Hierarchical clustering for disentangling population vulnerability to asthma.

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 R2 of 0.064. Five coherent population types are identified, spanning upland-plateau, lowland-valley, affluent suburban, peri-urban, and urban-core settings and reflecting multidimensional vulnerability profiles. These findings suggest that population clustering provides a principled approach to vulnerability-based stratification, supporting a more differentiated assessment of asthma burden. The proposed methodology may be transferable to broader geographic settings and related respiratory health applications.