ArticleNature communications2026
An updated modeling framework and sensitivity analysis of methodology for the climate health vulnerability index.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Encoding Justice with Data: Environmental Justice Screening Tools and the Limits of Quantification.Current environmental health reports · 2026Review
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7 authors.
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Abstract
Climate change and its severe health impacts raise serious concerns about climate justice. To measure a population's vulnerability to climate change, researchers often apply indicator-based composite indices. In this work, we present a modeling framework for constructing a climate health vulnerability index (CHVI) and examine how methodological choices influence the identification of vulnerable communities. Using 44 indicators in New York State and two structural designs-inductive (principal component analysis) and deductive (indicator aggregation)-we conducted multiple sensitivity analyses to evaluate the robustness of CHVI outcomes. The deductive design was less sensitive to model inputs and specifications than the inductive design. Among the construction steps, principal component selection and indicator normalization were the most influential factors for the inductive and deductive designs, respectively. Quantification of climate-change-related vulnerability through transparent and reproducible index development can inform policy planning and resource allocation for the most disadvantaged populations.
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