Measuring the Effect of Mental Health on Type 2 Diabetes

Abstract

There are many putative risk factors for type 2 diabetes (T2D), and the causal relationship between these factors and diabetes has been established. Socio-environmental and biological approaches are increasingly used to infer causality, and research is needed to understand the causal role of these factors in diabetes risk. Therefore, this study investigated the extent to which the treatment factor of stress induces the risk of diabetes through socio-environmental and biological factors. We present machine learning-based causal inference results generated using DoWhy, a Python library that provides a four-step causal inference method consisting of modeling, identification, estimation, and refutation steps. This study used 253,680 examples collected by the Behavioral Risk Factor Surveillance System (BRFSS), created a causal model based on a socio-environmental model, and tested the statistical significance of the obtained estimates. We identified several causal relationships and attempted various refutations. The results show that mental health problems increase the incidence of diabetes by about 15% (mean value: 0.146). The causal effect was evaluated based on the causal model, and the reliability of causal inference was proved through three refutation tests.