Regional variations in multimorbidity burden among office-based physicians in Germany

Abstract

Background Multimorbidity is associated with higher utilization of healthcare services. However, many countries do not consider multimorbidity when estimating physician supply. The main aim of this study was to assess how regional multimorbidity levels can be integrated when estimating the need for office-based physician supply. Methods Claims data were used to measure and compare the proportions of multimorbid patients of GPs, ophthalmologists, orthopaedic specialists and neurologists, and examine spatial variations through Bernoulli cluster analysis of regional multimorbidity levels. To explore the interrelationship between current capacities and spatial occurrence of high-rate clusters, clusters were compared with the current supply of physicians. Results About 17 239 488 individuals out of approximately 67 million records were classified as multimorbid. Multimorbidity levels varied greatly between physician disciplines (31.5–60.1%). Bernoulli cluster analysis demonstrated that many high-rate areas were found for all specialized physicians, but clusters varied partially by size and location. The comparison with current physician supply at cluster level showed that more than a third of clusters with a significantly higher share of morbid patients seeing a GP are met, on an average, by GP supply below targeted values. In turn, clusters with significantly higher multimorbidity levels of specialized physicians were met, on an average, by supply that exceeded targeted values. Conclusion Our study offers an approach to how to include discipline-specific multimorbidity at area level when estimating physician supply and discusses its relevance. The outcomes of our article can be used by policymakers to advance current planning strategies and to improve the quality of office-based care.

Spatial–temporal distribution patterns and influencing factors analysis of comorbidity prevalence of chronic diseases among middle-aged and elderly people in China: focusing on exposure to ambient fine particulate matter (PM2.5)

“…For instance, Guo Xiaorong et al used spatial autocorrelation methods to investigate the spatial distribution and patterns of multimorbidity among the elderly in China in 2015 [ 21 ]. Isabel et al employed Bernoulli cluster analysis to determine regional disparities in the burden of comorbid diseases [ 22 ]. Regarding the analysis of influencing factors, current studies often employ basic global or local regression methods to analyze cross-sectional data.…”