ArticleBMJ open2022
Risk profiles of frequent outpatients among public assistance recipients in Japan: a retrospective cohort study using a classification and regression trees algorithm.
Article in BMJ open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- Representativeness of Social Surveys among Older Individuals Living in Poverty: Who Were Left Behind?JMA journal · 2025Review
- Community-level social capital and polypharmacy among public assistance recipients in Japan: A multilevel cross-sectional study.SSM - population health · 2025Article
- Health Statuses of People in Poverty Receiving Public Assistance in Japan: A Scoping Review.JMA journal · 2024Article
- Sociodemographic inequities in unscheduled asthma care visits among public assistance recipients in Japan: additional risk by household composition among workers.BMC health services research · 2023Article
- Research on Injury Causes and Prevention Effect of College Rowing Athletes Based on Multiple Regression and Residual Algorithm.Journal of environmental and public health · 2022Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesAlthough 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.
designWe conducted a retrospective cohort study.
settingWe used secondary data from the public assistance databases of six municipalities in Japan.
participantsThe 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.
resultsThe 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).
conclusionsUsing 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.
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