Health Analytics Network, Pittsburgh, PA 15237, USA.
Department of Statistics, Mathematics & Computer Application, Bihar Agricultural University, Bhagalpur 813210, India.
Int J Environ Res Public Health. 2024 Jun 14;21(6):771. doi: 10.3390/ijerph21060771.
Social and Environmental Determinants of Health (SEDH) provide us with a conceptual framework to gain insights into possible associations among different human behaviors and the corresponding health outcomes that take place often in and around complex built environments. Developing better built environments requires an understanding of those aspects of a community that are most likely to have a measurable impact on the target SEDH. Yet data on local characteristics at suitable spatial scales are often unavailable. We aim to address this issue by application of different data disaggregation methods.
We applied different approaches to data disaggregation to obtain small area estimates of key behavioral risk factors, as well as geospatial measures of green space access and walkability for each zip code of Allegheny County in southwestern Pennsylvania.
Tables and maps of local characteristics revealed their overall spatial distribution along with disparities therein across the county. While the top ranked zip codes by behavioral estimates generally have higher than the county's median individual income, this does not lead them to have higher than its median green space access or walkability.
We demonstrated the utility of data disaggregation for addressing complex questions involving community-specific behavioral attributes and built environments with precision and rigor, which is especially useful for a diverse population. Thus, different types of data, when comparable at a common local scale, can provide key integrative insights for researchers and policymakers.
社会和环境决定健康因素（SEDH）为我们提供了一个概念框架，使我们能够深入了解人类行为之间可能存在的关联，以及这些行为通常在复杂的建筑环境中或周围发生的相应健康结果。要改善建筑环境，就需要了解社区中最有可能对目标 SEDH 产生可衡量影响的那些方面。然而，在适当的空间尺度上，关于当地特征的数据往往不可用。我们旨在通过应用不同的数据分解方法来解决这个问题。
我们应用了不同的数据分解方法来获得关键行为风险因素的小区域估计值，以及宾夕法尼亚州西南部阿勒格尼县每个邮政编码的绿地可达性和可步行性的地理空间度量。
当地特征的表格和地图显示了它们在全县范围内的总体空间分布及其差异。虽然行为估计值排名最高的邮政编码通常高于该县的中位数个人收入，但这并不意味着它们的绿地可达性或可步行性高于该县的中位数。
我们展示了数据分解在解决涉及社区特定行为属性和建筑环境的复杂问题方面的实用性，具有精确性和严谨性，这对于多样化的人群尤其有用。因此，不同类型的数据在共同的本地尺度上具有可比性时，可以为研究人员和政策制定者提供关键的综合见解。
