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 rise in one‐person households is a worldwide trend. This means that informal care is less available, particularly for elderly people, with important implications for health‐care utilisation and health expenditure. This paper uses a two‐part model to examine the relationship between living alone and hospitalisations in Australia in terms of both the likelihood and the length of hospitalisation. The results show living alone increases the probability of hospitalisation by 2.9 percentage points and length of stay by 3.8 days for people aged 45 and above. Further analysis indicates that these impacts depend on the length of living alone. Additionally, the probability and the length of hospitalisation vary depending on whether the cause of living alone is separation/divorce, widowhood or never having married.
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.
Background: Regional variation in the use of health care services is widespread. Identifying and understanding the sources of variation and how much variation is unwarranted 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 unwarranted 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 unwarranted. Future work should try to explain the remaining unexplained variation. Keywords : GP usage, Regional variation, Statistical Local Areas, Australia
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.