An epigenome-wide analysis of DNA methylation, racialized and economic inequities, and air pollution.

Importance: DNA methylation (DNAm) provides a plausible mechanism by which adverse exposures become embodied and contribute to health inequities, due to its role in genome regulation and responsiveness to social and biophysical exposures tied to societal context. However, scant epigenome-wide association studies (EWAS) have included structural and lifecourse measures of exposure, especially in relation to structural discrimination. Objective: Our study tests the hypothesis that DNAm is a mechanism by which racial discrimination, economic adversity, and air pollution become biologically embodied. Design: A series of cross-sectional EWAS, conducted in My Body My Story (MBMS, biological specimens collected 2008-2010, DNAm assayed in 2021); and the Multi Ethnic Study of Atherosclerosis (MESA; biological specimens collected 2010-2012, DNAm assayed in 2012-2013); using new georeferenced social exposure data for both studies (generated in 2022). Setting: MBMS was recruited from four community health centers in Boston; MESA was recruited from four field sites in: Baltimore, MD; Forsyth County, NC; New York City, NY; and St. Paul, MN. Participants: Two population-based samples of US-born Black non-Hispanic (Black NH), white non-Hispanic (white NH), and Hispanic individuals (MBMS; n=224 Black NH and 69 white NH) and (MESA; n=229 Black NH, n=555 white NH and n=191 Hispanic). Exposures: Eight social exposures encompassing racial discrimination, economic adversity, and air pollution. Main outcome: Genome-wide changes in DNAm, as measured using the Illumina EPIC BeadChip (MBMS; using frozen blood spots) and Illumina 450k BeadChip (MESA; using purified monocytes). Our hypothesis was formulated after data collection. Results: We observed the strongest associations with traffic-related air pollution (measured via black carbon and nitrogen oxides exposure), with evidence from both studies suggesting that air pollution exposure may induce epigenetic changes related to inflammatory processes. We also found suggestive associations of DNAm variation with measures of structural racial discrimination (e.g., for Black NH participants, born in a Jim Crow state; adult exposure to racialized economic residential segregation) situated in genes with plausible links to effects on health. Conclusions and Relevance: Overall, this work suggests that DNAm is a biological mechanism through which structural racism and air pollution become embodied and may lead to health inequities.

Epigenetic clocks and research implications of the lack of data on whom they have been developed: a review of reported and missing sociodemographic characteristics.

Epigenetic clocks are increasingly being used as a tool to assess the impact of a wide variety of phenotypes and exposures on healthy ageing, with a recent focus on social determinants of health. However, little attention has been paid to the sociodemographic characteristics of participants on whom these clocks have been based. Participant characteristics are important because sociodemographic and socioeconomic factors are known to be associated with both DNA methylation variation and healthy ageing. It is also well known that machine learning algorithms have the potential to exacerbate health inequities through the use of unrepresentative samples - prediction models may underperform in social groups that were poorly represented in the training data used to construct the model. To address this gap in the literature, we conducted a review of the sociodemographic characteristics of the participants whose data were used to construct 13 commonly used epigenetic clocks. We found that although some of the epigenetic clocks were created utilizing data provided by individuals from different ages, sexes/genders, and racialized groups, sociodemographic characteristics are generally poorly reported. Reported information is limited by inadequate conceptualization of the social dimensions and exposure implications of gender and racialized inequality, and socioeconomic data are infrequently reported. It is important for future work to ensure clear reporting of tangible data on the sociodemographic and socioeconomic characteristics of all the participants in the study to ensure that other researchers can make informed judgements about the appropriateness of the model for their study population.

Contextualizing Response Rates During the COVID-19 Pandemic: Experiences From a Boston-Based Community Health Centers Study.

OBJECTIVES: The focus of this study was to calculate and contextualize response rates for a community-based study conducted during the COVID-19 pandemic, a topic on which scant data exist, and to share lessons learned from recruiting and enrolling for implementation of future studies. DESIGN: The Life+Health Study, a cross-sectional population-based study designed to advance novel methods to measure and analyze multiple forms of discrimination for population health research. SETTING: The study recruited participants from 3 community health centers in Boston, Massachusetts, between May 2020 and July 2022. PARTICIPANTS: A total of 699 adult participants between the ages of 25 and 64 years who were born in the United States and had visited one of the health centers within the last 2 years. MAIN OUTCOME MEASURES: The response rate was calculated as follows: (number of completions + number of dropouts)/(dropouts + enrollments). To contextualize this response rate, we synthesized evidence pertaining to local COVID-19 case counts, sociopolitical events, pandemic-related restrictions and project protocol adjustments, and examples of interactions with patients. RESULTS: Our study had a lower-than-expected response rate (48.4%), with the lowest rates from the community health centers serving primarily low-income patients of color. Completion rates were lower during periods of higher COVID-19 case counts. We describe contextual factors that led to challenges and lessons learned from recruiting during the pandemic, including the impact of US sociopolitical events. CONCLUSIONS: The Life+Health Study concluded recruitment during the pandemic with a lower-than-expected response rate, as also reported in 4 other US publications focused on the impact of COVID-19 on response rates in community-based studies. Our results provide an example of the impact of the pandemic and related US sociopolitical events on response rates that can serve as a framework for contextualizing other research conducted during the pandemic and highlight the importance of best practices in research recruitment with underserved populations.

Decreasing Survey Response Rates in the Time of COVID-19: Implications for Analyses of Population Health and Health Inequities.

Objectives. To examine whether, and if so how, US national and state survey response rates changed after the onset of the COVID-19 pandemic. Methods. We compared the change in response rates between 2020 and 2019 of 6 (3 social and economic, 3 health focused) major US national surveys (2 with state response rates). Results. All the ongoing surveys except 1 reported relative decreases (∼29%) in response rates. For example, the household response rate to the US Census American Community Survey decreased from 86.0% in 2019 to 71.2% in 2020, and the response rate of the US National Health Interview Survey decreased from 60.0% to 42.7% from the first to the second quarter of 2020. For all surveys, the greatest decreases in response rates occurred among persons with lower income and lower education. Conclusions. Socially patterned decreases in response rates pose serious challenges and must be addressed explicitly in all studies relying on data obtained since the onset of the pandemic. Public Health Implications. Artifactual reduction of estimates of the magnitude of health inequities attributable to differential response rates could adversely affect efforts to reduce these inequities. (Am J Public Health. 2023;113(6):667-670. https://doi.org/10.2105/AJPH.2023.307267).

Use of Correct and Incorrect Methods of Accounting for Age in Studies of Epigenetic Accelerated Aging: Implications and Recommendations for Best Practices.

Motivated by our conduct of a literature review on social exposures and accelerated aging as measured by a growing number of epigenetic "clocks" (which estimate age via DNA methylation (DNAm) patterns), we report on 3 different approaches in the epidemiologic literature-1 incorrect and 2 correct-on the treatment of age in these and other studies using other common exposures (i.e., body mass index and alcohol consumption). Among the 50 empirical articles reviewed, the majority (n = 29; 58%) used the incorrect method of analyzing accelerated aging detrended for age as the outcome and did not control for age as a covariate. By contrast, only 42% used correct methods, which are either to analyze accelerated aging detrended for age as the outcome and control for age as a covariate (n = 16; 32%) or to analyze raw DNAm age as the outcome and control for age as a covariate (n = 5; 10%). In accord with prior demonstrations of bias introduced by use of the incorrect approach, we provide simulation analyses and additional empirical analyses to illustrate how the incorrect method can lead to bias towards the null, and we discuss implications for extant research and recommendations for best practices.

Using Implicit Measures of Discrimination: White, Black, and Hispanic Participants Respond Differently to Group-Specific Racial/Ethnic Categories vs. the General Category "People of Color" in the USA.

Recent studies showed that implicit measures are valuable instruments for assessing exposure to discrimination and predicting negative physical conditions. Between March 10, 2020, and April 1, 2020, we conducted three experiments (577 participants) in the USA to evaluate the use of group-specific vs. general race/ethnicity categories in implicit measures of discrimination. We measured implicit discrimination and attitudes towards the general race/ethnicity category "people of color" (POC) and two specific race/ethnicity categories (i.e., "Black people" and "Hispanic people"). Implicit discrimination and attitudes were assessed using the Brief Implicit Association Test (B-IAT). Among participants (mean age = 37, standard deviation = 10.5), 50% identified as White non-Hispanic (NH), 33.3% as Black NH, and 16.7% as Hispanic; 71.7% were female and 72.2% had a bachelor's degree or higher. We found an implicit discrimination towards target groups and an in-group preference among all participant groups only when specific race/ethnicity categories were used in the B-IAT. When the general category POC was used, we observed a discrimination towards POC only for Black NH participants, while White NH participants showed no discrimination. Similarly, Black NH participants showed no in-group preference for POC, but did show an in-group preference for Black people. These results suggest that using the category POC in implicit measures may be inappropriate when evaluating discrimination and attitudes towards Black and Hispanic individuals as it may not capture specific experiences of discrimination and identity in these groups.

Epigenetic aging & embodying injustice: US My Body My Story and Multi-Ethnic Atherosclerosis Study.

Importance: Epigenetic accelerated aging is associated with exposure to social and economic adversity and may increase risk of premature morbidity and mortality. However, no studies have included measures of structural racism and few have compared estimates within or across the 1st and 2nd generation of epigenetic clocks (the latter additionally trained on phenotypic data). Objective: To determine if accelerated epigenetic aging is associated with exposures to diverse measures of racialized, economic, and environmental injustice measured at different levels and time periods. Design: Cross-sectional My Body My Story Study (MBMS; US, 2008-2010) and Exam 5 Multi-Ethnic Atherosclerosis Study (MESA; US, 2010-2012). MBMS DNA extraction: 2021; linkage of structural measures to MBMS and MESA: 2022. Setting: MBMS recruited a random sample of US-born Black non-Hispanic (BNH) and white non-Hispanic (WNH) participants from 4 community health centers in Boston, MA. The MESA Exam 5 epigenetic component included 975 randomly selected US-born BNH, WNH, and Hispanic participants from four field sites: Baltimore, MD; Forsyth County, NC; New York City, NY; St. Paul, MN. Participants: US-born persons (MBMS: 224 BNH, 69 WNH; MESA: 229 BNH, 555 WNH, 191 Hispanic). Main outcome and measures: 10 epigenetic clocks (six 1st generation; four 2nd generation), computed using DNA methylation data (DNAm) from blood spots (MBMS; N = 293) and purified monocytes (MESA; N = 975). Results: Among Black non-Hispanic MBMS participants, epigenetic age acceleration was associated with being born in a Jim Crow state by 0.14 standard deviations (95% confidence interval [CI] 0.00, 0.27) and with birth state conservatism (0.06, 95% CI 0.00, 0.05), pooling across all clocks, as was low parental education for both Black non-Hispanic and white non-Hispanic MBMS participants (respectively: 0.24, 95% CI 0.08, 0.39, and 0.27, 95% CI 0.03, 0.51. Adult impoverishment was positively associated with the pooled 2nd generation clocks among the MESA participants (Black non-Hispanic: 0.06, 95% CI 0.01, 0.12; white non-Hispanic: 0.05, 95% CI 0.01, 0.08; Hispanic: 0.07, 95% CI 0.01, 0.14). Conclusions and Relevance: Epigenetic accelerated aging may be one of the biological mechanisms linking exposure to racialized and economic injustice to well-documented inequities in premature morbidity and mortality.

The impact of low input DNA on the reliability of DNA methylation as measured by the Illumina Infinium MethylationEPIC BeadChip.

DNA methylation (DNAm) is commonly assayed using the Illumina Infinium MethylationEPIC BeadChip, but there is currently little published evidence to define the lower limits of the amount of DNA that can be used whilst preserving data quality. Such evidence is valuable for analyses utilizing precious or limited DNA sources. We used a single pooled sample of DNA in quadruplicate at three dilutions to define replicability and noise, and an independent population dataset of 328 individuals (from a community-based study including US-born non-Hispanic Black and white persons) to assess the impact of total DNA input on the quality of data generated using the Illumina Infinium MethylationEPIC BeadChip. We found that data are less reliable and more noisy as DNA input decreases to 40ng, with clear reductions in data quality; and that low DNA input is associated with a reduction in power to detect EWAS associations, requiring larger sample sizes. We conclude that DNA input as low as 40ng can be used with the Illumina Infinium MethylationEPIC BeadChip, provided quality checks and sensitivity analyses are undertaken.

Statistical implications of endogeneity induced by residential segregation in small-area modelling of health inequities.

Health inequities are assessed by health departments to identify social groups disproportionately burdened by disease and by academic researchers to understand how social, economic, and environmental inequities manifest as health inequities. To characterize inequities, group-specific small-area health data are often modeled using log-linear generalized linear models (GLM) or generalized linear mixed models (GLMM) with a random intercept. These approaches estimate the same marginal rate ratio comparing disease rates across groups under standard assumptions. Here we explore how residential segregation combined with social group differences in disease risk can lead to contradictory findings from the GLM and GLMM. We show that this occurs because small-area disease rate data collected under these conditions induce endogeneity in the GLMM due to correlation between the model's offset and random effect. This results in GLMM estimates that represent conditional rather than marginal associations. We refer to endogeneity arising from the offset, which to our knowledge has not been noted previously, as "offset endogeneity". We illustrate this phenomenon in simulated data and real premature mortality data, and we propose alternative modeling approaches to address it. We also introduce to a statistical audience the social epidemiologic terminology for framing health inequities, which enables responsible interpretation of results.

Breast Cancer Incidence, Hormone Receptor Status, Historical Redlining, and Current Neighborhood Characteristics in Massachusetts, 2005-2015.

BACKGROUND: Scant research has analyzed contemporary US cancer incidence rates in relation to historical redlining (ie, 1930s US federally imposed residential segregation), implemented via the color-coded federal Home Owners' Loan Corporation (HOLC) maps. METHODS: We analyzed Massachusetts Cancer Registry data for all patients with primary invasive breast cancer (BC) diagnosed in 2005-2015 among women in the 28 Massachusetts municipalities with digitized 1930s HOLC maps. Multilevel Poisson regression estimated BC incidence rate ratios (IRR), overall and by tumor estrogen receptor (ER-positive, ER-negative) and progesterone receptor (PR-positive, PR-negative) status, in relation to HOLC grade and contemporary census tract (CT) social characteristics. RESULTS: Net of age and racialized group, the extremes of BC incidence were detected by combinations of HOLC grade and contemporary CT racialized economic segregation. Compared with CTs with the best HOLC grade (A + B) and most privileged contemporary characteristics (T1), for all, ER-positive and PR-positive BC, incidence was highest in T1 and mixed HOLC grade CTs (eg, IRRER+; Mixed-T1 = 1.10, 95% confidence interval [CI] = 1.01 to 1.21) and lowest in CTs with most concentrated racialized economic deprivation (T3) and no HOLC grade (eg, IRRER+; No Grade-T3 = 0.85, 95% CI = 0.75 to 0.95). For ER-negative and PR-negative BC, incidence was highest in CTs with the most contemporary deprivation, but the best HOLC grade (eg, IRRER-; A+B-T3 = 1.27, 95% CI = 0.93 to 1.75) and lowest in T1 and worst HOLC-graded CTs (eg, IRRER-; D-T1 = 0.84, 95% CI = 0.56 to 1.25). CONCLUSION: Breast cancer risk may be shaped by combined histories of redlining and present-day CT characteristics.

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.

The target/perpetrator brief-implicit association test (B-IAT): an implicit instrument for efficiently measuring discrimination based on race/ethnicity, sex, gender identity, sexual orientation, weight, and age.

BACKGROUND: To date, research assessing discrimination has employed primarily explicit measures (i.e., self-reports), which can be subject to intentional and social desirability processes. Only a few studies, focusing on sex and race/ethnicity discrimination, have relied on implicit measures (i.e., Implicit Association Test, IAT), which permit assessing mental representations that are outside of conscious control. This study aims to advance measurement of discrimination by extending the application of implicit measures to multiple types of discrimination and optimizing the time required for the administration of these instruments. METHODS: Between September 27th 2019 and February 9th 2020, we conducted six experiments (984 participants) to assess implicit and explicit discrimination based on race/ethnicity, sex, gender identity, sexual orientation, weight, and age. Implicit discrimination was measured by using the Brief-Implicit Association Test (B-IAT), a new validated version of the IAT developed to shorten the time needed (from ≈15 to ≈2 min) to assess implicit mental representations, while explicit discrimination was assessed using self-reported items. RESULTS: Among participants (mean age = 37.8), 68.6% were White Non-Hispanic; 69% were females; 76.1% were heterosexual; 90.7% were gender conforming; 52.8% were medium weight; and 41.5% had an advanced level of education. Overall, we found implicit and explicit recognition of discrimination towards all the target groups (stronger for members of the target than dominant groups). Some exceptions emerged in experiments investigating race/ethnicity and weight discrimination. In the racism experiment, only people of Color showed an implicit recognition of discrimination towards the target group, while White people were neutral. In the fatphobia experiment, participants who were not heavy showed a slight implicit recognition of discrimination towards the dominant group, while heavy participants were neutral. CONCLUSIONS: This study provides evidence that the B-IAT is a valuable tool for quickly assessing multiple types of implicit discrimination. It shows also that implicit and explicit measures can display diverging results, thus indicating that research would benefit from the use of both these instruments. These results have important implications for the assessment of discrimination in health research as well as in social and psychological science.

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.

COVID-19 and Overall Mortality Inequities in the Surge in Death Rates by Zip Code Characteristics: Massachusetts, January 1 to May 19, 2020.

Objectives. To address evidence gaps in COVID-19 mortality inequities resulting from inadequate race/ethnicity data and no socioeconomic data.Methods. We analyzed age-standardized death rates in Massachusetts by weekly time intervals, comparing rates for January 1 to May 19, 2020, with the corresponding historical average for 2015 to 2019 stratified by zip code social metrics.Results. At the surge peak (week 16, April 15-21), mortality rate ratios (comparing 2020 vs 2015-2019) were 2.2 (95% confidence interval [CI] = 1.4, 3.5) and 2.7 (95% CI = 1.4, 5.5) for the lowest and highest zip code tabulation area (ZCTA) poverty categories, respectively, with the 2020 peak mortality rate 1.1 (95% CI = 1.0, 1.3) times higher in the highest than the lowest poverty ZCTA. Similarly, rate ratios were significantly elevated for the highest versus lowest quintiles with respect to household crowding (1.7; 95% CI = 1.0, 2.9), racialized economic segregation (3.1; 95% CI = 1.8, 5.3), and percentage population of color (1.8; 95% CI = 1.6, 2.0).Conclusions. The COVID-19 mortality surge exhibited large inequities.Public Health Implications. Using zip code social metrics can guide equity-oriented COVID-19 prevention and mitigation efforts.

Structural Racism, Historical Redlining, and Risk of Preterm Birth in New York City, 2013-2017.

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.

A Structural Model of Social Determinants of the Metabolic Syndrome.

Objectives: The metabolic syndrome (MetS) refers to a cluster of interrelated physiological characteristics that are associated with an increased risk of cardiovascular disease and diabetes. While the clinical usefulness of the MetS has been the subject of controversy for years, increasingly sophisticated methods are being used to measure the concept. Participants: Study of community health center patients who were not diabetic; study group was evenly divided between Black and White adults. Main Outcome Measures: Latent MetS score and MetS status based on the five-point scale developed by the National Cholesterol Education Panel (NCEP). Methods: Structural equation modeling of MetS incorporating the effects of race/ethnicity, racial discrimination, socioeconomic position (SEP), and selected mediating variables. Results: The largest influences on latent MetS scores were SEP (negative relationship) and male gender (higher scores for men). Two mediating variables, physical activity and stress-related eating, had smaller impacts. Self-reported racial discrimination was associated with cynical hostility but did not influence the MetS level among nondiabetics. Despite higher NCEP scores and MetS prevalence rates for Blacks compared with Whites, race did not have direct effect on MetS levels when adjusted for the other characteristics in our model. Conclusions: Neither race nor self-reported racial discrimination had direct effects on MetS level in our structural model. The large effects of socioeconomic position and male gender were not mediated by the other variables in the model.

Cancer Stage at Diagnosis, Historical Redlining, and Current Neighborhood Characteristics: Breast, Cervical, Lung, and Colorectal Cancers, Massachusetts, 2001-2015.

In the 1930s, maps created by the federal Home Owners' Loan Corporation (HOLC) nationalized residential racial segregation via "redlining," whereby HOLC designated and colored in red areas they deemed to be unsuitable for mortgage lending on account of their Black, foreign-born, or low-income residents. We used the recently digitized HOLC redlining maps for 28 municipalities in Massachusetts to analyze Massachusetts Cancer Registry data for late stage at diagnosis for cervical, breast, lung, and colorectal cancer (2001-2015). Multivariable analyses indicated that, net of age, sex/gender, and race/ethnicity, residing in a previously HOLC-redlined area imposed an elevated risk for late stage at diagnosis, even for residents of census tracts with present-day economic and racial privilege, whereas the best historical HOLC grade was not protective for residents of census tracts without such current privilege. For example, a substantially elevated risk of late stage at diagnosis occurred among men with lung cancer residing in currently privileged areas that had been redlined (risk ratio = 1.17, 95% confidence interval: 1.06, 1.29), whereas such risk was attenuated among men residing in census tracts lacking such current privilege (risk ratio = 1.01, 95% confidence interval: 0.94, 1.08). Research on historical redlining as a structural driver of health inequities is warranted.

Severe sociopolitical stressors and preterm births in New York City: 1 September 2015 to 31 August 2017.

BACKGROUND: Severe stressors can induce preterm birth (PTB; gestation <37 weeks), with such stressors including social and economic threats, interpersonal violence, hate crimes and severe sociopolitical stressors (ie, arising from political leaders' threatening rhetoric or from political legislation). We analysed temporal changes in risk of PTB among immigrant, Hispanic and Muslim populations targeted in the US 2016 presidential election and its aftermath. METHODS: Trend analysis of all singleton births in New York City from 1 September 2015 to 31 August 2017 (n=230 105). RESULTS: Comparing the period before the US presidential nomination (1 September 2015 to 31 July 2016) to the post-inauguration period (1 January 2017 to 31 August 2017), the overall PTB rate increased from 7.0% to 7.3% (relative risk (RR): 1.04; 95% CI 1.00 to 1.07). Among Hispanic women, the highest post-inauguration versus pre-inauguration increase occurred among foreign-born Hispanic women with Mexican or Central American ancestry (RR: 1.15; 95% CI 1.01 to 1.31). The post-inauguration versus pre-inauguration PTB rate also was higher for women from the Middle East/North Africa and from the travel ban countries, although non-significant due to the small number of events. CONCLUSION: Severe sociopolitical stressors may contribute to increases in the risk of PTB among targeted populations.