Multilevel Analysis Of Individual Heterogeneity And Discriminatory Accuracy Research Articles

Sociodemographic Inequalities in Student Achievement: An Intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA)

Sociodemographic Inequalities in Student Achievement: An Intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA)

Intersectional inequalities in health anxiety: multilevel analysis of individual heterogeneity and discriminatory accuracy in the SOMA.SOC study.

Intersectional approaches are needed to disaggregate the complex interaction of social identities contributing to (mental) health disparities. Health anxiety represents an overlooked public mental health issue. Therefore, intersectional inequalities in health anxiety were examined using multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA). Analyses are based on cross-sectional data of the adult population living in Germany (N = 2,413). Health anxiety was assessed with the Whiteley Index-7. Applying intersectional MAIHDA, health anxiety in the intersectional strata of gender, history of migration, and income was predicted. Discriminatory accuracy was assessed via the intra-class correlation and the proportional change in variance. Analyses revealed additive social inequalities in health anxiety with greatest impact of low income but no clear intersectional gradient. Most affected by health anxiety were females who immigrated themselves with low income, males whose parent(s) immigrated with low income, and males who immigrated themselves with medium income. Intersectional approaches contribute to a more comprehensive understanding of (mental) health disparities. In addition to general efforts to counteract health inequalities, combining universal screening and targeted psychotherapeutic treatment seems promising to specifically reduce inequalities in health anxiety.

Depression at the intersection of race/ethnicity, sex/gender, and sexual orientation in a nationally representative sample of US adults: A design-weighted intersectional MAIHDA.

This study examined how race/ethnicity, sex/gender, and sexual orientation intersect under interlocking systems of oppression to socially pattern depression among US adults. With cross-sectional data from the 2015-2020 National Survey on Drug Use and Health (NSDUH; n=234,722), we conducted design-weighted multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) under an intersectional framework to predict past-year and lifetime major depressive episode (MDE). With 42 intersectional groups constructed from seven race/ethnicity, two sex/gender, and three sexual orientation categories, we estimated age-standardized prevalence and excess/reduced prevalence attributable to two-way or higher interaction effects. Models revealed heterogeneity across groups, with prevalence ranging from 1.9-19.7% (past-year) and 4.5-36.5% (lifetime). Approximately 12.7% (past-year) and 12.5% (lifetime) of total individual variance were attributable to between-group differences, indicating key relevance of intersectional groups in describing the population distribution of depression. Main effects indicated, on average, people who were White, women, gay/lesbian, or bisexual had greater odds of MDE. Main effects explained most between-group variance. Interaction effects (past-year: 10.1%; lifetime: 16.5%) indicated a further source of heterogeneity around averages with groups experiencing excess/reduced prevalence compared to main effects expectations. We extend the MAIHDA framework to calculate nationally representative estimates from complex sample survey data using design-weighted, Bayesian methods.

Intersectional inequalities in the transition to grandparenthood and cognitive functioning: A longitudinal Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA).

In aging societies, more people become vulnerable to experiencing cognitive decline. Simultaneously, the role of grandparenthood is central for older adults and their families. Our study investigates inequalities in the level and trajectories of cognitive functioning among older adults, focusing on possible intersectional effects of social determinants and grandparenthood as a life course transition that may contribute to delaying cognitive decline. Using longitudinal data from the Survey of Health, Ageing and Retirement in Europe, we analyzed a sample of 19,953 individuals aged 50-85 without grandchildren at baseline. We applied Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy to investigate variation in cognitive functioning across 48 intersectional strata, defined by sex/gender, migration, education, and occupation. We allowed the impact of becoming a grandparent on cognitive functioning trajectories to vary across strata by including random slopes. Intersectional strata accounted for 17.43% of the overall variance in cognitive functioning, with most of the stratum-level variation explained by additive effects of the stratum-defining characteristics. Transition to grandparenthood was associated with higher cognitive functioning, showing a stronger effect for women. Stratum-level variation in the grandparenthood effect was modest, especially after accounting for interactions between grandparenthood and the stratum-defining variables. This study highlights the importance of social determinants for understanding heterogeneities in older adults' level of cognitive functioning and its association with the transition to grandparenthood. Cumulative disadvantages negatively affect cognitive functioning, hence adopting an intersectional lens is useful to decompose inequalities and derive tailored interventions to promote equal healthy aging.

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.

Abstract 04: Intersectional Inequities in Obesity Among Multiracial Adults by Urbanicity: A Random Effects MAIHDA Analysis

Based on 2020 Census Bureau data, the percentage of people who reported multiple races changed more than all of the race alone groups, increasing from 2.9% of the population (9 million people) in 2010 to 10.2% of the population (33.8 million) in 2020. Yet, the majority of national health surveys aggregate all individuals who identify with multiple races into one heterogeneous group. We hypothesize this aggregation masks important differences in terms of health. Beyond the aggregation, there are multiple dimensions of race/ethnicity including racial self-identification (the race(s) one identifies with), racial category (the census category one selects), Hispanic ethnicity, and others that do not ideally overlap among multiracial adults. For multiracial adults, it is unknown how these multiple contexts of race and ethnicity intersect with one another. We evaluated the intersection of racial self-identification, racial category, and Hispanic ethnicity on body mass index (BMI) among multiracial adults as a second-level social identity strata. We then examined if these intersectional inequities in BMI were differently patterned in urban, suburban, and rural areas. We used multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) among multiracial adults in the US (n=42,632) with a three-year (2010-2012) pooled sample of the Behavioral Risk Factor Surveillance System (BRFSS). We observed intersectional effects on BMI inequities. Most of the effects of social identity on BMI were explained by the combination of racial self-identification, racial category, and Hispanic ethnicity among multiracial adults (85.3%). Those living in a rural area had considerably higher BMIs than those living in an urban or suburban area. This is especially true for individuals with racial self-identification of White-Black, White-Native Hawaiian/Pacific Islander (NHPI), Black-American Indian/Alaska Native (AIAN), Asian-NHPI, and White-Black-AIAN and those who chose the racial category of NHPI. For example, individuals who self-identified as White-Black had an average BMI 4.5 kg/m 2 (p<0.01) higher in rural areas compared to White-Blacks living in urban areas. The magnitude of this BMI gap across urban-rural environments varied considerably across social identity strata ranging from 35.3 kg/m 2 among Non-Hispanic White-Black-AIAN with no preference for one race to 22.5 kg/m 2 among Non-Hispanic White-Asian with no preference for one race. This study highlights aspects of the social construction of race and patterning across the urban-rural divide in evaluation of BMI/obesity. This study brings to light inequities within people who identify as multiracial and highlights sub-populations to focus future policy and intervention research on.

Methodological insights into multilevel analysis of individual heterogeneity and discriminatory accuracy: An empirical examination of the effects of strata configurations on between-stratum variance and of fixed effects across hierarchical levels.

This study aims to advance the Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) approach by addressing two key questions. First, it investigates the impact of using increasingly complex combinations of variables to create intersectional strata on between-stratum variance, measured by the variance partitioning coefficients (VPCs). Second, it examines the stability of coefficients for fixed effects across models with an increasing number of hierarchical levels. The analysis is performed using data from a survey of over 42,000 respondents on the prevalence of gender-based violence in European research organisations conducted in 2022. Results indicate that the number of intersectional strata is not significantly related to the proportion of the total variance attributable to the variance between intersectional strata in the MAIHDA approach. Moreover, the coefficients remain relatively stable and consistent across models with increasing complexity, where levels about organisations and countries are added. The analysis concludes that the MAIHDA approach can be flexibly applied for different research purposes, either to better account for structures of power and inequality; or to provide intersectionality-sensitive estimates. The findings underscore the need for researchers to clarify the specific aims of using MAIHDA, whether descriptive or inferential, and highlight the approach's versatility in addressing intersectionality within quantitative research. The study contributes to the literature by offering empirical evidence on the methodological considerations in applying the MAIHDA approach, thereby aiding in its more effective use for intersectional research.

Health inequalities at the intersection of multiple social determinants among under five children residing Nairobi urban slums: An application of multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA).

In this analysis we examine through an intersectionality lens how key social determinants of health (SDOH) are associated with health conditions among under-five children (<5y) residing in Nairobi slums, Kenya. We used cross-sectional data collected from Nairobi slums between June and November 2012 to explore how multiple interactions of SDoH shape health inequalities in slums. We applied multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) approach. We constructed intersectional strata for each health condition from combinations of significant SDoH obtained using univariate analyses. We then estimated the intersectional effects of health condition in a series of MAIHDA logistic regression models distinguishing between additive and interaction effects. We quantified discriminatory accuracy (DA) of the intersectional strata by means of the variance partitioning coefficient (VPC) and the area under the receiver operating characteristic curve (AUC-ROC). The total participants were 2,199 <5y, with 120 records (5.5%) dropped because health conditions were recorded as "not applicable". The main outcome variables were three health conditions: 1) whether a child had diarrhea or not, 2) whether a child had fever or not, and 3) whether a child had cough or not in the previous two weeks. We found non-significant intersectional effects for each health condition. The head of household ethnic group was significantly associated with each health condition. We found good DA for diarrhea (VPC = 9.0%, AUC-ROC = 76.6%) an indication of large intersectional effects. However, fever (VPC = 1.9%, AUC-ROC = 66.3%) and cough (VPC = 0.5%, AUC-ROC = 61.8%) had weak DA indicating existence of small intersectional effects. Our study shows pathways for SDoH that affect diarrhea, cough, and fever for <5y living in slums are multiplicative and shared. The findings show that <5y from Luo and Luhya ethnic groups, recent migrants (less than 2 years), and households experiencing CHE are more likely to face worse health outcomes. We recommend relevant stakeholders to develop strategies aimed at identifying these groups for targeted proportionate universalism based on the level of their need.

Mapping socio-geographical disparities in the occurrence of teenage maternity in Colombia using multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA)

BackgroundThe prevalence of teenage pregnancy in Colombia is higher than the worldwide average. The identification of socio-geographical disparities might help to prioritize public health interventions.AimTo describe variation in the probability of teenage maternity across geopolitical departments and socio-geographical intersectional strata in Colombia.MethodsA cross-sectional study based on live birth certificates in Colombia. Teenage maternity was defined as a woman giving birth aged 19 or younger. Multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) was applied using multilevel Poisson and logistic regression. Two different approaches were used: (1) intersectional: using strata defined by the combination of health insurance, region, area of residency, and ethnicity as the second level (2) geographical: using geopolitical departments as the second level. Null, partial, and full models were obtained. General contextual effect (GCE) based on the variance partition coefficient (VPC) was considered as the measure of disparity. Proportional change in variance (PCV) was used to identify the contribution of each variable to the between-strata variation and to identify whether this variation, if any, was due to additive or interaction effects. Residuals were used to identify strata with potential higher-order interactions.ResultsThe prevalence of teenage mothers in Colombia was 18.30% (95% CI 18.20–18.40). The highest prevalence was observed in Vichada, 25.65% (95% CI: 23.71–27.78), and in the stratum containing mothers with Subsidized/Unaffiliated healthcare insurance, Mestizo, Rural area in the Caribbean region, 29.08% (95% CI 28.55–29.61). The VPC from the null model was 1.70% and 9.16% using the geographical and socio-geographical intersectional approaches, respectively. The higher PCV for the intersectional model was attributed to health insurance. Positive and negative interactions of effects were observed.ConclusionDisparities were observed between intersectional socio-geographical strata but not between geo-political departments. Our results indicate that if resources for prevention are limited, using an intersectional socio-geographical approach would be more effective than focusing on geopolitical departments especially when focusing resources on those groups which show the highest prevalence. MAIHDA could potentially be applied to many other health outcomes where resource decisions must be made.

Intersectional analysis of social disparities in type 2 diabetes risk among adults in Germany: results from a nationwide population-based survey

BackgroundDifferences in type 2 diabetes risk have been reported for several sociodemographic determinants including sex/gender or socioeconomic status. From an intersectional perspective, it is important to not only consider the role of social dimensions individually, but also their intersections. This allows for a deeper understanding of diabetes risk and preventive needs among diverse population groups.MethodsAs an intersectionality-informed approach, multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) was used in a population-based sample of adults without known diabetes in Germany from the cross-sectional survey “Disease knowledge and information needs– Diabetes mellitus (2017)”. Diabetes risk was assessed by the German Diabetes Risk Score (GDRS, range 0-122 points), estimating the individual risk of developing type 2 diabetes within the next 5 years based on established self-reported risk factors. Nesting individuals in 12 intersectional strata defined by combining sex/gender, educational level, and history of migration, we calculated measures to quantify the extent to which individual differences in diabetes risk were explained at strata level, and how much this was due to additive or multiplicative intersectional effects of social determinants.ResultsDrawing on data of 2,253 participants, we found good discriminatory accuracy of intersectional strata (variance partition coefficient = 14.00% in the simple intersectional model). Model-predicted GDRS means varied between 29.97 (corresponding to a “low risk” of < 2%) in women with high educational level and a history of migration, and 52.73 (“still low risk” of 2–5%) in men with low educational level without a history of migration. Variance in GDRS between strata was mainly explained by additive effects of social determinants (proportional change in variance to intersectional interaction model = 77.95%) with being male and having low educational level being associated with higher GDRS. There was no evidence of multiplicative effects in individual strata.ConclusionsType 2 diabetes risk differed between intersectional strata and can to some extent be explained at strata level. The role of intersectional effects was minor and needs to be further investigated. Findings suggest a need for specific preventive measures targeted at large groups with increased diabetes risk, such as men and persons with low educational level.

What does the MAIHDA method explain?

Multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) is a new approach to quantitative intersectional modelling. Along with an outcome of interest, MAIHDA entails the use of two sets of independent variables. These include group demographics such as race, gender, and poverty status as well as strata which are constructs such as Black female poor, Black female wealthy, and White female poor. These constructs represent the combination of the demographic variables. To operationalize the approach, an initial random intercepts model with strata as a level 2 context is specified. Then, another model is specified that includes the strata as well as the demographic variables as level 1 fixed effects. As such, it is argued that MAIHDA uniquely identifies the additive and intersectional effects for any given outcome. In this paper we show that MAIHDA falls short of this promise: the strata are an individual-level composite variable not a level 2 context. Rather than being analogous to neighborhoods as contexts, strata are analogous to socio-economic status which is a combination of individual-level demographic variables, albeit often presented as a group-level characteristic. The result is that the demographic variables are inserted in both level 2 and 1. This duplication across the levels in MAIHDA means that there is a built-in collinearity across the levels and that the models are mis-specified and, therefore, redundant. We conclude that single-level models with the demographic variables and interactions or with the strata as fixed effects are still the more accurate models for quantitative intersectional 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.

Intersecting gender identity and racial/ethnic inequities in eating disorder risk factors, symptoms, and diagnosis among U.S. college students: An intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy.

There are documented inequities in eating disorders (EDs) by gender and race/ethnicity, yet, little is known about population-level prevalence of ED risk factors, symptoms, and diagnosis at the intersection of diverse gender and racial/ethnic identities. Data from the Healthy Minds Study 2015-2019 (N = 251,310 U.S. university students) were used in a multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA). Participants were nested in 35 intersectional strata given by all combinations of 5 gender and 7 racial/ethnic categories. Multilevel logistic models with participants at level 1 and intersectional strata at level 2 were used to estimate stratum-specific predicted prevalence estimates for self-reported thin-ideal internalization, ED symptoms, and ED diagnosis. The variance partition coefficient (VPC) was calculated to quantify the contextual effect of the strata. There was considerable heterogeneity in the predicted prevalence of our ED outcomes across the strata (e.g., .3%-18.3% for ED diagnoses). There were large disparities in all three outcomes, with transgender participants of color having a higher predicted prevalence than expected based on the additive effects of gender and race/ethnicity. Moderation by race/ethnicity was also apparent, such that racial/ethnic disparities were wider within the cisgender groups relative to the transgender groups. VPCs indicated that ~10% of the total variance in ED outcomes was due to intersectionality between gender and race/ethnicity, over and above variance due to individual-level differences. Findings suggest that gender and racial/ethnic disparities in EDs are interrelated, underscoring the need to develop preventive interventions centering health equity. Despite evidence that sexism, racism, and cissexism (i.e., anti-transgender prejudice) can impact EDs risk, little research examines the social patterning of EDs at the intersection of diverse gender and racial/ethnic identities. Using data from a sample of 250,000 U.S. university students, this study found that gender and racial/ethnic disparities in eating disorder risk are interrelated, highlighting the need to develop health equity centered preventive interventions.

Adverse Childhood Experiences and Sexual Orientation: An Intersectional Analysis of Nationally Representative Data

This study compared the prevalence of adverse childhood experiences across intersections of sexual orientation, gender, race/ethnicity, and economic status. Data collected between 1994 and 2018 from 12,519 participants in the National Longitudinal Study of Adolescent to Adult Health were analyzed in 2023 to generate adverse childhood experience prevalence estimates. Unadjusted 1-way ANOVAs and multivariate regressions were performed to compare differences in independent and cumulative adversity measures by sexual orientation, gender, race/ethnicity, and poverty status. A multilevel analysis of individual heterogeneity and discriminatory accuracy was conducted to estimate adversity scores across 24 groups that were stratified by sexual orientation, gender, race/ethnicity, and poverty status. Adolescents with same-sex attractions and adults who identified with a sexual minority group reported more adverse childhood experiences overall than straight participants, although associations varied by type of adversity. Strikingly, adversity scores were higher among White youth with same-sex attractions than among Black youth with same-sex attractions, among more economically advantaged bisexual adults than among poorer ones, and among poor White participants than among poor Black and Hispanic participants, suggesting that the combination of disadvantaged and marginalized statuses does not necessarily correspond with greater childhood adversity. A multilevel analysis of individual heterogeneity and discriminatory accuracy interaction model showed that sexual orientation and poverty status contributed significant variance to cumulative adversity scores, whereas gender and race/ethnicity did not. The results show that disparities in adverse experiences can be more fully and accurately represented when sexual orientation and other social identities are modeled as intersectional configurations. Given that adverse childhood experiences are linked to morbidity and mortality, the findings have salient implications for understanding health disparities that affect population subgroups.

An Intersectional Approach to Quantifying the Impact of Geographic Remoteness and Health Disparities on Quality-Adjusted Life Expectancy: Application to Australia

ObjectivesAn intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) is a novel method for exploring the interaction between sociodemographic characteristics that affect health outcomes. This study explores the interaction between geographic remoteness and socioeconomic status on health outcomes in Australia from an intersectional perspective. MethodsData from a cross-sectional survey were matched with data from the Australian Bureau of Statistics and the Australian Institute of Health and Welfare. To explore the effect of health-related quality of life on life expectancy, quality-adjusted life expectancy (QALE) was estimated through applying utility values derived from the EQ-5D-5L to life table data from the Australian Bureau of Statistics. The effect of geographic remoteness on QALE was quantified using multivariable linear regression. An intersectional MAIHDA was performed to explore differences in mean QALE across strata formed by intersections of age, sex, and Socioeconomic Indexes for Areas score. ResultsBased on multivariable linear modeling, QALE declined significantly with increasing remoteness (inner regional, −1.0 years [undiscounted]; remote/very remote, −3.3 years [undiscounted]) (P < .001). In contrast, life expectancy was only significantly different between participants in remote/very remote areas and major cities (β-coefficient, −2.4; 95% CI −4.4 to −0.4; P = .016). No intersectional interaction effects between strata on QALE were found in the MAIHDA. ConclusionsQALE has considerable value as a metric for exploring disparities in health outcomes. Given that no intersectional interactions were identified, our findings support broad interventions that target the underlying social determinants of health appropriately reduce disparities versus interventions targeting intersectional interactions.

Intersectional Prevalence of Suicide Ideation, Plan, and Attempt Based on Gender, Sexual Orientation, Race and Ethnicity, and Rurality

Suicidal thoughts and behaviors (STBs) are major public health problems, and some social groups experience disproportionate STB burden. Studies assessing STB inequities for single identities (eg, gender or sexual orientation) cannot evaluate intersectional differences and do not reflect that the causes of inequities are due to structural-level (vs individual-level) processes. To examine differences in STB prevalence at the intersection of gender, sexual orientation, race and ethnicity, and rurality. This cross-sectional study used adult data from the 2015-2019 National Survey on Drug Use and Health (NSDUH), a population-based sample of noninstitutionalized US civilians. Data were analyzed from July 2022 to March 2023. Outcomes included past-year suicide ideation, plan, and attempt, each assessed with a single question developed for the NSDUH. Intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) models were estimated, in which participants were nested within social strata defined by all combinations of gender, sexual orientation, race and ethnicity, and rurality; outcome prevalence estimates were obtained for each social stratum. Social strata were conceptualized as proxies for exposure to structural forms of discrimination that contribute to health advantages or disadvantages (eg, sexism, racism). The analytic sample included 189 800 adults, of whom 46.5% were men; 53.5%, women; 4.8%, bisexual; 93.0%, heterosexual; 2.2%, lesbian or gay; 18.8%, Hispanic; 13.9%, non-Hispanic Black; and 67.2%, non-Hispanic White. A total of 44.6% were from large metropolitan counties; 35.5%, small metropolitan counties; and 19.9%, nonmetropolitan counties. There was a complex social patterning of STB prevalence that varied across social strata and was indicative of a disproportionate STB burden among multiply marginalized participants. Specifically, the highest estimated STB prevalence was observed among Hispanic (suicide ideation: 18.1%; 95% credible interval [CrI], 13.5%-24.3%) and non-Hispanic Black (suicide plan: 7.9% [95% CrI, 4.5%-12.1%]; suicide attempt: 3.3% [95% CrI, 1.4%-6.2%]) bisexual women in nonmetropolitan counties. In this cross-sectional study, intersectional exploratory analyses revealed that STB prevalence was highest among social strata including multiply marginalized individuals (eg, Hispanic and non-Hispanic Black bisexual women) residing in more rural counties. The findings suggest that considering and intervening in both individual-level (eg, psychiatric disorders) and structural-level (eg, structural discrimination) processes may enhance suicide prevention and equity efforts.

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.

Modelling Intersectionality within Quantitative Research

In the last decades, and especially since 2010, the intersectionality paradigm is being increasingly used as theoretical framework for examining and explaining social inequalities in various areas – both with qualitative and quantitative methods. So far, there is no standard for applying intersectionality in quantitative social research. Therefore, this paper aims to present how intersectionality can be applied in quantitative research and to elaborate on and evaluate several methodological approaches in this regard. More precisely, we describe the tenets of three different quantitative approaches: multivariate linear models, conventional multilevel analysis, and multilevel analysis of individual heterogeneity and discriminatory accuracy (MIAHDA). We compare the three approaches and outline their methodological benefits and limitations.