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

Abstract

Objective: To develop indicators of vulnerability for coronavirus disease 2019 (covid-19) infection in Los Angeles County (LAC) by race and neighborhood characteristics. Design: Development of indicators that combines pre-existing medical vulnerabilities with social and built-environment data by zip code tabulation areas (ZTCAs). Setting: Neighborhoods in LAC categorized by race/ethnicity ranked into quintiles by relative vulnerability: Non-Hispanic white; Black; Latinx: Cambodians, Hmong and Laotians combined (CHL); and Other Asians. Data Sources: AskCHIS Neighborhood Edition, American Community Survey 2014-2018, and California Department of Parks and Recreation. Main Outcome Measures: 1) Pre-Existing Health Condition, 2) Barriers to Accessing Healthcare, 3) Built Environment Risk, and 4) CDC's Social Vulnerability. Results: Neighborhoods most vulnerable to covid-19 are characterized by significant clustering of racial minorities, low income households and unmet medical needs. An overwhelming 73% of Blacks reside in the neighborhoods with the two highest quintiles of pre-existing health conditions, followed by Latinx (70%) and CHL (60%), while 60% of whites reside in low or the lowest vulnerable neighborhoods. For the Barriers to Accessing Healthcare indicator, 40% of Latinx reside in the highest vulnerability places followed by Blacks, CHL and other Asians (29%, 22%, and 16% respectively), compared with only 7% of Whites reside in such neighborhoods. The Built Environment Indicator finds CHL (63%) followed by Latinx (55%) and Blacks (53%) reside in the neighborhoods designated as high or the highest vulnerability compared to 32% of Whites residing in these neighborhoods. The Social Vulnerability Indicator finds 42% of Blacks and Latinx and 38% of CHL residing in neighborhoods of high vulnerability compared with only 8% of Whites residing these neighborhoods. Conclusions: Vulnerability to covid-19 infections differs by neighborhood and racial/ethnic groups. Our vulnerability indicators when utilized in decision-making of re-openings or resource distribution such as testing, vaccine distribution, hotel rooms for quarantine and other covid-19-related resources can provide an equity driven data approach for the most vulnerable.

How to Best Protect People With Diabetes From the Impact of SARS-CoV-2: Report of the International COVID-19 and Diabetes Summit

“…Health conditions alone do not put individuals at risk for morbidity and mortality from COVID-19, but those conditions do put individuals at risk when they are paired with detrimental SDoH. 24 …”

County-level barriers in the COVID-19 vaccine coverage index and their associations with willingness to receive the COVID-19 vaccine across racial/ethnic groups in the U.S.

“…These studies using the SVI highlighted the impact of social vulnerabilities on COVID-19 vaccination intentions. Additionally, public health scholars emphasized the importance of both social vulnerabilities as well as healthcare system barriers when assessing structural factors associated with COVID-19 vaccination (15). In February 2021, the CDC released a second composite measure developed by Surgo Ventures that extended the SVI (16).…”

An AHP-based regional COVID-19 vulnerability model and its application in China

“…At present, many relevant studies have used various methods to construct vulnerability models related to COVID-19 and involve many possible risk factors, such as blood type, HIV, gene and genetic factors (El-Shitany et al 2021 ; Pollitt et al 2020 ). In addition to pathological factors, the vulnerability of social factors, such as population density, age structure of the population, ethnicity, and regional health levels, is also worth studying (Ong et al 2021 ; DeCaprio et al 2020 ; Chen et al 2021 ). In these studies, the AHP method was also applied to the assessment of regional risk levels (Rahman et al 2020 ; Mahato et al 2020 ; Sarwar and Imran 2021 ).…”