Assessing the informative value of macroeconomic indicators for public health forecasting
Shome Chakraborty, Fardil Khan, Soutik Ghosal
TL;DR
Macroeconomic indicators may serve as leading signals for public health capacity, informing forecasts of workforce, entrepreneurship in health services, and healthcare infrastructure. The study compares optimization-based neural nets (including SGD, Adam, and L--BFGS), GAMs, ARIMA with exogenous variables, and Random Forests using monthly US data and a lagged predictor design: $y_t = f(\boldsymbol{X}_{t-1};\theta)$. Across fixed and rolling evaluation designs, indicators consistently improve forecasts for workforce $EM.T$ and infrastructure $CTS.T$, while gains are smaller for $BA.T$, with more stability observed in models that emphasize regularization. The findings suggest macroeconomic surveillance can augment digital public health monitoring, provided models are carefully chosen and validated to handle regime shifts and nonstationarity; limitations include the single-lag setup and a selective indicator set, inviting broader indicator integration and causal analyses in future work.
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.