Beene Daniel, Lin Yan, Hoover Joseph H, Shi Xun
Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, USA.
Geography and Environmental Studies, Center for the Advancement of Spatial Informatics Research and Education (ASPIRE), University of New Mexico, Albuquerque, USA.
Int J Geogr Inf Sci. 2025 Mar 31. doi: 10.1080/13658816.2025.2482718.
Rural-urban classification schemes are frequently used in ecological studies of population health. However, the algorithms used to produce these classifications as well as their underlying assumptions may not match their intended use in health research. Here, we focus on the spatial distribution of features of the physical environment that are related to health - such as healthcare - to examine the extent to which eight classification schemes capture the heterogeneous context of rural places. We further explore how well rural-urban classifications distinguish between different types of rural places by comparing rural Tribal reservations with other rural areas in the American southwest. Because health services and infrastructure are often distributed through state and federal programs to underserved populations in rural areas, this approach speaks to the broader political implications in how rural communities are defined and represented. Results indicate that rural-urban classifications do not adequately reflect heterogeneous contexts within and across rural places. We advocate for more appropriate population health models that explain contextual differences in the relationship between health and place.
城乡分类方案常用于人口健康的生态研究。然而，用于生成这些分类的算法及其潜在假设可能与它们在健康研究中的预期用途不匹配。在这里，我们关注与健康相关的物理环境特征（如医疗保健）的空间分布，以研究八种分类方案在多大程度上捕捉了农村地区的异质背景。我们通过比较美国西南部的农村部落保留地与其他农村地区，进一步探讨城乡分类在区分不同类型农村地区方面的表现。由于卫生服务和基础设施通常通过州和联邦项目分配给农村地区的服务不足人群，这种方法涉及到农村社区如何被定义和呈现的更广泛政治影响。结果表明，城乡分类不能充分反映农村地区内部和之间的异质背景。我们主张采用更合适的人口健康模型来解释健康与地点之间关系的背景差异。
