We are complex beings: comparison of statistical methods to capture and account for intersectionality.

OBJECTIVES: Intersectionality conceptualises how different parts of our identity compound, creating unique and multifaceted experiences of oppression. Our objective was to explore and compare several quantitative analytical approaches to measure interactions among four sociodemographic variables and interpret the relative impact of axes of marginalisation on self-reported health, to visualise the potential elevated impact of intersectionality on health outcomes. DESIGN: Secondary analysis of National Epidemiologic Survey on Alcohol and Related Conditions-III, a nationally representative cross-sectional study of 36 309 non-institutionalised US citizens aged 18 years or older. PRIMARY OUTCOME MEASURES: We assessed the effect of interactions among race/ethnicity, disability status, sexual orientation and income level on a self-reported health outcome with three approaches: non-intersectional multivariate regression, intersectional multivariate regression with a single multicategorical predictor variable and intersectional multivariate regression with two-way interactions. RESULTS: Multivariate regression with a single multicategorical predictor variable allows for more flexibility in a logistic regression problem. In the fully fitted model, compared with individuals who were white, above the poverty level, had no disability and were heterosexual (referent), only those who were white, above the poverty level, had no disability and were gay/lesbian/bisexual/not sure (LGBQ+) demonstrated no significant difference in the odds of reporting excellent/very good health (aOR=0.90, 95% CI=0.71 to 1.13, p=0.36). Multivariate regression with two-way interactions modelled the extent that the relationship between each predictor and outcome depended on the value of a third predictor variable, allowing social position variation at several intersections. For example, compared with heterosexual individuals, LGBQ+ individuals had lower odds of reporting better health among whites (aOR=0.94, 95% CI=0.93 to 0.95) but higher odds of reporting better health among Black Indigenous People of Color (BIPOC) individuals (aOR=1.13, 95% CI=1.11 to 1.15). CONCLUSION: These quantitative approaches help us to understand compounding intersectional experiences within healthcare, to plan interventions and policies that address multiple needs simultaneously.

County-level jail incarceration, community economic distress, rurality, and preterm birth among women in the US South.

Introduction: The USA has higher rates of preterm birth and incarceration than any other developed nation, with rates of both being highest in Southern states and among Black Americans, potentially due to rurality and socioeconomic factors. To test our hypothesis that prior-year county-level rates of jail admission, economic distress, and rurality were positively associated with premature birth rates in the county of delivery in 2019 and that the strength of these associations is greater for Black women than for White or Hispanic women, we merged five datasets to perform multivariable analysis of data from 766 counties across 12 Southern/rural states. Methods: We used multivariable linear regression to model the percentage of babies born premature, stratified by Black (Model 1), Hispanic (Model 2), and White (Model 3) mothers. Each model included all three independent variables of interest measured using data from the Vera Institute, Distressed Communities Index, and Index of Relative Rurality. Results: In fully fitted stratified models, economic distress was positively associated with premature births among Black (F = 33.81, p < 0.0001) and White (F = 26.50, p < 0.0001) mothers. Rurality was associated with premature births among White mothers (F = 20.02, p < 0.0001). Jail admission rate was not associated with premature births among any racial group, and none of the study variables were associated with premature births among Hispanic mothers. Conclusions: Understanding the connections between preterm birth and enduring structural inequities is a necessary scientific endeavor to advance to later translational stages in health-disparities research.

COVID-19 vaccination rates vary by community vulnerability: A county-level analysis.

We used the COVID-19 Community Vulnerability Index and 7 theme scores to assess associations between vulnerability and county-level COVID-19 vaccination (n = 2415 counties) through May 25th, 2021. When comparing vaccination rates among quintiles of CCVI scores, Theme 3 (housing type, transportation, household composition, and disability) was associated with the largest disparity, with the least vulnerable counties (Q1) having 33% higher rates of vaccination among individuals aged 18+ (53.5% vs 40.2%) compared to counties with the highest vulnerability (Q5). Using generalized linear models with binomial distributions and log links, we found that a 10-point increase in the CCVI index, socioeconomic vulnerability, housing type and composition, and epidemiological factors were associated with at least a 1.0 percentage point decline in county-level vaccination. The association between community vulnerability and lower vaccination rates suggests the need for continued efforts for equitable COVID-19 vaccination across marginalized communities.