Urban environments are socially heterogeneous, yet the influence of socioeconomic structure on the population dynamics of indoor pests of health significance remains poorly understood. The common bed bug, Cimex lectularius , is often associated with low socioeconomic status, but whether “reservoir” populations are associated with socially vulnerable communities is unclear. We tested whether census-derived measures of social vulnerability predict the spatial distribution of genetically distinct bed bug reservoir populations across multiple U.S. urban regions. We analyzed genome-wide single nucleotide polymorphism (SNP) data from 195 infestations collected between 2018 and 2022 in Oklahoma, Ohio, and Illinois. Population structure, genetic diversity estimates, and effective population size were estimated. Census tract-level vulnerability was quantified using the Centers for Disease Control and Prevention Social Vulnerability Index (SVI; four themes). Associations between genetic clusters and SVI themes were tested. Bed bug populations exhibited patterns of genetic structure within each state, with significant differentiation among clusters (mean F ST ≈ 0.06–0.19), low heterozygosity, high inbreeding, and small effective population sizes (mean Ne 3.5–5.5). Multiple genetically differentiated clusters were identified within each urban region, consistent with geographically diffuse reservoir populations linked through human-mediated dispersal. In Oklahoma and Illinois, cluster membership was non-randomly associated with SVI themes, particularly socioeconomic status and household characteristics ( P < 0.05), whereas no significant associations were detected in Ohio. Importantly, clusters occurred across both high- and low-vulnerability census tracts. Fine-scale social structure shapes the spatial organization of urban bed bug populations, but reservoir populations are not confined to socially vulnerable communities. Effective vector and pest management strategies should, therefore, emphasize coordinated, community-wide approaches rather than targeting disadvantaged neighborhoods alone.
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Lewis et al. (2026) studied this question.
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