Overcoming combination fatigue: Addressing high-dimensional effect measure modification and interaction in clinical, biomedical, and epidemiologic research using multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA).

Growing interest in precision medicine, gene-environment interactions, health equity, expanding diversity in research, and the generalizability results, requires researchers to evaluate how the effects of treatments or exposures differ across numerous subgroups. Evaluating combination complexity, in the form of effect measure modification and interaction, is therefore a common study aim in the biomedical, clinical, and epidemiologic sciences. There is also substantial interest in expanding the combinations of factors analyzed to include complex treatment protocols (e.

Intersectional inequalities in industrial air toxics exposure in the United States.

Environmental justice and health research demonstrate unequal exposure to environmental hazards at the neighborhood-level. We use an innovative method-eco-intersectional multilevel (EIM) modeling-to assess intersectional inequalities in industrial air toxics exposure across US census tracts in 2014. Results reveal stark inequalities in exposure across analytic strata, with a 45-fold difference in average exposure between most and least exposed.

The racialization of privacy: racial formation as a family affair.

A right to family privacy is considered a cornerstone of American life, and yet access to it is apportioned by race. Our notion of the "racialization of privacy" refers to the phenomenon that family privacy, including the freedom to create a family uninhibited by law, pressure, and custom, is delimited by race. Building upon racial formation theory, this article examines three examples: the Native American boarding school system (1870s to 1970s), eugenic laws and practices (early/mid 1900s), and contemporary deportation.

Intersectional environmental justice and population health inequalities: A novel approach.

Drawing on the traditions of environmental justice, intersectionality, and social determinants of health, and using data from the EPA's NATA 2014 estimates of cancer risk from air toxics, we demonstrate a novel quantitative approach to evaluate intersectional environmental health risks to communities: Eco-Intersectional Multilevel (EIM) modeling. Results from previous case studies were found to generalize to national-level patterns, with multiply marginalized tracts with a high percent of Black and Latinx residents, high percent female-headed households, lower educational attainment, and metro location experiencing the highest risk. Overall, environmental health inequalities in cancer risk from air toxics are: (1) experienced intersectionally at the community-level, (2) significant in magnitude, and (3) socially patterned across numerous intersecting axes of marginalization, including axes rarely evaluated such as gendered family structure.

Open Campus Policies: How Built, Food, Social, and Organizational Environments Matter for Oregon's Public High School Students' Health.

Open campus policies that grant access to the off-campus food environment may influence U.S. high school students' exposure to unhealthy foods, yet predictors of these policies are unknown.

Reintegrating contexts into quantitative intersectional analyses of health inequalities.

Quantitative intersectional analyses often overlook the roles of contexts in shaping intersectional experiences and outcomes. This study advances a novel approach for integrating quantitative intersectional methods with models of contextual-level determinants of health inequalities. Building on recent methodological advancements, I propose an adaptation of intersectional MAIHDA (multilevel analysis of individual heterogeneity and discriminatory accuracy) where respondents are nested hierarchically in social strata defined by gender, race/ethnicity and socioeconomic classifications interacted with contextual classifications.

Multilevel versus single-level regression for the analysis of multilevel information: The case of quantitative intersectional analysis.

Intersectional MAIHDA involves applying multilevel models in order to estimate intercategorical inequalities. The approach has been validated thus far using both simulations and empirical applications, and has numerous methodological and theoretical advantages over single-level approaches, including parsimony and reliability for analyzing high-dimensional interactions. In this issue of SSM, Lizotte, Mahendran, Churchill and Bauer (hereafter "LMCB") assert that there has been insufficient clarity on the interpretation of fixed effects regression coefficients in intersectional MAIHDA, and that stratum-level residuals in intersectional MAIHDA are not interpretable as interaction effects.

Modeling the intersectionality of processes in the social production of health inequalities.

Background: The recent pair of studies by Bauer and Scheim make substantial contributions to the literature on intersectionality and health: a validation study of the Intersectional Discrimination Index and a study outlining a promising analytic approach to intersectionality that explicitly considers the roles of social processes in the production of health inequalities.

Rationale: In this commentary, I situate Bauer and Scheim's contribution within the wider landscape of intersectional scholarship. I also respond to emerging concerns about the value of descriptive intersectional approaches, in particular the critique that such approaches blunt the critical edge and transformative aims of intersectionality.

Adding interactions to models of intersectional health inequalities: Comparing multilevel and conventional methods.

Examining health inequalities intersectionally is gaining in popularity and recent quantitative innovations, such as the development of intersectional multilevel methods, have enabled researchers to expand the number of dimensions of inequality evaluated while avoiding many of the theoretical and methodological limitations of the conventional fixed effects approach. Yet there remains substantial uncertainty about the effects of integrating numerous additional interactions into models: will doing so reveal statistically significant interactions that were previously hidden or explain away interactions seen when fewer dimensions were considered? Furthermore, how does the multilevel approach compare empirically to the conventional approach across a range of conditions? These questions are essential to informing our understanding of population-level health inequalities. I address these gaps using data from the National Longitudinal Study of Adolescent to Adult Health by evaluating conventional and multilevel intersectional models across a range of interaction conditions (ranging from six points of interaction to more than ninety, interacting gender, race/ethnicity/immigration status, parent education, family income, and sexual identification), different model types (linear and logistic), and seven diverse dependent variables commonly examined by health researchers: body mass index, depression, general self-rated health, binge drinking, cigarette use, marijuana use, and other illegal drug use.

Intersectionality and depression in adolescence and early adulthood: A MAIHDA analysis of the national longitudinal study of adolescent to adult health, 1995-2008.

Depression in adolescents and young adults remains a pressing public health concern and there is increasing interest in evaluating population-level inequalities in depression intersectionally. A recent advancement in quantitative methods-multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA)-has many practical and theoretical advantages over conventional models of intercategorical intersectionality, including the ability to more easily evaluate numerous points of intersection between axes of marginalization. This study is the first to apply the MAIHDA approach to investigate mental health outcomes intersectionally in any population.

Reimagining infertility: a critical examination of fertility norms, geopolitics and survey bias.

While it is estimated that 15% of couples worldwide are infertile, this figure hinges critically on the quality, inclusiveness and availability of infertility data sources. Current infertility data and statistics fail to account for the infertility experiences of some social groups. We identify these people as the invisible infertile, and refer to their omission from infertility data and statistics-whether intentional or unintentional-as the process of invisibilization.

Scientific consensus, the law, and same sex parenting outcomes.

While the US Supreme Court was considering two related cases involving the constitutionality of same-sex marriage, one major question informing that decision was whether scientific research had achieved consensus regarding how children of same-sex couples fare. Determining the extent of consensus has become a key aspect of how social science evidence and testimony is accepted by the courts. Here, we show how a method of analyzing temporal patterns in citation networks can be used to assess the state of social scientific literature as a means to inform just such a question.

Economic growth and marine biodiversity: influence of human social structure on decline of marine trophic levels.

We assessed the effects of economic growth, urbanization, and human population size on marine biodiversity. We used the mean trophic level (MTL) of marine catch as an indicator of marine biodiversity and conducted cross-national time-series analyses (1960-2003) of 102 nations to investigate human social influences on fish catch and trends in MTL. We constructed path models to examine direct and indirect effects relating to marine catch and MTL.