Frontline work and racial disparities in social and economic pandemic stressors during the first COVID-19 surge

OBJECTIVE

To assess the magnitude of racial-ethnic disparities in pandemic-related social stressors and examine frontline work's moderating relationship on these stressors.

DATA SOURCES

Employed Californians' responses to the Institute for Governmental Studies (IGS) poll from April 16-20, 2020, were analyzed. The Pandemic Stressor Scale (PSS) assessed the extent to which respondents experienced or anticipated problems resulting from the inability to pay for basic necessities, job instability, lacking paid sick leave, unavailability of childcare, and reduced wages or work hours due to COVID-19.

STUDY DESIGN

Mixed-effects generalized linear models estimated (1) racial-ethnic disparities in pandemic stressors among workers during the first COVID-19 surge, adjusting for covariates, and (2) tested the interaction between race-ethnicity and frontline worker status, which includes a subset of essential workers who must perform their job on-site, to assess differential associations of frontline work by race-ethnicity.

DATA COLLECTION

The IGS poll data from employed workers (n = 4795) were linked to the 2018 Centers for Disease Control and Prevention Social Vulnerability Index at the zip code level (N = 1068).

PRINCIPAL FINDINGS

The average PSS score was 37.34 (SD = 30.49). Whites had the lowest PSS score (29.88, SD = 26.52), and Latinxs had the highest (50.74, SD = 32.61). In adjusted analyses, Black frontline workers reported more pandemic-related stressors than White frontline workers (PSS = 47.73 vs. 36.96, p < 0.001). Latinxs reported more pandemic stressors irrespective of frontline worker status. However, the 5.09-point difference between Latinx frontline and non-frontline workers was not statistically different from the 4.6-point disparity between White frontline and non-frontline workers.

CONCLUSION

Latinx workers and Black frontline workers disproportionately reported pandemic-related stressors. To reduce stress on frontline workers during crises, worker protections like paid sick leave, universal access to childcare, and improved job security are needed, particularly for those disproportionately affected by structural inequities, such as racially minoritized populations.

Impact of state Medicaid expansion on cross-sector health and social service networks: Evidence from a longitudinal cohort study

OBJECTIVE

To examine the impact of state Medicaid expansion on the delivery of population health activities in cross-sector health and social services networks. Community networks are multisector, interorganizational networks that provide services ranging from the direct provision of individual social services to the implementation of population-level initiatives addressing community outcomes.

DATA SOURCES

We used data measuring the composition of cross-sector population health networks 2006-2018 National Longitudinal Survey of Public Health Systems (NALSYS) linked with the Area Health Resource File.

STUDY DESIGN

A difference-in-differences approach was used to examine the impact of expansion on organization engagement in population health activities and network structure.

DATA COLLECTION/EXTRACTION METHODS

Stratified random sampling of local public health jurisdictions in the United States. We restricted our data to jurisdictions serving populations of 100,000 or more and states that had NALSYS observations across all time periods, resulting in a final sample size of 667.

PRINCIPAL FINDINGS

Results from our adjusted difference-in-differences estimates indicated that Medicaid expansion was associated with a 2.3 percentage point increase in the density of population health networks (p < 0.10). Communities in states that expanded Medicaid experienced significant increases in the participation of local public health, local government, hospital, nonprofit, insurer, and K-12 schools. Of the organizations with significant increases in expansion communities, nonprofits (7.7 percentage points, p < 0.01), local public health agencies (6.5 percentage points, p < 0.01), hospitals (5.8 percentage points, p < 0.01), and local government agencies (6.0 percentage points, p < 0.05) had the largest gains.

CONCLUSIONS

Our study found increases in cross-sector participation in population health networks in states that expanded Medicaid compared with nonexpansion states, suggesting that additional coverage gains are associated with positive changes in population health network structure.

The effect of a public transportation expansion on no-show appointments

OBJECTIVE

To test whether there were fewer missed medical appointments ("no-shows") for patients and clinics affected by a significant public transportation expansion.

STUDY SETTING

A new light rail line was opened in a major metropolitan area in June 2014. We obtained electronic health records data from an integrated health delivery system in the area with over three million appointments at 97 clinics between 2013 and 2016.

STUDY DESIGN

We used a difference-in-differences research design to compare whether no-show appointment rates differentially changed among patients and clinics located near versus far from the new light rail line after it opened. Models included fixed effects to account for underlying differences across clinics, patient zip codes, and time.

DATA EXTRACTION METHODS

We obtained data from an electronic health records system representing all appointments scheduled at 97 outpatient clinics in this system. We excluded same-day, urgent care, and canceled appointments.

PRINCIPAL FINDINGS

The probability of no-show visits differentially declined by 0.5 percentage points (95% confidence interval [CI]: -0.9 to -0.1), or 4.5% relative to baseline, for patients living near the new light rail compared to those living far from it, after the light rail opened. The effects were stronger among patients covered by Medicaid (-1.6 percentage points [95% CI: -2.4 to -0.8] or 9.5% relative to baseline).

CONCLUSIONS

Improvements to public transit may improve access to health care, especially for people with low incomes.

The short-term effects of the earned income tax credit on health care expenditures among US adults

OBJECTIVE

To test the hypothesis that the earned income tax credit (EITC)-the largest US poverty alleviation program-affects short-term health care expenditures among US adults.

DATA SOURCES

Adult participants in the 1997-2012 waves of the US Medical Expenditure Panel Survey (MEPS) (N = 1 282 080).

STUDY DESIGN

We conducted difference-in-differences analyses, comparing health care expenditures among EITC-eligible adults in February (immediately following EITC refund receipt) with expenditures during other months, using non-EITC-eligible individuals to difference out seasonal variation in health care expenditures. Outcomes included total out-of-pocket expenditures as well as spending on specific categories such as outpatient visits and inpatient hospitalizations. We conducted subgroup analyses to examine heterogeneity by insurance status.

PRINCIPAL FINDINGS

EITC refund receipt was not associated with short-term changes in total expenditures, nor any expenditure subcategories. Results were similar by insurance status and robust to numerous alternative specifications.

CONCLUSIONS

EITC refunds are not associated with short-term changes in health care expenditures among US adults. This may be because the refund is spent on other expenses, because of income smoothing, or because of similar refund-related variation in health care expenditures among noneligible adults. Future studies should examine whether other types of income supplementation affect health care expenditures, particularly among individuals in poverty.

Decomposing Mortality Disparities in Urban and Rural U.S. Counties

OBJECTIVE

To understand the role of county characteristics in the growing divide between rural and urban mortality from 1980 to 2010.

DATA SOURCE

Age-adjusted mortality rates for all U.S. counties from 1980 to 2010 were obtained from the CDC Compressed Mortality File and combined with county characteristics from the U.S. Census Bureau, the Area Health Resources File, and the Inter-University Consortium for Political and Social research.

STUDY DESIGN

We used Oaxaca-Blinder decomposition to assess the extent to which rural-urban mortality disparities are explained by observed county characteristics at each decade.

PRINCIPAL FINDINGS

Decomposition shows that, at each decade, differences in rural/urban characteristics are sufficient to explain differences in mortality. Furthermore, starting in 1990, rural counties have significantly lower predicted mortality than urban counties when given identical county characteristics. We find changes in the effect of characteristics on mortality, not the characteristics themselves, drive the growing mortality divide.

CONCLUSIONS

Differences in economic and demographic characteristics between rural and urban counties largely explain the differences in age-adjusted mortality in any given year. Over time, the role these characteristics play in improving mortality has increased differentially for urban counties. As characteristics continue changing in importance as determinants of health, this divide may continue to widen.