Rural-urban differences in estimated life expectancy associated with neighborhood-level cumulative social and environmental determinants
Introduction
Neighborhood area-level factors are known to have a variety of impacts on health and health-related outcomes. Several studies have demonstrated association of neighborhood environments with health outcomes (e.g., asthma, cardiovascular disease, and mental health outcomes), engaging in healthy behaviors (e.g., increased walking, physical activity, and social interactions), and increased life expectancy and decreased premature mortality (Brewer et al., 2017; Diez Roux et al., 2016; Kemp et al., 2016; Diez-Roux and Mair, 2010; Dwyer-Lindgren et al., 2017; Ding et al., 2011). Numerous neighborhood area-level factors have been identified as potentially being involved in determining health. These factors include aspects of the built environment (street connectivity, zoning, and proximity of amenities and hazards to residential areas), the social environment, and the natural environment (green space preservation) (Barnett et al., 2017; Dadvand et al., 2014; Maantay, 2001; Nolen et al., 2014; Rundle et al., 2009; Rundle et al., 2013; Van Cauwenberg et al., 2014; Van Dyck et al., 2013). Interest in the impacts of neighborhood area-level factors on health continues to grow (Casper et al., 2019).
However, studies often examine these neighborhood-level determinants, and their impacts on health and health outcomes, independently of each other. In reality, however, these factors may interact to form the totality of the health-related exposures we encounter in our neighborhoods (Prochaska et al., 2019a). A number of tools have been developed to begin examining the cumulative impacts these disparate sources of risk potentially contribute to health outcomes (Prochaska et al., 2019a; Sexton, 2012; Salinas et al., 2012; Gallagher et al., 2015; Seeman et al., 2001). These tools range in terms of complexity, expertise required to operationalize, scope, and engagement of diverse stakeholders. However, a straightforward, easy to implement tool that does not rely on complex software, and can integrate expertise and knowledge from a range of stakeholders may be a welcome addition to the current inventory of tools available.
To better understand the complexities of neighborhood-level determinants of health, and their interactions, while also incorporating local context expertise (community) with content expertise (academic) (Corburn, 2002). Prochaska and colleagues developed the Neighborhood Potential Health Impact Score (NPHIS), a tool to systematically estimate the relative potential health impacts of a neighborhood's environment on the health of the neighborhood's residents given locally available data, context, and the latest scientific expertise (Prochaska et al., 2019b). This tool was first developed and utilized in an effort to identify locations suitable for rebuilding public housing following a large-scale natural disaster (Nolen et al., 2014). Since then, the NPHIS has also been applied in examining potential neighborhood health impacts on health in an environmental justice community, and has been shown to have strong face validity, but the tool has yet to be validated against a key health-related outcome variable. One such outcome is life expectancy, estimates of which have recently become available at very localized (i.e., neighborhood-level) geographies. This study seeks to determine how the NPHIS performs in predicting estimated life expectancy (a key measure of population health (National Research Council (US) Panel to Advance a Research Program on the Design of National Health Accounts, 2010)) across a diverse range of geographic, sociodemographic, racial and ethnic neighborhoods across the State of Texas.
Section snippets
Methods
We examined the relationship between estimated life expectancy and potential neighborhood health impacts among all 5265 census tracts in Texas. Census tracts are relatively small geographic areas with boundaries set by the U.S. Census Bureau. They typically have populations of between 1200 and 8000 people and tend to respect both political (e.g., municipal) as well as natural (e.g., rivers) boundaries (Bureau USC, 2018). Census tracts, though not ideal as proxies for actual neighborhood
Results
Overall, 4709 census tracts were included in this analysis. 3992 census tracts (85.6%) were in metropolitan counties. Texas is geographically vast, and much of this area is rural. Considering that the average population of a census tract is between 1200 and 8000 individuals, it follows that the more densely populated urban counties would have more census tracts than the less densely populated rural counties. Mean estimated life expectancy among census tracts in metropolitan counties was
Discussion
Neighborhood-level factors play a role in determining a host of health and health-related outcomes but are often studied individually with no regard for possible interactive or synergistic effects. This study applies a health impact assessment tool (the Neighborhood Potential Health Impact Score) to model the collective potential impact of a number of neighborhood-level determinants of health on life expectancy within census tracts across Texas. The results indicate that the NPHIS we built is
Funding
This study was conducted with the support of the Texas Medical Center's Health Policy Institute and by NIEHS Center Grants P30 ES006676 and P30 ES030285. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
CRediT authorship contribution statement
John D. Prochaska: Conceptualization, Formal analysis, Funding acquisition, Project administration, Writing - original draft, Writing - review & editing. Daniel C. Jupiter: Conceptualization, Formal analysis, Methodology, Validation, Visualization, Writing - review & editing. Scott Horel: Data curation, Formal analysis, Writing - original draft, Writing - review & editing. Jennifer Vardeman: Conceptualization, Methodology, Writing - review & editing. James N. Burdine: Conceptualization,
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