Assessing the informative value of macroeconomic indicators for public health forecasting

TL;DR

This study evaluates the predictive value of macroeconomic indicators for public health capacity targets using various models on U.S. data, finding they are useful signals especially for workforce and infrastructure metrics.

Contribution

It systematically assesses the informativeness of macroeconomic indicators for health system forecasting and compares multiple modeling approaches for stability and accuracy.

Findings

Macroeconomic indicators reliably predict workforce and infrastructure measures.

Models with stability focus perform better during economic volatility.

Predictive signals vary across different health system targets.

Abstract

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 examine whether selected macroeconomic indicators contain predictive information for several capacity-related public health targets, including employment in the health and social assistance workforce, new business applications in the sector, and health care construction spending. Using monthly U.S. time series data, we evaluate 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 alternative model fitting designs. Across evaluation settings, we find that macroeconomic indicators provide a consistent and reproducible predictive signal for some public health targets, particularly workforce and infrastructure 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.