Health care use in response to health shocks: Does socio-economic status matter?

We investigate how utilization of primary care, specialist care, and emergency department (ED) care (and the mix across the three) changes in response to a change in health need. We determine whether any changes in utilization are impacted by socio-economic status. The use of a unique Australian data set that consists of a large survey linked to multiple years of detailed administrative records enables us to better control for individual heterogeneity and allows us to exploit changes in health that are related to the onset of two health shocks: a new diagnosis of diabetes and heart disease. We extend the analysis by also examining changes to patient out-of-pocket costs. We find significant differences in the mix between primary and specialist care use according to income and type of health shock but no evidence of using ED as a substitute for other care. Our results indicate that low- and high-income patients navigate very different pathways for their care following the onset of diabetes and to a lesser extent heart disease. These pathways appear to be chosen on the basis of ability to pay, rather than the most effective or efficient bundle of care delivered through a combination of GP and specialist care.

Does more investment in primary care improve health system performance?

This study examines the association between primary care investment and performance, in 34 OECD countries for 2005-15. Specifically, we explore whether an increasing investment in primary care is associated with improved performance, and whether particular characteristics of organisation and delivery are associated with a better return on primary care investment. We take advantage of new data sources that provide rich information on health and health systems as well as economic and distributional characteristics. Multilevel modelling was utilised to analyse cross-country variation. The results show that greater investment in primary care does not improve health system performance for complex targets (i.e., no reduction in preventable hospital admissions) though there is modest improvement in breast and cervical cancer screening rates. We also found that those countries in which GPs are more aware of health promotion/preventive activities achieve higher screening rates with the same amount of investment. The findings imply that primary care investment strategies need to look beyond high-level expenditure and characteristics of primary care strength, to institutional and funding arrangements and how these link to policy goals. Despite broad enthusiasm for strengthening primary care in general, we conclude that primary care policy needs to be appropriately targeted to improve health system performance.

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

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.

Bleeding Hearts, Profiteers, or Both: Specialist Physician Fees in an Unregulated Market.

This study shows that, in an unregulated fee-setting environment, specialist physicians practise price discrimination on the basis of their patients' income status. Our results are consistent with profit maximisation behaviour by specialists. These findings are based on a large population survey that is linked to administrative medical claims records. We find that, for an initial consultation, specialist physicians charge their high-income patients AU$26 more than their low-income patients. While this gap equates to a 19% lower fees for the poorest patients (bottom 25% of the household income distribution), it is unlikely to remove the substantial financial barriers they face in accessing specialist care. There are large variations across specialties, with neurologists exhibiting the largest fee gap between the high-income and low-income patients. Several possible channels for deducing the patient's income are examined. We find that patient characteristics such as age, health concession card status and private health insurance status are all used by specialists as proxies for income status. These characteristics are particularly important to further practise price discrimination among the low-income patients but are less relevant for the high-income patients. Copyright © 2016 John Wiley & Sons, Ltd.

The age profile of the location decision of Australian general practitioners.

The unbalanced distribution of general practitioners (GPs) across geographic areas has been acknowledged as a problem in many countries around the world. Quantitative information regarding GPs' location decision over their lifecycle is essential in developing effective initiatives to address the unbalanced distribution and retention of GPs. This paper describes the age profile of GPs' location decision and relates it to individual characteristics. I use the Medicine in Australia: Balancing Employment and Life (MABEL) survey of doctors (2008-2012) with a sample size of 5810 male and 5797 female GPs. I employ a mixed logit model to estimate GPs' location decision. The results suggest that younger GPs are more prepared to go to rural and remote areas but they tend to migrate back to urban areas as they age. Coming from a rural background increases the likelihood of choosing rural areas, but with heterogeneity: While male GPs from a rural background tend to stay in rural and remote areas regardless of age, female GPs from a rural background are willing to migrate to urban areas as they age. GPs who obtain basic medical degrees overseas are likely to move back to urban areas in the later stage of their careers. Completing a basic medical degree at an older age increases the likelihood of working outside major cities. I also examine factors influencing GPs' location transition patterns and the results further confirm the association of individual characteristics and GPs' location-age profile. The findings can help target GPs who are most likely to practise and remain in rural and remote areas, and tailor policy initiatives to address the undesirable distribution and movement of GPs according to the identified heterogeneous age profile of their location decisions.