key: cord-0794913-lkxm52jo
authors: Juhn, Young J.; Wheeler, Philip; Wi, Chung-Il; Bublitz, Joshua; Ryu, Euijung; Ristagno, Elizabeth; Patten, Christi
title: Role of Geographic Risk Factors in COVID-19 Epidemiology: Longitudinal Geospatial Analysis
date: 2021-07-12
journal: Mayo Clin Proc Innov Qual Outcomes
DOI: 10.1016/j.mayocpiqo.2021.06.011
sha: 592333ab76ab930ac183b77875e9223ce6211861
doc_id: 794913
cord_uid: lkxm52jo

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.

The fast spread of the infectious coronavirus disease-2019 (COVID- 19) , caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has created a worldwide pandemic with high morbidity and mortality rates since January 2020. 1 Current research on COVID-19 largely focuses on clinical and biological factors for the risk of COVID-19, while public communications, community health interventions and allocation of resources could benefit from community-based contextual data of patients and populations such as precise geographic distributions and residential units, given the well-recognized health effects of the places in which people live, 2 and other social determinants of health (SDH). [3] [4] [5] For example, despite the reported geographic clusters nationally 4, 5 and limited access to centralized COVID-19 testing facilities, testing and tracing efforts could be guided by more precise geospatial clusters of COVID-19 cases and their associated characteristics. Since COVID-19 vaccines are now available, how best to prioritize and reach out to populations with disproportionate burdens of COVID-19 is critical.

Some surveillance research performed geospatial analysis for COVID-19 at either county or state levels in the US, [6] [7] [8] [9] however, to date, no studies have performed longitudinal geospatial analysis to identify hotspots (high geographic clusters of COVID-19 cases) at a community level or to characterize population characteristics of those residing in identified hotspots based on contextual factors, e.g., type of residential unit and socioeconomic environment. This information could help us better understand racial/ethnic disparities of the burden of COVID-19 [10] [11] [12] [13] and the extent to which SDH (socioeconomic, geospatial, and residential building features) account for such disparities. To address these gaps, we performed a longitudinal geospatial analysis for COVID-19 in Olmsted County, Minnesota, a community of the Midwestern region.

J o u r n a l P r e -p r o o f For people tested multiple times, the date of the first negative test was retained for temporal analysis purposes, unless superseded by a positive test. In that case, the date of the first positive test was used for temporal analysis. The unit of analysis was persons tested, and not tests. For calculating the prevalence of COVID-19 cases, we used the REP census for Olmsted County urban population (denominator) (n=123,939). Case density was weighted as described in the Geospatial Analysis section below by following our previously reported GIS analysis methods. 21, 22 Geospatial analysis: This procedure was applied for each semi-monthly period and cumulatively for the J o u r n a l P r e -p r o o f

March through the end of study period. For the combined analysis for the entire study period (March thru October, 2020), we determined relative hotspots in urban areas over the period from the first positive test through the end of the analysis by using kernel density analysis with a half-mile bandwidth. Urban areas had a population density sufficiently high to enable using a half-mile bandwidth in kernel density analysis, enabling more precise location of hotspots. We limited the analysis to geocoded cases.

Hotspots for the combined analysis (March through October) were defined as case concentrations that (1) were in the 95 th percentile of case density AND (2) had a relative difference equivalent to at least 33% higher than expected case density. Relative difference was derived using the formula RD=(OCDw-ECD)/ECD, where RD = relative density, OCDw = weighted observed case density, and ECD = expected cased density based on the average incidence applied to the REP population. In many rural areas of Olmsted County, population density was low enough that expected case density would be close to zero. Focusing on the cumulative time period from March 11 through October 31 limited hotspots to ten areas, all within the City of Rochester. Thus, we focused our geospatial analysis on COVID-19 epidemiology in the City of Rochester.

3. Temporal trend analysis: To determine temporal differences in the spatial locations of hotspots, we mapped urban hotspots. We examined cases and testing over the following periods: all of March 2020 (3/11-3/31), early April (4/1-4/15), late April, early May, late May, and so on, For urban areas (operationalized as areas within municipal boundaries), we mapped the kernel density of weighted positive cases using a half-mile bandwidth and identified hotspots as defined above.

J o u r n a l P r e -p r o o f Data analysis: Apart from geospatial and temporal trend analysis for COVID-19 cases in the community, we compared study subjects who were tested for COVID-19 and positive COVID-19 tests using descriptive analysis. Similarly, we compared populations residing within and outside hotspots in the community. We characterized study subjects with age, sex, race/ethnicity, and a validated individual level socioeconomic measure called HOUSES index 23-48 with regard to their COVID-19 test status (Table 1 ) and residence status in hotspots (Table 2 ). Geospatial analysis was performed using ArcMap 10.4.1 (produced by ESRI).

Characteristics of study subjects: Of 123,939 Olmsted County urban residents included in the analysis, 53% were women and the mean age (SD) was 40.3 years old (23.6) ( Table 1) . Based on self-report, 77% were white, 8% African American, and 7% Asian; 7% reported Hispanic ethnicity. Addresses were successfully geocoded for 97% (n=140,829) of the full County sample.

The populations residing in urban areas (the City of Rochester and other small cities) in Olmsted County, MN account for 88% of Olmsted County population.

Prevalence of COVID-19, temporal trends and characteristics COVID-19 cases: As of October 31, 2020, a total of 37,141 subjects (30% of urban residents) were tested at least once of whom 2,433 (2%) tested positive accounting for 86% of the total number of COVID-19 cases in Olmsted County, MN during the study period. Since the first COVID-19 case was confirmed on March 11, the total number of new cases per month initially increased until July and decreased during summer. Subsequently, the total number of cases significantly increased since September and reached a peak in October (Figure 1 ). Figure 2 shows temporal (semi-monthly) trends of COVID-19 cases in relation to demographics (2a for age, 2b for sex, 2c for race/ethnicity and 2d J o u r n a l P r e -p r o o f for socioeconomic status). The proportion of African Americans (8.3% of Olmsted County urban population) among COVID-19 cases were significantly higher between April and June (45%) than Whites (77% of population, 30% of cases) and dramatically decreased since then (34% of urban cases in July and 5% in October). A majority of positive cases since July was driven by Whites, especially from September (76% of urban cases). Despite disparities in COVID-19 cases, no significant differences in testing rates were found: 30% (Black), 30% (Hispanic), 25% Figure 1 . Based on the geospatial analysis for the entire study period as shown in Figure 3 , overall, urban hotspots were concentrated in three types of areas: (1) low income family APT complexes, (2) MHCs, and (3) nearby moderate income SFH residential areas. Table 2 summarizes characteristics between those residing in hotspots and those outside hotspots. Ten urban hotspots meeting the 95 th percentile and relative difference of 0.33 or higher thresholds were limited to only the city of Rochester. Urban cases affected 1,512 addresses with 2,433 positive tested persons. Ten urban hotspots accounted for 590 cases at 220 addresses (2.68 cases/parcel), compared to 1,843 cases at 1,292 addresses in areas outside hotspots (1.43 cases /parcel). Overall, 12% population residing in hotspot areas accounted for 24% of all urban COVID-19 cases. People living in hotspots tend to be minorities (eg, African American, Hispanic) and from lower SES background.

To our knowledge, this is the first longitudinal geospatial analysis for COVID-19 epidemiology at a county level in the United States. Our geospatial trend analyses showed hotspots for COVID-19 are a major unrecognized geographic risk factor for COVID-19 and appears to be Our study has a few important strengths. First, our study is a population-based study leveraging a self-contained health care environment and REP, electronic data repository for our community population. Our study is the first longitudinal temporal geospatial analysis for COVID-19 epidemiology in a Midwest community with low dissimilarity index. The prevalence of COVID-19 was weighted by the number of tests and population size and was characterized by individual level SES for study population. Also, our study has some limitations. Some COVID-19 tests J o u r n a l P r e -p r o o f and cases might be missed in our data surveillance system if they were performed outside our study setting, and 5% of the population did not authorize to use their medical records for research. Our study setting has a unique feature such as higher proportion (22%) of health care workers which might affect interpretation of our study results with caution. Our geospatial analysis results were not tested for statistical significance given the frequent update of the results (semi-monthly).

In conclusion, our longitudinal geospatial analysis reveals novel geographic and residential risk factors which might significantly account for overall burden of COVID-19 and its associated racial/ethnic and socioeconomic disparities in the community. Results could geospatially guide community outreach efforts (e.g., public health education, testing/tracing, and vaccine roll out) for populations at risk for COVID-19. 

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We would like to thank Ms. Meaghan Sherden at Olmsted County Public Health Services for sharing her comments on the potential source for the transmission of COVID-19 in the community during the second spike. Also, we would like to thank Mrs. Kelly Okeson for her administrative assistance for preparing the manuscript.

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