General practice activity in Australia 2007–08

Australian GP Statistics and Classification Centre

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.
The Australian General Practice Statistics and Classification Centre (previously Unit) is a collaborating unit of the Australian Institute of Health and Welfare and the University of Sydney, situated within the Family Medicine Research Centre at Westmead Hospital. It fulfils the obligation of the Australian Institute of Health and Welfare to collect statistics regarding general practitioners, their patients and their patients’ care.
Companion publication:
Britt H, Miller GC, Charles J, Henderson J, Bayram C, Harrison C, Valenti L, Fahridin S, Pan Y, O’Halloran J 2008. General practice activity in Australia 1998–99 to 2007–08: 10 year data tables. General practice series no. 23. Cat. no. GEP 23. Canberra: AIHW.
Recent related publications:
Britt H, Miller GC, Charles J, Bayram C, Pan Y, Henderson J, Valenti L, O’Halloran J, Harrison C, Fahridin S 2008. General practice activity in Australia 2006–07. General practice series no. 21. Cat. no. GEP 21. Canberra: AIHW.
Britt H, Miller GC, Henderson J, Bayram C 2007. Patient-based substudies from BEACH: abstracts and research tools 1999–2006. General practice series no. 20. Cat. no. GEP 20. Canberra: AIHW.
Britt H, Miller GC, Charles J, Pan Y, Valenti L, Henderson J, Bayram C, O’Halloran J, Knox S 2007. General practice activity in Australia 2005–06. General practice series no. 19. Cat. no. GEP 19. Canberra: AIHW.
Britt H, Miller GC, Knox S, Charles J, Pan Y, Henderson J, Bayram C, Valenti L, Ng A, O’Halloran J 2005. General practice activity in Australia 2004–05. General practice series no. 18. Cat. no. GEP 18. Canberra: AIHW.
Knox S, Britt H, Pan Y, Miller GC, Bayram C, Valenti L, Charles J, Henderson J, Ng A, 
O’Halloran J 2005. Locality matters: the influence of geography on general practice activity in Australia 1998–2004. General practice series no. 17. Cat. no. GEP 17. Canberra: AIHW.
Britt H, Miller GC, Knox S, Charles J, Valenti L, Pan Y, Henderson J, Bayram C, O’Halloran J, Ng A 2004. General practice activity in Australia 2003–04. General practice series no. 16. Cat. no. GEP 16. Canberra: AIHW.
Australian General Practice Statistics and Classification Centre 2007. SAND abstracts from the BEACH program. Sydney: AIHW & University of Sydney. Viewed 25 October 2007, <www.fmrc.org.au/publications/SAND_abstracts.htm>.
Britt H, Miller GC, Knox S, Charles J, Valenti L, Bayram C, O’Halloran J, Henderson J, Pan Y, Harrison C 2004. General practice activity in the states and territories of Australia 1998–2003. General practice series no. 15. Cat. no. GEP 15. Canberra: AIHW.
Britt H, Miller GC, Knox S, Charles J, Valenti L, Henderson J, Pan Y, Bayram C, Harrison C 2003. General practice activity in Australia 2002–03. General practice series no. 14. Cat. no. GEP 14. Canberra: AIHW.
Britt H, Knox S, Miller GC 2003. Changes in pathology ordering by general practitioners in Australia, 1998–2001. General practice series no. 13. Cat. no. GEP 13. Canberra: AIHW.
O’Halloran J, Britt H, Valenti L, Harrison C, Pan Y, Knox S 2003. Older patients attending general practice 2000–02. General practice series no. 12. Cat. no. GEP 12. Canberra: AIHW.
GENERAL PRACTICE SERIES
Number 22

BEACH
Bettering the Evaluation
And Care of Health

General practice activity in Australia 2007–08

Helena Britt, Graeme C Miller, Janice Charles, Joan Henderson, Clare Bayram, 
Christopher Harrison, Lisa Valenti, Salma Fahridin, Ying Pan, Julie O’Halloran

October 2008

A joint report by the University of Sydney and the Australian Institute of Health and Welfare
Cat. no. GEP 22

© Australian Institute of Health and Welfare and the University of Sydney 2008

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, Media and Communications 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 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 74024 828 0

Suggested citation 
Britt H, Miller GC, Charles J, Henderson J, Bayram C, Harrison C, Valenti L, Fahridin S, Pan Y, O’Halloran J 2008. General practice activity in Australia 2007–08. General practice series no. 22. 
Cat. no. GEP 22. Canberra: Australian Institute of Health and Welfare.
Keywords
Australia, Delivery of Health Care/statistics & numerical data, Family Practice/statistics & numerical data, Health Care Surveys/methods.
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
School of Public Health
University of Sydney
Acacia House
Westmead Hospital
Westmead NSW 2145
Phone: +61 2 9845 8151
Fax: +61 2 9845 8155
Email: gpstats@fmrc.org.au

Published by the Australian Institute of Health and Welfare

Foreword
This 2007–08 BEACH (Bettering the Evaluation And Care of Health) report presents empirical data of Australian general practice: the quantification of patients consulting their family doctor, the health problems encountered, and the diagnostic and therapeutic interventions that were initiated. The rich and thick descriptive information presented here is vital for health care policy and for primary care development. For those acquainted with the BEACH project, the presentation of these data could be seen as ‘business as usual’: this is the 10th annual report since the project started exploring general practice performance in 1998. In that 10-year period, the research team of the Australian General Practice Statistics and Classification Centre, in their happy collaboration of the University of Sydney and the Australian Institute of Health and Welfare, have developed impressive expertise to collect and corroborate all this information. For that reason, I would like to stress—in congratulating the research team with this splendid jubilee publication—how unique and how important their work is. It is important for Australian health care and for Australian primary care, but the BEACH expertise has an impact well beyond the Australian borders. 
General practitioners work in direct contact with local communities and respond to the specific health needs of the patients in their community. The 2007–08 BEACH data illustrate this, for example in reporting that more than a third of the workload in general practice is currently directed at patients with chronic diseases—in particular hypertension, depressive disorder, diabetes, lipid disorders and osteoarthritis. This exemplifies also the generic nature of general practice, dealing with all patient groups for all health problems in all stages. But to appreciate the role of general practice and primary care, it is important to understand why patients came to contact their family doctor in the first place and how the episode of care started. The report stresses the importance of risk factors and lifestyles in the daily work of general practitioners and practice nurses. 
Essential in the BEACH project is the processing of health information over the recording of a mere diagnosis and procedure, and this is the international impact of this report. General practice has always been a feature of health care in local communities, but for a long time, the domain of the most common health problems encountered in society have remained the least studied. It required a thorough methodology, based on a comprehensive classification—the International Classification of Primary Care (ICPC), developed by the World Organization of Family Doctors (Wonca)—to open general practice for research and gain insight in its function. The leadership of the BEACH team have been instrumental in developing ICPC, and, on the other hand, the BEACH data demonstrate the richness of ICPC as an integral component of health informatics. That is where the 10 years of data collection in the context of Australian general practice has an impact well beyond its contribution to Australian health care.
Nothing beats empirical data in raising the appetite for more data, and the current classifications like ICPC are only the beginning of an integrated, comprehensive health informatics system. Improving the compatibility of ICPC as the primary care system with other major systems (International Classification of Diseases, ICD, Systematized Nomenclature of Medicine – Clinical Terms, SNOMED CT) is at this moment on the agenda. Wonca, through its International Classification Committee, is involved in this, and the BEACH experience will continue to make an invaluable contribution here.

In my judgement, the BEACH report presents the most convincing case of why such comprehensive general practice data are essential for health care policy, though possibly a bit hidden in all the information. For that reason, I would like to spell it out in concluding this foreword: of all health problems presented in general practice, less than 1 in 10 is referred. In other words, general practitioners, practice nurses and other members of the primary care team take the full care of more than 90% of all health problems that present to a general practitioner. This is the primary care lead in health care, and further improving population health requires a focus here. This report presents an invaluable basis for this.

Professor Chris van Weel MD, PhD, FRCGP
President
World Organization of Family Doctors (Wonca)

Acknowledgments
The Australian General Practice Statistics and Classification Centre (formerly the General Practice Statistics and Classification Unit) wishes to thank the 953 general practitioners who participated in BEACH between April 2007 and March 2008. 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 2007–08. 
•	Australian Government Department of Health and Ageing
•	Australian Institute of Health and Welfare
•	National Prescribing Service Ltd 
•	AstraZeneca Pty Ltd (Australia) 
•	Janssen-Cilag Pty Ltd 
•	Merck, Sharp and Dohme (Australia) Pty Ltd 
•	Pfizer Australia 
•	Abbott Australasia 
•	Sanofi-Aventis 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 Divisions of General Practice, the Australian College of Rural and Remote Medicine, and the Consumers Health Forum, and 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 Gervaise Woods, and for the valuable contribution of the general practitioner recruitment staff (Errol Henderson and Jan Fitzgerald) and data entry staff. We recognise the contribution of past members of the BEACH team, particularly Stephanie Knox. 
We appreciate the cooperation of the Primary Care Division of the Australian Government Department of Health and Ageing in regularly supplying general practitioner random samples and national Medicare data. 
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.
Contents
Abbreviations	xi
Executive summary	xiii
1	Overview	1
1.1	Background	1
1.2	The BEACH program	2
1.3	Issues when using BEACH data with other national data	4
1.4	Access to BEACH data	7
2	Methods	9
2.1	Sampling methods	9
2.2	Recruitment methods	9
2.3	Data elements	10
2.4	Supplementary Analysis of Nominated Data	11
2.5	The BEACH relational database	11
2.6	Statistical methods	12
2.7	Classification of data	12
2.8	Quality assurance	15
2.9	Validity and reliability	16
2.10	Other BEACH applications	17
3	The sample	19
3.1	Response rate	19
3.2	Representativeness of the GP sample	20
3.3	Weighting the data	22
3.4	Representativeness of the final encounter sample	22
3.5	The weighted data set	23
4	The participating GPs	24
4.1	Characteristics of the GP participants	24
4.2	Computer use at GP practices	27
5	The encounters	29
5.1	Content of the encounters	29
5.2	Encounter type	30
5.3	Consultation length	34
6	The patients	35
6.1	Age–sex distribution of patients at encounter	35
6.2	Other patient characteristics	35
6.3	Patient reasons for encounter	36
7	Problems managed	42
7.1	Number of problems managed at encounter	42
7.2	Problems managed by ICPC-2 chapter	44
7.3	Problems managed by ICPC-2 component	46
7.4	Most frequently managed problems	47
7.5	Most common new problems	49
7.6	Most frequently managed chronic problems	50
7.7	Work-related problems managed	52
8	Overview of management	54
9	Medications	57
9.1	Source of medications	57
9.2	Prescribed medications	57
9.3	Medications supplied by GPs	64
9.4	Medications advised for over-the-counter purchase	65
10	Other treatments	67
10.1	Number of other treatments	67
10.2	Clinical treatments	68
10.3	Procedural treatments	70
11	Referrals and admissions	73
11.1	Number of referrals and admissions	73
11.2	Most frequent referrals	73
11.3	Problems most often referred	74
12	Investigations	77
12.1	Number of investigations	77
12.2	Pathology ordering	78
12.3	Imaging ordering	80
12.4	Other investigations	83
13	Practice nurse activity	84
13.1	Practice nurse Medicare claims versus practice nurse activity	85
13.2	Discussion	88
14	Patient risk factors	90
14.1	Body mass index	90
14.2	Smoking (patients aged 18 years and over)	93
14.3	Alcohol consumption (patients aged 18 years and over)	96
14.4	Risk factor profile of adult patients	98
15	SAND abstracts and research tools	101
References	1
Glossary	7
Appendices	10
Appendix 1: Example of a 2007–08 recording form	10
Appendix 2: GP characteristics questionnaire, 2007–08	1
Appendix 3: Dissemination of results from the BEACH program	2
Appendix 4: Code groups from ICPC-2 and ICPC-2 PLUS	2
Appendix 5: Chronic code groups from ICPC-2 and ICPC-2 PLUS	2
List of tables	3
List of figures	5

Abbreviations
ABS	Australian Bureau of Statistics	
AGPSCC	Australian General Practice Statistics and Classification Centre, University of Sydney, a collaborating unit of the Australian Institute of Health and Welfare	
AHS	allied health service	
AIHW	Australian Institute of Health and Welfare	
ASGC	Australian Standard Geographical Classification	
ATC	Anatomical Therapeutic Chemical (classification)	
BEACH	Bettering the Evaluation And Care of Health	
BMI	body mass index	
CAPS	Coding Atlas for Pharmaceutical Substances	
CI	confidence interval (in this report 95% CI is used)	
CRF/CKD	chronic renal failure/chronic kidney disease	
CT	computerised tomography	
DoHA	Australian Government Department of Health and Ageing	
DVA	Australian Government Department of Veterans’ Affairs	
encs	encounters	
ESR	erythrocyte sedimentation rate	
EUC	electrolytes, urea and creatinine	
FMRC	Family Medicine Research Centre	
GP	general practitioner	
GPSCU	General Practice Statistics and Classification Unit (now the Australian General Practice Statistics and Classification Centre, AGPSCC)	
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	
LCL	lower confidence limit	
MBS	Medicare Benefits Schedule	
M,C&S	microscopy, culture and sensitivity	
NDSHS	National Drug Strategy Household Survey	
NESB	non-English-speaking background	
NSAID	non-steroidal anti-inflammatory drug	
OTC	over-the-counter (that is, medications advised for over-the-counter purchase)	
PBS	Pharmaceutical Benefits Scheme	
QA	quality assurance (in this case the Quality Assurance Program of the Royal Australian College of General Practitioners)	
RACGP	Royal Australian College of General Practitioners	
RFE(s)	reason(s) for encounter (see Glossary)	
RRMA	Rural, Remote and Metropolitan Areas	
SAND	Supplementary Analysis of Nominated Data	
SAS	Statistical Analysis System	
TC	total cholesterol	
UCL	upper confidence limit	
WHO	World Health Organization	
Wonca	World Organization of Family Doctors	

Executive summary
This report summarises results from the 10th year of the BEACH (Bettering the Evaluation And Care of Health) program, from April 2007 to March 2008, from a sample of 95,300 
patient encounters with 953 general practitioners (GPs). 
BEACH is a continuous cross-sectional national study of general practice activity in Australia that began in April 1998. A summary report containing 10 years of BEACH data General practice activity in Australia 1998–99 to 2007–08: 10 year data tables is available from <www.aihw.gov.au/publications/index.cfm/subject/19> (AIHW catalogue number GEP 23).
Method (chapters 2 and 3)
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 Australian Government Department of Health and Ageing. GPs are approached by letter and followed up by telephone recruitment. 
Participating GPs complete details about 100 consecutive patient encounters on structured paper encounter forms, and provide information about themselves and their practice. Results are reported in terms of GP and patient characteristics, patient reasons for encounter, problems managed and management techniques used. 
A section at the bottom of each encounter form investigates aspects of patient health or health care delivery not covered by the consultation-based data. All GPs are asked to provide start and finish times, and complete patient risk factor data at 40 of their 100 encounters, including self-reported height and weight (for calculation of body mass index), smoking status and alcohol consumption.
The general practitioners (Chapter 4)
Of the 953 participating GPs: 
•	63% were male, and 34% were aged 55 years or older 
•	almost 60% were in practices of fewer than five full-time equivalent GPs 
•	74% had graduated in Australia
•	72% practised in major cities 
•	50% were Fellows of the Royal Australian College of General Practitioners 
•	86% worked in an accredited practice 
•	71% worked in a practice that employed practice nurse(s)
•	74% worked 6–10 clinical sessions, and only 11% worked more than 10 sessions per week 
•	45% provided their own or cooperative after-hours care
•	97% worked in a computerised practice, with computers used mainly for prescribing and billing purposes. 

The encounters (Chapter 5)
Data were available for 95,300 encounters (953 GPs x 100 encounters), which, after weighting, amounted to 95,898 encounters.
For 98.6% of encounters, the patient was seen by the GP (direct encounters). About 97% of these were claimable through the Medicare Benefits Schedule (MBS) or the Australian Government Department of Veterans’ Affairs (DVA), the majority (82.1%) as standard surgery consultations and 9.9% as long surgery consultations. Short and prolonged surgery consultations, and home and residential aged care visits were relatively rare. 
In a subsample of 29,956 MBS/DVA-claimable encounters containing start and finish times, mean consultation length was 15.1 minutes (95% CI: 14.8–15.3), the median length was 13.0 minutes.
The patients (Chapter 6)
The patient was female at the majority (57.1%) of encounters. Patients aged less than 25 years accounted for 21.2% of encounters; 25–44 years for 23.4%; 45–64 years for 28.1% and 65 years and over for 27.3%. 
The patient was new to the practice at 8.6% of encounters. Almost 42% of encounters were with patients who held a Commonwealth concession card, and at 0.9% of encounters the patient identified as an Aboriginal person or Torres Strait Islander.
There were 146,696 reasons for encounter (RFEs) recorded (153 RFEs per 100 encounters). RFEs of a general nature were most common (40.1 per 100 encounters), followed by respiratory (20.6 per 100), and musculoskeletal (15.4) and skin (15.4) problems. The 30 most common RFEs represented more than half the total, and 18 of these were symptoms or complaints (for example, cough, back complaints and rash). However, of the top five RFEs four were requests for check-ups, prescriptions, test results or immunisation. 
Problems managed (Chapter 7)
There were 145,078 problems managed (151 per 100 encounters), the number managed at encounter increasing steadily with patient age. More problems were managed at encounters with female (154.4 per 100 encounters), than with male patients (147.4). 
Problems of the respiratory system (19.4 per 100 encounters) were most often managed, followed by those of a general and unspecified nature, cardiovascular problems and musculoskeletal problems. 
The 30 most frequently managed problems accounted for more than half the total. The most common were: hypertension (9.9 per 100 encounters), check-ups (6.3), upper respiratory tract infection (6.2), immunisation (5.2) and depression (4.0). 
Problems new to the patient (38.1% of the total), were led by upper respiratory tract infections (4.8 per 100 encounters), immunisation/vaccination (2.8) and acute bronchitis (1.7).
Chronic problems accounted for more than a third (34.6%) of all problems managed, the most common being non-gestational hypertension (18.8%), depressive disorder (7.5%), non-gestational diabetes (7.3%), lipid disorders (7.0%) and osteoarthritis (4.9%).
Overview of management (Chapter 8)
At the 95,898 encounters, GPs undertook 211,029 management activities including medications, other treatments, referrals and admissions, and tests and investigations. 

Medications (Chapter 9)
Of the 98,439 medications recorded (103 per 100 encounters and 68 per 100 problems managed), 80.3% were prescribed, 9.9% GP supplied, and 9.8% advised for over-the-counter (OTC) purchase. Extrapolation to all GP Medicare-claimed encounters (2007–08), suggests 88 million prescriptions, 10.8 million GP-supplied medications and 10.8 million advised OTC purchases. 
Prescribed medications: There were 79,051 prescriptions recorded (82 per 100 encounters and 55 per 100 problems managed). The 30 most often prescribed accounted for 44.3% of the total. Of the top five, two were antibiotics, two were plain or combination paracetamol, and the fifth was atorvastatin. Where GPs specified ‘number of repeats’ (for 60,733 prescriptions), 34.5% had no repeats, and 33.8% had five repeats. 
Medications supplied by GPs: Vaccines accounted for about 70% of the 9,702 GP supplied medications, influenza virus vaccine and the Papillomavirus vaccine being most common. 
Medications advised for over-the-counter purchase: Of the 9,686 medications recommended for OTC purchase, analgesics made up almost one-third, led by paracetamol and ibuprofen.
Other treatments (Chapter 10)
A total of 49,130 other treatments were recorded in the management of patient morbidity, at a rate of 51.2 per 100 encounters.
Clinical treatments: There were 33,121 clinical treatments recorded (34.5 per 100 encounters), in the management of one in five (20.6%) problems. The most common were general advice and education (7.2 per 100 encounters), counselling about the problem under management (4.3), nutrition and weight counselling (4.2), advice about treatment (3.5) and psychological counselling (3.2). 
Procedural treatments: There were 16,009 procedures recorded (16.7 per 100 encounters) in the management of 10.3% of all problems. The most frequent were excisions/biopsies, (3.4 per 100 encounters), local injections (2.3), dressings (2.2) and physical medicine/ rehabilitation (1.3 per 100). 
Referrals and admissions (Chapter 11)
There were 12,008 referrals to other health services, (12.5 per 100 encounters) for 8.3% of all problems managed, most frequently to specialists (8.0 referrals per 100 encounters) and allied health services (3.4 per 100). Very few referrals were made to hospitals, emergency departments or other medical services.
The most common specialist referrals were to surgeons (11%), orthopaedic surgeons (9%), dermatologists (8%) and ophthalmologists (8%). 
About 34% of referrals to allied health services were to physiotherapists, 19% to psychologists, 9% to podiatrists or chiropodists and 7% to dietitians or nutritionists. 
Tests and investigations (Chapter 12)
Pathology was ordered at 17.4% of encounters (for 13.1% of problems managed) and imaging was ordered at 8.3% of encounters (for 5.7% of problems managed).
Pathology: The GPs recorded 41,375 orders for pathology tests/batteries of tests, at a rate of 43.2 per 100 encounters. Chemistry tests accounted for more than half of all pathology test orders, the most common being lipids (4.3 orders per 100 encounters), liver function (3.1), EUC (3.0), and glucose/glucose tolerance (2.5). The problems contributing most to pathology test orders were diabetes, hypertension, general check-ups and lipid disorders. 
Imaging: There were 9,143 imaging test orders recorded, at a rate of 9.5 per 100 encounters. Diagnostic radiology accounted for half of all imaging test orders while ultrasound accounted for a further 35.2%. The problems contributing most to total imaging ordered were back complaint, osteoarthritis and fracture.
Practice nurse activity (Chapter 13) 
Practice nurses were involved in 5,712 GP–patient encounters (6.0% of all encounters), assisting in the management of 4.1% of problems. Only one-third (34.7%) of these encounters had a Medicare practice nurse item number recorded.
Practice nurses provided 6,283 other treatments, representing 11.9% of all other treatments recorded. The majority (84.7%) of these were procedural (28.9% of all procedures recorded), the most frequent being injections (mainly for immunisation/vaccinations) (40.4%) and dressing/pressure/compression (22.3%). 
Clinical treatments accounted for only 7.0% of practice nurse activity, and nurses provided less than 2% of all clinical treatments. Most common were administrative work (25.7% of nurse clinical activity), general advice and education (18.6%), treatment advice (7.6%) and counselling about the problem under management (7.2%).
The most common other investigations performed by the nurse were electrical tracings (such as electrocardiogram) (56.8% of other investigations) and physical function tests (38.5%).
Patient risk factor substudies (Chapter 14)
Overweight and obesity in adults: Sample size was 31,062 patients (aged 18 years and over) with 952 GPs. More than half (59.3%) the sample were overweight (35.4%); or obese (23.9%). Prevalence of overweight/obesity was higher among males (66.1%) than females (54.8), and most prevalent in those aged 45–74 years. Extrapolation to all patients who attended a GP at least once suggests 58.8% of the patient population are overweight (35.3%) or obese (23.5%). 
Overweight and obesity in children: Sample size was 3,046 patients aged 2–17 years, with 801 GPs. Three in ten children (28.3%) were overweight (17.1%) or obese (11.2%). Prevalence did not differ among male and female children sampled. 
Smoking status: Sample size was 31,652 adults (aged 18 years and over) with 952 GPs, 16.5% of whom were current daily smokers, 2.9% occasional smokers, and 27.9% previous smokers. Smoking was more prevalent among men (19.8%) than women (14.4%), and among younger adults. Extrapolation of these estimates to all patients who attended a GP at least once suggests 19.3% are daily smokers, 3.5% occasional, 25.7% previous, and 51.5% have never smoked.
Alcohol consumption: Sample size was 30,796 adults (aged 18 years and over) with 951 GPs. At-risk alcohol consumption levels were reported by 26.2%, being more prevalent among males (31.7%) than females (22.6%), and most prevalent in the 18–24 year age group. Extrapolation of these estimates to all patients who attended a GP at least once suggests that 29.3% of the patient population are at-risk drinkers.

1	Overview
This publication is the 10th annual report and the 22nd 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 2007 to March 2008 inclusive, using details of 95,300 encounters between general practitioners (GPs) and patients (about a 0.1% sample of all general practice encounters) from a random sample of 953 practising GPs across the country. In parallel with the release of this report, a summary of results from all 10 years of the BEACH program is published on the web in a report called General practice activity in Australia 1998–99 to 2007–08: 10 year data tables, at <www.aihw.gov.au/publications/index.cfm/subject/19> (AIHW catalogue number GEP 23). A third book using the 10 years of BEACH data collected to date will investigate in more detail changes in general practice activity in specific areas of interest, including the National Health Priority Areas. It will be published in mid-2009.
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 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.
The BEACH program is unique. It 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 million encounters from 9,874 participants representing about 7,400 individual GPs, or almost half of the GP sample frame from which the BEACH samples are drawn.
GPs provided by far the majority of the 111 million general practice services paid by Medicare in Australia in 2007–08, at an average rate of about five visits per head of population per year.1 BEACH gives us some understanding of the content of these encounters and of the services and treatments that GPs provide. 
1.1	Background
GPs are the first port of call in the Australian health care system. In 2007–08, they claimed about 107 million items of service (not including practice nurse item number claims) through Medicare and provided an estimated additional 5.4 million services that were paid for by other funders (such as workers compensation, state Government) or not charged for at all.2
About 88% of the Australian population visited a GP at least once in 2005–06.3 Previous research using BEACH data suggested that in 2001–02, people in Australia spent on average 83 minutes with a GP per head of population. This compared with about 56 minutes per head in New Zealand and about 30 minutes per head in the United States during the same period.4 
In December 2007, the population of Australia was estimated to be 21.2 million people.5 In 2005–06, national expenditure on health was estimated to be $86.9 billion, 9% of gross domestic product, with governments funding two-thirds the total health expenditure at an average of $4,226 per person.6,7
•	In 2005, in Australia, 49,393 medical practitioners were working as clinicians, of whom 44% were primary care providers. Of these, 80% were recognised general practitioners and 20% were other primary care medical practitioners.8
	There were 98 full-time equivalent practising primary care practitioners per 100,000 people in Australia in 2005.8 
	By far the majority of visits to GPs are funded through the Commonwealth Medicare Benefits Schedule (MBS). In the 2007–08 financial year, there were about 107 million general practice services (excluding practice nurse items) paid through Medicare at an average of about five GP services per person.1 This equates to about 293,000 services per day, or more than 2 million per week. 
	In 2007, the primary cost to Medicare for GP items was over $4 billion.1 Up-to-date estimates of secondary costs generated by GPs could not be located.
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 (about a 0.1% sample of all general practice encounters) 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 the Primary and Ambulatory Care Division of the Australian Government Department of Health and Ageing (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 each 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
	to establish an ongoing database of GP–patient encounter information
	to 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 11th year. The database for the first 10 years includes data for approximately 990,000 GP–patient encounters from almost 9,900 participating GPs. Each year the AGPSCC publishes an annual report of BEACH results through the AIHW. This publication reports results from the previous BEACH data year (that is, April 2007 to March 2008) on a national basis to provide an overview of general practice activity.
Other reports use the database for secondary analyses of a selected topic or for a specific research question. Recent examples are a comparative study of general practice activity in each of the states and territories of Australia9, a comparative study of activity in rural and metropolitan areas of Australia10, and a report of more than 100 BEACH substudies (including abstracts of results and the research tools).11 All annual reports and other BEACH reports can be downloaded from <www.fmrc.org.au/publications/> (go to Books—General Practice Series) or from <www.aihw.gov.au/publications/index.cfm/subject/19>.
The strengths of the BEACH program
BEACH tells us about what happens at clinical encounters between patients and GPs. It tells us about the relationships between the characteristics of the GP workforce, the patients they manage, the problems that are presented to and managed by GPs, and the treatment provided for each problem. It also provides a reliable continuous measure of changes in general practice since 1998.
We are often asked to outline the strengths of the BEACH program when compared with general practice activity data from other sources. These strengths are summarised below.
	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.12
	Our access to a regular random sample of recognised GPs in active practice, through the Australian Government Department of Health and Ageing (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). In contrast, systems such as electronic health records rely on the GP to complete all 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 manner.
	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 the user with 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 a well-structured classification system 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 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 consolidate information from multiple national data sources. Integration of data from multiple sources 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 safety net threshold—when the threshold increases, those that then fall under the threshold will no longer be counted in the PBS for non-Commonwealth concession cardholders13
	have no record of the problem being managed. 
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 prescriptions not filled by the patient (and this also applies to the PBS).
These differences have a major impact on the numbers of prescriptions counted but 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.13
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.
	The MBS consultation data provided by DoHA do not usually include data about patients and encounters funded through DVA. 
	The MBS data include those GP services that have been billed to Medicare. BEACH includes all consultations, irrespective of who pays for them (if anyone).
	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 2007–08, 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 that almost half the enhanced primary care items claimed through the MBS came from about 6% of active GPs.14 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.
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.
	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 the DoHA pathology payment system. This means that the tests recorded in the MBS include only those charged for, not all those that were done. 
	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 increasing2, 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.
Those interested in GP pathology ordering will find more detailed information from the BEACH program in Pathology ordering by general practitioners in Australia 1998.15 A study of changes in pathology ordering patterns between 1998–99 and 2000–0116 is also available through the Family Medicine Research Centre (FMRC) website <www.fmrc.org.au/publications/> (go to Books—General Practice Series). We are currently investigating recent trends in pathology ordering by GPs under a Quality Use of Pathology project funded by the Diagnostics and Technology Branch of DoHA.
Imaging data from the MBS
Some of the issues discussed regarding pathology data also apply to imaging data. Although coning 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 Imaging orders by general practitioners in Australia 1999–0017, at the Family Medicine Research Centre website.
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.18 
	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.19
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 population20,21, with around 88% of Australians visiting a GP at least once in any year.3 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 SAND (Supplementary Analysis of Nominated Data) substudy (see Section 2.4) of disease prevalence among patients seen in general practice. Those interested in disease prevalence should refer to the recently published papers: Estimating prevalence of common chronic morbidities in Australia3, and Prevalence and patterns of multimorbidity in Australia.22 
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 range of topics in journals and professional magazines. Appendix 3 lists all published material from BEACH, available from <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 2.4. 
Abstracts for all SAND substudies from April 1999 to July 2006 inclusive were published in Patient-based substudies from BEACH: abstracts and research tools 1999–2006.11 Abstracts of results and the research tools used in SAND substudies conducted between August 2006 and March 2007 were published in General practice activity in Australia 2006–072 and those conducted from April 2007 to January 2008 are included in Chapter 15 of this report.
Abstracts of results for all SAND substudies are also available on the FMRC’s website <www.fmrc.org.au/publications/SAND_abstracts.htm>.
Participating organisations
Organisations providing funding for the BEACH program receive summary reports of the encounter data quarterly, and standard reports 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 are further ‘de-identified’. Patient data are not identifiable, 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 only provided in the form of grouped output (for example, GPs aged less than 35 years) to any external organisation.
External purchasers of standard reports
Non-contributing organisations may purchase standard reports or other ad hoc analyses. Charges are available on request. The AGPSCC should be contacted for further information. 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 approximately 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 one week, 50 weeks a year
	each GP can be selected only once per quality assurance (QA) triennium (that is once every three years)
	the encounter information is recorded by the GPs on structured paper encounter forms (Appendix 1)
	each GP participant also completes 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.
On a quarterly basis the Primary and Ambulatory Care Division of DoHA updates the sample frame from the Medicare records, 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.
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 forward 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 about 10 days before the planned start date.
	Each GP receives a telephone reminder in the first days of 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 up to 3 months after the set recording time.
	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: encounter data, GP characteristics 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 data collected include the following:
	Encounter data: date of consultation, type of consultation (direct/indirect), 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 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).
	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 (if applicable).
	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 (if 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 if other treatment was provided by practice nurse (tick box)
–	new referrals to medical specialists, allied health professionals and hospital
–	investigations including pathology tests, imaging and other investigations ordered at the encounter.
	GP characteristics: age and sex, years in general practice, number of GP sessions worked per week, number of full-time equivalent GPs working in the practice, postcode of major practice address, country of graduation, postgraduate general practice training and Fellow of the RACGP status, after-hours care arrangements, use of computers in the practice, whether the practice is accredited, whether it is a teaching practice, work undertaken in other clinical settings and hours worked in direct patient care.
2.4	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.
	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 is 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 for all SAND substudies from April 1999 to July 2006 inclusive were published in Patient-based substudies from BEACH: abstracts and research tools 1999–2006.11 Abstracts of results and the research tools used in SAND substudies conducted between August 2006 and March 2007 were published in General practice activity in Australia 2006–072 and those conducted from April 2007 to January 2008 are included in Chapter 15 of this report.
Abstracts of results for all SAND substudies are also available on the FMRC’s website <www.fmrc.org.au/publications/SAND_abstracts.htm>.
2.5	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.
	all types of management are directly related to the problem being treated.

Figure 2.1: The BEACH relational database
Flow chart diagram.
Note: FTE—full time equivalent; NESB—non-English-speaking background; SAND—Supplementary Analysis of Nominated Data.
2.6	Statistical methods
The analysis of the 2007–08 BEACH data was conducted with Statistical Analysis System (SAS) version 9.1.323, 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, age, 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 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. We use procedures in SAS version 9.1.3. to calculate the intracluster correlation and adjust the confidence intervals accordingly.23 
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)24:
	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 Classifications25, and is the declared national standard in Australia for reporting of health data from general practice and patient self-reported health information.26
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. These are independent in each chapter and both can be used for patient RFEs or problems managed.
Components 2 to 6 cover the process of care, and are common throughout all chapters. The processes of care, including referrals, other (non-pharmacological) treatments and orders for pathology and imaging, are classified in these process components of ICPC-2. Component 2 (diagnostic, screening and prevention) is also often applied in describing the problem managed (for example, check-up, immunisation).
The ICPC-2 is an excellent epidemiological tool. The diagnostic and symptomatic 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. It has approximately 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 vocabulary of terms classified according to the ICPC, now called ICPC-2 PLUS.27 This is an interface terminology, developed by the FMRC from all the terms used by GPs in studies such as the Australian Morbidity and Treatment Survey 1990–9128, the Morbidity and Therapeutic Index 1992–1998 (a clinical audit tool that was available to GPs), and BEACH 1998–2008 that together have included close to 1.5 million encounter records. 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 studying health information management or medical science) 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)
Table and key diagram showing Version 2.
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—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 from <www.aihw.gov.au/publications/index.cfm/subject/19>.
Reporting morbidity with groups of ICPC-2 codes
When recording problems managed, the GP 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. A list of codes included in these groups are provided in Appendix 4.
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, for example, 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 are grouped within all the appropriate ICPC‑2 codes. This group is labelled ‘Osteoarthritis*’—the asterisk again indicating multiple codes, but in this case they are PLUS codes rather than ICPC-2 codes. A list of codes included in these groups are provided in Appendix 4.
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 list29 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 the 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 re-grouped all pathology and imaging ICPC-2 PLUS codes into MBS standard groups. This allows comparison of data between data sources. These 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)30 classification, which is the Australian standard for classifying medications at the generic level.
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 necessarily 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 2007–08 annual results for pharmaceutical data, the CAPS database is used in tables of the ‘most frequent medications’ (tables 9.2 to 9.4 inclusive).
	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. A 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 on a regular basis.
2.9	Validity and reliability
A discussion of the reliability and validity of the BEACH program has been published elsewhere.31 In this section we touch 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.32 Other studies have reported the degree to which GP-reported patient RFEs and problems managed accurately reflect those recalled by the patient33 and the reliability of secondary coding of RFEs34 and problems managed.28 The validity of ICPC as a tool with which to classify the data has also been investigated in earlier work.35
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. Bentsen36 and Barsky37 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.38 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.39,40 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.36 Two other factors that might affect GPs’ descriptions of patient RFEs have been identified: while individuals may select the same stimuli, some label each stimulus separately whereas others cluster them under one label; individuals differ in the number of stimuli they select (selective perception).41
The extent to which therapeutic decisions may influence the diagnostic label selected has also been discussed. Howie42 and Anderson39 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.39 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.43 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.44 Differences in the way male and female GPs label problems also appear to be independent of such influences.45
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.46 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’.39 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.47,48 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.49
2.10	Other BEACH applications
The BEACH method can be applied in various health settings. In the past the AGPSCC has used the method to conduct a variety of studies in collaboration with other organisations. Examples of past studies are described below.
In 2004, a study was conducted in collaboration with Monash University and the Victorian Metropolitan Alliance. The BEACH methods were used to measure the experience gained by GP registrars during each stage of their training. The results will help to better define the areas in which registrars should receive training, and identify areas in which they are not gaining experience.
Another registrar study was conducted in 2003 as a consultancy for North Coast GP Training Ltd and the Institute of General Practice Education. This study looked at the clinical activities of registrars compared with those of their supervisors, to assess their education program in terms of actual practice.
A study in the Victoria Community Health Centres was done in 2004 in collaboration with the Victorian Department of Human Services. The project aimed to provide information about the clinical role of Community Health Service GPs and the characteristics of the patients they see, and how these may differ from the ‘average’ GP in Australia. The department will use the results to help them plan future health services.
From 2002–04, the BEACH methods were used in the Alternative Pathway Program to assess the educational needs of each GP enrolled in the program. The Alternative Pathway Program was conducted by the National Consortium for Education in Primary Medical Care. The results for each GP were used in identifying specific educational needs and in planning an educational program for the individual practitioner.
In 2002–03, the AGPSCC conducted a longitudinal, matched, controlled trial of active computerised data collection compared with paper-based data collection in the western, north-western and south-western areas of Sydney. Software was developed that reflected the data elements collected in BEACH; the software did not interact with any clinical system being used by GPs. This study demonstrated that active GP computerised data collection in structured, stand-alone software does not provide a reliable and valid measure of GP activity and could not be adopted at this stage as an acceptable alternative to the paper-based data collection methods currently being used.50
As BEACH collects data nationally it is possible to analyse data at a level specific to local areas. For example, reports have been published comparing general practice in the different states and territories of Australia and investigating the differences between metropolitan and rural general practice. The research team has also developed Statistical Evaluation Areas (referred to as SEAs) that allow the provision of localised data for divisions of general practice.
3	The sample
This chapter describes the sample and sampling methods used in the BEACH program. The methods are only summarised in this chapter. For those wanting more detailed explanation, the BEACH methods are described in Chapter 2.
A summary of the total BEACH sample are reported for each year from 1998–99 to 2007–08 in the 10 year summary report General practice activity in Australia 1998–99 to 2007–08: 10 year data tables available from <www.aihw.gov.au/publications/index.cfm/subject/19> 
(AIHW catalogue number GEP 23).
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 Australia data by the Primary and Ambulatory Care Division of DoHA (see Chapter 2).
Contact was attempted with 3,884 GPs—12.6% could not be contacted. The majority of these had moved, retired or died, and were untraceable (Table 3.1). It is notable that of GPs approached who were aged less than 35 years, 26.1% were no longer at that practice and could not be traced. These would largely be registrars moving through practices during training. In contrast, 11.4% of GPs aged 35 years and over were not traceable (results not tabled).
The final participating sample consisted of 953 practitioners, representing 28.1% of those who were contacted and available, and 24.5% 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 = 3,884)	Per cent of contacts established (n = 3,394)	
Letter sent and phone contact attempted	3,884	100.0	—	
No contact 	490	12.6	—	
	No phone number	35	0.9	—	
	Moved/retired/deceased	228	5.9	—	
	Unavailable (overseas, maternity leave, etc)	36	0.9	—	
	No contact after five calls	191	4.9	—	
Telephone contact established	3,394	87.4	100.0	
	Declined to participate	2,075	53.4	61.1	
	Agreed but withdrew	366	9.4	10.8	
	Agreed and completed	953	24.5	28.1	
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.
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 no significant differences in GP characteristics between the final sample and all GPs in the sample frame, in terms of sex, age, distribution across states, or across Rural, Remote and Metropolitan Area classes. However, participants who graduated in a country other than Australia were slightly under-represented when compared with the total sample.
Data on the number of Medicare A1 items of service claimed in the previous quarter were also provided by DoHA for each GP in the original sample, but not for all GPs in the sample frame. These data were used to determine the ‘activity level’ of each GP. The final sample included a greater proportion of GPs with an activity level of 375–750 services in the previous quarter, and a smaller proportion of GPs in the > 1,500 services category, compared with non-participants. There was no difference between the proportions of participants and non-participants in the 751–1,500 services group. There was a significant difference (p = 0.041) in the mean number of A1 items claimed by participants (1,194 claims for the quarter) compared with the GPs who declined to participate (1,244 for the quarter) (Table 3.3). Comparisons of the median scores for each group showed a difference of fewer than five consultations per week. It is possible that the time required to participate in BEACH may be a greater issue for busier GPs. BEACH also 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 cannot be assumed, however, that a GP seeing 15 patients per day 3 days per week is any less ‘busy’ than a GP seeing 15 patients per day 5 days per week.
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 = 953)	Number	Per cent of GPs (n = 18,291) 	
Sex (2 = 0.36, p = 0.55)					
	Males	602	63.2	11,730	64.1	
	Females	351	36.8	6,561	35.9	
Age (2 = 3.61, p = 0.31)					
	< 35 years	74	7.8	1,285	7.0	
	35–44 years	210	22.2	3,980	21.8	
	45–54 years	344	36.4	6,366	34.8	
	> 54 years	317	33.5	6,660	36.4	
 (continued)
Table 3.2 (continued): 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 = 953)	Number	Per cent of GPs (n = 18,291)	
Place of graduation (2 = 5.72, p = 0.017)					
	Australia	698	73.5	12,772	69.8	
	Overseas	252	26.5	5,519	30.2	
State (2 = 13.81, p = 0.055)					
	New South Wales	314	33.0	6,174	33.8	
	Victoria	246	25.8	4,568	25.0	
	Queensland	169	17.8	3,444	18.8	
	South Australia	93	9.8	1,538	8.4	
	Western Australia	74	7.8	1,663	9.1	
	Tasmania	29	3.1	494	2.7	
	Australian Capital Territory	13	1.4	289	1.6	
	Northern Territory	14	1.5	121	0.7	
RRMA (2 = 12.0, p = 0.062)					
	Capital	645	67.8	12,127	66.3	
	Other metropolitan	67	7.0	1,411	7.7	
	Large rural	66	6.9	1,131	6.2	
	Small rural	45	4.7	1,250	6.8	
	Other rural	108	11.3	2,081	11.4	
	Remote centre	7	0.7	140	0.8	
	Other remote	14	1.5	151	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 A1 Medicare items during the most recent 3-month Medicare Australia data period. Data 
provided by the Primary Care Division of the Australian Government Department of Health and Ageing.
Note: RRMA—Rural, Remote and Metropolitan Area classification.
Table 3.3: Activity level of participating and non-participating GPs
Variable	Participants(a) 
(n = 953)	Non-participants(a) 
(n = 2,441)	
	Number 
of claims	Per cent
 of GPs	Number 
of claims	Per cent
 of GPs	
Activity (2 = 6.09, p = 0.048)					
	374–750 services in previous quarter	267	28.0	631	25.9	
	750–1,500 services in previous quarter	443	46.5	1,085	44.5	
	> 1,500 services in previous quarter	243	25.5	725	29.7	
Mean activity level (t = 2.05, p = 0.041)	1,193.7	—	1,243.5	—	
Median activity level	1,050	—	1,107	—	
Standard deviation	629.77	—	639.18	—	
(a)	Missing data removed.
3.3	Weighting the data
Activity weights: In BEACH, each GP provides details of 100 consecutive encounters. There is considerable variation in the number of services provided by different GPs 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 Medicare A1 items claimed by the GP in the previous 12 months (data supplied by DoHA).
Age–sex weights: In most previous years, BEACH has had an under-representation of young GPs. In order to achieve comparable estimates and precision, GP age–sex weights were applied to the data sets for these years in post-stratification weighting. In the current year (2007–08) this under-representation did not occur, but post-stratification weighting was again applied for consistency of method.
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 3.4 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 BEACH A1 MBS/DVA-claimable encounters was compared with that of patients at all encounters claimed as MBS/DVA A1 items of service in the 2007–08 study period (data provided by DoHA).
As shown in Table 3.4, there is an excellent fit of the MBS and BEACH age and sex distribution both with and without weighting, with no age–sex category varying by more than 20% from the population distribution. The range of raw precision ratios (0.91–1.14) 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 0.93–1.12 range.
Table 3.4: Age–sex distribution of patients at BEACH and MBS A1 services
	BEACH			
Sex/age	Raw(a)	Weighted(b)	Australia(c)	Precision ratios	
	Number	Per cent 
(n = 75,300)	Number	Per cent
(n = 76,111)	Per cent	Raw(a)	Weighted(c)	
Male								
	< 1 year	896	1.2	847	1.1	1.2	1.00	1.09	
	1–4 years	1,951	2.6	1,911	2.5	2.8	1.08	1.12	
	5–14 years	2,179	2.9	2,322	3.1	3.3	1.14	1.06	
	15–24 years	2,328	3.1	2,546	3.3	3.3	1.06	1.00	
	25–44 years	5,955	7.9	6,459	8.5	8.5	1.08	1.00	
	45–64 years	8,521	11.3	9,362	12.3	11.8	1.04	0.96	
	65–74 years	4,087	5.4	4,467	5.9	5.8	1.07	0.98	
	75+ years	3,825	5.1	4,056	5.3	5.3	1.04	1.00	
(continued)
Table 3.4 (continued): Age–sex distribution of patients at BEACH and MBS A1 services
Sex/age	BEACH			
	Raw(a)	Weighted(b)	Australia(c)	Precision ratios	
	Number	Per cent
(n = 75,300)	Number	Per cent
(n = 76,111)	Per cent	Raw(a)	Weighted(c)	
Female								
	< 1 year	798	1.1	760	1.0	1.0	0.91	1.00	
	1–4 years	1,716	2.3	1,713	2.3	2.4	1.04	1.04	
	5–14 years	2,083	2.8	2,182	2.9	3.2	1.14	1.10	
	15–24 years	4,920	6.5	4,779	6.3	6.2	0.95	0.98	
	25–44 years	11,955	15.9	11,212	14.7	14.6	0.92	0.99	
	45–64 years	12,604	16.7	12,129	15.9	15.6	0.93	0.98	
	65–74 years	5,456	7.2	5,471	7.2	6.7	0.93	0.93	
	75+ years	6,026	8.0	5,893	7.7	8.4	1.05	1.09	
(a)	Unweighted data, A1 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)	Data provided by the Primary Care Division of the Australian Government Department of Health and Ageing.
Note: A1 Medicare services—see Glossary. Only encounters with a valid age and sex are included in the comparison.
3.5	The weighted data set
The final unweighted data set from the 10th 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 3.5.
Table 3.5: The BEACH data set
Variable	Raw	Weighted	
General practitioners	953	953.1	
Encounters	95,300	95,897.7	
Reasons for encounter	146,405	146,695.5	
Problems managed	147,724	145,078.0	
Medications	96,488	98,439.3	
Other treatments	51,332	49,129.8	
Referrals	13,747	12,941.8	
Imaging	9,469	9,143.0	
Pathology	45,597	41,375.4	

4	The participating GPs
This chapter reports data collected between April 2007 and March 2008 about the participating GPs and their practices from the 10th year of the BEACH program. Data on GP and practice characteristics are reported for each year from 1998–99 to 2007–08 in the 10 year summary report General practice activity in Australia 1998–99 to 2007–08: 10 year data tables available from <www.aihw.gov.au/publications/index.cfm/subject/19> (AIHW catalogue number GEP 23).
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 953 participants:
•	63% were male, and 34% were aged 55 years or older
•	more than half had been in general practice for more than 20 years
•	almost 60% were in practices of fewer than five full-time equivalent GPs
•	74% had graduated in Australia
•	72% practised in major cities (classified using the Australian Standard Geographical Classification)
•	27% conducted some consultations in a language other than English
•	50% were Fellows of the RACGP
•	86% worked in an accredited practice
•	71% worked in a practice that employed practice nurse(s)
•	74% worked 6–10 clinical sessions per week, 15% worked fewer than six sessions per week, and only 11% worked more than 10 sessions per week.
•	39% spent more than 40 hours each week on direct patient care services
•	two in five had provided care in a residential aged care facility in the previous month
•	45% provided their own or cooperative after-hours care, and two in five employed a deputising service for after-hours patient care (multiple responses allowed)
•	over one-quarter (27%) bulk-billed Medicare for all patients; 47% bulk-billed for all consultations with pensioner/Commonwealth concession cardholders, and one-third (36%) bulk-billed for all consultations with children (multiple responses allowed)
•	more than half (55%) worked in a teaching practice for undergraduates or registrars, or both.
Those interested in the clinical activity of overseas trained doctors will find more information in Bayram et al. Clinical activity of overseas-trained doctors practising in general practice in Australia.51
Readers interested in the effects of GP age on clinical practice will find more information in Charles et al. The independent effect of age of general practitioner on clinical practice.52

Table 4.1: Characteristics of participating GPs and their practices
GP characteristic	Number(a)	Per cent of GPs(a)
 (n = 953)	
Sex	Male	602	63.2	
	Female	351	36.8	
Age (missing = 8)			
	< 35 years	74	7.8	
	35–44 years	210	22.2	
	45–54 years	344	36.4	
	55+ years	317	33.5	
Years in general practice (missing = 7)			
	< 2 years	6	0.6	
	2–5 years	94	9.9	
	6–10 years	122	12.9	
	11–19 years	195	20.6	
	20+ years	529	55.9	
Size of practice—Full-time equivalent GPs (missing = 23)			
	< 2	164	17.6	
	2–4 	383	41.2	
	5–9 	297	31.9	
	10+ 	86	9.2	

Practice location by RRMA (missing = 1)			
	Capital	645	67.8	
	Other metropolitan	67	7.0	
	Large rural	66	6.9	
	Small rural	45	4.7	
	Other rural	108	11.3	
	Remote central	7	0.7	
	Other remote, offshore	14	1.5	
Practice location by ASGC Remoteness structure (missing = 1)			
	Major cities	687	72.2	
	Inner regional	166	17.4	
	Outer regional	82	8.6	
	Remote	12	1.3	
	Very remote	5	0.5	
Place of graduation (missing = 3)			
	Australia	698	73.5	
	United Kingdom	65	6.8	
	Asia	93	9.8	
	Europe	25	2.6	
	Africa	41	4.3	
	New Zealand	13	1.4	
(continued)
Table 4.1 (continued): Characteristics of participating GPs and their practices
GP characteristic	Number(a)	Per cent of GPs(a)
(n = 953)	
Consult in languages other than English (missing = 4)			
	< 25% of consultations	194	20.4	
	25–50% of consultations	29	3.0	
	> 50% of consultations	34	3.6	
Currently in general practice training program (missing = 4)	27	2.8	
Fellow of RACGP (missing = 5)	476	50.2	
Accredited practice (missing = 5)	818	86.3	
Practice nurse at major practice address (missing = 3)	677	71.3	
Sessions per week (missing = 9)			
	< 6 per week	145	15.4	
	6–10 per week	696	73.9	
	11+ per week	103	10.9	
Direct patient care hours (worked) per week (missing = 25)			
	<= 10 hours	3	0.3	
	11–20 hours	81	8.7	
	21–40 hours	486	52.4	
	41–60 hours	298	32.1	
	60+ hours	60	6.5	
Patient care provided in previous month(b) (missing = 14)			
	As a locum	38	4.0	
	In a deputising service	24	2.5	
	In a residential aged care facility	392	41.6	
	As a salaried/sessional hospital medical officer	29	3.1	
	None of the above	456	48.4	
After-hours arrangements(b) (missing = 6)			
	Practice does its own	314	33.2	
	Cooperative with other practices	107	11.3	
	Deputing service	404	42.7	
	Referral to other service (e.g. emergency hospital department)	78	8.2	
	Other arrangement	26	2.7	
Bulk-billing(b) (missing = 4)			
	All patients	258	27.2	
	All pension/Commonwealth concession cardholders	442	46.6	
	Some pension/Commonwealth concession cardholders	197	20.8	
	All children	339	35.7	
	Some children	240	25.3	
	Selected other patients	560	59.0	
(continued)
Table 4.1 (continued): Characteristics of participating GPs and their practices
GP characteristic	Number(a)	Per cent of GPs(a)
(n = 953)	
Major practice a teaching practice (missing = 5)			
	Not a teaching practice	425	44.8	
	Yes—for undergraduates only	216	22.8	
	Yes—for GP registrars only	93	9.8	
	Yes—for both undergraduates and registrars	213	22.5	
(a)	Missing data removed.
(b)	Multiple responses allowed.
Note: RRMA—Rural, Remote and Metropolitan Areas classification; ASGC—Australian Standard Geographical Classification; 
RACGP—Royal Australian College of General Practitioners.
4.2	Computer use at GP practices
Table 4.2 shows the proportion of participating GPs who worked in a practice in which computers were used for each of five listed activities. Note that these results refer to computer availability or use at the practice level and may not reflect the use of computers by individual GPs. 
Information about reported individual GP’s use of computers at the practice can be found in Henderson et al. Extent and utilisation of computerisation in Australian general practice.53 Those interested in the effect of computerisation on quality of care in general practice will find more detailed information in Henderson The effect of computerisation on the quality of care in Australian general practice.54
Table 4.2 shows that:
•	only 3.3% of GPs worked in a non-computerised practice
•	computers were used mainly for prescribing and billing 
•	four-fifths had computers available for other administrative purposes
•	more than four-fifths had computers available for medical records
•	nearly four-fifths were in practices that had Internet and/or email available.
Table 4.2: Computer applications available/used at major practice address
Computer use	Number	Per cent of GPs (n = 953)(a)	Per cent of GPs with computers (n = 922)(a)	
Not at all	31	3.3	—	
Prescribing	845	89.3	92.3	
Billing	818	86.5	89.4	
Medical records	783	82.8	85.6	
Other administrative	765	80.9	83.6	
Internet/email	746	78.9	81.5	
Missing	7	—	—	
(a)	Missing data removed.

Table 4.3 lists the top 10 combinations of computer use by participants’ practices.
•	63% of GPs indicated that their practice used computers for all five listed purposes—billing, prescribing, medical records, other administrative purposes and Internet/email.
•	Within the top 10 combinations, 70% of GPs reported computer availability/use for both medical records and Internet/email purposes.
•	Prescribing was the only application included in all of the top 10 combinations of availability/use.
•	Billing was the second most frequently available function, with medical records third, and Internet/email use ranking fourth.
Table 4.3: Top 10 combinations of computer applications at major practice address
Combination	Number	Per cent of GPs (n = 953)(a)	Per cent of GPs with computers (n = 922)(a)	
All five uses	600	63.4	65.6	
Billing + prescribing + medical records + other administrative	57	6.0	6.2	
Billing + prescribing + other admin + Internet/email	35	3.7	3.8	
Billing + prescribing + medical records + Internet/email	31	3.3	3.4	
Billing + prescribing + medical records 	25	2.6	2.7	
Billing + prescribing	16	1.7	1.7	
Prescribing + medical records + other admin + Internet/email	15	1.6	1.6	
Prescribing + medical records + Internet/email	14	1.5	1.5	
Billing + prescribing + Internet/email	14	1.5	1.5	
Billing + prescribing + other administrative	7	0.7	0.8	
(a)	Missing data removed.
5	The encounters
This chapter describes the content and type of encounters recorded in the 10th year of the BEACH program. Data about the encounters are also reported for each year from 1998–99 to 2007–08 in the 10 year report General practice activity in Australia 1998–99 to 2007–08: 10 year data tables available from <www.aihw.gov.au/publications/index.cfm/subject/19> 
(AIHW catalogue number GEP 23).
5.1	Content of the encounters
In 2007–08, details of 95,898 encounters (weighted data) were available from 953 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 put forward 153 RFEs and GPs managed about 151 problems per 100 encounters.
	New problems accounted for nearly 40% of all problems, being managed at a rate of 58 per 100 encounters.
	Chronic problems accounted for 35% of all problems managed, managed at a rate of 52 chronic problems per 100 encounters.
	Work-related problems were managed at a rate of 2.8 per 100 encounters.
	Medications were the most common treatment choice (68 per 100 problems managed), and most of these were medications prescribed (rather than supplied or advised), at a rate of 54.5 per 100 problems managed.
	Clinical treatments (such as advice and counselling) were provided at a rate of 22.8 per 100 problems.
	There were eight referrals for care elsewhere for every 100 problems managed, most often to medical specialists (5.3 referrals per 100 problems) and less frequently to allied health professionals (2.3 referrals per 100 problems).
	GPs placed 28.5 orders for pathology tests and 6.3 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 = 95,898)	95% LCL	95% UCL	Rate per 100 problems 
(n = 145,078)	95% LCL	95% UCL	
General practitioners	953	—	—	—	—	—	—	
Encounters	95,898	—	—	—	—	—	—	
Reasons for encounter	146,696	153.0	151.1	154.8	—	—	—	
Problems managed	145,078	151.3	149.2	153.4	—	—	—	
	New problems	55,300	57.7	56.3	59.1	38.1	37.1	39.1	
	Work-related	2,719	2.8	2.6	3.1	1.9	1.7	2.0	
	Chronic problems	50,132	52.2	50.4	54.1	34.6	33.6	35.5	
Medications	98,439	102.7	100.3	105.0	67.9	66.5	69.2	
	Prescribed	79,051	82.4	80.3	84.6	54.5	53.2	55.8	
	GP-supplied	9,702	10.1	9.5	10.7	6.7	6.3	7.1	
	Advised OTC	9,686	10.1	9.3	10.9	6.7	6.2	7.2	
Other treatments	49,130	51.2	48.9	53.6	33.9	32.4	35.3	
	Clinical*	33,121	34.5	32.5	36.5	22.8	21.6	24.1	
	Procedural*	16,009	16.7	15.9	17.5	11.0	10.5	11.6	
Referrals	12,008	12.5	12.0	13.0	8.3	8.0	8.6	
	Specialist*	7,647	8.0	7.6	8.3	5.3	5.1	5.5	
	Allied health services*	3,305	3.4	3.2	3.7	2.3	2.1	2.4	
	Hospital*	381	0.4	0.3	0.5	0.3	0.2	0.3	
	Emergency department*	210	0.2	0.2	0.3	0.1	0.1	0.2	
	Other medical services*	83	0.1	0.1	0.1	0.1	0.0	0.1	
	Other referrals*	382	0.4	0.3	0.5	0.3	0.2	0.3	
Pathology	41,375	43.2	41.3	45.0	28.5	27.4	29.6	
Imaging	9,143	9.5	9.2	9.9	6.3	6.1	6.5	
Other investigations	934	1.0	0.9	1.1	0.6	0.6	0.7	
*	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(s) was claimable for the encounter the GP was 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 the GP was 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.
In Table 5.3 and Table 5.4 for comparability with previous years 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 5.5 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 
2007–08. Overall there were 83,418 encounters where at least one MBS/DVA item number was recorded. Only one item number was recorded at three-quarters of BEACH encounters said to be claimable from the MBS/DVA.
Table 5.2: Overview of MBS items recorded
Variable	Number	Per cent of encounters	
Encounters at which one MBS item was recorded	63,131	75.7	
Encounters at which two MBS items were recorded	18,912	22.7	
Encounters at which three MBS items were recorded	1,376	1.6	
Total encounters at which at least one item was recorded	83,418	100.0	
Note: Eleven encounters at which only a bulk-billing item number was recorded are not included in this table.
Table 5.3 reports the breakdown of encounter type (by payment source), counting a single Medicare item number per encounter (where applicable). 
	Indirect encounters (where the patient was not seen by the GP) accounted for 1.4% of all encounters.
	Direct encounters (patient was seen by the GP) accounted for 98.6% of all encounters.
	Direct encounters where the GP indicated that no charge was made occurred infrequently, at a rate of 0.4 per 100 encounters.
	About 97% of all direct encounters were claimable either through Medicare or the DVA.
	Encounters payable through workers compensation accounted for 2.3% of encounters.
	Encounters payable through other sources (including hospital paid encounters) accounted for 0.7% of encounters.
	There were 27 encounters where the only item recorded related to practice nurse activity, but the GP had indicated that she or he had seen the patient him/herself. There were 14 encounters at which a practice nurse item was recorded and the GP had indicated that they had not seen the patient. These were counted as indirect encounters.

Table 5.3: Type of encounter
Type of encounter	Number	Per cent of all encounters(a)
(n = 95,858)	95% LCL	95% UCL	Per cent of direct encounters
(n = 86,359)	
General practitioners	953	—	—	—	—	
Indirect encounters(b)	1,225	1.4	1.2	1.6	—	
	Practice nurse only items (indirect encs)	14	0.0	0.0	0.0	—	
Direct encounters	86,359	98.6	98.4	98.8	100.0	
	No charge	386	0.4	0.4	0.5	0.4	
	MBS/DVA items of service (all)(b)(c)	83,418	95.2	94.9	95.6	96.6	
	MBS/DVA items of service (GPs only)	83,376	95.2	94.8	95.5	96.5	
	Practice nurse only items (direct encs)	27	0.0	0.0	0.1	0.0	
	Workers compensation	2,000	2.3	2.1	2.5	2.3	
	Other paid (hospital, state, etc.)	577	0.7	05	0.8	0.7	
Practice nurse only items (unspecified)	2	0.0	0.0	0.0	—	
Subtotal	87,586	—	—	—	—	
Missing(d)	8,311	—	—	—	—	
Total encounters	95,898	—	—	—	—	
(a)	Missing data removed from analysis.
(b)	Six encounters involving chronic disease management or case conference items were recorded as indirect encounters.
(c)	Includes 14 indirect encounters at which a practice nurse item only was recorded and 2 unspecified encounters at which a practice nurse item was recorded.
(d)	If the ‘Patient not seen’ box was ticked, and MBS items other than chronic disease management items or case conference items were recorded, those items were included as missing data.
Note: LCL—lower confidence limit; UCL—upper confidence limit; MBS—Medicare Benefits Schedule; encs—encounters; DVA—Australian Government Department of Veterans’ Affairs.
Table 5.4 provides a summary of the MBS items recorded in BEACH, counting one item number only, using the same method described for Table 5.3. This provides comparable data to those reported in previous years. 
	Standard surgery consultations accounted for the majority (82.1%) of MBS/DVA‑claimable consultations, at a rate of 78.2 per 100 encounters.
	Almost 1 in 10 MBS/DVA encounters were long surgery consultations.
	Short and prolonged surgery consultations, home visits and residential aged care consultations were relatively rare. Very few encounters occurring in hospitals were recorded.
	Chronic disease management items, health assessments and GP mental health care items were all recorded rarely. There were six case conferences recorded during the 2007–08 BEACH year.
Those interested in GP activity at residential aged care facilities will find more information in O’Halloran et al. General practitioner consultations at residential aged-care facilities.55 

Table 5.4: Summary of MBS/DVA items recorded (counting one item number per encounter only)
MBS/DVA item	Number	Rate per 100 encounters(a)
(n = 95,858)	95% LCL	95% UCL	Per cent of Medicare-paid GP items (n = 83,376)	
Short surgery consultations	990	1.1	0.9	1.3	1.2	
Standard surgery consultations	68,455	78.2	77.0	79.3	82.1	
Long surgery consultations	8,231	9.4	8.8	10.0	9.9	
Prolonged surgery consultations	559	0.6	0.5	0.8	0.7	
Home visits	822	0.9	0.5	1.3	1.0	
Hospital	130	0.1	0.1	0.2	0.2	
Residential aged care facility	1,007	1.2	0.9	1.4	1.2	
Health assessments	294	0.3	0.3	0.4	0.4	
Chronic disease management items	451	0.5	0.4	0.6	0.5	
Case conferences	6	0.0	0.0	0.0	0.0	
GP mental health care	682	0.8	0.7	0.9	0.8	
Incentive payments	129	0.1	0.1	0.2	0.2	
Other items	1,620	1.8	1.4	2.3	1.9	
Total MBS/DVA items of service (GPs only)	83,376	95.2	94.8	95.5	100.0	
(a)	Missing data removed from analysis.
Note: LCL—lower confidence limit; UCL—upper confidence limit; MBS—Medicare Benefits Schedule; DVA—Australian 
Government Department of Veterans’ Affairs.
Table 5.5 provides the distribution of all Medicare item numbers recorded across Medicare item number groups. Overall, there were 105,081 MBS item numbers recorded in BEACH in 2007–08. An average of 1.1 items was recorded at encounters where at least one MBS item was recorded.
Surgery consultations (including short, standard, long and prolonged) were the most commonly recorded type of item number, at 94% of the encounters where at least one item was recorded. They accounted for 74.5% of all MBS items recorded in BEACH.
The second most commonly recorded were items for bulk-billed incentive payments, which accounted for 16.0% of all items recorded. Items for hospital, residential aged care and home visits were together recorded at one in every 50 encounters (2%). Practice nurse items were recorded at 2.0% of all encounters (Table 5.5). For a more detailed breakdown of practice nurse item numbers, and related data on practice nurse activity, refer to Chapter 13.

Table 5.5: 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	78,235	74.5		78,235	93.8	92.9	94.6	
Hospital, residential aged care and home visits	1,959	1.9		1,959	2.3	1.8	2.9	
Health assessments	356	0.3		356	0.4	0.4	0.5	
Chronic disease management items (including case conferences)	878	0.8		648	0.8	0.6	0.9	
Incentive payments	141	0.1		141	0.2	0.1	0.2	
Acupuncture	228	0.2		228	0.3	0.1	0.4	
Bulk-billed incentive payment(c)	16,819	16.0		16,813	20.2	18.1	22.2	
Practice nurse services	2,073	2.0		2,047	2.5	2.1	2.8	
Diagnostic procedures and investigations	545	0.5		540	0.6	0.5	0.8	
Therapeutic procedures	417	0.4		415	0.5	0.4	0.6	
Surgical operations	1,335	1.3		1,291	1.5	1.3	1.8	
Diagnostic imaging services	10	0.0		10	0.0	0.0	0.0	
Pathology services	256	0.2		253	0.3	0.2	0.4	
Other items	1,076	1.0		1,072	1.3	0.8	1.8	
GP mental health care items	753	0.7		753	0.9	0.8	1.0	
Total items/encounters	105,081	100.0		83,376	—	—	—	
(a)	Up to three MBS items could be recorded at each encounter. Missing data removed from analysis.
(b)	Identifies encounters where at least one item from a MBS group was recorded.
(c)	Includes 10 encounters with only a bulk-billing service item recorded at the encounter.
Note: LCL—lower confidence limit; UCL—upper confidence limit; MBS—Medicare Benefits Schedule.
5.3	Consultation length
In a subsample of 29,956 BEACH encounters containing start and finish times for all MBS/DVA-claimable encounters, the mean length of consultation in 2007–08 was 15.1 minutes (95% CI: 14.8–15.3). The median length was 13.0 minutes (results not tabled).
For A1 MBS/DVA-claimable encounters, the mean length of consultation in 2007–08 was 14.8 minutes (95% CI: 14.6–15.1), and the median length was 13.0 minutes (results not tabled). Methods describing the substudy from which consultation length data are collected are described in Section 2.4. The determinants of consultation length have been investigated by Britt et al. in Determinants of consultation length in Australian general practice.56
6	The patients
This chapter reports data collected between April 2007 and March 2008 about the characteristics of patients and their reasons for encounter from the 10th year of the BEACH program. Data on patient characteristics and reasons for encounter are reported for each year from 1998–99 to 2007–08 in the 10 year summary report General practice activity in Australia 1998–99 to 2007–08: 10 year data tables available from <www.aihw.gov.au/publications/index.cfm/subject/19> (AIHW catalogue number GEP 23).
6.1	Age–sex distribution of patients at encounter
The age–sex distribution of patients at the 95,898 encounters is shown in Figure 6.1. Females accounted for the greater proportion of encounters (57.1%). This was reflected across all age groups except for children aged less than 15 years (Figure 6.1; Table 6.1).
Patients aged less than 25 years accounted for 21.2% of encounters; those aged 25–44 years accounted for 23.4% of encounters, patients aged 45–64 years accounted for 28.1% and those aged 65 years and over accounted for 27.3% of encounters (Table 6.1).
Figure 6.1: Age-sex distribution of patients at encounter
Vertical bar chart showing, for male and female, per cent on y-axis and age on x-axis. Also includes table as follows:
	<1	1–4	5–14	15–24	25–44	45–64	65–74	75+	
Male	1.0	2.3	2.8	3.4	8.8	12.6	5.7	6.3	
Female	0.9	2.0	2.7	6.1	14.6	15.5	6.9	8.4	
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.
6.2	Other patient characteristics
Table 6.1 provides a view of other characteristics of the patients. In summary:
•	the patient was new to the practice at 8.6% of encounters.
•	almost 42% of encounters were with patients who held a Commonwealth concession card and 2.8% were with persons who held a Repatriation health card.
•	at 9.9% of encounters the patient was from a non-English-speaking background.
•	at 0.9% of encounters the patient identified themselves as an Aboriginal person or Torres Strait Islander.

Table 6.1: Characteristics of the patients at encounters
Patient characteristics	Number	Per cent of encounters (n = 95,898)	95% LCL	95% 
UCL	
Sex (missing)(a)	876				
	Males	40,761	42.9	42.1	43.7	
	Females	54,261	57.1	56.3	57.9	
Age group (missing)(a)	784				
	< 1 year	1,864	2	1.8	2.1	
	1–4 years	4,114	4.3	4.1	4.6	
	5–14 years	5,214	5.5	5.2	5.8	
	15–24 years	9,004	9.5	9.0	9.9	
	25–44 years	22,289	23.4	22.7	24.1	
	45–64 years	26,695	28.1	27.5	28.6	
	65–74 years	11,961	12.6	12.1	13.1	
	75+ years	13,972	14.7	13.9	15.5	
Other characteristics(b)					
	New patient to practice	8,136	8.6	7.8	9.4	
	Commonwealth concession card 	40,065	41.8	40.3	43.3	
	Repatriation health card	2,658	2.8	2.5	3.0	
	Non-English-speaking background	9,457	9.9	8.2	11.5	
	Aboriginal person	751	0.8	0.6	1.0	
	Torres Strait Islander	99	0.1	0.0	0.2	
	Aboriginal person and Torres Strait Islander	26	0.0	0.0	0.0	
(a)	Missing data removed.
(b)	Missing data for each of the listed ‘other’ patient characteristics were counted as a no response.
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.57
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 symptoms that relate to a single problem managed at the encounter, or multiple RFEs that relate to multiple problems managed at the encounter.
Number of reasons for encounter
There were 146,696 RFEs recorded. At 58.9% of encounters only one RFE was recorded and at 29.1% two RFEs were recorded (Table 6.2). Patients presented on average with 153.0 RFEs per 100 encounters, or 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 = 95,898)	Per cent of
encounters	95%
LCL	95%
UCL	
One RFE	56,525	58.9	57.7	60.2	
Two RFEs	27,949	29.1	28.5	29.8	
Three RFEs	11,425	11.9	11.2	12.6	
Total	95,898	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 40.1 per 100 encounters, with requests for prescriptions and test results most frequently recorded. RFEs related to the respiratory system arose at a rate of 20.6 per 100 encounters, while those related to the musculoskeletal system and the skin were each recorded at a rate of 15.4 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 = 146,696)	Rate per 100
encounters(b)
(n = 95,898)	95% 
LCL	95% 
UCL	
General & unspecified	38,441	26.2	40.1	39.0	41.2	
	Prescription NOS	8,191	5.6	8.5	8.0	9.1	
	Results tests/procedures NOS	6,147	4.2	6.4	6.0	6.8	
	Check-up—general*	3,803	2.6	4.0	3.7	4.2	
	Immunisation/vaccination—general	2,919	2.0	3.0	2.8	3.2	
	Fever	2,057	1.4	2.1	1.8	2.5	
	Administrative procedure NOS	1,885	1.3	2.0	1.8	2.1	
	Weakness/tiredness	1,302	0.9	1.4	1.2	1.5	
	Blood test NOS	1,132	0.8	1.2	1.0	1.3	
	Chest pain NOS	1,015	0.7	1.1	1.0	1.1	
	Observation/health education/advice/diet NOS	952	0.6	1.0	0.9	1.1	
	Other referrals NEC 	876	0.6	0.9	0.8	1.0	
	Trauma/injury NOS	783	0.5	0.8	0.7	0.9	
	Other reason for encounter NEC 	730	0.5	0.8	0.6	0.9	
	Clarify/discuss patient RFE/demand NOS	670	0.5	0.7	0.6	0.8	
Respiratory	19,764	13.5	20.6	19.7	21.5	
	Cough	5,992	4.1	6.2	5.8	6.7	
	Throat complaint	3,143	2.1	3.3	3.0	3.6	
	Upper respiratory tract infection	2,154	1.5	2.2	2.0	2.5	
	Immunisation/vaccination—respiratory	1,436	1.0	1.5	1.2	1.8	
	Nasal congestion/sneezing	1,337	0.9	1.4	1.2	1.6	
	Asthma	723	0.5	0.8	0.7	0.8	
	Shortness of breath, dyspnoea	703	0.5	0.7	0.7	0.8	
Musculoskeletal	14,793	10.1	15.4	14.9	15.9	
	Back complaint*	3,041	2.1	3.2	3.0	3.4	
	Knee complaint	1,271	0.9	1.3	1.2	1.4	
	Foot/toe complaint	1,045	0.7	1.1	1.0	1.2	
	Shoulder complaint	971	0.7	1.0	0.9	1.1	
	Neck complaint	885	0.6	0.9	0.8	1.0	
	Leg/thigh complaint	877	0.6	0.9	0.8	1.0	
	Injury musculoskeletal NOS	709	0.5	0.7	0.6	0.8	
Skin	14,787	10.1	15.4	14.8	16.1	
	Rash*	2,383	1.6	2.5	2.3	2.6	
	Skin complaint	1,367	0.9	1.4	1.3	1.5	
	Check-up—skin*	1,339	0.9	1.4	1.1	1.7	
	Swelling*	1,019	0.7	1.1	1.0	1.2	
(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 = 146,696)	Rate per 100
encounters(b)
(n = 95,898)	95% 
LCL	95% 
UCL	
Cardiovascular	10,756	7.3	11.2	10.6	11.8	
	Check-up—cardiovascular*	5,133	3.5	5.4	5.0	5.7	
	Hypertension/high blood pressure*	1,971	1.3	2.1	1.8	2.3	
	Prescription—cardiovascular	916	0.6	1.0	0.8	1.2	
Digestive	9,922	6.8	10.3	10.0	10.7	
	Abdominal pain*	1,682	1.1	1.8	1.6	1.9	
	Diarrhoea	1,388	0.9	1.4	1.3	1.6	
	Vomiting	1,027	0.7	1.1	1.0	1.2	
Psychological	7,526	5.1	7.8	7.5	8.2	
	Depression*	1,954	1.3	2.0	1.9	2.2	
	Anxiety*	1044	0.7	1.1	1.0	1.2	
	Sleep disturbance	951	0.6	1.0	0.9	1.1	
Endocrine & metabolic	6,223	4.2	6.5	6.1	6.8	
	Diabetes (non-gestational)*	1,204	0.8	1.3	1.1	1.4	
	Prescription—endocrine/metabolic	952	0.6	1.0	0.9	1.1	
Female genital system	4,981	3.4	5.2	4.8	5.6	
	Female genital check-up/pap smear*	1,842	1.3	1.9	1.7	2.1	
Neurological	4,593	3.1	4.8	4.6	5.0	
	Headache	1,559	1.1	1.6	1.5	1.8	
	Vertigo/dizziness	1,070	0.7	1.1	1.0	1.2	
Ear	3,452	2.4	3.6	3.4	3.8	
	Ear pain	1,305	0.9	1.4	1.3	1.5	
Pregnancy & family planning	3,115	2.1	3.2	3.0	3.5	
	Pre/postnatal check-up*	664	0.5	0.7	0.6	0.8	
	Oral contraception*	663	0.5	0.7	0.6	0.8	
Urology	2,414	1.6	2.5	2.4	2.7	
Eye	2,405	1.6	2.5	2.4	2.6	
Blood	1,310	0.9	1.4	1.2	1.5	
Male genital system	1,178	0.8	1.2	1.1	1.3	
Social	1,035	0.7	1.1	1.0	1.2	
Total RFEs	146,696	100.0	153.0	151.1	154.8	
(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. Nearly half (44.3%) of patient RFEs were expressed in terms of symptoms or complaints (for example, ‘tired’, ‘fever’). RFEs were described in diagnostic terms for 18.2% of RFEs (for example, ‘about my diabetes’, ‘for my depression’). The remaining 37.5% 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	Number	Per cent of total RFEs
(n = 146,696)	Rate per 100
encounters(a)
(n = 95,898)	95% 
LCL	95% 
UCL	
Symptoms & complaints	64,933	44.3	67.7	65.8	69.6	
Diagnosis, diseases	26,659	18.2	27.8	26.3	29.3	
Diagnostic & preventive procedures	24,542	16.7	25.6	24.7	26.5	
Medications, treatments & therapeutics	14,434	9.8	15.1	14.3	15.8	
Referrals & other RFEs	7,321	5.0	7.6	7.2	8.1	
Results	6,555	4.5	6.8	6.4	7.2	
Administrative	2,252	1.5	2.4	2.2	2.5	
Total RFEs	146,696	100.0	153.0	151.1	154.8	
(a)	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, 18 were descriptive of symptoms such as cough, throat and back complaints and rash. However, four of the top five RFEs reflected requests for a process of care (that is, requests for check-ups, prescriptions, test results and immunisations) and together accounted for a quarter of all RFEs (25.8%) (Table 6.5).

Table 6.5: Most frequent patient reasons for encounter
Patient reason for encounter	Number	Per cent of total RFEs
(n = 146,696)	Rate per 100 encounters(a)
(n = 95,898)	95%
LCL	95% 
UCL	
Check-up—all*	13,880	9.5	14.5	13.8	15.1	
Prescription—all*	12,004	8.2	12.5	11.9	13.2	
Test results*	7,321	5.0	7.6	7.2	8.1	
Cough	5,992	4.1	6.2	5.8	6.7	
Immunisation/vaccination—all*	4,585	3.1	4.8	4.4	5.1	
Throat complaint	3,143	2.1	3.3	3.0	3.6	
Back complaint*	3,041	2.1	3.2	3.0	3.4	
Rash*	2,383	1.6	2.5	2.3	2.6	
Upper respiratory tract infection	2,154	1.5	2.2	2.0	2.5	
Fever	2,057	1.4	2.1	1.8	2.5	
Hypertension/high blood pressure*	1,971	1.3	2.1	1.8	2.3	
Depression*	1,954	1.3	2.0	1.9	2.2	
Administrative procedure NOS	1,885	1.3	2.0	1.8	2.1	
Abdominal pain*	1,682	1.1	1.8	1.6	1.9	
Headache	1,559	1.1	1.6	1.5	1.8	
Diarrhoea	1,388	0.9	1.4	1.3	1.6	
Skin complaint	1,367	0.9	1.4	1.3	1.5	
Nasal congestion/sneezing	1,337	0.9	1.4	1.2	1.6	
Ear pain	1,305	0.9	1.4	1.3	1.5	
Weakness/tiredness 	1,302	0.9	1.4	1.2	1.5	
Knee complaint	1,271	0.9	1.3	1.2	1.4	
Diabetes—all*	1,214	0.8	1.3	1.1	1.4	
Blood test NOS	1,132	0.8	1.2	1.0	1.3	
Vertigo/dizziness	1,070	0.7	1.1	1.0	1.2	
Foot/toe complaint	1,045	0.7	1.1	1.0	1.2	
Anxiety*	1,044	0.7	1.1	1.0	1.2	
Vomiting	1,027	0.7	1.1	1.0	1.2	
Swelling*	1,019	0.7	1.1	1.0	1.2	
Chest pain NOS	1,015	0.7	1.1	1.0	1.1	
Shoulder complaint	971	0.7	1.0	0.9	1.1	
Subtotal 	83,121	56.7	—	—	—	
Total RFEs	146,696	100.0	153.0	151.1	154.8	
(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.

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. Problems that specifically included management by a practice nurse are reported additionally 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 of problems 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, 2.2 per 100 encounters) can be regarded as equivalent to ‘asthma is managed at 2.2% 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 1998–99 to 2007–08 in the 10 year summary report General practice activity in Australia 1998–99 to 2007–08: 10 year data tables available from <www.aihw.gov.au/publications/index.cfm/subject/19> (AIHW catalogue number GEP 23).
7.1	Number of problems managed at encounter
There were 145,078 problems managed, at a rate of 151.3 per 100 encounters. 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 one-quarter of encounters and almost 1 in 10 encounters (8.8%) involved the management of three problems. The management of four problems at an encounter was less common (2.7% 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	60,418	63.0	61.7	64.3	
Two problems	24,400	25.4	24.7	26.2	
Three problems	8,458	8.8	8.3	9.3	
Four problems	2,622	2.7	2.4	3.0	
Total	95,898	100.0	—	—	
Note: LCL—lower confidence limit; UCL—upper confidence limit.
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 (154.4 per 100 encounters, 95% CI: 152.1–156.6) than at those with male patients (147.4 per 100 encounters, 95% CI: 145.2–149.6) (results not tabled). 
Figure 7.1 shows the age–sex-specific rates of problems managed, and 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% confidence intervals
Stacked line chart showing, for male and female, rate per 100 encounters on y-axis and age group (years) on x-axis. Also includes table as follows:
	<1	1-4	5-14	15-24	25-44	45-64	65-74	75+	
Male	119.8	115.2	114.4	120.2	133.6	154.9	173.7	173.8	
Female	118.4	113.3	116.9	139.2	140.5	163.1	178.5	179.7	

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 (19.4 per 100 encounters)—in particular upper respiratory tract infection, acute bronchitis and asthma
•	problems of a general and unspecified nature (such as immunisations, check-ups and prescriptions)
•	cardiovascular problems (such as hypertension and cardiac check-ups) 
•	musculoskeletal problems (such as arthritis and back complaints) 
•	skin problems (such as contact dermatitis and solar keratosis/sunburn) (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 = 145,078)	Rate per 100 encounters(b) (n = 95,898)	95% 
LCL	95% 
UCL	
Respiratory	18,641	12.9	19.4	18.8	20.1	
	Upper respiratory tract infection	5,943	4.1	6.2	5.7	6.7	
	Acute bronchitis/bronchiolitis	2,303	1.6	2.4	2.2	2.6	
	Asthma	2,089	1.4	2.2	2.0	2.3	
	Immunisation/vaccination—respiratory	1,712	1.2	1.8	1.5	2.1	
	Sinusitis 	1,254	0.9	1.3	1.2	1.4	
	Tonsillitis*	928	0.6	1.0	0.9	1.1	
	Chronic obstructive pulmonary disease	744	0.5	0.8	0.7	0.9	
General & unspecified	17,107	11.8	17.8	17.1	18.5	
	Immunisation/vaccination—general	2,989	2.1	3.1	2.9	3.3	
	General check-up*	2,407	1.7	2.5	2.3	2.7	
	Prescription NOS	1,252	0.9	1.3	1.1	1.5	
	Results tests/procedures NOS	1,233	0.8	1.3	1.1	1.5	
	Viral disease, other/NOS	1,168	0.8	1.2	1.1	1.4	
	Abnormal results/investigations NOS	779	0.5	0.8	0.7	0.9	
	Administrative procedures NOS	699	0.5	0.7	0.6	0.8	
Cardiovascular	16,860	11.6	17.6	16.8	18.3	
	Hypertension*	9,496	6.5	9.9	9.4	10.5	
	Cardiac check-up*	1,141	0.8	1.2	1.0	1.4	
	Ischaemic heart disease*	1,046	0.7	1.1	1.0	1.2	
	Atrial fibrillation/flutter	984	0.7	1.0	0.9	1.1	
(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 = 145,078)	Rate per 100 encounters(b) (n = 95,898)	95% 
LCL	95% 
UCL	
Musculoskeletal	16,572	11.4	17.3	16.7	17.8	
	Arthritis—all*	3,460	4.2	6.3	6.0	6.7	
		Osteoarthritis*	2,484	1.7	2.6	2.4	2.8	
	Back complaint*	2,624	1.8	2.7	2.6	2.9	
	Sprain/strain*	1,509	1.0	1.6	1.4	1.7	
	Fracture*	959	0.7	1.0	0.9	1.1	
	Osteoporosis	928	0.6	1.0	0.9	1.1	
	Injury musculoskeletal NOS	841	0.6	0.9	0.8	1.0	
	Bursitis/tendonitis/synovitis NOS	789	0.5	0.8	0.7	0.9	
Skin	16,474	11.4	17.2	16.5	17.9	
	Contact dermatitis	1,719	1.2	1.8	1.7	1.9	
	Solar keratosis/sunburn	1,297	0.9	1.4	1.1	1.6	
	Malignant neoplasm skin	1,145	0.8	1.2	1.0	1.4	
	Laceration/cut	847	0.6	0.9	0.8	1.0	
	Skin disease, other	768	0.5	0.8	0.7	0.9	
Endocrine & metabolic	12,401	8.6	12.9	12.3	13.5	
	Diabetes, non-gestational*	3,698	2.5	3.9	3.6	4.1	
	Lipid disorders	3,541	2.4	3.7	3.4	4.0	
	Vitamin/nutritional deficiency	875	0.6	0.9	0.8	1.0	
	Obesity (BMI > 30)	682	0.5	0.7	0.6	0.8	
	Hypothyroidism/myxoedema	670	0.5	0.7	0.6	0.8	
Psychological	11,009	7.6	11.5	10.9	12.0	
	Depression*	3,822	2.6	4.0	3.8	4.2	
	Anxiety*	1,691	1.2	1.8	1.6	1.9	
	Sleep disturbance	1,547	1.1	1.6	1.5	1.7	
Digestive	10,282	7.1	10.7	10.4	11.1	
	Oesophageal disease	2,219	1.5	2.3	2.2	2.5	
	Gastroenteritis*	1,583	1.1	1.7	1.5	1.8	
Female genital system	5,562	3.8	5.8	5.4	6.2	
	Female genital check-up/pap smear*	1,722	1.2	1.8	1.6	2.0	
	Menopausal complaint	797	0.5	0.8	0.7	0.9	
Pregnancy & family planning	3,752	2.6	3.9	3.6	4.2	
	Pregnancy*	1,279	0.9	1.3	1.2	1.5	
	Oral contraception*	1,236	0.9	1.3	1.2	1.4	
Ear	3,593	2.5	3.8	3.6	3.9	
	Acute otitis media/myringitis	1,013	0.7	1.1	1.0	1.2	
	Excessive ear wax	719	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 = 145,078)	Rate per 100 encounters(b) (n = 95,898)	95% 
LCL	95% 
UCL	
Neurological	3,434	2.4	3.6	3.4	3.7	
Urology	3,002	2.1	3.1	3.0	3.3	
	Urinary tract infection*	1,525	1.1	1.6	1.5	1.7	
Eye	2,464	1.7	2.6	2.4	2.7	
	Infectious conjunctivitis	683	0.5	0.7	0.6	0.8	
Male genital system	1,698	1.2	1.8	1.7	1.9	
Blood	1,559	1.1	1.6	1.5	1.8	
Social	668	0.5	0.7	0.6	0.8	
Total problems	145,078	100.0	151.3	149.2	153.4	
(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; BMI—body mass index.
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.
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, almost two-thirds of problems were expressed as diagnoses or diseases (64.8%), with the majority of other problems described as symptoms or complaints (21.4%), or as diagnostic or preventive procedures such as check-ups (9.4%). 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 terms of a test result or an administrative procedure, or as a prescription.

Table 7.3: Distribution of problems managed, by ICPC-2 component
ICPC-2 component	Number	Per cent of
total problems (n = 145,078)	Rate per 100
encounters(a) (n = 95,898)	95%
 LCL	95%
 UCL	
Diagnosis, diseases	94,044	64.8	98.1	96.2	99.9	
Symptoms & complaints	30,982	21.4	32.3	31.5	33.1	
Diagnostic & preventive procedures	13,594	9.4	14.2	13.5	14.8	
Medications, treatments & therapeutics	2,820	1.9	2.9	2.7	3.2	
Results	1,679	1.2	1.8	1.6	1.9	
Referrals & other RFEs	1,135	0.8	1.2	1.1	1.3	
Administrative	823	0.6	0.9	0.8	1.0	
Total problems 	145,078	100.0	151.3	149.2	153.4	
(a)	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
Overall, there were 151.3 problems managed per 100 encounters. 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’ (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 far right-hand column in Table 7.4 lists the percentage of each problem that was new to the patient, indicating the first presentation of a problem to a medical practitioner. This can provide a measure of general practice incidence. For example, only 6.7% of all contacts with diabetes were new problems to the patient. In contrast, more than three-quarters of upper respiratory tract infection problems were new to the patient. 
The most common problems managed were hypertension (9.9 per 100 encounters), check‑ups (6.3 per 100), upper respiratory tract infections (6.2 per 100), immunisation/vaccination (5.2) and depression (4.0) (Table 7.4). 

Table 7.4: Most frequently managed problems
Problem managed	Number	Per cent of
total problems (n = 145,078)	Rate per 100
encounters(a)
 (n = 95,898)	95%
LCL	95%
UCL	Per cent
 of new problems(b)	
Hypertension*	9,496	6.5	9.9	9.4	10.5	6.1	
Check-up—all*	6,080	4.2	6.3	6.0	6.7	39.0	
Upper respiratory tract infection	5,943	4.1	6.2	5.7	6.7	77.0	
Immunisation/vaccination—all*	5,015	3.5	5.2	4.8	5.6	53.1	
Depression*	3,822	2.6	4.0	3.8	4.2	16.0	
Diabetes—all*	3,717	2.6	3.9	3.6	4.1	6.7	
Lipid disorders*	3,541	2.4	3.7	3.4	4.0	13.4	
Arthritis—all*	3,460	2.4	3.6	3.4	3.8	19.1	
Back complaint*	2,624	1.8	2.7	2.6	2.9	24.8	
Acute bronchitis/bronchiolitis	2,303	1.6	2.4	2.2	2.6	72.4	
Oesophageal disease	2,219	1.5	2.3	2.2	2.5	17.6	
Asthma	2,089	1.4	2.2	2.0	2.3	16.7	
Prescription—all*	1,880	1.3	2.0	1.7	2.2	5.1	
Contact dermatitis	1,719	1.2	1.8	1.7	1.9	45.3	
Anxiety*	1,691	1.2	1.8	1.6	1.9	20.4	
Test results*	1,679	1.2	1.8	1.6	1.9	29.7	
Gastroenteritis*	1,583	1.1	1.7	1.5	1.8	80.8	
Sleep disturbance	1,547	1.1	1.6	1.5	1.7	16.0	
Urinary tract infection*	1,525	1.1	1.6	1.5	1.7	64.2	
Sprain/strain*	1,509	1.0	1.6	1.4	1.7	59.6	
Solar keratosis/sunburn	1,297	0.9	1.4	1.1	1.6	47.1	
Pregnancy*	1,279	0.9	1.3	1.2	1.5	35.9	
Viral disease, other/NOS	1,254	0.9	1.3	1.2	1.4	67.2	
Malignant neoplasm skin	1,236	0.9	1.3	1.2	1.4	17.5	
Ischaemic heart disease*	1,145	0.8	1.2	1.0	1.4	52.3	
Sinusitis acute/chronic	1,141	0.8	1.2	1.0	1.4	12.4	
Acute otitis media/myringitis	1,046	0.7	1.1	1.0	1.2	8.9	
Atrial fibrillation/flutter	1,013	0.7	1.1	1.0	1.2	72.1	
Abnormal test results*	984	0.7	1.0	0.9	1.1	6.4	
Subtotal	73,837	50.9	—	—	—	—	
Total problems	145,078	100.0	151.3	149.2	153.4	38.1	
(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, or a problem that has been managed previously by any medical practitioner. Table 7.5 lists the most common new problems managed in general practice in 2007–08, in decreasing order of frequency. Overall, 55,300 problems (38.1% of all problems) were specified as being new, being managed at a rate of 57.7 per 100 encounters.
The far right-hand column of this table shows the proportion of total contacts with this problem that was reported as being a new problem for the patient. This provides an idea of the incidence of each problem. For example, the 610 new cases of depression represented only 16.0% of all GP contacts with diagnosed depression, suggesting that more than four out of five contacts for depression were for ongoing management. In contrast, four out of five gastroenteritis cases were first consultations to a medical practitioner for this episode of gastroenteritis. The balance (19%) would have been 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.
The most common new problems managed at general practice encounters were upper respiratory tract infections (4.8 per 100 encounters), immunisations/vaccinations (2.8), acute bronchitis (1.7), gastroenteritis (1.3) and general check-ups (1.2) (Table 7.5).
Table 7.5: Most frequently managed new problems
New problem managed	Number	Per cent of total
 new problems (n = 55,300)	Rate per 100
 encounters(a)
(n = 95,898)	95%
 LCL	95% UCL	Per cent 
of this problem(b)	
Upper respiratory tract infection	4,578	8.3	4.8	4.4	5.2	77.0	
Immunisation/vaccination—all*	2,661	4.8	2.8	2.5	3.0	53.1	
Acute bronchitis/bronchiolitis	1,668	3.0	1.7	1.6	1.9	72.4	
Gastroenteritis*	1,278	2.3	1.3	1.2	1.5	80.8	
General check-up*	1,168	2.1	1.2	1.1	1.3	48.5	
Urinary tract infection*	979	1.8	1.0	0.9	1.1	64.2	
Sprain/strain*	899	1.6	0.9	0.8	1.0	59.6	
Viral disease, other/NOS	895	1.6	0.9	0.8	1.1	76.6	
Sinusitis acute/chronic 	843	1.5	0.9	0.8	1.0	67.2	
Contact dermatitis 	779	1.4	0.8	0.7	0.9	45.3	
Acute otitis media/myringitis	731	1.3	0.8	0.7	0.8	72.1	
Female genital check-up*	712	1.3	0.7	0.6	0.8	41.4	
Tonsillitis*	677	1.2	0.7	0.6	0.8	73.0	
Back complaint*	651	1.2	0.7	0.6	0.7	24.8	
Solar keratosis/sunburn	611	1.1	0.6	0.5	0.7	47.1	
Depression*	610	1.1	0.6	0.6	0.7	16.0	
Malignant neoplasm skin	599	1.1	0.6	0.5	0.7	52.3	
Hypertension*	578	1.0	0.6	0.5	0.7	6.1	
Conjunctivitis, infectious	527	1.0	0.5	0.5	0.6	77.1	
(continued)
Table 7.5 (continued): Most frequently managed new problems
New problem managed	Number	Per cent of total
 new problems (n = 55,300)	Rate per 100
 encounters(a)
(n = 95,898)	95%
 LCL	95% UCL	Per cent 
of this problem(b)	
Test results*	499	0.9	0.5	0.4	0.6	29.7	
Bursitis/tendonitis/synovitis NOS	496	0.9	0.5	0.5	0.6	62.8	
Osteoarthritis*	486	0.9	0.5	0.4	0.6	19.6	
Lipid disorders*	476	0.9	0.5	0.4	0.6	13.4	
Pregnancy*	459	0.8	0.5	0.4	0.5	35.9	
Abnormal test results*	450	0.8	0.5	0.4	0.5	46.1	
Fracture*	433	0.8	0.5	0.4	0.5	45.2	
Excessive ear wax	428	0.8	0.4	0.4	0.5	59.5	
Skin disease, other	413	0.7	0.4	0.4	0.5	53.7	
Skin injury, other	407	0.7	0.4	0.4	0.5	69.3	
Oesophagus disease	391	0.7	0.4	0.4	0.5	17.6	
Subtotal	26,382	47.7	—	—	—	—	
Total new problems	55,300	100.0	57.7	56.3	59.1	—	
(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 chronic condition list classified according to ICPC-2 was applied to the BEACH data set.29 More than a third (34.6%) of the problems managed in general practice were chronic in nature. At least one chronic problem was managed at 39.6% of encounters (95% CI: 38.6–40.7), and chronic problems were managed at an average rate of 52.3 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 group may be found in Appendix 5, <www.aihw.gov.au/publications/index.cfm/subject/19>.
Table 7.6 shows the most frequently managed chronic problems in Australian general practice in decreasing order of frequency. The top seven chronic problems made up more than half of all chronic problems managed; these were non-gestational hypertension (18.8% of chronic conditions), depressive disorder (7.5%), non-gestational diabetes (7.3%), lipid disorders (7.0%), osteoarthritis (4.9%), oesophageal disease (4.4%) and asthma (4.1%).

Table 7.6: Most frequently managed chronic problems
Chronic problem managed	Number	Per cent of total
chronic problems (n = 50,132)	Rate per 100
 encounters(a) (n = 95,898)	95%
 LCL	95%
UCL	
Hypertension (non-gestational)**	9,486	18.8	9.9	9.3	10.4	
Depressive disorder	3,796	7.5	4.0	3.7	4.2	
Diabetes (non-gestational)**	3,698	7.3	3.9	3.6	4.1	
Lipid disorders*	3,541	7.0	3.7	3.4	4.0	
Osteoarthritis*	2,484	4.9	2.6	2.4	2.8	
Oesophageal disease	2,219	4.4	2.3	2.2	2.5	
Asthma	2,089	4.1	2.2	2.0	2.3	
Malignant neoplasm skin	1,145	2.3	1.2	1.0	1.4	
Ischaemic heart disease*	1,046	2.1	1.1	1.0	1.2	
Atrial fibrillation/flutter	984	2.0	1.0	0.9	1.1	
Osteoporosis	928	1.8	1.0	0.9	1.1	
Back syndrome with radiating pain 	875	1.7	0.9	0.8	1.0	
Chronic obstructive pulmonary disease	744	1.5	0.8	0.7	0.9	
Obesity (BMI > 30)	682	1.4	0.7	0.6	0.8	
Hypothyroidism/myxoedema	670	1.3	0.7	0.6	0.8	
Migraine	624	1.2	0.7	0.6	0.7	
Heart failure	607	1.2	0.6	0.6	0.7	
Gout	573	1.1	0.6	0.5	0.7	
Arthritis (excluding osteoarthritis and rheumatoid arthritis)**	523	1.0	0.5	0.5	0.6	
Schizophrenia	472	0.9	0.5	0.4	0.6	
Shoulder syndrome	435	0.9	0.5	0.4	0.5	
Rheumatoid arthritis	435	0.9	0.5	0.4	0.5	
Anaemia (chronic)**	426	0.8	0.4	0.4	0.5	
Dementia 	417	0.8	0.4	0.3	0.5	
Acne (chronic)**	406	0.8	0.4	0.4	0.5	
Anxiety disorder	395	0.8	0.4	0.3	0.5	
Overweight	318	0.6	0.3	0.3	0.4	
Back syndrome without radiating pain	317	0.6	0.3	0.3	0.4	
Vertiginous syndrome	313	0.6	0.3	0.3	0.4	
Endocrine/metabolic/nutritional disease other 	291	0.6	0.3	0.3	0.4	
Subtotal	40,939	80.7	—	—	—	
Total chronic problems	50,132	100.0	52.3	50.4	54.1	
(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 (see Appendix 4, <www.aihw.gov.au/publications/index.cfm/subject/19>).
**	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 <www.aihw.gov.au/publications/index.cfm/subject/19> for codes included in analysis of chronic conditions).
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, workplace exposures or a pre-existing condition that has been significantly exacerbated by work activity or workplace exposure. Work-related problems were managed at a rate of 2.8 per 100 general practice encounters in 2007–08 (Table 7.7).
The most common group of work-related problems were musculoskeletal problems, accounting for almost two-thirds (59.2%) of work-related problems and managed at a rate of 1.7 per 100 general practice encounters. One in ten musculoskeletal problems managed in general practice were work-related. The most common musculoskeletal work-related problems were back complaints (14.6% of work-related problems), sprains and strains (11.3%), musculoskeletal injury (9.0%) and fractures (3.3%).
Work-related psychological problems accounted for 8.3% of total work-related problems and were managed at a rate of 0.2 per 100 encounters. These psychological problems accounted for only 2.1% of total psychological problems managed in general practice. The most commonly managed work-related psychological problems were depression (3.2% of work‑related problems) and acute stress reaction (2.1%).
Preventive checks related to the patient’s work accounted for 5.7% of work-related problems and were performed at a rate of 0.2 per 100 encounters. The majority of these preventive checks were check-ups classified in the general and unspecified chapter of ICPC-2, including pre-employment and employment check-ups.
Other work-related problems not covered in the above groups accounted for 26.7% of work‑related problems and included skin injuries not elsewhere classified (3.6% of work‑related problems), lacerations (2.9%) and administrative procedures (2.5%).
Although back complaint was the most commonly managed individual work-related problem (accounting for 14.6% of work-related problems), it accounted for only 15.1% of the management of all back complaints. In contrast, musculoskeletal injury (not otherwise specified) accounted for 9.0% of work-related problems but represented 29.3% of all musculoskeletal injuries (not otherwise specified) managed (Table 7.7).

Table 7.7: Work-related problems, by type and most frequently managed individual problems
Work-related problem managed	Number	Percentage of total
work-related problems (n = 2,719)	Rate per 100
 encounters (n = 95,898)	95%
 LCL	95%
UCL	Percentage of this problem(a) 	
Musculoskeletal problems	1,611	59.2	1.7	1.5	1.8	9.7	
	Back complaint*	396	14.6	0.4	0.4	0.5	15.1	
	Sprain/strain*	307	11.3	0.3	0.3	0.4	20.3	
	Injury musculoskeletal NOS	246	9.0	0.3	0.2	0.3	29.3	
	Fracture*	90	3.3	0.1	0.1	0.1	9.4	
	Shoulder syndrome	80	3.0	0.1	0.1	0.1	18.4	
	Bursitis/tendonitis/synovitis NOS	66	2.4	0.1	0	0.1	8.4	
	Acute internal knee damage	55	2.0	0.1	0	0.1	20.1	
	Tennis elbow	53	2.0	0.1	0	0.1	22.9	
	Neck symptom/complaint	40	1.5	0.0	0	0.1	12.2	
Psychological problems	226	8.3	0.2	0.2	0.3	2.1	
	Depression*	88	3.2	0.1	0.1	0.1	2.3	
	Acute stress reaction	57	2.1	0.1	0	0.1	10.1	
Preventive checks	156	5.7	0.2	0.1	0.2	1.6	
	General check-up*	129	4.8	0.1	0.1	0.2	5.4	
Other work-related problems	726	26.7	0.8	0.7	0.8	0.7	
	Injury skin, other	98	3.6	0.1	0.1	0.1	16.7	
	Laceration/cut	79	2.9	0.1	0.1	0.1	9.3	
	Administrative procedures NOS	67	2.5	0.1	0.0	0.1	9.6	
Total work-related problems	2,719	100.0	2.8	2.6	3.1	—	
(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.
8	Overview of management
The BEACH survey form allowed 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 1998–99 to 2007–08 are reported for each year in the 10 year report General practice activity in Australia 1998–99 to 2007–08: 10 year data tables, see <www.aihw.gov.au/publications/index.cfm/subject/19>.
At the 95,898 recorded encounters, GPs undertook 211,029 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 occurring more frequently than procedural treatments (Table 8.1).
For an ‘average’ 100 GP–patient encounters, GPs provided 82 prescriptions, provided 35 clinical treatments, undertook 17 procedures, made 8 referrals to specialists and 4 to allied health services, and placed 43 pathology test orders and 10 imaging test orders.
Table 8.1: Summary of management
Management type	Number	Rate per 100 encounters
(n = 95,898)	95%
 LCL	95% 
UCL	Rate per 100 problems
(n = 145,078)	95% 
LCL	95% UCL	
Medications	98,439	102.7	100.3	105.0	67.9	66.5	69.2	
	Prescribed	79,051	82.4	80.3	84.6	54.5	53.2	55.8	
	GP-supplied	9,702	10.1	9.5	10.7	6.7	6.3	7.1	
	Advised OTC	9,686	10.1	9.3	10.9	6.7	6.2	7.2	
Other treatments	49,130	51.2	48.9	53.6	33.9	32.4	35.3	
	Clinical	33,121	34.5	32.5	36.5	22.8	21.6	24.1	
	Procedural	16,009	16.7	15.9	17.5	11.0	10.5	11.6	
Referrals	12,008	12.5	12.0	13.0	8.3	8.0	8.6	
	Specialist	7,647	8.0	7.6	8.3	5.3	5.1	5.5	
	Allied health	3,305	3.5	3.2	3.7	2.3	2.1	2.4	
	Hospital	381	0.4	0.3	0.5	0.3	0.2	0.3	
	Emergency department	210	0.2	0.2	0.3	0.1	0.1	0.2	
	Other medical services	83	0.1	0.1	0.1	0.1	0.0	0.1	
	Other referral	382	0.4	0.3	0.5	0.3	0.2	0.3	
Pathology	41,375	43.2	41.3	45.0	28.5	27.4	29.6	
Imaging	9,143	9.5	9.2	9.9	6.3	6.1	6.5	
Other investigations ordered(a)	934	1.0	0.9	1.1	0.6	0.6	0.7	
Total management activities	211,029	220.1	—	—	145.5	—	—	
(a)	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 at the surgery.
Note: LCL—lower confidence limit; UCL—upper confidence limit; OTC—over-the-counter.
Another perspective emerges in analysis of 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 91.9% of encounters and for 86.3% of problems managed.
•	At least one medication or other treatment was given for nearly three-quarters of the problems managed.
•	At least one medication (most commonly prescribed) was prescribed, supplied or advised for more than half the problems managed.
•	At least one other treatment (most commonly clinical) was provided for nearly one-third of problems managed.
•	At least one referral (most commonly to a specialist) was made for 8% of problems managed.
•	At least one investigation (most commonly pathology) was requested for 18% 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 = 95,898)	Number of problems	Per cent of total problems(a)
(n = 145,078)	
At least one management type	88,098	91.9	125,160	86.3	
	At least one medication or other treatment	78,792	82.2	106,252	73.2	
		At least one medication 	61,715	63.4	78,454	54.1	
			At least one prescription	51,369	53.6	64,484	44.4	
			At least one GP-supplied	7,583	7.9	7,758	5.3	
			At least one OTC advised	8,568	8.9	8,821	6.1	
		At least one other treatment	38,222	39.9	43,877	30.2	
			At least one clinical treatment	26,374	27.5	29,937	20.6	
			At least one procedural treatment	14,420	15.0	14,974	10.3	
	At least one referral	11,302	11.8	12,024	8.3	
		At least one referral to a specialist	7,360	7.7	7,735	5.3	
		At least one referral to allied health	3,193	3.1	3,338	2.3	
		At least one referral to hospital	381	0.4	394	0.3	
		At least one referral to emergency department	210	0.2	221	0.2	
		At least one referral to other medical services	83	0.1	88	0.1	
		At least one referral NOS	380	0.4	398	0.3	
	At least one investigation	22,846	23.8	26,247	18.1	
		At least one pathology order	16,639	17.4	19,033	13.1	
		At least one imaging order	7,958	8.3	8,205	5.7	
		At least one other investigation(b)	887	0.9	908	0.6	
(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 at the surgery.
Note: OTC—over-the-counter; NOS—not otherwise specified.
The combinations of management types related to each problem were then 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 of the problems managed
•	as a double component for 19% 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 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 = 145,078)	Per cent of total encounters
 (n = 95,898)	
No recorded management	13.7	8.1	
1+ management recorded	86.3	91.9	
y						37.2	31.6	
	y					9.8	7.2	
y	y					6.4	10.4	
					y	5.1	2.9	
		y				4.5	3.9	
			y			4.2	3.2	
y					y	2.8	4.4	
y		y				2.7	4.4	
				y		2.3	1.8	
y			y			1.3	2.8	
		y			y	1.3	1.3	
	y				y	1.2	1.4	
y				y		1.1	1.9	
	y		y			0.9	1.1	
y	y				y	0.6	1.7	
				y	y	0.5	0.6	
y	y		y			0.3	1.1	
y		y			y	0.3	1.0	
	y	y				0.3	0.7	
			y		y	0.3	0.4	
	y			y		0.3	0.4	
Note: 1+—at least one specified management type.

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:
–	enter 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) from which they were mapped to the international Anatomical Therapeutic Chemical (ATC) classification.30 
	Results are reported in this chapter at drug group, subgroup and generic level using ATC levels 1, 3 and 5. Individual medications are also reported at the CAPS generic level, the equivalent of ATC Level 5 (see Section 2.7).
Data on medications are reported for each year from 1998–99 to 2007–08 in the 10 year summary report General practice activity in Australia 1998–99 to 2007–08: 10 year data tables available from <www.aihw.gov.au/publications/index.cfm/subject/19> (AIHW catalogue number GEP 23).
Readers interested in adverse drug events will find more detailed information from the BEACH program in Miller et al. Adverse drug events in general practice patients in Australia.58
9.1	Source of medications
A total of 98,439 medications were recorded, at rates of 103 per 100 encounters and 68 per 100 problems managed (Table 8.1).
•	Four out of five medications (80.3% of all medications) were prescribed.
•	One in ten (9.9%) medications were supplied to the patient by the GP.
•	One in ten medications (9.8%) were recommended by the GP for OTC purchase.
If these are extrapolated to the 107 million general practice Medicare-claimed encounters in Australia in 2007–08, GPs in Australia:
	prescribed medications on more than 88 million occasions
	supplied 10.8 million medications directly to the patient
	recommended medications for OTC purchase on 10.8 million occasions.
9.2	Prescribed medications
There were 79,051 prescriptions recorded, at rates of 82 per 100 encounters and 55 per 100 problems managed. GPs recorded 86.1% of prescribed medications by brand (proprietary) name and 13.9% by their generic (non-proprietary) name (results not tabled).
On a per problem basis:
•	no prescription was given for half (55.6%) of all problems managed
•	one prescription was given for 36.7% of problems managed
•	two prescriptions were given for 5.9% of problems managed
•	three or more prescriptions were rarely given (1.9% 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 on x-axis. Also includes table as follows:
	Nil	One	Two	Three	Four	
Per cent	55.6	36.7	5.9	1.4	0.5	
Number of repeats
For 60,733 prescriptions (76.8% 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 34.5% of these prescriptions, the GP specified that no repeats had been prescribed, and for 33.8% five repeats were ordered. The latter proportion reflects the PBS provision of one month’s supply and five repeats for many medications used for chronic conditions such as hypertension. The ordering of one or two repeats (16.8% and 10.2%) was also quite common.
Age–sex-specific rates of prescribed medications
Age–sex-specific analysis found similar prescription rates per 100 encounters for males and females (82.1 and 82.7, 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 a rate of 56 per 100 encounters with patients aged less than 25 years rising to 110 per 100 encounters for patients aged 65 years and over (results not tabled).
Figure 9.3, however, demonstrates that the age-based increase lessens if the prescription rate is related to problems. This suggests that the increased prescription rate in older patients is largely accounted for by the increased number of health problems they have managed at an encounter.
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. Also includes table as follows:
	Nil	One	Two	Three	Four	Five	> Five	
Per cent	34.5	16.8	10.2	3.7	0.8	33.8	0.1	

Figure 9.3: Age-sex specific prescription rates per 100 problems managed
Stacked line chart showing, for male and female, rate per 100 encounters on y-axis and age group (years) on x-axis. Also includes table as follows:
	<1	1–4	5–14	15–24	25–44	45–64	65–74	75+	
Male	37	47.1	47.3	46.9	51.2	57.7	61.7	61.8	
Female	39.3	47.3	47	44.7	45.8	55.3	61.7	63.2	

Types of medications prescribed
Table 9.1 shows the distribution of prescribed medications using the WHO ATC classification.30 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 and as a rate per 100 encounters with 95% confidence intervals.

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 scripts
(n = 79,051)	Rate per 100 encs(a)
(n = 95,898)	95% LCL	95% UCL	
Nervous system		17,051	21.6	17.8	17.1	18.5	
 	Other analgesics and antipyretics 	4,933	6.2	5.1	4.8	5.5	
 	 	Paracetamol	2,381	3.0	2.5	2.2	2.7	
 	 	Paracetamol combinations excl. psycholeptics	1,922	2.4	2.0	1.8	2.2	
 	 	Acetylsalicylic acid	622	0.8	0.7	0.6	0.7	
 	Antidepressants	 	3,254	4.1	3.4	3.2	3.6	
 	 	Sertraline	586	0.7	0.6	0.5	0.7	
 	Opioids	2,921	3.7	3.1	2.8	3.3	
 	 	Oxycodone	989	1.3	1.0	0.9	1.2	
 	 	Tramadol	817	1.0	0.9	0.8	0.9	
 	Anxiolytics	 	1,855	2.4	1.9	1.7	2.1	
 	 	Diazepam	1,057	1.3	1.1	1.0	1.2	
 	 	Oxazepam	537	0.7	0.6	0.5	0.6	
 	Hypnotics and sedatives	 	1,592	2.0	1.7	1.5	1.8	
 	 	Temazepam	1,054	1.3	1.1	1.0	1.2	
 	 	Venlafaxine	531	0.7	0.6	0.5	0.6	
 	Antipsychotics	 	1,056	1.3	1.1	1.0	1.2	
 	 	Prochlorperazine	544	0.7	0.6	0.5	0.6	
 	Antiepileptics	 	516	0.7	0.5	0.5	0.6	
Cardiovascular system 	15,863	20.1	16.5	15.6	17.5	
 	Lipid modifying agents, plain	3,407	4.3	3.6	3.3	3.8	
 		Atorvastatin	1,664	2.1	1.7	1.6	1.9	
 		Simvastatin	879	1.1	0.9	0.8	1.0	
 	ACE inhibitors, plain 	2,487	3.2	2.6	2.4	2.8	
		Perindopril	1,133	1.4	1.2	1.1	1.3	
		Ramipril	772	1.0	0.8	0.7	0.9	
	Angiotensin ii antagonists, plain 	2,114	2.7	2.2	2.0	2.4	
		Irbesartan	919	1.2	1.0	0.9	1.1	
		Candesartan	597	0.8	0.6	0.5	0.7	
		Telmisartan	503	0.6	0.5	0.4	0.6	
	Beta blocking agents	 	1,651	2.1	1.7	1.6	1.9	
	 	Atenolol	835	1.1	0.9	0.8	1.0	
	Selective calcium channel blockers with 
mainly vascular effects 	1,510	1.9	1.6	1.4	1.7	
	 	Amlodipine	689	0.9	0.7	0.6	0.8	
	Angiotensin ii antagonists, combinations 	1,215	1.5	1.3	1.1	1.4	
	 	Irbesartan and diuretics	723	0.9	0.8	0.7	0.9	
(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 scripts
(n = 79,051)	Rate per 100 encs(a)
(n = 95,898)	95% LCL	95% UCL	
	High-ceiling diuretics	 	584	0.7	0.6	0.5	0.7	
	 	Furosemide	581	0.7	0.6	0.5	0.7	
	ACE inhibitors, combinations 	551	0.7	0.6	0.5	0.6	
	Selective calcium channel blockers with direct 
cardiac effects 	497	0.6	0.5	0.5	0.6	
Anti-infectives for systemic use	 	15,219	19.3	15.9	15.3	16.5	
 	Beta-lactam antibacterials, penicillins 	5,930	7.5	6.2	5.9	6.5	
 		Amoxicillin	3,330	4.2	3.5	3.2	4.2	
 	 	Amoxicillin and enzyme inhibitor	1,648	2.1	1.7	1.6	1.9	
 	Other beta-lactam antibacterials 	2,962	3.8	3.1	2.9	3.3	
 	 	Cefalexin	2,333	3.0	2.4	2.3	2.6	
 	 	Cefaclor	565	0.7	0.6	0.5	0.7	
 	Macrolides, lincosamides and streptogramins 	2,168	2.7	2.3	2.1	2.5	
 	 	Roxithromycin	1,174	1.5	1.2	1.1	1.4	
 	Viral vaccines	 	954	1.2	1.0	0.9	1.1	
 	Tetracyclines	822	1.0	0.9	0.8	1.0	
 	 	Doxycycline	702	0.9	0.7	0.6	0.8	
 	Sulfonamides and trimethoprim 	622	0.8	0.7	0.6	0.7	
Alimentary tract and metabolism	7,561	9.6	7.9	7.5	8.3	
	Drugs for peptic ulcer and gastro-oesophageal reflux	2,868	3.6	3.0	2.8	3.2	
	 	Esomeprazole	1,121	1.4	1.2	1.1	1.3	
	 	Pantoprazole	510	0.6	0.5	0.5	0.6	
	 	Omeprazole	478	0.6	0.5	0.4	0.6	
	Propulsives	 	612	0.8	0.6	0.6	0.7	
	 	Metoclopramide	530	0.7	0.6	0.5	0.6	
	Blood glucose lowering drugs, excl. insulins 	2,035	2.6	2.1	1.9	2.3	
	 	Metformin	1,134	1.4	1.2	1.1	1.3	
	 	Gliclazide	512	0.7	0.5	0.5	0.6	
Respiratory system 	4,871	6.2	5.1	4.8	5.4	
 	Adrenergics, inhalants 	2,515	3.2	2.6	2.4	2.8	
	 	Salbutamol	1,241	1.6	1.3	1.2	1.4	
	 	Salmeterol and other drugs for obstructive airways disease	775	1.0	0.8	0.7	0.9	
	Other drugs for obstructive airways 
disease, inhalants 	772	1.0	0.8	0.7	0.9	
	Decongestants and other nasal preparations 
for topical use 	731	0.9	0.8	0.7	0.8	
(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 scripts
(n = 79,051)	Rate per 100 encs(a)
(n = 95,898)	95% LCL	95% UCL	
Musculoskeletal system 	4,544	5.8	4.7	4.4	5.0	
	Anti-inflammatory and antirheumatic products, 
non-steroid 	3,315	4.2	3.5	3.2	3.7	
	 	Meloxicam	899	1.1	0.9	0.8	1.1	
	 	Diclofenac	743	0.9	0.8	0.7	0.9	
	Drugs affecting bone structure and mineralization 	620	0.8	0.7	0.6	0.7	
Dermatologicals 	3,740	4.7	3.9	3.7	4.1	
	Corticosteroids, plain 	2,322	2.9	2.4	2.3	2.6	
	 	Mometasone	724	0.9	0.8	0.7	0.8	
	 	Betamethasone	688	0.9	0.7	0.6	0.8	
Genitourinary system and sex hormones 	3,237	4.1	3.4	3.2	3.6	
	Hormonal contraceptives for systemic use 	1,664	2.1	1.7	1.6	1.9	
	 	Levonorgestrel and estrogen	931	1.2	1.0	0.9	1.1	
	Estrogens 		596	0.8	0.6	0.6	0.7	
Sensory organs 	2,341	3.0	2.4	2.3	2.6	
	Anti-infectives 		967	1.2	1.0	0.9	1.1	
		Chloramphenicol	896	1.1	0.9	0.9	1.0	
	Corticosteroids and anti-infectives in combination 	616	0.8	0.6	0.6	0.7	
Blood and blood-forming organs 	2,056	2.6	2.1	2.0	2.3	
	Antithrombotic agents 		1,475	1.9	1.5	1.4	1.7	
		Warfarin	1,030	1.3	1.1	0.9	1.2	
Systemic hormonal preparations, excl. sex hormones 	1,827	2.3	1.9	1.8	2.1	
	Corticosteroids for systemic use, plain 	1,126	1.4	1.2	1.1	1.3	
		Prednisolone	742	0.9	0.8	0.7	0.9	
	Thyroid preparations 		639	0.8	0.7	0.6	0.7	
		Levothyroxine sodium	636	0.8	0.7	0.6	0.7	
Antineoplastic and immunomodulating agents 	355	0.5	0.4	0.3	0.4	
Various 	243	0.3	0.3	0.2	0.3	
Antiparasitic products, insecticides and repellents 	143	0.2	0.2	0.1	0.2	
Total prescribed medications	79,051	100.0	82.4	80.3	84.6	
(a)	Column will not add to 100, as multiple prescriptions could be written at each encounter, and only the most frequent Level 3 and Level 5 drugs are included.
Note: ATC—Anatomical Therapeutic Chemical classification; scripts—prescriptions; encs—encounters; LCL—lower confidence limit; 
UCL—upper confidence limit; excl—excluding; 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 accounted for 44.3% of all prescribed medications. Of the top five medications, two were antibiotics, two were plain or combination paracetamol, and the fifth was atorvastatin, a lipid-modifying agent, which was among the top five for the first time.
Readers interested in changes in antibiotic prescribing information over time will find more information in Pan et al. Antibiotic prescribing in Australian general practice: How has it changed from 1990–91 to 2002–03?59
Table 9.2: Most frequently prescribed medications (CAPS generic level)
Generic medication	Number	Per cent of scripts 
(n = 79,051)	Rate per 100
 encounters(a)
(n = 95,898)	95%
 LCL 	95% 
UCL 	
Amoxycillin	3,330	4.2	3.5	3.2	3.7	
Paracetamol	2,381	3.0	2.5	2.2	2.7	
Cephalexin	2,333	3.0	2.4	2.3	2.6	
Paracetamol/Codeine	1,816	2.3	1.9	1.7	2.1	
Atorvastatin	1,664	2.1	1.7	1.6	1.9	
Amoxycillin/Potassium clavulanate	1,648	2.1	1.7	1.6	1.9	
Salbutamol	1,283	1.6	1.3	1.2	1.5	
Roxithromycin	1,174	1.5	1.2	1.1	1.4	
Metformin	1,134	1.4	1.2	1.1	1.3	
Perindopril	1,133	1.4	1.2	1.1	1.3	
Esomeprazole	1,121	1.4	1.2	1.1	1.3	
Diazepam	1,057	1.3	1.1	1.0	1.2	
Temazepam	1,054	1.3	1.1	1.0	1.2	
Warfarin sodium	1,030	1.3	1.1	0.9	1.2	
Oxycodone	989	1.3	1.0	0.9	1.2	
Levonorgestrel/Ethinyloestradiol	931	1.2	1.0	0.9	1.1	
Irbesartan	919	1.2	1.0	0.9	1.1	
Meloxicam	899	1.1	0.9	0.8	1.1	
Chloramphenicol eye	896	1.1	0.9	0.9	1.0	
Simvastatin	879	1.1	0.9	0.8	1.0	
Atenolol	835	1.1	0.9	0.8	1.0	
Tramadol	817	1.0	0.9	0.8	0.9	
Fluticasone/Salmeterol	775	1.0	0.8	0.7	0.9	
Ramipril	772	1.0	0.8	0.7	0.9	
Mometasone	724	0.9	0.8	0.7	0.8	
Irbesartan/Hydrochlorothiazide	723	0.9	0.8	0.7	0.9	
Doxycycline	702	0.9	0.7	0.7	0.8	
Amlodipine	689	0.9	0.7	0.6	0.8	
(continued)
Table 9.2 (continued): Most frequently prescribed medications (CAPS generic level)
Generic medication	Number	Per cent 
of scripts 
(n = 79,051)	Rate per 100
 encounters(a)
(n = 95,898)	95%
 LCL 	95% 
UCL 	
Betamethasone topical	688	0.9	0.7	0.6	0.8	
Diclofenac sodium systemic	659	0.8	0.7	0.6	0.8	
Subtotal	35,053	44.3	—	—	—	
Total prescribed medications	79,051	100.0	82.4	80.3	84.6	
(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: Scripts—prescriptions; LCL—lower confidence limit; UCL—upper confidence limit.
9.3	Medications supplied by GPs
GPs supplied their patients with 9,702 medications in this study, at a rate of 10.1 medications per 100 encounters. At least one medication was supplied at 7.9% of encounters for 5.3% of problems. Table 9.3 shows the most commonly supplied medications at the CAPS generic level (ATC Level 5 equivalent), with vaccines accounting for about 70% of this group. The Papillomavirus vaccine, which was government-funded for a GP-implemented catch-up program for women aged 18–26 years, was the second most commonly supplied medication.
Table 9.3: Medications most frequently supplied by GPs
Generic medication	Number	Per cent of 
GP-supplied 
(n = 9,702)	Rate per 100
 encounters(a)
(n = 95,898)	95%
 LCL 	95% 
UCL 	
Influenza virus vaccine	1,403	14.5	1.5	1.2	1.7	
Papillomavirus (HPV) vaccine	917	9.5	1.0	0.9	1.1	
Pneumococcal vaccine	563	5.8	0.6	0.5	0.7	
Vitamin B12 (Cobalamin)	350	3.6	0.4	0.3	0.4	
Mumps/Measles/Rubella vaccine	299	3.1	0.3	0.3	0.4	
Haemophilus B vaccine	255	2.6	0.3	0.2	0.3	
Polio vaccine oral sabin/injection	216	2.2	0.2	0.2	0.3	
ADT–CDT (diphtheria–tetanus) vaccine	202	2.1	0.2	0.2	0.3	
Diphtheria–pertussis–tetanus–polio vaccine	180	1.9	0.2	0.1	0.2	
Chickenpox (Varicella zoster)	169	1.8	0.2	0.1	0.2	
Meningitis vaccine	168	1.7	0.2	0.1	0.2	
Diphtheria–pertussis–tetanus–hepatitis B vaccine	164	1.7	0.2	0.1	0.2	
Meloxicam	161	1.7	0.2	0.1	0.2	
Diphtheria–pertussis–tetanus–hep B–polio–Hib vaccine	159	1.6	0.2	0.1	0.2	
Rotavirus vaccine	139	1.4	0.1	0.1	0.2	
Metoclopramide	124	1.3	0.1	0.1	0.2	
Hepatitis B vaccine	116	1.2	0.1	0.1	0.2	
Hepatitis A vaccine	112	1.2	0.1	0.1	0.2	
Allergen treatment	102	1.1	0.1	0.1	0.1	
(continued)
Table 9.3 (continued): Medications most frequently supplied by GPs
Generic medication	Number	Per cent of 
GP-supplied 
(n = 9,702)	Rate per 100
 encounters(a)
(n = 95,898)	95%
 LCL 	95% 
UCL 	
Typhoid vaccine (Salmonella typhi)	101	1.0	0.1	0.1	0.1	
Hepatitis A and B vaccine	90	0.9	0.1	0.1	0.1	
Haemophilus B–hepatitis B vaccine	88	0.9	0.1	0.1	0.1	
Methylprednisolone	85	0.9	0.1	0.1	0.1	
Esomeprazole	83	0.9	0.1	0.1	0.1	
Triple antigen (diphtheria–pertussis–tetanus)	80	0.8	0.1	0.1	0.1	
Betamethasone systemic	78	0.8	0.1	0.1	0.1	
Hepatitis A–salmonella typhi vaccine	76	0.8	0.1	0.1	0.1	
Medroxyprogesterone	73	0.8	0.1	0.1	0.1	
Lignocaine	64	0.7	0.1	0.0	0.1	
Salbutamol	59	0.6	0.1	0.0	0.1	
Subtotal	6,678	68.8	—	—	—	
Total medications supplied	9,702	100.0	10.1	9.5	10.7	
(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: LCL—lower confidence limit; UCL—upper confidence limit.
9.4	Medications advised for over-the-counter purchase
The GPs recorded 9,686 medications as recommended for OTC purchase, at rates of 10.1 per 100 encounters and 6.7 per 100 problems managed. At least one OTC medication was recorded as advised at 8.9% of encounters and for 6.1% of problems. Table 9.4 shows the top 30 advised medications at the CAPS generic level (ATC Level 5 equivalent). Analgesics made up almost one-third of this group.
Table 9.4: Most frequently advised over-the-counter medications
Generic medication	Number	Per cent of OTC
(n = 9,686)	Rate per 100 
encounters(a)
(n = 95,898)	95%
 LCL	95%
 UCL	
Paracetamol	2,442	25.2	2.6	2.2	2.9	
Ibuprofen	549	5.7	0.6	0.5	0.7	
Loratadine	194	2.0	0.2	0.2	0.3	
Diclofenac topical	179	1.9	0.2	0.1	0.2	
Saline bath/solution/gargle	174	1.8	0.2	0.1	0.2	
Sodium chloride topical nasal	161	1.7	0.2	0.1	0.2	
Sodium–potassium–citric–glucose	155	1.6	0.2	0.1	0.2	
Fexofenadine	138	1.4	0.1	0.1	0.2	
Aspirin	134	1.4	0.1	0.1	0.2	
(continued)
Table 9.4 (continued): Most frequently advised over-the-counter medications
Generic medication	Number	Per cent of OTC
(n = 9,686)	Rate per 100 encounters(a)
(n = 95,898)	95%
 LCL	95%
 UCL	
Cetirzine	123	1.3	0.1	0.1	0.2	
Paracetamol–codeine	120	1.2	0.1	0.1	0.2	
Clotrimazole topical	120	1.2	0.1	0.1	0.2	
Cold and Flu medication NEC	112	1.2	0.1	0.0	0.2	
Glucosamine	104	1.1	0.1	0.1	0.1	
Hyoscine butylbromide	103	1.1	0.1	0.1	0.1	
Hydrocortisone/Clotrimazole	100	1.0	0.1	0.1	0.1	
Brompheniramine–phenylephrine	99	1.0	0.1	0.1	0.1	
Cream–ointment–lotion NEC	97	1.0	0.1	0.1	0.1	
Codeine–paracetamol–pseudoephidrine	94	1.0	0.1	0.0	0.2	
Sorbolene–glycerol–cetomac	91	0.9	0.1	0.1	0.1	
Bromhexine	82	0.9	0.1	0.0	0.1	
Vitamin C (ascorbic acid)	81	0.8	0.1	0.0	0.2	
Simple analgesics	80	0.8	0.1	0.0	0.1	
Clotrimazole vaginal	78	0.8	0.1	0.1	0.1	
Psyllium hydrophillic mucilloid	72	0.8	0.1	0.1	0.1	
Chlorpheniramine–pseudoephidrine	70	0.7	0.1	0.0	0.1	
Multivitamins with minerals	69	0.7	0.1	0.0	0.1	
Promethazine hydrochloride	67	0.7	0.1	0.1	0.1	
Pholcodine	66	0.7	0.1	0.0	0.1	
Loperamide	66	0.7	0.1	0.0	0.1	
Subtotal	6,021	62.2	—	—	—	
Total medications advised	9,686	100.0	10.1	9.3	10.9	
(a)	Column will not add to 100 because 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; LCL—lower confidence limit; UCL—upper confidence limit; NEC—not elsewhere classified.
10	Other treatments
The survey form allowed GPs to record up to two other treatments for each problem managed at the encounter. Other treatments included all clinical and procedural treatments provided. These groups are defined in Appendix 4, <www.aihw.gov.au/publications/index.cfm/subject/19>. Patient observations that were regarded as 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 1998–99 to 2007–08 in the 10 year report General practice activity in Australia 1998–99 to 2007–08: 10 year data tables available from <www.aihw.gov.au/publications/index.cfm/subject/19> (AIHW catalogue number GEP 23).
10.1	Number of other treatments
Other treatments were commonly provided in the management of patient morbidity. In 2007–08, a total of 49,130 other treatments were recorded, at a rate of 51.2 per 100 encounters. Two-thirds of these were clinical treatments (Table 10.1).
Table 10.1: Summary of other treatments
	Number	Rate per 100 encs
(n = 95,898)	95% LCL	95% UCL	Rate per 100 problems
(n = 145,078)	95% LCL	95% UCL	
Other treatments	49,130	51.2	48.9	53.6	33.9	32.4	35.3	
	Clinical treatments	33,121	34.5	32.5	36.5	22.8	21.6	24.1	
	Procedural treatments	16,009	16.7	15.9	17.5	11.0	10.5	11.6	
	At least one other treatment	38,222	39.9	38.3	41.4	—	—	—	
Note: Encs—encounters; LCL—lower confidence limit; UCL—upper confidence limit.
Table 10.2 shows the proportion of problems for which at least one other treatment was given. 
	In nearly two-thirds of the problems that were managed with another treatment, no concurrent pharmacological treatment was provided.
	Nearly one in five problems were managed with a clinical treatment. Of these, nearly two-thirds were not provided with medication for that problem.
	A procedure was undertaken in the management of 10.3% of problems, with no pharmacological management given for two-thirds 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 = 145,078)	95% LCL	95% UCL	
At least one other treatment 	43,877	100.0	30.2	29.1	31.4	
	Without pharmacological treatment	27,798	64.6	19.2	18.4	19.9	
At least one clinical treatment 	29,937	100.0	20.6	19.6	21.7	
	Without pharmacological treatment	18,563	63.3	12.8	12.1	13.5	
At least one procedural treatment	14,974	100.0	10.3	9.8	10.8	
	Without pharmacological treatment 	9,812	66.5	6.8	6.4	7.1	
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 2007–08, there were 33,121 clinical treatments recorded, at a rate of 34.5 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, and as a rate per 100 encounters with 95% confidence limits.
General advice and education was the most frequently recorded clinical treatment, at a rate of 7.2 per 100 encounters. The most common preventive activity was counselling about nutrition and weight (4.2 per 100 encounters). There were a number of other groups that also could be considered preventive in nature, including counselling/advice for exercise, smoking, prevention, lifestyle and alcohol. Together, the abovementioned preventive treatments accounted for 14.4% of all clinical treatments, provided at a rate of 7.4 per 100 encounters. Psychological counselling was provided at a rate of 3.2 per 100 encounter, and advice and education about medication was given at a rate of 2.0 per 100 encounters (Table 10.3).

Table 10.3: Most frequent clinical treatments
Clinical treatment	Number	Per cent of other treatments
(n = 49,130)	Rate per 100 encounters (n = 95,898)	95% LCL	95% UCL	
Advice/education*	6,872	14.0	7.2	6.3	8.1	
Counselling—problem*	4,149	8.5	4.3	3.8	4.9	
Counselling/advice—nutrition/weight*	4,041	8.2	4.2	3.8	4.6	
Advice/education—treatment*	3,310	6.7	3.5	3.1	3.8	
Counselling—psychological*	3,065	6.2	3.2	2.9	3.4	
Advice/education—medication*	1,944	4.0	2.0	1.8	2.2	
Sickness certificate*	1,622	3.3	1.7	1.4	2.0	
Other admin/document*	1,463	3.0	1.5	1.4	1.7	
Reassurance, support	1,322	2.7	1.4	1.2	1.6	
Counselling/advice—exercise*	1,245	2.5	1.3	1.1	1.5	
Counselling/advice—smoking*	581	1.2	0.6	0.5	0.7	
Counselling/advice—prevention*	501	1.0	0.5	0.4	0.6	
Counselling/advice—lifestyle*	413	0.8	0.4	0.3	0.5	
Counselling/advice—alcohol*	359	0.7	0.4	0.3	0.4	
Family planning*	334	0.7	0.4	0.3	0.4	
Observe/wait*	329	0.7	0.3	0.2	0.4	
Counselling/advice—health/body*	288	0.6	0.3	0.2	0.4	
Subtotal 	31,840	64.8	—	—	—	
Total clinical treatments 	33,121	67.4	34.5	32.5	36.5	
*	Includes multiple ICPC-2 or ICPC-2 PLUS codes (see Appendix 4, <www.aihw.gov.au/publications/index.cfm/subject/19>).
Note: Includes the most common clinical treatments, those accounting for > 0.5% of all other treatments. LCL—lower confidence limit; 
UCL—upper confidence limit.
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 a clinical treatment was 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 29,937 problems (20.6% of all problems).
	The 10 most common problems managed with a clinical treatment accounted for almost one-third of all problems for which a clinical treatment was provided.
	Depression and upper respiratory tract infections were the most frequently managed problems with a clinical treatment, each at a rate of 1.8 per 100 encounters.
	Half the contacts with depression involving management with a clinical treatment did not result in a medication being prescribed/advised/supplied.
	Twenty-nine per cent of upper respiratory tract infection contacts involved a clinical treatment, with nearly 60% of these being managed without medication.
	One in ten hypertension contacts resulted in a clinical treatment, with 45% of these being managed without medication.
	A clinical treatment was used at one-quarter of diabetes contacts, and approximately two-thirds of these did not involve medication.
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 = 95,898)	95%
LCL	95%
UCL	Per cent
 of this
problem(b)	Per cent of treated
 problems no meds(c)	
Upper respiratory tract infection	1,729	5.8	1.8	1.6	2.0	29.1	58.9	
Depression*	1,720	5.8	1.8	1.6	1.9	45.0	50.9	
Hypertension*	1,168	3.9	1.2	1.1	1.4	12.3	45.1	
Lipid disorders*	915	3.1	1.0	0.9	1.1	25.8	59.9	
Diabetes*	885	3.0	0.9	0.8	1.0	23.8	64.1	
Anxiety*	753	2.5	0.8	0.7	0.9	44.5	60.5	
Gastroenteritis*	761	2.5	0.8	0.7	0.9	48.1	61.4	
Test results*	598	2.0	0.6	0.5	0.7	35.6	93.4	
Back complaint*	521	1.7	0.5	0.5	0.6	19.9	52.4	
Viral disease, other/NOS	464	1.6	0.5	0.4	0.6	39.7	69.5	
Subtotal 	9,515	31.8	—	—	—	—	—	
Total problems with clinical treatments	29,937	100.0	31.2	29.5	33.0	—	—	
(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; meds—medications; NOS—not otherwise specified.
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 16,009 procedural treatments provided in these general practice encounters during 2007–08 (Table 10.1).
Most frequent procedures
Table 10.5 lists the most common procedural treatments provided by GPs. 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 from an external provider. A summary of all investigations (both undertaken and ordered) is provided in Table 12.6.
There were 16,009 procedures recorded at a rate of 16.7 per 100 encounters. The most frequently recorded group of procedures in 2007–08 were excisions, at a rate of 3.4 per 100 encounters, and accounting for 6.7% of all other treatments. Other procedural treatments that were frequently recorded included local injections (2.3 per 100 encounters), dressings (2.2 per 100 encounters) and physical medicine/rehabilitation (1.3 per 100 encounters) (Table 10.5).
Table 10.5: Most frequent procedural treatments
Procedural treatment	Number	Per cent 
of other
treatments
(n = 49,130)	Rate per 100 encounters
 (n = 95,898)	95%
 LCL	95%
 UCL	
Excision/removal tissue/biopsy/destruction/
debridement/cauterisation*	3,304	6.7	3.4	3.1	3.8	
Local injection/infiltration*(a)	2,166	4.4	2.3	2.1	2.5	
Dressing/pressure/compression/tamponade*	2,146	4.4	2.2	2.1	2.4	
Physical medicine/rehabilitation*	1,232	2.5	1.3	1.1	1.5	
Incision/drainage/flushing/aspiration/removal body fluid*	1,139	2.3	1.2	1.1	1.3	
Pap smear*	1,039	2.1	1.1	0.9	1.2	
Repair/fixation—suture/cast/prosthetic device (apply/remove)*	881	1.8	0.9	0.8	1.0	
Other therapeutic procedures/surgery NEC*	740	1.5	0.8	0.6	0.9	
Electrical tracings*	542	1.1	0.6	0.5	0.6	
Physical function test*	491	1.0	0.5	0.4	0.6	
Urine test*	420	0.9	0.4	0.4	0.5	
INR test	352	0.7	0.4	0.3	0.5	
Check-up—practice nurse	348	0.7	0.4	0.3	0.4	
Other preventive procedures/high-risk medication*	322	0.7	0.3	0.3	0.4	
Subtotal 	15,121	30.8	—	—	—	
Total procedural treatments 	16,009	32.6	16.7	15.9	17.5	
(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: Includes the most common procedural treatments, those accounting for > 0.5% of all other treatments. LCL—lower confidence limit; 
UCL—upper confidence limit; NEC—not elsewhere classified.
Problems managed with a procedural treatment
Table 10.6 lists the top 10 problems managed with a procedural treatment. It also demonstrates the proportion of contacts with each problem that was managed with a procedure, and the proportion of problems managed with a procedure that had no concomitant medication.
	A total of 14,974 problems involved a procedural treatment in their management (10.3% of all problems).
	The top 10 problems accounted 36.0% of all problems for which a procedure was used.
	Solar keratosis/sunburn was the most common problem managed with a procedure, with a procedure undertaken for nearly 70% of all contacts.
	Almost half the malignant skin neoplasm contacts were managed with a procedural treatment, and the vast majority of these did not have a medication prescribed, supplied or advised.

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 encs(a)
(n = 95,898)	95% LCL	95% UCL	Per cent of this problem(b)	Per cent of treated problems no meds(c)	
Solar keratosis/sunburn 	885	5.9	0.9	0.8	1.1	68.2	96.4	
Female genital check-up* 	877	5.9	0.9	0.8	1.0	51.0	96.7	
Laceration/cut 	666	4.5	0.7	0.6	0.8	78.6	77.3	
Excessive ear wax 	546	3.7	0.6	0.5	0.6	76.0	93.4	
Malignant neoplasm skin 	521	3.5	0.5	0.4	0.6	45.5	96.6	
Warts 	485	3.2	0.5	0.4	0.6	77.7	95.1	
Chronic ulcer skin (including varicose ulcer)	378	2.5	0.4	0.3	0.5	74.8	76.2	
General check-up* 	362	2.4	0.4	0.3	0.5	15.0	86.5	
Sprain/strain* 	363	2.4	0.4	0.3	0.5	24.0	59.3	
Skin symptom/complaint	309	2.1	0.3	0.2	0.4	48.0	95.0	
Subtotal 	5,392 	36.0	—	—	—	—	—	
Total problems with procedural treatments	14,974 	100.0	15.6	14.9	16.4	—	—	
(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: Encs—encounters; LCL—lower confidence limit; UCL—upper confidence limit; meds—medications.
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 specialists, allied health professionals, hospitals for admission, emergency departments or other medical services. Referrals to hospital outpatient clinics and other GPs were classified as referrals to other medical services.
Data on referrals and admissions are reported for each year from 1998–99 to 2007–08 in the 10 year summary report General practice activity in Australia 1998–99 to 2007–08: 10 year data tables available from <www.aihw.gov.au/publications/index.cfm/subject/19> 
(AIHW catalogue number GEP 23).
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 11.8% of all encounters, and for 8.3% of all problems managed. The most frequent referrals were to specialists, followed by referrals to allied health services. 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 = 95,898)	95% LCL	95% UCL	Rate per 100 problems
(n = 145,078)	95% LCL	95% UCL	
At least one referral(a)	11,302	11.8	11.3	12.2	8.3	8.0	8.6	
Referrals	12,008	12.5	12.0	13.0	8.3	8.0	8.6	
	Specialist	7,647	8.0	7.6	8.3	5.3	5.1	5.5	
	Allied health service	3,305	3.4	3.2	3.7	2.3	2.1	2.4	
	Hospital	381	0.4	0.3	0.5	0.3	0.2	0.3	
	Emergency department	210	0.2	0.2	0.3	0.1	0.1	0.2	
	Other medical services	83	0.1	0.1	0.1	0.1	0.0	0.1	
	Other referrals	382	0.4	0.3	0.5	0.3	0.2	0.3	
(a)	Rate per 100 problems for at least one referral is calculated using a numerator of number of individual problems with a referral (n = 11,277).
Note: LCL—lower confidence limit; UCL—upper confidence limit.
11.2	Most frequent referrals
There were 12,008 referrals to other health services, at a rate of 12.5 per 100 encounters. Table 11.2 shows the specialists and allied health service groups to whom GPs most often referred. The most common referrals were to surgeons (11%), orthopaedic surgeons (9%), dermatologists (8%) and ophthalmologists (8%). About 34% of referrals to allied health services were to physiotherapists, 19% to psychologists, 9% to podiatrists or chiropodists and 7% to dietitians or nutritionists.
Table 11.2: The most frequent referrals, by type
Professional/organisation	Number	Per cent of referrals	Per cent of referral group	Rate per 100 encounters
(n = 95,898)	95% LCL	95% UCL	
Medical specialist 	7,647	63.7	100.0	8.0	7.6	8.3	
	Surgeon 	803	6.7	10.5	0.8	0.8	0.9	
	Orthopaedic surgeon 	653	5.4	8.6	0.7	0.6	0.7	
	Dermatologist	634	5.3	8.3	0.7	0.6	0.7	
	Ophthalmologist	631	5.3	8.3	0.7	0.6	0.7	
	Cardiologist 	521	4.3	6.8	0.5	0.5	0.6	
	Ear, nose and throat 	492	4.1	6.4	0.5	0.5	0.6	
	Gastroenterologist	476	4.0	6.2	0.5	0.4	0.6	
	Gynaecologist	419	3.5	5.5	0.4	0.4	0.5	
	Urologist 	278	2.3	3.6	0.3	0.3	0.3	
	Paediatrician	224	1.9	2.9	0.2	0.2	0.3	
Subtotal: top 10 specialist referrals 	5,133	42.7	67.1	—	—	—	
Allied health and other professionals 	3,305	27.5	100.0	3.5	3.2	3.7	
	Physiotherapy 	1,132	9.4	34.3	1.2	1.1	1.3	
	Psychologist 	635	5.3	19.2	0.7	0.6	0.7	
	Podiatrist/chiropodist	299	2.5	9.0	0.3	0.3	0.4	
	Dietitian/nutritionist	215	1.8	6.5	0.2	0.2	0.3	
	Dentist 	178	1.5	5.4	0.2	0.1	0.2	
	Audiologist 	98	0.8	3.0	0.1	0.1	0.1	
	Optometrist 	79	0.7	2.4	0.1	0.1	0.1	
	Breast clinic	67	0.6	2.0	0.1	0.1	0.1	
	Diabetes education 	64	0.5	2.0	0.1	0.0	0.1	
	Counsellor 	62	0.5	1.9	0.1	0.0	0.1	
Subtotal: top 10 allied health referrals	2,830	23.6	85.6	—	—	—	
Total referrals	12,008	100.0	—	12.5	12.0	13.0	
Note: LCL—lower confidence limit; UCL—upper confidence limit.
11.3	Problems most often referred
A referral to a specialist was provided in the management of 7,820 problems. The 10 problems most commonly referred to a specialist accounted for 19.5% of all problems referred to a specialist, and those most often referred were pregnancy (3.1% of problems referred to a specialist), diabetes, malignant skin neoplasm and osteoarthritis (Table 11.3).
Table 11.3 also shows the rate of referral per 100 contacts for each problem. Pregnancy and malignant neoplasm were the problem most likely to result in a referral to a specialist.

Table 11.3: The 10 problems most frequently referred to a medical specialist
Problem managed	Number	Per cent of problems referred 	Rate per 100 encs 
(n = 95,898) 	95% LCL	95% UCL	Rate per 100 contacts of this problem(a)	
Pregnancy*	238	3.1	0.3	0.2	0.3	18.6	
Diabetes—all*	226	2.9	0.2	0.2	0.3	6.1	
Malignant skin neoplasm	214	2.7	0.2	0.2	0.3	18.7	
Osteoarthritis*	155	2.0	0.2	0.1	0.2	6.2	
Back complaint*	138	1.8	0.1	0.1	0.2	5.3	
Sleep disturbance	117	1.5	0.1	0.1	0.2	7.6	
Depression*	114	1.5	0.1	0.1	0.2	3.0	
Ischaemic heart disease*	112	1.4	0.1	0.1	0.1	10.7	
Abnormal test results*	105	1.4	0.1	0.1	0.1	10.8	
Oesophagus disease	103	1.3	0.1	0.1	0.1	4.7	
Subtotal: top 10 problems referred to a specialist	1,522	19.5	—	—	—	—	
Total problems referred to specialist 	7,820	100.0	8.2	7.8	8.5	—	
(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: Encs—encounters; LCL—lower confidence limit; UCL—upper confidence limit.
There were 3,422 problems referred to an allied health professional or service. The 10 most common of these accounted for 47.9% of all problems referred to allied health services, with depression the most common. 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).
Table 11.4: The 10 problems most frequently referred to allied health services
Problem managed	Number	Per cent of problems referred 	Rate per 100 encs
(n = 95,898)	95% LCL	95% UCL	Rate per 100 contacts of this problem(a)	
Depression*	414	12.1	0.4	0.4	0.5	10.8	
Back complaint* 	273	8.0	0.3	0.2	0.3	10.4	
Sprain/strain*	206	6.0	0.2	0.2	0.3	13.7	
Diabetes—all*	197	5.8	0.2	0.2	0.3	5.3	
Anxiety* 	131	3.8	0.1	0.1	0.2	7.7	
Teeth/gum disease	121	3.5	0.1	0.1	0.2	28.8	
Osteoarthritis*	109	3.2	0.1	0.1	0.1	4.4	
Musculoskeletal injury NOS	65	1.9	0.1	0.1	0.1	7.8	
Bursitis/tendonitis/synovitis NOS	64	1.9	0.1	0.1	0.1	8.1	
Obesity	60	1.7	0.1	0.0	0.1	8.7	
Subtotal: top 10 problems referred to AHS	1,639	47.9	—	—	—	—	
Total problems referred to AHS 	3,422	100.0	3.6	3.3	3.8	—	
(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: Encs—encounters; LCL—lower confidence limit; UCL—upper confidence limit; NOS—not otherwise specified; AHS—allied health service.
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, with one in four contacts being referred to hospital.
Table 11.5: The 10 problems most frequently referred to hospital
Problem managed	Number	Per cent of problems referred 	Rate per 100 encs (n = 95,898)	95% LCL	95% UCL	Rate per 100 contacts of this problem(a)	
Pregnancy* 	33	8.4	0.03	0.02	0.05	2.6	
Appendicitis	13	3.3	0.01	0.01	0.02	25.4	
Pneumonia	13	3.3	0.01	0.01	0.02	4.7	
Fracture*	12	3.1	0.01	0.00	0.02	1.3	
Phlebitis/thrombophlebitis	8	2.1	0.01	0.00	0.02	3.2	
Stroke/cerebrovascular accident	8	2.0	0.01	0.00	0.02	3.7	
Atrial fibrillation/flutter	7	1.9	0.01	0.00	0.02	0.8	
Skin infection, other	7	1.9	0.01	0.00	0.02	2.6	
Abortion, spontaneous	7	1.7	0.01	0.00	0.01	8.1	
Heart failure 	6	1.6	0.01	0.00	0.01	1.1	
Subtotal: top 10 problems referred for admission	116	29.5	—	—	—	—	
Total problems referred to hospital	394	100.0	0.41	0.35	0.47	—	
(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: Encs—encounters; LCL—lower confidence limit; UCL—upper confidence limit.

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 1998–99 to 2007–08 in the 10 year summary report General practice activity in Australia 1998–99 to 2007–08: 10 year data tables available from <www.aihw.gov.au/publications/index.cfm/subject/19> (AIHW catalogue number GEP 23).
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 (76.6%) of encounters.
At least one pathology test order was recorded at 17.4% of encounters (for 13.1% of problems managed), and at least one imaging test was ordered at 8.3% of encounters (for 5.7% of problems managed).
Table 12.1: Number of encounters and problems for which pathology or imaging ordered
Pathology/imaging test ordered	Number of encounters 	Per cent of encounters (n = 95,898)	95% LCL	95% UCL	Number of problems	Per cent of problems (n = 145,078)	95% LCL	95% UCL	
Pathology and imaging ordered	2,191	2.3	2.1	2.4	1,539	1.1	1.0	1.1	
Pathology only ordered	14,448	15.1	14.5	15.6	17,494	12.1	11.6	12.5	
Imaging only ordered	5,767	6.0	5.8	6.3	6,667	4.6	4.4	4.8	
No tests ordered	73,492	76.6	75.9	77.3	119,379	82.3	81.8	82.8	
At least one pathology ordered	16,639	17.4	16.7	18.0	19,033	13.1	12.7	13.6	
At least one imaging ordered	7,958	8.3	8.0	8.6	8,205	5.7	5.4	5.9	
At least one other investigation ordered	887	0.9	0.8	1.0	908	0.6	0.6	0.7	
At least one other investigation performed in the practice	1,204	1.3	1.1	1.4	1,212	0.8	0.7	0.9	
At least one other investigation ordered or performed	2,030	2.1	1.9	2.3	2,064	1.4	1.3	1.5	
Note: LCL—lower confidence limit; UCL—upper confidence limit.
12.2	Pathology ordering
A comprehensive report on pathology ordering by GPs in Australia in 1998, written by the then General Practice Statistics and Classification Unit (GPSCU) using BEACH data, was published on the Internet by the Diagnostics and Technology Branch of the then Department of Health and Aged Care in 2000.15 A report on changes in pathology ordering by GPs from 1998 to 2001 was also produced by the GPSCU as an AIHW–University of Sydney book in the GP series in 2003.16 Readers may wish to compare those results with the information presented below. 
Nature of pathology orders at encounter
The GPs recorded 41,375 orders for pathology tests/batteries of tests, at a rate of 43.2 per 100 encounters.
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, and as a rate per 100 encounters with 95% confidence limits.
The pathology tests recorded were grouped according to the categories set out in 
Appendix 4, <www.aihw.gov.au/publications/index.cfm/subject/19>. The main pathology groups reflect those used in previous analyses of pathology tests by Medicare Australia (MBS groups).60
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, liver function (3.1), EUC (3.0), and glucose/glucose tolerance (2.5 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 = 95,898)	95% LCL	95% UCL	
Chemistry* 	23,916	57.8	100.0	24.9	23.6	26.2	
	Lipids* 	4,079	9.9	17.1	4.3	4.0	4.5	
	Liver function* 	2,954	7.1	12.4	3.1	2.8	3.4	
	EUC*	2,848	6.9	11.9	3.0	2.7	3.2	
	Glucose/tolerance*	2,429	5.9	10.2	2.5	2.3	2.8	
	Thyroid function* 	2,324	5.6	9.7	2.4	2.2	2.6	
	Multibiochemical analysis* 	1,886	4.6	7.9	2.0	1.7	2.2	
	Chemistry; other* 	1,258	3.0	5.3	1.3	1.1	1.5	
	Ferritin* 	1,191	2.9	5.0	1.2	1.1	1.4	
	HbA1c*	990	2.4	4.1	1.0	0.9	1.2	
	Prostate specific antigen* 	826	2.0	3.5	0.9	0.8	0.9	
	Hormone assay*	745	1.8	3.1	0.8	0.6	0.9	
	C reactive protein 	591	1.4	2.5	0.6	0.5	0.7	
(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 = 95,898)	95% LCL	95% UCL	
Haematology* 	7,572	18.3	100.0	7.9	7.5	8.3	
	Full blood count* 	5,602	13.5	74.0	5.8	5.5	6.2	
	ESR 	917	2.2	12.1	1.0	0.8	1.1	
	Coagulation* 	793	1.9	10.5	0.8	0.7	0.9	
Microbiology* 	5,432	13.1	100.0	5.7	5.3	6.0	
	Urine M,C&S* 	1,709	4.1	31.5	1.8	1.7	1.9	
	Microbiology; other* 	755	1.8	13.9	0.8	0.7	0.9	
	Hepatitis serology* 	498	1.2	9.2	0.5	0.4	0.6	
	Faeces M,C&S* 	326	0.8	6.0	0.3	0.3	0.4	
	Vaginal swab and M,C&S 	313	0.8	5.8	0.3	0.3	0.4	
	HIV*	304	0.7	5.6	0.3	0.3	0.4	
	Chlamydia*	290	0.7	5.3	0.3	0.3	0.4	
Cytology* 	1,806	4.4	100.0	1.9	1.7	2.1	
	Pap smear* 	1,765	4.3	97.7	1.8	1.6	2.0	
Other NEC* 	950	2.3	100.0	1.0	0.8	1.2	
	Blood test 	424	1.0	44.6	0.4	0.3	0.6	
	Other test NEC	344	0.8	36.2	0.4	0.3	0.4	
Tissue pathology* 	740	1.8	100.0	0.8	0.6	0.9	
	Histology; skin	652	1.6	88.1	0.7	0.6	0.8	
Immunology* 	633	1.5	100.0	0.7	0.6	0.7	
	Immunology, other*	286	0.7	45.3	0.3	0.2	0.4	
	Antinuclear antibodies	167	0.4	26.5	0.2	0.1	0.2	
Simple basic tests* 	175	0.4	100.0	0.2	0.1	0.2	
Infertility/pregnancy*	152	0.4	100.0	0.2	0.1	0.2	
Total pathology tests 	41,375	100.0	—	43.2	41.3	45.0	
*	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; 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 orders per 100 specified problems when at least one test is ordered. For example, 29.3% of contacts with diabetes resulted in pathology orders, and when pathology was ordered for diabetes, 283 tests were ordered per 100 diabetes contacts that resulted in a pathology test order. In contrast, only 10.9% of contacts with hypertension problems resulted in a pathology test, but the resulting test orders accounted for almost as many tests (6.8%) as did diabetes.
Table 12.3: The 10 problems for which pathology was most frequently ordered
Problem managed	Number of problems	Number of problem–path combinations(a)	Per cent of problem–path combinations(a)	Per cent of problems with test(b)	Rate of path orders per 100 problems with pathology(c)	
Diabetes—all* 	3,717	3,078	7.2	29.3	282.9	
Hypertension* 	9,496	2,896	6.8	10.9	278.6	
General check-up* 	2,407	2,564	6.0	33.7	315.7	
Lipid disorders	3,541	2,181	5.1	27.6	222.8	
Female genital check-up* 	1,722	1,593	3.7	78.9	117.2	
Weakness/tiredness general 	625	1,473	3.4	62.4	377.9	
Blood test NOS 	413	1,193	2.8	85.2	339.5	
Urinary tract infection*	1,525	957	2.2	54.7	114.7	
Pregnancy*	1,279	881	2.1	31.9	215.7	
Abnormal test results*	976	864	2.0	52.1	169.9	
Subtotal	25,701	17,680	41.3	—	—	
Total problems	145,078	42,835	100.0	13.1	225.1	
(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 41,375 pathology test orders and 42,835 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: Path—pathology; 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 GPSCU using BEACH data, and published by the AIHW and the University of Sydney in 2001.17
Nature of imaging orders at encounter
There were 9,143 imaging test orders recorded, at a rate of 9.5 per 100 encounters. 
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, and as a rate per 100 encounters with 95% confidence limits. Diagnostic radiology accounted for half of all imaging test orders while ultrasound accounted for a further 35.2%.

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 = 95,898)	95% LCL	95% UCL	
Diagnostic radiology*	4,599	50.3	100.0	4.8	4.6	5.0	
	X-ray; chest	1,009	11.0	21.9	1.1	1.0	1.1	
	X-ray; knee	449	4.9	9.8	0.5	0.4	0.5	
	Mammography; female	329	3.6	7.2	0.3	0.3	0.4	
	Test; densitometry 	303	3.3	6.6	0.3	0.3	0.4	
	X-ray; foot/feet 	253	2.8	5.5	0.3	0.2	0.3	
	X-ray; hip	224	2.4	4.9	0.2	0.2	0.3	
	X-ray; shoulder 	201	2.2	4.4	0.2	0.2	0.2	
	X-ray; ankle	176	1.9	3.8	0.2	0.1	0.2	
	X-ray; wrist 	142	1.6	3.1	0.2	0.1	0.2	
	X-ray; spine; lumbosacral	137	1.5	3.0	0.1	0.1	0.2	
	X-ray; spine; lumbar 	131	1.4	2.8	0.1	0.1	0.2	
	X-ray; hand 	131	1.4	2.8	0.1	0.1	0.2	
	X-ray; abdomen	109	1.2	2.4	0.1	0.1	0.1	
	X-ray; spine; cervical	99	1.1	2.1	0.1	0.1	0.1	
	X-ray; finger(s)/thumb	95	1.0	2.1	0.1	0.1	0.1	
	X-ray; spine; thoracic	82	0.9	1.8	0.1	0.1	0.1	
Ultrasound*	3,215	35.2	100.0	3.4	3.2	3.5	
	Ultrasound; pelvis	515	5.6	16.0	0.5	0.5	0.6	
	Ultrasound; shoulder 	293	3.2	9.1	0.3	0.3	0.3	
	Ultrasound; breast; female 	278	3.0	8.7	0.3	0.2	0.3	
	Ultrasound; abdomen	276	3.0	8.6	0.3	0.2	0.3	
	Ultrasound; obstetric	230	2.5	7.2	0.2	0.2	0.3	
	Test; doppler 	140	1.5	4.3	0.2	0.1	0.2	
	Echocardiography	139	1.5	4.3	0.2	0.1	0.2	
	Ultrasound; kidney	104	1.1	3.2	0.1	0.1	0.1	
	Ultrasound; renal tract 	100	1.1	3.1	0.1	0.1	0.1	
	Ultrasound; scrotum	90	1.0	2.8	0.1	0.1	0.1	
	Ultrasound; abdomen upper	79	0.9	2.4	0.1	0.1	0.1	
	Ultrasound; leg	75	0.8	2.3	0.1	0.1	0.1	
	Ultrasound; neck	70	0.8	2.2	0.1	0.1	0.1	
	Ultrasound; thyroid	69	0.8	2.1	0.1	0.1	0.1	
Computerised tomography*	1,164	12.7	100.0	1.2	1.1	1.3	
	CT scan; brain	181	2.0	15.6	0.2	0.2	0.2	
	CT scan; abdomen 	175	1.9	15.1	0.2	0.2	0.2	
	CT scan; spine; lumbar	133	1.5	11.4	0.1	0.1	0.2	
	CT scan; head	111	1.2	9.6	0.1	0.1	0.1	
	CT scan; spine; lumbosacral	94	1.0	8.1	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 = 95,898)	95% LCL	95% UCL	
	CT scan; chest	79	0.9	6.8	0.1	0.1	0.1	
	CT scan; sinus	76	0.8	6.5	0.1	0.1	0.1	
Nuclear medicine imaging*	105	1.1	100.0	0.1	0.1	0.1	
	Scan; bone(s)	62	0.7	59.6	0.1	0.0	0.1	
Magnetic resonance imaging	60	0.7	100.0	0.1	0.0	0.1	
Total imaging tests	9,143	100.0	—	9.5	9.2	9.9	
*	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 describes the most common problems for which imaging was ordered, in decreasing frequency order of problem–imaging combinations. The most common problem for which imaging was ordered was back complaint, accounting for 5.7% of orders, followed by osteoarthritis (4.5%), and fracture (4.2%). 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, 36.7% of contacts with fractures resulted in an imaging test and 109.6 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*	2,624	524	5.7	17.0	117.7	
Osteoarthritis*	2,484	413	4.5	14.6	113.8	
Fracture*	959	386	4.2	36.7	109.6	
Sprain/strain*	1,509	319	3.5	17.5	120.7	
Injury musculoskeletal NOS	841	300	3.3	31.2	114.4	
Pregnancy*	1,279	292	3.2	22.2	102.6	
Abdominal pain*	628	253	2.7	37.1	108.5	
Bursitis/tendonitis/synovitis NOS 	789	204	2.2	22.7	113.5	
Breast lump/mass (female)	183	198	2.2	72.2	150.0	
Injury skin; other	587	153	1.7	21.6	120.4	
Subtotal	11,883	3,042	32.9	—	—	
Total problems	145,078	9,221	100.0	5.7	112.4	
(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 9,143 imaging test orders and 9,221 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. There were a total of 934 other investigations ordered by GPs during the study year and 1,256 other investigations undertaken by the GP or practice staff during the study year. This means there were 2,190 total other investigations either ordered or undertaken in the practice (Table 12.6).
Most frequent other investigations
The first half of Table 12.6 lists the most common other investigations ordered by GPs. The second half lists the most common other investigations undertaken in the practice by GPs or practice staff. The total number of these investigations ordered by the GP or undertaken in the practice is shown in the table. Each investigation is expressed as a percentage of ordered or undertaken other investigations, and as a rate per 100 encounters with 95% confidence limits. 
Table 12.6: Most frequent other investigations ordered by GPs or performed in the practice
Investigation ordered	Number	Per cent of ordered investigations	Rate per 100 encounters
 (n = 95,898)	95%
LCL	95%
UCL	
Electrical tracings*	450	48.1	0.5	0.4	0.5	
Diagnostic endoscopy*	261	27.9	0.3	0.2	0.3	
Physical function test* 	190	20.4	0.2	0.1	0.3	
Other diagnostic procedures*	24	2.5	0.0	0.0	0.0	
Subtotal 	925	98.9	—	—	—	
Total other investigations ordered	934	100.0	1.0	0.9	1.1	
Investigation undertaken in the practice	Number	Per cent of undertaken investigations	Rate per 100 encounters
 (n = 95,898)	95%
LCL	95%
UCL	
Electrical tracings*	542	43.2	0.6	0.5	0.6	
Physical function test* 	491	39.1	0.5	0.4	0.6	
Other diagnostic procedures*	185	14.7	0.2	0.1	0.3	
Diagnostic endoscopy*	37	2.9	0.0	0.0	0.1	
Subtotal 	1,255	99.9	—	—	—	
Total other investigations undertaken	1,256	100.0	1.3	1.1	1.5	
Total other investigations ordered or undertaken in the practice	2,190	—	2.3	2.1	2.5	
*	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.
13	Practice nurse activity
This section describes the activities of practice nurses recorded in association with the 
GP–patient encounters recorded by the GPs in BEACH.
In November 2004, four 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 recording form for the 2005–06 BEACH year was amended to allow the capture of this information.
•	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 gave the ’other treatment’.
The survey form allowed 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, <www.aihw.gov.au/
publications/index.cfm/subject/19>.
In November 2007, a new Medicare item number was added for practice nurse services. This item, 10997, was only available to BEACH 2007–08 participants between November 2007 and March 2008 inclusive. The seven practice nurse Medicare items available during the 2007–08 BEACH data period are listed with a short description in Table 13.1.61
This section investigates:
	the distribution of the Medicare items claimed for practice nurses (the total number of these items was reported as one group in Table 5.5)
	treatments provided by practice nurses in association with the GP-recorded encounters
	problems for which the practice nurse provided the treatment in direct association with the GP-recorded encounters.
In Chapter 10, all treatments (other than medications) 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 that were not counted in Chapter 10. GPs are also instructed not to record their taking of routine clinical measurements, such as blood pressure. However, where the practice nurse 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 the practice nurse 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 versus practice nurse activity
Practice nurses were involved in 5,712 GP–patient encounters, assisting in the management of 5,909 problems. However, only 2,073 practice nurse item numbers were recorded as claimable from Medicare, and practice nurse items accounted for 2.0% of all Medicare items recorded in 2007–08 (Table 5.5). At two-thirds (65.3%) of encounters at which the practice nurse performed a clinical or procedural activity, no practice nurse item number was recorded as claimable (results not tabled).
Distribution of practice nurse item numbers claimed at encounters
GPs recorded 2,073 practice nurse item numbers at 2,047 encounters (Table 5.5). Almost all the practice nurse item numbers recorded for the BEACH encounters were for immunisations/vaccinations (64.1%) and wound treatments (34.4%). Items claimed for practice nurse conduct of cervical smears and/or preventive checks were very few (17 in total), as were services provided to a person with chronic disease, each accounting for less than 1% of all recorded practice nurse item numbers (Table 13.1).
Table 13.1: Distribution of practice nurse item numbers recorded at encounter
Medicare item number	Short descriptor	Number 	Per cent
 of total	
10993	Immunisation	1,330	64.1	
10994(a)	Cervical smear and preventive checks	4	0.2	
10995(a)	Cervical smear and preventive checks—women 20–69 years, no smear in past 4 years	2	0.1	
10996	Wound treatment (other than normal aftercare)	713	34.4	
10997(b)	Service provided to a person with a chronic disease by a practice nurse or registered Aboriginal Health Worker 	14	0.7	
10998(c)	Cervical smear	5	0.3	
10999(c)	Cervical smear—women 20–69 years, no smear in past 4 years	6	0.3	
Total	All Medicare practice nurse item numbers	2,073	100.0	
(a)	Item number introduced in November 2006.
(b)	Item number introduced in November 2007. 
(c)	Item numbers introduced in November 2004 but broadened in 2006, so they are now not limited to services in rural areas.
Treatments provided by practice nurses
As reported in Chapter 10, GPs reported 49,130 other treatments. A further 3,634 injections were given for immunisation (not reported in Chapter 10). In total 52,764 other treatments were recorded.
At least one practice nurse activity was recorded at 5,712 encounters—6.0% of all encounters. Nurses were involved in the management of 5,909 problems (4.1% of all problems managed by the participating GPs). Practice nurses provided 6,283 other treatments, representing 11.9% of all other treatments recorded at BEACH encounters. The majority (84.7%) of the practice nurse activity was procedural in nature. These procedures represented 28.9% of all procedures recorded. Other investigations conducted in the surgery accounted for 8.3% of practice nurse activity and represented 41.5% of all other investigations undertaken during BEACH encounters. In contrast, while clinical treatments accounted for 7.0% of practice nurse activity, the practice nurse provided less than 2% of all clinical treatments (Table 13.2).
Table 13.2: Summary of treatments given by practice nurse
Treatment	Performed/assisted by the practice nurse		Performed by the GP		Total number recorded(a) 	
	Number	Per cent 
of total 		Number	Per cent 
of total			
Procedural treatments(a)	5,322	28.9		13,065	71.1		18,387	
Clinical treatments	440	1.3		32,681	98.7		33,121	
Other investigations 	521	41.5		735	58.5		1,256	
All other treatments	6,283	11.9		46,481	88.1		52,764	
(a)	Procedural treatments here includes all injections for immunisations/vaccinations (n = 3,634). 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 5,322 procedures done by practice nurses, 40.4% were injections (which were mainly for immunisations/vaccinations) and a further 22.3% were dressing/pressure/compression/
tamponade. Together these accounted for more than half of all procedures undertaken by practice nurses. Incision/drainage/aspirations made up 7.3%, and check-ups 6.5% of procedures done by the nurse. Practice nurses also undertook a wide range of other procedural activities in association with the GP encounters. The most common are listed in Table 13.3.
Administrative and documentation work was the most frequently recorded clinical treatment, accounting for 25.7% of the clinical treatments provided by nurses, followed by general advice/education (18.6%), advice about treatment (7.6%), counselling about the problem under management (7.2%) and counselling about nutrition or weight (6.7%).
The most common other investigations performed by the nurse were electrical tracings (such as electrocardiogram) (56.8% of other investigations) and physical function tests (38.5%) (Table 13.3).
Problems managed with practice nurse involvement
The problems managed most often with the assistance of a practice nurse in association with the consultation were immunisation/vaccination (28.5% of all problems managed with the involvement of a practice nurse), followed by laceration/cut (5.8%) and chronic skin ulcer (5.7%) (Table 13.4).
Practice nurses were involved in the management of a wide range of problems in association with the GP encounters. The most common are listed in Table 13.4.

Table 13.3: Most frequent treatments provided by practice nurses
Treatment	Number	Per cent of group(a)	Rate per 100 encs involving practice nurse (n = 5,712)(a)	95% LCL	95% UCL	
Procedural treatments	5,323	100.0	93.2	91.5	94.8	
	Local injection/infiltration*	2,153	40.4	37.7	34.7	40.7	
	Dressing/pressure/compression/tamponade*	1,185	22.3	20.7	18.7	22.8	
	Incision/drainage/flushing/aspiration/removal body fluid*	387	7.3	6.8	5.6	7.9	
	Check-up—practice nurse*	348	6.5	6.1	4.8	7.4	
	Repair/fixation-suture/cast/prosthetic device (apply/remove)*	285	5.3	5.0	4.2	5.7	
	INR test	282	5.3	4.9	3.6	6.2	
	Excision/removal issue/biopsy/destruction/
debride/cauterise*	278	5.2	4.9	3.8	5.9	
	Urine test*	121	2.3	2.1	1.3	3.0	
	Other procedures/minor surgery NEC*	84	1.6	1.5	1.0	2.0	
	Glucose test	55	1.0	1.0	0.7	1.3	
	Physical medicine/rehabilitation*	35	0.7	0.6	0.2	1.1	
	Pregnancy test*	30	0.6	0.5	0.3	0.8	
	Pap smear	30	0.6	0.5	0.3	0.8	
Clinical treatments	440	100.0	7.7	6.2	9.2	
	Other admin/document*	113	25.7	2.0	1.4	2.6	
	Advice/education*	82	18.6	1.4	0.8	2.1	
	Advice/education—treatment*	33	7.6	0.6	0.4	0.8	
	Counselling—problem*	32	7.2	0.6	0.3	0.8	
	Counselling/advice—nutrition/weight*	29	6.7	0.5	0.1	0.9	
Other investigations	521	100.0	9.1	7.5	10.8	
	Electrical tracings*	296	56.8	5.2	4.3	6.1	
	Physical function tests*	201	38.5	3.5	2.3	4.7	
(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
treatment 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: Encs—encounters; LCL—lower confidence limit; UCL—upper confidence limit; NEC—not elsewhere classified.

Table 13.4: The most common problems managed with the involvement of practice nurse
Problem managed	Number	Per cent of problems
involving practice nurse (n = 5,909)	Rate per 100 
encs involving 
practice nurse(a)
(n = 5,712)	95%
LCL	95%
UCL	
Immunisation/vaccination—all*	1,684	28.5	29.5	26.7	32.2	
Laceration/cut	345	5.8	6.0	5.0	7.0	
Chronic ulcer skin (including varicose ulcer)	266	4.5	4.7	3.7	5.6	
General check-up*	244	4.1	4.3	3.1	5.4	
Diabetes—all*	173	2.9	3.0	2.4	3.7	
Atrial fibrillation/flutter	162	2.7	2.8	2.0	3.6	
Excessive ear wax	160	2.7	2.8	2.2	3.4	
Malignant neoplasm skin	146	2.5	2.6	1.8	3.3	
Hypertension*	100	1.7	1.8	1.2	2.3	
Skin infection, post-traumatic	90	1.5	1.6	1.0	2.1	
Asthma	70	1.2	1.2	0.9	1.6	
Abrasion/scratch/blister	66	1.1	1.2	0.6	1.7	
Repair/fixate-suture/cast/prosthetic device (apply/remove)*	65	1.1	1.1	0.8	1.5	
Burns/scalds	63	1.1	1.1	0.8	1.4	
Total problems involving practice nurse	5,909	100.0	103.4	102.7	104.2	
(a)	Rate of nurse provision of treatment for selected problem per 100 total encounters.
*	Includes multiple ICPC-2 or ICPC-2 PLUS codes (see Appendix 4, <www.aihw.gov.au/publications/index.cfm/subject/19>).
Note: 	Encs—encounters; LCL—lower confidence limit; UCL—upper confidence limit.
13.2	Discussion
These results suggest that many GPs are using practice nurses to provide immunisations/ vaccinations and, to a lesser degree, for dressings. However, they also suggest very little use of the cervical smear/preventive check practice nurse item numbers.
If extrapolated, the 2,047 encounters at which a practice nurse item number was recorded as claimable (at 2.0% of Medicare-claimable encounters in BEACH) (Table 5.4) to the 111 million general practice Medicare items claimed (including practice nurse items)1, represent an estimated 2.2 million claims were made from Medicare for practice nurse services associated with GP consultations. The MBS claims data for practice nurse item numbers for the 2007–08 BEACH year show that 4.6 million practice nurse items were claimed for that period.1 This suggests that about 2.4 million services were provided and claimed for practice nurse activities conducted independently of direct GP–patient consultations. 
The MBS data suggest that 62.2% of the claims were for immunisation/vaccinations (item 10993), 34.6% were for wound dressings (item 10996) and only 1.4% were for the cervical smear/preventive check items (10994, 10995, 10998, 10999).62 This compares with BEACH data of 64.1% of the claims being for immunisations/vaccinations, 34.4% for wound dressings and 0.8% for cervical smear/preventive check items. This suggests that more of the wound management and cervical smear/preventive checks are being done (and claimed for) through direct appointments with the practice nurse, as directed by the GP.
In 2005–06, the research team suggested that the low uptake of practice nurse items covering cervical smears may have been partly due to the geographic limitations placed on these item numbers at that time, and on the difficulty of separating the cervical smear from the total clinical activity of a well woman check. These checks often involve (in addition to a cervical smear) a pelvic examination and a breast check, and may also involve discussion of sexual issues and contraception, which in turn may result in prescription of medication. Practice nurses cannot prescribe medication.
The geographic limitations, and the broadening of the cervical smear item numbers in November 2006 removed some of these limitations. But these actions appear to have had little effect on uptake rate of these item numbers. 
Comparison of the services provided by practice nurses (Table 13.3) with the common problems for which these services were provided (Table 13.4) suggests that about 78% of the local injections/infiltrations recorded for practice nurses were given for immunisation/
vaccinations, and about 22% were for other types of injections and therefore not eligible to be claimed through Medicare. Table 13.1 suggests that only 1,330 (79%) of the estimated 1,684 immunisations/vaccinations involving the practice nurses were actually claimed through Medicare. 
Table 13.3 shows that nurses dealt with 1,185 dressing/pressure/compression/tamponades in conjunction with the GP encounter, but only 713 claims were made for Medicare payment for wound treatment (Table 13.1). This suggests that about 60% of the dressings recorded for practice nurses were claimable under Medicare. Some of the dressings may be follow-up encounters where the follow-up treatment (aftercare) is included in the initial Medicare claim (claimed in the past), and may therefore not be claimable for the practice nurse.
A comparison of practice nurse activity from 2005–06 to 2007–08 is provided in the 10 year summary report General practice activity in Australia 1998–99 to 2007–08: 10 year data tables available from <www.aihw.gov.au/publications/index.cfm/subject/19> (AIHW catalogue number GEP 23).
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.3 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 about their lifestyle choices on an individual basis. 
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 include self-reported height and weight (for calculation of body mass index, BMI), 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 1998–99 to 2007–08 in the 10 year summary report General practice activity in Australia 1998–99 to 
2007–08: 10 year data tables available from <www.aihw.gov.au/publications/index.cfm/subject/19> (AIHW catalogue number GEP 23).
Summaries of results from all SAND substudies from April 1999 to July 2006 inclusive have been published in Patient-based substudies from BEACH: abstracts and research tools 1999–2006.11 Abstracts of results and the research tools used in SAND substudies conducted between August 2006 and March 2007 were published in General practice activity in Australia 2006–072 and those conducted from April 2007 to January 2008 are included in Chapter 15 of this report.
14.1	Body mass index
It is estimated that overweight and obesity accounted for 7.5% of the total burden of disease in Australia in 2003, and ranked third63, an increase from 4.3% of total burden and sixth rank in 1996.64 The 1999–00 Australian Diabetes, Obesity and Lifestyle Study (AusDiab) estimated that 60% of Australians aged over 25 years were overweight or obese (BMI > 25). Men were more likely to be overweight or obese than women (67% compared with 52%).65 
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 recent WHO recommendations66 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).67 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. developed a method which calculates the age–sex-specific BMI cut-off levels for overweight and obesity specific to children aged 2–17 years.68 There are three categories defined for childhood BMI: underweight/normal, 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.67 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. has been used for defining overweight and obesity in children (aged 2–17 years).68
Results
Body mass index of adults
The sample size was 31,062 patients aged 18 years and over at encounters with 952 GPs.
	More than half (59.3%) of the patients were overweight or obese—23.9% obese and 35.4% overweight (Table 14.1).
	Only 2.5% of patients were underweight (Table 14.1).
	Four out of ten adult patients had a BMI that was in the normal range (Table 14.1).
	Males were more likely to be overweight or obese (66.1%, 95% CI: 65.0–67.2) than females (54.8%, 95% CI: 53.7–55.8) (results not tabled).
	Overweight/obesity was most prevalent among male patients aged 45–64 years (74.5%) and those aged 65–74 years (72.0%) (Figure 14.1).
	Among female patients overweight/obesity was most prevalent in those aged 
65–74 years (66.9%) and 45–64 years (63.3%) (Figure 14.1).
	Underweight was most prevalent in patients aged 18–24 years and 75 years and over. Of young adults (18–24 years), 6.6% of females and 2.3% of males were underweight, and among those aged 75 years and over, 4.9% of women and 1.9% of men were underweight (Figure 14.2).
These results are consistent with those of the 1999–00 AusDiab study65 (60% of adults aged > 25 years were overweight or obese) and with the ABS 2004–05 figures from the National Health Survey, which reported that 53% of adults aged 18 or more were overweight or obese.69
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 attending general practice patients. 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 will attend a GP at least once. For the first time in this report we have weighted the BEACH sample to estimate the BMI of the GP–patient population (that is, the 14 million adult patients who have attended a GP at least once), using the method described by Knox et al. (2008).3 
The estimates for the GP–patient population (after adjusting for age–sex attendance patterns) suggest that 23.5% of the patient population were obese, 35.3% were overweight, 38.9% were normal weight and 2.3% 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 = 12,126)	Per cent in patient population
(95% CI)(b)	Per cent in BEACH sample
(95% CI)
(n = 18,703)	Per cent 
in patient population
(95% CI)(b)	Per cent 
in BEACH sample
(95% CI)
(n = 31,062)	Per cent 
in patient population
(95% CI)(b)	
Obese	23.1
(22.1–24.1)	22.8
(21.8–23.8)	24.3
(23.5–25.2)	23.9
(23.0–24.8)	23.9
(23.1–24.6)	23.5
(22.7–24.2)	
Overweight	43.0
(42.0–44.0)	42.1
(41.0–43.2)	30.4
(29.7–31.2)	29.7
(28.9–30.4)	35.4
(34.7–36.0)	35.3
(34.6–36.0)	
Normal	32.7
(31.6–33.8)	34.0
(32.7–35.2)	41.9
(40.9–43.0)	43.1
(42.1–44.2)	38.3
(37.4–39.2)	38.9
(38.0–39.9)	
Underweight	1.2
(1.0–1.4)	1.2
(0.9–1.4)	3.3
(3.0–3.6)	3.3
(3.0–3.6)	2.5
(2.3–2.7)	2.3
(2.1–2.5)	
(a)	Patient sex was not recorded for 233 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 million.
Note: BMI—body mass index; CI—confidence interval.
Body mass index of children
BMI was calculated for 3,046 patients aged 2–17 years at encounters with 801 GPs.
	Three in 10 children (28.3%, 95% CI: 26.4–30.2) were classed as overweight or obese—this consists of 11.2% (95% CI: 10.0–12.5) obese and 17.1% (95% CI: 15.7–18.5) overweight (results not tabled).
	There was no difference in prevalence of overweight/obesity among male (29.3%, 
95% CI: 26.8–31.8) and female children (27.4%, 95% CI: 25.0–29.9) (results not tabled).
	The age-specific rates of obesity followed similar patterns for both sexes (figures 14.3 and 14.4).
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. Also includes table as follows:
	18–24	25–44	45–64	65–74	75+	
Male	40.8	61.9	74.5	72	59.4	
Female	33.3	47.9	63.3	66.9	54.4	
Figure 14.2: Age-sex-specific rates of underweight adults
Vertical bar chart showing, for male and female, per cent on y-axis and age group (years) on x-axis. Also includes table as follows:
	18–24	25–44	45–64	65–74	75+	
Male	2.3	1.1	0.8	0.9	1.9	
Female	6.6	3.4	1.8	2.3	4.9	
Figure 14.3: Age specific rates of obesity, overweight and normal/underweight in male children
Stacked line chart showing, for weight range, per cent on y-axis and age group (years) on x-axis. Also includes table as follows:
	2–4	5–8	9–12	13–17	
Male obese	16	13	11.9	7	
Male overweight	11.2	14.9	20.9	23.1	
Male under/normal	72.8	72.2	67.2	69.9	
Figure 14.4: Age specific rates of obesity, overweight and normal/underweight in female children
Stacked line chart showing, for weight range, per cent on y-axis and age group (years) on x-axis. Also includes table as follows:
	2–4	5–8	9–12	13–17	
Female obese	9.5	13.9	10.2	9.3	
Female overweight	14.0	15.1	21.1	17.5	
Female under/normal	76.5	71.0	68.7	73.2	

14.2	Smoking (patients aged 18 years and over)
Tobacco smoking is the leading cause of drug-related death and hospital separations in Australia.70 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 200363, a decrease from 9.7% of total burden in 1996.64 According to the 2004 National Drug Strategy Household Survey (NDSHS), 17.4% of Australians aged 14 years and over smoked daily: 18.6% of males and 16.3% of females.71
Method
GPs were instructed to ask adult patients (18 years and over):
•	What best describes your smoking status? 	Smoke daily
	Smoker 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 31,652 adult patients was established at encounters with 952 GPs. Table 14.2 shows that:
	16.5% of adult patients were daily smokers
	significantly more male (19.8%) than female patients (14.4%) were daily smokers
	only 2.9% of adult patients were occasional smokers
	more than a quarter of adults (27.9%) were previous smokers.
Daily smoking was most prevalent among younger adult patients (aged 18–24 years and 
25–44 years), with almost one in four of these patients reporting daily smoking. Almost 60% of male and 25% of female patients aged 75 years and over were previous smokers, 
but only 5% of males and 4% of females in this age group were daily smokers (figures 14.5 and 14.6).
Estimation of smoking in the adult general practice patient population
The BEACH study reports data about patient smoking habits from a sample of the attending general practice patients. 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 (about 14 million adults). For the first time in this report we have weighted the BEACH sample to estimate the smoking status among the GP–patient population, using the method described by Knox et al. (2008).3 
The estimates for the GP–patient population (after adjusting for age–sex attendance patterns) show that 19.3% of the patient population were daily smokers, 3.5% were occasional smokers, 25.7% were previous smokers and 51.5% had never smoked. Male patients in the total general practice population were significantly more likely to be daily (23.4%), occasional (4.1%) and previous smokers (30.5%) than females patients (15.9%, 3.0% and 21.7%, respectively) (Table 14.2). 

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 = 12,335)	Per cent in patient population
(95% CI)(b)	Per cent in BEACH sample
(95% CI)
(n = 19,081)	Per cent in patient population
(95% CI)(b)	Per cent 
in BEACH sample
(95% CI)
(n = 31,652)	Per cent in patient population
(95% CI)(b)	
Daily	19.8
(18.8–20.8)	23.4
(22.2–24.5)	14.4
(13.7–15.2)	15.9
(15.1–16.7)	16.5
(15.8–17.3)	19.3
(18.5–20.1)	
Occasional	3.3
(2.9–3.7)	4.1
(3.6–4.6)	2.6
(2.3–2.9)	3.0
(2.7–3.3)	2.9
(2.7–3.2)	3.5
(3.2–3.9)	
Previous	36.5
(35.3–37.7)	30.5
(29.4–31.6)	22.3
(21.4–23.1)	21.7
(20.8–22.5)	27.9
(27.1–28.6)	25.7
(24.9–26.5)	
Never	40.4
(39.2–41.6)	42.0
(40.7–43.3)	60.7
(59.6–61.7)	59.4
(58.3–60.5)	52.7
(51.7–53.6)	51.5
(50.4–52.5)	
(a)	Patient sex was not recorded for 236 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 million.
Note: CI—confidence interval.

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. Also includes table as follows:
	18–24	25–44	45–64	65–74	75+	
Male daily	25.4	31.4	21.8	10.8	5.3	
Male previous	11.4	19.4	35.8	51.3	58.5	
Male never	54.6	43.9	39.4	36.3	35.7	

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. Also includes table as follows:
	18–24	25–44	45–64	65–74	75+	
Female daily	20.4	19.6	16.1	7.1	4.1	
Female previous	10.5	20.4	24.6	27.0	24.7	
Female never	62.7	55.9	57.3	65.2	70.9	

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 morbidity70 (in particular ischaemic heart disease).72 The National Health and Medical Research Council 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.72 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%.63
The 2004 NDSHS found that 9.8% of people aged 14 years and over (10.1% of males and 9.6% of females) drank at levels considered to be risky or high risk for their health in the long term.71 This risk level of alcohol consumption was based on the NHMRC 2001 guidelines.73 The NDSHS also found that 35.4% of people aged 14 years and over (40.3% of males and 30.7% of females) drank alcohol during the preceding 12 months at levels that put their health at risk in the short term.71
Method
To measure alcohol consumption, BEACH uses three items from the WHO Alcohol Use Disorders Identification Test (AUDIT)74, with scoring for an Australian setting.75 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.75
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
•	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 is present at the consultation.
Results
Patients’ self-reported alcohol consumption was recorded at 30,796 adult patient (18 years and over) encounters with 951 GPs.
	More than one-quarter of adults reported drinking alcohol at at-risk levels (26.2%) (Table 14.3).
	At-risk drinking was more prevalent among male patients (31.7%) than female patients (22.6%) (Table 14.3).
	At-risk drinking was most prevalent in the 18–24 year age group, particularly among men. In this age group half of the males and more than one-third 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 a little lower than those for short-term risk from the NDSHS.76 This is likely to be due to the difference in the age ranges studied (14 years and over in NDSHS and 18 years and over in BEACH), and to differences in the age–sex distributions of the study populations. 
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 attending general practice patients. 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 (about 14 million adults). For the first time in this report we have weighted the BEACH sample to estimate the alcohol consumption among the GP–patient population, using the method described by Knox et al. (2008).3 
The estimates for the GP–patient population (after adjusting for age–sex attendance patterns) show that 29.3% of the patient population were at-risk drinkers, 44.2% were responsible drinkers and 26.5% were non-drinkers. Male patients in the total general practice population were significantly more likely to be at-risk drinkers (35.7%) than females patients (24.0%) (Table 14.3). 
Readers interested in the relationship between morbidity managed and alcohol consumption will find more information in Proude et al. The relationship between self-reported alcohol intake and the morbidities managed by GPs in Australia.77

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 = 12,071)	Per cent in patient population
(95% CI)(a)	Per cent in BEACH sample
(95% CI)
(n = 18,725)	Per cent in patient population
(95% CI)(a)	Per cent in BEACH sample
(95% CI)
(n = 30,796)	Per cent in patient population
(95% CI)(a)	
At-risk drinker	31.7
(30.5–32.9)	35.7
(34.3–37.1)	22.6
(21.6–23.5)	24.0
(23.0–25.0)	26.2
(25.3–27.1)	29.3
(28.3–30.3)	
Responsible drinker	47.6
(46.4–48.8)	45.0
(43.8–46.3)	42.6
(41.6–43.7)	43.4
(42.4–44.5)	44.6
(43.7–45.5)	44.2
(43.3–45.1)	
Non-drinker	20.7
(19.6–21.8)	19.3
(18.2–20.4)	34.8
(33.5–36.1)	32.6
(31.3–33.9)	29.3
(28.2–30.3)	26.5
(25.5–27.5)	
(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 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-at-risk, per cent on y-axis and age group (years) on x-axis. Also includes table as follows:
	18–24	25–44	45–64	65–74	75+	
Male at-risk	50.6	40.4	34.5	23.8	13.1	
Female at-risk	36.9	25.8	23.0	15.7	12.9	

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 are considered risk factors. A risk factor profile was prepared for 30,002 adult patients (aged 18 years and over) (Table 14.4).
	Half of the adult respondents had one risk factor. The most common was overweight (22.6% of adults) followed by obesity (16.0%).
	One in five patients had two risk factors, the most common combinations being:
–	overweight and at-risk alcohol consumption—7.1% of patients
–	obesity and at-risk alcohol consumption—4.1% of patients
–	daily smoking and at-risk alcohol consumption—3.3% of patients.
	A small group of patients (4.1%) 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 (29.9%) than males (20.2%).
	One-third of males (31.8%) had two or three risk factors compared with one-fifth (18.7%) of females.
Estimation of the risk profile of the adult general practice patient population
The BEACH study reports data about patient risk factors from a sample of the attending general practice patients. 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 (about 14 million adults). For the first time in this report we have weighted the BEACH sample to estimate the risk factor profile among the GP–patient population, using the method described by Knox et al. (2008).3 
The estimates for the GP–patient population (after adjusting for age–sex attendance patterns) show that: 
•	one-quarter of patients had no risk factors (24.8%)
•	almost half of the adult patients had one risk factor (48.2%). The most common was overweight (20.9% of adults) followed by obesity (14.8%)
•	one in five patients had two risk factors (21.9%). The most common combinations were overweight and at-risk alcohol consumption (7.7%), followed by obese and at-risk alcohol consumption (4.3%)
•	one in twenty patients had three risk factors (Table 14.4).
Table 14.5 shows the estimation of number of risk factors in the total GP–patient population by sex. Male patients in the total patient population were significantly more likely to have two (28.2%) or three risk factors (7.2) and significantly less likely to have none (18.8%) or one risk factor (45.8%) than female patients (16.7%, 3.3%, 29.8% and 50.2%, respectively).
Table 14.4: Risk factor profile of patients (aged 18 years and over)
Number of risk factors	Number	Per cent BEACH sample 
(95%CI)
(n = 30,002)	Per cent in patient population 
(95% CI)(a)	
No risk factors	7,821	26.1
(25.3–26.9)	24.8
(23.9–25.6)	
One risk factor	15,022	50.1
(49.4–50.8)	48.2
(47.5–48.9)	
	Overweight only	6,779	22.6
(22.0–23.2)	20.9
(20.3–21.6)	
	Obese only	4,808	16.0
(15.4–16.6)	14.8
(14.2–15.4)	
	At-risk alcohol level only	2,308	7.7
(7.2–8.2)	8.3
(7.7–8.8)	
	Current daily smoker only	1,127	3.8
(3.5–4.0)	4.2
(3.9–4.5)	
Two risk factors	5,935	19.8
(19.1–20.4)	21.9
(21.2–22.7)	
	Overweight and at-risk alcohol level	2,131	7.1
(6.7–7.5)	7.7
(7.2–8.1)	
	Obese and at-risk alcohol level	1,225	4.1
(3.8–4.3)	4.3
(4.1–4.6)	
(continued)

Table 14.4 (continued): Risk factor profile of patients (aged 18 years and over)
Number of risk factors	Number	Per cent BEACH sample 
(95%CI)
(n = 30,002)	Per cent in patient population 
(95% CI)(a)	
	Daily smoker and at-risk alcohol level	982	3.3
(3.0–3.5)	4.0
(3.7–4.3)	
	Overweight and current daily smoker	931	3.1
(2.9–3.4)	3.5
(3.2–3.8)	
	Obese and current daily smoker	666	2.2
(2.0–2.4)	2.4
(2.2–2.7)	
Three risk factors	1,224	4.1
(3.8–4.4)	5.1
(4.7–5.4)	
	Overweight and current daily smoker and at-risk alcohol level	775	2.6
(2.4–2.8)	3.3
(3.0–3.5)	
	Obese and current daily smoker and at-risk alcohol level	449	1.5
(1.3–1.7)	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 million.
Note: CI—confidence interval.
Table 14.5: Number of risk factors, by patient sex
Number of risk factors 	Number	Per cent in BEACH sample within sex
(95% CI)	Per cent in patient population within sex
(95% CI)(a)	
Male patients	11,784	100.0	—	
	No risk factors	2,378	20.2
(19.2–21.1)	18.8
(17.8–19.8)	
	One risk factor	5,657	48.0
(47.0–49.0)	45.8
(44.7–46.9)	
	Two risk factors	3,051	25.9
(24.9–26.9)	28.2
(27.1–29.3)	
	Three risk factors	698	5.9
(5.4–6.4)	7.2
(6.6–7.8)	
Female patients	18,218	100.0	—	
	No risk factors	5,443	29.9
(28.9–30.8)	29.8
(28.8–30.9)	
	One risk factor	9,365	51.4
(50.6–52.3)	50.2
(49.4–51.1)	
	Two risk factors	2,884	15.8
(15.2–16.5)	16.7
(16.0–17.4)	
	Three risk factors	526	2.9
(2.6–3.2)	3.3
(3.0–3.6)	
(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 million.
Note: CI—confidence interval.

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 2.4. All substudies have been approved by the AIHW Ethics Committee (on behalf of the AIHW and the University of Sydney).
The 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.
Data from the SAND substudies conducted in the first year of BEACH (1998–99) were published in Measures of health and health care delivery in general practice in Australia.78 
Abstracts of results and research tools for the SAND studies undertaken in 1999–2006 were published in Patient-based substudies from BEACH: abstracts and research tools 1999–2006 in July 2007.11 Abstracts and research tools for substudies conducted in 2006–07 that were not included in that report were published in General practice activity in Australia 2006–07.2 
This chapter includes the abstracts and research tools for SAND substudies conducted from April 2007 to January 2008. SAND substudies conducted in February and March 2008 will be reported in General practice activity in Australia 2008–09 to be published in 2009.
Abstracts of results from all SAND studies are also available from the FMRC’s website <www.fmrc.org.au/publications/SAND_abstracts.htm>.
The subjects covered in the abstracts from 2007–08 BEACH year are listed in Table 15.1, with the sample size for each topic.
Table 15.1: SAND abstracts for 2007–08 and sample size for each 

Abstract number	Subject	Number of respondents	Number of GPs	
111	Adverse drug events in general practice patients	8,602	294	
112	Prevalence and management of chronic pain	3,131	108	
113	Management of hypertension and hypercholesterolaemia among general practice patients	3,160	112	
114	Chronic kidney disease among general practice patients	5,924	195	
115	Type 2 diabetes among general practice patients	2,784	86	
116	Schizophrenia and bipolar disorder among general practice patients	3,374	116	
117	Lipid management in patients with high-risk conditions	8,834	301	
118	Risk factors for osteoporosis among general practice patients	2,613	89	
119	Management of diabetes among general practice patients	5,989	204	
120	Management of asthma among general practice patients	2,987	101	
121	Gastrointestinal symptoms and management among general practice patients	3,293	112	

SAND abstract number 111 from the BEACH program 2007–08
Subject: Adverse drug events in general practice patients
Organisation 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. The number, cause and severity of these adverse events, GP confidence in causation and number of resulting hospitalisations.
Sample: 8,602 encounters from 294 GPs; data collection period: 16/01/2007 – 19/02/2007; 17/07/2007 – 20/08/2007; 25/09/2007 – 29/10/2007. 
Method: Detailed in the paper entitled SAND Method 2007–08 available at 
<www.fmrc.org.au/publications/SAND_abstracts.htm>. 
Summary of results
The age–sex distribution of respondents was similar to the distribution for all BEACH encounters, with the majority (60.1%) of patients being female.
Of the 8,602 respondents, 801 (9.3%; CI: 8.4–10.3) had experienced an adverse drug event in the previous 6 months. Among male patients, 7.5% (95% CI: 6.4–8.6) reported having an adverse drug event, significantly lower than the 10.5% (95% CI: 9.4–11.7) of female patients. The proportion of patients who reported an adverse drug event increased with age group of patient from 3.3% of infants <1 year to 13.1% of patients aged 75 years or more. 
Selective serotonin reuptake inhibitors (SSRIs) were the medication group most frequently reported as the cause of adverse events, but only accounted for 6.1% of the medications, due to the wide variety of medications named. HMG CoA reductase inhibitors (statins) were the second most commonly reported, accounting for 5.0% of the total adverse event medications. Of the 822 medications, the most common individual medications causing adverse events were amoxicillin, which accounted for 3.9%, paracetamol/codeine (3.2%), perindopril (3.0%) and atorvastatin (2.9%). 
Of 783 adverse drug events, GPs indicated that in 75% the cause was a recognised side-effect. Drug sensitivity was the reported cause in 9.5%, and allergy in 8.4%. Just 0.8% indicated drug interaction as the cause, and contraindication was recorded in only one case (0.1%). 
For 48.1% of patients, the adverse drug events were classed as mild, for 41.3% they were moderate, and for 10.5% they were classed as severe. 
Of 764 patients with an adverse drug event for whom this information was known, 35 (4.6%) were hospitalised due to the event. Of 369 patients with a mild event, two (0.5%) were hospitalised, of 317 patients with a moderate event, 9 (2.8%) were hospitalised, and of the 77 patients with a severe event, 24 (31.2%) were hospitalised. 
Information regarding GP confidence in causality was available for 781 of the 801 patients with an adverse event. On a scale of 1 to 6 (1=not confident to 6=completely confident) the median level of confidence was 5. For almost 40% of events, the level was ‘completely confident’.
The following page contains the recording form and instructions with which the data in this abstract were collected.
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SAND abstract number 112 from the BEACH program 2007–08
Subject: Prevalence and management of chronic pain
Organisation supporting this study: Janssen-Cilag Pty Ltd 
Issues: The prevalence of chronic pain in patients attending general practice; causal conditions of the chronic pain: cancer, osteoarthritis, other arthritis, back problems, other conditions; the severity of pain; current methods for chronic pain management for these patients; reasons for non-use of opioids when opioids were not used. 
Sample: 3,131 respondents from 108 GPs; data collection period: 27/03/07 – 30/04/07 and 21/08/07 – 24/09/07.
Method: Detailed in the paper entitled SAND Method: 2007–08 available at 
<www.fmrc.org.au/publications/SAND_abstracts.htm>. Chronic pain grades were defined according to Von Korff M, Ormel J et al. Pain 1992; 50(2):133–149. Pain was graded from Grade I (low disability/low intensity) to Grade IV (high disability/high intensity).
Summary of results
The age–sex distribution of the sample reflected that of all BEACH participants. Of the 3,131 respondents, 548 (17.5%; 95% CI: 15.0–20.0) reported having chronic pain. The prevalence of chronic pain increased significantly with patient age (p<0.0001). Sex-specific rates showed no significant difference between males and females in the prevalence of chronic pain.
Of the 548 patient with chronic pain, 543 advised causal condition (multiple responses were allowed). Of these, 84.9% (n=461) reported one causal condition only, 13.4% (n=73) reported two and 1.7% (n=9) reported three conditions. Nearly half (49.7%; n=270) of patients with chronic pain indicated osteoarthritis as a cause, 30.4% (n=165) indicated back problems, 7.9% (n=43) other arthritis, 3.1% (n=17) cancer, and a further 25.6% (n=139) indicated ‘other conditions’ as a cause of their chronic pain. Of the 118 known ‘other conditions’ 49.2% were musculoskeletal in nature.
Of the 548 patients with chronic pain, 529 provided responses about severity of pain. Of these, 30.6% were at Grade I, 37.2% at Grade II, 25.5% at Grade III, and 6.6% at Grade IV. There was no significant difference in the average pain grading (Grade II) across causal conditions, although 11.8% of patients with back problems were at Grade IV compared with 5.0% of patients with osteoarthritis, and 2.4% of patients with other arthritis. Grade IV pain was also reported in 9.7% of patients with other conditions, and 2 of the 17 patient with cancer (11.8%).
Management method responses were provided for 538 of the 548 patients with chronic pain. The majority (79.2%; n=426) used medication only, while 11.7% (n=63) used medication and other methods, 2.6% (n=14) used other methods only (for example, physiotherapy, exercise, massage), and 6.5% (n=35) were using neither medication nor other methods. The most commonly used medications were ‘other analgesics’ (42.0%; n=226). NSAIDs/COX-IIs were taken by 29.6% (n=159) of patients, weaker opioids (e.g. tramadol, codeine preparations) by 28.6% (n=154), and antidepressants by 8.9% (n=48) of patients. The most common reasons for not taking opioids were that they were not needed (46%; n=134), side effects (14.8%; n=43), patient choice (12.1%; n=35), and concerns about dependence (5.0%; n=15).
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SAND abstract number 113 from the BEACH program 2007–08
Subject: Management of hypertension and hypercholesterolaemia among general practice patients 
Organisations supporting this study: AstraZeneca Pty Ltd (Australia)
Issues: The prevalence of diagnosed hypertension (HT) and/or hypercholesterolaemia in general practice patients; the proportion of these patients who also have diagnosed chronic heart failure (CHF), microalbuminuria, diabetes type 2, left ventricular hypertrophy (LVH); the medications taken for the management of HT and/or hypercholesterolaemia, and the proportion that are taking no medication for either condition, or are managing their HT/hypercholesterolaemia with diet and exercise only; the proportion for whom a change to medication regimen was made at that encounter, and the reasons for change.
Sample: 3,160 respondents from 112 GPs; data collection period: 27/03/2007 – 30/04/2007 and 21/08/2007 – 24/09/2007.
Method: Detailed in the paper entitled SAND Method 2007–08 available at <www.fmrc.org.au/publications/SAND_abstracts.htm>.
Summary of results
The age–sex distribution of the respondents was similar to the distribution for all BEACH encounters, with the majority of patients (59.3%) being female.
Of the 3,160 patients, 873 (27.6%, 95% CI: 24.6–30.7) had HT and 690 (21.8%, 95% CI: 
19.5–24.2) had hypercholesterolaemia. Three and a half per cent of patients with HT and 5.6% of those with hypercholesterolaemia had been diagnosed at today’s encounter. There were 1,115 patients (35.3%) who had HT and/or hypercholesterolaemia. Of the 3,160 patients, 13.5% had HT only, 7.7% had hypercholesterolaemia only, 14.2% had both conditions and 64.7% had neither condition. Of the 1,115 patients with HT and/or hypercholesterolaemia, 5.7% had CHF, 4.0% had microalbuminuria, 16.2% had type 2 diabetes mellitus, and 4.0% had LVH.
Of the 1,115 respondents, 1,110 provided information about current treatment, of whom 86.1% were currently taking at least one HT/hypercholesterolaemia medication and 13.9% were not currently taking medication. Of the 1,189 medications taken by 794 patients for the management of HT, perindopril was the most commonly prescribed medication (10.0% of HT medications). Of the 539 medications taken by 518 patients for the management of hypercholesterolaemia, atorvastatin was the most commonly prescribed medication (45.3% of hypercholesterolaemia medications). There were 154 (13.9%) patients who were not taking a medication for either condition.
One in twelve patients (8.7%) were managed with diet/exercise alone, 85.7% (n=951) were managed with medication alone, and 57 patients (5.1%) were not being managed with either medication or diet/exercise. A change in the medication regimen was made at today’s encounter for 126 patients (11.4%). No change was made for 984 patients (88.7%). The reason for change was indicated for 113 patients, with lack of BP control being the main reason (52.2%).
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SAND abstract number 114 from the BEACH program 2007–08
Subject: Chronic kidney disease among general practice patients 
Organisation supporting this study: Abbott Australasia Pty Ltd 
Issues: The proportion of patients attending general practice who have undergone a kidney function test in the previous 12 months; prevalence of chronic kidney disease among patients attending general practice; the stage of kidney disease for these patients; the comorbidities and risk factors of patients with chronic kidney disease; the management of chronic kidney disease for patients attending general practice. 
Sample: 5,924 respondents from 195 GPs; data collection period: 01/05/2007 – 04/06/2007 and 25/09/2007 – 29/10/2007.
Method: Detailed in the paper entitled SAND Method 2007–08 available at <www.fmrc.org.au/publications/SAND_abstracts.htm>. Stages of disease were defined according to National Kidney Foundation Guidelines.
Summary of results
The age–sex distribution of the sample reflected that of all BEACH participants. Of the 5,924 respondents, 2,960 (50.0%, 95% CI: 46.2–55.1) had had a kidney function test in the previous 12 months, 31.5% (n=1,867) a glomerular function test, 45.6% (n=2,699) a serum creatinine test, and 3.7% (n=219) another kidney function test. Age-specific test rates showed that the likelihood of being tested increased significantly with patient age, with 85.4% of patients aged 75 years and over having been tested. Sex-specific rates showed no significant difference between males and females in the proportion tested.
Of the 5,729 patients for whom a response was recorded, 332 (5.8%, 95% CI: 4.8–6.8) had been diagnosed with chronic renal failure/chronic kidney disease (CRF/CKD). Of the 332, 73.8% had been diagnosed by a GP and 26.2% by a specialist. While there was no difference in the diagnosed prevalence between males and females, the age-specific rate showed that 24.1% (95% CI: 20.5–27.7) of patients aged 75 years and over had diagnosed CRF/CKD. 
Of the 322 diagnosed patients with a response about comorbidities, 75.8% had hypertension, 46.3% had dyslipidaemia, 34.2% had diabetes, 21.1% had proteinurea, 10.9% had anaemia and 1.6% had hyperparathyroidism. Of patients with CRF/CKD 6.8% were current smokers, and 9.3% had none of the listed conditions. Stage of disease was provided for 328 of the 332 diagnosed patients. The majority (55.8%) were at Stage 3. For patients aged 75 years and over 63.8% were at Stage 3, while only 1.1% of patients in this age group were at Stage 1 of the disease. 
Management method responses were provided for 326 (98.2%) CRF/CKD patients. Half (51.1%) were being managed by a GP only, more that one-third (38.7%) by a GP and specialist, and 10.2% by a specialist only. Of the 222 respondents to questions about the type of management, 67.1% (n=149) were managed by diet; 14.4% (n=32) by Vitamin D supplements; and 56.3% (n=125) were managed with other methods, most commonly the management of risk factors and other diseases including: cardiovascular problems, diabetes, dyslipidaemia or anaemia. Less frequent managements were haemopoetic agents, dialysis, advice about fluids, and avoidance of non-steroidal anti-inflammatory drugs. 
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SAND abstract number. 115 from the BEACH program 2007–08
Subject: Type 2 diabetes among general practice patients 
Organisations supporting this study: National Prescribing Service Ltd
Issues: The prevalence of Type 2 diabetes among patients attending general practice, their most recent HbA1c level and the duration since their last test; their current blood pressure level; the proportion of these patients currently taking aspirin and/or clopidogrel, an ACE inhibitor; the proportion who also have ischaemic heart disease (IHD), cerebrovascular disease (CVD), peripheral vascular disease (PVD), microalbuminuria/proteinuria. 
Sample: 2,784 respondents from 86 GPs; data collection period: 01/05/2007 – 04/06/2007.
Method: Detailed in the paper entitled SAND Method 2007–08 available at <www.fmrc.org.au/publications/SAND_abstracts.htm>.
Summary of results
Of the 2,784 respondents, 215 (7.7%, 95% CI: 6.6–8.9) had Type 2 diabetes. Prevalence increased steadily by patient age from 0.4% of patients aged under 24 years to 18.8% of those aged 65–74 years. It then decreased slightly (though not significantly) to 15.3% among patients aged 75 years and over. Where patient sex was provided (n=2,758), prevalence was estimated as 8.9% (95% CI: 7.1–10.8) among males and 6.8% (95% CI: 5.6–8.1) among females, these results were not significantly different.
Of 192 patients with Type 2 diabetes for whom HbA1c levels were recorded, over half (54.7%) had an optimal HbA1c level of <= 7.0% (n=105), with the remaining 45.3% (n=87) having levels considered high (> 7.0%). Of the patients who had a HbA1c level > 7.0%, one‑third had a HbA1c level greater than 8.0%. 
The time since the last HbA1c was provided for 169 patients, 146 (86.4%) of whom had been tested in the previous 6 months and 18 (10.7%) in the previous 7–12 months, with 5 patients (3.0%) having not had a HbA1c test for more than 12 months. 
Blood pressure (BP) was recorded for 192 of the 215 patients with Type 2 diabetes. Two-fifths (40.6%) of these had BP defined as high-normal according to the National Heart Foundation classification, 16.7% had normal BP, 34.4% had isolated systolic hypertension and 8.3% had high BP. 
Of 205 respondents with Type 2 diabetes who provided medication information, half (48.3%) were taking aspirin only (39.0% prescribed; 9.3% OTC), and 5.9% were taking clopidogrel only. There were four patients (2.0%) who were taking both prescribed aspirin and clopidogrel. Overall, 56.1% of patients were taking aspirin, clopidogrel or both medications. 
Two-thirds (n=136) of patients for whom ACE inhibitor status was provided (n=206) were taking an ACE inhibitor medication. Eighty-one patients (39.7%) were taking an ACE inhibitor with aspirin or clopidogrel, and 53 patients were taking the ACE inhibitor alone. 
Almost half (47.6%) of 206 respondents with Type 2 diabetes also had at least one of the four listed cardiovascular related conditions/symptoms (IHD 33.5%, CVD 9.2%, PVD 15.1% and microalbuminuria/proteinuria 18.9%). 
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SAND abstract number 116 from the BEACH program 2007–08
Subject: Schizophrenia and bipolar disorder among general practice patients
Organisations supporting this study: Janssen-Cilag Pty Ltd
Issues: The proportion of patients attending general practice who had a history of schizophrenia or bipolar disorder; the management plans these patients were on (shared care plan with a community mental health centre (CMHC), private psychiatrist, treatment order or a discharge plan from hospital or CMHC); number of GP visits by these patients in the previous 3 months; management of general health risk factors in these patients. 
Sample: 3,374 respondents from 116 GPs; data collection period: 5/06/2007 – 16/07/2007 and 30/10/2007 – 31/11/2007.
Method: Detailed in the paper entitled SAND Method 2007–08 available at <www.fmrc.org.au/publications/SAND_abstracts.htm>.
Summary of results 
Patient sex was provided at 3,353 encounters, with 60.7% (95% CI: 57.9–63.6) being female patients, which is slightly higher than the proportion in the BEACH 2006–07 data (56.3 95% CI: 55.5–57.1). Patient age was provided at 3,349 encounters. The age distribution of patients was similar to that reported for all 2006–07 BEACH encounters.
Of the 3,374 respondents, 50 had a history of schizophrenia (schizophrenia/schizoaffective/
schizophreniform/paranoid psychosis) (1.5%, 95% CI: 1.0–1.9), and 36 had a history of bipolar disorder (1.1%, 95% CI: 0.6–1.5), with no differences in age- and sex-specific rates.
Of the 48 patients with schizophrenia who responded, 20 (41.7%) were being managed as part of a shared care program with a CMHC; 11 (22.9%) with a management plan with a private psychiatrist, and over one-third with none of the listed plans. Of the 32 patients with bipolar who responded, nearly half (n=15, 47%) were using none of the listed plans, one-third (n=11, 34%) had a management plan with a private psychiatrist, and 5 (16%) had a shared care plan with a CMHC.
Of the 46 patients with schizophrenia who responded, the median number of visits to a GP in the previous 3 months was 5.0, and for the 40 who responded, the median number of visits where schizophrenia was managed was 3.0. Of the 34 bipolar patients who responded, the median number of visits was 2.5, and for the 31 who responded the median number of treatment visits was 1.0. Nearly half of the bipolar patients (n=14, 45%) did not have their bipolar treated in the previous 3 months.
Of the 49 schizophrenia patients who responded, 27 (55%) had their cardiovascular risks/
hypertension checked or managed; 25 (51%) had obesity/high BMI checked or managed; and 24 (49%) had diabetes/high blood glucose checked or managed, either at the current encounter or in the previous 3 months. Of the 33 bipolar patients who responded, 23 (70%) had their cardiovascular risks/hypertension checked or managed; 12 (36%) had obesity/
high BMI checked or managed; and over half had diabetes/high blood glucose checked or managed (n=20, 61%), either at the current encounter or in the previous 3 months.
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SAND abstract number 117 from the BEACH program 2007–08 
Subject: Lipid management in patients with high-risk conditions
Organisations supporting this study: Merck, Sharp & Dohme (Australia) Pty Ltd and AstraZeneca Pty Ltd (Australia). 
Issues: Prevalence of selected high-risk conditions among patients attending general practice; current lipid levels; whether target levels were met; lipid lowering management; proportion who had cholesterol test in conjunction with current encounter; proportion ever managed by a specialist for dyslipidaemia; type of specialist; future management plan.
Sample: 8,834 patients from 301 GPs; data collection period: 06/06/2006 – 14/08/2006, 05/06/2007 – 16/07/2007 and 30/10/2007 – 03/12/2007.
Method: Detailed in the paper entitled SAND Method 2007–08 available at <www.fmrc.org.au/publications/SAND_abstracts.htm>. High-risk conditions listed: coronary heart disease (CHD), diabetes, hypertension, familial hypercholesterolaemia, elevated cholesterol, family history of CHD and peripheral vascular disease. This abstract is an update of SAND abstract number 99, as additional data were collected in 2007–08.
Summary of results 
The age and sex distributions of respondents were similar to the distributions for all BEACH encounters, with the majority (59.1%) of patients being female.
From the 8,834 encounters, 3,725 (42.2%, 95% CI: 40.2–44.1) patients had at least one of the listed high-risk conditions, the most common being hypertension and elevated cholesterol (24.6% and 18.1%, respectively). Age-specific rates increased with age to 79.7% 
(95% CI: 77.0–82.5) among patients aged 75 years and over. One-fifth of patients (21.7%) indicated they had only one of the listed high-risk conditions and 20.5% had two or more. The rest of these analyses are limited to the 3,725 encounters with patients with at least one listed high-risk condition.
Total cholesterol (TC) level was provided for 2,928 patients; the average TC level was 5.1 mmol/L. Female patients had a significantly higher average level (5.3, 95% CI: 5.2–5.3) than males (4.9, 95% CI: 4.9–5.0). GP opinion was 55.5% of 2,600 respondents had reached target TC levels. Average high density lipoprotein (HDL) level was 1.5 mmol/L (among 2,448 respondents), 82.8% (of 2,139 respondents) having reached target HDL level. Average low density lipoprotein (LDL) level was 2.9 mmol/L (among 2,367 respondents), 59.7% (of 2,069 respondents) having reached target level. Average triglyceride (TG) level was 1.7 mmol/L (among 2,783 respondents), 73.8% (of 2,364 respondents) having reached target TG level.
Of 3,410 patients for whom information on current lipid medication was available, 1,442 (42.3%) were currently taking 1,471 lipid medications. Atorvastatin accounted for 47.7%, simvastatin for 31.5% and pravastatin for 9.7% of these. Of 2,527 respondents, 57.7% indicated diet and/or exercise advice was a current lipid management strategy. 
Of the 3,506 respondents to the question on cholesterol monitoring, 32.1% were tested in conjunction with the current consultation. Specialists had at some time managed 11.3% of 3,387 patients for dyslipidaemia, usually a cardiologist (63.1% of 287 patients for whom specialist type was recorded). Of the 3,462 respondents, changes to medication were planned for 15.1%: 2.9% to increase the dose of the same medication; 2.1% to add a new medication.
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SAND abstract number 118 from the BEACH program 2007–08 
Subject: Risk factors for osteoporosis among general practice patients 
Organisation supporting this study: National Prescribing Service Ltd
Issues: The proportion of patients on medication for osteoporosis; type of medication taken: bisphosphonate, raloxifene, hormone replacement therapy, teriparatide, strontium, vitamin D, calcium; risk factors and history of fracture after minor trauma; proportion with history of fracture referred for bone mineral density (BMD) scan or x-ray; proportion diagnosed with osteoporosis.
Sample: 2,613 patients from 89 GPs; data collection period: 17/07/2007 – 20/08/2007.
Method: Detailed in the paper entitled SAND Method 2007–08 available at <www.fmrc.org.au/publications/SAND_abstracts.htm>. Osteoporosis risk factor test from International Osteoporosis Foundation: <www.iofbonehealth.org>.
Summary of results 
The age–sex distribution of respondents was similar to the distribution for all BEACH encounters, with the majority of patients (60.0%) being female. 
Of 2,218 respondents to the medication question, 343 (15.5%, 95% CI: 12.7–18.3) were using at least one of the listed medications for osteoporosis: 204 patients (9.2%) used a calcium supplement; 142 (6.4%) a bisphosphonate, 84 patients (3.8%) a vitamin D supplement, and 52 patients (2.3%) used hormone replacement therapy.
There were 617 patients (23.8% of 2,592 respondents) who had at least one risk factor and/or had suffered a fracture after minor trauma, and the incidence was significantly higher for female patients (28.9%, 95% CI: 25.1–32.7) than for male patients (15.9%, 95% CI: 12.4–19.3). The likelihood of risk factor and/or fracture after minor trauma rose significantly with age of patient: 11.0% (95% CI: 8.0–14.1) among those aged 25–44 years, 30.2% (95% CI: 25.2–35.2) among those aged 45–64 years, 39.9% (95% CI: 33.9–45.8) among those aged 65–74 years, and a marginally higher rate, 52.9% (95% CI: 45.8–60.0), among patients aged 75 years and over. 
More than half (51.9%) of the 617 patients who had at least one of the risk factors and/or fracture had been referred previously for screening. Of the 293 patients for whom screening method was known, 47.8% were referred for bone mineral density scan, 37.2% for both 
x-ray and BMD, and 15.0% for an x-ray only. A significantly greater proportion of female patients were referred for screening compared with male patients: of 446 female patients, 59.4% (95% CI: 53.0–65.9) had been referred for screening, while among 163 male patients, 31.3% (95% CI: 21.7–40.8) had been referred. 
Of 312 respondents who had been screened, just over half (n=162, 51.9%) were diagnosed with osteoporosis. Over half (54.1%) of the 159 patients whose aged was known were aged 75 years and over. There was no significant difference between screened male and female patients in the likelihood of diagnosed osteoporosis. Fracture information was available for 154 of the 162 osteoporosis patients, with over two-thirds (68.2%) having had a fracture. Of 156 respondents with osteoporosis, 92.3% were taking at least one of the listed medications.
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SAND abstract number 119 from the BEACH program 2007–08
Management of diabetes among general practice patients
Organisation supporting this study: Sanofi-Aventis Australia Pty Ltd
Issues: The prevalence of Type 1 and Type 2 diabetes in patients attending general practice; frequency and type of referrals given in past year for patients with diabetes; proportion of patients taking insulin or other medications for diabetes management; type of insulin used.
Sample: 5,989 patients from 204 GPs; data collection period: 21/08/2007 – 24/09/2007 and 01/12/2007 – 21/01/2008.
Method: Detailed in the paper entitled SAND Method 2007–08 available at <www.fmrc.org.au/publications/SAND_abstracts.htm>. 
Summary of results
The age and sex distribution of respondents was similar to the distributions for all BEACH encounters. Of the 5,989 respondents, 561 (9.4%, 95% CI: 8.3–10.4) had either Type 1 or 
Type 2 diabetes. The majority of patients had Type 2 diabetes (8.5% of respondents, 95% CI: 7.4–9.5, n=506), and 55 patients (0.9% of respondents, 95% CI: 0.6–1.3) had Type 1 diabetes. 
The proportion of patients with Type 2 diabetes rose significantly with age of patient, to 17.7% of those aged 65–74 years. Males (10.6%, 95% CI: 9.1–12.0) were significantly more likely than females (6.8%, 95% CI: 5.7–7.9) to have Type 2 diabetes. Age and sex did not influence the prevalence of Type 1 diabetes.
Of the 55 patients with Type 1 diabetes, 47 responded to referral questions and 42 (89.4%) had received referrals in the previous year. Patients with Type 1 diabetes were most often referred to ophthalmologists (63.8% of patients, n=30), endocrinologists (59.6%, n=28) and diabetes nurses (38.3%, n=18). Of 481 respondents with Type 2 diabetes, at least one referral had been given to 86.9% of patients in the previous year (n=418). The majority of referrals were to ophthalmologists (63.0% of patients), followed by podiatrists (35.1%), diabetes nurses (34.9%), dietitians (34.1%) and endocrinologists (23.1%).
Of the 47 patients with Type 1 diabetes who responded to medication use questions, insulin use was reported by 59.6% (n=28). Of these, 25 patients (53.2%) were using basal insulin, 5 (10.6%) used intermediate-acting insulin and 15 (31.9%) used fast-acting insulin. Twenty patients (42.6%) were taking 27 diabetes medications other than insulin. Of these medications, metformin was taken by 11 patients (40.7%), gliclazide by 8 (29.6%) and glimepiride by 2 (7.4%).
For patients with Type 2 diabetes, 488 responded to medication use questions, and 70 (14.3%) were using insulin. Basal insulin was used by 49 patients (10.0%), intermediate-acting insulin by 17 (3.5%) and 16 used fast-acting insulin (3.3%). Medications other than insulin were taken by 341 patients (69.9%). Of these, more than half were taking metformin (53.3%) and 145 gliclazide (29.1%).
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SAND abstract number 120 from the BEACH program 2007–08
Management of asthma among general practice patients
Organisations supporting this study: AstraZeneca Pty Ltd (Australia)
Issues: The prevalence of asthma in the general practice population; severity of asthma; frequency of general practice visits by patients with asthma; frequency of general practice visits where asthma is managed; time since last asthma visit; medications taken for the management of asthma; type and provider of asthma management at the current encounter.
Sample: 2,987 patients from 101 GPs; data collection period: 30/10/2007 – 03/12/2007.
Method: Detailed in the paper entitled SAND Method 2007–08 available at <www.fmrc.org.au/publications/SAND_abstracts.htm>. For this study, severity classes for children and adults were adapted from the National Asthma Council Asthma Management Handbook (1998). 
Summary of results
The age distribution of respondents was similar to the distribution for all BEACH encounters, with patients aged 45–64 years accounting for 27.0% of encounters. There were significantly fewer male patients in this study (39.0%, 95% CI: 35.7–42.3) compared with all BEACH encounters (43.7%, 95% CI: 42.9–44.5).
Of the 2,987 respondents, 403 (13.5%, 95% CI: 11.9–15.1) had been diagnosed with asthma. Prevalence among children (0–17 years, n=398) was 17.1% (95% CI: 12.7–21.4), and among adults (n=2,577) was 13.0% (95% CI: 11.2–14.7). The age-specific rate of asthma was highest for those aged 15–17 years (33.3%), steadily declining to 9.9% of those aged 75 years and over. There was no difference in the prevalence of asthma between males (12.4%) and females (14.2%). 
For 80.6% of children with asthma who answered the severity question (n=67), severity was ‘infrequent’. Of the 330 patients aged 18 years and over with asthma, severity was ‘very mild’ for 42.7%, ‘mild’ for 29.4%, ‘moderate’ for 22.1% and ‘severe’ for 5.8%. 
Of 392 respondents with asthma, 10.2% had not visited a GP for any reason in the previous 12 months, 9.4% had visited once, 28.8% had between 2 and 4 visits, and 51.5% had more than 4 visits. For 396 respondents, 46.2% had not had asthma managed in the previous 12 months, 23.5% once, and 30.3% twice or more. Of 171 respondents who had not had asthma managed in the previous 12 months, 70.2% stated that it was more than 2 years since their asthma had been managed by a GP.
Of 392 respondents who answered the question about medication use, 77.3% were taking at least one of the medications listed; over half (53.6%) a short-acting beta agonist (SABA); and 30.1% a combination inhaled corticosteroid/long-acting beta agonist (ICS/LABA). More than one in five patients (22.7%) were not taking any asthma medication.
Asthma had been managed at 76 of the encounters. Management of asthma by the GP most often involved general questions about asthma (72.4%, n=55). Asthma symptoms were discussed with the GP at 65.8% of encounters (n=50) and therapy was reviewed at 55.3% (n=42). Practice nurses were rarely involved in asthma management at these encounters.
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SAND abstract number 121 from the BEACH program 2007–08 
Subject: Gastrointestinal symptoms and management among general practice patients
Organisation supporting this study: Janssen-Cilag Pty Ltd 
Issues: The proportion of patients who have had listed gastrointestinal (GI) symptoms: heartburn or epigastric pain, acid regurgitation, early satiety, nausea/vomiting, bloating, belching; severity of symptoms; the proportion of patients with GI symptoms who had sought treatment and the source of treatment; whether GP was the source of treatment, diagnosis and regimen of medication prescribed/advised.
Sample: 3,293 patients from 112 GPs; data collection period: 4/12/2007 – 21/01/2008.
Method: Detailed in the paper entitled SAND Method 2007–08 available at <www.fmrc.org.au/publications/SAND_abstracts.htm>.
Summary of results 
There were some differences in the age–sex distribution, with fewer patients aged 5–14 years and fewer males (40.0%, 95% CI: 37.3–42.8), compared with all 2006–07 BEACH encounters (43.7%, 95% CI: 42.9–44.5). 
Of the 3,293 respondents, 990 (30.1%, 95% CI: 27.0–33.2) had experienced heartburn, reflux or other GI symptoms, and over two-thirds of these patients indicated the problem was current or in the previous 12 months. GI symptoms were significantly more common in the older age groups (40.6% of those aged 65–74 years and 39.5% of those aged 75 years and over). Heartburn or epigastric pain was indicated for 79.3% of 986 respondents, and acid regurgitation for 41.0%. Early satiety was the least common symptom, indicated for only 5.5% of patients. For the majority of patients the severity of GI symptoms was defined as mild or moderate. However, for 20.2% of patients with heartburn or epigastric pain, and for 17.2% of patients with bloating the symptoms were severe.
Of 980 respondents to a multiple response question on treatment, 768 (78.4%) had sought treatment. Of these, 28.5% had sought treatment from a supermarket/pharmacy, and, of 166 supermarket/pharmacy medications recorded, mylanta accounted for 41.0% and quick‑eze for 25.9%. 
Treatment had been sought from a GP by 654 patients (85.2% of those who sought treatment), and a diagnosis was recorded for 562 of these respondents: 437 (77.8%) were diagnosed with oesophageal disease, and for 89.9% of these patients the management was medication. Esomeprazole accounted for over one-quarter (26.9%) of the 581 initial medications prescribed by the GP, followed by omeprazole (20.7%) and pantoprazole (13.6%). 
Medication review status could be calculated for 502 patients. For 296 patients (59.0%) on an initial medication, there was no change after review. Medication was ceased after review for 13 (2.6%) patients. For 104 patients (20.7%), the medication was changed to a new medication after review. Information was available for 308 patients on the approximate number of months into treatment when the initial medication was first reviewed. Of these, 47.4% were reviewed 1 month into treatment with an initial medication. 
The following page contains the recording form and instructions with which the data in this abstract were collected.

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References
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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 that entitles the holder to reduced cost medicines under the Pharmaceutical Benefits Scheme and a limited number of 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 which 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 exposures 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).
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.
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 2007–08 recording form

This form has been excluded from this document. Please contact feedback@aihw.gov.au  for more information.
Appendix 2: GP characteristics questionnaire,
2007–08
This form has been excluded from this document. Please contact feedback@aihw.gov.au  for more information.
Appendix 3: Dissemination of results from the BEACH program
Available from <www.aihw.gov.au/publications/index.cfm/subject/19>
A full list of BEACH publications is also available from the Family Medicine Research Centre website: < www.fmrc.org.au/publications/>.

Appendix 4: Code groups from ICPC-2 and
ICPC-2 PLUS
Available from <www.aihw.gov.au/publications/index.cfm/subject/19>

Appendix 5: Chronic code groups from ICPC-2 and ICPC-2 PLUS
Available from <www.aihw.gov.au/publications/index.cfm/subject/19>
List of tables
Table 3.1: 	Recruitment and participation rates	19
Table 3.2: 	Comparison of BEACH participants and all active recognised GPs in Australia (the 
sample frame)	20
Table 3.3: 	Activity level of participating and non-participating GPs	21
Table 3.4: 	Age–sex distribution of patients at BEACH and MBS A1 services	22
Table 3.5: 	The BEACH data set	23
Table 4.1: 	Characteristics of participating GPs and their practices	25
Table 4.2: 	Computer applications available/used at major practice address	27
Table 4.3: 	Top 10 combinations of computer applications at major practice address	28
Table 5.1: 	Summary of morbidity and management	30
Table 5.2: 	Overview of MBS items recorded	31
Table 5.3: 	Type of encounter	32
Table 5.4: 	Summary of MBS/DVA items recorded (counting one item number per encounter only)	33
Table 5.5: 	Medicare item number distribution across item number groups	34
Table 6.1: 	Characteristics of the patients at encounters	36
Table 6.2: 	Number of patient reasons for encounter	37
Table 6.3: 	Distribution of patient reasons for encounter, by ICPC-2 chapter and most frequent individual reasons for encounter within chapter	38
Table 6.4: 	Distribution of RFEs by ICPC-2 component	40
Table 6.5: 	Most frequent patient reasons for encounter	41
Table 7.1: 	Number of problems managed at an encounter	43
Table 7.2: 	Distribution of problems managed, by ICPC-2 chapter and most frequent individual problems within chapter	44
Table 7.3: 	Distribution of problems managed, by ICPC-2 component	47
Table 7.4: 	Most frequently managed problems	48
Table 7.5: 	Most frequently managed new problems	49
Table 7.6: 	Most frequently managed chronic problems	51
Table 7.7: 	Work-related problems, by type and most frequently managed individual problems	53
Table 8.1: 	Summary of management	54
Table 8.2: 	Encounters and problems for which management was recorded	55
Table 8.3: 	Most common management combinations	56
Table 9.1: 	Distribution of prescribed medications, by ATC levels 1, 3 and 5	60
Table 9.2: 	Most frequently prescribed medications (CAPS generic level)	63
Table 9.3: 	Medications most frequently supplied by GPs	64
Table 9.4: 	Most frequently advised over-the-counter medications	65
Table 10.1: 	Summary of other treatments	67
Table 10.2: 	Relationship between other treatments and pharmacological treatments	68
Table 10.3: 	Most frequent clinical treatments	69
Table 10.4: 	The 10 most common problems managed with a clinical treatment	70
Table 10.5: 	Most frequent procedural treatments	71
Table 10.6: 	The 10 most common problems managed with a procedural treatment	72
Table 11.1: 	Summary of referrals and admissions	73
Table 11.2: 	The most frequent referrals, by type	74
Table 11.3: 	The 10 problems most frequently referred to a medical specialist	75
Table 11.4: 	The 10 problems most frequently referred to allied health services	75
Table 11.5: 	The 10 problems most frequently referred to hospital	76
Table 12.1: 	Number of encounters and problems for which pathology or imaging ordered	77
Table 12.2: 	Distribution of pathology orders across MBS pathology groups and most frequent individual test orders within group	78
Table 12.3: 	The 10 problems for which pathology was most frequently ordered	80
Table 12.4: 	The most frequent imaging tests ordered, by MBS group	81
Table 12.5: 	The 10 problems for which an imaging test was most frequently ordered	82
Table 12.6: 	Most frequent other investigations ordered by GPs or performed in the practice	83
Table 13.1: 	Distribution of practice nurse item numbers recorded at encounter	85
Table 13.2: 	Summary of treatments given by practice nurse	86
Table 13.3: 	Most frequent treatments provided by practice nurses	87
Table 13.4: 	The most common problems managed with the involvement of practice nurse	88
Table 14.1: 	Patient body mass index (aged 18 years and over)	92
Table 14.2: 	Patient smoking status (aged 18 years and over)	95
Table 14.3: 	Patient alcohol consumption (aged 18 years and over)	98
Table 14.4: 	Risk factor profile of patients (aged 18 years and over)	99
Table 14.5: 	Number of risk factors, by patient sex	100
Table 15.1: 	SAND abstracts for 2007–08 and sample size for each	101

List of figures
Figure 2.1: The BEACH relational database
Figure 2.2: The structure of the International Classification of Primary Care—Version 2 (ICPC-2)
Figure 6.1: Age-sex distribution of patients at encounter
Figure 7.1: Age–sex-specific rates of problems managed per 100 encounters with  95% confidence intervals
Figure 9.1: Number of medications prescribed per problem
Figure 9.2: Number of repeats ordered per prescription
Figure 9.3: Age-sex specific prescription rates per 100 problems managed
Figure 14.1: Age-sex-specific rates of overweight/obesity in adults
Figure 14.2: Age-sex-specific rates of underweight adults
Figure 14.3: Age specific rates of obesity, overweight and normal/underweight in male children
Figure 14.4: Age specific rates of obesity, overweight and normal/underweight in female children
Figure 14.5: Smoking status – male age-specific rates
Figure 14.6: Smoking status – female age-specific rates
Figure 14.7: Age-sex-specific rates of at-risk alcohol consumption