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Recurrent Home Flooding in Detroit, MI 2012–2020: Results of a Household Survey

Author

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  • Peter S. Larson

    (Social Environment and Health Program, Survey Research Center, Institute for Social Research, The University of Michigan, Ann Arbor, MI 48109, USA
    Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA)

  • Carina Gronlund

    (Social Environment and Health Program, Survey Research Center, Institute for Social Research, The University of Michigan, Ann Arbor, MI 48109, USA)

  • Lyke Thompson

    (Center for Urban Studies, Wayne State University, Detroit, MI 48202, USA)

  • Natalie Sampson

    (Department of Health and Human Services, University of Michigan-Dearborn, 19000 Hubbard Drive, Fairlane Center South, Dearborn, MI 48126, USA)

  • Ramona Washington

    (Center for Urban Studies, Wayne State University, Detroit, MI 48202, USA)

  • Jamie Steis Thorsby

    (Healthy Urban Waters, Wayne State University, Detroit, MI 48202, USA)

  • Natalie Lyon

    (Healthy Urban Waters, Wayne State University, Detroit, MI 48202, USA)

  • Carol Miller

    (Healthy Urban Waters, Wayne State University, Detroit, MI 48202, USA)

Abstract

Household flooding has wide ranging social, economic and public health impacts particularly for people in resource poor communities. The determinants and public health outcomes of recurrent home flooding in urban contexts, however, are not well understood. A household survey was used to assess neighborhood and household level determinants of recurrent home flooding in Detroit, MI. Survey activities were conducted from 2012 to 2020. Researchers collected information on past flooding, housing conditions and public health outcomes. Using the locations of homes, a “hot spot” analysis of flooding was performed to find areas of high and low risk. Survey data were linked to environmental and neighborhood data and associations were tested using regression methods. 4803 households participated in the survey. Flooding information was available for 3842 homes. Among these, 2085 (54.26%) reported experiencing pluvial flooding. Rental occupied units were more likely to report flooding than owner occupied homes (Odd ratio (OR) 1.72 [95% Confidence interval (CI) 1.49, 1.98]). Housing conditions such as poor roof quality and cracks in basement walls influenced home flooding risk. Homes located in census tracts with increased percentages of owner occupied units (vs. rentals) had a lower odds of flooding (OR 0.92 [95% (CI) 0.86, 0.98]). Household factors were found the be more predictive of flooding than neighborhood factors in both univariate and multivariate analyses. Flooding and housing conditions associated with home flooding were associated with asthma cases. Recurrent home flooding is far more prevalent than previously thought. Programs that support recovery and which focus on home improvement to prevent flooding, particularly by landlords, might benefit the public health. These results draw awareness and urgency to problems of urban flooding and public health in other areas of the country confronting the compounding challenges of aging infrastructure, disinvestment and climate change.

Suggested Citation

  • Peter S. Larson & Carina Gronlund & Lyke Thompson & Natalie Sampson & Ramona Washington & Jamie Steis Thorsby & Natalie Lyon & Carol Miller, 2021. "Recurrent Home Flooding in Detroit, MI 2012–2020: Results of a Household Survey," IJERPH, MDPI, vol. 18(14), pages 1-16, July.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:14:p:7659-:d:596984
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    References listed on IDEAS

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    1. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    2. Yaella Depietri & Timon McPhearson, 2018. "Changing urban risk: 140 years of climatic hazards in New York City," Climatic Change, Springer, vol. 148(1), pages 95-108, May.
    3. Akaike, Hirotugu, 1981. "Likelihood of a model and information criteria," Journal of Econometrics, Elsevier, vol. 16(1), pages 3-14, May.
    4. Greene, G. & Paranjothy, S. & Palmer, S.R., 2015. "Resilience and vulnerability to the psychological harm from flooding: The role of social cohesion," American Journal of Public Health, American Public Health Association, vol. 105(9), pages 1792-1795.
    5. Dianne Lowe & Kristie L. Ebi & Bertil Forsberg, 2013. "Factors Increasing Vulnerability to Health Effects before, during and after Floods," IJERPH, MDPI, vol. 10(12), pages 1-53, December.
    6. Torsten Grothmann & Fritz Reusswig, 2006. "People at Risk of Flooding: Why Some Residents Take Precautionary Action While Others Do Not," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 38(1), pages 101-120, May.
    7. Haynes, Katharine & Coates, Lucinda & van den Honert, Rob & Gissing, Andrew & Bird, Deanne & Dimer de Oliveira, Felipe & D’Arcy, Rebecca & Smith, Chloe & Radford, Deirdre, 2017. "Exploring the circumstances surrounding flood fatalities in Australia—1900–2015 and the implications for policy and practice," Environmental Science & Policy, Elsevier, vol. 76(C), pages 165-176.
    8. Julia Woodhall-Melnik & Caitlin Grogan, 2019. "Perceptions of Mental Health and Wellbeing Following Residential Displacement and Damage from the 2018 St. John River Flood," IJERPH, MDPI, vol. 16(21), pages 1-18, October.
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