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BMC Health Services Research
Published in: BMC Health Services Research 1/2020

Open Access 01-12-2020 | Research article

What explains the regional variation in the use of general practitioners in Australia?

Authors: Chunzhou Mu, Jane Hall

Published in: BMC Health Services Research | Issue 1/2020

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Abstract

Background

Regional variation in the use of health care services is widespread. Identifying and understanding the sources of variation and how much variation is unexplained can inform policy interventions to improve the efficiency and equity of health care delivery.

Methods

We examined the regional variation in the use of general practitioners (GPs) using data from the Social Health Atlas of Australia by Statistical Local Area (SLAs). 756 SLAs were included in the analysis. The outcome variable of GP visits per capita by SLAs was regressed on a series of demand-side factors measuring population health status and demographic characteristics and supply-side factors measuring access to physicians. Each group of variables was entered into the model sequentially to assess their explanatory share on regional differences in GP usage.

Results

Both demand-side and supply-side factors were found to influence the frequency of GP visits. Specifically, areas in urban regions, areas with a higher percentage of the population who are obese, who have profound or severe disability, and who hold concession cards, and areas with a smaller percentage of the population who reported difficulty in accessing services have higher GP usage. The availability of more GPs led to higher use of GP services while the supply of more specialists reduced use. 30.56% of the variation was explained by medical need. Together, both need-related and supply-side variables accounted for 32.24% of the regional differences as measured by the standard deviation of adjusted GP-consultation rate.

Conclusions

There was substantial variation in GP use across Australian regions with only a small proportion of them being explained by population health needs, indicating a high level of unexplained clinical variation. Supply factors did not add a lot to the explanatory power. There was a lot of variation that was not attributable to the factors we could observe. This could be due to more subtle aspects of population need or preferences and therefore warranted. However, it could be due to practice patterns or other aspects of supply and be unexplained. Future work should try to explain the remaining unexplained variation.
Appendix
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Footnotes
1
For example, vulnerable groups include people who are less than 16 years old, or those who are concession card holders. Concession card holders are predominantly aged pensioners, certain social security allowance recipients, and people from low-income families. They are entitled to access to prescription medicines at a cheaper rate. Also, patients with concession cards are more likely to be bulk billed or charged lower fees by physicians than the general patients.
 
2
These data are part of the Public Health Information Development Unit’s Social Health Atlas series
 
3
The delimitation of SLAs is based on the boundaries of incorporated bodies of local government. These bodies are the Local Government Councils and the geographical areas which they administer
 
4
The statistics for Australian Capital Territory are missing and there is no data for the areas that are unincorporated in the corresponding state or with unknown ABS cell adjustment
 
5
The 756 SLAs account for around 70% of all 1094 SLAs contained in the raw data set while around 87% of the whole population have been covered by them, indicating that SLAs with lower population were dropped.
 
6
The score for Australia is 1000 as a benchmark
 
7
The information on EDs, such as name, hospital type (public or private), postcode, and address are obtained from MyHospitals, accessed at <http://​www.​myhospitals.​gov.​au/​>
 
8
The explanatory power of various control variables also depends on the sequences in which these variables enter the regression. We consider that controlling for variables measuring medical need is the natural sequence to start with. Permuting the order of the control variables, i.e. controlling for supply-side factors first and then adding demand-side factors into the regression, changed the results slightly: supply-side factors reduced the standard deviation of GP usage by about 2% and demand-side factors increased the explanatory share to 32.24%.
 
9
Given that the densities of GPs and specialists are constructed at the LGA level, we undertake an analysis with standard errors being clustered at LGA level. Robust results are obtained and are available upon request
 
10
Full sets of results are available upon request
 
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Metadata
Title
What explains the regional variation in the use of general practitioners in Australia?
Authors
Chunzhou Mu
Jane Hall
Publication date
01-12-2020
Publisher
BioMed Central
Published in
BMC Health Services Research / Issue 1/2020
Electronic ISSN: 1472-6963
DOI
https://doi.org/10.1186/s12913-020-05137-1

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