General practitioner databases in Australia

General practitioner (family physician) workforce in Australia: comparing geographic data from surveys, a mailing list and medicare

Background

Good quality spatial data on Family Physicians or General Practitioners (GPs) are key to accurately measuring geographic access to primary health care. The validity of computed associations between health outcomes and measures of GP access such as GP density is contingent on geographical data quality. This is especially true in rural and remote areas, where GPs are often small in number and geographically dispersed. However, there has been limited effort in assessing the quality of nationally comprehensive, geographically explicit, GP datasets in Australia or elsewhere.

Our objective is to assess the extent of association or agreement between different spatially explicit nationwide GP workforce datasets in Australia. This is important since disagreement would imply differential relationships with primary healthcare relevant outcomes with different datasets. We also seek to enumerate these associations across categories of rurality or remoteness.

Method

We compute correlations of GP headcounts and workload contributions between four different datasets at two different geographical scales, across varying levels of rurality and remoteness.

Results

The datasets are in general agreement with each other at two different scales. Small numbers of absolute headcounts, with relatively larger fractions of locum GPs in rural areas cause unstable statistical estimates and divergences between datasets.

Conclusion

In the Australian context, many of the available geographic GP workforce datasets may be used for evaluating valid associations with health outcomes. However, caution must be exercised in interpreting associations between GP headcounts or workloads and outcomes in rural and remote areas. The methods used in these analyses may be replicated in other locales with multiple GP or physician datasets.

Selected demographic, social and work characteristics of the Australian general medical practitioner workforce: comparing capital cities with regional areas

The aim of the present study was to compare selected characteristics of the Australian general medical practitioner workforce in capital cities and regional areas. Data were derived from the 1996 Census of Population and Housing. Characteristics included age, sex, full- or part-time work, place of birth and change in residential address. Analyses were performed for each state and territory in Australia, the statistical division containing each capital city and all other statistical divisions in each state and territory. Of the 26,359 general medical practitioners identified, 68% were male. More female than male general medical practitioners were aged < 45 years (74 vs 52%, respectively; P < 0.0001). The proportion of general medical practitioners aged < 35 years was higher in capital cities (30%) than regional areas (24%; P < 0.0001). Overall, 32% of the general medical practitioner workforce was female and almost 50% of those aged < 35 years were female. The proportion of female general medical practitioners was higher in capital cities than regional areas, by up to 30%. While 13% of male general medical practitioners reported part-time work, 42% of females also reported part-time work and these figures were similar in capital cities and regional areas. Approximately 40% of the Australian general medical practitioner workforce was born outside Australia and while fewer migrants have entered in recent years they were more likely to be living in regional areas than the capitals. The census provides useful medical workforce data. The regional workforce tends to be made up of more males and is older than in capital cities. Monitoring trends in these characteristics could help to evaluate initiatives aimed at addressing regional workforce issues.

Inequitable distribution of general practitioners in Australia: analysis by state and territory using census data

The objective of this study was to describe the distribution of general practitioners in each State and Territory, stratified by statistical division and adjusted for estimated community need. The location of general practitioners was obtained from the 1996 Census of Population and Housing. Community need was estimated from crude death rates supplied by the Australian Bureau of Statistics. On average there are 920 people per full-time general practitioner in Australia. Three States are relatively oversupplied by up to 5% (Australian Capital Territory) and the rest are relatively undersupplied by up to 12% (Western Australia). Adjusted for estimated need, the Australian Capital Territory is oversupplied by 71% compared with all of Australia, while Western Australia is undersupplied by 15%. More marked differences occur within the States, with the statistical division containing each capital city in each State that is relatively overserved. The greatest oversupply is in Sydney, where 33% fewer people share a full-time general practitioner than the whole of New South Wales (adjusted for need, oversupply in Sydney is 63%). Relative undersupply is greatest in Queensland, with 133% more people sharing each general practitioner in the north-west statistical division compared with the whole State. The distribution of general practitioners between and within States and Territories is unequal and inequitable. In each State, capital cities tend to be relatively oversupplied compared with more rural areas. While these data do not inform the absolute level of service needed in a community; they do suggest that strategies to redress the inequitable distribution are required.

Inequitable distribution of general practitioners in Australia: estimating need through the Robin Hood Index

OBJECTIVE

From Census data, to document the distribution of general practitioners in Australia and to estimate the number of general practitioners needed to achieve an equitable distribution accounting for community health need.

METHODS

Data on location of general practitioners, population size and crude mortality by statistical division (SD) were obtained from the Australian Bureau of Statistics. The number of patients per general practitioner by SD was calculated and plotted. Using crude mortality to estimate community health need, a ratio of the number of general practitioners per person: mortality was calculated for all Australia and for each SD (the Robin Hood Index). From this, the number of general practitioners needed to achieve equity was calculated.

RESULTS

In all, 26,290 general practitioners were identified in 57 SDs. The mean number of people per general practitioner is 707, ranging from 551 to 1887. Capital city SDs have most favourable ratios. The Robin Hood Index for Australia is 1, and ranges from 0.32 (relatively under-served) to 2.46 (relatively over-served). Twelve SDs (21%) including all capital cities and 65% of all Australians, have a Robin Hood Index > 1. To achieve equity per capita 2489 more general practitioners (10% of the current workforce) are needed. To achieve equity by the Robin Hood Index 3351 (13% of the current workforce) are needed.

CONCLUSIONS

The distribution of general practitioners in Australia is skewed. Nonmetropolitan areas are relatively underserved. Census data and the Robin Hood Index could provide a simple means of identifying areas of need in Australia.

Methodological issues in medical workforce analysis: implications for regional Australia

Medical workforce data have a profound impact on health policy formulation, but derived doctor population ratios (DPR) are often more relevant to plotting national trends than providing a detailed regional or local workforce perspective. Regional workforce data may be more useful if national approaches are augmented by local information. In developing a detailed workforce analysis for one region of Australia, the authors encountered several challenging methodological issues, including the accuracy of medical workforce databases, clarity of definition of community boundaries, interpretation of workforce definitions and the difficulty accounting for local community needs. This paper discusses the implications for regional workforce research.

Identifying the active general practice workforce in one division of general practice: the utility of public domain databases

OBJECTIVE

To identify the non-specialist medical practitioner workforce engaged in active general practice in the region served by the Division of General Practice-Northern Tasmania and to determine the usefulness of public domain databases for enumeration of individual non-specialists providing general practice services.

METHODS

A masterlist of the active general practice workforce was compiled by obtaining the names and addresses/postcodes of all non-specialist medical practitioners who were listed in at least one of nine public domain databases and who were confirmed by selected local medical practitioners to be in active general practice in the three months prior to 30 June 1994. This masterlist was used in calculating the sensitivity and positive predictive value (PPV) of each of the nine databases for enumerating non-specialist practitioners in active general practice.

RESULTS

Combining the databases resulted in a list of 475 practitioners, which was refined to 139 practitioners who, by our criteria, were in active general practice. Databases had a range of sensitivities and PPVs, but those with high sensitivity tended to have low PPVs, and vice versa. The most useful database for enumerating these practitioners was the mailing list for Australian Family Physician (sensitivity, 94%; PPV, 0.79).

CONCLUSIONS

When used alone, no single database had both high sensitivity and high positive predictive value for identifying the active general practice workforce. Combining multiple databases may improve precision. Developing methods to identify recent departures from local active practice has the potential to improve the PPV of existing highly sensitive databases.

A controlled trial of educational visiting to improve benzodiazepine prescribing in general practice

A randomised controlled trial studied the effect of an educational visit on benzodiazepine prescribing. An approximately representative sample of 286 general practitioners was allocated to an intervention or a control group. Rates of benzodiazepine prescriptions were derived from two comprehensive self-report surveys seven months apart. Two months after the first survey the intervention group received an educational visit and supporting material from a doctor or pharmacist, ostensibly unconnected with the surveys. The overall benzodiazepine prescribing rate fell by 23.7 per cent from the first to the second surveys, from 4.93 to 3.76 prescriptions per 100 encounters (P < 0.001). Anxiety and insomnia diagnosis rates also declined from 4.68 to 3.76 per 100 encounters (19.7 per cent). After adjusting for confounders, there was a differential downward trend in prescriptions per diagnosis of insomnia but not to a statistical level. The same was true of initial prescriptions per insomnia diagnosis. In a subsidiary analysis selecting only new insomnia diagnoses, the intervention had a strong effect in reducing initial prescriptions (odds ratio 0.18, 95 per cent confidence interval 0.04 to 0.73). No effect was seen on prescribing for anxiety diagnoses. Educational practice visiting for benzodiazepine prescribing in anxiety, as we conducted it, is not justified in an unselected population of general practitioners. Specific education on prescribing for insomnia is probably useful. Our interpretation of the reduction in benzodiazepine prescribing is that probably there was an effect from self-monitoring alone which overwhelmed a main-analysis intervention effect. Retrospective diagnosis may also have obscured a real intervention effect.

Training for rural general practice

OBJECTIVE

To identify requirements for vocational training and continuing education programs in rural general practice.

DESIGN

A questionnaire was sent to all 487 rural doctors and 140 metropolitan and 140 provincial city general practitioners (GPs) in Queensland. A sample of medical educators, health professional and consumer representatives and rural doctors was also interviewed. Responses were compared by geographical area, practice characteristics and level of postgraduate training.

RESULTS

There are significant differences between rural and urban practice profiles. Rural doctors have to practise a range of clinical skills in an environment with restricted access to health professional support, although the need for advanced training in procedural or other skills depends on the type of rural practice. Rural and urban doctors want more influence in determining continuing medical education (CME) programs. Interactive learning methods were rated as the most effective education methods by both rural and urban GPs. Rural doctors were less likely to consider that they spent enough time on CME.

CONCLUSION

Vocational training programs should accommodate various rural career objectives, including those requiring advanced levels of procedural work. There is a significant unmet demand for CME tailored to the needs of individual doctors, both rural and urban, but distance and isolation may make this more critical in rural practice. These issues need to be addressed as training opportunities can contribute to improved retention of the rural medical workforce.

A sampling framework for rural and remote doctors

A pilot survey by telephone interview, followed by a questionnaire of all rural doctors identified in Queensland, was used develop both a definition of rural practice that distinguishes it from urban general practice and a classification of rural and remote practice which assists in sampling of rural doctors. Questionnaire responses in specific geographic areas were compared using chi-square and Mantel-Haenszel chi-square tests. Several factors were found to differentiate rural from urban general practice consistently, thereby enabling a functional definition of rural practice to be developed. Within the broad group of rural doctors, gradients in practice characteristics were found to differentiate doctors in larger rural centres from those in smaller, more remote communities. These gradients were related to the distance and time of travel from support services. They formed the basis of a complex classification of rural and remote general practice. This functional definition of rural and remote medical practice should be considered by researchers of rural medicine issues when sampling rural and remote doctors. The strategies used in this study could be adapted for use in considering practice characteristics of other rural health professions.