ArticleEnvironmental advances2026
Hierarchical clustering for disentangling population vulnerability to asthma.
Article in Environmental advances, 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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Abstract
Asthma is influenced by air pollution and weather conditions, yet asthma-related responses to these exposures may vary across communities due to heterogeneity in population vulnerability-defined here as a context represented by socioeconomic, demographic, and landscape conditions. We propose a clustering framework to characterize region-level population vulnerability prior to exposure-asthma modeling. Using statewide longitudinal data from Pennsylvania (2011-2019), socioeconomic, demographic, and landscape factors are aggregated at the region level and clustered through a hierarchical algorithm to define distinct population types. The algorithm combines outcome-informed feature weighting with agglomerative hierarchical clustering based on Euclidean distance and Ward linkage. These groups are subsequently incorporated into stratified modeling of age-adjusted asthma hospitalization rates in relation to pollutant and weather conditions. Compared with statewide pooled modeling, the group-specific approach improves exposure-asthma characterization and predictive performance, with an absolute overall increase in R
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