCL—lower confidence limit; UCL—upper confidence limit.

12.5	Computerised tomography scans of the lumbar and lumbosacral spine (CT-LS) 
Concerns have been raised regarding the frequency of use of computerised tomography (CT) scans and the consequences of the significant radiation level to which patients are exposed.75 CT scan of the lumbar or lumbosacral spine (CT-LS) was the most commonly ordered CT scan in general practice, ordered at 231 encounters (2.4% of all imaging; 18.1% of CT scans) for the management of 233 problems. This represents about 270,000 GP–patient encounters at which a CT-LS was ordered in general practice across Australia in 2009–10. This section investigates lumbar CT scans (ICPC-2 Plus: L41057) and lumbosacral CT scans (ICPC-2 Plus: L41057). Figure 12.1 shows the patients and problems for which a CT-LS was ordered and describes other management for the same problem. 
Patient age and sex 
The sex distribution of patients receiving CT-LS (40.9% male) was similar to that of all patients attending general practice (43.1%; Table 6.1). The age distribution differed, with more patients at CT-LS encounters aged 45–64 years (43.9%) and fewer aged less than 25 years (5.8%) compared with the total sample (21.1% aged < 25 years; Table 6.1). 
Patients aged 45–64 years were those most likely to have an CT-LS ordered (at 0.4% of encounters in this age group), followed by patients aged 65–74 years (0.3%). CT-LSs were ordered at 0.2% of encounters with male and females patients.
Reasons for encounter 
The reason for encounter most often given by these patients was a back complaint (66.7 per 100 CT-LS encounters), followed by leg/thigh symptom or complaint (19.6). 
Problems for which CT-LS was ordered
While the majority (58.5%) of CT-LSs were ordered as part of the ongoing management of a previously diagnosed problem, 41.8% were ordered for problems that were new to the patient (had not been managed previously by any doctor). For the group of the top four conditions, a CT-LS was ordered at about 5 per 100 encounters for these problems. The rate of CT-LS ordering ranged from 9.5 per 100 encounters for back syndrome with radiating pain to less than 2 for patients with back syndrome without radiating pain. For patients with a new problem, the overall order rate for the top four conditions was 7.5 per 100 encounters, ranging from 15 for patients with back syndrome with radiating pain to less than 1 per 100 encounters for patients with back syndrome without radiating pain.
The NHMRC National Institute of Clinical Studies guidelines on ‘Lumbar imaging in acute non-specific low back pain’ 2008 suggest that imaging of the lumbar spine in acute non-specific back pain is not indicated at initial presentation unless ‘red flag’ signs or symptoms are present.76 American College of Radiology guidelines make similar recommendations, but also suggest that magnetic resonance imaging (MRI) scanning is the most appropriate investigation once a decision to investigate has been made.77 However, in Australia, Medicare rebates are not available for MRI scans ordered by GPs. BEACH data do not contain information on the presence of symptoms or signs that might indicate the presence of ‘red flags’, but it is clear from these data that GPs are selecting patients with clinically more severe forms of low back pain, as indicated by the clinical label, to investigate with CT scans. 
Other management actions provided at encounters where CT-LS was ordered
At least one medication was prescribed, supplied or advised for over-the-counter purchase for more than 40% of problems for which a CT-LS was ordered. GPs also commonly provided other treatments (including clinical and procedural treatments) (for 16.1% of CT-LS problems), referrals (11.3%), and less commonly pathology (4.1%).
Figure 12.1: CT scans of LS spine ordered in general practice, 2009–10
Diagram
(a)	Expressed as a per cent of problems for which CT-LS was ordered. 
(b)	Expressed as a rate per 100 problems for which CT-LS was ordered.
(c)	Expressed as a rate per 100 encounters at which CT-LS was ordered. 
(d)	Age and sex-specific rates, per cent of encounters involving CT-LS in each age or sex group.
*	Includes multiple ICPC-2 or ICPC-2 PLUS codes (see Appendix 4).

12.6	Changes in investigations over the decade 2000–01 to 2009–10
Data on investigations are reported for each year from 2000–01 to 2009–10 in Chapter 12 of the web-based companion report General practice activity in Australia 2000–01 to 2009–10: 10 year data tables.1 In that report, changes over time are measured as change in the management of problems (that is, as a rate per 100 problems). This reflects change in how GPs are managing problems, and accounts for the significant increase in the number of problems managed per encounter over the decade (see Section 7.9). The major changes are highlighted below.
•	At least one pathology test was ordered for 10.6% of problems managed in 2000–01 rising to 13.2% of problems in 2009–10. The largest increase was in orders for chemical pathology, which increased from 10.8 per 100 problems in 2000–01 to 16.9 per 100 problems in 2009–10. Haematology increased at a slower rate, from 4.0 per 100 problems in 2000–01 to 5.4 in 2009–10. Microbiology test orders increased from 3.2 per 100 problems in 2000–01 to 4.1 in 2009–10. There was a far smaller increase in order rates for immunology, a marginal increase in orders for tissue pathology and simple tests, and no increases in the other test groups.
•	Between 2000–01 and 2009–10 the number of problems managed per 100 encounters rose from 144.5 to 153.3 (Table 5.1). Both the rise in the number of tested problems and the rise in the number of problems managed at encounter contributed to an overall increase in the proportion of encounters involving a pathology test. These rose from 13.8% of encounters in 2000–01 to 17.7% in 2009–10, which is almost 6.8 million more encounters at which pathology was ordered in 2009–10 than in 2000–01.
•	The number of pathology tests ordered increased from 20.5 tests (or battery of tests) per 100 problems managed in 2000–01 to 29.3 per 100 problems in 2009–10. The rate of pathology orders per 100 encounters increased from 29.7 per 100 encounters in 2000–01 to 45.0 in 2009–10, which extrapolates to approximately 22.7 million more tests (or battery of tests) ordered in 2009–10. 
•	At least one imaging test was ordered for 4.8% of all problems managed in 2000–01, rising to 5.7% of all problems in 2009–10. The proportion of encounters generating imaging orders increased from 6.8% in 2000–01 to 8.5% in 2009–10, resulting in an estimated 3.1 million more encounters nationally at which imaging was ordered in 
2009–10.
•	The number of imaging tests ordered increased from 5.3 tests (or battery of tests) per 100 problems managed in 2000–01 to 6.4 per 100 problems in 2009–10. Total imaging orders per 100 encounters also increased significantly from 7.7 per 100 encounters in 2000–01 to 9.8 in 2009–10, suggesting there were almost 3.7 million more imaging orders in 2009–10 than in 2000–01.

13	Practice nurse activity
This section describes the activities of practice nurses recorded in association with the 
GP–patient encounters by the GPs in BEACH.
In February 2004, two Medicare item numbers were introduced into the MBS that allowed GPs to claim for specified tasks undertaken by a practice nurse under the direction of the GP. The BEACH recording form (see Appendix 1) was amended to allow the capture of this information from April 2005 onwards.
•	GPs were allowed to record multiple (up to three) Medicare item numbers where appropriate, rather than be limited to one item number.
•	In the ‘other treatments’ section, for each problem managed GPs were asked to tick the ‘practice nurse’ box if the treatment recorded was provided by the practice nurse rather than by the GP. If the box was not ticked it was assumed that the GP provided the ‘other treatment’.
The survey form allows GPs to record up to two other treatments for each problem managed at the encounter. Other treatments include all clinical and procedural treatments provided at the encounters. These groups are defined in Appendix 4.
Since February 2004 additional practice nurse items have been added. The seven practice nurse Medicare items recorded by GPs during the 2009–10 BEACH data period are listed with a short description in Table 13.2. 
This section investigates: the distribution of the practice nurse Medicare items recorded; treatments provided by practice nurses in association with the GP-recorded encounters; and the problems for which practice nurses provided treatments (in direct association with the GP-recorded encounters).
In Chapter 10, all clinical and procedural treatments recorded by the GPs were reported, irrespective of whether they were provided by the GP or by a practice nurse. As in previous years, injections recorded in the provision of immunisations and vaccinations were not included, as these are already counted as pharmacological management. In contrast, this section, being a description of practice nurse activity, reports only the activities indicated as being conducted by a practice nurse and includes the injections for immunisation/
vaccination (when given by a practice nurse). GPs are also instructed not to record their taking of routine clinical measurements, such as blood pressure. However, where practice nurses undertook these activities at the consultation, and it was recorded as a practice nurse activity, they have been included in the analysis in this chapter.
When viewing these results, it must be remembered that these practice nurse data will not include activities undertaken by practice nurses during the GP’s BEACH recording period that were outside (not associated with) the recorded encounter. Such activities could include Medicare-claimable activities (for example, immunisations/vaccinations) provided under instruction from the GP but not provided at the time of the encounter recorded in BEACH, or provision of other services not currently claimable from Medicare (for example, dietary advice on a one-to-one basis, or in a group situation).
13.1	Practice nurse Medicare claims and practice nurse activity
There were 9,154 (9.0%) GP–patient encounters for which at least once practice nurse item and/or nurse activity was recorded. However, for 155 of these their activity was not described. At the remaining 8,999 encounters a practice nurse was involved in the management of 9,542 problems (6.1% of all problems managed at all encounters). Simple extrapolation of these results suggests that during 2009–10 practice nurses were involved in about 10.5 million GP–patient consultations. 
At only 4,161 encounters (4.1% of all encounters, and 45.5% of the 9,154 encounters involving practice nurses) was a practice nurse Medicare item recorded (Table 13.1), and 4,216 practice nurse items recorded. Practice nurse items accounted for 3.7% of all Medicare items recorded in 2009–10 (Table 5.6). At more than half (54.5%) of encounters in which practice nurses were involved, no practice nurse item number was recorded as claimable (Table 13.1).
Table 13.1: Summary of practice nurse involvement at encounter
Variable	Number	
Total encounters 	101,349	
Encounters involving practice nurse 	9,154	
	Encounters at which practice nurse activity described	8,999	
	Encounters with practice nurse item number(s) but activity not described 	155	
Encounters at which one or more practice nurse item numbers were recorded as claimable 	4,161	
Total problems managed (n)	155,373	
Problems managed with practice nurse involvement	9,542	
Proportions	Per cent (95% CI)	
Encounters involving practice nurses as a proportion of total encounters 	9.0 (8.2–9.9)	
Practice nurse claimable encounters as a proportion of total encounters 	4.1 (3.6–4.6)	
Proportion of practice nurse involved encounters for which one or more practice item numbers were claimed from Medicare 	45.5 (42.1–48.8)	
Problems involving practice nurses as a proportion of total problems (95% CI)	6.1 (5.6–6.7)	
Note: CI—confidence interval.
Distribution of practice nurse item numbers claimed at encounters
A total of 4,216 practice nurse item numbers were recorded at 4,161 encounters. Three‑quarters of the practice nurse item numbers recorded were for immunisations (74.9%), and a further 21.3% were for wound treatments. Items recorded for practice nurse services to a person with chronic disease accounted for 3.0%, and those claimed for practice nurse conduct of cervical smears (with or without preventive checks) for 0.5% of total practice nurse item numbers recorded. Recorded claims for health checks by nurses were few. 
Comparison of the distribution of BEACH practice nurse item numbers recorded and the distribution of the 6.85 million claims made for such items from Medicare in the same data period demonstrated a good fit (Table 13.2).
Table 13.2: Distribution of practice nurse item numbers recorded at encounter
Medicare item number	Short descriptor	Number 	Per cent
 of total	Per cent of Medicare practice nurse claims
(n = 6.85 million)	
10993(a)	Immunisation	3,157	74.9	70.2	
10996(a)	Wound treatment (other than normal aftercare)	898	21.3	24.9	
10997(b)	Service provided to a person with a chronic disease by a practice nurse or registered Aboriginal Health Worker 	125	3.0	2.9	
10994(c)	Cervical smear and preventive checks	15	0.4	 
1.4
(all cervical smears)	
10995(c)	Cervical smear and preventive checks—women aged 20–69 years, no smear in previous 4 years	1	0.0		
10998(d)	Cervical smear	6	0.1		
00711(e)	Health check by a practice nurse or registered Aboriginal Health Worker	14	0.3	0.0	
Total	All Medicare practice nurse item numbers	4,216	100.0	100.0	
(a)	Item number introduced in February 2004.
(b)	Item number introduced in July 2007. 
(c)	Item number introduced in November 2006.
(d)	Item number introduced in January 2005.
(e)	Item number introduced in July 2008.
Note: there were no recordings of items: 16400—Antenatal services provided by midwives, practice nurses and Aboriginal health workers in rural and remote areas; 10987—Follow-up services provided by a practice nurse or Aboriginal health worker for an Indigenous person who has received a health check. 
Source: Total Medicare practice nurse claims—Medicare health statistics.5,78 

Treatments provided by practice nurses
As reported in Chapter 10, GPs reported 53,243 other treatments at encounter. A further 5,917 injections were given for immunisations by a practice nurse (not reported in Chapter 10). In total 59,159 other treatments were recorded. Practice nurses provided 10,078 other treatments (representing 17% of all other treatments recorded at BEACH encounters) at a rate of 9.9 per 100 recorded encounters. The majority (93.0%) of the practice nurse activity was procedural, and these procedures represented 39.6% of all procedures recorded. In contrast, clinical treatments accounted for 7.0% of practice nurse activity, but practice nurses provided only 2.0% of all recorded clinical treatments (Table 13.3).
Table 13.3: Summary of treatments given by practice nurse
Treatment	Perform/assisted by practice nurse	Performed by the GP	Total number recorded(a)	
	Number	Row per cent 
of total	Number	Row per cent 
of total		
Procedural treatments(a)	9,371	39.6	14,304	60.4	23,675	
Clinical treatments	707	2.0	34,777	98.0	35,484	
All other treatments	10,078	17.0	49,081	83.0	59,159	
(a)	Procedural treatments here include all injections given by a practice nurse for immunisations/vaccinations (n = 5,917). These are not included in the summary of the content of encounter in Table 5.1, summary of management in Table 8.1 or in the analyses of other treatments in Chapter 10, because the immunisation/vaccination is already counted as a prescription or GP-supplied medication.
Of the 9,371 procedures done by practice nurses, 48.3% were injections (which were mainly for immunisations/vaccinations), and a further 15.2 % were dressing/pressure/ compression/tamponade. Together these accounted for about two‑thirds of all procedures undertaken by practice nurses in association with the recorded GP encounters. Check-ups made up 7.3% of procedures done by the nurse, followed by incision/drainage/aspirations (6.5%) and INR tests (4.3%). Practice nurses also undertook a wide range of other procedural activities in association with the GP encounters. The most common are listed in Table 13.4.
Other administrative procedure (which includes administrative/documentation work but excludes provision of sickness certificates) was the most frequently recorded clinical activity, accounting for 29.3% of the 707 clinical treatments provided by nurses, followed by general advice/education (15.7%), counselling about nutrition or weight (7.4%) and counselling for the problem under management (7.2%) (Table 13.4). 

Table 13.4: Most frequent activities done by a practice nurse
Activity	Number	Per cent of group(a)	Rate per 100 encounters involving practice nurse(a)
 (n = 8,999)	95% LCL	95% UCL	
Procedural treatments	9,371	100.0	104.1	102.4	105.9	
	Local injection/infiltration*	4,527 	48.3	50.3	47.0	53.6	
	Dressing/pressure/compression/tamponade*	1,424 	15.2	15.8	14.2	17.5	
	Check-up—practice nurse*	683 	7.3	7.6	4.0	11.1	
	Incision/drainage/flushing/aspiration/removal body fluid*	609 	6.5	6.8	5.4	8.1	
	INR test	407 	4.3	4.5	3.5	5.5	
	Repair/fixation-suture/cast/prosthetic device 
	(apply/remove)*	356 	3.8	4.0	3.3	4.6	
	Electrical tracings*	328 	3.5	3.6	3.1	4.2	
	Excision/removal tissue/biopsy/destruction/
	debridement/cauterisation*	263 	2.8	2.9	2.2	3.6	
	Physical function test*	257 	2.7	2.9	2.1	3.6	
	Urine test*	120 	1.3	1.3	0.8	1.8	
	Other therapeutic procedures/surgery NEC*	91 	1.0	1.0	0.7	1.3	
	Other diagnostic procedures NEC*	62 	0.7	0.7	0.4	1.0	
	Pap smear*	60 	0.6	0.7	0.4	0.9	
	Glucose test	55 	0.6	0.6	0.4	0.8	
Clinical treatments	707	100.0	7.9	5.9	9.9	
	Other administrative procedure*	207	29.3	2.3	1.6	3.0	
	Advice/education*	111	15.7	1.2	0.6	1.9	
	Counselling/advice—nutrition/weight*	52	7.4	0.6	0.3	0.8	
	Advice/education—prevention*	51	7.3	0.6	0.2	0.9	
	Counselling—problem*	51	7.2	0.6	0.3	0.9	
(a)	Figures do not total 100, as more than one treatment can be performed by a practice nurse at each encounter and only those individual
 treatments accounting for ≥ 0.5% of total treatments by practice nurse are included.
*	Includes multiple ICPC-2 or ICPC-2 PLUS codes (see Appendix 4, <www.aihw.gov.au/publications/index.cfm/subject/19>).
Note: LCL—lower confidence limit; UCL—upper confidence limit; INR—international normalised ratio; NEC—not elsewhere classified.

13.2	Problems managed with practice nurse involvement
Practice nurses were involved in management of a wide variety of problems in association with the GP encounters. The problems managed most often were immunisation/vaccination (38.3% of all problems managed with the involvement of a practice nurse), followed by check-ups (5.2%), laceration/cut (4.2%) and chronic skin ulcer (3.8%). Other common problems that involved practice nurses at the consultations are listed in Table 13.5.
Table 13.5: The most common problems managed with involvement of practice nurses
Problem managed	Number	Per cent of problems involving practice nurse
(n = 9,542)	Rate per 100 encounters with recorded practice nurse activity(a)
(n = 8,999)	95%
LCL	95%
UCL	
Immunisation/vaccination—all*	3,653	38.3	40.6	37.3	43.9	
Check-up—all*	492	5.2	5.5	4.6	6.3	
Laceration/cut	403	4.2	4.5	3.8	5.1	
Chronic ulcer skin (including varicose ulcer)	364	3.8	4.0	3.3	4.8	
Atrial fibrillation/flutter	226	2.4	2.5	1.8	3.2	
Malignant neoplasm skin	193	2.0	2.1	1.7	2.6	
Diabetes—all*	177	1.9	2.0	1.5	2.4	
Excessive ear wax	176	1.8	2.0	1.5	2.4	
Hypertension*	163	1.7	1.8	1.2	2.4	
Skin infection—post traumatic	160	1.7	1.8	1.3	2.2	
Blood test—all*	137	1.4	1.5	0.8	2.2	
Observation/health education/advice/diet—all*	101	1.1	1.1	0.1	2.1	
Vitamin/nutritional deficiency	96	1.0	1.0	0.8	1.3	
Pregnancy*	94	1.0	1.0	0.5	1.6	
Subtotal	6,431	67.5	—	—	—	
Total problems involving practice nurse	9,542	100.0	106.0	104.8	107.3	
(a)	Rate of nurse provision of treatment at encounter for selected problem per 100 total encounters in which a practice nurse was involved.
*	Includes multiple ICPC-2 or ICPC-2 PLUS codes (see Appendix 4, <www.aihw.gov.au/publications/index.cfm/subject/19>). 
Note: LCL—lower confidence limit; UCL—upper confidence limit. 

13.3	Discussion
These results suggest that many GPs are claiming Medicare items for practice nurses to provide immunisations and, to a lesser degree, wound treatments, but are infrequently using the cervical smear/preventive check practice nurse item numbers. 
The following section extrapolates these results to national estimates and considers them in light of Medicare claims data.5 
•	Extrapolation of the 9,154 encounters involving a practice nurse (9.0% of all encounters) to the 116.8 million GP service items claimed through Medicare in 2009–10 suggest there were 10.5 million encounters nationally that involved practice nurses. 
•	Extrapolation of the 10,078 activities ascribed to practice nurses in BEACH (9.9 per 100 encounters) to a national estimate suggest there were 11.6 million such activities conducted as part of GP–patient encounters nationally.
•	Extrapolation of the 4,216 practice nurse items claimed (at a rate of 4.2 per 100 BEACH encounters) suggests that GPs claimed 4.9 million practice nurse items for activities the nurses undertook in relation to the GP–patient encounters. 
These data suggest that nationally in 2009–10 there were:
•	about 6.7 million (11.6 million activities minus 4.9 million claims) practice nurse clinical activities undertaken in association with GP–patient encounters that were not claimable or not claimed through Medicare.
•	about 2.0 million (6.85 million claims from Table 13.2 minus the estimated 4.9 million that were for activities associated with the encounters) practice nurse items claimed for practice nurse activities conducted independently of direct GP–patient consultations (that is, services provided separately from the encounter, and therefore not reported by GPs in BEACH encounter records).
There is no means by which the number of practice nurse clinical activities undertaken independently of the GP–patient encounters for which no claim was made can be estimated, either because the activity did not qualify for Medicare payment, or because the practice simply failed to claim.
Comparison of the services provided by practice nurses (Table 13.4) with the common problems for which these services were provided (Table 13.5) suggests that about 80% of the local injections/infiltrations recorded as given by practice nurses were for immunisations, and about 20% were for other types of injections, and therefore not eligible to be claimed through Medicare. 
Table 13.4 shows that nurses dealt with 1,424 dressing/pressure/compression/tamponades in conjunction with the GP encounter, but only 898 claims were made for Medicare payment for wound treatment (Table 13.2). This suggests that about 63% of the dressings recorded for practice nurses were claimable under Medicare. Some of the dressings may have been follow-up encounters where the follow-up treatment (aftercare) was included in the initial Medicare claim (claimed in the past), and may therefore not have been claimable for the practice nurse.
It is clear that practice nurses undertook a wide variety of other activities at the BEACH encounters that did not qualify for Medicare reimbursement. 
13.4	Changes in practice nurse activity, 2005–06 to 2009–10
A comparison of practice nurse activity from 2005–06 to 2009–10 is provided in Chapter 13 of the 10-year summary report General practice activity in Australia 1999–00 to 2008–09: 10 year data tables.1
Changes are summarised below.
•	Encounters involving a practice nurse as a proportion of all encounters more than doubled from 4.2% in 2005–06 to 9.0% in 2008–09. This suggests that in 2009–10, practice nurses were involved in about 10.5 million GP–patient encounters, 6.3 million more than in 2005–06. 
•	Between 2005–06 and 2008–09 the proportion of encounters with practice nurse activity for which a Medicare practice nurse item number was recorded remained constant at 
36–39%. However, in 2009–10 there was a sudden increase (to 45.5%) that did not quite reach statistical significance. 
•	Provision of clinical treatments by a practice nurse (such as advice and health education) at GP encounters remained infrequent, at less than 1 clinical treatment per 100 encounters.
•	The number of procedures (including tests) undertaken by practice nurses at GP–patient encounters more than doubled from 4.0 per 100 encounters in 2005–06 to 9.2 per 100 in 2009–10, nurses now doing 40% of all procedures recorded at BEACH encounters.
•	There were increases in practice nurse provision of local injections, check-ups, and INR testing. There were decreases in their rate of: dressings; repair/fixation; electrical tracings; excisions; and physical function tests. An increase in clinical treatments from 5.2 to 7.9 per 100 GP encounters, was largely accounted for by an increase in administrative procedures. 

14	Patient risk factors
General practice is a useful intervention point for health promotion because about 88% of Australians visit a GP at least once in any given year.20 GPs, through ongoing professional education, have substantial knowledge of population health, screening programs and other interventions. They are also in an ideal position to advise patients about the benefits of health screening, and to counsel patients individually about their lifestyle choices. 
Since April 1998, a section on the bottom of each encounter form has been used to investigate aspects of patient health or health care delivery not covered by general practice consultation‑based information. These additional substudies are referred to as SAND (Supplementary Analysis of Nominated Data). The SAND methods are described in Section 2.4. 
The patient risk factors measured in BEACH include body mass index (BMI) (calculated using self-reported height and weight), self-reported alcohol consumption and smoking status. Patient risk factors are investigated for a subsample of 40 of the 100 patient encounters recorded by each GP. An example of the encounter form with the patient risk factor SAND questions is included in Appendix 1. The methods used in the risk factor substudies reported in this chapter are described in each section below.
Data on patient risk factors measured in SAND are reported for each year from 2000–01 to 2009–10 in the companion report General practice activity in Australia 2000–01 to 2009–10: 10 year data tables.1
Abstracts of results and the research tools used in all SAND substudies from April 1998 to March 2010 have been published. Those:
•	from April 1998 to March 1999 were published in Measures of health and health care delivery in general practice in Australia22
•	from April 1999 to July 2006 were published in Patient-based substudies from BEACH: abstracts and research tools 1999–200623
•	since August 2006 have been published in each of the general practice activity annual reports 24-26
•	conducted in the 2009–10 BEACH year are provided in Chapter 15 of this publication.
14.1	Body mass index
High body mass was the third highest contributor to the total burden of disease in Australia in 2003, accounting for 7.5% of the total burden79, an increase from 4.3% of total burden and sixth rank in 1996.80 The 2007–08 National Health Survey (NHS) estimated that, based on measured data, 62% of Australians aged 18 years and over were overweight or obese (BMI > 25). Men were more likely to be overweight or obese than women (68% compared with 55%).16 The 2007–08 NHS also reported that 25% of children aged 5–17 years were classified as overweight or obese, with boys and girls having similar rates of overweight/obesity (26% and 24% respectively).16

Method
Patient BMI was investigated for a subsample of 40 of the 100 patient encounters. Each GP was instructed to ask the patient (or their carer in the case of children):
•	What is your height in centimetres (without shoes)?
•	What is your weight in kilograms (unclothed)?
Metric conversion tables (feet and inches; stones and pounds) were provided to the GP.
The BMI for an individual was calculated by dividing weight (kilograms) by height (metres) squared. The WHO recommendations81 for BMI groups were used, which specify that an adult (18 years and over) with a BMI:
•	less than 18.5 is underweight
•	greater than or equal to 18.5 and less than 25 is normal
•	greater than or equal to 25 and less than 30 is overweight
•	of 30 or more is obese.
The reported height for adult patients was checked against sex-appropriate upper and lower height limits from the Australian Bureau of Statistics (ABS).82 Encounters with adults whose reported heights were outside the sex-appropriate limits were excluded from the analysis.
The standard BMI cut-offs described above are not appropriate in the case of children. Cole et al. (2000 & 2007) developed a method that calculates the age–sex-specific BMI cut-off levels for overweight and obesity specific to children aged 2–17 years.83,84 There are four categories defined for childhood BMI: underweight, normal weight, overweight and obese. This method, based on international data from developed Western cultures, is applicable 
in the Australian setting. The reported height of children was checked against 
age–sex‑appropriate upper and lower height limits from the ABS and Centres for Disease Control (CDC).82,85 Encounters with children whose reported heights were outside either of the age–sex-appropriate limits were excluded from the analysis.
The BEACH data on BMI are presented separately for adults (aged 18 years and over) and children (aged 2–17 years). The standard BMI cut-offs have been applied for the adult sample, and the method described by Cole et al. (2000 & 2007) has been used for defining overweight and obesity in children (aged 2–17 years).83,84 
Results
Body mass index of adults
The sample size was 31,932 patients aged 18 years and over at encounters with 984 GPs.
•	More than half (60.2%) of the patients were overweight or obese—25.9% obese and 34.4% overweight (Table 14.1).
•	More than one-third (37.3%) of adult patients had a BMI in the normal range and 2.4% were underweight (Table 14.1).
•	Males were more likely to be overweight or obese (67.6%, 95% CI: 66.5–68.7) than females (55.8%, 95% CI: 54.8–56.8) (results not tabled).
•	Overweight/obesity was most prevalent among male patients aged 45–64 years (75.1%) and those aged 65–74 years (73.9%) (Figure 14.1).
•	Among female patients, overweight/obesity was most prevalent in those aged 
65–74 years (68.8%) and 45–64 years (63.3%) (Figure 14.1).
•	Underweight was most prevalent among patients aged 18–24 years and 75 years and over. Of young adults (18–24 years), 7.0% of females and 3.0% of males were underweight, and among those aged 75 years and over, 5.0% of women and 2.2% of men were underweight (Figure 14.2).
The overall and sex-specific prevalence estimates were consistent with the ABS 2007–08 figures from the National Health Survey (based on measured BMI data), which reported that 62% of adults aged 18 and over (68% of men and 55% of females) were overweight or obese.16 
Readers interested in prevalence of the three WHO-defined levels of obesity will find more information and discussion in Chapter 7 of General practice in Australia, health priorities and policies 1998 to 2008.86 
Estimation of body mass index for the adult general practice patient population
The BEACH study reports data about patient BMI from a sample of the patients attending general practice. As older people attend a GP more often than young adults, and females attend more often than males, they have a greater chance of being selected in the subsample. This leads to a greater proportion of older and female patients in the sample when compared with the total population who attend a GP at least once in a year. The 2009–10 BEACH sample has been weighted to estimate the BMI of the GP–patient population (that is, the 14.3 million adult patients who attended a GP at least once in 2009–10), using the method described by Knox et al. (2008) applied individually to each of the years of the study.20 
The estimates for the adult GP–patient population (after adjusting for age–sex attendance patterns) suggest that 25.4% of the patient population were obese, 34.4% were overweight, 38.0% were normal weight and 2.2% were underweight (Table 14.1). 
Table 14.1: Patient body mass index (aged 18 years and over)
	Male(a)	Female(a)	Total respondents	
BMI class	Per cent in BEACH sample
(95% CI)
(n = 11,945)	Per cent 
in patient population
(95% CI)(b)	Per cent in BEACH sample
(95% CI)
(n = 19,735)	Per cent 
in patient population
(95% CI)(b)	Per cent in BEACH sample
(95% CI)
(n = 31,932)	Per cent 
in patient population
(95% CI)(b)	
Obese	25.5
(24.6–26.5)	25.1
(24.1–26.1)	26.2
(25.3–27.0)	25.6
(24.7–26.5)	25.9
(25.2–26.6)	25.4
(24.6–26.1)	
Overweight	42.1
(41.1–43.0)	40.9
(39.9–41.9)	29.6
(28.9–30.3)	28.8
(28.1–29.6)	34.4
(33.7–35.0)	34.4
(33.8–35.1)	
Normal	31.6
(30.2–32.3)	32.9
(31.7–34.0)	41.1
(40.1–42.0)	42.4
(41.4–43.4)	37.3
(36.5–38.2)	38.0
(37.1–38.9)	
Underweight	1.2
(1.0–1.4)	1.1
(0.9–1.4)	3.2
(2.9–3.5)	3.1
(2.8–3.5)	2.4
(2.2–2.6)	2.2
(2.0–2.4)	
(a)	Patient sex was not recorded for 252 respondents.
(b)	Estimation of BMI among the total adult general practice patient population (that is, patients aged 18 years and over who have attended a GP at least once) (n = 14.3 million).
Note: BMI—body mass index; CI—confidence interval.
Figure 14.1: Age–sex-specific rates of overweight/obesity in adults
Vertical bar chart showing, for male and female, per cent on y-axis and age group (years) on x-axis.
Figure 14.2: Age–sex-specific rates of underweight in adults
Vertical bar chart showing, for male and female, per cent on y-axis and age group (years) on x-axis.
Body mass index of children
BMI was calculated for 3,183 patients aged 2–17 years at encounters with 829 GPs.
•	Just over one-quarter of children (27.6%, 95% CI: 25.8–29.4) were classed as overweight or obese—9.6% (95% CI: 8.4–10.8) obese and 18.0% (95% CI: 16.7–19.4) overweight (results not tabled).
•	There was no difference in prevalence of overweight/obesity among male (28.0%, 
95% CI: 25.3–30.6) and female children (27.3%, 95% CI: 24.9–29.7) (results not tabled).
•	The age-specific rates of obesity followed similar patterns for both sexes (figures 14.3 and 14.4).
Readers interested in further detail and discussion of overweight and obesity in children attending general practice will find more information in Cretikos et al. (2008) General practice management of overweight and obesity in children and adolescents in Australia.87
Figure 14.3: Age-specific rates of obesity, overweight, normal weight and underweight in 
male children
Stacked line chart showing, for weight type, per cent on y-axis and age group (years) on x-axis. 
Figure 14.4: Age-specific rates of obesity, overweight, normal weight and underweight in female children
Stacked line chart showing, for weight type, per cent on y-axis and age group (years) on x-axis. 
14.2	Smoking (patients aged 18 years and over)
Tobacco smoking is the leading cause of drug-related death and hospital separations in Australia.88 It has been identified as the risk factor associated with the greatest disease burden, accounting for 7.8% of the total burden of disease in Australia in 200379, a decrease from 9.7% of total burden in 1996.80 According to the 2007 National Drug Strategy Household Survey (NDSHS), 16.6% of Australians aged 14 years and over smoked daily: 18.0% of males and 15.2% of females.89
Method
GPs were instructed to ask adult patients (18 years and over):
•	What best describes your smoking status? 	Smoke daily
	Smoke occasionally
	Previous smoker
	Never smoked
Respondents were limited to adults aged 18 years and over because there are ethical concerns about approaching the younger patient group to ask for information on smoking for survey purposes. In addition, the reliability of this information from patients aged less than 18 years may be compromised if a parent is present at the consultation.
Results
The smoking status of 32,744 adult patients was established at encounters with 986 GPs. Table 14.2 shows that:
•	15.1% of adult patients were daily smokers
•	significantly more male (18.1%) than female patients (13.3%) were daily smokers
•	only 2.7% of adult patients were occasional smokers
•	more than a quarter of adults (28.2%) were previous smokers.
Table 14.2: Patient smoking status (aged 18 years and over)
	Male(a)	Female(a)	Total respondents	
Smoking status	Per cent in BEACH sample
(95% CI)
(n = 12,260)	Per cent in patient population
(95% CI)(b)	Per cent in BEACH sample
(95% CI)
(n = 20,224)	Per cent in patient population
(95% CI)(b)	Per cent in BEACH sample
(95% CI)
(n = 32,744)	Per cent in patient population
(95% CI)(b)	
Daily	18.1
(17.1–19.1)	21.4
(20.2–22.6)	13.3
(12.6–14.0)	14.6
(13.9–15.4)	15.1
(14.4–15.8)	17.7
(16.9–18.5)	
Occasional	3.1
(2.8–3.5)	3.9
(3.4–4.3)	2.4
(2.2–2.7)	2.8
(2.5–3.1)	2.7
(2.5–2.9)	3.3
(3.0–3.6)	
Previous	36.9
(35.8–38.1)	30.6
(29.5–31.7)	22.8
(22.0–23.7)	21.9
(21.1–22.7)	28.2
(27.4–29.0)	25.9
(25.1–26.6)	
Never	41.8
(40.6–43.0)	44.1
(42.8–45.4)	61.5
(60.4–62.5)	60.7
(59.6–61.7)	54.0
(53.1–55.0)	53.1
(52.1–54.1)	
(a)	Patient sex was not recorded for 260 respondents.
(b)	Estimation of the smoking status of the total adult general practice patient population (that is, patients aged 18 years and over who have attended a GP at least once) (n = 14.3 million).
Note: CI—confidence interval.
Daily smoking was most prevalent among younger adult patients aged 18–24 years and 
25–44 years, with 19% and 22% respectively (results not tabled). Almost 60% of male and 25% of female patients aged 75 years and over were previous smokers, but only 4.9% of males and 3.2% of females in this age group were daily smokers (figures 14.5 and 14.6).
Figure 14.5: Smoking status—male age-specific rates
Stacked line chart showing, for smoking status, per cent on y-axis and age group (years) on x-axis.
Figure 14.6: Smoking status—female age-specific rates
Stacked line chart showing, for smoking status, per cent on y-axis and age group (years) on x-axis.
Estimation of smoking in the adult general practice patient population
The BEACH study reports data about patient smoking habits from a sample of patients attending general practice. As older people attend a GP more often than young adults, and females attend more often than males, they have a greater chance of being selected in the subsample. This leads to a greater proportion of older and female patients in the sample when compared with the total population who attend a GP at least once in a year (about 14.3 million adults in 2009–10). The 2009–10 BEACH sample has been weighted to estimate the smoking status among the GP–patient population, using the method described by Knox et al. (2008) applied individually to each of the years of the study.20 
The estimates for the GP–patient population (after adjusting for age–sex attendance patterns) suggest that 17.7% of the patient population were daily smokers, 3.3% were occasional smokers, 25.9% were previous smokers and 53.1% had never smoked. Male patients in the total general practice population were significantly more likely to be daily (21.4%), occasional (3.9%) and previous smokers (30.6%) than females patients (14.6%, 2.8% and 21.9%, respectively) (Table 14.2). 
14.3	Alcohol consumption (patients aged 18 years and over)
In people aged 65 years and over, low to moderate consumption of alcohol has been found to have a preventive effect against selected causes of morbidity88 (in particular ischaemic heart disease).90 The National Health and Medical Research Council (NHMRC) in a review of the evidence concluded that in young women there was no evidence of any cardiovascular mortality benefit from alcohol consumption, and in young men any benefit was outweighed by alcohol-related other causes of death.90 In 2003, alcohol consumption accounted for 3.3% of the total burden of disease in Australia; however, after taking into account the benefit derived from low to moderate alcohol consumption, this fell to 2.3%.79
The 2007–08 NHS classified alcohol use of those aged 15 years or more based on the estimated average daily consumption of alcohol during the previous week. They found that 12.6% drank at levels considered to be risky (14.4% of males and 10.8% of females).16
The 2007 NDSHS found that 10.3% of people aged 14 years and over (10.2% of males and 10.5% of females) drank at levels considered to be risky or high risk for their health in the long term.89 The NDSHS also found that 20.4% of people aged 14 years and over (23.7% of males and 17.2% of females) drank alcohol during the preceding 12 months at levels that put their health at risk in the short term.89 These alcohol consumption risk levels are based on the NHMRC 2001 guidelines.91
The NHMRC 2001 alcohol guidelines91 have been rescinded. In February 2009 the NHMRC published a revised edition of evidence-based alcohol guidelines, which are significantly different from those in 2001, and use the concept of progressively increasing risk of harm with the amount of alcohol consumed, rather than specifying ‘risky’ and ‘high risk’ levels of drinking.92 For this reason the definitions earlier developed by WHO continue to be applied in this report (see ‘Method’ below).93
Method
To measure alcohol consumption, BEACH uses three items from the WHO Alcohol Use Disorders Identification Test (AUDIT)93, with scoring for an Australian setting.94 Together, these three questions assess ‘at-risk’ alcohol consumption. The scores for each question range from zero to four. A total (sum of all three questions) score of five or more for males or four or more for females suggests that the person’s drinking level is placing him or her at risk.94
GPs were instructed to ask adult patients (18 years and over):
•	How often do you have a drink containing alcohol?	Never
	Monthly or less
	Once a week/fortnight
	2–3 times a week
	4 times a week or more
•	How many standard drinks do you have on a typical day when you are drinking? 		_______________
•	How often do you have six or more standard drinks on one occasion? 
	Never
	Less than monthly
	Monthly
	Weekly
	Daily or almost daily
A standard drinks chart was provided to each GP to help the patient identify the number of standard drinks consumed.
Respondents were limited to adults aged 18 years and over because there are ethical concerns about approaching the younger patient group to ask for information on alcohol consumption for survey purposes. In addition, the reliability of this information from patients aged less than 18 years may be compromised if a parent or guardian is present at the consultation.
Results
Patients’ self-reported alcohol consumption was recorded at 31,771 adult patient (18 years and over) encounters with 984 GPs.
•	About one-quarter of adults reported drinking alcohol at at-risk levels (26.5%) (Table 14.3).
•	At-risk drinking was more prevalent among male patients (31.6%) than female patients (23.4%) (Table 14.3).
•	At-risk drinking was most prevalent in those aged 18–24 years, particularly among men. In this age group almost half of the males and two in five of the females reported at-risk alcohol consumption (Figure 14.7).
•	The proportion of patients who were at-risk drinkers decreased with age for both males and females (Figure 14.7).
These estimates are not comparable with the 2007–08 NHS16 or the 2007 NDSHS89 as they all use different concepts for defining alcohol consumption and risk, and different adult populations (patients aged 18 years or more for BEACH, persons aged 15 years or more for the NHS, and persons aged 14 years or more for the NDSHS). 
Readers interested in the relationship between morbidity managed and alcohol consumption will find more information in Proude et al. (2006) The relationship between self-reported alcohol intake and the morbidities managed by GPs in Australia.95 
Table 14.3: Patient alcohol consumption (aged 18 years and over)
	Male	Female	Total respondents	
Alcohol consumption	Per cent in BEACH sample
(95% CI)
(n = 11,974)	Per cent in patient population
(95% CI)(a)	Per cent in BEACH sample
(95% CI)
(n = 19,797)	Per cent in patient population
(95% CI)(a)	Per cent in BEACH sample
(95% CI)
(n = 31,771)	Per cent in patient population
(95% CI)(a)	
At-risk drinker	31.6
(30.4–32.8)	35.2
(33.9–36.6)	23.4
(22.5–24.4)	24.9
(23.9–25.9)	26.5
(25.7–27.4)	29.7
(28.7–30.6)	
Responsible drinker	47.6
(46.4–48.8)	45.3
(44.0–46.6)	42.5
(41.5–43.6)	43.0
(42.0–44.1)	44.4
(43.5–45.3)	44.1
(43.1–45.0)	
Non-drinker	20.8
(19.7–21.9)	19.5
(18.3–20.7)	34.0
(32.8–35.3)	32.0
(30.8–33.3)	29.1
(28.0–30.1)	26.3
(25.2–27.3)	
(a)	Estimation of the alcohol consumption of the total adult general practice patient population (that is, patients aged 18 years and over who have attended a GP at least once) (n = 14.3 million).
Note: CI—confidence interval.
Figure 14.7: Age–sex-specific rates of at-risk alcohol consumption
Stacked line chart showing, for male and female, per cent on y-axis and age group (years) on x-axis.
Estimation of alcohol consumption in the adult general practice patient population
The BEACH study reports data about patient alcohol consumption from a sample of the patients attending general practice. As older people attend a GP more often than young adults, and females attend more often than males, they have a greater chance of being selected in the subsample. This leads to a greater proportion of older and female patients in the sample when compared with the total population who attend a GP at least once in a year (about 14.3 million adults). The 2009–10 BEACH sample has been weighted to estimate the smoking status among the GP–patient population, using the method described by Knox et al. (2008) applied individually to each of the years of the study.20 
The estimates for the GP–patient population (after adjusting for age–sex attendance patterns) suggest that 29.7% of the patient population were at-risk drinkers, 44.1% were responsible drinkers and 26.3% were non-drinkers. Male patients in the total general practice population were significantly more likely to be at-risk drinkers (35.2%) than female patients (24.9%) (Table 14.3). 
14.4	Risk factor profile of adult patients
All patient risk factor questions (BMI, smoking and alcohol consumption) were asked of the same subsample of patients. This allows us to build a risk profile of this sample of adult patients. For the purposes of this analysis, being overweight or obese, a daily smoker or an at-risk drinker were considered risk factors. A risk factor profile was prepared for 30,795 adult patients (aged 18 years and over) (Table 14.4).
•	About half (50.3%) of the adult respondents had one risk factor. The most common was overweight (22.1% of adults) followed by obesity (17.2%).
•	One in five patients had two risk factors, the most common combinations being:
–	overweight and at-risk alcohol consumption—7.3% of patients
–	obesity and at-risk alcohol consumption—4.9% of patients
–	daily smoking and at-risk alcohol consumption—3.0% of patients.
•	A small group of patients (3.8%) had all three risk factors.
Table 14.5 shows the number of risk factors by patient sex.
•	Females were significantly more likely to have no risk factors (29.5%) or one risk factor (51.2%) than males (19.6% and 49.0% respectively).
•	Almost one-third of males (31.4%) had two or three risk factors compared with about one in five (19.4%) females.
Estimation of the risk profile of the adult general practice patient population
The 2009–10 BEACH sample has been weighted to estimate the risk profile of the GP–patient population, using the method described by Knox et al. (2008) applied individually to each of the years of the study.20 
The estimates for the GP–patient population (after adjusting for age–sex attendance patterns) show that: 
•	one-quarter of patients had no risk factors (24.5%)
•	about half of the adult patients had one risk factor (48.6%), the most common being overweight (20.7% of adults) followed by obesity (15.8%)
•	one in five patients had two risk factors (22.2%), the most common combinations being overweight and at-risk alcohol consumption (7.9%), followed by obesity and at-risk alcohol consumption (5.2%)
•	1 in 20 patients had three risk factors (Table 14.4).
Table 14.4: Risk factor profile of patients (aged 18 years and over)
Number of risk factors	Number	Per cent in BEACH sample 
(n = 30,795)	95% LCL	95% UCL	Per cent in patient population(a)	95% LCL	95% UCL	
No risk factors	7,937	25.8	25.0	26.5	24.5	23.7	25.3	
One risk factor	15,502	50.3	49.6	51.0	48.6	47.8	49.3	
	Overweight only	6,816	22.1	21.5	22.7	20.7	20.1	21.3	
	Obese only	5,290	17.2	16.6	17.8	15.8	15.2	16.3	
	At-risk alcohol level only	2,330	7.6	7.1	8.0	8.1	7.6	8.7	
	Current daily smoker only	926	3.0	2.8	3.3	4.0	3.6	4.3	
Two risk factors	6,178	20.1	19.5	20.7	22.2	21.5	22.9	
	Overweight and at-risk alcohol level	2,251	7.3	6.9	7.7	7.9	7.5	8.4	
	Obese and at-risk alcohol level	1,517	4.9	4.6	5.2	5.2	4.9	5.5	
	Daily smoker and at-risk alcohol level	926	3.0	2.8	3.3	3.7	3.3	4.0	
	Overweight and current daily smoker	772	2.5	2.3	2.7	2.8	2.6	3.0	
	Obese and current daily smoker	712	2.3	2.1	2.5	2.6	2.3	2.8	
Three risk factors	1,178	3.8	3.6	4.1	4.8	4.4	5.1	
	Overweight and current daily smoker and at-risk alcohol level	733	2.4	2.2	2.6	3.0	2.7	3.2	
	Obese and current daily smoker and at-risk alcohol level	445	1.4	1.3	1.6	1.8	1.6	2.0	
(a)	Estimation of the risk factor profile of the total adult general practice patient population (that is, patients aged 18 years and over who have attended a GP at least once) (n = 14.3 million).
Note: LCL—lower confidence limit; UCL—upper confidence limit.
Table 14.5: Number of risk factors, by patient sex
	Male	Female	
Number of risk factors	Per cent in BEACH sample (95% CI)
(n = 11,613)	Per cent in patient population
(95% CI)(a)	Per cent in BEACH sample (95% CI)
(n = 19,812)	Per cent in patient population
(95% CI)(a)	
No risk factors	19.6
(18.7–20.5)	18.7
(17.7–19.7)	29.5
(28.6–30.4)	29.5
(28.5–30.5)	
One risk factor	49.0
(48.0–50.0)	46.8
(45.7–47.8)	51.2
(50.3–52.0)	50.1
(49.2–50.9)	
Two risk factors	25.8
(24.9–26.8)	27.8
(26.8–28.9)	16.6
(15.9–17.2)	17.4
(16.6–18.1)	
Three risk factors	5.6
(5.1–6.1)	6.7
(6.1–7.3)	2.8
(2.5–3.0)	3.1
(2.8–3.4)	
(a)	Estimation of the risk factor profile of the total adult general practice patient population (that is, patients aged 18 years and over 
who have attended a GP at least once) (n = 14.3 million).
Note: CI—confidence interval.

14.5	Changes in patient risk factors over the decade 2000–01 to 2009–10
To investigate changes over time in these patient risk factors, data tables reporting results for each year from 2000–01 to 2009–10 are published in the companion report General practice activity in Australia 2000–01 to 2009–10: 10 year data tables.1
The major changes between 2000–01 and 2009–10 are summarised below.
•	The prevalence of obesity in adults attending general practice increased significantly, from 20.2% in 2000–01 to 25.9% in 2009–10, an increase apparent in both male and female patients. The prevalence of overweight in adults was steady over this time period at about 34%.
•	The prevalence of overweight and obesity in children aged 2–17 years remained fairly static from 2000–01 to 2009–10, with about 10–11% of children being obese and about 18% overweight.
•	Prevalence of daily and occasional smoking decreased significantly in adults aged 
18 years and over, from 19.3% and 4.4% respectively in 2000–01, to 15.1% and 2.7% in 2009–10.
•	The prevalence of at-risk alcohol consumption among adults aged 18 years and over remained fairly static at about 26% between 2001–02 and 2009–10. 

15	SAND abstracts and research tools
Since BEACH began in April 1998, a section on the bottom of each encounter form has been used to investigate aspects of patient health or health care delivery not covered by general practice consultation-based information. These additional substudies are referred to as SAND (Supplementary Analysis of Nominated Data). The SAND methods are described in Section 2.5. All substudies have been approved by the AIHW Ethics Committee (on behalf of the AIHW and the University of Sydney).
The Australian General Practice Statistics and Classification Centre (AGPSCC) and participating stakeholders of the BEACH program select topics for investigation in each of the SAND studies. In each BEACH year, up to 20 substudies can be conducted in addition to the study of patient risk behaviours (see Chapter 14). Topics are often repeated to increase the size of the sample and its statistical power.
This chapter includes the abstracts and research tools for SAND substudies conducted from April 2009 to March 2010. The subjects covered in the abstracts in this chapter are listed in Table 15.1, with the sample size for each topic.
Table 15.1: SAND abstracts for 2009–10 and sample size for each 

Abstract number	Subject	Number of respondents 	Number 
of GPs	
143	Prevalence of premature ejaculation(a)	656	83	
144	GP ordering of full blood counts and lipid profiles for general practice patients	5,629	193	
145	Schizophrenia and bipolar disorder management and visit frequency	2,564	89	
146	Antiplatelet medication and gastrointestinal problems in general practice patients	3,289	111	
147	Depressive disorders, management and comorbidities	3,278	111	
148	Type 2 diabetes, and blood glucose, lipid and blood pressure medication management	3,021	103	
149	Dyslipidaemia and lipid management	2,960	103	
150	Chronic pain in general practice patients	2,780	94	
151	Lipid medication use and cardiovascular risk in patients seen in general practice(b)	2,312	94	
152	Migraine and acute/rescue medication use in general practice patients	3,098	105	
153	Diabetes management and insulin initiation	3,087	105	
154	Pneumococcal vaccine and pneumonia in general practice patients	2,662	90	
155	Chronic kidney disease among general practice patients(c)	2,297	98	
156	Osteoarthritis, rheumatoid arthritis and ankylosing spondylitis and acid suppressants use	2,919	97	
157	Depression and antidepressant use in general practice patients	5,704	195	
158	Hypertension and benign prostatic hyperplasia(a)	1,003	91	
159	Dementia screening, prevalence and management	2,690	91	
160	Prevalence, cause, manifestation and severity of adverse pharmacological events	5,497	189	
161	Chronic obstructive pulmonary disease in general practice patients	2,842	97	
(a)	Substudy limited to male patients aged 18 years and over.
(b)	Substudy limited to adult patients aged 18 years and over.
(c)	Substudy limited to patients aged 24 years and over.

SAND abstract number 143: Prevalence of premature ejaculation
Organisation supporting this study: Janssen-Cilag Pty Ltd 
Issues: Prevalence of premature ejaculation (PE) in adult male general practice patients; validation of the Premature Ejaculation Diagnostic Tool in Australian general practice.
Sample: 656 adult male encounters from 83 GPs; data collection period: 02/12/08 – 19/01/09. 
Method: Detailed in the paper entitled SAND method: 2009–10 at: 
<www.fmrc.org.au/publications/SAND_abstracts.htm>. Methods for this study: The Premature Ejaculation Diagnostic Tool was defined according to Symonds T, Perelman MA et al. Eur Urol 2007; 52(2):565–73. A GP information sheet and a patient card with a copy of the questions were provided to assist with completion of the form. 
Summary of results
Of the 2,669 patients in the total sample, 36.6% (95% CI: 33.2–40.0) were male. This proportion of male patients was significantly smaller than the average for BEACH in 2008–09 (42.4%, 95% CI: 41.5–43.3). This was possibly due to the sensitive nature of the study topic. Male patients may have declined to participate, or GPs may have chosen not to record encounters with male patients.
Of the 972 male patients, 796 were adults (aged 18 years and over), and of these patients 656 completed questions about PE. Of these 57.5% reported being currently sexually active, 39.5% were previously sexually active, and 2.7% had never been sexually active.
Self-perceived PE status was recorded by 463 (72.6%) of the 638 currently/previously sexually active adult male patients in this study; 18.1% (n=84) felt they had PE and 81.9% (n=379) felt they did not. Of the 84 patients with PE, 45.2% (n=38) reported that they had always (or nearly always) had it since their first sexual contact, 42.9% (n=36) developed consistent PE ‘at a certain age’, and 11.9% (n=10) developed consistent PE at the same time or after experiencing erectile dysfunction.
Of the 638 currently/previously sexually active patients, the length of time to ejaculation after penetration was recorded for 420 (65.8%). One-quarter (n=101) reported ejaculation within 2 minutes, and three-quarters reported ejaculation more than 2 minutes after penetration. Of the 82 patients who self-reported PE and responded to this question, 57 (69.5%) reported ejaculation within 2 minutes of penetration. Of the 338 patients who did not feel they had PE and responded to this question, 44 (13.0%) reported ejaculation within 2 minutes of penetration. 
All five questions that form the Premature Ejaculation Diagnostic Tool (PEDT) were answered by 416 currently/previously sexually active adult male patients. Of the 81 patients who felt they had PE, the PEDT score suggested 77.8% had PE, 7.4% had possible PE and 14.8% did not have it. Of the 335 patients who did not feel they had PE, the PEDT score suggested 3.6% had PE, 8.7% had possible PE and 87.8% did not have it.
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SAND abstract number 144: GP ordering of full blood counts and lipid profiles for general practice patients 
Organisation supporting this study: Australian GP Statistics and Classification Centre
Issues: The proportion of patients who receive lipid or full blood count (FBC) tests at that day’s encounter; person initiating lipid/FBC tests; purpose of testing; differential diagnoses of investigative FBC tests; morbidities of patients receiving lipid tests.
Sample: 5,629 patients from 193 GPs; data collection period: 15/07/2008 – 18/08/2008 and 22/09/2009 – 16/10/2009.
Method: Detailed in the paper entitled SAND method 2009–10 at: <www.fmrc.org.au/publications/SAND_abstracts.htm>.
Summary of results
The age and sex distribution of patients did not differ from all 2008–09 BEACH encounters. Of 5,629 patients, 693 (12.3%, 95% CI: 11.1–13.5) had a lipid and/or FBC test ordered at that day’s encounter: 465 (8.3%) had a lipid test and 530 (9.4%) had a FBC. Sex was known for 459 patients with lipid tests: males had a higher order rate at 9.9% (95% CI: 8.4–11.4) than females 7.0% (95% CI: 5.9–8.1). There was no sex difference in the order rate of FBC tests.
Lipid tests
Of the 465 patients with lipid test ordered, 456 indicated who initiated the test order. The majority were suggested by GPs (87.1%), while 11.2% were suggested by patients, and 1.8% by another health professional.
Purpose(s) of ordering lipid test(s) were recorded for 455 patients. Monitoring was the most common reason for ordering (58.5% of patients), followed by investigative/diagnostic (18.2%), primary prevention (18.0%), secondary prevention (17.8%), and opportunistic testing (that is, adding the test once the decision to order was already made) (7.3%).
Of 452 respondents with a lipid test ordered, 43.8% had dyslipidaemia, 43.1% had hypertension, 25.7% were obese, 18.8% had a family history of dyslipidaemia, 18.8% had diabetes, and 12.2% had another cardiovascular disease. At least one of these morbidities/risk factors was present in 84.3% of patients receiving a lipid test.
FBC tests
Of the 530 patients with FBC ordered, 486 indicated who initiated the test order. The majority were suggested by GPs (92.2%), while 6.0% were suggested by the patient, and the remaining 1.9% were suggested by another health professional.
Purpose(s) of ordering FBC tests were recorded for 500 patients. An investigative or diagnostic purpose was the most common reason for ordering FBC tests (48.0% of patients), followed by monitoring (35.0%), opportunistic testing (10.6%), primary prevention (8.0%), and secondary prevention (4.8%).
GPs recorded 349 differential diagnoses for 230 patients with investigative FBC ordered. The most common was anaemia (29.2% of diagnoses), while iron deficiency (a common cause of anaemia) was indicated for a further 3.4%. Infections and infectious conditions were recorded for 28.7% of diagnoses, particularly unspecified infections (8.0% of diagnoses), and respiratory infections (6.0%).
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SAND abstract number 145: Schizophrenia and bipolar disorder management and visit frequency
Organisation supporting this study: Janssen-Cilag Pty Ltd
Issues: The proportion of general practice patients with a history of schizophrenia, schizoaffective disorder, schizophreniform, paranoid psychosis or bipolar disorder; whether the condition was managed under a specific plan or program; the number of visits to a GP in the previous 3 months (including the current consultation); the number of visits at which schizophrenia/bipolar disorder was managed; current medications used to manage the condition and who initiated the medication use (GP or specialist). 
Sample: 2,564 patients from 89 GPs; data collection period: 31/03/2009 – 04/05/2009. 
Method: Detailed in the paper entitled SAND method 2009–10 at: <www.fmrc.org.au/publications/SAND_abstracts.htm>.
Summary of results 
The age and sex distribution of respondents did not differ from that of patients at all 2007–08 BEACH encounters.
Of the 2,564 respondents, 74 (2.9%, 95% CI: 2.0–3.7) had a history of either schizophrenia‑related problems or bipolar disorder. Of these patients, 32 (1.3%, 95% CI: 0.7–1.8) had schizophrenia, schizoaffective disorder, schizophreniform or paranoid psychosis, and 43 (1.7%, 95% CI: 1.1–2.3) had bipolar disorder. 
The majority of schizophrenia-related problems (87.5%) and bipolar problems (83.7%) were in patients aged 25–64 years. There was no significant difference between males and females in the proportion who had either of the conditions. 
Of 28 respondents with schizophrenia-related problems, 13 were managed as part of a shared care program with a community mental health centre, and 8 patients were part of a management plan with a private psychiatrist. Of 28 respondents, the median number of GP visits in the previous 3 months was four, at which schizophrenia-related problems were managed twice. Of the 32 patients with schizophrenia-related problems 40.6% (n=13) were on one medication, 37.5% (n=12) were on two, and 12.5% (n=4) of patients were taking no medication. Quetiapine and risperidone were most commonly recorded, and together accounted for more than one‑third of the medications. There were 45 medications with known initiator: 10 (22.2%) were initiated by a GP, and 35 (77.8%) by a specialist.
Of 42 respondents with bipolar disorder, 13 were part of a shared care program with a community mental health centre and 17 were being managed as part of a management plan with a private psychiatrist. For the 43 bipolar patients, the median number of visits to a GP in the previous 3 months was 3, at which bipolar disorder was managed once. Of the 43 patients, 34.9% were on one medication, 30.2% were on two, 16.3% were on three, 7.0% were on four, and 11.6% of bipolar patients were taking no medication. Sodium valproate, quetiapine and olanzapine were most commonly recorded, and together accounted for about 36% of the medications. For the 71 medications with known initiator, 28 (39.4%) were initiated by a GP and 43 (60.6%) by a specialist.
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SAND abstract number 146: Antiplatelet medication and gastrointestinal problems in general practice patients
Organisations supporting this study: AstraZeneca Pty Ltd
Issues: Prevalence of patients at risk of cardiovascular disease. For those at risk: current antiplatelet medications and daily dose; proportion with gastrointestinal (GI) problems and medications prescribed for those problems; the pattern of use of proton pump inhibitors (PPI); and timing of initiation of antiplatelet and PPI use.
Sample: 3,298 respondents from 111 GPs; data collection period: 05/05/2009–08/06/2009. 
Method: Detailed in the paper entitled SAND method 2009–10 at: <www.fmrc.org.au/publications/SAND_abstracts.htm>.
Summary of results 
Age and sex of patient were recorded at 3,277 encounters. The age distribution did not differ from all 2007–08 BEACH encounters. Sex distribution was significantly different: 37.6% (95% CI: 34.6–40.5) of encounters were with male patients, a significantly lower proportion than in total 2007–08 BEACH encounters (42.9%, 95% CI: 42.1–43.7).
GPs determined that 1,117 (33.9%) patients were at risk of cardiovascular disease, the proportion rising significantly with age. Among patients aged 25–44 years, 10.6% (95% CI: 7.9–13.3) were at risk, while among those aged 75 years and over, 76.9% (95% 
CI: 72.0–81.9) were at risk. There was no statistically significant difference between the sexes.
Information on antiplatelet use was available for 1,078 at risk patients. A total of 575 medications were recorded (multiple responses were allowed). Half of the patients (50.5%, 95% CI: 46.0–54.9) took at least one antiplatelet medication: 475 patients (44.1%) taking aspirin, 8.2% taking clopidogrel, and 1.1% taking an aspirin/dipyridamole combination. 
Of 1,089 at risk respondents with GI problem data, 437 (40.1%) had at least one GI problem: 31.8% had gastro-oesophageal reflux disease (GORD), 6.5% had dyspepsia, 2.3% had peptic ulcer disease, and 4.5% had other GI problems/symptoms. 
Of 430 respondents with a GI problem and medication data, 393 (91.4%) were taking a medication for their problems, and 366 (89.5%) of these were taking a PPI (80.3% of these as a continuous medication). Of a total of 410 medications for GI problems, esomeprazole accounted for 31.2%, pantoprazole for 21.7% and omeprazole 20.0%.
Of the 475 patients currently taking aspirin as an antiplatelet (either alone or with clopidogrel), GI problem status was recorded for 469. Of these, 202 (43.1%) had at least one GI problem: 169 (36.0%) had GORD, 5.5% had dyspepsia, 2.4% had peptic ulcer disease, and 3.2% had other GI problems/symptoms.
Of 167 at-risk patients with GORD and on antiplatelet aspirin, 156 (93.4%) were taking a PPI, and for 122 of these patients (80.3%), the regimen was continuous medication. For 114 at risk patients with GORD and on antiplatelet aspirin, timing of initiation of antiplatelet and PPI use was known: 54.4% started on a PPI first and 45.6% started on antiplatelet aspirin first.
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SAND abstract number 147: Depressive disorders, management and comorbidities
Organisation supporting this study: Wyeth Australia Pty Ltd
Issues: The proportion of general practice patients who had a diagnosed depressive disorder at the time of encounter; type of depressive disorder; comorbidities present among these patients; current medications taken for the management of depressive disorder; proportion of patients who were adequately controlled with the current treatment; and for those whose depressive disorder was not adequately controlled, the current management plan.
Sample: 3,278 patients from 111 GPs; data collection period: 05/05/2009 – 08/06/2009. 
Method: Detailed in the paper entitled SAND method 2009–10 at: <www.fmrc.org.au/publications/SAND_abstracts.htm>. Method for this study: DSM-IV-TR criteria for major depression supplied (Source: American Psychiatric Association 2000. Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision. Washington, DC: American Psychiatric).
Summary of results 
The age and sex distributions of respondents did not differ from that of patients at all 
2007–08 BEACH encounters. Of the 3,278 respondents, 594 (18.1%, 95% CI: 16.1–20.2) currently had a diagnosed depressive disorder. The prevalence was low (0.6%, 
95% CI: 0.0–1.4) in patients aged less than 15 years, and did not differ from the average in all other age groups. Prevalence was significantly higher for females (20.9%, 95% CI: 18.4–23.3) than males (13.9%, 95% CI: 11.2–16.5).
For the 579 patients who specified the type of depressive disorder, 216 (37.3%) had mixed anxiety-depressive disorder, 174 (30.1%) had major depressive disorder, 162 (28.0%) had generalised depressive disorder, 26 (4.5%) had bipolar disorder, and 12 patients (2.1%) had another depressive disorder (4 of whom had postnatal depression). 
Details of comorbidities were provided for 554 patients, of whom 495 (89.4%) had at least one comorbidity. The prevalence common comorbidities were anxiety (47.5%), insomnia (29.1%), hypertension (27.3%) and arthritis (24.2%). Comorbidities other than those listed were recorded for 172 patients (31.1% of respondents), with chronic obstructive pulmonary disease the most commonly recorded, followed by osteoporosis and dementia. 
Of 569 respondents with a depressive disorder, 108 (19.0%) were taking no medication for their disorder. There were 569 medications for depressive disorder listed for the 461 patients taking medication. Sertraline was the most common (13.2%), followed by venlafaxine (10.9%). For 502 of the 569 medications, the initiator of the medication was known, and 360 (71.7%) of these were initiated by the GP.
Of the 461 patients taking at least one medication for depressive disorder, information on current management plan was available for 453. Four out of five (78.4%) of these patients had their depressive disorder adequately controlled, with the GP not planning on changing management. For 20 patients (4.4%), the plan was to stay on the same medication but increase the dosage. For 10 patients (2.2%), the plan was to change to another medication, for 11 patients (2.4%) a new medication was to be added, and 43 patients (9.5%) were to be referred to another professional. Other management plans for depressive disorder were recorded for 32 (7.1%) patients, including 10 patients who were to be admitted to hospital.
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SAND abstract number 148: Type 2 diabetes, and blood glucose, lipid and blood pressure medication management
Organisation supporting this study: Merck Sharp and Dohme (Australia) Pty Ltd 
Issues: The prevalence of diagnosed Type 2 diabetes in general practice patients; for these patients, the most recent levels of HbA1c, total cholesterol, low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol and blood pressure; proportion currently taking medication for management of blood glucose levels; proportion currently taking mono, dual, triple or quadruple medication therapy; proportion currently using insulin (alone or in combination with oral medication) for management of blood glucose levels; proportion who meet the Pharmaceutical Benefits Schedule (PBS) criteria for subsidy of lipid-lowering medications; proportion of eligible patients taking a lipid-lowering medication; proportion of patients with Type 2 diabetes taking a lipid-lowering medication, and those taking a medication for hypertension. 
Sample: 3,021 respondents from 103 GPs; data collection period: 09/06/2009–13/07/2009. 
Method: Detailed in the paper entitled SAND method 2009–10 at: <www.fmrc.org.au/publications/SAND_abstracts.htm>. Methods for this study: A card was supplied to participating GPs to assist in defining patient eligibility for the PBS subsidy.
Summary of results 
The age–sex distribution of respondents did not differ from the distribution for all BEACH encounters, with 42.9% being male, and 26.5% of patients aged 45–64 years.
Of 3,021 respondents, 271 (9.0%) had diagnosed Type 2 diabetes. Of 3,000 patients for whom sex was known, prevalence of Type 2 diabetes was higher among male (11.5%, 95% CI: 
9.2–13.8) than female patients (7.2%, 95% CI: 5.6–8.8). Of 3,006 patients for whom age was known, prevalence was higher among patients aged 45–64 years (12.6%) than among patients aged 25–44 years (2.6%).
Of 244 respondents, the average HbA1c level was 7.3%, 57.0% having a level <7.0%. For 255 respondents, the average total cholesterol level was 4.6, 38.8% having levels 4.0–4.99. For 228 respondents, the average LDL cholesterol level was 2.5, 44.3% having levels of 2.5 or higher. For 233 respondents, the average HDL cholesterol level was 1.3, 64.4% having levels greater than 1.0. Of 248 respondents for blood pressure, only 8.1% were in the normal range.
Of 263 Type 2 diabetes respondents to the medication question, 205 (78.0%) were taking at least one medication to manage their blood glucose. Monotherapy was recorded for 108 patients (52.7%), dual for 85 patients (41.5%), and triple therapy for 12 patients (5.9%). None were on quadruple therapy. Of 47 patients taking insulin, 40.4% were taking insulin only, and 59.6% were taking insulin with at least one other medication.
Of 258 Type 2 diabetes respondents to the lipid medication question, 179 (69.4%) were currently taking a lipid-lowering medication, and of 255 respondents to the hypertension medication question, 188 (73.7%) were currently on medication for hypertension. Of 241 Type 2 diabetes patients for whom a response to the question on PBS subsidy eligibility was recorded, 206 (85.5%) met the criteria for subsidy of lipid-lowering medications, and of these, 165 (80.5%) were currently taking a lipid-lowering medication. 
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SAND abstract number 149: Dyslipidaemia and lipid management
Organisation supporting this study: Abbott Australasia
Issues: The proportion of general practice patients who were having their lipids managed for diagnosed dyslipidaemia and/or other risk factors/conditions; current lipid lowering medication; most recent levels of total cholesterol (TC), low-density lipoprotein (LDL), high‑density lipoprotein (HDL) cholesterol and triglycerides, all in mmol/L; GP opinion on whether lipids had reached target; lipid subfraction/s targeted with current medication.
Sample: 2,960 respondents from 103 GPs; data collection period: 09/06/2009—13/07/2009.
Method: Detailed in the paper entitled SAND method 2009–10 at: <www.fmrc.org.au/publications/SAND_abstracts.htm>.
Summary of results 
The age–sex distribution of respondents did not differ from the distribution for all 
2008–09 BEACH encounters, with the majority of patients (56.6%) being female. 
Of the 2,960 surveyed patients, 796 (26.9%, 95% CI: 24.0–29.8) were having their lipids managed. One in five (n=663, 22.4%, 95% CI: 19.6–25.2) had diagnosed dyslipidaemia and 167 (5.6%, 95% CI: 3.9–7.4) were having lipids managed for other risk factors/conditions (multiple response allowed). Lipid management rates did not differ significantly for males (30.7%) and females (24.0%). The proportion of adult patients under lipid management increased with age, from 7.1% of those aged 25–44 years to 57.1% of those aged 75 years and over.
Of 796 patients with lipids being managed, lipid medication status was available for 783 patients. Three-quarters of these (n=607, 77.5%, 95% CI: 73.1–82.0) were using lipid lowering medication. The remaining 22.5% were having their lipids managed without lipid medication. Of the 607 patients on lipid medication, 575 (94.7%) were using a statin; of these 557 (96.9%) were on a single statin and 18 (3.1%) were on statin combination medication. 
Of the 796 patients with lipids being managed, most recent TC, LDL, HDL and triglyceride data was available for 759, 693, 701, and 745 patients respectively. One-quarter (n=187, 24.6%) of respondents had TC <4.0, and the proportion of patients reaching target (TC<4.0) was significantly higher in males (35.2%) than females (14.6%). The mean TC was 4.9 (sd=1.3), 4.6 for males and 5.2 for females. Almost half (45.6%), and significantly more males (53.3%) than females (38.4%), had LDL<2.5. The mean LDL was 2.8 (sd=1.1), 2.6 for males and 2.9 for females. Four in five (79.5%), and significantly more females (89.7%) than males (69.7%), had HDL>1.0. The mean HDL was 1.4 (sd=0.4), 1.2 for males and 1.5 for females. Almost half (46.4%) had triglycerides<1.5, with similar proportions for males (48.8%) and females (44.4%). The mean triglyceride level was 1.7 (sd=1.0), 1.8 for males and 1.7 for females.
According to the GPs clinical opinion, target was reached for 61.6%, 58.4%, 82.5% and 68.7% of patients for TC, LDL, HDL and triglycerides respectively.
Of the 607 patients who had lipids managed by medication, 81.1% had total cholesterol targeted, 76.6% had LDL specifically targeted, 36.7% had HDL specifically targeted and 43.7% had triglycerides specifically targeted (multiple response allowed).
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SAND abstract number 150: Chronic pain in general practice patients 
Organisation supporting this study: Janssen-Cilag Pty Ltd 
Issues: The proportion of patients attending general practice who suffer from chronic pain; conditions causing chronic pain; severity of pain (by pain severity grades) for these patients; management of chronic pain; GP and patient satisfaction with current pain management. 
Sample: 2,780 patients from 94 GPs; data collection period: 14/07/2009 – 17/08/2009.
Method: Detailed in the paper entitled SAND method 2009–10 at: <www.fmrc.org.au/publications/SAND_abstracts.htm>. Method for this study: Chronic pain was defined as ‘pain experienced every day for three months in the six months prior to this consultation’ (Blyth FM et al. 2001). Severity was graded as: Grade I = low disability/low intensity; Grade II = low disability/high intensity; Grade III = high disability/moderately limiting; Grade IV = high disability/severely limiting (Von Korff M et al. 1992). Satisfaction was graded on a scale of 1 (highly dissatisfied) to 5 (highly satisfied). Pain impact was measured with the ‘Living better with pain’ log (American Chronic Pain Association 2005), from 1 (best) to 10 (worst). These definitions were supplied on a card for participating GPs.
Summary of results
The age–sex distribution of patients differed significantly from that of patients at all 2008–09 BEACH encounters, with greater proportions aged 1–4 years and 25–44 years, and lesser proportions aged 65–74 years and being male patients. Of the 2,780 respondents, 523 (18.8%, 95% CI: 16.3–21.3) had chronic pain. The age-specific rates showed prevalence increasing with patient age. Sex-specific rates showed no significant difference between sexes in chronic pain prevalence. 
The ‘cause of pain’ was given for 510 (97.5%) patients: cancer was the cause for 2.4% of these; osteoarthritis for 48.6%; other arthritis for 7.1%; and back problems for 29.2%. Nearly one‑third (29.2%) of patients nominated ‘other condition’ as the cause of their chronic pain, 65.1% of these being musculoskeletal conditions, and 14.7% neurological conditions. Pain severity was recorded for 500 patients (95.6%), and ranked as Grade I for 23.6%, Grade II for 37.8% of, Grade III for 29.6%, and Grade IV for 9.0% of these patients.
Current management was reported for 496 patients (94.3%), of whom 52.6% were currently managing their chronic pain with medication only. Medication in combination with other treatment was used by 31.9%, while 8.5% were using other managements (no medication), and 7.1% were not using any type of pain management. For the 419 patients taking medication, 623 medications were recorded, of which 32.9% was paracetamol, and 10.0% was paracetamol/ codeine. Oxycodone (6.7%), meloxicam (5.8%) and tramadol (5.8%), were also frequently recorded. A total of 247 other management methods were reported for the 200 patients using them, physiotherapy (29.6%), heat therapy (10.5%) and exercise (8.9%) being most common. 
GP satisfaction with pain management was recorded for 497 patients, and patient satisfaction with pain management for 502 patients. The mean GP satisfaction level was 2.5, and the mean patient satisfaction level was 2.7. For 498 patient respondents who ranked the impact of pain (when in pain) on activity, sleep and mood (1 = best; 10 = worst), the mean level of impact on activity was 4.7, on sleep was 4.8, and on mood was 4.8.
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SAND abstract number 151: Lipid medication use and cardiovascular risk in patients seen in general practice
Organisations supporting this study: Pfizer Australia Pty Ltd 
Issues: Prevalence of overweight and obesity, smoking, high blood pressure, high total cholesterol, ischaemic heart disease, erectile dysfunction, angina, previous myocardial infarction, previous stroke, other cardiovascular disease risk factor, Type 2 diabetes (and most recent HbA1C result). Current use of lipid lowering agents, and for users: recent results of total cholesterol. HDL, LDL, and triglycerides and past results (prior to start of lipid therapy). 
Sample: 2,312 adult respondents (18 years and over) from 94 GPs; data collection period: 14/07/09 – 17/08/09.
Method: Detailed in the paper entitled SAND method 2009–10 at: <www.fmrc.org.au/publications/SAND_abstracts.htm>.
Summary of results 
Patient age was provided for 2,303 respondents whose age distribution (7.4% 18–24 years, 31.5% 25–44 years, 31.2% 45–64 years and 29.9% 65 years and over) did not significantly differ from that of all adults at BEACH encounters 2008–09. Females were significantly over‑represented in this sample (n=2,300), 66.4%, compared with 61.1% of adults at all BEACH encounters 2007–08. 
Of the 2,211patients for whom BMI could be calculated, 34.1% (95% CI: 31.8–36.3) were overweight and 25.5% (95% CI: 22.7–28.2) were obese. Of the 2,087 patients for whom risk factor information was provided 41.8% had none of those listed, 28.4% had one, 15.5% two and 14.3% three or more: 34.0% had high blood pressure, 27.9% high total cholesterol; 12.8% were current smokers; 8.0% had ischaemic heart disease, 5.1% previous myocardial infarction, 3.6% angina, 2.9% erectile dysfunction, 2.7% previous stroke, 5.9% had ‘other’ cardiovascular risk factors, and 10.5% had Type 2 diabetes (n=218). Current HbA1c, known for 85.8% of Type 2 diabetes patients (n=187), averaged 7.14.
Of 2,312 surveyed patients, 23.8% (n=551) were currently on lipid medication, 55.1% were not, and for 21.1%, lipid status was not known. There were 570 lipid medications recorded, 88.4% being statins, 4.7% statin combinations, 1.8% fibrates and 5.1% other lipid medications. 
Of 548 respondents on current lipid medication, 539 (98.4%) had at least one cardiovascular risk factor: 78.1% had high total cholesterol, 67.2% had high blood pressure, 28.8% had Type 2 diabetes, 24.6% had ischaemic heart disease, 16.2% had previous myocardial infarction, 11.3% had angina, 7.9% were current smokers, 5.8% had erectile dysfunction, 6.4% had previous stroke and 12.6% had ‘other’ cardiovascular risk factors.
Recent test results were recorded for a varying number of respondents on lipid medication. Mean recent total cholesterol level (n=515) was 4.5, mean LDL cholesterol level (n=477) was 2.4, mean HDL (n=483) was 1.4, and mean triglyceride level (n=497) was 1.7. Women on lipid lowering medication had a significantly higher mean total cholesterol and HDL than males. 
Measures recorded prior to commencement of lipid medication, showed the mean total cholesterol level (n=276) was 6.3, average LDL level (n=228) was 3.8, mean HDL level (n=242) was 1.4, and the mean triglyceride level (n=253) was 2.3. 
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SAND abstract number 152: Migraine and acute/rescue medication use in general practice patients
Organisation supporting this study: Merck Sharp and Dohme (Australia) Pty Ltd
Issues: The proportion of general practice patients who suffer from migraine attacks. For patients who suffer migraine: frequency per month; type and detail of acute/rescue medication used at time of attack; current and previous use of triptan medication; cardiovascular safety concerns.
Sample: 3,098 respondents from 105 GPs; data collection period: 18/08/2009 – 21/09/2009.
Method: Detailed in the paper entitled SAND method 2009–10 at: <www.fmrc.org.au/publications/SAND_abstracts.htm>.
Summary of results
The age–sex distribution of respondents did not differ from the distribution for all 2008–09 BEACH encounters, with the majority of patients (59.8%) being female. Patients aged 
25–44 years and 45–64 years accounted for 22.5% and 27.6% of the sample respectively.
Of the 3,098 surveyed patients, 259 (8.4%, 95% CI: 6.7–10.0) suffered from migraine attacks. Prevalence of migraine was significantly higher among females (10.6%, 95% CI: 8.5–12.6) than males (5.1%, 95% CI: 3.4–6.8), and was highest among patients aged 15–24 years, 
25–44 years and 45–64 years (15.5%, 11.1% and 11.0% respectively). Of 249 patients with migraine who reported attack frequency, 55.8% had less than one migraine per month, 18.9% had one per month, and 25.3% had two or more per month. Reported migraine frequency per month were did not differ for males and females.
Of 249 respondents with migraine, 37 (14.9%) currently used no acute/rescue medication at the time of an attack, and 212 (85.1%) used prescribed and/or advised over-the-counter (OTC) acute/rescue medication. The proportion of patients taking OTC acute medication did not differ by migraine frequency. Patients experiencing two or more migraines per month were significantly more likely to use prescribed acute/rescue medication (47.6%, 95% CI: 33.3–62.0) than those who had less than one attack per month (18.8%, 95% CI: 12.7–25.0). 
Of the 72 migraine patients currently taking prescribed acute/rescue medication, 71 gave details of these medications. About half (54.9%, n=39) were currently using a triptan, most commonly sumatriptan (40.9%, n=29), followed by zolmitriptan (9.9%, n=7), and naratriptan (4.2%, n=3). Almost half (47.9%, n=34) were currently using other acute/rescue prescribed medications, paracetamol combinations being the most commonly listed (n=19).
Of the 212 migraine patients currently on acute/rescue medication/s (OTC or prescribed), 190 gave information about whether they had ever tried a triptan. One-third (32.6%, n=62) had tried a triptan, 120 (63.2%) had never tried a triptan, and the GP indicated ‘Don’t know ’ for 8 (4.2%). Of the 62 patients who had tried a triptan, 39 (62.9%) were currently taking a triptan.
Of the 120 migraine patients on current acute/rescue medication who had never tried a triptan, 112 gave information on cardiovascular risk concerns with triptan use. Of the 112 respondents, 10 (8.9%) had never tried a triptan due to concerns about cardiovascular safety.
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SAND abstract number 153: Diabetes management and insulin initiation
Organisation supporting this study: Sanofi-Aventis Australia Pty Ltd
Issues: Prevalence of diagnosed Type 1 and Type 2 diabetes in general practice patients; for these patients, time since diagnosis, most recent HbA1c result and time since last test; proportion currently taking a medication for management of blood glucose levels; proportion currently taking a mono, dual triple or quadruple medication therapy for management of blood glucose levels; proportion currently using insulin (alone or in combination with oral medication); for patients using insulin: HbA1c level before initiation of insulin, the number of years insulin used, and who initiated the insulin. 
Sample: 3,087 respondents from 105 GPs; data collection period: 18/08/2009 – 21/09/2009. 
Method: Detailed in the paper entitled SAND method 2009–10 at: <www.fmrc.org.au/publications/SAND_abstracts.htm>.
Summary of results 
The age–sex distribution of respondents did not differ from the distribution for all BEACH encounters, with 41.1% of patients being male, and 28.6% of patients aged 45–64 years.
Of 3,087 respondents, 258 (8.4%) had diagnosed diabetes. Of these, 31 (1.0%) had Type 1, and 227 (7.4%) Type 2 diabetes. Prevalence of diabetes rose significantly with age. For the 3,065 patients for whom age was known, prevalence was 3.0% for those aged 25–44 years, 9.8% for those aged 45–64 years, 15.9% for those aged 65–74 years, and 17.4% for those aged 75 years and over. Prevalence of diabetes did not differ significantly between male and female patients for the 3,062 patients for whom sex was known. 
Of 250 respondents with diabetes, the majority (40.0%) had been diagnosed 5–10 years earlier. Of the 31 patients with Type 1 diabetes, one-third (32.3%) had been diagnosed more than 20 years earlier. Of 219 respondents with Type 2 diabetes, 33.3% had been diagnosed less than 5 years earlier and 42.5% had been diagnosed 5–10 years earlier. 
Of 232 diabetes patients with known HbA1c, 53.0% had levels less than 7%. About one‑quarter of Type 1 diabetes patients (27.6%) and 56.7% of Type 2 diabetes patients had a level of less than 7%. Of 233 patients, 50.2% had been tested in the previous 3 months. 
Of 250 respondents with diabetes, 193 (77.2%) were currently taking at least one blood glucose medication. The majority of the 250 respondents (n=135; 54.0%) were taking only oral medication, 30 patients (12.0%) were taking insulin only, 28 (11.2%) were taking a combination of oral medication and insulin. Almost two-thirds of the 193 patients (n=119; 61.7%) were on mono therapy, 62 (32.1%) were on dual therapy, 11 (5.7%) were on triple therapy, and one patient was on quadruple therapy.
Of 58 diabetes patients taking insulin, HbA1c level before insulin use was known for 29. Of these, 24 (82.8%) had levels >8% and 5 patients (17.2%) had levels 7–8%. For 57 patients for whom duration was known, 29.8% had been on insulin for more than 10 years. 
Of 56 patients taking insulin, almost half (44.6%) had had their insulin initiated by an endocrinologist only, 19 (33.9%) had their insulin initiated by a GP only, and 12 (21.4%) had their insulin initiated by a GP in consultation with an endocrinologist.
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SAND abstract number 154: Pneumococcal vaccine and pneumonia in general practice patients 
Organisation supporting this study: Wyeth Australia Pty Ltd 
Issues: The proportion of general practice patients who received a pneumococcal vaccination: in the previous 5 years; the proportion who received the vaccinations in the previous 12 months, and the month of vaccination; indications for pneumococcal vaccination; the proportion of general practice patients who had been diagnosed with pneumonia in the previous 12 months; month of pneumonia diagnosis; impact of pneumonia on daily life (slight, moderate, severe, very severe).
Sample: 2,662 respondents from 90 GPs; data collection period: 22/09/2009 – 26/10/2009. 
Method: Detailed in the paper entitled SAND method 2009–10 at: <www.fmrc.org.au/publications/SAND_abstracts.htm>.
Summary of results 
The age-sex distribution of the respondents did not differ from the distribution for all BEACH respondents in 2008–09, with the majority of patients being female (57.8%). Patients aged 45–64 years accounted for 26.1% of the sample.
Of the 2,662 respondents, 24.4% (n=650, 95% CI: 21.3–27.6) had been given a pneumococcal vaccine in the previous 5 years. Of these, 36.5% were aged 75 years or more, and 28.2% were aged less than 15 years. Children aged less than 15 years (48.2%) and those aged 65 years and over (66.6%) had the highest vaccination rates over the previous 5 years.
Of the 601 respondents as to when vaccination had been done, 150 patients (25.0%) had been vaccinated in the previous year, and 132 of these knew the month of vaccination—March and September (each with 17.4%) were most common. After adjusting for general practice attendance frequencies by age and sex, it was estimated that 4.9% of patients who attended general practice at least once had a pneumococcal vaccine in that year.
Reason for vaccination (multiple responses allowed) was recorded for 533 patients. Of these, 62.1% were vaccinated because they were aged 65 years or more, and 27.8% were vaccinated as part of the routine childhood immunisation program. Another medical risk factor was given as a reason for 16.1% of patients, being a smoker was listed for 3.2%, and 0.9% were vaccinated because they came from an Aboriginal or Torres Strait Islander background and were aged over 50 years.
Of the 2,641 respondents who answered the question on pneumonia diagnosis, 62 (2.4%) had been diagnosed with pneumonia in the previous year. Of 48 respondents, almost one-third of had been diagnosed in September (n=14, 29.2%).
Of the 62 patients with diagnosed pneumonia, 28 (45.2%) had been vaccinated before their diagnosis of pneumonia, 48.4% had not, and 6.5% did not know. There were 56 respondents to the question on impact of pneumonia on daily life: 27 of them (48.2%) judged the impact as ‘severe’, and 21.4% judged it ‘very severe’.
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SAND abstract number 155: Chronic kidney disease among general practice patients 
Organisation supporting this study: Abbott Australasia Pty Ltd 
Issues: For patients aged 24 years and over attending general practice: the proportion who had their kidney function assessed in the previous 12 months; the proportion with comorbidities and/or risk factors for chronic kidney disease (CKD); the prevalence of CKD; the stages of kidney disease for patients with CKD. For patients at stages 3–5 of CKD: the management of blood pressure (BP), total cholesterol (TC), and HbA1c levels; underlying causes of CKD. 
Sample: 2,297 patients aged 24 years and over, from 98 GPs; data collection period: 27/10/2009—30/11/2009.
Method: Detailed in the paper entitled SAND method 2009–10 at: <www.fmrc.org.au/publications/SAND_abstracts.htm>. Method for this study: Stages of disease were defined according to National Kidney Foundation Guidelines.
Summary of results
There were 2,943 patients sampled, 2,353 of whom were 24 years and over, and 2,297 (97.6%) of these responded to kidney test questions. Nearly two-thirds (64.8%, 95% CI: 61.3–68.4) had had at least one kidney function test in the previous 12 months: 40.7% a glomerular filtration rate test, 63.8% a serum creatinine test, and 13.6% a proteinuria/microalbuminuria test. 
Age-specific test rates showed that the likelihood of being tested increased significantly with patient age, with 86.3% of patients aged 75 years and over having had at least one test. There was no significant difference between males and females in the proportion tested.
Responses to risk factors/comorbidities were recorded for 2,268 patients: 34.0% had hypertension; 14.8% were obese (BMI > 30); 11.6% had diabetes; 8.4% were current smokers; and 2.5% had a family history of CKD. One in four patients (26.2%) had no risk factors/comorbidities; 75.7% of the 1,675 patients with at least one risk factor had had a kidney function test; and 35.9% of the 593 with no risk factors had been tested. 
Of the 2,255 patients for whom a response was recorded, 259 (11.5%) had been diagnosed with CKD, and 55.6% of those were aged 75 years and over. There was no significant difference in diagnosed prevalence between males and females. Stage of disease was provided for 249 diagnosed patients: 10.2% were at Stage 1; 31.0% were at Stage 2; 47.8% were at Stage 3; 7.1% were at Stage 4; and 1.6% were at Stage 5. 
Of the 144 patients at stages 3–5 of CKD, response rates to management questions varied—of 142 respondents, 42.3% had had a renal ultrasound in the previous 5 years; of 140 respondents, 52.1% had the quantity of proteinuria assessed; of 142 respondents, 28.9% had been referred to a nephrologist; and of 137, 73.7% were currently taking an ACE inhibitor/A2RA. 
Indicator levels for patients at stages 3–5 (response rates varied by question) showed that 34.5% (of 142) had BP of < 130/80; 29.8% (of 131) had a TC of < 4; and 59.5% (of 42) had an HbA1c of < 7. The underlying cause of CKD had been established for 68 of 112 respondents (60.7%). The most common causal condition was hypertension (27.9%, n=19), followed by Type 2 diabetes (20.6%, n=14).
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SAND abstract number 156: Osteoarthritis, rheumatoid arthritis and ankylosing spondylitis and acid suppressants use
Organisation supporting this study: AstraZeneca Pty Ltd (Australia)
Issues: The proportion of general practice patients with diagnosed osteoarthritis (OA), rheumatoid arthritis (RA) or ankylosing spondylitis (AS); the medications currently taken for management of OA/RA/AS; the reasons for the most recent change in medication regimen for OA/RA/AS; the proportion of patients with OA/RA/AS taking acid suppressants; the reasons for acid suppressant use related to mediations taken for OA/RA/AS.
Sample: 2,919 patients from 97 GPs; data collection period: 27/10/2009—30/11/2009.
Method: Detailed in the paper entitled SAND method 2009–10 at: <www.fmrc.org.au/publications/SAND_abstracts.htm>.
Summary of results 
The age-sex distribution of respondents did not differ from the distribution for all 2008–09 BEACH encounters. Of the 2,895 respondents, 755 (26.1%, 95% CI: 23.5–28.7) had OA, 33 (1.1%, 95% CI: 0.7–1.6) had RA, and 3 patients (0.1%, 95% CI: 0.0–0.2) had AS. In total, 786 patients (27.2%, 95% CI: 24.5–29.8) had at least one of these conditions. The prevalence of at least one condition rose significantly as age of patient increased, from 28.7% (95% CI: 25.3–32.2) in patients aged 25–44 years to 66.1% (95% CI: 60.5–71.8) in patients aged 75 years and over. There was no sex-specific difference in prevalence of OA/RA/AS.
Of 740 respondents with osteoarthritis, only 550 (74.3%) were currently taking medication. There were 718 medications recorded, of which 48.3% were over-the-counter (OTC) analgesics, 18.9% were ‘other medications’, and 16.9% were coxibs or meloxicam. The OTC analgesic paracetamol accounted for 47.9% of medications for osteoarthritis. (Refer to Box 1 for medication groups.)
Of 27 respondents with rheumatoid arthritis only, 24 (88.9%) were currently taking medication. There were 51 medications recorded, of which 38 (74.5%) were in the ‘other medication’ group, most commonly methotrexate (25.5%). There were five medications (9.8%) in the coxibs and meloxicam group.
Of the 580 patients currently taking medication for OA/RA/AS, information on the reason for most recent change of medication or regimen was available for 482. Lack of efficacy was the most common reason given, cited by 7.7% of patients. Fear of side effects was the reason given by 2.9% of patients, cost of medication was recorded for 2.3%, patient’s request for 1.5%, and side effect for 0.8%. No change was recorded for 412 patients (85.5%).
Of 726 respondents, 266 patients (36.6%) were currently taking acid suppressants. Of the 261 acid suppressants recorded, esomeprazole (n=80, 30.7%) was most commonly taken, followed by pantoprazole (n=65, 24.9%) and omeprazole (n=49, 18.8%). Of 264 respondents, 113 (42.8%) indicated that their acid suppressant use was related to their use of OA/RA/AS medication. Reasons for acid suppressant use were: treatment of gastrointestinal symptoms (n=73, 64.6%), prevention of gastrointestinal symptoms (n=29, 25.7%), treatment of ulcer or bleed (n=8, 7.1%), and prevention of ulcer or bleed (n=6, 5.3%).
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Box 1: Medication groups and Anatomical Therapeutic Chemical (ATC) codes
Medication group	ATC code 	ATC label for the specified ATC code	
Non-selective non-steroidal anti-inflammatory (NSAID) drugs	All M01A
excluding (M01AH, M01AX05, M01AC06)	Anti-inflammatory and antirheumatic products, non-steroids
excluding (coxibs, glucosamine, meloxicam)	
Coxibs and meloxicam	M01AH	Coxibs 	
	M01AC06	Meloxicam	
OTC analgesic	N02B	other analgesics and antipyrectics	
	B01AC06	acetylsalicylic acid	
Other medication	M01BA	Anti-inflammatory/antirheumatic agents combined with corticosteroids	
	M01C	Specific antirheumatic agents	
	M02	Topical products for joint and muscular pain	
	M05B	Drugs affecting bone structure and mineralisation	
	A07E	Intestinal anti-inflammatory agents	
	H02A	Corticosteroids for systemic use, plain	
	L01B	Antimetabolites	
	L04A	Immunosuppressants	
	N02A	Opioids	
	N06A	Antidepressants	
	P01BA02	Hydroxychloroquine	
Complementary therapy	M01AX05	Glucosamine	
	M01BX
	Other anti-inflammatory/antirheumatic agents combined with corticosteroids—for example, glucosamine+chondroitin 	
	V03 	All other therapeutic products	
	V06D 	Other nutrients	
	A12A	Calcium	
	B03B 	Vitamin B12 and folic acid	
	A09A	Digestives, including enzymes	
Source: World Health Organization Collaborating Centre for Drug Statistics Methodology 2009. Anatomical Therapeutic Chemical (ATC) classification index with Defined Daily Doses (DDDs). January 2009 ed. Oslo: WHO. 	

SAND abstract number 157: Depression and antidepressant use in general practice patients 
Organisation supporting this study: Wyeth Australia Pty Ltd
Issues: Prevalence of current diagnosed depression; presence of listed comorbidities (anxiety, insomnia, back complaint, hypertension, lipid disorder, diabetes, asthma, ischaemic heart disease, gastro-oesophageal reflux disease, arthritis, cancer, other) in patients with depression; their current use of antidepressants; medication side effects thought (GP clinical opinion) due to the antidepressant medication(s). 
Sample: 5,704 patients from 195 GPs; data collection periods: 24/02/2009—30/03/2009 and 01/12/2009–18/01/2010. Note: results from the first of these data periods were previously reported as SAND Abstract number 142, in General practice activity in Australia 2008–09.
Method: Detailed in the paper entitled SAND method 2009–10 at: 
<www.fmrc.org.au/publications/SAND_abstracts.htm>. 
Summary of results
The sex distribution of surveyed patients did not differ from that of patients at all BEACH encounters 2008–09, 42.4% being male. However, this sample was significantly older than patients at all encounters, a greater proportion being aged 65 years and over. 
The prevalence of current diagnosed depression among the 5,704 patients was 16.4%, (95% CI: 15.0–17.9), and significantly higher among females (18.8%, 95% CI: 16.9–20.6) than males (13.2%, 95% CI 11.6–14.8). Prevalence was highest among those aged 25–44 years (21.6%) and 45–64 years (21.9%), then decreased significantly to 12.2% among those aged 75 years or more. Of the 936 patients with depression, 840 (89.7%) had at least one comorbidity and 66.6% had two or more. Most common were anxiety (56.7%), insomnia (34.1%), hypertension (27.6%), back complaint (24.3%), lipid disorder (19.7%) and gastro-oesophageal reflux disease (18.5%).
Of the 936 patients with diagnosed depression, 915 (97.8%) responded to the antidepressant question. Of these, 695 (76.0%) were taking antidepressant(s), and 659 patients gave details of 689 antidepressants being taken. About half (51.5%) of these were selective serotonin reuptake inhibitors—largely accounted for by sertraline (14.5% of all antidepressants), citalopram (11.6%) and escitalopram (10.4%). A further 12.6% were non-selective monoamine reuptake inhibitors; 2.4% were monoamine oxidase A inhibitors (selective or non-selective); and 33.4% were ‘other antidepressants’ (venlafaxine being most common at 17.1%). The presence/absence of side-effects of antidepressants was reported for 650 (93.5%) of the 695 patients: 451 (69.4%) reported no side-effects; and 199 (30.6%) reported 301 side effects (average 1.5 per patient). The most common side effects were sedation (10.8% of those on antidepressants), weight gain (9.9%) and sexual dysfunction (7.5%). 
Among those taking antidepressants, prevalence of the common comorbidities paralleled that of all patients with depression. GPs detailed 2,035 other prescribed medications for 627 patients on antidepressant(s) for depression (average 3.2 per patient). Patients on no other medications reported fewer side effects of antidepressants, but reported side effects of antidepressants did not increase with increased numbers of other prescribed medications. 
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SAND abstract number 158: Hypertension and benign prostatic hyperplasia
Organisation supporting this study: Sanofi-Aventis Australia Pty Ltd
Issues: Prevalence of diagnosed hypertension, benign prostatic hyperplasia (BPH), and/or other prostate or urinary problems in male general practice patients aged 18 years and over; percentage of patients with at least one of the conditions who were currently taking prazosin; the indication for which prazosin was prescribed; medications currently taken for BPH and/or other prostate or urinary problems, and the health professional who initiated each of these medications (GP alone, specialist alone, GP and specialist, other). 
Sample: 1,003 adult male respondents from 91 GPs; data collection period: 01/12/2009 – 18/01/2010. 
Method: Detailed in the paper entitled SAND method 2009–10 at: <www.fmrc.org.au/publications/SAND_abstracts.htm>.
Summary of results 
The age distribution of respondents did not differ from that of all male patients aged 18 years and over at BEACH encounters, with 28.5% aged 18–45 years, 35.9% aged 
45–64 years, and 35.7% aged 65 years and over.
Of the 1,003 respondents, 377 (37.6%) had diagnosed hypertension and 100 (10.0%) had diagnosed BPH. There were 58 (5.8%) patients who had other prostate/urinary conditions. A total of 439 (43.8%) patients had at least one of the conditions, among whom 288 had hypertension only, 58 had hypertension and BPH, and 35 had BPH only. There were 5 patients who had all three conditions. 
Of the 439 patients with at least one of the conditions, 426 responded to the prazosin questions, and 6.1% (n=26) of them were using prazosin: 4.7% (n=17) of those with hypertension; 19.0% (n=19) of those with BPH; and 5.2% (n=3) of those with another prostate/urinary condition. 
Of the 26 patients on prazosin, 12 were prescribed it for BPH only, 8 were prescribed it for hypertension but not BPH, and 6 were prescribed it for hypertension and BPH. 
Of the 151 patients with BPH and/or other prostate/urinary conditions, 126 gave medication details: 38 (30.2%) were currently on medication and 88 (69.8%) were on no medications for these conditions. There were 41 medications listed for the 38 patients, with prazosin accounting for almost half (48.8%) of these, while tamsulosin made up one-quarter (24.4%). 
Of the 100 patients with BPH, 85 provided medication details, with 27 patients taking 29 medications. Prazosin accounted for 62.1% of these medications, and tamsulosin 24.1%.
Of all medications taken for BPH and/or other prostate/urinary conditions, about one-third (36.8%) were initiated by a GP, and the same proportion was initiated by a specialist. About one-quarter (23.7%) were initiated by both a GP and a specialist.
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SAND abstract number 159: Dementia screening, prevalence and management
Organisation supporting this study: Pfizer Australia Pty Ltd
Issues: The proportion of general practice patients who have been screened for dementia; reason(s) for screening; test(s) used for screening; proportion of screened patients diagnosed with dementia; management of patients with dementia; dementia risk factors present in patients who had not been screened; proportion of unscreened patients with whom GPs had discussed dementia risk.
Sample: 2,690 patients from 91 GPs; data collection period: 19/01/2010 – 22/02/2010. 
Method: Detailed in the paper entitled SAND method 2009–10 at: <www.fmrc.org.au/publications/SAND_abstracts.htm>.
Summary of results 
The age distribution of respondents was significantly different from patients at all 2008–09 BEACH encounters, with a higher proportion of patients aged 25–44 years. The sex distribution was similar to that of all patients at 2008–09 BEACH encounters.
Of the 2,690 respondents, 226 (8.4%, 95% CI: 6.0–10.8) had been screened for dementia at some time. The percentage of patients screened rose significantly with age to reach 44.1% among patients aged 75 years and over. There was no significant difference between males and females in the proportion of patients screened.
Among the 214 screened patients for whom screening reasons were recorded, ‘concern of the GP’ was a reason for screening 35.5%, dementia signs and symptoms were cited as a reason for 30.8%, ‘family concerns’ for 20.1%, family history of dementia for 10.3%, and concern of other health professional for 8.4%. Other reasons for dementia screening were reported for over one-third (34.1%) of these patients. Health assessment accounted for 49.3%, and age of patient accounted for 14.1% of the other reasons specified.
The MMSE (mini mental state exam) was used for 90.7% of the 226 screened patients, and GP-COG (GP assessment of cognition) for 10.2% of patients. The other listed tests were not often used.
Dementia diagnosis status was recorded for 221 of the 226 screened patients. A total of 54 patients (24.4%) were diagnosed with dementia. Of those patients with dementia, 72.2% received a referral, 42.6% were being monitored (no treatment), 29.6% received medication, and for 24.1%, another action was recorded—for example, residential care.
Of the 2,464 patients who had not been screened for dementia, 2,403 gave details of risk factors. Three out of five patients (60.9%) had no risk factors, while 23.4% had one risk factor, 10.7% had two, and 5.0% had three or more. The most common risk factor was cardiovascular disease (14.9% of patients), followed by dyslipidaemia (14.7%), being a smoker (12.7%), diabetes (7.6%), and family history of dementia (7.5%).
GPs were asked if they had ever discussed dementia risk factors with the patient. Of 2,383 respondents who had never been screened, GPs had discussed dementia risk factors with 11.1% of them. They had discussed dementia risk with 21.8% of the 923 unscreened respondents with at least one risk factor. 
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SAND abstract number 160: Prevalence, cause, manifestation and severity of adverse pharmacological events
Organisations supporting this study: Australian GP Statistics and Classification Centre
Issues: The proportion of general practice patients who have experienced an adverse event
resulting from the use of a medication during the preceding 6 months. For the most recent event, the cause, clinical manifestation, severity, duration and any resulting hospitalisation.
Sample: 5,497respondents from 189 GPs; data collection period: 19/01/2010–29/03/2010. 
Method: Detailed in the paper entitled SAND method 2009–10 at: <www.fmrc.org.au/publications/SAND_abstracts.htm>.
Summary of results 
Sex of patient was recorded at 5,463 encounters, and 63.5% (95% CI: 61.4–65.7) of these were with female patients, a significantly higher proportion than in total 2009–10 BEACH encounters (60.4%, 95% CI: 59.5–61.3). The age distribution did not differ from patients at all 2009–10 BEACH encounters.
Of the 5,497 respondents, 466 (8.5%; CI: 7.4–9.6) had experienced an adverse drug event in the previous 6 months. There was no difference in occurrence of adverse events between the sexes. The proportion of patients who reported an adverse drug event increased with age, from 1.8% of infants aged less than 1 year to 11.6% of patients aged 75 years or more. 
Of 484 drugs suspected of causing adverse events, ‘natural opium alkaloids’ and ‘other antidepressants’ were the medication groups most often cited. However, they accounted for only 4.8% and 4.6% respectively of the medications, due to the wide variety of medications implicated. The most common individual medications were atorvastatin, which accounted for 2.5%, tramadol (2.3%), amlodipine (1.9%), and metformin (1.9%). 
Among 442 respondents, the most commonly listed manifestations/symptoms of the adverse event were digestive in nature (28.1% of all manifestations), followed by skin problems (16.4%), and problems which were general and unspecified (14.2%). At individual condition level, the most common were nausea (9.1% of all listed manifestations), followed by localised rash (8.3%), vomiting (6.0%), vertigo/dizziness (3.6%) and diarrhoea (3.5%). Within individual drug groups, opioids most commonly caused vomiting (drug specific rate 15.3%), nausea (13.9%) and/or constipation (12.5%); antidepressants caused sleep disturbance (13.4%), anti-arthritics caused epigastric pain (19.4%), lipids caused muscle pain (30.8%) and penicillins caused rashes (28.6%) and diarrhoea (19.1%).
Among 446 respondents, the adverse drug event was classed as mild for 41.7%, moderate for 46.2%, and severe for 11.7%. 
Of 445 patients with an adverse drug event for whom this information was known, 5.4% were hospitalised due to the event. Of 52 patients with a severe event, 28.9% were hospitalised. Information on the duration of the event was available for 441 patients. For 42.2% the adverse event lasted for less than 1 week, for 19.1% it lasted 1–2 weeks, for 14.3% it lasted 3–4 weeks, for 15.2% it lasted 1–2 months, and for 9.3% it lasted more than 3 months.
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SAND abstract number 161: Chronic obstructive pulmonary disease in general practice patients 
Organisation supporting this study: Novartis Pharmaceuticals Australia Pty Ltd 
Issues: For patients attending general practice—the proportion who had diagnosed chronic obstructive pulmonary disease (COPD) with or without asthma; the severity of COPD; factors contributing to diagnosis of COPD; the proportion with asthma diagnosed before COPD; medication taken for management of COPD/COPD with asthma; the proportion who had medication changes at the current encounter, and the reasons for these changes. 
Sample: 2,842 patients from 97 GPs; data collection period: 23/02/2010—29/03/2010.
Method: Detailed in the paper entitled SAND method 2009–10 at: <www.fmrc.org.au/publications/SAND_abstracts.htm>. Methods for this study: GOLD COPD guidelines were used to categorise severity of COPD (< www.goldcopd.com/>).
Summary of results
There were 2,939 patients sampled, and 2,842 (96.7%) of these responded to asthma and COPD questions. The age distribution did not differ from that of patients at all BEACH encounters in 2008–09, but the sex distribution differed significantly (35.1% male compared with 42.4% male at all 2008–09 BEACH encounters). 
Of the 2,842 respondents, 273 patients (9.6%, 95% CI: 8.1–11.2) currently had diagnosed asthma without COPD, 79 (2.8%, 95% CI: 2.0–3.5) had COPD without asthma, 65 (2.3%, 95% CI: 1.6–3.0) had both COPD and asthma, and 2,425 (85.3%, 95%CI: 83.3–87.4) had neither. The highest prevalence was in patients aged 65–74 years. There was no significant difference between proportions of males and females with COPD.
Severity was reported for 142 patients with COPD, 37.3% had mild COPD, 43.0% had moderate COPD, 1 in 10 (11.3%) had severe COPD, and 8.5% had very severe COPD.
Of the 144 patients with COPD, factors contributing to the diagnosis were reported for 142 (98.6%). Some were health states/risk factors, and some were diagnostic factors. Each section of this question had a different number of respondents: of 126 patients, 123 (97.6%) selected ‘clinical history/symptoms’ as a contributing factor; of 80 respondents, 32 (40.0%) selected ‘non-response to bronchodilator’; of 128 respondents, 117 (91.4%) nominated smoking history; of 68 respondents, 19 (27.9%) selected environmental irritants; of 101 respondents, 83 (82.2%) reported spirometry testing; and of 110 respondents, 83 (75.5%) selected chest x-ray. Twelve patients reported other factors, 5 of whom had CT scans.
Of the 65 patients with both COPD and asthma, 38 of 56 respondents (67.9%) reported having asthma diagnosed before the COPD diagnosis.
Medication use questions were answered by 137 of the 144 patients with COPD, and 117 of these (85.4%) were taking at least one. These 117 patients reported a total of 232 medications. The most frequently reported were tiotropium (29.7%) and salbutamol (25.0%). Twelve patients (11.1% of 108 respondents) reported having medication changed at the current encounter. For 5 patients, progression of disease was the reason for the change, and 2 patients had medication changed due to lack of efficacy.
The following pages contain the recording form and instructions with which the data in this abstract were collected.

This information has been excluded from this format. For more information please contact feedback@aihw.gov.au 

Severity of Chronic Obstructive Pulmonary Disease (COPD) reference card
Severity	Measure 	Symptoms	
Mild	FEV1/FVC < 0.7
FEV1 80% predicted	Characterised by mild airflow limitation.
Symptoms of chronic cough and sputum production may be present.	
Moderate	FEV1/FVC < 0.7
FEV1 50 and <80% predicted
	Characterised by worsening airflow limitation.
Shortness of breath typically developing on exertion, chronic cough and sputum production may also be present.	
Severe	FEV1/FVC < 0.7
FEV1 30 and <50% predicted
	Characterised by further worsening of airflow limitation.
Greater shortness of breath, reduced exercise capacity, fatigue, and repeated exacerbations that almost always have an impact on patients’ quality of life.	
Very severe	FEV1/FVC < 0.7
FEV1 <30% predicted or FEV1 <50% predicted plus chronic respiratory failure(a)	Characterised by severe airflow limitation. 
Quality of life is very appreciably impaired and exacerbations may be life threatening.	
(a)	Respiratory failure is defined as arterial pressure of oxygen (PaO2) <8.0 kPa (60 mm Hg) with or without arterial partial pressure of CO2 (PaCO2)>6.7 kPa (50 mm Hg) while breathing at sea level.
Note:	FEV1—post bronchodilator forced expiratory volume in one second; FVC—forced vital capacity (maximal inspiration); FEV1/FVC—ratio of forced expiratory volume to forced vital capacity. 

Source: Rabe KF, Hurd S, Anzueto A, Barnes PJ, Buist SA, Calverley P et al. 2007. Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary. Am J Respir Crit Care Med 176(6):532-555.

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Glossary
A1 Medicare items: Medicare item numbers 1, 2, 3, 4, 13, 19, 20, 23, 24, 25, 33, 35, 36, 37, 38, 40, 43, 44, 47, 48, 50, 51, 601, 602.
Aboriginal: The patient identifies himself or herself as an Aboriginal person.
Activity level: The number of general practice A1 Medicare items claimed during the previous 3 months by a participating GP.
Allied and other health professionals: Those who provide clinical and other specialised services in the management of patients, including physiotherapists, occupational therapists, dietitians, dentists and pharmacists.
Chapters (ICPC-2): The main divisions within ICPC-2. There are 17 chapters primarily representing the body systems.
Chronic problem: see Diagnosis/problem: Chronic problem.
Commonwealth concession card: An entitlement card provided by the Australian Government, which entitles the holder to reduced-cost medicines under the Pharmaceutical Benefits Scheme and some other concessions from state and local government authorities.
Complaint: A symptom or disorder expressed by the patient when seeking care.
Component (ICPC-2): In ICPC-2 there are seven components that act as a second axis across all chapters.
Consultation: See Encounter.
Diagnosis/problem: A statement of the provider’s understanding of a health problem presented by a patient, family or community. GPs are instructed to record at the most specific level possible from the information available at the time. It may be limited to the level of symptoms.
•	New problem: The first presentation of a problem, including the first presentation of a recurrence of a previously resolved problem, but excluding the presentation of a problem first assessed by another provider.
•	Old problem: A previously assessed problem that requires ongoing care, including follow‑up for a problem or an initial presentation of a problem previously assessed by another provider.
•	Chronic problem: A medical condition characterised by a combination of the following characteristics: duration that has lasted or is expected to last 6 months or more, a pattern of recurrence or deterioration, a poor prognosis, and consequences or sequelae that impact on an individual’s quality of life. (Source: O’Halloran J, Miller GC, Britt H 2004. Defining chronic conditions for primary care with ICPC-2. Fam Pract 21(4):381–6). 
•	Work-related problem: Irrespective of the source of payment for the encounter, it is likely in the GP’s view that the problem has resulted from work-related activity or workplace exposure, or that a pre-existing condition has been significantly exacerbated by work activity or workplace exposure.
Encounter (enc): Any professional interchange between a patient and a GP.
•	Indirect: Encounter where there is no face-to-face meeting between the patient and the GP but a service is provided (for example, prescription, referral).
•	Direct: Encounter where there is a face-to-face meeting of the patient and the GP.
Direct encounters can be further divided into:
–	Medicare-claimable
▪	Surgery consultations: Encounters identified by any one of MBS item numbers 3, 23, 36, 44, 52, 53, 54, 57, 5000, 5020, 5040, 5060, 5200, 5203, 5207, 5208.
▪	Home visits: Encounters identified by any one of MBS item numbers 
4, 24, 37, 47, 58, 59, 60, 65, 5003, 5023, 5043, 5063, 5220, 5223, 5227, 5228.
▪	Hospital encounters: Encounters identified by any one of MBS item numbers 
19, 33, 40, 50, 87, 89, 90, 91.
▪	Residential aged care facility: Encounters identified by any one of MBS item numbers 20, 35, 43, 51, 92, 93, 95, 96, 5010, 5028, 5049, 5067, 5260, 5263, 5265, 5267.
▪	Health assessments: Encounters identified by any one of MBS item numbers 700, 702, 704, 706, 708, 710, 712.
▪	Chronic disease management items: Encounters identified by any one of MBS item numbers 720, 721, 722, 723, 724, 725, 726, 727, 728, 729, 730, 731.
▪	Case conferences: Encounters identified by any one of MBS item numbers 
734, 736, 738, 740, 742, 744, 746, 749, 757, 759, 762, 765, 768, 771, 773, 775, 778, 779.
▪	Incentive payments: Encounters identified by any one of MBS item numbers 
2497, 2501, 2503, 2504, 2506, 2507, 2509, 2517, 2518, 2521, 2522, 2525, 2526, 2546, 2547, 2552, 2553, 2558, 2559, 2574, 2575, 2577, 2578, 2598, 2600, 2603, 2606, 2610, 2613, 2616, 2620, 2622, 2624, 2631, 2633, 2635, 2664, 2666, 2668, 2673, 2675, 2677, 2704, 2705, 2707, 2708.
▪	Other MBS encounters: Encounters identified by an MBS item number that does not identify place of encounter (see A1 Medicare items).
–	Workers compensation: Encounters paid by workers compensation insurance.
–	Other paid: Encounters paid from another source (for example, state).
General practitioner (GP): A medical practitioner who provides primary comprehensive and continuing care to patients and their families within the community (Royal Australian College of General Practitioners).
GP consultation service items: Includes GP services provided under the MBS professional services category including MBS items classed as A1, A2, A5, A6, A7, A14, A17, A18, A19, A20, A22 and selected items provided by GPs classified in A11, A15 and A27.
Medication: Medication that is prescribed, provided by the GP at the encounter or advised for over-the-counter purchase.
Medication rates: The rate of use of all medications, including medications that were prescribed, supplied by the GP and advised for over-the-counter purchase.
Medication status:
•	New: The medication prescribed/provided at the encounter/advised is being used for the management of the problem for the first time.
•	Continuation: The medication prescribed/provided at the encounter/advised is a continuation or repeat of previous therapy for this problem.
•	Old: See Continuation.
Morbidity: Any departure, subjective or objective, from a state of physiological wellbeing. In this sense, sickness, illness and morbid conditions are synonymous.
Patient status: The status of the patient to the practice.
•	New patient: The patient has not been seen before in the practice.
•	Old patient: The patient has attended the practice before.
Practice nurse involvement: Encounters at which a practice nurse MBS item number and/or a treatment (either clinical or procedural) was recorded as done by a practice nurse.
Prescribed rates: The rate of use of prescribed medications (that is, does not include medications that were GP-supplied or advised for over-the-counter purchase).
Problem managed: See Diagnosis/problem.
Provider: A person to whom a patient has access when contacting the health care system.
Reasons for encounter (RFEs): The subjective reasons given by the patient for seeing or contacting the general practitioner. These can be expressed in terms of symptoms, diagnoses or the need for a service.
Recognised GP: A medical practitioner who is:
•	vocationally recognised under Section 3F of the Health Insurance Act, or
•	a holder of the Fellowship of the Royal Australian College of General Practitioners who participates in, and meets the requirements for, quality assurance and continuing medical education as defined in the Royal Australian College of General Practitioners (RACGP) Quality Assurance and Continuing Medical Education Program, or
•	undertaking an approved placement in general practice as part of a training program for general practice leading to the award of the Fellowship of the Royal Australian College of General Practitioners, or undertaking an approved placement in general practice as part of some other training program recognised by the RACGP as being of equivalent standard. (Source: Commonwealth Department of Health and Aged Care 2001. Medicare benefits schedule book. Canberra: DHAC). 
Referral: The process by which the responsibility for part or all of the care of a patient is temporarily transferred to another health care provider. Only new referrals to specialists and allied health professionals, and for hospital and residential aged care facility admissions arising at a recorded encounter are included. Continuation referrals are not included. Multiple referrals can be recorded at any one encounter.
Repatriation health card: An entitlement card provided by the Department of Veterans’ Affairs that entitles the holder to access a range of Repatriation health care benefits, including access to prescription and other medications under the Pharmaceutical Benefits Scheme.
Rubric: The title of an individual code in ICPC-2.
Significant: This term is used to refer to a statistically significant results. Statistical significance is measured at the 95% confidence level in this report. 
Torres Strait Islander: The patient identifies himself or herself as a Torres Strait Islander person.
Work-related problem: See Diagnosis/problem.

Appendices
Appendix 1: Example of a 2009–10 recording form

This information has been excluded from this format. For more information please contact feedback@aihw.gov.au 
Appendix 2: GP characteristics questionnaire,
2009–10
This information has been excluded from this format. For more information please contact feedback@aihw.gov.au 

Appendix 3: Dissemination of results from the BEACH program
Available at: <www.aihw.gov.au/publications/index.cfm/subject/19>. Click on the link to the report and see ‘Additional material’.
A full list of BEACH publications is also available at the Family Medicine Research Centre‘s website: < www.fmrc.org.au/publications/>.
Appendix 4: Code groups from ICPC-2 and
ICPC-2 PLUS
Available at: <www.aihw.gov.au/publications/index.cfm/subject/19>. Click on the link to the report and see ‘Additional material’.
Appendix 5: Chronic code groups from ICPC-2 and ICPC-2 PLUS
Available at: <www.aihw.gov.au/publications/index.cfm/subject/19>. Click on the link to the report and see ‘Additional material’.

List of tables
Table 2.1: 	Number of general practice professional services claimed from Medicare Australia each financial year, 2000–01 to 2009–10 (‘000)	34
Table 3.1: 	Recruitment and participation rates	36
Table 3.2: 	Comparison of BEACH participants and all active recognised GPs in Australia (the sample frame)	38
Table 3.3: 	Comparison of all active recognised GPs in Australia (the sample frame), GPs in the sample from Medicare claims data (drawn by DoHA), and BEACH participants 2009–10	39
Table 3.4: 	Activity level of participating and non-participating GPs	40
Table 3.5: 	Age–sex distribution of patients at BEACH and MBS GP consultation service items	41
Table 3.6: 	The BEACH data set, 2009–10	42
Table 4.1: 	Characteristics of participating GPs and their practices	44
Table 4.2: 	Computer applications available/used at major practice address	47
Table 5.1: 	Summary of morbidity and management	50
Table 5.2: 	Overview of MBS items recorded	51
Table 5.3 	Breakdown of MBS/DVA items of service according to provider (counting one item number per encounter)	51
Table 5.4: 	Type of encounter at which a source of payment was recorded for the encounter 
(counting one item number per encounter)	52
Table 5.5: 	Summary of GP only MBS/DVA items recorded (counting one item number per 
encounter)	53
Table 5.6: 	Medicare item number distribution across item number groups	54
Table 6.1: 	Characteristics of the patients at encounters	58
Table 6.2: 	Number of patient reasons for encounter	59
Table 6.3: 	Distribution of patient reasons for encounter, by ICPC-2 chapter and most frequent individual reasons for encounter within chapter	60
Table 6.4: 	Distribution of RFEs by ICPC-2 component	62
Table 6.5: 	Most frequent patient reasons for encounter	63
Table 7.1: 	Number of problems managed at an encounter	66
Table 7.2: 	Distribution of problems managed, by ICPC-2 chapter and most frequent individual problems within chapter	67
Table 7.3: 	Distribution of problems managed, by ICPC-2 component	70
Table 7.4: 	Most frequently managed problems	71
Table 7.5: 	Most frequently managed new problems	72
Table 7.6: 	Most frequently managed chronic problems	74
Table 7.7: 	Work-related problems, by type and most frequently managed individual problems	76
Table 8.1: 	Summary of management	80
Table 8.2: 	Encounters and problems for which management was recorded	81
Table 8.3: 	Most common management combinations	82
Table 9.1: 	Distribution of prescribed medications, by ATC levels 1, 3 and 5	87
Table 9.2: 	Most frequently prescribed medications	91
Table 9.3: 	Medications most frequently supplied by GPs	93
Table 9.4: 	Most frequently advised over-the-counter medications	95
Table 10.1: 	Summary of other treatments	103
Table 10.2: 	Relationship between other treatments and pharmacological treatments	104
Table 10.3: 	Most frequent clinical treatments	105
Table 10.4: 	The 10 most common problems managed with a clinical treatment	106
Table 10.5: 	Most frequent procedural treatments	107
Table 10.6: 	The 10 most common problems managed with a procedural treatment	108
Table 11.1: 	Summary of referrals and admissions	110
Table 11.2: 	Most frequent referrals, by type	111
Table 11.3: 	The 10 problems most frequently referred to a medical specialist	112
Table 11.4: 	The 10 problems most frequently referred to allied health services	113
Table 11.5: 	The 10 problems most frequently referred to hospital	113
Table 11.6: 	The 10 problems most frequently referred to an emergency department	114
Table 12.1: 	Number of encounters and problems for which pathology or imaging was ordered	115
Table 12.2: 	Distribution of pathology orders across MBS pathology groups and most frequent individual test orders within group	116
Table 12.3: 	The 10 problems for which pathology was most frequently ordered	118
Table 12.4: 	The most frequent imaging tests ordered, by MBS group	119
Table 12.5: 	The 10 problems for which an imaging test was most frequently ordered	120
Table 12.6: 	Other investigations ordered by GPs or performed in the practice	121
Table 13.1: 	Summary of practice nurse involvement at encounter	126
Table 13.2: 	Distribution of practice nurse item numbers recorded at encounter	127
Table 13.3: 	Summary of treatments given by practice nurse	128
Table 13.4: 	Most frequent activities done by a practice nurse	129
Table 13.5: 	The most common problems managed with involvement of practice nurses	130
Table 14.1: 	Patient body mass index (aged 18 years and over)	135
Table 14.2: 	Patient smoking status (aged 18 years and over)	137
Table 14.3: 	Patient alcohol consumption (aged 18 years and over)	140
Table 14.4: 	Risk factor profile of patients (aged 18 years and over)	142
Table 14.5: 	Number of risk factors, by patient sex	142
Table 15.1: 	SAND abstracts for 2009–10 and sample size for each	144

List of figures
Figure 2.1: 	The BEACH relational database	12
Figure 2.2: 	The structure of the International Classification of Primary Care—Version 2 (ICPC-2)	29
Figure 3.1: 	Age distribution of all patients at BEACH and MBS GP consultation services 2009–10	42
Figure 3.2: 	Age distribution of male patients at BEACH and MBS GP consultation services 2009–10	42
Figure 3.3: 	Age distribution of female patients at BEACH and MBS GP consultation services 2009–10	42
Figure 6.1: 	Age–sex distribution of patients at encounter	56
Figure 7.1: 	Age–sex-specific rates of problems managed per 100 encounters with 95% CI	66
Figure 7.2: 	Management of back problems in general practice, 2009–10	65
Figure 9.1: 	Number of medications prescribed per problem	85
Figure 9.2: 	Number of repeats ordered per prescription	85
Figure 9.3: 	Age–sex-specific prescription rates per 100 problems managed	86
Figure 9.4: 	Systemic antibiotics prescribed or supplied in general practice, 2009–10	85
Figure 9.5: 	Systemic antibiotic medications prescribed in general practice, 1998–99 to 
2009–10, 95% CI	98
Figure 9.6: 	Distribution of problems for which systemic antibiotics were prescribed in 
2009–10	98
Figure 9.7: 	Proportion of problems that have a systemic antibiotic prescription recorded, 1998–99 to 2009–10, 95% CI	99
Figure 9.8: 	Management rates of systemic antibiotic related problems, 1998–99 to 2009–10, 95% CI	99
Figure 9.9: 	National estimated number of annual systemic antibiotic prescriptions by GPs, by problem managed (millions), 1998–99 to 2009–10, 95% CI	100
Figure 12.1: 	CT scans of LS spine ordered in general practice, 2009–10	116
Figure 14.1: 	Age–sex-specific rates of overweight/obesity in adults	136
Figure 14.2: 	Age–sex-specific rates of underweight in adults	136
Figure 14.3: 	Age-specific rates of obesity, overweight, normal weight and underweight in male children	136
Figure 14.4: 	Age-specific rates of obesity, overweight, normal weight and underweight in female children	136
Figure 14.5: 	Smoking status—male age-specific rates	137
Figure 14.6: 	Smoking status—female age-specific rates	137
Figure 14.7: 	Age–sex-specific rates of at-risk alcohol consumption	140