COVID-19 Medical Vulnerability Indicators: A Predictive, Local Data Model for Equity in Public Health Decision Making

Paul M. Ong, Chhandara Pech, Nataly Rios Gutierrez and Vickie M. Mays
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Paul M. Ong: Department of Urban Planning, UCLA Center for Neighborhood Knowledge, UCLA Luskin School of Public Policy, Los Angeles, CA 90095, USA
Chhandara Pech: Department of Urban Planning, UCLA Center for Neighborhood Knowledge, UCLA Luskin School of Public Policy, Los Angeles, CA 90095, USA
Nataly Rios Gutierrez: Department of Urban Planning, UCLA Center for Neighborhood Knowledge, UCLA Luskin School of Public Policy, Los Angeles, CA 90095, USA
Vickie M. Mays: Departments of Psychology and Health Policy & Management, UCLA Fielding School of Public Health, Los Angeles, CA 90095, USA

IJERPH, 2021, vol. 18, issue 9, 1-23

Abstract: This article reports the outcome of a project to develop and assess a predictive model of vulnerability indicators for COVID-19 infection in Los Angeles County. Multiple data sources were used to construct four indicators for zip code tabulation areas: (1) pre-existing health condition, (2) barriers to accessing health care, (3) built environment risk, and (4) the CDC’s social vulnerability. The assessment of the indicators finds that the most vulnerable neighborhoods are characterized by significant clustering of racial minorities. An overwhelming 73% of Blacks reside in the neighborhoods with the two highest levels of pre-existing health conditions. For the barriers to accessing health care indicator, 40% of Latinx reside in the highest vulnerability places. The built environment indicator finds that selected Asian ethnic groups (63%), Latinx (55%), and Blacks (53%) reside in the neighborhoods designated as high or the highest vulnerability. The social vulnerability indicator finds 42% of Blacks and Latinx and 38% of selected Asian ethnic group residing in neighborhoods of high vulnerability. The vulnerability indicators can be adopted nationally to respond to COVID-19. The metrics can be utilized in data-driven decision making of re-openings or resource distribution such as testing, vaccine distribution and other pandemic-related resources to ensure equity for the most vulnerable.

Keywords: COVID-19; health disparities; social vulnerability index; Black; Latinx; Cambodian; hmong; Laotians; Asians; built environment (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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