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

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.

Geographic disparities and temporal changes of diabetes-related mortality risks in Florida: a retrospective study.

Background: Over the last few decades, diabetes-related mortality risks (DRMR) have increased in Florida. Although there is evidence of geographic disparities in pre-diabetes and diabetes prevalence, little is known about disparities of DRMR in Florida. Understanding these disparities is important for guiding control programs and allocating health resources to communities most at need. Therefore, the objective of this study was to investigate geographic disparities and temporal changes of DRMR in Florida. Methods: Retrospective mortality data for deaths that occurred from 2010 to 2019 were obtained from the Florida Department of Health. Tenth International Classification of Disease codes E10-E14 were used to identify diabetes-related deaths. County-level mortality risks were computed and presented as number of deaths per 100,000 persons. Spatial Empirical Bayesian (SEB) smoothing was performed to adjust for spatial autocorrelation and the small number problem. High-risk spatial clusters of DRMR were identified using Tango's flexible spatial scan statistics. Geographic distribution and high-risk mortality clusters were displayed using ArcGIS, whereas seasonal patterns were visually represented in Excel. Results: A total of 54,684 deaths were reported during the study period. There was an increasing temporal trend as well as seasonal patterns in diabetes mortality risks with high risks occurring during the winter. The highest mortality risk (8.1 per 100,000 persons) was recorded during the winter of 2018, while the lowest (6.1 per 100,000 persons) was in the fall of 2010. County-level SEB smoothed mortality risks varied by geographic location, ranging from 12.6 to 81.1 deaths per 100,000 persons. Counties in the northern and central parts of the state tended to have high mortality risks, whereas southern counties consistently showed low mortality risks. Similar to the geographic distribution of DRMR, significant high-risk spatial clusters were also identified in the central and northern parts of Florida. Conclusion: Geographic disparities of DRMR exist in Florida, with high-risk spatial clusters being observed in rural central and northern areas of the state. There is also evidence of both increasing temporal trends and Winter peaks of DRMR. These findings are helpful for guiding allocation of resources to control the disease, reduce disparities, and improve population health.

Geographic disparities and predictors of COVID-19 vaccination in Missouri: a retrospective ecological study.

Background: Limited information is available on geographic disparities of COVID-19 vaccination in Missouri and yet this information is essential for guiding efforts to improve vaccination coverage. Therefore, the objectives of this study were to (a) investigate geographic disparities in the proportion of the population vaccinated against COVID-19 in Missouri and (b) identify socioeconomic and demographic predictors of the identified disparities. Methods: The COVID-19 vaccination data for time period January 1 to December 31, 2021 were obtained from the Missouri Department of Health. County-level data on socioeconomic and demographic factors were downloaded from the 2020 American Community Survey. Proportions of county population vaccinated against COVID-19 were computed and displayed on choropleth maps. Global ordinary least square regression model and local geographically weighted regression model were used to identify predictors of proportions of COVID-19 vaccinated population. Results: Counties located in eastern Missouri tended to have high proportions of COVID-19 vaccinated population while low proportions were observed in the southernmost part of the state. Counties with low proportions of population vaccinated against COVID-19 tended to have high percentages of Hispanic/Latino population (p = 0.046), individuals living below the poverty level (p = 0.049), and uninsured (p = 0.015) populations. The strength of association between proportion of COVID-19 vaccinated population and percentage of Hispanic/Latino population varied by geographic location. Conclusion: The study findings confirm geographic disparities of proportions of COVID-19 vaccinated population in Missouri. Study findings are useful for guiding programs geared at improving vaccination coverage and uptake by targeting resources to areas with low proportions of vaccinated individuals.

Geographic disparities and temporal changes of COVID-19 hospitalization risks in North Dakota.

Background: Although the burden of the coronavirus disease 2019 (COVID-19) has been different across communities in the US, little is known about the disparities in COVID-19 burden in North Dakota (ND) and yet this information is important for guiding planning and provision of health services. Therefore, the objective of this study was to identify geographic disparities of COVID-19 hospitalization risks in ND. Methods: Data on COVID-19 hospitalizations from March 2020 to September 2021 were obtained from the ND Department of Health. Monthly hospitalization risks were computed and temporal changes in hospitalization risks were assessed graphically. County-level age-adjusted and spatial empirical Bayes (SEB) smoothed hospitalization risks were computed. Geographic distributions of both unsmoothed and smoothed hospitalization risks were visualized using choropleth maps. Clusters of counties with high hospitalization risks were identified using Kulldorff's circular and Tango's flexible spatial scan statistics and displayed on maps. Results: There was a total of 4,938 COVID-19 hospitalizations during the study period. Overall, hospitalization risks were relatively stable from January to July and spiked in the fall. The highest COVID-19 hospitalization risk was observed in November 2020 (153 hospitalizations per 100,000 persons) while the lowest was in March 2020 (4 hospitalizations per 100,000 persons). Counties in the western and central parts of the state tended to have consistently high age-adjusted hospitalization risks, while low age-adjusted hospitalization risks were observed in the east. Significant high hospitalization risk clusters were identified in the north-west and south-central parts of the state. Conclusions: The findings confirm that geographic disparities in COVID-19 hospitalization risks exist in ND. Specific attention is required to address counties with high hospitalization risks, especially those located in the north-west and south-central parts of ND. Future studies will investigate determinants of the identified disparities in hospitalization risks.

Sero-prevalence and risk factors associated with brucellosis in dairy cattle of Sylhet District, Bangladesh: A cross-sectional study.

BACKGROUND: Brucellosis is an emerging disease that causes a significant impact on productive and reproductive performance in dairy cattle. Though Brucella is a pivotal microorganism for dairy cattle, the scenario of brucellosis in Sylhet District is unknown. OBJECTIVES: A cross-sectional study was carried out to assess the prevalence and determinants associated with brucellosis in dairy cattle of Sylhet District. METHODS: A total of 386 sera and data on determinants from 63 dairy herds were collected from 12 sub-districts using simple random sampling. The sera were tested with Rose Bengal Brucella antigen test, Brucella abortus plate agglutination test and serum agglutination test to find out the sero-positivity. RESULTS: Overall, 17.09% (95% CI: 13.67-21.18) prevalence in cows were calculated. Relatively higher prevalence (56.08%; 95% CI: 42.23-70.32) was recorded in cows having parity ≥4 and were at higher risk (OR = 7.28) than the other cows with parity 0-3. Prevalence was significantly higher in cows with history of abortion 90.63% (95% CI: 75.79-96.76), repeat breeding 79.17% (95% CI: 65.74-88.27) and reproductive abnormalities 48.54% (95% CI: 39.12-58.07). Farm-level prevalence was high in farms with the previous history of abortion 95.45% (95% CI: 78.20-99.19) and repeat breeding 90.00% (95% CI: 74.38-96.54). CONCLUSIONS: The prevalence was high in Sylhet district, which might be a public health concern. Therefore, this study would represent the baseline information for guiding brucellosis control and prevention.

Prevalence of Eimeria spp. with associated risk factors in dairy calves in Sylhet, Bangladesh.

BACKGROUND: Bovine eimeriosis is thought to be very important for the productivity and health of cattle all over the world. Despite the importance of cattle farming in Sylhet, little is known about the prevalence of bovine Eimeria spp. and the risk factors connected with it. OBJECTIVES: We conducted a study to evaluate the prevalence, species diversity and associated risk factors of Eimeria spp. in a population of 50 cattle farms from 12 upazilas (sub-district) in Sylhet district. METHODS: Faecal samples were collected randomly from a total of 554 calves ranging in age from 1 month to 2 years old during a period of 7 months. We used Flotation and McMaster techniques for parasitological examination. Species identification was done by using their morphological and morphometric characteristics. RESULTS: Out of 554 calves, 308 were found to be positive for Eimeria species (55.60%). Seven species of Eimeria were identified. Among the identified species, E. bovis (38.98%), E. zuernii (26.17%) and E. alabamensis (22.38%) were found to be the most prevalent species. Mixed and species-specific Eimeria infection were (24.73%; 95% CI 21.32-28.49) and (30.87%; 95% CI 27.17-34.84), respectively. In addition, the highest prevalence was observed at Zakigonj (68%; 95% CI 58.34-76.33) and the lowest at Companygonj (40%; 95% CI 30.94-49.80). Eimeria species intensity ranged between 50 and 76,550 oocyst per gram of faeces. Analysis of associated risk factors by using multivariate logistic regression analysis revealed that age, gender and body condition were significantly (p < 0.05) associated with Eimeria infection. CONCLUSIONS: Based on these present findings, it can be assumed that 'coccidia belong to the most prevalent pathogens in the population of calves in the study area'. Thus, the findings of this study could be used as tools for adoptive surveillance and effective control and prevention of the disease in cattle populations in this region.

Isolation and characterization of novel reassortant mammalian orthoreovirus from pigs in the United States.

Mammalian orthoreovirus (MRV) infects multiple mammalian species including humans. A United States Midwest swine farm with approximately one thousand 3-month-old pigs experienced an event, in which more than 300 pigs showed neurological signs, like "down and peddling", with approximately 40% mortality. A novel MRV was isolated from the diseased pigs. Sequence and phylogenetic analysis revealed that the isolate was a reassortant virus containing viral gene segments from three MRV serotypes that infect human, bovine and swine. The M2 and S1 segment of the isolate showed 94% and 92% nucleotide similarity to the M2 of the MRV2 D5/Jones and the S1 of the MRV1 C/bovine/Indiana/MRV00304/2014, respectively; the remaining eight segments displayed 93%-95% nucleotide similarity to those of the MRV3 FS-03/Porcine/USA/2014. Pig studies showed that both MRV-infected and native contact pigs displayed fever, diarrhoea and nasal discharge. MRV RNA was detected in different intestinal locations of both infected and contact pigs, indicating that the MRV isolate is pathogenic and transmissible in pigs. Seroconversion was also observed in experimentally infected pigs. A prevalence study on more than 180 swine serum samples collected from two states without disease revealed 40%-52% positive to MRV. All results warrant the necessity to monitor MRV epidemiology and reassortment as the MRV could be an important pathogen for the swine industry and a novel MRV might emerge to threaten animal and public health.