An observational, sequential analysis of the relationship between local economic distress and inequities in health outcomes, clinical care, health behaviors, and social determinants of health

In the late 2010s in the US, less prosperous counties had worse values than more prosperous ones in 29 of 33 measures of Health Outcomes, Clinical Care, Health Behaviors, the Physical Environment, and Social and Economic Factors; for 26 of those measures, during a time of economic growth across the US, there was a progressive worsening of measure values with each move from a higher to a lower prosperity county. Further, with four exceptions, measures in the least prosperous counties worsened more than those in the most prosperous counties between approximately 2015 and 2019, suggesting that inequities in health and SDOH measures associated with economic prosperity increased during that period.

The general stepwise nature of the relationship between increasing economic distress and the measures we studied suggests a structural relationship that has led to a systemic and sequential worsening of health as one descends the economic prosperity ladder. Our findings support that local economic prosperity is associated with health status, health outcomes, and health care quality in Medicare fee-for-service patients [5,6] and other populations [3, 14‐18]. Further, for most of the measures we examined, the gap between the least and most prosperous counties widened in the immediate pre-pandemic period.

Not all measures demonstrated a stepwise worsening with increasing local economic distress. Physical Environment and Social and Economic Factors measures showed distinct patterns, both cross-sectionally and over time. Nonetheless, measures in the Physical Environment were invariably worse in the least prosperous counties.

It is worth noting that we found an inverse relationship between reporting of excessive drinking and local economic prosperity, though binge drinking increased across all prosperity quintiles between 2015 and 2019. Indeed, binge drinking is more common in members of higher household incomes and those with greater educational attainment. [19] It is possible that binge drinking is more culturally accepted in higher-income groups, or that alcohol consumption is relatively expensive compared to other drugs. The inverse relationship between price increases and alcohol use has long been documented; [20] studies of the relative prices of alcohol and illicit drugs, particularly in lower prosperity areas, should be conducted.

Our study has several limitations. First, our results are derived from data in two relatively close time periods in a relatively stable financial time; studies of different time periods may have different results. Importantly, we evaluated periods before the COVID-19 pandemic and reports suggest that economic and health inequities have increased since COVID-19 began; [21] therefore, our results might underestimate current inequities. Further, analyses of other time periods – for instance, during the 2008 financial crisis – might generate different results. Second, measures are not adjusted for local demographic factors that may impact measure values. For example, Blacks are more likely than Whites to have diabetes, [22] lower life expectancy, [23] and low birth weight babies; [24] Blacks are also more likely than Whites to live in areas with lower economic prosperity and may experience different outcomes than Whites living in the same economic conditions after admission for heart failure. [25] While demographic factors may partially explain our findings (for instance, among 25–34 year old participants in the Behavioral Risk Factor Surveillance System between 2009 and 2012, after demographic adjustments county-level economic opportunity was found to independently contribute to measures of Health Outcomes and Health Behaviors [26], demographic adjustment offers policymakers few pragmatic solutions if health equity is to be color-blind: changing the demographic makeup of a county cannot be a reasonable policy platform. While demographic adjustment is important in real-world policy development, in this observational study, we did not make demographic adjustments because we were concerned that demographic adjustment might inadvertently justify a demographic group’s obtaining lower quality care or less care access. Third, we limited our evaluation to county-level quintiles of economic prosperity and did not evaluate other potentially contributing factors, such as rural-urban disparities or geographic variation in health outcomes. To be sure, it is likely that more rural counties and more counties in the southeastern United States more persistently and commonly experience local economic distress. [27] Nonetheless, that reality does not refute our findings: it suggests that more rural and southeastern counties experience worse measures of health outcomes, clinical care measures, health behaviors, the physical environment, and social and economic factors. Fourth, our analysis was performed at the county quintile level: we did not seek to identify outliers, such as low prosperity counties with excellent health outcomes or vice-versa. In future work, particularly should robust longitudinal data become available, such analyses might give insights into reasons why counties become more prosperous or healthier and might identify causal pathways between population health and local prosperity. Finally, we did not have access to data that might have explored the relationship between our findings and the degree to which: particular measures – for instance, life expectancy or premature mortality – might be amenable to intervention by targeted risk factor modification at the population level; [28] the local political environment or other unmeasured factors might contribute to our findings; or the influence of geographically proximal economic distress might influence local economic distress. Each of these areas warrants further research that likely would require multi-decade datasets, across a variety of global economic conditions.

Despite these limitations, our findings suggest that investment in low prosperity qualified ‘economic opportunity zones’ might not only improve local economic conditions, but also improve community health, [29] thereby reducing health inequities, regardless of the demographic makeup of those areas. While our findings are associative and not causative, aforementioned studies used natural experimental methods and suggest that improving economic conditions might generate health benefits, rather rapidly. [7,8] Should more longitudinal and robust data become available, policymakers might be able to model the impact that targeted efforts to improve local economic conditions might have on measures of local population health.