Geographic disparities and temporal changes of COVID-19 incidence risks in North Dakota, United States

Nirmalendu Deb Nath; Md Marufuzzaman Khan; Matthew Schmidt; Grace Njau; Agricola Odoi

doi:10.1186/s12889-023-15571-5

Geographic disparities and temporal changes of COVID-19 incidence risks in North Dakota, United States

BMC Public Health. 2023 Apr 20;23(1):720. doi: 10.1186/s12889-023-15571-5.

Authors

Nirmalendu Deb Nath¹, Md Marufuzzaman Khan², Matthew Schmidt³, Grace Njau³, Agricola Odoi⁴

Affiliations

¹ Department of Biomedical and Diagnostic Sciences, College of Veterinary Medicine, University of Tennessee, Knoxville, TN, USA.
² Department of Public Health, College of Education, Health, and Human Sciences, University of Tennessee, Knoxville, TN, USA.
³ North Dakota Department of Health and Human Services, Special Projects and Health Analytics, Bismarck, ND, USA.
⁴ Department of Biomedical and Diagnostic Sciences, College of Veterinary Medicine, University of Tennessee, Knoxville, TN, USA. aodoi@utk.edu.

Abstract

Background: COVID-19 is an important public health concern due to its high morbidity, mortality and socioeconomic impact. Its burden varies by geographic location affecting some communities more than others. Identifying these disparities is important for guiding health planning and service provision. Therefore, this study investigated geographical disparities and temporal changes of the percentage of positive COVID-19 tests and COVID-19 incidence risk in North Dakota.

Methods: COVID-19 retrospective data on total number of tests and confirmed cases reported in North Dakota from March 2020 to September 2021 were obtained from the North Dakota COVID-19 Dashboard and Department of Health, respectively. Monthly incidence risks of the disease were calculated and reported as number of cases per 100,000 persons. To adjust for geographic autocorrelation and the small number problem, Spatial Empirical Bayesian (SEB) smoothing was performed using queen spatial weights. Identification of high-risk geographic clusters of percentages of positive tests and COVID-19 incidence risks were accomplished using Tango's flexible spatial scan statistic. ArcGIS was used to display and visiualize the geographic distribution of percentages of positive tests, COVID-19 incidence risks, and high-risk clusters.

Results: County-level percentages of positive tests and SEB incidence risks varied by geographic location ranging from 0.11% to 13.67% and 122 to 16,443 cases per 100,000 persons, respectively. Clusters of high percentages of positive tests were consistently detected in the western part of the state. High incidence risks were identified in the central and south-western parts of the state, where significant high-risk spatial clusters were reported. Additionally, two peaks (August 2020-December 2020 and August 2021-September 2021) and two non-peak periods of COVID-19 incidence risk (March 2020-July 2020 and January 2021-July 2021) were observed.

Conclusion: Geographic disparities in COVID incidence risks exist in North Dakota with high-risk clusters being identified in the rural central and southwest parts of the state. These findings are useful for guiding intervention strategies by identifying high risk communities so that resources for disease control can be better allocated to communities in need based on empirical evidence. Future studies will investigate predictors of the identified disparities so as to guide planning, disease control and health policy.

Keywords: COVID-19; FlexScan; Geographic disparities; Geographic information system; North Dakota; Spatial epidemiology.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Bayes Theorem
COVID-19* / epidemiology
Humans
Incidence
North Dakota / epidemiology
Retrospective Studies