IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0087144.html
   My bibliography  Save this article

Community-Wide Health Risk Assessment Using Geographically Resolved Demographic Data: A Synthetic Population Approach

Author

Listed:
  • Jonathan I Levy
  • Maria Patricia Fabian
  • Junenette L Peters

Abstract

Background: Evaluating environmental health risks in communities requires models characterizing geographic and demographic patterns of exposure to multiple stressors. These exposure models can be constructed from multivariable regression analyses using individual-level predictors (microdata), but these microdata are not typically available with sufficient geographic resolution for community risk analyses given privacy concerns. Methods: We developed synthetic geographically-resolved microdata for a low-income community (New Bedford, Massachusetts) facing multiple environmental stressors. We first applied probabilistic reweighting using simulated annealing to data from the 2006–2010 American Community Survey, combining 9,135 microdata samples from the New Bedford area with census tract-level constraints for individual and household characteristics. We then evaluated the synthetic microdata using goodness-of-fit tests and by examining spatial patterns of microdata fields not used as constraints. As a demonstration, we developed a multivariable regression model predicting smoking behavior as a function of individual-level microdata fields using New Bedford-specific data from the 2006–2010 Behavioral Risk Factor Surveillance System, linking this model with the synthetic microdata to predict demographic and geographic smoking patterns in New Bedford. Results: Our simulation produced microdata representing all 94,944 individuals living in New Bedford in 2006–2010. Variables in the synthetic population matched the constraints well at the census tract level (e.g., ancestry, gender, age, education, household income) and reproduced the census-derived spatial patterns of non-constraint microdata. Smoking in New Bedford was significantly associated with numerous demographic variables found in the microdata, with estimated tract-level smoking rates varying from 20% (95% CI: 17%, 22%) to 37% (95% CI: 30%, 45%). Conclusions: We used simulation methods to create geographically-resolved individual-level microdata that can be used in community-wide exposure and risk assessment studies. This approach provides insights regarding community-scale exposure and vulnerability patterns, valuable in settings where policy can be informed by characterization of multi-stressor exposures and health risks at high resolution.

Suggested Citation

  • Jonathan I Levy & Maria Patricia Fabian & Junenette L Peters, 2014. "Community-Wide Health Risk Assessment Using Geographically Resolved Demographic Data: A Synthetic Population Approach," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-10, January.
    • Handle: RePEc:plo:pone00:0087144
    DOI: 10.1371/journal.pone.0087144
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0087144
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0087144&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0087144?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Sexton, K. & Linder, S.H., 2011. "Cumulative risk assessment for combined health effects from chemical and nonchemical stressors," American Journal of Public Health, American Public Health Association, vol. 101(SUPPL. 1), pages 81-88.
    2. Beckman, Richard J. & Baggerly, Keith A. & McKay, Michael D., 1996. "Creating synthetic baseline populations," Transportation Research Part A: Policy and Practice, Elsevier, vol. 30(6), pages 415-429, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Susan M. Rogers & James Rineer & Matthew D. Scruggs & William D. Wheaton & Phillip C. Cooley & Douglas J. Roberts & Diane K. Wagener, 2014. "A Geospatial Dynamic Microsimulation Model for Household Population Projections," International Journal of Microsimulation, International Microsimulation Association, vol. 7(2), pages 119-146.
    2. Jian Liu & Xiaosu Ma & Yi Zhu & Jing Li & Zong He & Sheng Ye, 2021. "Generating and Visualizing Spatially Disaggregated Synthetic Population Using a Web-Based Geospatial Service," Sustainability, MDPI, vol. 13(3), pages 1-16, February.
    3. Templ, Matthias & Meindl, Bernhard & Kowarik, Alexander & Dupriez, Olivier, 2017. "Simulation of Synthetic Complex Data: The R Package simPop," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i10).
    4. Chen, Zhiwei & Li, Xiaopeng, 2021. "Unobserved heterogeneity in transportation equity analysis: Evidence from a bike-sharing system in southern Tampa," Journal of Transport Geography, Elsevier, vol. 91(C).
    5. Brownstone, David, 1997. "Multiple Imputation Methodology for Missing Data, Non-Random Response, and Panel Attrition," University of California Transportation Center, Working Papers qt2zd6w6hh, University of California Transportation Center.
    6. Stefano Guarino & Enrico Mastrostefano & Massimo Bernaschi & Alessandro Celestini & Marco Cianfriglia & Davide Torre & Lena Rebecca Zastrow, 2021. "Inferring Urban Social Networks from Publicly Available Data," Future Internet, MDPI, vol. 13(5), pages 1-45, April.
    7. Zou, Tianqi & Aemmer, Zack & MacKenzie, Don & Laberteaux, Ken, 2022. "A framework for estimating commute accessibility and adoption of ridehailing services under functional improvements from vehicle automation," Journal of Transport Geography, Elsevier, vol. 102(C).
    8. Philip C. Cooley & Sarah M. Bartsch & Shawn T. Brown & William D. Wheaton & Diane K. Wagener & Bruce Y. Lee, 2016. "Weekends as social distancing and their effect on the spread of influenza," Computational and Mathematical Organization Theory, Springer, vol. 22(1), pages 71-87, March.
    9. Hongtai Huang & Timothy M. Barzyk, 2016. "Connecting the Dots: Linking Environmental Justice Indicators to Daily Dose Model Estimates," IJERPH, MDPI, vol. 14(1), pages 1-15, December.
    10. Kathleen Hibbert & Nicolle S. Tulve, 2019. "State-of-the-Science Review of Non-Chemical Stressors Found in a Child’s Social Environment," IJERPH, MDPI, vol. 16(22), pages 1-27, November.
    11. Christopher L Burdett & Brian R Kraus & Sarah J Garza & Ryan S Miller & Kathe E Bjork, 2015. "Simulating the Distribution of Individual Livestock Farms and Their Populations in the United States: An Example Using Domestic Swine (Sus scrofa domesticus) Farms," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-21, November.
    12. Leah Zilversmit Pao & Emily W. Harville & Jeffrey K. Wickliffe & Arti Shankar & Pierre Buekens, 2019. "The Cumulative Risk of Chemical and Nonchemical Exposures on Birth Outcomes in Healthy Women: The Fetal Growth Study," IJERPH, MDPI, vol. 16(19), pages 1-16, October.
    13. Sean F. Reardon & Lindsay Fox & Joseph Townsend, 2015. "Neighborhood Income Composition by Household Race and Income, 1990–2009," The ANNALS of the American Academy of Political and Social Science, , vol. 660(1), pages 78-97, July.
    14. He, Brian Y. & Zhou, Jinkai & Ma, Ziyi & Chow, Joseph Y.J. & Ozbay, Kaan, 2020. "Evaluation of city-scale built environment policies in New York City with an emerging-mobility-accessible synthetic population," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 444-467.
    15. Juste Raimbault, 2019. "Second-order control of complex systems with correlated synthetic data," Post-Print halshs-02376968, HAL.
    16. Ming Yi & Achla Marathe, 2013. "Policy Trap and Optimal Subsidization Policy under Limited Supply of Vaccines," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-9, July.
    17. Lovelace, Robin & Ballas, Dimitris & Watson, Matt, 2014. "A spatial microsimulation approach for the analysis of commuter patterns: from individual to regional levels," Journal of Transport Geography, Elsevier, vol. 34(C), pages 282-296.
    18. Rich, Jeppe & Mulalic, Ismir, 2012. "Generating synthetic baseline populations from register data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(3), pages 467-479.
    19. Richard Todd Niemeier & Pamela R.D. Williams & Alan Rossner & Jane E. Clougherty & Glenn E. Rice, 2020. "A Cumulative Risk Perspective for Occupational Health and Safety (OHS) Professionals," IJERPH, MDPI, vol. 17(17), pages 1-19, August.
    20. Kay, Andrew I. & Noland, Robert B. & Rodier, Caroline J., 2014. "Achieving reductions in greenhouse gases in the US road transportation sector," Energy Policy, Elsevier, vol. 69(C), pages 536-545.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0087144. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.