Predicting Self-Rated Health Across the Life Course: Health Equity Insights from Machine Learning Models.

Abstract

Self-rated health is a strong predictor of mortality and morbidity. Machine learning techniques may provide insights into which of the multifaceted contributors to self-rated health are key drivers in diverse groups. We used machine learning algorithms to predict self-rated health in diverse groups in the Behavioral Risk Factor Surveillance System (BRFSS), to understand how machine learning algorithms might be used explicitly to examine drivers of self-rated health in diverse populations. We applied three common machine learning algorithms to predict self-rated health in the 2017 BRFSS survey, stratified by age, race/ethnicity, and sex. We replicated our process in the 2016 BRFSS survey. We analyzed data from 449,492 adult participants of the 2017 BRFSS survey. We examined area under the curve (AUC) statistics to examine model fit within each group. We used traditional logistic regression to predict self-rated health associated with features identified by machine learning models. Each algorithm, regularized logistic regression (AUC: 0.81), random forest (AUC: 0.80), and support vector machine (AUC: 0.81), provided good model fit in the BRFSS. Predictors of self-rated health were similar by sex and race/ethnicity but differed by age. Socioeconomic features were prominent predictors of self-rated health in mid-life age groups. Income [OR: 1.70 (95% CI: 1.62-1.80)], education [OR: 2.02 (95% CI: 1.89, 2.16)], physical activity [OR: 1.52 (95% CI: 1.46-1.58)], depression [OR: 0.66 (95% CI: 0.63-0.68)], difficulty concentrating [OR: 0.62 (95% CI: 0.58-0.66)], and hypertension [OR: 0.59 (95% CI: 0.57-0.61)] all predicted the odds of excellent or very good self-rated health. Our analysis of BRFSS data show social determinants of health are prominent predictors of self-rated health in mid-life. Our work may demonstrate promising practices for using machine learning to advance health equity.