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

Background Socioeconomic status has long been associated with population health and health outcomes. While ameliorating social determinants of health may improve health, identifying and targeting areas where feasible interventions are most needed would help improve health equity. We sought to identify inequities in health and social determinants of health (SDOH) associated with local economic distress at the county-level. Methods For 3,131 counties in the 50 US states and Washington, DC (wherein approximately 325,711,203 people lived in 2019), we conducted a retrospective analysis of county-level data collected from County Health Rankings in two periods (centering around 2015 and 2019). We used ANOVA to compare thirty-three measures across five health and SDOH domains (Health Outcomes, Clinical Care, Health Behaviors, Physical Environment, and Social and Economic Factors) that were available in both periods, changes in measures between periods, and ratios of measures for the least to most prosperous counties across county-level prosperity quintiles, based on the Economic Innovation Group’s 2015–2019 Distressed Community Index Scores. Results With seven exceptions, in both periods, we found a worsening of values with each progression from more to less prosperous counties, with least prosperous counties having the worst values (ANOVA p < 0.001 for all measures). Between 2015 and 2019, all except six measures progressively worsened when comparing higher to lower prosperity quintiles, and gaps between the least and most prosperous counties generally widened. Conclusions In the late 2010s, the least prosperous US counties overwhelmingly had worse values in measures of Health Outcomes, Clinical Care, Health Behaviors, the Physical Environment, and Social and Economic Factors than more prosperous counties. Between 2015 and 2019, for most measures, inequities between the least and most prosperous counties widened. Our findings suggest that local economic prosperity may serve as a proxy for health and SDOH status of the community. Policymakers and leaders in public and private sectors might use long-term, targeted economic stimuli in low prosperity counties to generate local, community health benefits for vulnerable populations. Doing so could sustainably improve health; not doing so will continue to generate poor health outcomes and ever-widening economic disparities.

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 William B Weeks 1* , Ji E Chang 2 , José A Pagán 2 , Ann Aerts 3 , James N Weinstein 4 and Juan Lavista Ferres 1

Background
Socioeconomic status has long been associated with population health and health outcomes in industrial countries.[1][2][3] In the United States (US), among older adults enrolled in traditional Medicare, living in areas of high local economic distress (an index compiled from seven measures of local economic activity obtained from US Census Bureau, US Bureau of Labor Statistics, and American Community Survey data [4] has been associated with higher per-capita Medicare expenditures, lower care quality, higher mortality, [5] and less use of recommended services.[6] Improving local economic conditions in the US is associated with improved health outcomes in Medicare [7] and non-Medicare populations.[8]. The distribution of economic prosperity among US communities has undergone significant changes in recent decades, resulting in heightened inequality in local economic prosperity.[9] This has led to a growing interest in developing policies and resources that support both "places" and "people, " particularly in underserved communities.[10,11] Such policies recognize that socio-economic conditions are significant determinants of health and that ameliorating social determinants of health (SDOH) -the non-medical factors that influence health outcomes -may improve population health.[12] However, formulating an effective policy response requires identifying and targeting areas where interventions are most greatly needed, are achievable, and might have the largest impact on health equity.
To identify characteristics associated with such areas, we sought to identify cross-sectional and longitudinal inequities in health, clinical care, health behaviors, and SDOH associated with local economic distress at the county level, using 2015 and 2019 data from County Health Rankings.

Data collection and aggregation
For 3,131 counties in the 50 US states and Washington, DC (wherein 325,711,203 people lived in 2019), we obtained Distressed Communities Index (DCI) scores from the Economic Innovation Group.[4] Constructed from seven measures of local economic distress collected from the US Census, US Bureau of Labor Statistics, and the American Community Survey over the period 2015-2019, DCI scores are ranked percentiles that are equally distributed and range from 0 (most prosperous) to 100 (least prosperous).[4] For those counties, we collected 33 county-level attributes obtained from the 2015-2022 County Health Rankings [13] across five health and SDOH domains: Health Outcomes, Clinical Care, Health Behaviors, Physical Environment, and Social and Economic Factors.We limited measures to those available in both (approximately) 2015 and 2019, in essence using a convenience sample of available measures that approximately bookended the time period over which data were used to calculate DCI scores.Table 1 provides the measure name, definition, measure value orientation, periods of data collection, and year interval, across the five domains.Table 2 shows the original sources from which County Health Rankings obtained these measures.

Analysis
In both years, we compared the health and SDOH measures across prosperity quintiles, defined by county-level DCI scores (there were 626 counties in the most prosperous, highly prosperous, average, and unprosperous quintiles and 627 counties in the least prosperous quintile) using Analysis of Variance (ANOVA).We calculated the ratio of values for the least to the most prosperous county quintiles.Further, for each prosperity quintile, we calculated the change in values for each prosperity quintile between 2015 and 2019.Finally, we calculated the ratio of values for the least to the most prosperous counties between the first and second period and provided an indication of whether the gap between the least and most targeted economic stimuli in low prosperity counties to generate local, community health benefits for vulnerable populations.Doing so could sustainably improve health; not doing so will continue to generate poor health outcomes and ever-widening economic disparities.

Contributions to the literature
• Since local economic distress is associated with worse health outcomes, policymakers should consider returns to health as a motivator for improving economic distress.• The potential magnitude of the impact of reducing economic disparities on clinical quality, outcomes, and health behaviors should be considered when investing to improve local economic conditions.• Targeted economic stimuli to generate local, community health benefits for vulnerable populations living in the least prosperous areas may be an effective way to improve population health.
prosperous counties was widening, narrowing, or staying the same.We used SPSS v 28 (released 2022, Armonk, NY: IBM Corporation) for all analyses.

Results
In 2019, we found a progressive worsening of values with movement to a less prosperous quintile for all except five measures (ANOVA p < 0.001 for all measures) (Table 3).
All Health Outcomes values got progressively worse with worsening county-level prosperity.For example, diabetes is least prevalent in the most prosperous quintile of counties (8.84%); its prevalence progressively increases as prosperity decreases, reaching a peak in the least prosperous quintile of counties (13.49%).All Clinical Care metrics got progressively worse with worsening countylevel prosperity, although the mental health workforce measure did not do so in a strictly progressive manner (the second least prosperous quintile had the worst value).All Health Behavior metrics got progressively worse with lowering county-level prosperity except for excessive drinking, which showed the opposite pattern: 20.84% of the adult population reported excessive drinking in the most prosperous quintile of counties, but that proportion progressively fell with worsening prosperity to reach a nadir of 16.43% in the least prosperous quintile of counties.In the Physical Environment domain, measures of air quality and severe housing problems were worst in the least prosperous two quintiles, but there was not a progressive pattern of worsening.All Social and Economic Factors metrics got progressively worse with lowering county-level prosperity except for the membership association rate, where there was an inverse U-shaped pattern, with the least economically prosperous quintile having the worst value.Identical patterns were found when examining earlier data (Table 4), with the exception being that the membership association rate was second worst in the least prosperous quintile.When examining changes in measure values between earlier and later data collection periods, most measures demonstrated progressive worsening of values with worsening prosperity, suggesting that inequities increased over time (Table 5).There were several exceptions to this general rule: years potential life lost increased more in higher prosperity quintiles; the preventable hospitalization rate fell as prosperity worsened; the least prosperous quintile experienced the greatest absolute decline in the uninsurance rate; and there was a stepwise reduction in childhood poverty as prosperity decreased.There was no clear pattern in food insecurity, limited healthy food access, air quality, severe housing problems, or the membership association rate.While income equality worsened most in the least prosperous quintile counties, there was not a progressive, stepwise pattern.
When comparing ratios of values in the least to the most prosperous counties in 2015 and 2019, 20 measures demonstrated a widening of the gap between the least and most prosperous counties, 10 demonstrated a narrowing, and 3 remained the same (Table 6).

Discussion
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][15][16][17][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 Table 4 Results for the earlier data collection period (around 2015), by county prosperity quintile.All ANOVA differences across prosperity quintiles p < 0.001.Values in italics did not follow a stepwise worsening of measure value when moving from a higher to a lower prosperity quintile.Values in bold indicate measures where there was improvement in measure values when moving from a higher to a lower prosperity quintile 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.

Conclusions
Our findings suggest that local economic prosperity may serve as a proxy for the health and SDOH status of the community.Communities operate within the context of federal and state policies that shape local economic conditions including the allocation of resources and strategic priorities.[9] Together, policymakers, health plans, health systems, public health leaders, and leaders in corporate America should consider long-term, targeted economic stimuli to generate local, community health benefits for vulnerable populations living in the least prosperous areas.

Table 1
Measures collected, with domain, definition, orientation, periods obtained, and year interval between periods

Table 2
Measures collected, with domain and original source of data that was compiled in County Health Reports

Table 3
Results for the later collection period (around 2019), by county prosperity quintile.All ANOVA differences across prosperity quintiles p < 0.001.Values in italics did not follow a stepwise worsening of measure value when moving from a higher to a lower prosperity quintile.Values in bold indicate measures where there was improvement in measure values when moving from a higher to a lower prosperity quintile

Table 5
Change in measure values between the earlier and later data collection period, by county prosperity quintile.Values in italics did not follow a stepwise worsening of measure value when moving from a higher to a lower prosperity quintile.Values in bold indicate measures where there was improvement in measure values among the least prosperous counties

Table 6
Ratios of values in the least to the most prosperous counties in 2015 and 2019 and an indication of whether the gap between least and most prosperous counties is widening, narrowing, or staying the same