Incorporating disability within intersectional analysis: general challenges and French specificities

The usefulness of intersectional analysis is explored. Its implications for enhancing arts therapies practice are set out and analyzed in detail. In particular, intersectional analysis is discussed in relation to gender issues. A case study is then offered to illustrate the practical application of the concept. An argument is made for the introduction of further critical theory to be taught in our curriculums to enhance critical pedagogy.

BackgroundTuberculosis (TB) remains a major public health problem in Nepal, high in settings marked by prevalent gender and social inequities. Various social stratifiers intersect, either privileging or oppressing individuals based on their characteristics and contexts, thereby increasing risks, vulnerabilities and marganilisation associated with TB. This study aimed to assess the inclusiveness of gender and other social stratifiers in key health related national policies and the Health Management Information System (HMIS) of National Tuberculosis Programme (NTP) by conducting an intersectional analysis of TB cases recorded via HMIS.MethodsA desk review of key policies and the NTP’s HMIS was conducted. Retrospective intersectional analysis utilized two secondary data sources: annual NTP report (2017–2021) and records of 628 TB cases via HMIS 6.5 from two TB centres (2017/18–2018/19). Chi-square test and multi-variate analysis was used to assess the association between social stratifers and types of TB, registration category and treatment outcome.ResultsGender, social inclusion and concept of intersectionality are incorporated into various health policies and strategies but lack effective implementation. NTP has initiated the collection of age, sex, ethnicity and location data since 2014/15 through the HMIS. However, only age and sex disaggregated data are routinely reported, leaving recorded social stratifiers of TB patients static without analysis and dissemination. Furthermore, findings from the intersectional analysis using TB secondary data, showed that male more than 25 years exhibited higher odds [adjusted odds ratio (aOR) = 4.95, 95% confidence interval (CI): 1.60–19.06, P = 0.01)] of successful outcome compared to male TB patients less than 25 years. Similarly, sex was significantly associated with types of TB (P < 0.05) whereas both age (P < 0.05) and sex (P < 0.05) were significantly associated with patient registration category (old/new cases).ConclusionsThe results highlight inadequacy in the availability of social stratifiers in the routine HMIS. This limitation hampers the NTP’s ability to conduct intersectional analyses, crucial for unveiling the roles of other social determinants of TB. Such limitation underscores the need for more disaggregated data in routine NTP to better inform policies and plans contributing to the development of a more responsive and equitable TB programme and effectively addressing disparities.

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.

Intersectional feminism examines the interactive effects of systems of power and forms of oppression on people’s lives. It also examines the complex ways that gender, race, ethnicity, class, caste, citizenship status, sexuality, ability and other identities interact to shape people’s realities. Critical praxis occurs when people apply intersectional analysis to their own lives in order to guide practice. This chapter first discusses the development of intersectional feminism out of Black feminist theorizing. It then evaluates the degree to which feminist economists have incorporated intersectional analysis into their research. The chapter suggests pathways for feminist economists to reorient their research in accordance with intersectional theory and praxis. It discusses two ways that feminist economists can incorporate intersectional feminist understandings of race as both critical theory and praxis into their research to counter the omission of race within feminist economic analysis. Intersectionality involves analyzing disparities and relative position as well as historical factors and current practices that sustain inequities overtime.

Intercultural education is emphasized on both national and European level, an educational discourse elaborated out from the United Nations Declaration of Human Rights after the World War II. In the authors’ understanding intercultural approaches aim to critically explore categorizations as class, gender, etc. This study is an examination of intercultural/intersectional and gender-related issues in national curriculum as well as local curricula in teacher education courses for lower secondary school teachers at three universities in Sweden. The study analyses theories and ideologies underlying course designs together with the concept of teacher knowledge, focusing specifically on gender aspects intersecting with other social categorisations. The analysis show that gender issues and intersectional analysis are not prominent in teacher education in Sweden, neither in national nor local curricula but might be at stake within the concept of fundamental values as well as within a didactic discourse. However, intersectionality and gender issues vary at the universities which could be connected to different student groups but also different choices of theories and perspectives at the different universities. This raises questions on quality in teacher education as such and for the professional practice in the future.

The paper reflects on a changing public service project regarding women and intersectional analysis in Halifax, Canada. The project sought to facilitate collective mobilizations to challenge austerity and to imagine public services that meet the needs of the citizens who use them, and the workers that provide them. We provide an overview of the project, and then explore our attempt at adapting “multistrand” intersectional policy analysis (Hankivsky &amp; Cormier, 2011) to a community-based context. In considering the challenges and opportunities associated with this work, the paper concludes that the changing public service project created space for an innovative approach to community-based research that can guide both participatory policy analysis and collective action.

Using complementary intersection and segment analyses to identify crash hot spots

IntroductionPeriodontitis is a common inflammatory disease that compromises oral and systemic health. This study aimed to elucidate its molecular mechanisms and identify potential biomarkers for early diagnosis and precision treatment.MethodsWe integrated genome-wide association study (GWAS) and transcriptomic data from periodontitis patients and healthy controls. Summary data-based Mendelian randomization (SMR) and the heterogeneity in dependent instruments (HEIDI) test were used to identify genetically associated genes. Differentially expressed genes (DEGs) were identified using LIMMA, and weighted gene co-expression network analysis (WGCNA) revealed disease-related gene modules. Candidate biomarkers were prioritized through intersection analysis and evaluated using five machine learning algorithms. Causal relationships were further validated by two-sample Mendelian randomization (TSMR). Functional enrichment was assessed via gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA), and immune infiltration was analyzed using CIBERSORT.ResultsSMR identified 360 gene-trait associations, with 320 passing the HEIDI test, corresponding to 294 unique genes. DEGs were enriched in immune and neuronal development pathways. WGCNA uncovered nine gene modules associated with periodontitis. Intersection and machine learning analyses identified five key biomarkers—GPX2, IGKV2D-30, CD34, GSTA4, and NYNRIN—with strong predictive performance, validated by MR analysis (p < 0.05). Immune infiltration analysis revealed increased regulatory T cells, activated mast cells, and neutrophils, and decreased memory B cells and resting mast cells in periodontitis, with biomarker expression levels showing significant immune correlations.ConclusionThis integrative multiomics analysis uncovers causal genes and robust biomarkers involved in periodontitis pathogenesis, providing new insights for early detection and individualized treatment strategies. Further experimental validation is needed to confirm their functional roles in disease progression and therapeutic potential.

Traffic simulation is widely used for modeling, planning, and analyzing different strategies for traffic control and road development in a cost-efficient manner. In order to perform an intersection simulation, random vehicle trip data are typically applied to an intersection network, making them unrealistic. In this paper, we address this issue by presenting two different methods of incorporating actual turning movement count (TMC) data and comparing their similarity for intersection simulation and analysis. The TMC of three intersections in Las Vegas are estimated separately for one hour using a developed vision-based tracking system and they are incorporated into Simulation of Urban MObility (SUMO) for estimating traffic measurements and traffic signal design. t-tests with a 95% confidence interval on the simulation variables demonstrate the importance of using a route-based creation method which injects vehicles into a simulation environment based on the frame-level departure time. The intersection analyses and comparisons are performed based on estimated traffic measurements such as travel time, density, lane density, occupancy, and normalized waiting time. Since the critical edge of each intersection network is identified based on a higher normalized waiting time, new traffic signal designs are suggested based on the actual critical turning movements and improvements in vehicle travel time are achieved to better accommodate the actual traffic demand.

BackgroundChildren and adolescents are highly vulnerable to the impact of sustained stressors during developmentally sensitive times. We investigated how demographic characteristics intersect with socioeconomic dimensions to shape the social patterning of quality of life and mental health in children and adolescents, two years into the COVID-19 pandemic. MethodsWe used data from the prospective SEROCoV-KIDS cohort study of children and adolescents living in Geneva (Switzerland, 2022). We conducted an intersectional Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy by nesting participants within 48 social strata defined by intersecting sex, age, immigrant background, parental education and financial hardship in Bayesian multilevel logistic models for poor health-related quality of life (HRQoL, measured with PedsQL) and mental health difficulties (measured with the Strengths and Difficulties Questionnaire). ResultsAmong participants aged 2–17 years, 240/2096 (11.5%, 95%CI 10.1–12.9) had poor HRQoL and 105/2135 (4.9%, 95%CI 4.0–5.9) had mental health difficulties. The predicted proportion of poor HRQoL ranged from 3.4% for 6–11 years old Swiss girls with highly educated parents and no financial hardship to 34.6% for 12–17 years old non-Swiss girls with highly educated parents and financial hardship. Intersectional strata involving adolescents and financial hardship showed substantially worse HRQoL than their counterparts. Between-stratum variations in the predicted frequency of mental health difficulties were limited (range 4.4%–6.5%). ConclusionsWe found considerable differences in adverse outcomes across social strata. Our results suggest that, post-pandemic, interventions to address social inequities in HRQoL should focus on specific intersectional strata involving adolescents and families experiencing financial hardship, while those aiming to improve mental health should target all children and adolescents.

Intersectional analysis goes beyond consideration of single variables to examine the compounded impact at the intersections of, for example, gender and race, or geographical location and caste. The Guidelines for Intersectional Analysis in Science and Technology (GIST) help researchers, journal editors, and funding agencies systematically integrate intersectional analysis into relevant domains of science and technology. These guidelines serve as a roadmap for quantitative intersectional analysis throughout the research process&amp;mdash;from setting strategic research priorities and shaping research questions to data collection, analysis, and interpretation. Here we provide a checklist to facilitate author and journal editor compliance with the guidelines. We recommend that the GIST checklist be added to journals&amp;rsquo; &amp;ldquo;Information for Authors&amp;rdquo;. The goal is to reset the research default to include intersectional analysis, where appropriate. Intersectional analysis leads to better science: precision in research best guides effective social and environmental policies that, in turn, enhance global equity and sustainability.

The #MeToo movement that originated in 2017 has drawn attention to the problem of sexual misconduct in workplaces globally. This problem includes the prevalent use of nondisclosure agreements (NDAs) to perpetuate misconduct and conceal acts of wrongdoing. In response, legislatures in different countries have introduced acts with varying degrees of scope. Thus, two questions remain: how do these policies impact individuals with different intersecting identities, and how can these policies be improved? Using a case study and multi-level approach that draws on gender and intersectional policy analyses, this MRP answers these questions by examining four different subnational jurisdictions in Canada and the United States. The results demonstrate varying degrees of effective legislation, which lead to the central argument that legislatures should consider a total ban on the use of NDAs in cases of harassment and discrimination. The MRP concludes by providing recommendations for future policy development in the Canadian context.

Background Internationally there is a large treatment gap for common mental disorders and disparities across population groups but little understanding about whether people occupying multiple advantaged/disadvantaged statuses access mental health support differentially. This study examines discrimination and mental health service use (MHSU) at the intersections of social statuses. We hypothesised i) greater past-year discrimination among disadvantaged social statuses; ii) different patterns of MHSU/treatment among single, compared to multiple social status groups; and after accounting for need: iii) less MHSU/treatment among multiply disadvantaged than more advantaged social status groups; and, iv) less MHSU/treatment among those reporting discrimination. Methods English population-based data came from the 2014 Adult Psychiatric Morbidity Survey. Latent Class Analysis (LCA) was used to define intersectional social status groups. Multivariate logistic regression models were estimated to examine associations between MHSU/treatment and i) single social statuses and ii) latent classes of social status by gender, adjusting for confounders and need (physical/mental illness), followed by exposure to past year discrimination. Results Five-class LCA solutions were selected for men and women, characterised as: 1) retired White British; 2) employed migrants, 3) economically inactive migrants 4) employed White British, lower education/social class; and 5) employed White British, high education/social class. Discrimination was more common among disadvantaged groups, and patterns differed across single and intersectional analyses. After adjustments, MHSU/treatment was elevated among females (OR 1.88, 95% CI: 1.49–2.06) and sexual minorities (OR 1.63: 1.06–2.51) but lower among Black ethnic groups (OR 0.28: 0.14–0.56). Adjustments for discrimination attenuated associations for sexual minorities and Black respondents. Intersectionally, findings were similar by gender except the retired White British group, for whom MHSU/treatment was significantly elevated among women (OR 1.96: 1.32–2.90) but not men. Among women and men respectively, compared to the most advantaged group, greater odds of MHSU/treatment were found for ‘employed migrants’ (OR 2.50: 1.71–3.67; OR 2.56: 1.77–3.71), ‘economically inactive migrants’ (OR 4.47: 3.00–6.40; OR 4.60: 3.10–6.83) and ‘employed White British, low social class/education’ (OR 1.91: 1.32–2.79; OR 1.95: 1.43–2.63). Adjustments for discrimination had little influence. Conclusion Accounting for need, MHSU/treatment disparities are apparent but differ when considering single, or multiple social statuses. Single status analyses mask discrepancies observed intersectionally, while intersectional data-driven analyses miss inequities by minority statuses which do not distinguish latent classes. For some groups, discrimination may elevate, rather than inhibit MHSU/treatment. To better inform policy and practice, research should incorporate multiple and mixed-methods approaches to identify complexities of social stratification processes.

This chapter spotlights intersectionality and intersectional analysis–considering global social policy making and the quasi-concept of social cohesion. It is a quasi-concept, as the chapter argues, not least because no shared definition of social cohesion actually exists in the literature. But also, importantly, because of the way it is used empirically by policy makers, while at the same time remaining sufficiently flexible to be deployed by policymakers in policy instruments, at different levels, and within and across different spatial scales. The chapter also investigates and compares the work and policy approaches of the international institutions: the World Bank, the United Nations and the World Health Organization, as well as the work of regional actors like the EU. It ultimately argues that the concern for cohesive social relations during crisis and change continues to motivate both actions and analysis of institutions and actors now intervening at several scales, including global social policy.

The #MeToo movement that originated in 2017 has drawn attention to the problem of sexual misconduct in workplaces globally. This problem includes the prevalent use of nondisclosure agreements (NDAs) to perpetuate misconduct and conceal acts of wrongdoing. In response, legislatures in different countries have introduced acts with varying degrees of scope. Thus, two questions remain: how do these policies impact individuals with different intersecting identities, and how can these policies be improved? Using a case study and multi-level approach that draws on gender and intersectional policy analyses, this MRP answers these questions by examining four different subnational jurisdictions in Canada and the United States. The results demonstrate varying degrees of effective legislation, which lead to the central argument that legislatures should consider a total ban on the use of NDAs in cases of harassment and discrimination. The MRP concludes by providing recommendations for future policy development in the Canadian context.