What are the factors that set the stage for health status and outcomes in the United States (U.S.)? This complex question is rarely considered in a comprehensive way. The current study employs an artificial intelligence analysis to assess the accuracy of adding a measure of social capital (i.e., public trust) to the Ecological Framework of Population Health in predicting U.S. county-level life expectancy and COVID-19 mortality rates. Descriptive, cross-sectional, retrospective analysis The current study utilized several U.S. county-level datasets representing the Ecological Framework of Population Health, including measures of culture, politics, policy, socioeconomics, lifestyle behaviors, and both chronic disease risk factors and diagnoses. A social media generated index of social capital, i.e., public trust, was added to the framework as a crosscutting variable to determine its efficacy in predicting county-level life expectancy and COVID-19 mortality rates using a non-linear artificial intelligence statistical method. Analysis revealed significance in predicting both life expectancy (R 2 = 0.803) and COVID-19 deaths (R 2 = 0.548), with the optimal model employing 27 and 12 features, respectively. The public trust index was retained in both final models, ranking as the 5 th and 6 th most important predictors for life expectancy and COVID-19 mortality, respectively. The present study expands the body of work exploring forcing factors of population health by demonstrating the potential utility of a measure of public trust, derived from social media posts and integrated into the Ecological Framework of Population Health, in predicting important U.S. county-level health outcomes.
Arena et al. (Fri,) studied this question.