  Objective: Social-environmental data obtained from the U.S. Census is an
important resource for understanding health disparities, but rarely is the full
dataset utilized for analysis. A barrier to incorporating the full data is a
lack of solid recommendations for variable selection, with researchers often
hand-selecting a few variables. Thus, we evaluated the ability of empirical
machine learning approaches to identify social-environmental factors having a
true association with a health outcome.
  Materials and Methods: We compared several popular machine learning methods,
including penalized regressions (e.g. lasso, elastic net), and tree ensemble
methods. Via simulation, we assessed the methods' ability to identify census
variables truly associated with binary and continuous outcomes while minimizing
false positive results (10 true associations, 1,000 total variables). We
applied the most promising method to the full census data (p=14,663 variables)
linked to prostate cancer registry data (n=76,186 cases) to identify
social-environmental factors associated with advanced prostate cancer.
  Results: In simulations, we found that elastic net identified many
true-positive variables, while lasso provided good control of false positives.
Using a combined measure of accuracy, hierarchical clustering based on
Spearman's correlation with sparse group lasso regression performed the best
overall. Bayesian Adaptive Regression Trees outperformed other tree ensemble
methods, but not the sparse group lasso. In the full dataset, the sparse group
lasso successfully identified a subset of variables, three of which replicated
earlier findings.
  Discussion: This analysis demonstrated the potential of empirical machine
learning approaches to identify a small subset of census variables having a
true association with the outcome, and that replicate across empiric methods.
