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Tailoring Household Disaster Preparedness Interventions to Reduce Health Disparities: Nursing Implications from Machine Learning Importance Features from the 2018–2020 FEMA National Household Survey

Author

Listed:
  • Meghna Shukla

    (College of Nursing, Wayne State University, 5557 Cass Ave, Detroit, MI 48202, USA
    These authors contributed equally to this work.)

  • Taryn Amberson

    (Castner Incorporated, 1879 Whitehaven Road #150, Grand Island, NY 14072, USA
    Health Systems and Population Health School of Public Health, Department of Health Services Research, University of Washington, 1959 NE Pacific St., Seattle, WA 98195, USA
    Administration for Strategic Preparedness and Response, National Disaster Medical System, 200 Independence Ave., Washington, DC 20201, USA
    These authors contributed equally to this work.)

  • Tara Heagele

    (Hunter-Bellevue School of Nursing, Hunter College, The City University of New York, 425 East 25th Street, Office 427W, New York, NY 10010, USA)

  • Charleen McNeill

    (College of Nursing, University of Tennessee Health Science Center’s, Suite 140C, 874 Union Ave., Memphis, TN 38163, USA)

  • Lavonne Adams

    (Harris College of Nursing & Health Sciences, Texas Christian University, TCU Box 298620, Fort Worth, TX 76129, USA)

  • Kevin Ndayishimiye

    (Castner Incorporated, 1879 Whitehaven Road #150, Grand Island, NY 14072, USA)

  • Jessica Castner

    (Castner Incorporated, 1879 Whitehaven Road #150, Grand Island, NY 14072, USA
    Health Policy, Management and Behavior, School of Public Health, University at Albany, 1400 Washington Avenue, Albany, NY 14222, USA)

Abstract

Tailored disaster preparedness interventions may be more effective and equitable, yet little is known about specific factors associated with disaster household preparedness for older adults and/or those with African American/Black identities. This study aims to ascertain differences in the importance features of machine learning models of household disaster preparedness for four groups to inform culturally tailored intervention recommendations for nursing practice. A machine learning model was developed and tested by combining data from the 2018, 2019, and 2020 Federal Emergency Management Agency National Household Survey . The primary outcome variable was a composite readiness score. A total of 252 variables from 15,048 participants were included. Over 10% of the sample self-identified as African American/Black and 30.3% reported being 65 years of age or older. Importance features varied regarding financial and insurance preparedness, information seeking and transportation between groups. These results reiterate the need for targeted interventions to support financial resilience and equitable resource access. Notably, older adults with Black racial identities were the only group where TV, TV news, and the Weather Channel was a priority feature for household disaster preparedness. Additionally, reliance on public transportation was most important among older adults with Black racial identities, highlighting priority needs for equity in disaster preparedness and policy.

Suggested Citation

  • Meghna Shukla & Taryn Amberson & Tara Heagele & Charleen McNeill & Lavonne Adams & Kevin Ndayishimiye & Jessica Castner, 2024. "Tailoring Household Disaster Preparedness Interventions to Reduce Health Disparities: Nursing Implications from Machine Learning Importance Features from the 2018–2020 FEMA National Household Survey," IJERPH, MDPI, vol. 21(5), pages 1-23, April.
    • Handle: RePEc:gam:jijerp:v:21:y:2024:i:5:p:521-:d:1381137
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    References listed on IDEAS

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    3. Kyong-Jin Park & Bong-Woo Lee & Kyoo-Man Ha, 2022. "Examining the transition of natural disaster management for climate change," International Journal of Business Continuity and Risk Management, Inderscience Enterprises Ltd, vol. 12(2), pages 116-130.
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