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Objectives. To assess if historical redlining, the US government’s 1930s racially discriminatory grading of neighborhoods’ mortgage credit-worthiness, implemented via the federally sponsored Home Owners’ Loan Corporation (HOLC) color-coded maps, is associated with contemporary risk of preterm birth (< 37 weeks gestation). Methods. We analyzed 2013–2017 birth certificate data for all singleton births in New York City (n = 528 096) linked by maternal residence at time of birth to (1) HOLC grade and (2) current census tract social characteristics. Results. The proportion of preterm births ranged from 5.0% in grade A (“best”—green) to 7.3% in grade D (“hazardous”—red). The odds ratio for HOLC grade D versus A equaled 1.6 and remained significant (1.2; P < .05) in multilevel models adjusted for maternal sociodemographic characteristics and current census tract poverty, but was 1.07 (95% confidence interval = 0.92, 1.20) after adjustment for current census tract racialized economic segregation. Conclusions. Historical redlining may be a structural determinant of present-day risk of preterm birth. Public Health Implications. Policies for fair housing, economic development, and health equity should consider historical redlining’s impacts on present-day residential segregation and health outcomes.

We introduce the National Institute on Minority Health and Health Disparities (NIMHD) research framework, a product that emerged from the NIMHD science visioning process. The NIMHD research framework is a multilevel, multidomain model that depicts a wide array of health determinants relevant to understanding and addressing minority health and health disparities and promoting health equity. We describe the conceptual underpinnings of the framework and define its components. We also describe how the framework can be used to assess minority health and health disparities research as well as priorities for the future. Finally, we describe how fiscal year 2015 research project grants funded by NIMHD map onto the framework, and we identify gaps and opportunities for future minority health and health disparities research.

Reducing health disparities requires an understanding of the mechanisms that generate disparities. Life course approaches to health disparities leverage theories that explain how socially patterned physical, environmental, and socioeconomic exposures at different stages of human development shape health within and across generations and can therefore offer substantial insight into the etiology of health disparities. Life course approaches are informed by developmental and structural perspectives. Developmental perspectives emphasize how socially patterned exposures to risk factors during sensitive life stages shift health trajectories, whereas structural perspectives emphasize how social identity and position within socially patterned environments disproportionately allocate risk factors and resources, resulting in altered health trajectories. We conclude that the science of health disparities will be advanced by integrating life course approaches into etiologic and intervention research on health disparities. The following 4 strategies are offered to guide in this process: (1) advance the understanding of multiple exposures and their interactions, (2) integrate life course approaches into the understanding of biological mechanisms, (3) explore transgenerational transmission of health disparities, and (4) integrate life course approaches into health disparities interventions.

In the United States, people of color face disparities in access to health care, the quality of care received, and health outcomes. The attitudes and behaviors of health care providers have been identified as one of many factors that contribute to health disparities. Implicit attitudes are thoughts and feelings that often exist outside of conscious awareness, and thus are difficult to consciously acknowledge and control. These attitudes are often automatically activated and can influence human behavior without conscious volition. We investigated the extent to which implicit racial/ethnic bias exists among health care professionals and examined the relationships between health care professionals' implicit attitudes about racial/ethnic groups and health care outcomes. To identify relevant studies, we searched 10 computerized bibliographic databases and used a reference harvesting technique. We assessed eligibility using double independent screening based on a priori inclusion criteria. We included studies if they sampled existing health care providers or those in training to become health care providers, measured and reported results on implicit racial/ethnic bias, and were written in English. We included a total of 15 studies for review and then subjected them to double independent data extraction. Information extracted included the citation, purpose of the study, use of theory, study design, study site and location, sampling strategy, response rate, sample size and characteristics, measurement of relevant variables, analyses performed, and results and findings. We summarized study design characteristics, and categorized and then synthesized substantive findings. Almost all studies used cross-sectional designs, convenience sampling, US participants, and the Implicit Association Test to assess implicit bias. Low to moderate levels of implicit racial/ethnic bias were found among health care professionals in all but 1 study. These implicit bias scores are similar to those in the general population. Levels of implicit bias against Black, Hispanic/Latino/Latina, and dark-skinned people were relatively similar across these groups. Although some associations between implicit bias and health care outcomes were nonsignificant, results also showed that implicit bias was significantly related to patient-provider interactions, treatment decisions, treatment adherence, and patient health outcomes. Implicit attitudes were more often significantly related to patient-provider interactions and health outcomes than treatment processes. Most health care providers appear to have implicit bias in terms of positive attitudes toward Whites and negative attitudes toward people of color. Future studies need to employ more rigorous methods to examine the relationships between implicit bias and health care outcomes. Interventions targeting implicit attitudes among health care professionals are needed because implicit bias may contribute to health disparities for people of color.

Intersectionality, an indispensable critical theoretical framework for public health,1,2 is ideally suited to address the current \"deadly confluence of health, economic, and racial crises\" (Poteat, p. 91). Aligned with my invocation of intersectionality to lambaste the \"We're all in this together\" tropes of the COVID-19 era,3 this special section affirms an essential need for \"an intersectional public health lens that . . . embrace[s] rather than obscure[s] the heterogeneity of people's lived experience\" (Elnaiem, p. 93; quote p. 94) with new public health crises such as COVID19, and ongoing ones such as police brutality and HIV/AIDS (Aguayo-Romero, p. 101; Elnaiem; Poteat). The section also ventures into uncharted terrains such as epigenetics (Zota and VanNoy, p. 104) and artificial intelligence (Bauer and Lizotte, p. 98), and highlights the conceptual and methodological challenges of intersectionality research from the perspective of a group of National Institutes of Health (NIH) extramural research administrators (Alvidrez et al., p. 95).Informed by Collins's conceptualization of intersectionality as a \"broadbased knowledge project\"-a field of study, an analytical strategy, and critical praxis4(p3)-I characterize intersectionality's inroads into public health and its potential for addressing public health crises as a series of overlapping waves. Wave 1 was and is definitional, focused on intersectionality's history, core tenets, and relevance to public health. Wave 2 reflects the mainstreaming and flattening of intersectionality as it travels through traditional research organizations such as the National Academy of Sciences (NAS) and NIH. Wave 3 is analytical, reflecting the theoretical application of intersectionality to current public health crises. In this editorial, I highlight how this special section spans these waves and preview a fourth wave essential to addressing and resolving the current spate of multiple and interlocking public health crises.

Health disparities research in the United States over the past 2 decades has yielded considerable progress and contributed to a developing evidence base for interventions that tackle disparities in health status and access to care. However, health disparity interventions have focused primarily on individual and interpersonal factors, which are often limited in their ability to yield sustained improvements. Health disparities emerge and persist through complex mechanisms that include socioeconomic, environmental, and system-level factors. To accelerate the reduction of health disparities and yield enduring health outcomes requires broader approaches that intervene upon these structural determinants. Although an increasing number of innovative programs and policies have been deployed to address structural determinants, few explicitly focused on their impact on minority health and health disparities. Rigorously evaluated, evidence-based structural interventions are needed to address multilevel structural determinants that systemically lead to and perpetuate social and health inequities. This article highlights examples of structural interventions that have yielded health benefits, discusses challenges and opportunities for accelerating improvements in minority health, and proposes recommendations to foster the development of structural interventions likely to advance health disparities research.

We are not all in this together. My 32-year history with the HIV/AIDS epidemic in the United States-initially as an HIV/AIDS policy analyst and now as an HIV-prevention researcher- has provided the dubitable opportunity to witness how adroitly deadly viruses spotlight fissures of structural inequality. In the late 1980s, \"changing face\" was the term often used to describe the epidemic's transition from one that affected predominantly White and class-privileged gay and bisexual men to one that exacted a disproportionate toll on people at the most marginalized demographic intersections: Black and Latinx gay and bisexual men, cisgender and transgender women, injection drug users, and poor people.The epidemic curve ofHIV/AIDS in the United States has now flattened, to use the parlance of the day, but not for people marginalized by intersections of racism, sexism, classism, and transphobia. An HIV vaccine still eludes us, but biomedical interventions such as preexposure prophylaxis effectively reduce HIV transmission. Alas, not for all. Black people are still less likely to have access to preexposure prophylaxis than are their White counterparts. Thus, COVID-19's arrival made me dread what its \"changing face\" might portend. Newspaper headlines swiftly affirmed the disproportionate impact of COVID-19 in Black and Navajo communities and issued ominous warnings about the pandemic's future in poor White rural communities.My irritation with the ubiquitous phrase \"We're all in this together\" quickly ensued. Although seemingly innocuous and often well intentioned, the phrase reflects an intersectional color and class blinding that functions to obscure the structural inequities that befall Black and other marginalized groups, who bear the harshest and most disproportionate brunt of anything negative or calamitous: HIV/AIDS, hypertension, poverty, diabetes, climate change disasters, unemployment, mass incarceration, and, now, COVID-19.

Multilevel interventions can be uniquely effective at addressing minority health and health disparities, but they pose substantial methodological, data analytic, and assessment challenges that must be considered when designing and applying interventions and assessment. To facilitate the adoption of multilevel interventions to reduce health disparities, we outline areas of need in filling existing operational challenges to the design and assessment of multilevel interventions. We discuss areas of development that address overarching constructs inherent in multilevel interventions, with a particular focus on their application to minority health and health disparities. Our approach will prove useful to researchers, as it allows them to integrate information related to health disparities research into the framework of broader constructs with which they are familiar. We urge researchers to prioritize building transdisciplinary teams and the skills needed to overcome the challenges in designing and assessing multilevel interventions, as even small contributions can accelerate progress toward improving minority health and reducing health disparities. To make substantial progress, however, a concerted and strategic effort, including work to advance analytic techniques and measures, is needed.

Objectives. To examine 47 years of US urban and rural mortality trends at the county level, controlling for effects of education, income, poverty, and race. Methods. We obtained (1) Centers for Disease Control and Prevention WONDER (Wide-ranging ONline Data for Epidemiologic Research) data (1970–2016) on 104 million deaths; (2) US Census data on education, poverty, and race; and (3) Bureau of Economic Analysis data on income. We calculated ordinary least square regression models, including interaction models, for each year. We graphed standardized parameter estimates for 47 years. Results. Rural–urban mortality disparities increased from the mid-1980s through 2016. We found education, race, and rurality to be strong predictors; we found strong interactions between percentage poverty and percentage rural, indicating that the largest penalty was in high-poverty, rural counties. Conclusions. The rural–urban mortality disparity was persistent, growing, and large when compared to other place-based disparities. The penalty had evolved into a high-poverty, rural penalty that rivaled the effects of education and exceeded the effects of race by 2016. Public Health Implications. Targeting public health programs that focus on high-poverty, rural locales is a promising strategy for addressing disparities in mortality.