Machine learning approaches to the social determinants of health in the health and retirement study. (April 2018)

• Record Type: Journal Article
• Authors: Seligman, Benjamin; Tuljapurkar, Shripad; Rehkopf, David
• Is Part Of: SSM - population health. Volume 4(2018)
• Page Start: 95
• Page End: 99
• Publication Date: 2018-04
• Subjects: Social determinants of health -- Machine learning -- Neural network -- Biomarkers -- Health and retirement study; Social medicine -- Periodicals; Medical anthropology -- Periodicals; Public health -- Periodicals; Psychology -- Periodicals; Medicine -- Periodicals; 362.105
• DOI: 10.1016/j.ssmph.2017.11.008
• Languages: English
• ISSNs: 2352-8273

Abstract

Abstract: Background: Social and economic factors are important predictors of health and of recognized importance for health systems. However, machine learning, used elsewhere in the biomedical literature, has not been extensively applied to study relationships between society and health. We investigate how machine learning may add to our understanding of social determinants of health using data from the Health and Retirement Study. Methods: A linear regression of age and gender, and a parsimonious theory-based regression additionally incorporating income, wealth, and education, were used to predict systolic blood pressure, body mass index, waist circumference, and telomere length. Prediction, fit, and interpretability were compared across four machine learning methods: linear regression, penalized regressions, random forests, and neural networks. Results: All models had poor out-of-sample prediction. Most machine learning models performed similarly to the simpler models. However, neural networks greatly outperformed the three other methods. Neural networks also had good fit to the data ( R 2 between 0.4–0.6, versus <0.3 for all others). Across machine learning models, nine variables were frequently selected or highly weighted as predictors: dental visits, current smoking, self-rated health, serial-seven subtractions, probability of receiving an inheritance, probability of leaving an inheritance of at least $10, 000, number of children ever born, African-American race, and gender. Discussion: Some of the machine learning methods do not improve prediction or fit beyond simpler models, however, neural networks performed well. The predictors identified across models suggest underlying social factors that are important predictors of biological indicators of chronic disease, and that the non-linear and interactive relationships between variables fundamental to the neural network approach may be important to consider. Highlights: "Big data" methods may aid understanding of social determinants of health. Neural networks outperform other methods in prediction and variance explained. No individual machine learning method was readily interpretable. Variables in common among machine learning methods suggest core social determinants.