Describing Intersectional Health Outcomes: An Evaluation of Data Analysis Methods

Categorization of continuous covariates and complex regression models-friends or foes in intersectionality research

OBJECTIVES

To reduce health inequities, it is important to identify intersections in characteristics of individuals subject to privilege or disadvantage. Different proposals for that have recently been published. One approach (1) considers models specified with first- and all second-order effects and another (2) the stratification based on multiple covariates; both categorize continuous covariates. A simulation study was conducted in order to review both methods with regard to identification of intersections showing true differences, rate of false-positive results, and generalizability to independent data compared to an established approach (3) of backward variable elimination according to Bayesian information criterion (BE-BIC) combined with splines.

STUDY DESIGN AND SETTING

R software has been used to simulate the covariates age, sex, body mass index, education, and diabetes to examine their association with a continuous frailty score for osteoporosis using multiple linear regression. In setting 1, none of the covariates was associated with the frailty score, that is, only noise is present in the data. In setting 2, the covariates age, sex, and their interaction were associated with the frailty score, such that only females above 55 years formed an intersection associated with an increased frailty score. All approaches were compared under varying sample sizes (N = 200-3000) and signal-to-noise ratios (SNRs, 0.5-4) in 1000 replications. For model evaluation, bootstrap resampling was used. The models were fitted in internal learning data and then used to predict outcomes in the internal validation data. The mean squared error (MSE) was used for comparison and the frequency of false-positive findings calculated.

RESULTS

In setting 1, approaches 1 and 2 generated spurious effects in more than 90% of simulations across all sample sizes. In a smaller sample size, approach 3 (BE-BIC) selected 36.5% of the correct model, in larger sample size in 89.8% and always had a lower number of spurious effects. MSE in independent data was generally higher for approaches 1 and 2 when compared to 3. In setting 2, approach 1 selected most frequently the correct interaction but frequently showed spurious effects (>75%). Across all sample sizes and SNR, approach 3 generated least often spurious results and had lowest MSE in independent data.

CONCLUSION

Categorization of continuous covariates is detrimental to studies on intersectionality. Due to high and unrestricted model complexity, such approaches are prone to spurious effects and often lack interpretability. Approach 3 (BE-BIC) is considerably more robust against spurious findings, showed better generalizability to independent data, and can be used with most statistical software. For intersectionality research, we consider it most important to describe relevant differences between intersections and to avoid nonreproducible and spurious findings.

Methodologies for the collection of parameters to estimate dust/soil ingestion for young children

Background

Heavy metals, pesticides and a host of contaminants found in dust and soil pose a health risk to young children through ingestion. Dust/soil ingestion rates for young children can be estimated using micro-level activity time series (MLATS) as model inputs. MLATS allow for the generation of frequency and duration of children's contact activities, along with sequential contact patterns. Models using MLATS consider contact types, and transfer dynamics to assign mechanisms of contact and appropriate exposure factors for cumulative estimates of ingestion rates.

Objective

The objective of this study is to describe field implementation, data needs, advanced field collection, laboratory methodologies, and challenges for integrating into and updating a previously validated physical-stochastic MLATS-based model framework called the Child-Specific Aggregate Cumulative Human Exposure and Dose (CACHED) model. The manuscript focuses on describing the methods implemented in the current study.

Methods

This current multidisciplinary study (Dust Ingestion childRen sTudy [DIRT]) was implemented across three US regions: Tucson, Arizona; Miami, Florida and Greensboro, North Carolina. Four hundred and fifty participants were recruited between August 2021 to June 2023 to complete a 4-part household survey, of which 100 also participated in a field study.

Discussion

The field study focused on videotaping children's natural play using advanced unattended 360° cameras mounted for participants' tracking and ultimately conversion to MLATS. Additionally, children's hand rinses were collected before and after recording, along with indoor dust and outdoor soil, followed by advanced mass analysis. The gathered data will be used to quantify dust/soil ingestion by region, sociodemographic variables, age groups (from 6 months to 6 years), and other variables for indoor/outdoor settings within an adapted version of the CACHED model framework.

Significance

New innovative approaches for the estimation of dust/soil ingestion rates can potentially improve modeling and quantification of children's risks to contaminants from dust exposure.

Socioeconomic inequities in care experienced by women with breast cancer in England: An intersectional cross-sectional study

PURPOSE

Guided by the Intersectionality Framework, we examined the differential in breast cancer care experience across population subgroups in England.

METHODS

Secondary data analysis using the 2017/2018 National Cancer Patient Experience Surveys. We used disaggregated descriptive statistics (mean, standard errors, 95% confidence interval) to analyse 26,030 responses from female breast cancer patients to a question relating to overall care experience categorised by age, ethnicity, and sexual orientation in their intersection with social position. We then used logistic regression to investigate the odds (Odds Ratio (OR), 95% confidence intervals) of reporting positive care experience adjusting for patient, clinical, and Trust level factors.

RESULTS

Poorer care experience was predominantly reported by the most deprived younger and minoritised ethnic groups. Statistically significant findings were observed in adjusted multivariable analyses. Compared to patients aged 65-74 years, younger respondents were less likely to rate their care favourably (16-34 years old ORadj. = 0.55 (0.36-0.84). Compared to White British, Asian (ORadj. = 0.51 (0.39-0.66)) and Black African women (ORadj. = 0.53 (0.33-0.88)) were less likely to rate their care favourably. The least affluent respondents were less likely to rate their care favourably (ORadj. = 0.79 (0.64-0.97)).

CONCLUSION

There is evidence of inequity in overall cancer care experience among female breast cancer patients in England, particularly among women living at the specific intersection of age, ethnicity, and socioeconomic position. Future research is necessary to understand the mechanisms underlying breast cancer inequities. Policymakers, commissioners, and providers should consider the existence of multiple forms of marginalisation to inform improvement initiatives targeting patients at higher risk of vulnerability.

Causal Inference Challenges in the Relationship Between Social Determinants and Cardiovascular Outcomes

The effects of social determinants on cardiovascular outcomes are frequently estimated in epidemiologic analyses, but the profound causal and statistical challenges of this research program are not widely discussed. Here, we carefully review definitions and measures for social determinants of cardiovascular health and then examine the various assumptions required for valid causal inference in multivariable analyses of observational data, such as what one would typically encounter in cohorts, population surveys, health care databases, and vital statistics databases. We explain the necessity of the "well-defined exposure" and show how this goal relates to the "consistency assumption" that is necessary for valid causal inference. Well-defined exposure is especially challenging for social determinants of health because they are seldom simple atomistic interventions that are easily conceptualized and measured. We then review threats to valid inference that arise from confounding, selection bias, information bias, and positivity violations. Other causal considerations are reviewed and explained, such as correct model specification, absence of immortal time, and avoidance of the "Table 2 Fallacy," and their application to social determinants of cardiovascular outcomes are discussed. Fruitful approaches, including focusing on policy interventions and the "target trial" frameworks are proposed and provide a pathway for a more efficacious research program that can more reliably improve population health. Valid causal inference in this setting is quite challenging, but-with clever design and thoughtful analysis-the important role of social factors in patterning cardiovascular outcomes can be quantified and reported.

Methods for structural sexism and population health research: Introducing a novel analytic framework to capture life-course and intersectional effects

Accumulating evidence links structural sexism to gendered health inequities, yet methodological challenges have precluded comprehensive examinations into life-course and/or intersectional effects. To help address this gap, we introduce an analytic framework that uses sequential conditional mean models (SCMMs) to jointly account for longitudinal exposure trajectories and moderation by multiple dimensions of social identity/position, which we then apply to study how early life-course exposure to U.S. state-level structural sexism shapes mental health outcomes within and between gender groups. Data came from the Growing Up Today Study, a cohort of 16,875 children aged 9-14 years in 1996 who we followed through 2016. Using a composite index of relevant public policies and societal conditions (e.g., abortion bans, wage gaps), we assigned each U.S. state a year-specific structural sexism score and calculated participants' cumulative exposure by averaging the scores associated with states they had lived in during the study period, weighted according to duration of time spent in each. We then fit a series of SCMMs to estimate overall and group-specific associations between cumulative exposure from baseline through a given study wave and subsequent depressive symptomology; we also fit models using simplified (i.e., non-cumulative) exposure variables for comparison purposes. Analyses revealed that cumulative exposure to structural sexism: (1) was associated with significantly increased odds of experiencing depressive symptoms by the subsequent wave; (2) disproportionately impacted multiply marginalized groups (e.g., sexual minority girls/women); and (3) was more strongly associated with depressive symptomology compared to static or point-in-time exposure operationalizations (e.g., exposure in a single year). Substantively, these findings suggest that long-term exposure to structural sexism may contribute to the inequitable social patterning of mental distress among young people living in the U.S. More broadly, the proposed analytic framework represents a promising approach to examining the complex links between structural sexism and health across the life course and for diverse social groups.

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.

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.

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.

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.

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

BACKGROUND

The 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.

AIM

To describe variation in the probability of teenage maternity across geopolitical departments and socio-geographical intersectional strata in Colombia.

METHODS

A 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.

RESULTS

The 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.

CONCLUSION

Disparities 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.

A novel application of interrupted time series analysis to identify the impact of a primary health care reform on intersectional inequities in avoidable hospitalizations in the adult Swedish population

Primary health care (PHC) systems are a crucial instrument for achieving equitable population health, but there is little evidence of how PHC reforms impact equities in population health. In 2010, Sweden implemented a reform that promoted marketization and privatization of PHC. The present study uses a novel integration of intersectionality-informed and evaluative epidemiological analytical frameworks to disentangle the impact of the 2010 Swedish PHC reform on intersectional inequities in avoidable hospitalizations. The study population comprised the total Swedish population aged 18-85 years across 2001-2017, in total 129 million annual observations, for whom register data on sociodemographics and hospitalizations due to ambulatory care sensitive conditions were retrieved. Multilevel Analysis of Individual Heterogeneity and Discriminatory Analyses (MAIHDA) were run for the pre-reform (2001-2009) and post-reform (2010-2017) periods to provide a mapping of inequities. In addition, random effects estimates reflecting the discriminatory accuracy of intersectional strata were extracted from a series MAIHDAs run per year 2001-2017. The estimates were re-analyzed by Interrupted Time Series Analysis (ITSA), in order to identify the impact of the reform on measures of intersectional inequity in avoidable hospitalizations. The results point to a complex reconfiguration of social inequities following the reform. While the post-reform period showed a reduction in overall rates of avoidable hospitalizations and in age disparities, socioeconomic inequities in avoidable hospitalizations, as well as the importance of interactions between complex social positions, both increased. Socioeconomically disadvantaged groups born in the Nordic countries seem to have benefited the least from the reform. The study supports a greater attention to the potentially complex consequences that health reforms can have on inequities in health and health care, which may not be immediate apparent in conventional evaluations of either population-average outcomes, or by simple evaluations of equity impacts. Methodological approaches for evaluation of complex inequity impacts need further development.

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.

An Intersectional Analysis of Moral Distress and Intention to Leave Employment Among Long-Term Care Providers in British Columbia

Objectives: In this study, we aimed to explore the relationship between intersectional inequities and moral distress among those working in Long-Term Care (LTC) in British Columbia, Canada. Methods: This was a cross-sectional and retrospective study. We assessed moral distress, of 1678 respondents, using a modified Moral Distress Scale, and an equivalent distress mitigation score, at the intersections of gender and racial/ethnic identity. Then, we explored which worker attributes were more predictive of intention to leave work. Results: We found notable difference in experiences of moral distress across intersecting identities, including high moral distress scores among Indigenous men and women, and white women. Significant differences in mitigation scores were also found by intersectional identities. Discussion: Moral distress was the most important predictor of intention to leave work. The differences across racial and gender identity groups suggest the need for tailored interventions to address moral distress among LTC providers.

A Quantitative Intersectionality Analysis of HIV/STI Prevention and Healthcare Access Among Transgender and Nonbinary People

BACKGROUND

Transgender and nonbinary people experience substantial barriers to accessing healthcare, including prevention of HIV and other sexually transmitted infections (HIV/STI), due to structural inequities. We examined differences in insurance, HIV/STI prevalence, testing, and preexposure prophylaxis use among transgender and nonbinary people living in Washington State by race and ethnicity and gender.

METHODS

We pooled data from five 2019-2021 Washington State HIV/STI surveillance data sources to obtain a large and diverse sample of 1648 transgender and nonbinary participants. We calculated the risk difference (RD) for each outcome and used Poisson regression to estimate a surrogate measure of additive interaction-attributable proportion (AP)-that measures the proportion of the excess prevalence of the outcome observed at the intersection of gendered and racialized experience, beyond that expected from gender or race and ethnicity alone.

RESULTS

Participants reported overall high levels of poverty (29% incomes <$15,000 and 7% unstable housing). Certain groups, especially racial/ethnic minority transgender women, were disproportionately impacted by HIV/STIs (RDs from 20% to 43% and APs from 50% to 85%) and less likely to currently have insurance (RDs from 25% to 39% and APs from 74% to 93%) than that expected based on gendered or racialized experience alone.

CONCLUSIONS

Our findings highlight the heterogeneity in insurance access, HIV/STI positivity, and prevention utilization within transgender communities. We observed that a large proportion of increased HIV/STI prevalence among racial/ethnic minority transgender women was attributable to the intersection of gender and race and ethnicity. Our findings highlight the importance of trans-inclusive models of HIV/STI prevention that address multilevel barriers rooted in cissexism and structural racism.

More Than Identity: An Intersectional Approach to Understanding Mental-Emotional Well-Being of Emerging Adults by Centering Lived Experiences of Marginalization

Understanding social determinants that shape pertinent developmental shifts during emerging adulthood (i.e., ages 18-25 years) and their associations with psychological health requires a nuanced approach. In our exploratory study, we investigated how multiple social identities and lived experiences generated by systems of marginalization and power (e.g., racism, classism, sexism) intersect in connection to the mental-emotional well-being of emerging adults (EAs). Eating and Activity Over Time (EAT, 2010-2018) data were collected from 1,568 EAs (mean age = 22.2 (standard deviation, 2.0) years) recruited initially in 2010 from Minneapolis/St. Paul schools. Conditional inference tree analyses were employed to treat "social location" and systems of marginalization and power as interdependent social factors influencing EAs' mental-emotional well-being outcomes: depressive symptoms, stress, self-esteem, and self-compassion. Conditional inference tree analyses identified EAs' subgroups with differing mean levels of mental-emotional well-being outcomes, distinguished primarily by marginalized social experiences (e.g., discrimination, financial difficulties) rather than social identities themselves. The relative positioning of EAs' experiences of social marginalization (e.g., discrimination) to their social identities (e.g., race/ethnicity) suggests that the social experiences generated by systems of privilege and oppression (e.g., racism) are more adjacent social determinants of mental-emotional well-being than the social identities used in public health research to proxy the oppressive systems that give them social meaning.

Associations of past-year stigma and psychosocial syndemic conditions: Considerations for intersectional stigma measures among Black Sexual Minority Men

This secondary analysis of a mixed serostatus sample of Black sexual minority men (BSMM) used conditional inference tree methods to explore associations of past-year experienced stigma and psychosocial syndemic conditions. Experienced stigmas were attributed to race, sexuality, socioeconomic status, HIV status or some "other" reason. Psychosocial syndemic conditions studied included physical assault, intimate partner violence, polysubstance use, and depression symptomology. Data are from Promoting Our Worth, Equality and Resilience (POWER), a serial, cross-sectional study conducted between 2014-2017 (N=4430). Experiences of multiple stigmas were reported by n=938 (22.1%) of BSMM. Conditional inference tree results revealed that HIV-related stigma and its intersection with "other" stigma showed the greatest variance in psychosocial condition prevalence. Our findings suggest that when developing intercategorical intersectional analyses with BSMM, there are important stigmas for BSMM beyond those attributed to race, sexuality, and SES, particularly intersecting with HIV-related stigma. Conditional inference tree analysis shows promise in quantitative explorations of intersectional stigma with BSMM, but will benefit from the inclusion of additional forms of stigma, which should be considered by the field moving forward.

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.

Table 2 Fallacy in Descriptive Epidemiology: Bringing Machine Learning to the Table

There is a lack of rigorous methodological development for descriptive epidemiology, where the goal is to describe and identify the most important associations with an outcome given a large set of potential predictors. This has often led to the Table 2 fallacy, where one presents the coefficient estimates for all covariates from a single multivariable regression model, which are often uninterpretable in a descriptive analysis. We argue that machine learning (ML) is a potential solution to this problem. We illustrate the power of ML with an example analysis identifying the most important predictors of alcohol abuse among sexual minority youth. The framework we propose for this analysis is as follows: (1) Identify a few ML methods for the analysis, (2) optimize the parameters using the whole data with a nested cross-validation approach, (3) rank the variables using variable importance scores, (4) present partial dependence plots (PDP) to illustrate the association between the important variables and the outcome, (5) and identify the strength of the interaction terms using the PDPs. We discuss the potential strengths and weaknesses of using ML methods for descriptive analysis and future directions for research. R codes to reproduce these analyses are provided, which we invite other researchers to use.

Explaining biological differences between men and women by gendered mechanisms

BACKGROUND

The principal aim of this study was to explore if biological differences between men and women can be explained by gendered mechanisms.

METHODS

We used data from the 1958 National Child Development Study, including all the living subjects of the cohort at the outcome collection wave (44-45 years). We explored several biomarkers as outcomes: systolic blood pressure, triglycerides, LDL cholesterol, HbA1c, CRP, and cortisol. Three conceptualizations of gender have been used to define methodological strategies: (a) Gender as an individual characteristic; (b) Gender as an effect of sex on socio-behavioural characteristics; (c) Gender as an interaction between sex and the social environment, here the early-life social environment. We estimated the total effect of sex and the proportion of total effect of sex at birth eliminated by gender, measured by 3 different ways according to these 3 concepts, using g-computation.

RESULTS

The average level of each biomarker was significantly different according to sex at birth, higher in men for cardiometabolic biomarkers and higher in women for inflammatory and neuroendocrine biomarkers. The sizes of the differences were always smaller than one standard deviation but were larger than differences due to early-life deprivation, except for CRP. We observed gender mechanisms underlying these differences between men and women, even if the mediation effects were rarely statistically significant. These mechanisms were of three kinds: (1) mediation by socio-behavioural characteristics; (2) attenuation by gendered mechanisms; (3) interaction with early social environment. Indeed, we observed that being born into a deprived rather than non-deprived family increased metabolic and inflammatory biomarkers levels more strongly in females than in males.

CONCLUSIONS

The biological differences between men and women seem to not be purely explained by biological mechanisms. The exploration of gender mechanisms opens new perspectives, in terms of methodology, understanding and potential applications.

Multilevel modelling for measuring interaction of effects between multiple categorical variables: An illustrative application using risk factors for preeclampsia

BACKGROUND

Measuring multiple and higher-order interaction effects between multiple categorical variables proves challenging.

OBJECTIVES

To illustrate a multilevel modelling approach to studying complex interactions.

METHODS

We apply a two-level random-intercept linear regression to a binary outcome for individuals (level-1) nested within strata (level-2) defined by all observed combinations of multiple categorical exposure variables. As a pedagogic application, we analyse 36 strata defined by five risk factors of preeclampsia (parity, previous preeclampsia, chronic hypertension, multiple pregnancies, body mass index category) among 652,603 women in the Swedish Medical Birth Registry between 2002 and 2010.

RESULTS

The absolute risk of preeclampsia was 4% but was predicted to vary from 1% to 44% across strata. The stratum discriminatory accuracy was 30% according to the variance partition coefficient (VPC) and 0.73 according to the area under the receiver operating characteristic curve (AUC). While the risk heterogeneity across strata was primarily due to the main effects of the categories defining the strata, 5% of the variation was attributable to their two- and higher-way interaction effects. One stratum presented a positive interaction, and two strata presented negative interaction.

CONCLUSIONS

Multilevel modelling is an innovative tool for identifying and analysing higher-order interaction effects. Further work is needed to explore how this approach can best be applied to making causal inferences.

Lysmeral Exposure in Children and Adolescences Participating in the German Environmental Survey (2012-2015): Integrating Sex/Gender into Analysis

Comprehensive consideration of the biological and social diversities of sex and gender as well as their interdependencies is mostly missing in human biomonitoring (HBM) studies. Using the INGER sex/gender concept as theoretical background, we analyzed differences in exposure to lysmeral, a compound commonly found as a fragrance in cosmetics, personal care, and household products, in 2294 children and adolescents in Germany using decision tree, regression, and mediation analysis. The variables "sex assigned at birth" and "age", as well as well as use of personal care products and fabric conditioner proved to have the highest explanatory value. Mediating effects of behaviour associated with societal gender expectations were observed, as the use of cosmetics correlated highly with lysmeral metabolites concentrations in girls between 6 and 17 years, with the strongest effect in adolescents between 14 and 17 years old. In the youngest age group (3-5 years) boys showed higher concentration of the metabolite tert-butylbenzoic acid (TBBA) compared to girls of the same age but only if TBBA urine concentrations were normalized on creatinine. Our study offers the first retrospective sex/gender assessment of HBM data. It demonstrates the possibilities to rethink and broaden sex/gender analysis in existing HBM-studies and highlights the need for inclusion of new sex/gender concepts in the design of new studies.

Biomarkers for isolated congenital heart disease based on maternal amniotic fluid metabolomics analysis

INTRODUCTION

Congenital heart disease (CHD) is one of the most prevalent birth defects in the world. The pathogenesis of CHD is complex and unclear. With the development of metabolomics technology, variations in metabolites may provide new clues about the causes of CHD and may serve as a biomarker during pregnancy.

METHODS

Sixty-five amniotic fluid samples (28 cases and 37 controls) during the second and third trimesters were utilized in this study. The metabolomics of CHD and normal fetuses were analyzed by untargeted metabolomics technology. Differential comparison and randomForest were used to screen metabolic biomarkers.

RESULTS

A total of 2472 metabolites were detected, and they were distributed differentially between the cases and controls. Setting the selection criteria of fold change (FC) ≥ 2, P value < 0.01 and variable importance for the projection (VIP) ≥ 1.5, we screened 118 differential metabolites. Within the prediction model by random forest, PE(MonoMe(11,5)/MonoMe(13,5)), N-feruloylserotonin and 2,6-di-tert-butylbenzoquinone showed good prediction effects. Differential metabolites were mainly concentrated in aldosterone synthesis and secretion, drug metabolism, nicotinate and nicotinamide metabolism pathways, which may be related to the occurrence and development of CHD.

CONCLUSION

This study provides a new database of CHD metabolic biomarkers and mechanistic research. These results need to be further verified in larger samples.