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

Author

Listed:
  • Saurav Guha

    (Health Analytics Network, Pittsburgh, PA 15237, USA
    Department of Statistics, Mathematics & Computer Application, Bihar Agricultural University, Bhagalpur 813210, India)

  • Michael Alonzo

    (Department of Environmental Science, American University, Washington, DC 20016, USA)

  • Pierre Goovaerts

    (Biomedware, Inc., Ann Arbor, MI 48103, USA)

  • LuAnn L. Brink

    (Allegheny County Health Department, Pittsburgh, PA 15219, USA)

  • Meghana Ray

    (Health Analytics Network, Pittsburgh, PA 15237, USA
    Heed Lab, North Bethesda, MD 20723, USA)

  • Todd Bear

    (Department of Family Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA)

  • Saumyadipta Pyne

    (Health Analytics Network, Pittsburgh, PA 15237, USA
    Department of Statistics and Applied Probability, University of California, Santa Barbara, CA 93106, USA)

Abstract

Background: 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. 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. Results: 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. Conclusion: 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.

Suggested Citation

  • Saurav Guha & Michael Alonzo & Pierre Goovaerts & LuAnn L. Brink & Meghana Ray & Todd Bear & Saumyadipta Pyne, 2024. "Disaggregation of Green Space Access, Walkability, and Behavioral Risk Factor Data for Precise Estimation of Local Population Characteristics," IJERPH, MDPI, vol. 21(6), pages 1-17, June.
    • Handle: RePEc:gam:jijerp:v:21:y:2024:i:6:p:771-:d:1414572
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    References listed on IDEAS

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    4. Hill, Terrence D. & Angel, Ronald J., 2005. "Neighborhood disorder, psychological distress, and heavy drinking," Social Science & Medicine, Elsevier, vol. 61(5), pages 965-975, September.
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