Intersectionality and risk for ischemic heart disease in Sweden: Categorical and anti-categorical approaches

Considerations for Subgroup Analyses in Cluster-Randomized Trials Based on Aggregated Individual-Level Predictors

In research assessing the effect of an intervention or exposure, a key secondary objective often involves assessing differential effects of this intervention or exposure in subgroups of interest; this is often referred to as assessing effect modification or heterogeneity of treatment effects (HTE). Observed HTE can have important implications for policy, including intervention strategies (e.g., will some patients benefit more from intervention than others?) and prioritizing resources (e.g., to reduce observed health disparities). Analysis of HTE is well understood in studies where the independent unit is an individual. In contrast, in studies where the independent unit is a cluster (e.g., a hospital or school) and a cluster-level outcome is used in the analysis, it is less well understood how to proceed if the HTE analysis of interest involves an individual-level characteristic (e.g., self-reported race) that must be aggregated at the cluster level. Through simulations, we show that only individual-level models have power to detect HTE by individual-level variables; if outcomes must be defined at the cluster level, then there is often low power to detect HTE by the corresponding aggregated variables. We illustrate the challenges inherent to this type of analysis in a study assessing the effect of an intervention on increasing COVID-19 booster vaccination rates at long-term care centers.

Intersectional inequalities in paediatric infectious diseases: a national cohort study in Sweden

BACKGROUND

It is well known that socially deprived children are more likely to be hospitalised for infections. Less is known about how different social disadvantages interact. Therefore, we examine intersectional inequalities in overall, upper respiratory, lower respiratory, enteric and genitourinary infections in the first 5 years of life.

METHODS

We conducted a population-based retrospective cohort study of Swedish children born between 1998 and 2015. Inequalities were examined using analysis of individual heterogeneity and discriminatory accuracy as the analytical framework. A variable with 60 intersectional strata was created by combining information on maternal education, household income, sex/gender and maternal migration status. We estimated the incidence rates of infectious disease hospitalisation for each intersectional strata and the associations between intersectional strata and infectious disease hospitalisations using logistic regression models. We furthermore quantified the discriminatory ability of the intersectional strata with respect to infectious disease hospitalisation.

RESULTS

The study included 1785 588 children and 318 080 hospital admissions. The highest overall incidence of hospitalisations for infections was found in boys born to low-educated mothers who lived in families with the lowest household income. The overall incidence of infections was unrelated to household income in children born to highly educated mothers. The ability of the intersectional strata to discriminate between children with and without infections was poor.

CONCLUSION

We found that inequalities in paediatric infectious diseases were shaped by the intersections of different social disadvantages. These inequalities should be addressed by public health policies that reach all children.

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.

Latent Profile Analysis of Canadian Military Veterans With Chronic Pain Identifies 5 Meaningful Classes Through Self-Report Measures

The purpose of this study was to identify meaningful response patterns in self-report survey data collected from Canadian military veterans with chronic pain and to create an algorithm intended to facilitate triage and prioritization of veterans to the most appropriate interventions. An online survey was presented to former members of the Canadian military who self-identified as having chronic pain. Variables collected were related to pain, physical and mental interference, prior traumatic experiences, and indicators from each of the 7 potential drivers of the pain experience. Maximum likelihood estimation-based latent profile analysis was used to identify clinically and statistically meaningful profiles using the 7-axis variables, and classification and regression tree (CRT) analysis was then conducted to identify the most parsimonious set of indicators that could be used to accurately classify respondents into the most relevant profile group. Data from N = 322 veterans were available for analysis. The results of maximum likelihood estimation-based latent profile analysis indicated a 5-profile structure was optimal for explaining the patterns of responses within the data. These were: Mood-Dominant (13%), Localized Physical (24%), Neurosensory-Dominant (33%), Central-Dominant with complex mood and neurosensory symptoms (16%), and Trauma- and mood-dominant (14%). From CRT analysis, an algorithm requiring only 3 self-report tools (central symptoms, mood screening, bodily coherence) achieved 83% classification accuracy across the 5 profiles. The new classification algorithm requiring 16 total items may be helpful for clinicians and veterans in pain to identify the most dominant drivers of their pain experience that may be useful for prioritizing intervention strategies, targets, and relevant health care disciplines. PERSPECTIVE: This article presents the results of latent profile (cluster) analysis of responses to standardized self-report questionnaires by Canadian military veterans with chronic pain. It identified 5 clusters that appear to represent different drivers of the pain experience. The results could be useful for triaging veterans to the most appropriate pain care providers.

Redefining global cardiac surgery through an intersectionality lens

Although cardiovascular diseases are the leading cause of morbidity and mortality worldwide, six billion people lack access to safe, timely and affordable cardiac surgical care when needed. The burden of cardiovascular disease and disparities in access to care vary widely based on sociodemographic characteristics, including but not limited to geography, sex, gender, race, ethnicity, indigeneity, socioeconomic status and age. To date, the majority of cardiovascular, global health and global surgical research has lacked intersectionality lenses and methodologies to better understand access to care at the intersection of multiple identities and traditions. As such, global (cardiac) surgical definitions and health system interventions have been rooted in reductionism, focusing, at most, on singular sociodemographic characteristics. In this article, we evaluate barriers in global access to cardiac surgery based on existing intersectionality themes and literature. We further examine intersectionality methodologies to study access to cardiovascular care and cardiac surgery and seek to redefine the definition of 'global cardiac surgery' through an intersectionality lens.

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.

Parkinson's Disease Inequities in Daily Cognitive Activities: An Intersectional Approach

Objective

Intersectionality approaches to examining differences in Parkinson's disease (PD) based on racialized group, gender identity, and socioeconomic status (SES) are not well covered in the literature. Additionally, the differences in daily cognitive activities for persons diagnosed with PD by racialized group, gender, and SES are undetermined. This study was conducted to explore the differences in PD daily cognitive activities for diverse racialized groups by gender and SES.

Methods

This study was a secondary analysis of the Michael J. Fox Foundation's Fox Insight online clinical dataset. Persons with PD were partitioned into 16 racialized by gender groups (Black women, Indigenous men, Latina/x women, Asian men, etc.) that were used in within-group comparisons of low-, middle-, and high-SES-a new variable comprising education and income.

Results

Intersectional analyses revealed most items differed between low-SES and high-SES except for items associated with Black and Indigenous men, for whom significant differential item functioning was found between mid-SES and high-SES.

Conclusions

These findings revealed that within-group differences exist and may be missed in research in which social factors are adjusted for instead of included in the model.

Socioeconomic disparities in attention deficit hyperactivity disorder (ADHD) in Sweden: An intersectional ecological niches analysis of individual heterogeneity and discriminatory accuracy (IEN-AIHDA)

We aimed (i) to gain a better understanding of the demographic and socioeconomical distribution of ADHD risk in Sweden; and (ii) to contribute to the critical discussion on medicalization, i.e., the tendency to define and treat behavioural and social problems as medical entities. For this purpose, we analysed the risk of suffering from ADHD in the whole Swedish population aged between 5 and 60 years, across 96 different strata defined by combining categories of gender, age, income, and country of birth. The stratified analysis evidenced considerable risk heterogeneity, with prevalence values ranging from 0.03% in high income immigrant women aged 50-59, to 6.18% in middle income immigrant boys aged 10-14. Our study questions the established idea that behavioural difficulties conceptualized as ADHD should be primarily perceived as a neurological abnormality. Rather, our findings suggest that there is a strong sociological component behind how some individuals become impaired and subject to medicalization.

The Influence of Sex, Age, and Race on Coronary Artery Disease: A Narrative Review

Cardiovascular disease has remained one of the leading causes of mortality in the world. The basic pathophysiology of coronary artery disease (CAD) is a reduction of the blood flow in coronary vessels, leading to restricted blood flow to the heart muscle. Both modifiable and non-modifiable risk factors contribute to its multifactorial etiology. The clinical presentation ranges from asymptomatic to typical symptoms like chest pain, shortness of breath, and left arm or jaw pain. The purpose of this review is to investigate and analyze the variation of CAD depending on the biological sex, age, race, or ethnicity and how it might differ in the studied population while comparing the symptoms and prognosis of CAD. For this research, PubMed's database was used. A total of 926 articles were selected using pre-determined inclusion and exclusion criteria, with 74 articles eligible to be included in the narrative review. Studies were selected from the general population of patients with CAD, regardless of their severity, stage of diagnosis, and treatment plan. The scale for the assessment of non-systematic review articles (SANRA) was used to assess the quality of the study. As humans age, the incidence of CAD increases, and people over 75 are more likely to have multiple-vessel CAD. It has been observed that South Asians have the highest rate of CAD at 24%, while the White population has the lowest at 8%. The prevalence of CAD also depends on race, with the White population having the lowest rate at 3.2%, followed by Hispanics at 5%, Black women at 5.2%, and Black men at 5.7%. Younger Black women tend to have more chest pain. Men with CAD commonly experience chest pain, and women are more likely to present with atypical symptoms. Modifiable risk factors such as smoking and alcoholism are more commonly observed in young men than in young women. Coronary artery disease in the elderly, female, minority, and Black patients is associated with a higher mortality rate. Acknowledging the prevalence of certain risk factors, signs, results, and responses to treatment in certain socio-demographic groups, as well as the provision and accessibility of diagnosis and treatment, would lead to a better outcome for all individuals. The impact of this shift can range from an earlier diagnosis of CAD to a faster and more customized treatment plan tailored to each patient's individual requirements.

Disparities in fruit and vegetable intake at the intersection of gender and education in northern Sweden: a cross-sectional study

BACKGROUND

Even though the existence of inequalities in fruit and vegetable consumption has been well established, it is not clear how it is patterned across intersections of multiple social positions and identities. This study aims to investigate disparities in fruit and vegetable intake between groups at the intersection of education and gender in northern Sweden, and to estimate the discriminatory accuracy of the intersectional groups.

METHODS

Cross-sectional data from the 2018 Health on Equal Terms survey conducted in four regions in northern Sweden was used (N = 21,853). Four intersectional groups were created: high and low educated men, and high and low educated women. Prevalence differences corresponding to joint, referent, and excess intersectional inequalities, were estimated for three outcomes: inadequate fruit and vegetable intake combined, inadequate fruit intake, and inadequate vegetable intake. The discriminatory accuracy of the intersectional groups was estimated by the area under the receiver operating characteristic curve.

RESULTS

Low educated men had the highest prevalence of inadequate intake of fruits and vegetables combined (81.4%), fruits (83.4%), and vegetables (84.9%), while high educated women had the lowest (47.7, 60.2, and 51.8%, respectively). The joint disparities between high educated women and low educated men were both significant and substantial for all outcomes (34.6 percentage points (pp.), 25.2 pp., and 31.2 pp., adjusted), although differences in magnitude were noted between fruit and vegetable intake. The joint disparities were mostly explained by the two referent disparities for gender and education. The excess intersectional disparity - the part of the joint disparity not explained by either referent disparity - was negative for all three outcomes (-5.5 pp., - 4.2 pp., and - 4.6 pp. respectively, adjusted). The discriminatory accuracy of the intersectional groups was moderate (0.67, 0.65, and 0.68 respectively).

CONCLUSIONS

An intersectional approach can provide a more detailed view of inequalities in fruit and vegetable consumption between groups combining several social positions. The moderate discriminatory accuracy observed here suggests that interventions and policies aiming to reduce diet inequalities should not solely be targeted at certain groups, but also be universal.

Ideal cardiovascular health at ELSA-Brasil: non-additivity effects of gender, race, and schooling by using additive and multiplicative interactions

This study aims to assess the non-additivity effects of gender, race, and schooling on ideal cardiovascular health among participants of the Brazilian Longitudinal Study of Adult Health - ELSA-Brasil. This is a cross-sectional study using data from the baseline of ELSA-Brasil, conducted from 2008 to 2010. The American Heart Association defined a score of ideal cardiovascular health (ICH) as the sum of indicators for the presence of seven favorable health factors and behaviors: non-smoking, ideal body mass index, physical activity and healthy diet, adequate levels of total cholesterol, normal blood pressure, and absence of diabetes mellitus. Multiplicative and additive interactions between gender, race, and schooling were assessed using the Poisson regression model to discuss intersectionality. The mean cardiovascular health score was 2.49 (SD = 1.31). This study showed a positive interaction between gender and schooling (women with high school and higher education) in both additive and multiplicative scales for the score of ideal cardiovascular health. We observed a trend towards higher mean values of cardiovascular health for increased schooling, with a marked difference among women. The lowest cardiovascular health scores observed reinforce the importance of understanding the psychosocial experiences that influence health attitudes, access to health care, and healthy lifestyle choices, which affect ICH, to reduce inequities in health and propose more adequate public policies that assist and prevent cardiovascular diseases.

Decomposing intersectional inequalities in subjective physical and mental health by sex, gendered practices and immigration status in a representative panel study from Germany

Background

The mapping of immigration-related health inequalities remains challenging, since immigrant populations constitute a heterogenous socially constructed group whose health experiences differ by social determinants of health. In spite of the increasing awareness that population mobility and its effects on health are highly gendered, an explicit gender perspective in epidemiology is often lacking or limited.

Methods

To map inequalities in self-reported physical and mental health in Germany at the intersections of sex, gendered practices and immigration status, we used data from the German Socioeconomic Panel (SOEP) and applied an intercategorical intersectional approach conducting multilevel linear regression models. We differentiated between sex (male/female) as reported in the survey and gendered social practices, quantified through a gender score (on a femininity-masculinity continuum).

Results

We included 20,897 participants in our analyses. We saw an intersectional gradient for physical and mental health. Compared to the reference group, i.e. non-immigrant males with masculine gendered practices, physical and mental health steadily decreased in the intersectional groups that did not embody one or more of these social positions. The highest decreases in health were observed in the intersectional group of immigrant females with feminine gendered practices for physical health (-1,36; 95% CI [-2,09; -0,64]) and among non-immigrant females with feminine practices for mental health (-2,51; 95% CI [-3,01; -2,01]).

Conclusions

Patterns of physical and mental health vary along the intersectional axes of sex, gendered practices and immigration status. These findings highlight the relevance of intersections in describing population health statuses and emphasise the need to take them into account when designing public health policies aiming at effectively reducing health inequalities.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-13022-1.

Mapping mental health inequalities: The intersecting effects of gender, race, class, and ethnicity on ADHD diagnosis

While the effects of social stratification by gender, race, class, and ethnicity on health inequalities are well-documented, our understanding of the intersecting consequences of these social dimensions on diagnosis remains limited. This is particularly the case in studies of mental health, where "paradoxical" patterns of stratification have been identified. Using a Bayesian multi-level random-effects Poisson model and a nationally representative random sample of 138,009 households from the National Survey of Children's Health, this study updates and extends the literature on mental health inequalities through an intersectional investigation of one of the most commonly diagnosed psychiatric conditions of childhood/adolescence: attention-deficit hyperactivity disorder (ADHD). Findings indicate that gender, race, class, and ethnicity combine in mutually constitutive ways to explain between-group variation in ADHD diagnosis. Observed effects underscore the importance and feasibility of an intersectional, multi-level modelling approach and data mapping technique to advance our understanding of social subgroups more/less likely to be diagnosed with mental health conditions.

Conceptual foundations of organizational structure: re-structuring of women's health services

PURPOSE

Researchers often debate health service structure. Understanding of the practical implications of this debate is often limited by researchers' neglect to integrate participants' views on structural options with discourses those views represent. As a case study, this paper aims to discern the extent to which and how conceptual underpinnings of stakeholder views on women's health contextualize different positions in the debate over the ideal structure of health services.

DESIGN/METHODOLOGY/APPROACH

The researchers chose a self-standing, comprehensive women's health service facing the prospect of being dispersed into "mainstream" health services. The researchers gathered perspectives of 53 professional and consumer stakeholders in ten focus groups and seven semi-structured interviews, analyzed through inductive thematic analysis.

FINDINGS

"Women's marginalization" was the core theme of the debate over structure. The authors found clear patterns between views on the function of women's health services, women's health needs, ideal client group, ideal health service structure and particular feminist discourses. The desire to re-organize services into separate mainstream units reflected a liberal feminist discourse, conceiving marginalization as explicit demonstration of its effects, such as domestic abuse. The desire to maintain a comprehensive women's health service variously reflected post-structural feminism's emphasis on plurality of identities, and a radical feminist discourse, holding that womanhood itself constituted a category of marginalization - that is, merely being at risk of unmet health needs.

ORIGINALITY/VALUE

As a contribution to health organizational theory, the paper shows that the discernment of discursive underpinnings of particular stakeholder views can clarify options for the structure of health services.

The intersection race/skin color and gender, smoking and excessive alcohol consumption: cross sectional analysis of the Brazilian National Health Survey, 2013

This study aims to investigate whether the intersectional identities defined by race/skin color and gender are associated with smoking and excessive consumption of alcohol in a representative sample of Brazilian adults. This is a cross-sectional study with 48,234 participants in the Brazilian National Health Survey (PNS) - 2013. Crude and adjusted odds ratios (OR) and respective 95% confidence intervals (95%CI) were used to estimate the associations of intersectional categories of race/skin color and gender (white woman, brown woman, black woman, white man, brown man, black man) with smoking and excessive consumption of alcohol, based on the combination of weekly "days" and "servings". The prevalence of smoking varied from 10.6% for white women to 23.1% for black men, while the prevalence of elevated consumption of alcohol ranged from 3.3% to 14%, respectively. In comparison to white women, only white, brown, and black men presented greater chances of smoking, reaching the OR of 2.04 (95%CI: 1.66-2.51) in black men. As to excessive consumption of alcohol, all intersectional categories showed greater chances of consumption than white women, with the greatest magnitude in black men (OR = 4.78; 95%CI: 3.66-6.23). These associations maintained statistical significance after adjustments made for sociodemographic, behavioral, and health characteristics. Results demonstrated differences in smoking habit and excessive consumption of alcohol when the intersectional categories were compared to traditional analyses. These findings reinforce the significance of including intersectionality of race/skin color and gender in epidemiological studies.

An investigation of quantitative methods for assessing intersectionality in health research: A systematic review

Intersectionality is a theoretical framework that investigates how interlocking systems of power and oppression at the societal level influence the lived experiences of historically and socially marginalized groups. Currently, there are no consistent or widely adopted quantitative methods to investigate research questions informed by intersectionality theory. The objective of this systematic review is to describe the current landscape of quantitative methods used to assess intersectionality and to provide recommendations on analytic best practices for future research. We searched PubMed, EMBASE, and the Web of Science in December 2019 to identify studies using analytic quantitative intersectionality approaches published up to December 2019 (PROSPERO CRD42020162686). To be included in the study, articles had to: (1) be empirical research, (2) use a quantitative statistical method, (3) be published in English, and (4) incorporate intersectionality. Our initial search yielded 1889 articles. After screening by title/abstract, methods, and full text review, our final analytic sample included 153 papers. Eight unique classes of quantitative methods were identified, with the majority of studies employing regression with an interaction term. We additionally identified several methods which appear to be at odds with the key tenets of intersectionality. As quantitative intersectionality continues to expand, careful attention is needed to avoid the dilution of the core tenets. Specifically, emphasis on social power is needed as methods continue to be adopted and developed. Additionally, clear explanation of the selection of statistical approaches is needed and, when using regression with interaction terms, researchers should opt for use of the additive scale. Finally, use of methods that are potentially at odds with the tenets of intersectionality should be avoided.

Potential of Standard Perinatal Data for Measuring Violation of Birth Integrity

Background: Measuring the phenomenon of violation of birth integrity (vBI) (e.g., obstetric violence) relies in part on the availability and content of maternity care providers' data. The population coverage and linkage possibilities that these data provide make for a yet untapped potential. Although vBI is a complex phenomenon best measured with dedicated instruments, we argue that maternity care providers' data could contribute to enhance our knowledge of the manifestations and frequency of vBI, and allow for analyses across different sub-groups of the population. Looking into the German standardized perinatal data, we investigate which variables are relevant to vBI-related research, and how complete their reporting is.

Methods: First, we analyse state-of-the-art frameworks and recommendations, and, for each vBI-related domain, we search for and list corresponding variables in the perinatal data which could contribute to a better understanding of vBI issues. Second, we use an example and analyse the content of perinatal data obtained between 2013 and 2016 in the context of the BaBi birth cohort study set in Bielefeld, Germany. We use descriptive statistics to assess the completeness of the data.

Results: The vBI-related variables can be classified in three main categories: discrimination based on specific patient socio-demographic attributes (e.g., height and weight to calculate BMI before pregnancy, foreign origin), indication for medical interventions (i.e., medicalization-related variables: indication for cesarean sections and induction), and supportive care, in particular the mobilization dimension (e.g., continuous fetal heartbeat monitoring). The data analyses included 876 births, of which 601 were vaginal birth. We found poor reporting on demographic variables in terms of completeness. Medicalization and mobilization variables are better documented, although limited in scope.

Conclusions: Putting more emphasis on the completeness of standardized data could increase their potential for vBI-related research. Perinatal data alone are insufficient to assess vBI, but a broader, theory-informed discussion of indicators to be included in standardized datasets would contribute to capturing the different aspects of integrity violation in a more systematic way and expand the evidence-base on different types of vBI.

Intersectional inequalities in mental health across multiple dimensions of inequality in the Swedish adult population

Intersectionality has recently gained traction in health inequality research emphasizing multiple intersecting dimensions of inequality as opposed to the traditional unidimensional approaches. In this study inequalities in mental health were estimated across intersections of gender, income, education, occupation, country of birth, and sexual orientation. The outcomes and inequalities of intersectional strata were disentangled analogously to the possibilities described by intersectionality theory; as a result of either of the two inequality dimensions, as a result of the sum the dimensions, or as a unique outcome not equaling the sum. Furthermore the study examined the discriminatory accuracy of the six inequality dimensions as well as the intersectional space comprising 64 strata. The study population (N = 52,743) consists of a yearly random sample of the Swedish population 26-84 years between 2010 and 2015, from The Health on Equal Terms survey. Mental health was measured through a self-administered General Health Questionnaire (GHQ)-12, and sociodemographics through survey and linked register data. Intersectional inequalities in mental health were estimated for all pairwise combinations of inequality dimensions by joint inequalities, excess intersectional inequalities and referent inequalities. The findings of the study found that the sum of dimensions contributed to the overall (joint) inequality in mental health rather than a reinforced adverse effect of multiple disadvantages or the contribution by a single dimension. Nevertheless, the dimension of income was found to be the most important in terms of relative contribution. The discriminatory accuracy was low indicating that policy action targeting mental health should be universal rather than focusing on particular groups. The results highlight the unpredictable inequality patterns revealed by an intersectional approach, even for a single health outcome and within one country, and illustrate the need for empirical investigations into the actual population patterns in health that appear in the intersections of multiple disadvantages.

Social inequalities in the associations between urban green spaces, self-perceived health and mortality in Brussels: Results from a census-based cohort study

This study examines the associations between residential urban green spaces (UGS) and self-perceived health and natural cause mortality, applying an intersectional approach across gender, education and migrant background. We used data from the 2001 Belgian census linked to register data on emigration and mortality for the period 2001-2014, including 571,558 individuals aged 16-80 residing in Brussels (80% response rate). Residential UGS were assessed with the Normalized Difference Vegetation Index (NDVI) within a 300 m buffer from the residential address and perceived neighbourhood greenness. Multilevel logistic and Cox proportional hazards regression models were conducted to estimate associations between UGS and poor self-perceived health at baseline and natural cause mortality during follow-up. Residential UGS were inversely associated with both outcomes, but there were differences between groups. The strongest beneficial associations among women were found in the lower educated, regardless of their migrant background. For men the strongest association was found in those with tertiary education and Belgian origin. No significant beneficial associations were found in men originating from low and middle-income countries. Applying an intersectionality approach is crucial to understand health inequalities related to UGS exposure. Further research in different geographical contexts is needed to contrast our findings.

Intersectionality in quantitative research: A systematic review of its emergence and applications of theory and methods

Background

Intersectionality is a theoretical framework rooted in the premise that human experience is jointly shaped by multiple social positions (e.g. race, gender), and cannot be adequately understood by considering social positions independently. Used widely in qualitative studies, its uptake in quantitative research has been more recent.

Objectives

To characterize quantitative research applications of intersectionality from 1989 to mid-2020, to evaluate basic integration of theoretical frameworks, and to identify innovative methods that could be applied to health research.

Methods

Adhering to PRISMA guidelines, we conducted a systematic review of peer-reviewed articles indexed within Scopus, Medline, ProQuest Political Science and Public Administration, and PsycINFO. Original English-language quantitative or mixed-methods research or methods papers that explicitly applied intersectionality theoretical frameworks were included. Experimental studies on perception/stereotyping and measures development or validation studies were excluded. We extracted data related to publication, study design, quantitative methods, and application of intersectionality.

Results

707 articles (671 applied studies, 25 methods-only papers, 11 methods plus application) met inclusion criteria. Articles were published in journals across a range of disciplines, most commonly psychology, sociology, and medical/life sciences; 40.8% studied a health-related outcome. Results supported concerns among intersectionality scholars that core theoretical tenets are often lost or misinterpreted in quantitative research; about one in four applied articles (26.9%) failed to define intersectionality, while one in six (17.5%) included intersectional position components not reflective of social power. Quantitative methods were simplistic (most often regression with interactions, cross-classified variables, or stratification) and were often misapplied or misinterpreted. Several novel methods were identified.

Conclusions

Intersectionality is frequently misunderstood when bridging theory into quantitative methodology. Further work is required to (1) ensure researchers understand key features that define quantitative intersectionality analyses, (2) improve reporting practices for intersectional analyses, and (3) develop and adapt quantitative methods.

•

Multidisciplinary statistical methods for intersectional research were identified.

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Regression with intersectional variables was the most frequently used method.

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Decomposition analyses and decision trees were identified as promising methods.

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Intersectionality theory and methods literature was infrequently cited in studies.

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Improvements in reporting of quantitative intersectional research are needed.

Applying intersectionality theory in health promotion research and practice

Health promotion researchers and practitioners are grappling with how to address growing health inequalities for population groups. In particular, critiques of dominant behaviour change approaches draw attention to the need to engage with social theories to better understand the social and relational drivers of health. Public health researchers are increasingly acknowledging intersectionality as an important theoretical approach, providing a framework for investigating health inequalities by highlighting intersections of individuals' multiple identities within social systems of power that compound and exacerbate experiences of ill health. This article provides an overview of the diverse ways public health researchers and practitioners have applied intersectionality theory to better understand and address health inequalities. We map three key applications of intersectionality theory in public health: as an epistemological approach, as a methodological approach, and as a tool for action and intervention. Drawing on this work, we argue that health promotion researchers and practitioners can enhance engagement with intersectionality theory to address important challenges within the field. Through this article, we aim to inspire the continued exploration of intersectionality and offer some insights into opportunities and challenges for doing so in health promotion.

Intersectionality and heart failure: what clinicians and researchers should know and do

PURPOSE OF REVIEW

To review the application of intersectionality to heart failure. Intersectionality refers to the complex ways in which disenfranchisement and privilege intersect to reproduce and influence health and social outcomes.

RECENT FINDINGS

Intersectionality challenges approaches that focus on a single or small number of socio-demographic characteristics, such as sex or age. Instead, approaches should take account of the nature and effects of a full range of socio-demographic factors linked to privilege, including: race and ethnicity, social class, income, age, gender identity, disability, geography, and immigration status. Although credible and well established across many fields - there is limited recognition of the effects of intersectionality in research into heart disease, including heart failure. This deficiency is important because heart failure remains a common and burdensome syndrome that requires complex pharmacological and nonpharmacological care and collaboration between health professionals, patients and caregivers during and at the end-of-life.

SUMMARY

Approaches to heart failure clinical care should recognize more fully the nature and impact of patients' intersectionality- and how multiple factors interact and compound to influence patients and their caregivers' behaviours and health outcomes. Future research should explicate the ways in which multiple factors interact to influence health outcomes.

An intersectional analysis providing more precise information on inequities in self-rated health

Background

Intersectionality theory combined with an analysis of individual heterogeneity and discriminatory accuracy (AIHDA) can facilitate our understanding of health disparities. This enables the application of proportionate universalism for resource allocation in public health. Analyzing self-rated health (SRH) in Sweden, we show how an intersectional perspective allows for a detailed mapping of health inequalities while avoiding simplification and stigmatization based on indiscriminate interpretations of differences between group averages.

Methods

We analyzed participants (n=133,244) in 14 consecutive National Public Health Surveys conducted in Sweden in 2004–2016 and 2018. Applying AIHDA, we investigated the risk of bad SRH across 12 intersectional strata defined by gender, income and migration status, adjusted by age and survey year. We calculated odds ratios (with 95% confidence intervals) to evaluate between-strata differences, using native-born men with high income as the comparison reference. We calculated the area under the receiver operating characteristic curve (AU-ROC) to evaluate the discriminatory accuracy of the intersectional strata for identifying individuals according to their SRH status.

Results

The analysis of intersectional strata showed clear average differences in the risk of bad SRH. For instance, the risk was seven times higher for immigrated women with low income (OR 7.00 [95% CI 6.14–7.97]) than for native men with high income. However, the discriminatory accuracy of the intersectional strata was small (AU-ROC=0.67).

Conclusions

The intersectional AIHDA approach provides more precise information on the existence (or the absence) of health inequalities, and can guide public health interventions according to the principle of proportionate universalism. The low discriminatory accuracy of the intersectional strata found in this study warrants universal interventions rather than interventions exclusively focused on strata with a higher average risk of bad SRH.

Socio-economic disparities in the dispensation of antibiotics in Sweden 2016-2017: An intersectional analysis of individual heterogeneity and discriminatory accuracy

Aims: Antimicrobial resistance presents an increasingly serious threat to global public health, which is directly related to how antibiotic medication is used in society. Actions aimed towards the optimised use of antibiotics should be implemented on equal terms and according to the needs of the population. Previous research results on differences in antibiotic use between socio-economic and demographic groups in Sweden are not entirely coherent, and have typically focused on the effects of singular socio-economic variables. Using an intersectional approach, this study provides a more precise analysis of how the dispensation of antibiotic medication was distributed across socio-economic and demographic groups in Sweden in 2016–2017. Methods: Using register data from a nationwide cohort and adopting an intersectional analysis of individual heterogeneity and discriminatory accuracy, we map the dispensation of antibiotics according to age, sex, country of birth and income. Results: While women and high-income earners had the highest antibiotic dispensation prevalence, no large differences in the dispensation of antibiotics were identified between socio-economic groups. Conclusions: Public-health interventions aiming to support the reduced and optimised use of antibiotics should be directed towards the whole Swedish population rather than towards specific groups. Correspondingly, an increased focus on socio-economic or demographic factors is not warranted in interventions aimed at improving antibiotic prescription patterns among medical practitioners.

Systematic review of methods used to study the intersecting impact of sex and social locations on health outcomes

PURPOSE

Independent health impacts of sex or social circumstances are well-studied, particularly among older adults. Less theorized or examined is how combinations or intersections of these underpin differential health effects. Nevertheless, and often without naming it as such, an intersectional framework aligns with studies of social determinants of health, life-course epidemiology and eco-epidemiology. In this systematic review we examined and aimed to identify research methods used to operationalize, whether intentionally or inadvertently, interconnected effects of sex and social locations on health outcomes for 45+ year olds.

METHODS

Using broad search terms, numerous databases, and following Prisma guidelines, 732 of 9214 papers initially identified, met inclusion criteria for full review.

RESULTS

Of the 501 papers included after full review, methods used in considering intersections of sex and social circumstances/location(s) included regression (112 of 365 papers), growth curves (7 of 22), multilevel (15 of 25), decomposition (6 of 9), mediation (10 of 17), structural equation modelling (23 of 25), and other (2 of 3). Most (n = 157) approximated intersectional analyses by including interaction terms or sex-stratifying results.

DISCUSSION

Few authors used the inherent strength of some study methods to examine intersecting traits. As even fewer began with an intersectionality framework their subsequent failure to deliver cannot be faulted, despite many studies including data and methodologies that would support intersectional analyses. There appeared to be a gap, not in analytic potential but rather in theorizing that differential distributions of social locations describe heterogeneity within the categories 'men' and 'women' that can underlie differential, gendered effects on older adults' health. While SEM, mediation and decomposition analyses emerged as particularly robust methods, the unexpected outcome was finding how few researchers consider intersectionality as a potential predictor of health.

Non-response in a national health survey in Germany: An intersectionality-informed multilevel analysis of individual heterogeneity and discriminatory accuracy

BACKGROUND

Dimensions of social location such as socioeconomic position or sex/gender are often associated with low response rates in epidemiological studies. We applied an intersectionality-informed approach to analyze non-response among population strata defined by combinations of multiple dimensions of social location and subjective health in a health survey in Germany.

METHODS

We used data from the cross-sectional sample of the German Health Interview and Examination Survey for Adults (DEGS1) conducted between 2008 and 2011. Information about non-responders was available from a mailed non-responder questionnaire. Intersectional strata were constructed by combining all categories of age, sex/gender, marital status, and level of education in scenario 1. Subjective health was additionally used to construct intersectional strata in scenario 2. We applied multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) to calculate measures of discriminatory accuracy, proportions of non-responders among intersectional strata, as well as stratum-specific total interaction effects (intersectional effects). Markov chain Monte Carlo methods were used to estimate multilevel logistic regression models.

RESULTS

Data was available for 6,534 individuals of whom 36% were non-responders. In scenario 2, we found weak discriminatory accuracy (variance partition coefficient = 3.6%) of intersectional strata, while predicted proportions of non-response ranged from 20.6% (95% credible interval (CI) 17.0%-24.9%) to 57.5% (95% CI 48.8%-66.5%) among intersectional strata. No evidence for intersectional effects was found. These results did not differ substantially between scenarios 1 and 2.

CONCLUSIONS

MAIHDA revealed that proportions of non-response varied widely between intersectional strata. However, poor discriminatory accuracy of intersectional strata and no evidence for intersectional effects indicate that there is no justification to exclusively target specific intersectional strata in order to increase response, but that a combination of targeted and population-based measures might be appropriate to achieve more equal representation.

Precision public health: Mapping socioeconomic disparities in opioid dispensations at Swedish pharmacies by Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA)

BACKGROUND

In light of the opioid epidemic in the United States, there is growing concern about the use of opioids in Sweden as it may lead to misuse and overuse and, in turn, severe public health problems. However, little is known about the distribution of opioid use across different demographic and socioeconomic dimensions in the Swedish general population. Therefore, we applied an intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA), to obtain an improved mapping of the risk heterogeneity of and socioeconomic inequalities in opioid prescription receipt.

METHODS AND FINDINGS

Using data from 6,846,106 residents in Sweden aged 18 and above, we constructed 72 intersectional strata from combinations of gender, age, income, cohabitation status, and presence or absence of psychological distress. We modelled the absolute risk (AR) of opioid prescription receipt in a series of multilevel logistic regression models distinguishing between additive and interaction effects. By means of the Variance Partitioning Coefficient (VPC) and the area under the receiver operating characteristic curve (AUC), we quantified the discriminatory accuracy (DA) of the intersectional strata for discerning those who received opioid prescriptions from those who did not. The AR of opioid prescription receipt ranged from 2.77% (95% CI 2.69-2.86) among low-income men aged 18-34, living alone, without psychological distress, to 28.25% (95% CI 27.95-28.56) among medium-income women aged 65 and older, living alone, with psychological distress. In a model that conflated both additive and interaction effects, the intersectional strata had a fair DA for discerning opioid users from non-users (VPC = 13.2%, AUC = 0.68). However, in the model that decomposed total effects into additive and interaction effects, the VPC was very low (0.42%) indicating the existence of small interaction effects for a number of the intersectional strata.

CONCLUSIONS

The intersectional MAIHDA approach aligns with the aims of precision public health, through improving the evidence base for health policy by increasing understanding of both health inequalities and individual heterogeneity. This approach is particularly relevant for socioeconomically conditioned outcomes such as opioid prescription receipt. We have identified intersections of social position within the Swedish population at greater risk for opioid prescription receipt.

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.

Languishing in the crossroad? A scoping review of intersectional inequalities in mental health

The concept of intersectionality has gradually been introduced to health inequality research, adding depth and breadth to the way inequalities in health are approached. We conducted a scoping review with the purpose to systematically map, describe and analyze the literature about intersectional inequalities in mental health. For eligibility, the study had to analyze and report inequality defined by combinations of socioeconomic position, gender, race/ethnicity, sexual orientation or religion. The mental health outcome had to be measured as self-reported symptoms assessed through validated scales, or disorders assessed through diagnostic interviews. The search strategy was applied in two databases and the screening process yielded 20 studies. The interaction of intersectional positions showed no consistent patterns in mental health across studies, but both synergistic and antagonistic effects were observed. In most studies an absolute measure of inequality was used and few studies analyzed factors potentially explaining the intersectional inequalities. Taken together, the findings of this review highlight the value of assessing intersectional inequalities across population groups for priority setting and action on mental health inequalities.

Changing rate orders of race-gender heart disease death rates: An exploration of county-level race-gender disparities

A holistic view of racial and gender disparities that simultaneously compares multiple groups can suggest associated underlying contextual factors. Therefore, to more comprehensively understand temporal changes in combined racial and gender disparities, we examine variations in the orders of county-level race-gender specific heart disease death rates by age group from 1973-2015. We estimated county-level heart disease death rates by race, gender, and age group (35-44, 45-54, 55-64, 65-74, 75-84, ≥ 85, and ≥ 35) from the National Vital Statistics System of the National Center for Health Statistics from 1973-2015. We then ordered these rates from lowest to highest for each county and year. The predominant national rate order (i.e., white women (WW) < black women (BW) < white men (WM) < black men (BM)) was most common in younger age groups. Inverted rates for black women and white men (WW Collapse

Key Words

• BM, black men
• BW, black women
• CDC, Centers for Disease Control and Prevention
• County-level
• Gender
• Heart disease mortality
• ICD, International Classification of Diseases
• MCMC, Markov chain Monte Carlo
• NCHS, National Center for Health Statistics
• Race
• Spatiotemporal
• Temporal trends
• WM, white men
• WW, white women

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MESH Headings Collapse

Grants

• K01 HD074726 NICHD NIH HHS

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Affiliation(s)

Adam S. Vaughan Division for Heart Disease and Stroke Prevention, Centers for Disease Control and Prevention, 4770 Buford Hwy NE, Atlanta, GA 30341, United States
Harrison Quick Department of Epidemiology and Biostatistics, Dornsife School of Public Health, Drexel University, Nesbitt Hall, 3215 Market St., Philadelphia, PA 19104, United States
Linda Schieb Division for Heart Disease and Stroke Prevention, Centers for Disease Control and Prevention, 4770 Buford Hwy NE, Atlanta, GA 30341, United States
Michael R. Kramer Department of Epidemiology, Rollins School of Public Health, Emory University, 1518 Clifton Road NE, Atlanta, GA 30322, United States
Herman A. Taylor Cardiovascular Research Institute, Morehouse School of Medicine, 720 Westview Drive, Atlanta, GA 30310, United States
Michele Casper Division for Heart Disease and Stroke Prevention, Centers for Disease Control and Prevention, 4770 Buford Hwy NE, Atlanta, GA 30341, United States

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Intersectional decomposition analysis with differential exposure, effects, and construct

In recent years a wide array of proposals for bringing intersectional perspectives into quantitative studies of health disparities have appeared, from studies of interaction, predictive discrimination, to mediation. Bauer and Scheim, in a companion set of articles, extend these proposals by developing new attribution-blind measures of perceived discrimination and using VanderWeele's 3-way decomposition to quantify its contribution to disparities through differential exposure and differential effects (sometimes called differential vulnerability or susceptibility). In this commentary, after providing an overview of causal inference interpretations with social characteristics, we provide a broad overview of old and new decomposition methods in the social sciences literature and contrast their strengths and weaknesses for studying intersectional inequalities. We then examine how different forms of differential effects can be expressed within these decompositions and discuss their utility for the purpose of informing interventions for reducing disparities. Last, we discuss the tension in social sciences research when prominent explanatory variables represent constructs that are only defined or exist for certain marginalized populations and may not neatly fit within the decomposition methods framework. Through these discussions, we aim to provide greater conceptual clarity for applied researchers who are interested in using decomposition methods and other approaches to advance intersectional equity.

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.

Understanding the complexity of socioeconomic disparities in type 2 diabetes risk: a study of 4.3 million people in Sweden

OBJECTIVE

Investigating demographic and socioeconomic factors as intersecting rather than as separate dimensions may improve our understanding of the heterogeneous distribution of type 2 diabetes in the population. However, this complexity has scarcely been investigated and we still do not know the accuracy of these factors for predicting type 2 diabetes. Improved understanding of the demographic and socioeconomic disparities predicting type 2 diabetes risk in the population would contribute to more precise and effective public health interventions.

RESEARCH DESIGN AND METHODS

We analyzed the risk of type 2 diabetes among 4 334 030 individuals aged 40-84 years who by 2010 had resided in Sweden for at least 5 years. We stratified the study population into 120 strata defined by categories of age, gender, income, education, and immigration status. We calculated measures of absolute risk (prevalence) and relative risk (prevalence ratio), and quantified the discriminatory accuracy of the information for predicting type 2 diabetes in the population.

RESULTS

The distribution of type 2 diabetes risk in the population was highly heterogeneous. For instance, immigrated men aged 70-79 years with low educational achievement and low income had a risk around 32 times higher than native women aged 40-49 years with high income and high educational achievement (ie, 17.6% vs 0.5%). The discriminatory accuracy of the information was acceptable.

CONCLUSION

A more detailed, intersectional mapping of socioeconomic and demographic distribution of type 2 diabetes can assist in public health management aiming to reduce the prevalence of the disease.

Socioeconomic differences in body mass index in Spain: An intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy

Many studies have demonstrated the existence of simple, unidimensional socioeconomic gradients in body mass index (BMI). However, in the present paper we move beyond such traditional analyses by simultaneously considering multiple demographic and socioeconomic dimensions. Using the Spanish National Health Survey 2011–2012, we apply intersectionality theory and multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) to analyze 14,190 adults nested within 108 intersectional strata defined by combining categories of gender, age, income, educational achievement and living situation. We develop two multilevel models to obtain information on stratum-specific BMI averages and the degree of clustering of BMI within strata expressed by the intra-class correlation coefficient (ICC). The first model is a simple variance components analysis that provides a detailed mapping of the BMI disparities in the population and measures the accuracy of stratum membership to predict individual BMI. The second model includes the variables used to define the intersectional strata as a way to identify stratum-specific interactions. The first model suggests moderate but meaningful clustering of individual BMI within the intersectional strata (ICC = 12.4%). Compared with the population average (BMI = 26.07 Kg/m2), the stratum of cohabiting 18-35-year-old females with medium income and high education presents the lowest BMI (-3.7 Kg/m2), while cohabiting 36-64-year-old females with low income and low education show the highest BMI (+2.6 Kg/m2). In the second model, the ICC falls to 1.9%, suggesting the existence of only very small stratum specific interaction effects. We confirm the existence of a socioeconomic gradient in BMI. Compared with traditional analyses, the intersectional MAIHDA approach provides a better mapping of socioeconomic and demographic inequalities in BMI. Because of the moderate clustering, public health policies aiming to reduce BMI in Spain should not solely focus on the intersectional strata with the highest BMI, but should also consider whole population polices.

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.

Intra-Ethnic Racial Differences in Waterpipe Tobacco Smoking among Latinos?

BACKGROUND AND OBJECTIVES

This study examined differences in waterpipe smoking (both lifetime and current) by race and ethnicity. More specifically, we evaluated intra-ethnic racial differences among Latinos using a nationally representative sample.

METHODS

Pooled data from the National Adult Tobacco Survey (NATS) [2012-2014] was used, in which Log-Poisson multivariable regression models were deployed to determine the prevalence of waterpipe smoking behavior. Models were stratified by gender and we further investigated acculturation, controlling for relevant sociodemographic characteristics.

RESULTS

In fully-adjusted models assessing lifetime WTS, Black Latinos and White Latinos exhibited an increase prevalence of lifetime WTS compared to their non-Hispanic white counterparts. Once stratifying by gender, Black Latino men (PR = 1.49; 95% CI = 1.16, 1.90) exhibited increased prevalence of lifetime WTS compared to their non-Hispanic white men counterparts; although white Latino men (PR = 0.88; 95% CI = 0.80, 0.98) exhibited decreased prevalence compared to their non-Hispanic white male counterparts. Similar trends were found for current WTS among men. In fully adjusted models assessing lifetime WTS, among women, only white Latina's (PR = 1.23; 95% CI = 1.04, 1.46) exhibited increased prevalence compared to their non-Hispanic white women counterparts. When evaluating current WTS, Black Latina's (PR = 2.19; 95% CI = 1.32, 3.65) and white Latinas (PR = 1.28; 95% CI = 1.00, 1.63) exhibited increased prevalence of WTS compared to their non-Hispanic white women counterparts. Conclusions/Importance: Among the U.S. general adult population, intra-ethnic racial differences in WTS behaviors exist among Latinos; and is shaped by gender. Future efforts to eliminate racial disparities in WTS should be attentive intra-ethnic racial differences among Latinos.

Chronic Obstructive Pulmonary Disease in Sweden: An intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy

Socioeconomic, ethnic and gender disparities in Chronic Obstructive Pulmonary Disease (COPD) risk are well established but no studies have applied multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) within an intersectional framework to study this outcome. We study individuals at the first level of analysis and combinations of multiple social and demographic categorizations (i.e., intersectional strata) at the second level of analysis. Here we used MAIHDA to assess to what extent individual differences in the propensity of developing COPD are at the intersectional strata level. We also used MAIHDA to determine the degree of similarity in COPD incidence of individuals in the same intersectional stratum. This leads to an improved understanding of risk heterogeneity and of the social dynamics driving socioeconomic and demographic disparities in COPD incidence. Using data from 2,445,501 residents in Sweden aged 45–65, we constructed 96 intersectional strata combining categories of age, gender, income, education, civil- and migration status. The incidences of COPD ranged from 0.02% for young, native males with high income and high education who cohabited to 0.98% for older native females with low income and low education who lived alone. We calculated the intra-class correlation coefficient (ICC) that informs on the discriminatory accuracy of the categorizations. In a model that conflated additive and interaction effects, the ICC was good (20.0%). In contrast, in a model that measured only interaction effects, the ICC was poor (1.1%) suggesting that most of the observed differences in COPD incidence across strata are due to the main effects of the categories used to construct the intersectional matrix while only a minor share of the differences are attributable to intersectional interactions. We found conclusive interaction effects. The intersectional MAIHDA approach offers improved information to guide public health policies in COPD prevention, and such policies should adopt an intersectional perspective.

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We use multilevel analysis of individual heterogeneity and discriminatory accuracy.

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There is a clear difference in COPD incidence between intersectional strata.

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Intersectionality improves mapping of socioeconomic differences in COPD incidence.

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Preventive measures should be based on intersectional rather than classic analyses.

Sociodemographic patterns in pharmacy dispensing of medications for erectile dysfunction in Sweden

Purpose

The purpose of this study is to investigate the relationship between sociodemographic factors and pharmacy dispensing of medications for erectile dysfunction (ED) in the general population of middle-aged and elderly men. By considering a number of medical conditions that could promote or contraindicate use of ED medication, the analysis could help capture prescription patterns that might not be explained by medical needs.

Methods

Individual-level pharmacy dispensing data from 2006 for a population-based cohort of 216,148 men aged 45–79 years in the county Scania, Sweden, were analysed. Multiple logistic regression was applied, and area under the receiver operating characteristic curve (AUC) was calculated to quantify the discriminatory accuracy (DA) of the associations. National trends in pharmacy dispensing of ED medication between 2006 and 2016 were also analysed.

Results

Pharmacy dispensing of ED medication increased between 2006 and 2016, particularly among men aged 65–79 years (from 6.8 to 9.2%). Dispensing of ED medication was positively associated with higher socioeconomic position, and divorced and widowed men were more likely to fill a prescription with ED medication than married men. These associations remained after adjusting for medical conditions. The DA of the associations was, however, rather low (AUC = 0.69 among 45–64 year olds and AUC = 0.65 among 65–79 year olds).

Conclusions

Pharmacy dispensing of ED medication seem linked to the individuals socioeconomic position, age and marital status suggesting sociodemographic disparities in the pharmacy dispensing targeting sexual function. However, the low DA of the associations shows the limited capacity of these factors to predict ED medication use at the individual level.

The tyranny of the averages and the indiscriminate use of risk factors in public health: The case of coronary heart disease

Modern medicine is overwhelmed by a plethora of both established risk factors and novel biomarkers for diseases. The majority of this information is expressed by probabilistic measures of association such as the odds ratio (OR) obtained by calculating differences in average “risk” between exposed and unexposed groups. However, recent research demonstrates that even ORs of considerable magnitude are insufficient for assessing the ability of risk factors or biomarkers to distinguish the individuals who will develop the disease from those who will not. In regards to coronary heart disease (CHD), we already know that novel biomarkers add very little to the discriminatory accuracy (DA) of traditional risk factors. However, the value added by traditional risk factors alongside simple demographic variables such as age and sex has been the subject of less discussion. Moreover, in public health, we use the OR to calculate the population attributable fraction (PAF), although this measure fails to consider the DA of the risk factor it represents. Therefore, focusing on CHD and applying measures of DA, we re-examine the role of individual demographic characteristics, risk factors, novel biomarkers and PAFs in public health and epidemiology. In so doing, we also raise a more general criticism of the traditional risk factors’ epidemiology. We investigated a cohort of 6103 men and women who participated in the baseline (1991–1996) of the Malmö Diet and Cancer study and were followed for 18 years. We found that neither traditional risk factors nor biomarkers substantially improved the DA obtained by models considering only age and sex. We concluded that the PAF measure provided insufficient information for the planning of preventive strategies in the population. We need a better understanding of the individual heterogeneity around the averages and, thereby, a fundamental change in the way we interpret risk factors in public health and epidemiology.

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There is a plethora of differences in “average” risk between exposed and unexposed groups of individuals.

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Individual heterogeneity around average values is seldom considered in Public Health.

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Measures of discriminatory accuracy (DA) informs on the underlying individual heterogeneity.

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Most know risk factors and other categorizations associated with diseases have low DA.

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We need a fundamental change in the way we investigate risk factors and other categorizations in Public Health.

Absolute rather than relative income is a better socioeconomic predictor of chronic obstructive pulmonary disease in Swedish adults

BACKGROUND

While psychosocial theory claims that socioeconomic status (SES), acting through social comparisons, has an important influence on susceptibility to disease, materialistic theory says that socioeconomic position (SEP) and related access to material resources matter more. However, the relative role of SEP versus SES in chronic obstructive pulmonary disease (COPD) risk has still not been examined.

METHOD

We investigated the association between SES/SEP and COPD risk among 667 094 older adults, aged 55 to 60, residing in Sweden between 2006 and 2011. Absolute income in five groups by population quintiles depicted SEP and relative income expressed as quintile groups within each absolute income group represented SES. We performed sex-stratified logistic regression models to estimate odds ratios and the area under the receiver operator curve (AUC) to compare the discriminatory accuracy of SES and SEP in relation to COPD.

RESULTS

Even though both absolute (SEP) and relative income (SES) were associated with COPD risk, only absolute income (SEP) presented a clear gradient, so the poorest had a three-fold higher COPD risk than the richest individuals. While the AUC for a model including only age was 0.54 and 0.55 when including relative income (SES), it increased to 0.65 when accounting for absolute income (SEP). SEP rather than SES demonstrated a consistent association with COPD.

CONCLUSIONS

Our study supports the materialistic theory. Access to material resources seems more relevant to COPD risk than the consequences of low relative income.