Improving the evaluation of an integrated healthcare system using entropy balancing: Population health improvements in Gesundes Kinzigtal

BACKGROUND: Evidence of integrated healthcare networks' effect on population health is scarce. Moreover, current designs for evaluating such networks have shortcomings that can result in misleading conclusions. Our paper evaluates Gesundes Kinzigtal, a best-practice integrated healthcare network, using an innovative design that enlightens the discussion about health gains produced by integrated healthcare. RESEARCH QUESTION: What is the effect of Gesundes Kinzigtal on population health? METHODS: We evaluated the effect of the integrated healthcare initiative by performing a quasi-experimental evaluation based on entropy balancing. Integrated network participants were compared to a control group and followed for five years. Claims data from 2004 to 2018 was used. Population health outcomes correspond to survival (Cox hazard ratio, Kaplan-Meier curve), mortality ratio, mean age at the time of death, and years of life lost or gained. Design validity was evaluated by assessing group balance at baseline. Finally, we compared our results to those obtained using a previously published design for evaluating integrated networks. RESULTS: The treatment group was composed of 9083 network participants, compared to an equivalent control group, showing, respectively, a mortality ratio of 5.4% vs 7.5% (p < 0.05), mean age at the time of death of 80.1 vs 80.3 (p > 0.05) and a gain of 0.2 years of life per person for the treatment group (p > 0.05). The Cox hazard ratio (0.72; p < 0.05) and mean survival time (1784 vs 1768 days; p < 0.05) showed better survival for treated participants. Results using the previously published design were more favorable for the treatment group; however, the design excluded participants significantly associated with greater healthcare needs. DISCUSSION: The integrated network had a favorable effect on participants' mortality and survival risk. Previous evaluations based on propensity score matching might overestimate the network's impact on population health by excluding participants with greater healthcare needs.