Are Community Health Workers saving lives? A longitudinal analysis of state-level variation in Community Health Workforce.
*Names in bold indicate Presenter
Sharp increases in health care costs and pronounced health disparities across the United States are leading policy makers and academic researchers to consider alternative models of health care delivery. In part, health care costs in the United States are driven by a rise in prevalence of chronic diseases such as cancer and diabetes, paired with increase life expectancy, but, a substantial share of inefficiencies also stems from disparate access to health resources and asymmetric information about healthy behaviors. Especially among underserved communities, language barriers, along with cultural norms and traditions, present significant challenges, affecting treatment adherence and effectiveness, as well as prevalence of disease outcomes and risky behaviors.
To cope with these emerging challenges, community health workers (CHWs) have been discussed as promising interventions towards achieving more integrated, culturally sensitive and personalized models of care. However, to date, there is a lack of rigorous, quantitative evaluations of the effect that community worker programs have on both health outcomes and health expenditures.
We construct a longitudinal panel of state-level occupational data on community health workers from the Bureau of Labor Statistics (BLS), as well as health system capacity measures from the Kaiser Family Foundation and the BLS, demographic characteristics from the American Community Survey (ACS) and prevalence of smoking and drinking from the National Survey on Drug Use and Health (NSDUH), between the years of 2005 and 2015. Using this novel data source, we apply a linear multilevel regression model to gain a better understanding of the impact that CHW programs have on statewide health outcomes.
Main Findings and policy implications
Our findings suggest that states with a higher number of CHWs experience statistically significant reductions in mortality rates, a fact that is consistent across all of our alternative fixed and random effects model specifications. Further, when applying a hypothetical policy intervention of a 20% increase in each state’s CHW workforce, we show this intervention could save up to 40,000 lives per year. However, we observe considerable variation in CHW effectiveness between states, where states with higher mortality rates display much larger potential gains in mortality reduction from additional CHW workforce than states that are already at the forefront of using community resources as part of integrated health care teams.