Evaluating longitudinal ecological models linking scientific production to population-level indicators: a global case study in mental health research

BackgroundQuantifying the alignment between scientific production and population-level indicators remains a persistent methodological challenge in health research evaluation. While longitudinal ecological models have been increasingly used to explore associations between research output and societal outcomes, their feasibility, interpretability, and structural limitations have not been systematically examined. MethodsWe conducted a longitudinal ecological meta-research analysis integrating global bibliometric data on mental health publications with country-level indicators of mental disorders, mental health infrastructure, and subjective well-being. Analyses were stratified by World Bank income groups and implemented using a three-step framework comprising income specific linear regression models, random-effects meta-analyses, and meta-regressions to assess association patterns, heterogeneity, and potential moderators. ResultsScientific production was highly concentrated in high-income countries. Income-stratified regression models revealed divergent association patterns across contexts, with inverse associations observed in higher income groups and predominantly positive coefficients in low-income countries. Meta-analyses showed extreme between-group heterogeneity for most indicators, yielding largely attenuated pooled estimates. Only one subjective well-being indicator retained a significant pooled association. ConclusionsLongitudinal ecological models linking scientific production to population-level indicators can identify broad association patterns and structural asymmetries but are strongly constrained by contextual heterogeneity and data availability.