Cruz Jennifer L, Luke Douglas A, Ceballos Rachel M, Ramanadhan Shoba, Emmons Karen M
Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA, 02115, USA.
Brown School at Washington University in St Louis, 1 Brookings Drive, St. Louis, MO, 63130, USA.
SSM Popul Health. 2024 Dec 2;29:101729. doi: 10.1016/j.ssmph.2024.101729. eCollection 2025 Mar.
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.
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.
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.
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.
在人口健康研究中,农村地区通常是根据宽泛的人口密度指标来定义的,而这些指标无法体现农村地区多样且复杂的特征。虽然研究人员已经开发出更细致入微的方法来研究城市环境中邻里和区域对健康的影响,但类似方法很少应用于农村环境。为了填补这一空白,我们旨在探索美国农村地区背景异质性的维度。
我们进行了一项探索性潜在类别分析(LCA),以识别不同的农村类别。使用社区资本框架,我们整理并分析了美国所有农村普查区(n = 15,643)农村社区资本各领域具有全国代表性的数据。本研究的数据来自2018年至2021年期间的十个公开可用数据集。为了对我们的研究结果进行初步验证,我们检查了所确定的农村类别中的社会脆弱性指数(SVI)百分位排名。
一个四类模型解决方案最适合我们的数据。我们的LCA结果确定了四种不同的农村类别,它们在资本类型方面存在差异:偏远地区(n = 3,566,22.7%)、发达地区(n = 3,210,20.5%)、资源丰富地区(n = 4,896,31.3%)和适应性强地区(n = 3,981,25.4%)。这些类别之间的平均SVI百分位排名存在显著差异,资源丰富地区的平均百分位排名最低,适应性强地区的平均百分位排名最高。
我们在普查区层面识别出了不同类型的农村地区,这些地区呈现出社会梯度差异,如SVI百分位排名的变化所示。这些发现凸显出每个农村类别都有独特的社区资本组合。这种对农村地区进行细致入微概念化的方法为确定符合特定农村社区独特优势和需求的干预措施提供了机会。