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

Abstract

Background: 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.

Objective: 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.

Design: 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.

Participants: We analyzed data from 449,492 adult participants of the 2017 BRFSS survey.

Main measures: 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.

Key results: 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.

Conclusions: 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.

Keywords: healthcare disparities; machine learning; self-rated health; social determinants of health; socioeconomic factors.

Figure 1

Top variables of importance across age groups, 2017 BRFSS. Notes: Seven domains include demographics: age, sex, race, geographic division, state of residence, number of adults in the respondent’s household, marriage status, veteran status, number of children, and language spoken; clinical conditions: a self-reported history of cancer, asthma, depression, diabetes, stroke, cardiovascular disease, kidney disease, arthritis, COPD, skin cancer, body mass index, angina, or hypertension; functional status: difficulty doing errands, difficulty dressing, difficulty walking, difficulty communicating, blindness, or deafness; access to clinical care: delayed care due to cost, having a primary care physician, insurance status, and having had doctor visit in the previous year; health behavior: alcohol use, smoking status, e-cigarette use, use of chewing tobacco, exercise practices, drunk driving, seat belt use, Internet use in last 30 days, daily fruit consumption, and daily vegetable consumption; preventive care: having had an HIV test, having identified HIV risk factors, and having had a flu vaccine; socioeconomic status: education attainment, income category, homeownership, employment, and cell phone use.

Figure 2

Top variables of importance by domain and age group, 2017 BRFSS. Notes: Data analysis performed with regularized logistic regression machine learning algorithm.