Impact of Social Capital on Residents' Choice of Medical Institutions: A Study Based on Classification Tree Model and Logistic Regression

Abstract

To explore the influence of social capital on the local residents' choice of medical institutions and to provide a reference basis for promoting diagnosis and treatment services available at different tiers. A classification tree model was established using the exhaustive chi-square automatic interaction detection (Exhaustive CHAID) method to screen for factors influencing the residents' choice of medical institutions, and a logistic regression model was used to quantitatively analyze the interaction effect of the influencing factors. The classification tree model showed that there were four layers and eight terminal nodes, identifying a total of six influencing factors, including individual social capital, self-reported physical health, education, community social capital, chronic disease prevalence, and self-reported mental health. Logistic regression analysis showed that education (odds ratio [OR]=0.660, 95% confidence interval [CI]: 0.502-0.869), community social capital (OR=0.746, 95% CI: 0.589-0.943), and individual social capital (OR=0.405, 95% CI: 0.287-0.572) (P<0.001) had an impact on residents' choice of medical institution. There was an interaction between individual social capital and self-reported physical health on residents' choice of medical institution (OR=1.872, 95% CI: 1.180-2.969, P<0.05). Interventions in terms of social capital factors should be considered in order to promote the rational use of medical resources.