Intersectional social identities and loneliness: Evidence from a municipality in Switzerland

What does the MAIHDA method explain?

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

A dual-path psychosocial model of social determinants of health in the community: Results from the Cause Commune program

We tested a dual-path psychosocial framework of social vulnerability that considers the impact of socioeconomic resources and cognitive social capital on health, and whether they were mediated by an enabling psychosocial path (collective efficacy) and a disabling path (loneliness). A total of 1401 people (53.6% female, M_age  = 48.7, SD = 18.1) from a community in Switzerland participated in the study. Structural equation models showed that psychosocial factors were related to both social determinants and health outcomes and partially mediated their interrelation. Our model showed an adequate fit to the data (χ²  = 1,377.56, df = 341, p = 0.000, comparative fit index = 0.93, root mean square error of approximation = 0.05, standardized root mean-squared residual = 0.05). The findings highlight the role of psychosocial-relational factors in the processes of social vulnerability and would be of interest to researchers working on social vulnerability in the community.

Beyond Geography: Social Quality Environments and Health

The concept of social quality has garnered increasing attention as a composite indicator of the well-being of societies as well as individuals embedded within them. Prior research suggests four domains of social quality: socio-economic security, social cohesion, social inclusion, and social empowerment, based on the assumption that these domains influence health and well-being. In this paper, we investigate whether and to what extent social quality environments defined with reference to the cross-cutting social quality domains reliably predict various types of health, using data collected in a municipality in Switzerland as part of a participatory action research project. We found that social inclusion had the highest predictive power for mental health and functional health, while economic security had the highest predictive power for physical capacity and overall self-rated health. Results indicate interaction among various domains of social quality for a subset of health measures. Findings suggest that environments defined as combinations of social quality domains effectively distinguish between population segments with varying levels of health. Social quality represents a promising avenue for policy and intervention development, particularly from the social determinants of health perspective, as it jointly captures the multiple domains of social well-being relevant to population health.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11205-023-03073-1.

The effects of social determinants on children's health outcomes in Bangladesh slums through an intersectionality lens: An application of multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA)

Empirical evidence suggests that the health outcomes of children living in slums are poorer than those living in non-slums and other urban areas. Improving health especially among children under five years old (U5y) living in slums, requires a better understanding of the social determinants of health (SDoH) that drive their health outcomes. Therefore, we aim to investigate how SDoH collectively affects health outcomes of U5y living in Bangladesh slums through an intersectionality lens. We used data from the most recent national Urban Health Survey (UHS) 2013 covering urban populations in Dhaka, Chittagong, Khulna, Rajshahi, Barisal, Sylhet, and Rangpur divisions. We applied multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) to estimate the Discriminatory Accuracy (DA) of the intersectional effects estimates using Variance Partition Coefficient (VPC) and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). We also assessed the Proportional Change in Variance (PCV) to calculate intersectional effects. We considered three health outcomes: cough, fever, and acute respiratory infections (ARI) in U5y.We found a low DA for cough (VPC = 0.77%, AUC-ROC = 61.90%), fever (VPC = 0.87%, AUC-ROC = 61.89%) and ARI (VPC = 1.32%, AUC-ROC = 66.36%) of intersectional strata suggesting that SDoH considered do not collectively differentiate U5y with a health outcome from those with and without a health outcome. The PCV for cough (85.90%), fever (78.42%) and ARI (69.77%) indicates the existence of moderate intersectional effects. We also found that SDoH factors such as slum location, mother's employment, age of household head, and household's garbage disposal system are associated with U5y health outcomes. The variables used in this analysis have low ability to distinguish between those with and without health outcomes. However, the existence of moderate intersectional effect estimates indicates that U5y in some social groups have worse health outcomes compared to others. Therefore, policymakers need to consider different social groups when designing intervention policies aimed to improve U5y health outcomes in Bangladesh slums.