Intersectional Trajectories in Physical and Mental Health: A UK Longitudinal Study (2009-2024)

Technical Summary: Intersectional Trajectories in Physical and Mental Health (2009–2024)

Problem Statement
Existing research on health inequalities in the UK and internationally frequently examines socioeconomic position (SEP), gender, and ethnicity as single, additive axes. This approach obscures how these dimensions interlock to create unique positions of advantage and disadvantage. Furthermore, much of the intersectional research remains cross-sectional, limiting the understanding of how inequalities evolve across age-based trajectories and historical contexts. While life course theory posits that health differences unfold dynamically over time, and cohort analysis suggests that generational context shapes health outcomes, there is a lack of longitudinal studies that integrate intersectionality with life course epidemiology to model how physical and mental health trajectories vary simultaneously across gender, ethnicity, occupational class, and generation.

Methodology
This study employs a longitudinal intersectional approach using data from 14 waves (2009–2024) of the UK Household Longitudinal Study (UKHLS). The analytic sample comprises 83,304 individuals (483,887 observations) aged 16–103.

• Outcomes: Physical and mental health were measured using the SF-12 Physical Component Summary (PCS) and Mental Component Summary (MCS) scores.
• Intersectional Strata: The authors defined 398 intersectional strata based on four dimensions:
  • Gender: Male, Female (binary, aligned with UKHLS data).
  • Ethnicity: 10 categories (White British, White Irish, Other White, Indian, Pakistani, Bangladeshi, Caribbean, African, Mixed, Other).
  • Socioeconomic Position (SEP): Occupational class (NS-SEC: Managerial/Professional, Intermediate, Routine/Manual) and a distinct "No work history" category.
  • Generation: Silent (≤1945), Boomer (1946–1963), Gen X (1964–1979), Gen Y/Millennials (1980–1994), and Gen Z (1995+).
• Analytical Model: The study utilizes the longitudinal extension of Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA). This three-level model nests repeated observations (Level 1) within individuals (Level 2) within intersectional strata (Level 3).
  • Model 1: Estimates overall age-related trajectories (cubic polynomial) and partitions variance between individuals and strata.
  • Model 2: Adds main effects of the stratum-defining variables and their interactions with the linear age term. This allows for the estimation of how health trajectories differ by intersectional position and generation, capturing both intercept differences (baseline health) and slope differences (rates of change).
• Visualisation: Results were visualized using a custom RShiny application to plot predicted trajectories across observed age ranges for specific intersections.

Key Results
The analysis reveals that health inequalities are structurally differentiated by cohort and intersectional position, with distinct patterns for physical versus mental health.

• Physical Health (PCS): Inequalities widen with age, consistent with the theory of cumulative disadvantage.
  • Women, Bangladeshi and Pakistani groups, and those in routine/manual or "no work history" categories reported poorer physical health.
  • The rate of physical health decline was steeper for women and those with no work history.
  • Generational differences in physical health were modest within overlapping age ranges, though later-born cohorts showed some compression of favorable trajectories among advantaged groups.
• Mental Health (MCS): Inequalities were more evident among later-born cohorts, particularly at younger ages.
  • The "Boomer" generation exhibited a relatively flat mental health trajectory across its observed age range.
  • "Generation Z" showed lower predicted mental health scores in early adulthood.
  • Intersectional Nuance: Unlike physical health, mental health advantages were not stable. While White British managerial/professional men showed clear advantages in the Boomer generation, this advantage shifted in later generations. In Generations X, Y, and Z, African, Indian, Other White, and White Irish men often displayed more favorable mental health trajectories than White British men.
  • Critical Disadvantage: White British women with "no work history" emerged as a group with particularly poor predicted mental health trajectories in later-born cohorts (Gen X, Y, Z), with poorer predicted trajectories than their South Asian peers. This contrasts with older cohorts where White British groups often held relative advantages.
• Variance Partitioning: In both models, the addition of main effects and interactions (Model 2) significantly reduced the between-strata variance (Intercept VPC dropped from ~18% to ~3.7% for physical health; ~31% to ~17% for mental health), indicating that the intersectional variables explain a substantial portion of the heterogeneity. However, the majority of variance remained within individuals over time.

Key Contributions

1. Methodological Innovation: The study applies longitudinal MAIHDA to a large-scale UK dataset, demonstrating a robust method for modeling age-related health trajectories across 398 intersectional strata while accounting for individual heterogeneity.
2. Revealing Non-Additive Patterns: The findings challenge single-axis narratives. For instance, the "White British" advantage in mental health is not universal; it is generation-specific and often reversed in favor of other ethnic groups in younger cohorts. Similarly, the "no work history" category reveals a specific vulnerability for White British women in younger generations that differs from older cohorts.
3. Divergent Trajectories: The paper highlights that physical and mental health do not follow the same intersectional logic. Cumulative disadvantage drives physical health inequalities with age, whereas mental health inequalities are more strongly patterned by generational context and shifting social positions.
4. Policy Relevance: The results argue against interventions based on single categories (e.g., targeting only "women" or only "ethnic minorities"). Instead, they support "proportionate universalism," which combines broad population support with intensified action for specific intersections showing the poorest trajectories (e.g., young women with no work history, older ethnically minoritised groups facing cumulative physical decline).

Significance and Claims
The authors claim that this study provides novel longitudinal insights into how health inequalities unfold, demonstrating that single-axis approaches obscure significant population heterogeneity. By integrating intersectionality with life course theory, the study shows that health trajectories are not uniform but vary dynamically across intersecting social positions and historical generations.

The paper modestly notes that while it identifies complex patterns, it does not definitively separate age, period, and cohort effects, nor does it model the specific mechanisms (e.g., early-life exposures, specific policy changes) driving these trajectories. It acknowledges limitations regarding measurement invariance across ethnic groups and generations, and the exclusion of survey weights due to computational constraints of the random-slope MAIHDA model.

Ultimately, the study asserts that understanding health inequalities requires moving beyond additive models to examine how systems of power (racism, sexism, classism) interact to shape distinct, shifting trajectories of advantage and disadvantage across the life course. The findings suggest that policy responses must be nuanced, targeting specific intersectional groups experiencing the steepest declines or lowest baselines, rather than relying on broad, undifferentiated categories.