Extending intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) to study individual longitudinal trajectories, with application to mental health in the UK
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a new longitudinal method for analyzing intersectional inequalities over time. The approach reveals significant differences in life course trajectories across generations and social groups, highlighting additive and multiplicative inequalities in mental health.
Area Of Science
- Social Epidemiology
- Quantitative Psychology
- Sociology
Background
- The Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) is a key method for identifying intersectional inequalities.
- Existing MAIHDA applications are limited to cross-sectional data, failing to capture dynamic intersectional processes.
- Intersectional social determinants are increasingly recognized as evolving over the life course.
Purpose Of The Study
- To develop and illustrate a longitudinal version of the MAIHDA approach.
- To enable the analysis of life course trajectories of intersectional inequalities.
- To address conceptual challenges in defining intersectional groups over time.
Main Methods
- Development of a longitudinal MAIHDA framework integrating intersectionality and life course theories.
- Application to longitudinal mental health data from the United Kingdom Household Longitudinal Study (2009-2021).
- Consideration of changeable intersectional groups, generational differences, and age-period-cohort effects.
Main Results
- Significant differences in mental health trajectories were observed between generations and intersectional strata.
- Intersectional inequalities in mental health trajectories were found to be partly multiplicative and mostly additive.
- The longitudinal MAIHDA approach successfully captured dynamic changes in inequalities over the life course.
Conclusions
- The developed longitudinal MAIHDA approach offers a robust quantitative method for analyzing dynamic intersectional inequalities.
- This methodological advancement is crucial for social epidemiology and other fields studying evolving social determinants of health.
- The findings underscore the importance of considering life course and generational dynamics in understanding health disparities.
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