Age-based spatial disparities of COVID-19 incidence rates in the United States counties
COVID-19 incidence disparities have been documented in the literature, but the different driving factors among age groups have yet to be explicitly explained. This study proposes a community-based COVID-19 spatial disparity model, considering different levels of geographic units (individual and comm...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10249835/?tool=EBI |
| _version_ | 1797806999807721472 |
|---|---|
| author | Qian Huang |
| author_facet | Qian Huang |
| author_sort | Qian Huang |
| collection | DOAJ |
| description | COVID-19 incidence disparities have been documented in the literature, but the different driving factors among age groups have yet to be explicitly explained. This study proposes a community-based COVID-19 spatial disparity model, considering different levels of geographic units (individual and community), various contextual variables, multiple COVID-19 outcomes, and different geographic contextual elements. The model assumes the existence of age nonstationarity effects on health determinants, suggesting that health effects of contextual variables vary among place and age groups. Based on this conceptual model and theory, the study selected 62 county-level variables for 1,748 U.S. counties during the pandemic, and created an Adjustable COVID-19 Potential Exposure Index (ACOVIDPEI) using principal component analysis (PCA). The validation was done with 71,521,009 COVID-19 patients in the U.S. from January 2020 through June 2022, with high incidence rates shifting from the Midwest, South Carolina, North Carolina, Arizona, and Tennessee to the West and East coasts. This study corroborates the age nonstationarity effect of health determinants on COVID-19 exposures. These results empirically identify the geographic disparities of COVID-19 incidence rates among age groups and provide the evidentiary guide for targeting pandemic recovery, mitigation, and preparedness in communities. |
| first_indexed | 2024-03-13T06:15:55Z |
| format | Article |
| id | doaj.art-c9464ad961ce43c0b92fbb5c9e840f0f |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-03-13T06:15:55Z |
| publishDate | 2023-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-c9464ad961ce43c0b92fbb5c9e840f0f2023-06-11T05:31:30ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-01186Age-based spatial disparities of COVID-19 incidence rates in the United States countiesQian HuangCOVID-19 incidence disparities have been documented in the literature, but the different driving factors among age groups have yet to be explicitly explained. This study proposes a community-based COVID-19 spatial disparity model, considering different levels of geographic units (individual and community), various contextual variables, multiple COVID-19 outcomes, and different geographic contextual elements. The model assumes the existence of age nonstationarity effects on health determinants, suggesting that health effects of contextual variables vary among place and age groups. Based on this conceptual model and theory, the study selected 62 county-level variables for 1,748 U.S. counties during the pandemic, and created an Adjustable COVID-19 Potential Exposure Index (ACOVIDPEI) using principal component analysis (PCA). The validation was done with 71,521,009 COVID-19 patients in the U.S. from January 2020 through June 2022, with high incidence rates shifting from the Midwest, South Carolina, North Carolina, Arizona, and Tennessee to the West and East coasts. This study corroborates the age nonstationarity effect of health determinants on COVID-19 exposures. These results empirically identify the geographic disparities of COVID-19 incidence rates among age groups and provide the evidentiary guide for targeting pandemic recovery, mitigation, and preparedness in communities.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10249835/?tool=EBI |
| spellingShingle | Qian Huang Age-based spatial disparities of COVID-19 incidence rates in the United States counties PLoS ONE |
| title | Age-based spatial disparities of COVID-19 incidence rates in the United States counties |
| title_full | Age-based spatial disparities of COVID-19 incidence rates in the United States counties |
| title_fullStr | Age-based spatial disparities of COVID-19 incidence rates in the United States counties |
| title_full_unstemmed | Age-based spatial disparities of COVID-19 incidence rates in the United States counties |
| title_short | Age-based spatial disparities of COVID-19 incidence rates in the United States counties |
| title_sort | age based spatial disparities of covid 19 incidence rates in the united states counties |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10249835/?tool=EBI |
| work_keys_str_mv | AT qianhuang agebasedspatialdisparitiesofcovid19incidenceratesintheunitedstatescounties |