Risk profiles of frequent outpatients among public assistance recipients in Japan: a retrospective cohort study using a classification and regression trees algorithm

Abstract

[Objectives] Although several individual risk factors of frequent outpatient attendance (FOA) have previously been reported, identifying a specific risk profile is needed to provide effective intervention for impoverished citizens with complex biopsychosocial needs. We aimed to identify potential risk profiles of FOA among public assistance recipients in Japan by using classification and regression trees (CART) and discussed the possibilities of applying the CART to policypractice as compared with the results of conventional regression analyses. [Design] We conducted a retrospective cohort study. [Setting] We used secondary data from the public assistance databases of six municipalities in Japan. [Participants] The study population included all adults on public assistance in April 2016, observed until March 2017. We obtained the data of 15 739 people on public assistance. During the observational period, 435 recipients (2.7%) experienced FOA. [Outcome measure] We dichotomised a cumulative incidence of FOA during the study period into a binary variable of exhibiting FOA or not. We adopted the definition of FOA by the Ministry of Health, Labour, and Welfare: visiting the same medical institution more than 15 days a month. [Results] The results of the CART showed that an employed subpopulation with mental disabilities exhibited the highest risk of FOA (incidence proportion: 16.7%). Meanwhile, multiple Poisson regression showed that the adjusted incidence ratio of being unemployed (vs employed) was 1.71 (95% CI 1.13 to 2.59). [Conclusions] Using the CART model, we could identify specific risk profiles that could have been overlooked when considering only the risk factors obtained from regression analysis. Public health activities can be provided effectively by focusing on risk factors and the risk profiles.