Disaggregation of Green Space Access, Walkability, and Behavioral Risk Factor Data for Precise Estimation of Local Population Characteristics

• Published in: International journal of environmental research and public health 2024-06, Vol.21 (6), p.771
• Main Authors: Guha, Saurav, Alonzo, Michael, Goovaerts, Pierre, Brink, LuAnn L, Ray, Meghana, Bear, Todd, Pyne, Saumyadipta
• Format: Article
• Language: English
• Subjects: Behavior; Body mass index; Built environment; Built Environment - statistics & numerical data; Census of Population; Environment Design; Exercise; Health Behavior; Health disparities; Health risk assessment; Health risks; Humans; Mortality; Neighborhoods; Obesity; Parks, Recreational - statistics & numerical data; Pennsylvania; Polls & surveys; Postal codes; Public health; Residence Characteristics; Risk Factors; Smoking; Socioeconomic factors; Variables; Walking - statistics & numerical data
• ISSN: 1661-7827; 1660-4601; 1660-4601
• 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.