A quantitative approach to the intersectional study of mental health inequalities during the COVID-19 pandemic in UK young adults

Intersectional discrimination and mental health inequalities: a qualitative study of young women's experiences in Scotland

BACKGROUND

In 2021, Scotland became the first UK country to launch a Women's Health Plan. This policy signals increasing commitment to broader ambitions surrounding gender equality in health. Research shows a connection between discrimination and health, representing a contributor to health inequalities. There remains sparse evidence on how certain groups experience discrimination that could be useful for policymaking. This research set out to address this evidence gap through exploring how discrimination shapes young women's experiences of mental health and inequalities in Scotland.

METHODS

We interviewed women aged 16-25 years (n=28), living in Scotland, UK, adopting an intersectional approach to recruitment and data analysis. We used a semi-structured topic guide to facilitate open discussion about discrimination and health. Transcripts were analysed by two researchers using Thematic Analysis and NVivo software.

FINDINGS

We identified three themes that illuminate intersectional discrimination and the impact on mental health. The first outlines how experiences of discrimination in school, work and public spaces (and the anticipation of such) creates stress leading to mental health problems, particularly for participants from ethnic minority groups. The second highlights the lack of support for mental health, both at structural and interpersonal levels, which was viewed by young women as a form of intersectional discrimination, largely because of their gender and age. Finally, we developed a mid-level theory termed the 'chain of dismissal' that displays that for both physical or mental health symptoms, young women's concerns are immediately "written off" as anxiety-related and in turn a natural attribute of young women. These themes show that discrimination has the potential to amplify mental health problems for young women and is a likely contributor to health inequalities.

CONCLUSIONS

Structural disadvantages such as racism intersect with gender and age to compound the experience of discrimination for marginalised young women. To improve mental health and reduce health inequalities for young women, multi-level approaches are needed, with strong consideration of how the structural and cultural landscape as well as assumptions made by healthcare professionals have critical implications for young women's health.

Overcoming combination fatigue: Addressing high-dimensional effect measure modification and interaction in clinical, biomedical, and epidemiologic research using multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA)

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

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

Objectives

With aging societies, more people become vulnerable to experiencing cognitive decline. While normal aging is associated with a deterioration in certain cognitive abilities, little is known about how social determinants intersect to create late-life cognitive functioning inequalities. Simultaneously, the role of grandparenthood is central for older adults and their families. There are indications that social determinants intersect to modulate the effect of the transition to grandparenthood, but further evidence is needed. Our study investigates the relation of transition to grandparenthood with cognitive functioning and explores differences across intersectional strata.

Methods

Using longitudinal data from the Survey of Health, Ageing and Retirement in Europe, we analyzed a sample of 19,953 individuals aged 50-85 without grandchildren at the baseline. We applied Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy to investigate cognitive functioning differences across 48 intersectional strata, defined by sex/gender, migration, education, and occupation. We allowed the impact of becoming a grandparent to vary across strata by including random slopes.

Results

Intersectional strata accounted for 17.43% of the overall variance in cognitive functioning, with most of the stratum-level variation explained by additive effects of the stratum-defining characteristics. Transition to grandparenthood was associated with higher cognitive functioning, with a stronger effect for women. Stratum-level variation in the grandparenthood effect was modest.

Discussion

This study highlights the importance of social determinants for understanding heterogeneities in the association of transition to grandparenthood with cognitive functioning. Adopting an intersectional lens is useful to decompose inequalities and derive tailored interventions to promote equal healthy aging.