Clarifications on the intersectional MAIHDA approach: A conceptual guide and response to Wilkes and Karimi (2024).

Intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) has been welcomed as a new gold standard for quantitative evaluation of intersectional inequalities, and it is being rapidly adopted across the health and social sciences. In their commentary "What does the MAIHDA method explain?", Wilkes and Karimi (2024) raise methodological concerns with this approach, leading them to advocate for the continued use of conventional single-level linear regression models with fixed-effects interaction parameters for quantitative intersectional analysis. In this response, we systematically address these concerns, and ultimately find them to be unfounded, arising from a series of subtle but important misunderstandings of the MAIHDA approach and literature.

A tutorial for conducting intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA).

Intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (I-MAIHDA) is an innovative approach for investigating inequalities, including intersectional inequalities in health, disease, psychosocial, socioeconomic, and other outcomes. I-MAIHDA and related MAIHDA approaches have conceptual and methodological advantages over conventional single-level regression analysis. By enabling the study of inequalities produced by numerous interlocking systems of marginalization and oppression, and by addressing many of the limitations of studying interactions in conventional analyses, intersectional MAIHDA provides a valuable analytical tool in social epidemiology, health psychology, precision medicine and public health, environmental justice, and beyond.

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 inequities in the birthweight gap between twin and singleton births: A random effects MAIHDA analysis of 2012-2018 New York City birth data.

Birthweight is a widely-used biomarker of infant health, with inequities patterned intersectionally by maternal age, race/ethnicity, nativity/immigration status, and socioeconomic status in the United States. However, studies of birthweight inequities almost exclusively focus on singleton births, neglecting high-risk twin births. We address this gap using a large sample (N = 753,180) of birth records, obtained from the 2012-2018 New York City (NYC) Department of Health and Mental Hygiene, Bureau of Vital Statistics, representing 99% of all births registered in NYC, and a novel random coefficients intersectional MAIHDA (Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy) model.

The application of intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) to examine birthweight inequities in New York City.

Exploring the intersection of dimensions of social identity is critical for understanding drivers of health inequities. We used multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) to examine the intersection of age, race/ethnicity, education, and nativity status on infant birthweight among singleton births in New York City from 2012 to 2018 (N = 725,875). We found evidence of intersectional effects of various systems of oppression on birthweight inequities and identified U.

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.

An Intercategorical Ecology of Lead Exposure: Complex Environmental Health Vulnerabilities in the Flint Water Crisis.

In 2014, city and state officials channeled toxic water into Flint, Michigan and its unevenly distributed and corroding lead service lines (LSLs). The resulting Flint water crisis is a tragic example of environmental racism against a majority Black city and enduring racial and spatial disparities in environmental lead exposures in the United States. Important questions remain about how race intersected with other established environmental health vulnerabilities of gender and single-parent family structure to create unequal toxic exposures within Flint.

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.

Cross-classified multilevel models (CCMM) in health research: A systematic review of published empirical studies and recommendations for best practices.

Recognizing that health outcomes are influenced by and occur within multiple social and physical contexts, researchers have used multilevel modeling techniques for decades to analyze hierarchical or nested data. Cross-Classified Multilevel Models (CCMM) are a statistical technique proposed in the 1990s that extend standard multilevel modeling and enable the simultaneous analysis of non-nested multilevel data. Though use of CCMM in empirical health studies has become increasingly popular, there has not yet been a review summarizing how CCMM are used in the health literature.

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.

Adolescent Sexual Initiation: A Cross-Classified Multilevel Analysis of Peer Group-, School-, and Neighborhood-Level Influences.

Purpose: This study examines the simultaneous roles of neighborhood, school, and peer group contexts on variation in age of U.S. adolescent sexual initiation (coitarche).

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.

Does an uneven sample size distribution across settings matter in cross-classified multilevel modeling? Results of a simulation study.

Background: Recent advances in multilevel modeling allow for modeling non-hierarchical levels (e.g., youth in non-nested schools and neighborhoods) using cross-classified multilevel models (CCMM).

A multilevel approach to modeling health inequalities at the intersection of multiple social identities.

Rationale: Examining interactions between numerous interlocking social identities and the systems of oppression and privilege that shape them is central to health inequalities research. Multilevel models are an alternative and novel approach to examining health inequalities at the intersection of multiple social identities. This approach draws attention to the heterogeneity within and between intersectional social strata by partitioning the total variance across two levels.

Contextual Effects of Neighborhoods and Schools on Adolescent and Young Adult Marijuana Use in the United States.

Little is known about the unique contribution of schools vs neighborhoods in driving adolescent marijuana use. This study examined the relative contribution of each setting and the influence of school and neighborhood socioeconomic status on use. We performed a series of cross-classified multilevel logistic models predicting past 30-day adolescent (N = 18 329) and young adult (N = 13 908) marijuana use using data from Add Health.

Associations of Continuity and Change in Early Neighborhood Poverty With Adult Cardiometabolic Biomarkers in the United States: Results From the National Longitudinal Study of Adolescent to Adult Health, 1995-2008.

Limitations of extant research on neighborhood disadvantage and health include general reliance on point-in-time neighborhood measures and sensitivity to residential self-selection. Using data from the US Census and the 1995-2008 National Longitudinal Study of Adolescent to Adult Health, we applied conventional methods and coarsened exact matching to assess how cardiometabolic health varies among those entering, exiting, or remaining in poor and nonpoor neighborhoods. Within the full sample (n = 11,767), we found significantly higher systolic and diastolic blood pressures among those who entered or consistently lived in poor neighborhoods relative to those who never lived in poor neighborhoods.

Multiple contexts and adolescent body mass index: Schools, neighborhoods, and social networks.

Adolescent health and behaviors are influenced by multiple contexts, including schools, neighborhoods, and social networks, yet these contexts are rarely considered simultaneously. In this study we combine social network community detection analysis and cross-classified multilevel modeling in order to compare the contributions of each of these three contexts to the total variation in adolescent body mass index (BMI). Wave 1 of the National Longitudinal Study of Adolescent to Adult Health is used, and for robustness we conduct the analysis in both the core sample (122 schools; N = 14,144) and a sub-set of the sample (16 schools; N = 3335), known as the saturated sample due to its completeness of neighborhood data.

The persistent clustering of adult body mass index by school attended in adolescence.

Background: It is well known that adolescent body mass index (BMI) shows school-level clustering. We explore whether school-level clustering of BMI persists into adulthood.

Methods: Multilevel models nesting young adults in schools they attended as adolescents are fit for 3 outcomes: adolescent BMI, self-report adult BMI and measured adult BMI.

Disentangling the relative influence of schools and neighborhoods on adolescents' risk for depressive symptoms.

Objectives: Although schools and neighborhoods influence health, little is known about their relative importance, or the influence of one context after the influence of the other has been taken into account. We simultaneously examined the influence of each setting on depression among adolescents.

Methods: Analyzing data from wave 1 (1994-1995) of the National Longitudinal Study of Adolescent Health, we used cross-classified multilevel modeling to examine between-level variation and individual-, school-, and neighborhood-level predictors of adolescent depressive symptoms.