Assessment of Retrospective Covid-19 Spatial Clusters with Respect to Demographic Factors
Case Study of Kansas City, Missouri, United States
Bibliographic Data
| ID | 15514318 |
|---|---|
| Authors | Hadeel AlQadi (0000-0003-1478-1912, University of Missouri–Kansas City, corresponding author), Majid Bani-Yaghoub (0000-0002-7627-3940, University of Missouri–Kansas City), Sindhu Balakumar (University of Missouri–Kansas City), Siqi Wu (0000-0001-8234-6600, University of Missouri–Kansas City), Alex Francisco (El Paso County Public Health) |
| Year | 2021 |
| Volume | 18 |
| Issue | 21 |
| Pages | 11496-11496 |
| Publication date | 2021-11-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph182111496 |
| PMID | 34770012 |
| OpenAlex | W3208361589 |
| Language | EN |
| Citations received | 4 |
| References cited | 6 |
Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The United States (U.S.) has the highest number of reported COVID-19 infections and related deaths in the world, accounting for 17.8% of total global confirmed cases as of August 2021. As COVID-19 spread throughout communities across the U.S., it became clear that inequities would arise among differing demographics. Several researchers have suggested that certain racial and ethnic minority groups may have been disproportionately impacted by the spread of COVID-19. In the present study, we used the daily data of COVID-19 cases in Kansas City, Missouri, to observe differences in COVID-19 clusters with respect to gender, race, and ethnicity. Specifically, we utilized a retrospective Poisson spatial scan statistic with respect to demographic factors to detect daily clusters of COVID-19 in Kansas City at the zip code level from March to November 2020. Our statistical results indicated that clusters of the male population were more widely scattered than clusters of the female population. Clusters of the Hispanic population had the highest prevalence and were also more widely scattered. This demographic cluster analysis can provide guidance for reducing the social inequalities associated with the COVID-19 pandemic. Moreover, applying stronger preventive and control measures to emerging clusters can reduce the likelihood of another epidemic wave of infection
Cluster (spacecraft · Coronavirus disease 2019 (COVID-19 · Disease · Ethnic group · Geography · Infectious disease (medical specialty · Pandemic · Political science · Population · Scan statistic · Sociology · Statistics · COVID-19 and healthcare impacts · COVID-19 epidemiological studies · Data-Driven Disease Surveillance · Demography · Law · Medicine · Gerontology
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Then and now
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| Citations per year | 1.33 |
|---|---|
| Citation span | 2023 - 2026 (4) |
| Highly cited | No |