We consider several possible interpretations of the “effect of race” when regressions are run with race as an exposure variable, controlling also for various confounding and mediating variables. When adjustment is made for socioeconomic status early in a person’s life, we discuss under what contexts the regression coefficients for race can be interpreted as corresponding to the extent to which a racial inequality would remain if various socioeconomic distributions early in life across racial groups could be equalized. When adjustment is also made for adult socioeconomic status, we note how the overall racial inequality can be decomposed into the portion that would be eliminated by equalizing adult socioeconomic status across racial groups and the portion of the inequality that would remain even if adult socioeconomic status across racial groups were equalized. We also discuss a stronger interpretation of the “effect of race” (stronger in terms of assumptions) involving the joint effects of race-associated physical phenotype (e.g. skin color), parental physical phenotype, genetic background and cultural context when such variables are thought to be hypothetically manipulable and if adequate control for confounding were possible. We discuss some of the challenges with such an interpretation. Further discussion is given as to how the use of selected populations in examining racial disparities can additionally complicate the interpretation of the effects.

Analysts often use different conceptual definitions of a cohort effect, and therefore different statistical methods, which lead to differing empirical results. A definition often used in sociology assumes that cohorts have unique characteristics confounded by age and period effects, whereas epidemiologists often conceive that period and age effects interact to produce cohort effects. The present study aims to illustrate these differences by estimating age, period, and cohort (APC) effects on obesity prevalence in the U.S. from 1971–2006 using both conceptual approaches. Data were drawn from seven cross-sectional waves of the National Health and Nutrition Examination Survey. Obesity was defined as BMI≥30 for adults and ≥95th percentile for children under the age of 20. APC effects were estimated using the classic constraint-based method (first-order effects estimated and interpreted), the Holford method (first-order effects estimated but second-order effects interpreted), and median polish method (second-order effects are estimated and interpreted). Results indicated that all methods report significant age and period effects, with lower obesity prevalence in early life as well as increasing prevalence in successive surveys. Positive cohort effects for more recently born cohorts emerged based on the constraint-based model; when cohort effects were considered second-order estimates, no significant effects emerged. First-order estimates of age-period-cohort effects are often criticized because of their reliance on arbitrary constraints, but may be conceptually meaningful for sociological research questions. Second-order estimates are statistically estimable and produce conceptually meaningful results for epidemiological research questions. Age-period-cohort analysts should explicitly state the definition of a cohort effect under consideration. Our analyses suggest that the prevalence of obesity in the U.S. in the latter part of the 20th century rose across all birth cohorts, in the manner expected based on estimated age and period effects. As such, the absence or presence of cohort effects depends on the conceptual definition and therefore statistical method used.

Abstract Epidemiology of the U.S. COVID-19 outbreak focuses on individuals’ biology and behaviors, despite centrality of occupational environments in the viral spread. This demonstrates collusion between epidemiology and racial capitalism because it obscures structural influences, absolving industries of responsibility for worker safety. In an empirical example, we analyze economic implications of race-based metrics widely used in occupational epidemiology. In the U.S., White adults have better average lung function and worse hearing than Black adults. Both impaired lung function and hearing are criteria for Worker’s compensation, which is ultimately paid by industry. Compensation for respiratory injury is determined using a race-specific algorithm. For hearing, there is no race adjustment. Selective use of race-specific algorithms for workers’ compensation reduces industries’ liability for worker health, illustrating racial capitalism operating within public health. Widespread and unexamined belief in inherent physiological inferiority of Black Americans perpetuates systems that limit industry payouts for workplace injuries. We see a parallel in the epidemiology of COVID-19 disparities. We tell stories of industries implicated in the outbreak and review how they exemplify racial capitalism. We call on public health professionals to: critically evaluate who is served and neglected by data analysis; and center structural determinants of health in etiological evaluation.

Multigene assays highlight racial disparities in tumor subtype distribution that persist even in clinically defined subgroups. Differences in tumor biology (eg, HER2-enriched status) may be targetable to reduce disparities among clinically ER+/HER2- cases.

Identifying the exposures or interventions that exacerbate or ameliorate racial health disparities is one of social epidemiology’s fundamental goals. Introducing an interaction term between race and an exposure into a statistical model is commonly utilized in the epidemiologic literature to assess racial health disparities and the potential viability of a targeted health intervention. However, researchers may attribute too much authority to the interaction term and inadvertently ignore other salient information regarding the health disparity. In this article, we highlight empirical examples from the literature demonstrating limitations of over-reliance on interaction terms in health disparities research; we further suggest approaches for moving beyond interaction terms when assessing these disparities. We promote a comprehensive framework of three guiding questions for disparity investigation, suggesting examination of the group-specific differences in 1) outcome prevalence, 2) exposure prevalence, and 3) effect size. Our framework allows for better assessment of meaningful differences in population health and the resulting implications for interventions, demonstrating that interaction terms alone do not provide sufficient means for determining how disparities arise. The widespread adoption of this more comprehensive approach has the potential to dramatically enhance understanding of the patterning of health and disease and the drivers of health disparities.

Decades of historical practices like housing discrimination in Detroit have lasting impacts on communities. Perhaps the most explicit example is the practice of redlining in the 1930s, whereby lenders outlined financially undesirable neighborhoods, populated by minority families, on maps and prevented residents from moving to better resourced neighborhoods. Awareness of historical housing discrimination may improve research assessing the impacts of current neighborhood characteristics on health. Using the Detroit Neighborhood Health Study (DNHS), we assessed the association between two-year changes in home foreclosure rates following the 2007-2008 Great Recession, and residents’ five-year self-rated health trajectories (2008-2013); and estimated the confounding bias introduced by ignoring historical redlining practices in the city. We used both ecological and multilevel models to make inference about person- and community-level processes. In a neighborhood-level linear regression adjusted for confounders (including percent redlined); a 10 percentage-point slower foreclosure rate recovery was associated with an increase in prevalence of poor self-rated health of 0.31 (95% CI: −0.02-0.64). At the individual level, it was associated with a within-person increase in probability of poor health of 0.45 (95% CI: 0.15-0.72). Removing redlining from the model biased the estimated effect upward to 0.38 (95% CI: 0.07-0.69) and 0.56 (95% CI: 0.21-0.84) in the neighborhood and individual-level models, respectively. Stratum-specific foreclosure recovery effects indicate stronger influence in neighborhoods with a greater proportion of residents identifying as white and a greater degree of historic redlining. These findings support theory that structural discrimination has lasting influences on current neighborhood health effects, and suggests that historical redlining specifically may increase vulnerability to contemporary neighborhood foreclosures. Community interventions should consider historical discrimination in conjunction with current place-based indicators to more equitably improve population health.

IMPORTANCE Despite cross-sectional evidence linking racial residential segregation to hypertension prevalence among non-Hispanic blacks, it remains unclear how changes in exposure to neighborhood segregation may be associated with changes in blood pressure.OBJECTIVE To examine the association of changes in neighborhood-level racial residential segregation with changes in systolic and diastolic blood pressure over a 25-year period. DESIGN, SETTING, AND PARTICIPANTSThis observational study examined longitudinal data of 2280 black participants of the Coronary Artery Risk Development in Young Adults (CARDIA) study, a prospective investigation of adults aged 18 to 30 years who underwent baseline examinations in field centers in 4 US locations from March 25, 1985, to June 7, 1986, and then were re-examined for the next 25 years. Racial residential segregation was assessed using the Getis-Ord G i * statistic, a measure of SD between the neighborhood's racial composition (ie, percentage of black residents) and the surrounding area's racial composition. Segregation was categorized as high (G i * >1.96), medium (G i * 0-1.96), and low (G i * <0). Fixed-effects linear regression modeling was used to estimate the associations of within-person change in exposure to segregation and within-person change in blood pressure while tightly controlling for time-invariant confounders. Data analyses were performed between August 4, 2016, and February 9, 2017. MAIN OUTCOMES AND MEASURESWithin-person changes in systolic and diastolic blood pressure across 6 examinations over 25 years. RESULTSOf the 2280 participants at baseline, 974 (42.7%) were men and 1306 (57.3%) were women. Of these, 1861 (81.6%) were living in a high-segregation neighborhood; 278 (12.2%), a medium-segregation neighborhood; and 141 (6.2%), a low-segregation neighborhood. Systolic blood pressure increased by a mean of 0.16 (95% CI, 0.06-0.26) mm Hg with each 1-SD increase in segregation score after adjusting for interactions of time with age, sex, and field center. Of the 1861 participants (81.6%) who lived in high-segregation neighborhoods at baseline, reductions in exposure to segregation were associated with reductions in systolic blood pressure. Mean differences in systolic blood pressure were −1.33 (95% CI, −2.26 to −0.40) mm Hg when comparing high-segregation with medium-segregation neighborhoods and −1.19 (95% CI, −2.08 to −0.31) mm Hg when comparing high-segregation with low-segregation neighborhoods after adjustment for time and interactions of time with baseline age, sex, and field center. Changes in segregation were not associated with changes in diastolic blood pressure.CONCLUSIONS AND RELEVANCE Decreases in exposure to racial residential segregation are associated with reductions in systolic blood pressure. This study adds to the small but growing body of evidence that policies that reduce segregation may have meaningful health benefits.

Background and Purpose While studies have linked types of fatty acids with coronary heart disease, data on individual fatty acids and risk of ischemic stroke are limited. We aimed to examine the associations between serum fatty acid concentrations and incidence of ischemic stroke and its subtypes. Methods We conducted a prospective case-control study nested in the Women's Health Initiative Observational Study cohort of postmenopausal US women aged 50 to 79 years. Between 1993 and 2003, incident cases of ischemic stroke were matched 1:1 to controls on age, race, and length of follow-up (964 matched pairs). Conditional logistic regression was used to estimate odds ratios (OR) and 99.9% confidence intervals (CI) for ischemic stroke and its subtypes. Results The multivariable adjusted OR and 99.9% CI of ischemic stroke associated with a 1-standard deviation (SD) increment in serum fatty acid concentration was 1.38 (99.9% CI, 1.05-1.83) for linelaidic acid (18:2tt, SD=0.04%); 1.27 (99.9% CI, 1.06-1.51) for palmitic acid (16:0, SD=2.74%); 1.20 (99.9% CI, 1.01-1.43) for oleic acid (18:1n9, SD=2.32%); 0.72 (99.9% CI, 0.59-0.87) for docosapentaenoic acid (22:5n3, SD=0.18%); 0.72 (99.9% CI, 0.59-0.87) for docosahexaenoic acid (22:6n3, SD=0.91%); and 0.81 (99.9% CI, 0.67-0.98) for arachidonic acid (20:4n6, SD=2.02%). These associations were generally consistent for atherothrombotic and lacunar stroke, but not cardioembolic stroke. Conclusions These findings suggest that individual serum trans, saturated, and monounsaturated fatty acids are positively associated with particular ischemic stroke subtypes, while individual n3 and n6 polyunsaturated fatty acids are inversely associated.