Assessing the informative value of macroeconomic indicators for public health forecasting

Macroeconomic conditions influence the environments in which health systems operate, yet their value as leading signals of health-system capacity has not been systematically evaluated. In this study, we examined whether certain macroeconomic indicators contained predictive information for several capacity-related public health targets in the United States: employment in the health and social assistance workforce, new business applications in the sector, and health care construction spending. Using seasonally adjusted monthly time-series data collected from government sources, we evaluated multiple forecasting approaches-including neural network models with different optimization strategies, generalized additive models, random forests, and time series models with exogenous macroeconomic indicators-under different model fitting designs. Across the evaluation settings, we found that macroeconomic indicators are associated with improved predictive performance for some public health targets-particularly workforce measures-while other targets exhibit weaker or less stable predictability. Models emphasizing stability and implicit regularization tend to perform more reliably during periods of economic volatility. These findings suggest that macroeconomic indicators may serve as useful upstream signals for digital public health monitoring, while underscoring the need for careful model selection and validation when translating economic trends into health-system forecasting tools.