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Background: In population health research, rurality is often defined using broad population density measures, which fail to capture the diverse and complex characteristics of rural areas. While researchers have developed more nuanced approaches to study neighborhood and area effects on health in urban settings, similar methods are rarely applied to rural environments. To address this gap, we aimed to explore dimensions of contextual heterogeneity across rural settings in the US. Methods: We conducted an exploratory latent class analysis (LCA) to identify distinct classes of rurality. Using the Community Capitals Framework, we collated and analyzed nationally representative data for each domain of rural community capital across all rural census tracts in the US (n = 15,643). Data for this study were sourced from ten publicly available datasets spanning the years 2018-2021. To provide preliminary validation of our findings, we examined the Social Vulnerability Index (SVI) percentile rankings across the identified rural classes. Results: A four-class model solution provided the best fit for our data. Our LCA results identified four distinct classes of rurality that vary in terms of capital types: Outlying (n = 3,566, 22.7%), Developed (n = 3,210, 20.5%), Well-Resourced (n = 4,896, 31.3%), and Adaptable (n = 3,981, 25.4%). The mean SVI percentile rankings differed significantly across these classes, with Well-Resourced having the lowest and Adaptable the highest mean percentile rankings. Conclusions: We identified different types of rurality at the census tract level that fall along a social gradient as indicated by variation in SVI percentile rankings. These findings highlight that each rural class has a unique combination of community capitals. This nuanced approach to conceptualizing rurality provides the opportunity to identify interventions that meet specific rural communities' unique strengths and needs.
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