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.

翻译：宏观经济条件影响着卫生系统的运行环境，然而其作为卫生系统能力先行信号的价值尚未得到系统评估。本研究检验了选定的宏观经济指标是否对若干能力相关的公共卫生目标具有预测信息，包括卫生与社会援助行业就业人数、该领域新企业申请数量以及医疗保健建设支出。利用美国月度时间序列数据，我们在不同模型拟合设计下评估了多种预测方法，包括采用不同优化策略的神经网络模型、广义可加模型、随机森林以及包含外生宏观经济指标的时间序列模型。在所有评估情境中，我们发现宏观经济指标为部分公共卫生目标（特别是人力与基础设施指标）提供了持续且可复现的预测信号，而其他目标则表现出较弱或不稳定的可预测性。强调稳定性和隐式正则化的模型在经济波动时期往往表现更为可靠。这些发现表明，宏观经济指标可作为数字公共卫生监测的有效上游信号，同时强调在将经济趋势转化为卫生系统预测工具时，需要谨慎进行模型选择与验证。