Unraveling the drivers of regional variation in healthcare spending by analyzing prevalent chronic diseases

Abstract

Background: Population Health Management PHM is increasingly seen as a response to the sustainability problems healthcare systems are currently facing. Increasingly reduction of low-value care and inefficiencies are seen promising strategies within PHM. To indicate inefficiencies in health systems, previous studies examined regional variation in healthcare spending by analyzing a heterogeneous population. However, there is population heterogeneity that clouds a detailed interpretation which could be used to inform the interventions which may be directed at subpopulations in PHM. Therefore, we aimed to gain insight into the drivers of regional variation in healthcare spending by studying prevalent chronic diseases. Methods: We used 2012 secondary health survey data, linked with claims data, healthcare supply data and demographics at the individual level for 18 Dutch PHM 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 generalized linear mixed models. Results: Regions 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 variation was attributed to the regional level. For all samples, we found that demand variables explained 62-63% of the total variance. Self-reported health status was the most prominent predictor 28% of healthcare spending. Supply variables added nearly 0% but significantly to explaining 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. Conclusions: The extent to which regional variation in healthcare spending can be considered as inefficiency may differ between regions and disease-groups. Therefore, studying chronic diseases, in addition to the traditional approach where the general population is studied, provides more detailed insight into the causes of regional variation in healthcare spending. Future policies on which interventions could be targeted can benefit on these insights.