Effectiveness of the Covid-19 Community Vulnerability Index in explaining Covid-19 deaths
Bibliographic Data
| ID | 22082153 |
|---|---|
| Authors | Jinghua An (0000-0002-1650-3855, University of Illinois Chicago, corresponding author), Shelley Hoover (0000-0003-3197-3529, University of Illinois Chicago), Sreenivas Konda (University of Illinois Chicago), Sage J Kim (0000-0001-8939-5157, University of Illinois Chicago) |
| Year | 2022 |
| Volume | 10 |
| Pages | 953198-953198 |
| Publication date | 2022-09-23 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2022.953198 |
| PMID | 36211696 |
| OpenAlex | W4297010215 |
| Language | EN |
| Citations received | 1 |
| References cited | 9 |
Objectives: To explore the effectiveness of a COVID-19 specific social vulnerability index, we examined the relative importance of four COVID-19 specific themes and three general themes of the COVID-19 Community Vulnerability Index (CCVI) in explaining COVID-19 mortality rates in Cook County, Illinois. Methods: We counted COVID-19 death records from the Cook County Medical Examiner's Office, geocoded incident addresses by census tracts, and appended census tracts' CCVI scores. Negative binomial regression and Random Forest were used to examine the relative importance of CCVI themes in explaining COVID-19 mortality rates. Results: COVID-19 specific Themes 6 (High risk environments) and 4 (Epidemiological factors) were the most important in explaining COVID-19 mortality (incidence rate ratio (IRR) = 6.80 and 6.44, respectively), followed by a general Theme 2 (Minority status & language, IRR = 3.26). Conclusion: The addition of disaster-specific indicators may improve the accuracy of social vulnerability indices. However, variance for Theme 6 was entirely from the long-term care resident indicator, as the other two indicators were constant at the census tract level. Thus, CCVI should be further refined to improve its effectiveness in identifying vulnerable communities. Also, building a more robust local data infrastructure is critical to understanding the vulnerabilities of local places
Cartography · Census · Census tract · Computer security · Disease · Environmental health · Geocoding · Geography · Negative binomial distribution · Population · Psychological resilience · Rate ratio · Social vulnerability · Sociology · Statistics · Computer Science · COVID-19 epidemiological studies · Demography · Disaster Response and Management · Health disparities and outcomes · Mathematics · Medicine · Psychology · Social Psychology
Negative Binomial Regression
Association Between Social Vulnerability and a County’s Risk for Becoming a Covid-19 Hotspot — United States, June 1–July 25, 2020
Evaluating social vulnerability indicators
Social Vulnerability and Racial Inequality in Covid-19 Deaths in Chicago
On the Validity of Validation
Validation of a neighborhood-level Covid Local Risk Index in 47 large U.S. cities
How Valid Are Social Vulnerability Models
| Unique citing works | 1 |
|---|---|
| Citations per year | 0,33 |
| Citation span | 2023 - 2023 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |