tetano
Editor, Senior Moderator
Mayo Clin Proc Innov Qual Outcomes
. 2021 Jul 12.
doi: 10.1016/j.mayocpiqo.2021.06.011. Online ahead of print.
Role of Geographic Risk Factors in COVID-19 Epidemiology: Longitudinal Geospatial Analysis
Young J Juhn[SUP] 1 [/SUP], Philip Wheeler[SUP] 1 [/SUP], Chung-Il Wi[SUP] 1 [/SUP], Joshua Bublitz[SUP] 2 [/SUP], Euijung Ryu[SUP] 2 [/SUP], Elizabeth Ristagno[SUP] 1 [/SUP], Christi Patten[SUP] 2 [/SUP]
Affiliations
Abstract
Objective: To perform a geospatial and temporal trend analysis for coronavirus disease-2019 (COVID-19) in a Midwest community to identify and characterize hotspots for COVID-19.
Methods: We conducted a population-based longitudinal surveillance assessing the semi-monthly geospatial trends of the prevalence of test confirmed COVID-19 cases in Olmsted County, Minnesota, from March 11, 2020, to October 31, 2020. As urban areas accounted for 84% of population and 86% of all COVID-19 cases in Olmsted County, MN, we determined hotspots for COVID-19 in urban areas of Olmsted County (Rochester and other small cities), MN during the study period by using kernel density analysis with a half-mile bandwidth.
Results: As of October 31, 2020, a total of 37,141 subjects (30%) were tested at least once of whom 2,433 (6.6%) tested positive. Testing rates among race groups were similar: 29% (African American), 30% (Hispanic), 25% (Asian), and 31% (White). Ten urban hotspots accounted for 590 cases at 220 addresses (2.68 case/address), compared to 1,843 cases at 1,292 addresses in areas outside hotspots (1.43 case/address). Overall, 12% of population residing in hotspot areas accounted for 24% of all COVID-19 cases. Hotspots were concentrated in neighborhoods with low-income apartments and mobile home communities. People living in hotspots tended to be minorities and from lower socioeconomic background.
Conclusion: Geographic and residential risk factors might significantly account for overall burden of COVID-19 and its associated racial/ethnic and socioeconomic disparities. Results could geospatially guide community outreach efforts (e.g., testing/tracing, and vaccine roll out) for populations at risk for COVID-19.
Keywords: Acute Respiratory Infection, (ARI); COVID-19; Confidence interval, (CI); Coronavirus disease 2019, (COVID-19); Electronic Health Records, (EHRs); Human coronavirus, (HCov); Middle East respiratory syndrome (MERS)-coronavirus, (MERS-CoV); Reverse transcription polymerase chain reaction, (RT-PCR); SARS-CoV-2; Severe acute respiratory syndrome (SARS)-associated coronavirus, (SARS-CoV); Severe acute respiratory syndrome coronavirus 2, (SARS-CoV-2); Social determinants of health, (SDH); Socioeconomic status, (SES); epidemiology; geospatial analysis; social determinants of health.
.
. 2021 Jul 12.
doi: 10.1016/j.mayocpiqo.2021.06.011. Online ahead of print.
Role of Geographic Risk Factors in COVID-19 Epidemiology: Longitudinal Geospatial Analysis
Young J Juhn[SUP] 1 [/SUP], Philip Wheeler[SUP] 1 [/SUP], Chung-Il Wi[SUP] 1 [/SUP], Joshua Bublitz[SUP] 2 [/SUP], Euijung Ryu[SUP] 2 [/SUP], Elizabeth Ristagno[SUP] 1 [/SUP], Christi Patten[SUP] 2 [/SUP]
Affiliations
- PMID: 34308261
- PMCID: PMC8272975
- DOI: 10.1016/j.mayocpiqo.2021.06.011
Abstract
Objective: To perform a geospatial and temporal trend analysis for coronavirus disease-2019 (COVID-19) in a Midwest community to identify and characterize hotspots for COVID-19.
Methods: We conducted a population-based longitudinal surveillance assessing the semi-monthly geospatial trends of the prevalence of test confirmed COVID-19 cases in Olmsted County, Minnesota, from March 11, 2020, to October 31, 2020. As urban areas accounted for 84% of population and 86% of all COVID-19 cases in Olmsted County, MN, we determined hotspots for COVID-19 in urban areas of Olmsted County (Rochester and other small cities), MN during the study period by using kernel density analysis with a half-mile bandwidth.
Results: As of October 31, 2020, a total of 37,141 subjects (30%) were tested at least once of whom 2,433 (6.6%) tested positive. Testing rates among race groups were similar: 29% (African American), 30% (Hispanic), 25% (Asian), and 31% (White). Ten urban hotspots accounted for 590 cases at 220 addresses (2.68 case/address), compared to 1,843 cases at 1,292 addresses in areas outside hotspots (1.43 case/address). Overall, 12% of population residing in hotspot areas accounted for 24% of all COVID-19 cases. Hotspots were concentrated in neighborhoods with low-income apartments and mobile home communities. People living in hotspots tended to be minorities and from lower socioeconomic background.
Conclusion: Geographic and residential risk factors might significantly account for overall burden of COVID-19 and its associated racial/ethnic and socioeconomic disparities. Results could geospatially guide community outreach efforts (e.g., testing/tracing, and vaccine roll out) for populations at risk for COVID-19.
Keywords: Acute Respiratory Infection, (ARI); COVID-19; Confidence interval, (CI); Coronavirus disease 2019, (COVID-19); Electronic Health Records, (EHRs); Human coronavirus, (HCov); Middle East respiratory syndrome (MERS)-coronavirus, (MERS-CoV); Reverse transcription polymerase chain reaction, (RT-PCR); SARS-CoV-2; Severe acute respiratory syndrome (SARS)-associated coronavirus, (SARS-CoV); Severe acute respiratory syndrome coronavirus 2, (SARS-CoV-2); Social determinants of health, (SDH); Socioeconomic status, (SES); epidemiology; geospatial analysis; social determinants of health.
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