Geographic disparities and temporal changes of COVID-19 incidence risks in North Dakota, United States
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 p...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-04-01
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| Series: | BMC Public Health |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12889-023-15571-5 |
| _version_ | 1797840771952410624 |
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| author | Nirmalendu Deb Nath Md Marufuzzaman Khan Matthew Schmidt Grace Njau Agricola Odoi |
| author_facet | Nirmalendu Deb Nath Md Marufuzzaman Khan Matthew Schmidt Grace Njau Agricola Odoi |
| author_sort | Nirmalendu Deb Nath |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-09T16:20:07Z |
| format | Article |
| id | doaj.art-262fa458debb4c8cacd7060018e78035 |
| institution | Directory Open Access Journal |
| issn | 1471-2458 |
| language | English |
| last_indexed | 2024-04-09T16:20:07Z |
| publishDate | 2023-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Public Health |
| spelling | doaj.art-262fa458debb4c8cacd7060018e780352023-04-23T11:30:36ZengBMCBMC Public Health1471-24582023-04-0123111010.1186/s12889-023-15571-5Geographic disparities and temporal changes of COVID-19 incidence risks in North Dakota, United StatesNirmalendu Deb Nath0Md Marufuzzaman Khan1Matthew Schmidt2Grace Njau3Agricola Odoi4Department of Biomedical and Diagnostic Sciences, College of Veterinary Medicine, University of TennesseeDepartment of Public Health, College of Education, Health, and Human Sciences, University of TennesseeNorth Dakota Department of Health and Human Services, Special Projects and Health AnalyticsNorth Dakota Department of Health and Human Services, Special Projects and Health AnalyticsDepartment of Biomedical and Diagnostic Sciences, College of Veterinary Medicine, University of TennesseeAbstract 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.https://doi.org/10.1186/s12889-023-15571-5COVID-19Spatial epidemiologyGeographic disparitiesGeographic information systemFlexScanNorth Dakota |
| spellingShingle | Nirmalendu Deb Nath Md Marufuzzaman Khan Matthew Schmidt Grace Njau Agricola Odoi Geographic disparities and temporal changes of COVID-19 incidence risks in North Dakota, United States BMC Public Health COVID-19 Spatial epidemiology Geographic disparities Geographic information system FlexScan North Dakota |
| title | Geographic disparities and temporal changes of COVID-19 incidence risks in North Dakota, United States |
| title_full | Geographic disparities and temporal changes of COVID-19 incidence risks in North Dakota, United States |
| title_fullStr | Geographic disparities and temporal changes of COVID-19 incidence risks in North Dakota, United States |
| title_full_unstemmed | Geographic disparities and temporal changes of COVID-19 incidence risks in North Dakota, United States |
| title_short | Geographic disparities and temporal changes of COVID-19 incidence risks in North Dakota, United States |
| title_sort | geographic disparities and temporal changes of covid 19 incidence risks in north dakota united states |
| topic | COVID-19 Spatial epidemiology Geographic disparities Geographic information system FlexScan North Dakota |
| url | https://doi.org/10.1186/s12889-023-15571-5 |
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