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

  Objective  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.

  Methods  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.

  Results  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).

  Conclusion  Interventions in terms of social capital factors should be considered in order to promote the rational use of medical resources.