1965 US Voting Rights Act Impact on Black and Black Versus White Infant Death Rates in Jim Crow States, 1959-1980 and 2017-2021.

Objectives. To investigate the impact of the US Voting Rights Act (VRA) of 1965 on Black and Black versus White infant deaths in Jim Crow states. Methods. Using data from 1959 to 1980 and 2017 to 2021, we applied difference-in-differences methods to quantify differential pre-post VRA changes in infant deaths in VRA-exposed versus unexposed counties, controlling for population size and social, economic, and health system characteristics. VRA-exposed counties, identified by Section 4, were subject to government interventions to remove existing racist voter suppression policies. Results. Black infant deaths in VRA-exposed counties decreased by an average of 11.4 (95% confidence interval [CI] = 1.7, 21.0) additional deaths beyond the decrease experienced by unexposed counties between the pre-VRA period (1959-1965) and the post-VRA period (1966-1970). This translates to 6703 (95% CI = 999.6, 12 348) or 17.5% (95% CI = 3.1%, 28.1%) fewer deaths than would have been experienced in the absence of the VRA. The equivalent differential changes were not significant among the White or total population. Conclusions. Passage of the VRA led to pronounced reductions in Black infant deaths in Southern counties subject to government intervention because these counties had particularly egregious voter suppression practices. (Am J Public Health. 2024;114(3):300-308. https://doi.org/10.2105/AJPH.2023.307518).

Gerrymandering and the Packing and Cracking of Medical Uninsurance Rates in the United States.

CONTEXT: Technological innovation and access to big data have allowed partisan gerrymandering to increase dramatically in recent redistricting cycles. OBJECTIVE: To understand whether and how partisan gerrymandering, including "packing" and "cracking" (ie, respectively concentrating within or dividing specified social groups across political boundaries), distorts understanding of public health need when health statistics are calculated for congressional districts (CDs). DESIGN: Cross-sectional study using 2020 CDs and nonpartisan simulated districts. SETTING: United States, 2017-2021. PARTICIPANTS: United States residents. MAIN OUTCOME MEASURE: Percent with no medical insurance (uninsured), within-district variance of percent uninsured, and between-district variance of percent uninsured. RESULTS: At the state level, states where partisan redistricting plans showed greater evidence of partisan gerrymandering were more likely to contain CDs with more extreme values of uninsurance rates than districts in states with less evidence for gerrymandering (association between z-scores for gerrymandering and between-district variation in uninsurance = 0.25 (-0.04, 0.53), P  = .10). Comparing variation in uninsurance rates for observed CDs vs nonpartisan simulated districts across all states with more than 1 CD, in analyses stratified by state gerrymander status (no gerrymander, Democratic gerrymander, and Republican gerrymander), we found evidence of particularly extreme distortion of rates in Republican gerrymandered states, whereby Republican-leaning districts tended to have lower uninsurance rates (the percentage of Republican-leaning districts that were significantly lower than nonpartisan simulated districts was 5.1 times that of Democratic-leaning districts) and Democrat-leaning districts had higher uninsurance rates (the percentage of Democrat-leaning districts that were significantly higher than nonpartisan simulated districts was 3.0 times that of Republican-leaning districts). CONCLUSIONS: Partisan gerrymandering can affect determination of CD-level uninsurance rates and distort understanding of public health burdens.

Systematic review of how racialized health inequities are addressed in Epidemiologic Reviews articles (1979-2021): a critical conceptual and empirical content analysis and recommendations for best practices.

Critical analysis of the determinants of current and changing racialized health inequities, including the central role of racism, is an urgent priority for epidemiology, for both original research studies and epidemiologic review articles. Motivating our systematic overview review of Epidemiologic Reviews articles is the critical role of epidemiologic reviews in shaping discourse, research priorities, and policy relevant to the social patterning of population health. Our approach was first to document the number of articles published in Epidemiologic Reviews (1979-2021; n = 685) that either: (1) focused the review on racism and health, racial discrimination and health, or racialized health inequities (n = 27; 4%); (2) mentioned racialized groups but did not focus on racism or racialized health inequities (n = 399; 59%); or (3) included no mention of racialized groups or racialized health inequities (n = 250; 37%). We then conducted a critical content analysis of the 27 review articles that focused on racialized health inequities and assessed key characteristics, including (1) concepts, terms, and metrics used regarding racism and racialized groups (notably only 26% addressed the use or nonuse of measures explicitly linked to racism; 15% provided explicit definitions of racialized groups); (2) theories of disease distribution guiding (explicitly or implicitly) the review's approach; (3) interpretation of findings; and (4) recommendations offered. Guided by our results, we offer recommendations for best practices for epidemiologic review articles for addressing how epidemiologic research does or does not address ubiquitous racialized health inequities.

Opioid-related overdose deaths by race and neighborhood economic hardship in Chicago.

Compared to national findings, Chicago has both a higher rate of opioid-related overdose death and a markedly different distribution by demographics. The Chicago Department of Public Health analyzed fatal overdoses by level of neighborhood economic hardship. The highest rate of opioid-related deaths occurred in neighborhoods with high economic hardship (36.9 per 100,000 population) compared to medium- (20.5) and low- (12.3) hardship neighborhoods. However, these patterns were not consistent across racial/Hispanic ethnicity subgroups. These data support the need to consider the role of racism and other structural, social, and economic factors when designing interventions to reduce opioid-related overdose deaths.

Sex Disparities in COVID-19 Mortality Vary Across US Racial Groups.

BACKGROUND: Inequities in COVID-19 outcomes in the USA have been clearly documented for sex and race: men are dying at higher rates than women, and Black individuals are dying at higher rates than white individuals. Unexplored, however, is how sex and race interact in COVID-19 outcomes. OBJECTIVE: Use available data to characterize COVID-19 mortality rates within and between race and sex strata in two US states, with the aim of understanding how apparent sex disparities in COVID-19 deaths vary across race. DESIGN AND PARTICIPANTS: This observational study uses COVID-19 mortality data through September 21, 2020, from Georgia (GA) and Michigan (MI). MAIN MEASURES: We calculate age-specific rates for each sex-race-age stratum, and age-standardized rates for each race-sex stratum. We investigate the sex disparity within race groups and the race disparity within sex groups using age-standardized rate ratios, and rate differences. KEY RESULTS: Within race groups, men have a higher COVID-19 mortality rate than women. Black men have the highest rate of all race-sex groups (in MI: 254.6, deaths per 100,000, 95% CI: 241.1-268.2, in GA:128.5, 95% CI: 121.0-135.9). In MI, the COVID-19 mortality rate for Black women (147.1, 95% CI: 138.7-155.4) is higher than the rate for white men (39.1, 95% CI: 37.3-40.9), white women (29.7, 95% CI: 28.3-31.0), and Asian/Pacific Islander men and women. COVID-19 mortality rates in GA followed the same pattern. In MI, the male:female mortality rate ratio among Black individuals is 1.7 (1.5-2.0) while the rate ratio among White individuals is only 1.3 (1.2-1.5). CONCLUSION: While overall, men have higher COVID-19 mortality rates than women, our findings show that this sex disparity does not hold across racial groups. This demonstrates the limitations of unidimensional reporting and analyses and highlights the ways that race and gender intersect to shape COVID-19 outcomes.

Impact of Differential Privacy and Census Tract Data Source (Decennial Census Versus American Community Survey) for Monitoring Health Inequities.

Objectives. To investigate how census tract (CT) estimates of mortality rates and inequities are affected by (1) differential privacy (DP), whereby the public decennial census (DC) data are injected with statistical "noise" to protect individual privacy, and (2) uncertainty arising from the small number of different persons surveyed each year in a given CT for the American Community Survey (ACS).Methods. We compared estimates of the 2008-2012 average annual premature mortality rate (death before age 65 years) in Massachusetts using CT data from the 2010 DC, 2010 DC with DP, and 2008-2012 ACS 5-year estimate data.Results. For these 3 denominator sources, the age-standardized premature mortality rates (per 100 000) for the total population respectively equaled 166.4 (95% confidence interval [CI] = 162.2, 170.6), 166.4 (95% CI = 162.2, 170.6), and 166.3 (95% CI = 162.1, 170.5), and inequities in the range from best to worst quintile for CT racialized economic segregation were from 103.4 to 260.1, 102.9 to 258.7, and 102.8 to 262.4. Similarity of results across CT denominator sources held for analyses stratified by gender and race/ethnicity.Conclusions. Estimates of health inequities at the CT level may not be affected by use of 2020 DP data and uncertainty in the ACS data.

Association between Food Deserts and Gestational Diabetes Mellitus in a Large Metropolitan Area.

OBJECTIVE: The aim of this study is to determine the relationship between urban food deserts and frequency and obstetric outcomes related to gestational diabetes. STUDY DESIGN: We conducted a retrospective cohort study of singleton births in Chicago from 2010 to 2014. Birth certificate data were analyzed and geomapped by census tract. Census tracts were categorized as "food deserts" according to the USDA Food Access Research Atlas. The primary outcome was frequency of gestational diabetes. Secondary outcomes were assessed among women with gestational diabetes and their neonates. RESULTS: Of the 191,947 eligible women, 8,709 (4.5%) were diagnosed with gestational diabetes. Those in food deserts were more likely to be younger, obese, minority race/ethnicity, and multiparous. Women in food deserts were less likely to develop gestational diabetes (3.8 vs. 4.8%, p < 0.01; adjusted odds ratio 0.91, 95% confidence interval 0.86-0.96). Women with gestational diabetes did not experience worse maternal and neonatal outcomes after controlling for potential confounders. CONCLUSION: In contrast to prior work, women in Chicago living within food deserts were less likely to develop gestational diabetes and did not experience poorer outcomes, suggesting environmental factors other than food access contribute to perinatal outcomes.

A Gender Hypothesis of sex disparities in adverse drug events.

Pharmacovigilance databases contain larger numbers of adverse drug events (ADEs) that occurred in women compared to men. The cause of this disparity is frequently attributed to sex-linked biological factors. We offer an alternative Gender Hypothesis, positing that gendered social factors are central to the production of aggregate sex disparities in ADE reports. We describe four pathways through which gender may influence observed sex disparities in pharmacovigilance databases: healthcare utilization; bias and discrimination in the clinic; experience of a drug event as adverse; and pre-existing social and structural determinants of health. We then use data from the U.S. FDA Adverse Event Reporting System (FAERS) to explore how the Gender Hypothesis might generate novel predictions and explanations of sex disparities in ADEs in existing widely referenced datasets. Analyzing more than 3 million records of ADEs between 2014 and 2022, we find that patient-reported ADEs show a larger female skew than healthcare provider-reported ADEs and that the sex disparity is markedly smaller for outcomes involving death or hospitalization. We also find that the sex disparity varies greatly across types of ADEs, for example, cosmetically salient ADEs are skewed heavily female and sexual dysfunction ADEs are skewed male. Together, we interpret these findings as providing evidence of the promise of the Gender Hypothesis for identifying intervenable mechanisms and pathways contributing to sex disparities in ADEs. Rigorous application of the Gender Hypothesis to additional datasets and in future research studies could yield new insights into the causes of sex disparities in ADEs.

How Cumulative Statistics Can Mislead: The Temporal Dynamism of Sex Disparities in COVID-19 Mortality in New York State.

Overall, men have died from COVID-19 at slightly higher rates than women. But cumulative estimates of mortality by sex may be misleading. We analyze New York State COVID-19 mortality by sex between March 2020 and August 2021, demonstrating that 72.7% of the total difference in the number of COVID-19 deaths between women and men was accrued in the first seven weeks of the pandemic. Thus, while the initial surge in COVID-19 mortality was characterized by stark sex disparities, this article shows that disparities were greatly attenuated in subsequent phases of the pandemic. Investigating changes over time could help illuminate how contextual factors contributed to the development of apparent sex disparities in COVID-19 outcomes.

Sex disparities in COVID-19 outcomes in the United States: Quantifying and contextualizing variation.

This paper presents the first longitudinal study of sex disparities in COVID-19 cases and mortalities across U.S. states, derived from the unique 13-month dataset of the U.S. Gender/Sex COVID-19 Data Tracker. To analyze sex disparities, weekly case and mortality rates by sex and mortality rate ratios were computed for each U.S. state, and a multilevel crossed-effects conditional logistic binomial regression model was fitted to estimate the variation of the sex disparity in mortality over time and across states. Results demonstrate considerable variation in the sex disparity in COVID-19 cases and mortalities over time and between states. These data suggest that the sex disparity, when present, is modest, and likely varies in relation to context-sensitive variables, which may include health behaviors, preexisting health status, occupation, race/ethnicity, and other markers of social experience.

Comparing denominator sources for real-time disease incidence modeling: American Community Survey and WorldPop.

Across the United States public health community in 2020, in the midst of a pandemic and increased concern regarding racial/ethnic health disparities, there is widespread concern about our ability to accurately estimate small-area disease incidence rates due to the absence of a recent census to obtain reliable population denominators. 2010 decennial census data are likely outdated, and intercensal population estimates from the Census Bureau, which are less temporally misaligned with real-time disease incidence data, are not recommended for use with small areas. Machine learning-based population estimates are an attractive option but have not been validated for use in epidemiologic studies. Treating 2010 decennial census counts as a "ground truth", we conduct a case study to compare the performance of alternative small-area population denominator estimates from surrounding years for modeling real-time disease incidence rates. Our case study focuses on modeling health disparities in census tract incidence rates in Massachusetts, using population size estimates from the American Community Survey (ACS), the most commonly-used intercensal small-area population data in epidemiology, and WorldPop, a machine learning model for high-resolution population size estimation. Through simulation studies and an analysis of real premature mortality data, we evaluate whether WorldPop denominators can provide improved performance relative to ACS for quantifying disparities using both census tract-aggregate and race-stratified modeling approaches. We find that biases induced in parameter estimates due to temporally incompatible incidence and denominator data tend to be larger for race-stratified models than for area-aggregate models. In most scenarios considered here, WorldPop denominators lead to greater bias in estimates of health disparities than ACS denominators. These insights will assist researchers in intercensal years to select appropriate population size estimates for modeling disparities in real-time disease incidence. We highlight implications for health disparity studies in the coming decade, as 2020 census counts may introduce new sources of error.

Adverse Drug Events by Sex After Adjusting for Baseline Rates of Drug Use.

This cross-sectional study examines adverse drug events reported by sex in the US Food and Drug Administration (FDA) database after adjusting for drug use by males and females.