Extending intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) to study individual longitudinal trajectories, with application to mental health in the UK.

The intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) approach is gaining prominence in health sciences and beyond, as a robust quantitative method for identifying intersectional inequalities in a range of individual outcomes. However, it has so far not been applied to longitudinal data, despite the availability of such data, and growing recognition that intersectional social processes and determinants are not static, unchanging phenomena. Drawing on intersectionality and life course theories, we develop a longitudinal version of the intersectional MAIHDA approach, allowing the analysis not just of intersectional inequalities in static individual differences, but also of life course trajectories. We discuss the conceptualization of intersectional groups in this context: how they are changeable over the life course, appropriate treatment of generational differences, and relevance of the age-period-cohort identification problem. We illustrate the approach with a study of mental health using United Kingdom Household Longitudinal Study data (2009-2021). The results reveal important differences in trajectories between generations and intersectional strata, and show that trajectories are partly multiplicative but mostly additive in their intersectional inequalities. This article provides an important and much needed methodological contribution, enabling rigorous quantitative, longitudinal, intersectional analyses in social epidemiology and beyond.

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. Since readers new to MAIHDA may share confusion on these points, we take this opportunity to provide clarifications. Our response is organized around four important clarifications: (1) At what level are the additive main effect variables defined in intersectional MAIHDA models? (2) Do MAIHDA models have problems with collinearity? (3) Why does the Variance Partitioning Coefficient (VPC) tend to be small, and the Proportional Change in Variance (PCV) tend to be large in MAIHDA? and (4) What are the goals of MAIHDA analysis?

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. The approach allows for estimation of average differences between intersectional strata (stratum inequalities), in-depth exploration of interaction effects, as well as decomposition of the total individual variation (heterogeneity) in individual outcomes within and between strata. Specific advice for conducting and interpreting MAIHDA models has been scattered across a burgeoning literature. We consolidate this knowledge into an accessible conceptual and applied tutorial for studying both continuous and binary individual outcomes. We emphasize I-MAIHDA in our illustration, however this tutorial is also informative for understanding related approaches, such as multicategorical MAIHDA, which has been proposed for use in clinical research and beyond. The tutorial will support readers who wish to perform their own analyses and those interested in expanding their understanding of the approach. To demonstrate the methodology, we provide step-by-step analytical advice and present an illustrative health application using simulated data. We provide the data and syntax to replicate all our analyses.

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.g., multiple study arms or factorial randomization), comorbid medical conditions or risk factors, and sociodemographic and other subgroup identifiers. However, expanding the number of subgroup category combinations creates combination fatigue problems, including concerns over small sample size, reduced power, multiple testing, spurious results, and design and analytic complexity. Creative new approaches for managing combination fatigue and evaluating high-dimensional effect measure modification and interaction are needed. Intersectional MAIHDA (multilevel analysis of individual heterogeneity and discriminatory accuracy) has already attracted substantial interest in social epidemiology, and has been hailed as the new gold standard for investigating health inequities across complex intersections of social identity. Leveraging the inherent advantages of multilevel models, a more general multicategorical MAIHDA can be used to study statistical interactions and predict effects across high-dimensional combinations of conditions, with important advantages over alternative approaches. Though it has primarily been used thus far as an analytic approach, MAIHDA should also be used as a framework for study design. In this article, I introduce MAIHDA to the broader health sciences research community, discuss its advantages over conventional approaches, and provide an overview of potential applications in clinical, biomedical, and epidemiologic research.

Supporting High-impAct useRs in Emergency Departments (SHarED) quality improvement: a mixed-method evaluation.

BACKGROUND: The need to better manage frequent attenders or high-impact users (HIUs) in hospital emergency departments (EDs) is widely recognised. These patients often have complex medical needs and are also frequent users of other health and care services. The West of England Academic Health Science Network launched its Supporting High impAct useRs in Emergency Departments (SHarED) quality improvement programme to spread a local HIU intervention across six other EDs in five Trusts. AIM: SHarED aimed to reduce ED attendance and hospital admissions by 20% for enrolled HIUs. To evaluate the implementation of SHarED, we sought to learn about the experience of staff with HIU roles and their ED colleagues and assess the impact on HIU attendance and admissions. METHODS: We analysed a range of data including semistructured interviews with 10 HIU staff; the number of ED staff trained in HIU management; an ED staff experience survey; and ED attendances and hospital admissions for 148 HIUs enrolled in SHarED. RESULTS: Staff with HIU roles were unanimously positive about the benefits of SHarED for both staff and patients. SHarED contributed to supporting ED staff with patient-centred recommendations and provided the basis for more integrated case management across the health and care system. 55% of ED staff received training. There were improvements in staff views relating to confidence, support, training and HIUs receiving more appropriate care. The mean monthly ED attendance per HIU reduced over time. Follow-up data for 86% (127/148) of cases showed a mean monthly ED attendances per HIU reduced by 33%, from 2.1 to 1.4, between the 6 months pre-enrolment and post-enrolment (p<0.001). CONCLUSION: SHarED illustrates the considerable potential for a quality improvement programme to promote more integrated case management by specialist teams across the health and care system for particularly vulnerable individuals and improve working arrangements for hard-pressed staff.

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. Our results show evidence of intersectional inequities in birthweight outcomes for both twin and singleton births by maternal age, race/ethnicity, education, and nativity status. Twins have considerably lower predicted birthweights than singletons overall (-930 g on average), and this is especially true for babies born to mothers who are younger (11-19 years), older (40+), racial/ethnic minoritized, foreign-born, and have lower education. However, the magnitude of this birthweight 'gap' between twins and singletons varies considerably across social identity strata, ranging between 830.8 g (observed among 40+ year old Black foreign-born mothers with high school degrees) and 1013.7 g (observed among 30-39 year old Hispanic/Latina foreign-born mothers with less than high school degrees). This study underscored the needs of a high-risk population and the need for aggressive social policies to address health inequities and dismantle intersectional systems of marginalization, oppression, and socioeconomic inequality. In addition to our substantive contributions, we add to the growing methods literature on intersectional quantitative analysis by demonstrating how to apply intersectional MAIHDA with random coefficients and random slopes. We conclude with a discussion of the significant potential for this methodological extension in future research on inequities.

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.S.-born Black women as having infants of lower-than-expected birthweights. The MAIHDA approach should be used to identify intersectional causes of health inequities and individuals affected most to develop policies and interventions redressing inequities.

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. Low SES, multiply marginalized (high % Black, high % female-headed households) urban communities experienced highest risk. These inequalities were not described by additive effects alone, necessitating the use of interaction terms. We advance a critical intersectional approach to evaluating environmental injustices.

The association between maternal perinatal mental health and perfectionism: A systematic review and meta-analysis.

BACKGROUND: Perfectionism is an important feature of adult psychopathology. In the absence of a prior review of the role of perfectionism in perinatal psychopathology, we aimed to ascertain whether perfectionism was associated with symptoms of maternal perinatal depression and anxiety. METHOD: We followed PRISMA guidance (PROSPERO: 42019143369), estimated weighted effect sizes and tested possible moderators: timing (pre or post- natal), scales used to measure constructs, infant gender, temperament and age; and rated study quality. RESULTS: Fourteen studies met eligibility criteria. Perfectionism as a whole, and the perfectionistic concerns dimension, were moderately correlated with common maternal perinatal mental health difficulties r = .32 (95% Confidence Interval = 0.23 to 0.42). In sub-group analyses, perfectionistic concerns were associated with depression (r = .35, 95% CI = 0.26-0.43). We found no evidence of significant moderation of associations. LIMITATIONS: Included studies had methodological and conceptual limitations. All studies examined depression and two examined anxieties; all examined perfectionistic concerns and four examined perfectionist strivings. CONCLUSIONS: Perfectionism, namely perfectionistic concerns, is potentially associated with common maternal perinatal mental health problems. While further research is warranted, identification of perfectionism in the perinatal period may help focus resources for intervention, reducing the prevalence of perinatal mental health difficulties.

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. We address this question with (1) an "intercategorical ecology" framework that extends the "racial ecology" lens into the complex spatial and demographic dimensions of environmental health vulnerabilities and (2) a multivariate analysis using block-level data from the 2010 U.S. decennial census and a key dataset estimating the LSL connections for 56,038 land parcels in Flint. We found that blocks exposed to LSLs had, on average, higher concentrations of single-parent white, Black, and Latinx families. However, logistic regression results indicate that the likelihood of block exposure to LSLs was most consistently and positively associated with the percentage of single-father Black and single-mother Latina families, net of other racialized and gendered single-parent family structures, socioeconomic status, and the spatial concentration of LSL exposure.

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. EIM provides an innovative approach that will enable explicit consideration of structural/institutional social processes in the social production of intersectional and geospatial inequalities.

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. To address this gap, we performed a scoping review of empirical health studies using CCMM to: (a) evaluate the extent to which this statistical approach has been adopted; (b) assess the rationale and procedures for using CCMM; and (c) provide concrete recommendations for the future use of CCMM. We identified 118 CCMM papers published in English-language literature between 1994 and 2018. Our results reveal a steady growth in empirical health studies using CCMM to address a wide variety of health outcomes in clustered non-hierarchical data. Health researchers use CCMM primarily for five reasons: (1) to statistically account for non-independence in clustered data structures; out of substantive interest in the variance explained by (2) concurrent contexts, (3) contexts over time, and (4) age-period-cohort effects; and (5) to apply CCMM alongside other techniques within a joint model. We conclude by proposing a set of recommendations for use of CCMM with the aim of improved clarity and standardization of reporting in future research using this statistical approach.

Impact on Health-Related Quality of Life of Video-Assisted Thoracoscopic Surgery for Lung Cancer.

BACKGROUND: Video-assisted thoracoscopic surgery (VATS) approaches are increasingly used in lung cancer surgery, but little is known about their impact on patients' health-related quality of life (HRQL). This prospective study measured recovery and HRQL in the year after VATS for non-small cell lung cancer (NSCLC) and explored the feasibility of HRQL data collection in patients undergoing VATS or open lung resection. PATIENTS AND METHODS: Consecutive patients referred for surgical assessment (VATS or open surgery) for proven/suspected NSCLC completed HRQL and fatigue assessments before and 1, 3, 6 and 12 months post-surgery. Mean HRQL scores were calculated for patients who underwent VATS (segmental, wedge or lobectomy resection). Paired t-tests compared mean HRQL between baseline and expected worst (1 month), early (3 months) and longer-term (12 months) recovery time points. RESULTS: A total of 92 patients received VATS, and 18 open surgery. Questionnaire response rates were high (pre-surgery 96-100%; follow-up 67-85%). Pre-surgery, VATS patients reported mostly high (good) functional health scores [(European Organisation for Research and Treatment of Cancer) EORTC function scores > 80] and low (mild) symptom scores (EORTC symptom scores < 20). One-month post-surgery, patients reported clinically and statistically significant deterioration in overall health and physical, role and social function (19-36 points), and increased fatigue, pain, dyspnoea, appetite loss and constipation [EORTC 12-26; multidimensional fatigue inventory (MFI-20) 3-5]. HRQL had not fully recovered 12 months post-surgery, with reduced physical, role and social function (10-14) and persistent fatigue and dyspnoea (EORTC 12-22; MFI-20 2.7-3.2). CONCLUSIONS: Lung resection has a considerable detrimental impact on patients' HRQL that is not fully resolved 12 months post-surgery, despite a VATS approach.

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. We disagree with their second assertion; however, the authors are right to call for greater clarity. For this purpose, in this response we have three main objectives. (1) In their commentary, LMCB incorrectly describe model predictions based on MAIHDA fixed effects as estimates of "grand means" (or the mean of means), when they are actually "precision-weighted grand means." We clarify the differences between average predicted values obtained by different models, and argue that predictions obtained by MAIHDA are more suitable to serve as reference points for residual/interaction effects. This further enables us to clarify the interpretation of residual/interaction effects in MAIHDA and conventional models. Using simple simulations, we demonstrate conditions under which the precision-weighted grand mean resembles a grand mean, and when it resembles a population mean (or the mean of all individual observations) obtained using single-level regression, explaining the results obtained by LMCB and informing future research. (2) We construct a modification to MAIHDA that constrains the fixed effects so that the resulting model predictions provide estimates of population means, which we use to demonstrate the robustness of results reported by Evans et al. (2018). We find that stratum-specific residuals obtained using the two approaches are highly correlated (Pearson corr = 0.98, p < 0.0001) and no substantive conclusions would have been affected if the preference had been for estimating population means. However, we advise researchers to use the original, unconstrained MAIHDA. (3) Finally, we outline the extent to which single-level and MAIHDA approaches address the fundamental goals of quantitative intersectional analyses and conclude that intersectional MAIHDA remains a promising new approach for the examination of inequalities.

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. To demonstrate this approach I examine past-month adolescent cigarette use intersectionally by school- and neighborhood-poverty status in Wave 1 of the National Longitudinal Study of Adolescent to Adult Health (N = 17,234). I conclude by discussing the adaptability of this approach to a variety of research questions, including intersectional effects that vary by contextual exposures over time, positions in social networks, and exposures to social policies.

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). All three contexts have been shown to be important determinants of adolescent sexual and reproductive health outcomes but are typically examined separately, leaving a large gap in our understanding of their relative and joint importance. Furthermore, little is known about whether these contexts matter differently for boys and girls. METHODS: Using sociocentric network data from the National Longitudinal Study of Adolescent to Adult Health, we combine gender-stratified analyses, social network community detection (to identify teens' social cliques), and cross-classified multilevel modeling to simultaneously analyze gender, neighborhood, school, and peer group effects. These results are compared against results from traditional multilevel models (MLMs), which analyze the contexts individually. RESULTS: Evaluated separately in MLM, peer groups accounted for 6.79% of the total variation in coitarche, schools for 3.56%, and neighborhoods for 4.11%. Under simultaneous cross-classified multilevel modeling analysis, a different story emerges: peer groups and schools accounted for 3.66% and 3.19% of the total variation in coitarche, respectively, whereas neighborhood explained only 1.16% of the total variation. Stratified analyses indicate that gender modifies these associations. CONCLUSIONS: Results demonstrate that omitting any one of these contexts may lead to an overestimation of the importance of contexts included in models. When modeled simultaneously with neighborhoods, our findings suggest that peer groups and schools are meaningful contributing contexts to the variance in sexual initiation, and that these contexts matter differently for boys and girls.

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. Finally, I outline important future directions for intersectional scholarship modeling social processes, in particular, the need for addressing structural determinants of inequalities intersectionally. CONCLUSIONS: Whether a study is descriptive or analytic, engagement with theory is essential in order to maintain the critical and transformative edge of intersectionality. Theories of population health such as fundamental causes, social production, and ecosocial theory, should be framed and applied in explicitly intersectional terms. As the field moves toward intersectional evaluations of social processes, attention should be given to all ecological levels but especially the structural/institutional level. This attention includes considering interactions between intersectional social strata and contexts and considering the roles of structural-level discrimination in shaping population health outcomes intersectionally.

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. Findings suggest that adding categories to intersectional analyses will tend to reveal new points of interaction. Stratum-level results from the multilevel approach are robust to cross-classification by school context. Conventional and multilevel approaches differ substantially when tested empirically. I conclude with a detailed consideration of the origin of these differences and provide recommendations for future scholarship of intersectional health inequalities.

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. We examine intersectionality and depression among adolescents and young adults in the U.S. along dimensions of gender, race/ethnicity, immigration status, and family income using a large, nationally representative sample-the National Longitudinal Study of Adolescent to Adult Health. We find evidence of considerable inequalities between social strata, with women, racial/ethnic minorities, immigrants, and low income strata experiencing elevated depression scores. Importantly, the majority of between-strata variation is explained by additive main effects, with no strata experiencing statistically significant residual "interaction" effects. We compare these findings to previous intersectional research on depression and discuss possible sources of differences between MAIHDA and conventional intersectional models.

Associations Between Physical Function and Subjective Well-Being in Older Adults From Low- and Middle-Income Countries: Results From the Study on Global AGEing and Adult Health (SAGE).

BACKGROUND: Physical function is positively associated with subjective well-being in older adults from high-income nations. This study tests whether this association is evident in low- and middle-income countries. METHODS: Data were drawn from the study on global AGEing and adult health, using nationally representative samples of individuals over 50 years old from China, Ghana, India, Mexico, Russia, and South Africa. Participant interviews measured well-being (quality of life, mood, and happiness) and physical function (grip strength, usual and rapid gait speed). Logistic regressions tested relations between physical function and well-being variables within each country. RESULTS: Higher physical function measures exhibited moderate, yet significant, associations with increased odds of highly rated well-being (p < .05). However, higher gait speeds were unexpectedly associated with decreased odds of highly rated well-being (p < .05) in South Africa and Russia. CONCLUSION: These results suggest that physical function is generally positively associated with perceived well-being in older individuals from lower income nations.