Harmonising Depression and Anxiety Measures Across International Cohorts: A Framework Validated Using Measurement Invariance.

AIMS: Cross-national research on mental health trajectories requires harmonised measures that are valid and comparable. However, measurement scales often differ between cohorts in item content and cultural context. This study aimed to develop a framework for harmonising mental health measures across international cohorts, focussing on depression and anxiety symptoms. METHODS: We applied a structured, theory-driven approach to align item content across two longitudinal ageing cohorts that used different self-report instruments with heterogeneous item codings: ELSA-UK and ELSA-Brasil. Data were collected before, during, and after the COVID-19 pandemic, providing a natural experiment for examining temporal changes in mental health. The harmonisation process involved: (1) mapping items to DSM-5 symptom domains via expert review; (2) transforming response formats into harmonised ordinal indicators; (3) leveraging the Harmony AI tool to identify semantically equivalent items; and (4) testing measurement invariance across waves and cohorts using multi-group confirmatory factor analysis (MGCFA). RESULTS: Scalar invariance was achieved for depression in both longitudinal and cross-cohort models, enabling meaningful latent mean comparisons between cohorts. For anxiety, scalar invariance was supported within but not across cohorts, likely due to the limited number of conceptually matching items. CONCLUSION: The proposed harmonisation framework demonstrates the feasibility of robust cross-cultural comparisons of mental health constructs. Its success depends on the conceptual alignment and quality of available items. This approach provides a methodological template for future harmonisation efforts in global health and psychiatric epidemiology.