ArticleBMC health services research2018
Unraveling the drivers of regional variation in healthcare spending by analyzing prevalent chronic diseases.
Article in BMC health services research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
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- Weaker Effects of Educational Attainment on Chronic Medical Conditions in American Indian Alaska Native, Black, and Latino Adults: National Health Interview Survey 2023.Open journal of medical sciences · 2025Article
- Examining annual transitions in healthcare spending among U.S. medicare beneficiaries using multistate Markov models: Analysis of medicare current beneficiary survey data, 2003-2019.Preventive medicine reports · 2023Article
- Systematic population-wide ecological analysis of regional variability in disease prevalence.Heliyon · 2023Article
- Regional variation in long-term care spending in Japan.BMC public health · 2022Article
- A Framework for Understanding How Variation in Health Care Service Delivery Affects Work Disability Management.Journal of occupational rehabilitation · 2022Review
- Regional differences in healthcare costs further explained: The contribution of health, lifestyle, loneliness and mastery.TSG : tijdschrift voor gezondheidswetenschappen · 2022Article
- The Gini coefficient as a useful measure of malaria inequality among populations.Malaria journal · 2020Article
- What explains the regional variation in the use of general practitioners in Australia?BMC health services research · 2020Article
- Regional variation in healthcare spending and mortality among senior high-cost healthcare users in Ontario, Canada: a retrospective matched cohort study.BMC geriatrics · 2018Article
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4 authors.
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Abstract
backgroundTo indicate inefficiencies in health systems, previous studies examined regional variation in healthcare spending by analyzing the entire population. As a result, population heterogeneity is taken into account to a limited extent only. Furthermore, it clouds a detailed interpretation which could be used to inform regional budget allocation decisions to improve quality of care of one chronic disease over another. Therefore, we aimed to gain insight into the drivers of regional variation in healthcare spending by studying prevalent chronic diseases.
methodsWe used 2012 secondary health survey data linked with claims data, healthcare supply data and demographics at the individual level for 18 Dutch regions. We studied patients with diabetes (n = 10,767) and depression (n = 3,735), in addition to the general population (n = 44,694). For all samples, we estimated the cross-sectional relationship between spending, supply and demand variables and region effects using linear mixed models.
resultsRegions with above (below) average spending for the general population mostly showed above (below) average spending for diabetes and depression as well. Less than 1% of the a-priori total variation in spending was attributed to the regions. For all samples, we found that individual-level demand variables explained 62-63% of the total variance. Self-reported health status was the most prominent predictor (28%) of healthcare spending. Supply variables also explained, although a small part, of regional variation in spending in the general population and depression. Demand variables explained nearly 100% of regional variation in spending for depression and 88% for diabetes, leaving 12% of the regional variation left unexplained indicating differences between regions due to inefficiencies.
conclusionsThe extent to which regional variation in healthcare spending can be considered as inefficiency may differ between regions and disease-groups. Therefore, analyzing chronic diseases, in addition to the traditional approach where the general population is studied, provides more insight into the causes of regional variation in healthcare spending, and identifies potential areas for efficiency improvement and budget allocation decisions.
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