ArticleFrontiers in research metrics and analytics2026
Measuring scientific coherence between global neglected tropical disease research and population health indicators: a 25-year meta-research study.
Article in Frontiers in research metrics and analytics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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Abstract
Introduction: Despite major investments in neglected tropical diseases (NTDs) research over the past two decades, it remains unclear whether global scientific production aligns with population-level health indicators. To address this gap, we designed and implemented a comprehensive meta-research analysis pipeline to quantify the scientific coherence between global NTDs research activity and epidemiological indicators across World Health Organization (WHO) regions from 2000-2024. Methods: Using this pipeline, we linked 1,07,251 NTDrelated publications to 23 disease-specific indicators from WHO, World Bank, and Our World in Data databases. Region-year panels were analyzed using bivariate regressions and hierarchical mixed-eects models, with publication count as the predictor and disease indicators as dependent variables, adjusting for temporal trends and regional clustering. Results: Higher research activity was significantly associated with reductions in HIV incidence, tuberculosis incidence, malaria incidence, and schistosomiasis treatment requirements, with hierarchical models confirming consistent protective associations and moderate heterogeneity across regions. In contrast, indicators such as antibiotic consumption and total DALYs showed incoherent or null relationships, suggesting persistent misalignment between research intensity and broader health indicators. Discussion: By introducing a reproducible, data-driven framework for assessing research-indicators coherence, this study shows that although NTDs research contributes to epidemiological improvements, coherence remains incomplete. Incorporating coherence metrics into global health assessment may help align scientific investment with public health needs and enhance the translational impact of research systems.
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