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.

Persistent spatial clustering and predictors of pediatric La Crosse virus neuroinvasive disease risk in eastern Tennessee and western North Carolina, 2003-2020.

The combined region of eastern Tennessee and western North Carolina has a persistently high risk of pediatric La Crosse virus neuroinvasive disease (LACV-ND). To guide public health intervention in this region, the objectives of this retrospective ecological study were to investigate the geographic clustering and predictors of pediatric LACV-ND risk at the ZIP code tabulation area (ZCTA) level. Data on pediatric cases of LACV-ND reported between 2003 and 2020 were obtained from Tennessee Department of Health and North Carolina Department of Health and Human Services. Purely spatial and space-time scan statistics were used to identify ZCTA-level clusters of confirmed and probable pediatric LACV-ND cases from 2003-2020, and a combination of global and local (i.e., geographically weighted) negative binomial regression models were used to investigate potential predictors of disease risk from 2015-2020. The cluster investigation revealed spatially persistent high-risk and low-risk clusters of LACV-ND, with most cases consistently reported from a few high-risk clusters throughout the entire study period. Temperature and precipitation had positive but antagonistic associations with disease risk from 2015-2020, but the strength of those relationships varied substantially across the study area. Because LACV-ND risk clustering in this region is focally persistent, retroactive case surveillance can be used to guide the implementation of targeted public health intervention to reduce the disease burden in high-risk areas. Additional research on the role of climate in LACV transmission is warranted to support the development of predictive transmission models to guide proactive public health interventions.

Investigation of geographic disparities of diabetes-related hospitalizations in Florida using flexible spatial scan statistics: An ecological study.

BACKGROUND: Hospitalizations due to diabetes complications are potentially preventable with effective management of the condition in the outpatient setting. Diabetes-related hospitalization (DRH) rates can provide valuable information about access, utilization, and efficacy of healthcare services. However, little is known about the local geographic distribution of DRH rates in Florida. Therefore, the objectives of this study were to investigate the geographic distribution of DRH rates at the ZIP code tabulation area (ZCTA) level in Florida, identify significant local clusters of high hospitalization rates, and describe characteristics of ZCTAs within the observed spatial clusters. METHODS: Hospital discharge data from 2016 to 2019 were obtained from the Florida Agency for Health Care Administration through a Data Use Agreement with the Florida Department of Health. Raw and spatial empirical Bayes smoothed DRH rates were computed at the ZCTA level. High-rate DRH clusters were identified using Tango's flexible spatial scan statistic. Choropleth maps were used to display smoothed DRH rates and significant high-rate spatial clusters. Demographic, socioeconomic, and healthcare-related characteristics of cluster and non-cluster ZCTAs were compared using the Wilcoxon rank sum test for continuous variables and Chi-square test for categorical variables. RESULTS: There was a total of 554,133 diabetes-related hospitalizations during the study period. The statewide DRH rate was 8.5 per 1,000 person-years, but smoothed rates at the ZCTA level ranged from 0 to 101.9. A total of 24 significant high-rate spatial clusters were identified. High-rate clusters had a higher percentage of rural ZCTAs (60.9%) than non-cluster ZCTAs (41.8%). The median percent of non-Hispanic Black residents was significantly (p < 0.0001) higher in cluster ZCTAs than in non-cluster ZCTAs. Populations of cluster ZCTAs also had significantly (p < 0.0001) lower median income and educational attainment, and higher levels of unemployment and poverty compared to the rest of the state. In addition, median percent of the population with health insurance coverage and number of primary care physicians per capita were significantly (p < 0.0001) lower in cluster ZCTAs than in non-cluster ZCTAs. CONCLUSIONS: This study identified geographic disparities of DRH rates at the ZCTA level in Florida. The identification of high-rate DRH clusters provides useful information to guide resource allocation such that communities with the highest burdens are prioritized to reduce the observed disparities. Future research will investigate determinants of hospitalization rates to inform public health planning, resource allocation and interventions.

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.

Modeling county level COVID-19 transmission in the greater St. Louis area: Challenges of uncertainty and identifiability when fitting mechanistic models to time-varying processes.

We use a compartmental model with a time-varying transmission parameter to describe county level COVID-19 transmission in the greater St. Louis area of Missouri and investigate the challenges in fitting such a model to time-varying processes. We fit this model to synthetic and real confirmed case and hospital discharge data from May to December 2020 and calculate uncertainties in the resulting parameter estimates. We also explore non-identifiability within the estimated parameter set. We find that the death rate of infectious non-hospitalized individuals, the testing parameter and the initial number of exposed individuals are not identifiable based on an investigation of correlation coefficients between pairs of parameter estimates. We also explore how this non-identifiability ties back into uncertainties in the estimated parameters and find that it inflates uncertainty in the estimates of our time-varying transmission parameter. However, we do find that R0 is not highly affected by non-identifiability of its constituent components and the uncertainties associated with the quantity are smaller than those of the estimated parameters. Parameter values estimated from data will always be associated with some uncertainty and our work highlights the importance of conducting these analyses when fitting such models to real data. Exploring identifiability and uncertainty is crucial in revealing how much we can trust the parameter estimates.

Determinants of disparities of diabetes-related hospitalization rates in Florida: a retrospective ecological study using a multiscale geographically weighted regression approach.

BACKGROUND: Early diagnosis, control of blood glucose levels and cardiovascular risk factors, and regular screening are essential to prevent or delay complications of diabetes. However, most adults with diabetes do not meet recommended targets, and some populations have disproportionately high rates of potentially preventable diabetes-related hospitalizations. Understanding the factors that contribute to geographic disparities can guide resource allocation and help ensure that future interventions are designed to meet the specific needs of these communities. Therefore, the objectives of this study were (1) to identify determinants of diabetes-related hospitalization rates at the ZIP code tabulation area (ZCTA) level in Florida, and (2) assess if the strengths of these relationships vary by geographic location and at different spatial scales. METHODS: Diabetes-related hospitalization (DRH) rates were computed at the ZCTA level using data from 2016 to 2019. A global ordinary least squares regression model was fit to identify socioeconomic, demographic, healthcare-related, and built environment characteristics associated with log-transformed DRH rates. A multiscale geographically weighted regression (MGWR) model was then fit to investigate and describe spatial heterogeneity of regression coefficients. RESULTS: Populations of ZCTAs with high rates of diabetes-related hospitalizations tended to have higher proportions of older adults (p < 0.0001) and non-Hispanic Black residents (p = 0.003). In addition, DRH rates were associated with higher levels of unemployment (p = 0.001), uninsurance (p < 0.0001), and lack of access to a vehicle (p = 0.002). Population density and median household income had significant (p < 0.0001) negative associations with DRH rates. Non-stationary variables exhibited spatial heterogeneity at local (percent non-Hispanic Black, educational attainment), regional (age composition, unemployment, health insurance coverage), and statewide scales (population density, income, vehicle access). CONCLUSIONS: The findings of this study underscore the importance of socioeconomic resources and rurality in shaping population health. Understanding the spatial context of the observed relationships provides valuable insights to guide needs-based, locally-focused health planning to reduce disparities in the burden of potentially avoidable hospitalizations.

Antimicrobial susceptibility profiles of Staphylococcus aureus in cattle and humans in farming communities of Isingiro and Kamuli districts, Uganda.

Bacterial resistance to antimicrobials is fast becoming a big challenge as resistance to multiple drugs is rising rapidly. The emergence of resistant Staphylococcus aureus worldwide is life-threatening in both humans and animals and yet little is known about the burden of antimicrobial resistance (AMR) in developing countries including Uganda. Therefore, the aims of this study were to determine the prevalence of antimicrobial resistant S. aureus among humans and animals as well as assess the perceptions and practices of farmers in Kamuli and Isingiro districts in Uganda regarding AMR of S. aureus. A cross-sectional study was conducted between July and September 2020 in 147 randomly selected cattle-keeping households in Isingiro and Kamuli districts. A structured questionnaire uploaded in the Kobo-collect online data collection tool was used to assess farmers' perceptions and practices pertaining to AMR in each of the selected households. Nasal swabs (n = 147) were collected from both cattle and humans (farmers). Bacterial isolation and confirmation was done using Gram-staining and biochemical tests. This was followed by antimicrobial susceptibility testing (AST) using the Kirby Bauer disc diffusion method. Only 14/147 (9.5%) cattle samples and 45/147(30.6%) human samples tested positive for S. aureus. All cattle S. aureus isolates were resistant to Nitroimidazoles while 92.9% were resistant to Penicillins. None of the isolates were resistant to Fluoroquinolones and Aminoglycosides. All the 14 isolates exhibited AMR to at least one of the assessed antibiotics and 92.9% (13/14) showed evidence of multidrug resistance (MDR). Likewise, S. aureus human isolates showed high levels of resistance to Nitroimidazoles (100%) and Penicillins (93.3%), with none of the isolates having resistance to Aminoglycosides, and only one exhibiting resistance to Fluoroquinolones (2.2%). All the 45 human isolates exhibited AMR to at least one antibiotic while 93% (42/45) had MDR. Most farmers had good perceptions of AMR, with a significantly higher proportion of respondents from Isingiro than Kamuli showing a better understanding of AMR. Antibiotic prophylaxis was reported to be the least practiced measure of diseases and parasites control (17.0%), with more farmers in Isingiro (33.3%) undertaking it than those in Kamuli (1.3%) (p < 0.001). Penicillins and Nitroimidazoles were reported to be the most used antibiotics among cattle and humans. This study provides evidence of occurrence of S. aureus resistance to antimicrobials commonly used in both humans and livestock in Isingiro and Kamuli districts. Farmers had good perceptions regarding AMR as well as good antimicrobial use practices which can form a basis for mitigation of AMR.

An examination of geographic access to outpatient stroke rehabilitation services in Tennessee, a stroke belt state.

BACKGROUND: While over half of US stroke patients were discharged to home, estimates of geographic access to outpatient stroke rehab facilities are unavailable. The objective of our study was to assess distance and travel time to the nearest outpatient stroke rehab facility in Tennessee, a high stroke prevalence state. METHODS: We systematically scraped Google Maps with the terms "stroke", "rehabilitation", and "outpatient" to identify Tennessee stroke rehab facilities. We then averaged/aggregated Census block-level travel distance and travel time to determine the mean travel distance/time to a facility for each of the 95 Tennessee counties and the overall state. Comparisons of mean travel time/distance were made between rural and urban counties and between low, medium, and high stroke prevalence counties. RESULTS: We found that 79% of facilities were in urban areas. Significantly higher median of mean travel times and distances (p values both <0.001) were observed in rural (22.0 miles, 31.6 min) versus urban counties (10.5 miles, 18.4 min). High (21.5 miles, 32.5 min) and medium (18.7 miles, 28.3 minutes) stroke prevalence counties, which often overlap with rural counties, had significantly higher median of mean travel times and distance than low stroke prevalence counties (7.3 miles, 14.5 min). CONCLUSIONS: Rural Tennessee counties were faced with high stroke prevalence, inadequate facilities, and significantly greater travel distance and time to access care. Additional efforts to address transportation barriers and accelerate telerehabilitation implementation are crucial for improving equal access to stroke aftercare in these areas.

Investigation of predictors of severity of diabetes complications among hospitalized patients with diabetes in Florida, 2016-2019.

BACKGROUND: Severe diabetes complications impact the quality of life of patients and may lead to premature deaths. However, these complications are preventable through proper glycemic control and management of risk factors. Understanding the risk factors of complications is important in guiding efforts to manage diabetes and reduce risks of its complications. Therefore, the objective of this study was to identify risk factors of severe diabetes complications among adult hospitalized patients with diabetes in Florida. METHODS: Hospital discharge data from 2016 to 2019 were obtained from the Florida Agency for Health Care Administration through a Data Use Agreement with the Florida Department of Health. Adapted Diabetes Complications Severity Index (aDCSI) scores were computed for 1,061,140 unique adult patients with a diagnosis of diabetes. Severe complications were defined as those with an aDCSI ≥ 4. Population average models, estimated using generalized estimating equations, were used to identify individual- and area-level predictors of severe diabetes complications. RESULTS: Non-Hispanic Black patients had the highest odds of severe diabetes complications compared to non-Hispanic White patients among both males (Odds Ratio [OR] = 1.20, 95% Confidence Interval [CI]: 1.17, 1.23) and females (OR = 1.27, 95% CI: 1.23, 1.31). Comorbidities associated with higher odds of severe complications included hypertension (OR = 2.30, 95% CI: 2.23, 2.37), hyperlipidemia (OR = 1.29, 95% CI: 1.27, 1.31), obesity (OR = 1.24, 95% CI: 1.21, 1.26) and depression (OR = 1.09, 95% CI: 1.07, 1.11), while the odds were lower for patients with a diagnosis of arthritis (OR = 0.81, 95% CI: 0.79, 0.82). Type of health insurance coverage was associated with the severity of diabetes complications, with significantly higher odds of severe complications among Medicare (OR = 1.85, 95% CI: 1.80, 1.90) and Medicaid (OR = 1.83, 95% CI: 1.77, 1.90) patients compared to those with private insurance. Residing within the least socioeconomically deprived ZIP code tabulation areas (ZCTAs) in the state had a protective effect compared to residing outside of these areas. CONCLUSIONS: Racial, ethnic, and socioeconomic disparities in the severity of diabetes complications exist among hospitalized patients in Florida. The observed disparities likely reflect challenges to maintaining glycemic control and managing cardiovascular risk factors, particularly for patients with multiple chronic conditions. Interventions to improve diabetes management should focus on populations with disproportionately high burdens of severe complications to improve quality of life and decrease premature mortality among adult patients with diabetes in Florida.

Modeling impact of vaccination on COVID-19 dynamics in St. Louis.

The region of St. Louis, Missouri, has displayed a high level of heterogeneity in COVID-19 cases, hospitalization, and vaccination coverage. We investigate how human mobility, vaccination, and time-varying transmission rates influenced SARS-CoV-2 transmission in five counties in the St. Louis area. A COVID-19 model with a system of ordinary differential equations was developed to illustrate the dynamics with a fully vaccinated class. Using the weekly number of vaccinations, cases, and hospitalization data from five counties in the greater St. Louis area in 2021, parameter estimation for the model was completed. The transmission coefficients for each county changed four times in that year to fit the model and the changing behaviour. We predicted the changes in disease spread under scenarios with increased vaccination coverage. SafeGraph local movement data were used to connect the forces of infection across various counties.

Perceptions of the impact of COVID-19 in Tennessee, USA: a retrospective study.

Background: Despite high incidence and mortality risks associated with COVID-19 during the pandemic, stay-at-home orders and vaccination recommendations were met with varying levels of acceptance in Tennessee. Understanding perceptions of individuals regarding the health and economic impacts of COVID-19 is necessary to address public concerns while ensuring appropriate public health response. Therefore, the objectives of this study were to (a) investigate differences in opinions among residents of Tennessee regarding the impacts of COVID-19; and (b) identify socioeconomic and demographic predictors/determinants of these opinions. Methods: This retrospective cross-sectional study was conducted using survey data collected in nine waves during 2020. Distributions of survey-weighted sociodemographic characteristics and respondent perceptions of the impact of COVID-19 were computed. Weighted logistic models were used to investigate predictors of a number of perceptions: whether the health or economic impact was greater, concern for respondent's health, concern for family's health, and willingness to accept COVID-19 vaccine. Results: The study included a total of 9,754 survey respondents. Approximately equal percentages considered COVID-19 to have a greater economic (48.4%) versus health impact (51.6%). Just 40.1% of the respondents reported that they would definitely accept a COVID-19 vaccine. Age group, race, educational attainment, and household composition were significant (p < 0.05) predictors of all investigated perceptions regarding COVID-19. Lack of prior infection was the strongest predictor of the perception of COVID-19 having a greater impact on health (OR = 2.40, p < 0.001), concern for respondent's health (OR = 1.86, p = 0.002), and concern for family members' health (OR = 1.90, p = 0.001). Compared to males, females had higher odds of identifying the health impact of COVID-19 as greater (OR = 1.09, p = 0.041) and reporting concern for family health (OR = 1.14, p = 0.003). However, they had lower odds (OR = 0.63, p < 0.001) of willingness to accept vaccine than males. Conclusion: These findings improve our understanding of the drivers of health behaviors, including vaccine hesitancy, and are useful for guiding public health outreach/education programs.

A retrospective investigation of spatial clusters and determinants of diabetes prevalence: scan statistics and geographically weighted regression modeling approaches.

Background: Diabetes and its complications represent a significant public health burden in the United States. Some communities have disproportionately high risks of the disease. Identification of these disparities is critical for guiding policy and control efforts to reduce/eliminate the inequities and improve population health. Thus, the objectives of this study were to investigate geographic high-prevalence clusters, temporal changes, and predictors of diabetes prevalence in Florida. Methods: Behavioral Risk Factor Surveillance System data for 2013 and 2016 were provided by the Florida Department of Health. Tests for equality of proportions were used to identify counties with significant changes in the prevalence of diabetes between 2013 and 2016. The Simes method was used to adjust for multiple comparisons. Significant spatial clusters of counties with high diabetes prevalence were identified using Tango's flexible spatial scan statistic. A global multivariable regression model was fit to identify predictors of diabetes prevalence. A geographically weighted regression model was fit to assess for spatial non-stationarity of the regression coefficients and fit a local model. Results: There was a small but significant increase in the prevalence of diabetes in Florida (10.1% in 2013 to 10.4% in 2016), and statistically significant increases in prevalence occurred in 61% (41/67) of counties in the state. Significant, high-prevalence clusters of diabetes were identified. Counties with a high burden of the condition tended to have high proportions of the population that were non-Hispanic Black, had limited access to healthy foods, were unemployed, physically inactive, and had arthritis. Significant non-stationarity of regression coefficients was observed for the following variables: proportion of the population physically inactive, proportion with limited access to healthy foods, proportion unemployed, and proportion with arthritis. However, density of fitness and recreational facilities had a confounding effect on the association between diabetes prevalence and levels of unemployment, physical inactivity, and arthritis. Inclusion of this variable decreased the strength of these relationships in the global model, and reduced the number of counties with statistically significant associations in the local model. Conclusions: The persistent geographic disparities of diabetes prevalence and temporal increases identified in this study are concerning. There is evidence that the impacts of the determinants on diabetes risk vary by geographical location. This implies that a one-size-fits-all approach to disease control/prevention would be inadequate to curb the problem. Therefore, health programs will need to use evidence-based approaches to guide health programs and resource allocation to reduce disparities and improve population health.

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.

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.

Use of a colorimeter is a viable method to measure melanin and erythema content in the context of laser beam attenuation by use of a class IV laser in different tissues in dogs.

OBJECTIVE: Patient factors may alter laser photon attenuation, but these factors have not been adequately evaluated in live dogs. Our objective was to evaluate class IV laser beam attenuation (LBA) by canine tissues using a colorimeter to evaluate melanin and erythema indices. We hypothesized that greater melanin and erythema indices and unclipped hair would increase LBA, and these properties would vary among tissues. ANIMALS: 20 client-owned dogs. PROCEDURES: Between October 1 and December 1, 2017, colorimeter measurements and LBA in various tissues before and after clipping overlying hair were evaluated. Data were analyzed using generalized linear mixed models. Statistical significance was set at P < .05. RESULTS: LBA was greater in unclipped (98.6 ± 0.4%) than clipped hair (94.6 ± 0.4%). The least LBA occurred in the pinna (93%) while the greatest occurred in the caudal vertebra (100%) and caudal semitendinosis muscles (100%). Each mm of tissue thickness resulted in LBA of 11.6%. Each unit increase in melanin index resulted in a 3.3% increase in LBA. There was no association of LBA with erythema index. CLINICAL RELEVANCE: To our knowledge, this is the first study that evaluated LBA by different tissues in live dogs using a colorimeter to evaluate melanin and erythema indices. We recommend clipping hair prior to photobiomodulation to decrease laser beam attenuation and using increased laser doses in thicker tissues and dogs with high melanin content. The colorimeter may be helpful in customizing patient treatment dosimetry. Future studies are necessary to determine therapeutic laser doses for adequate photobiomodulation effects.

An epidemiological study of the predictors of multidrug resistance and methicillin resistance among Staphylococcus spp. isolated from canine specimens submitted to a diagnostic laboratory in Tennessee, USA.

Background: Understanding drivers of multidrug resistance (MDR) and methicillin resistance, which have increased among canine staphylococcal isolates, is essential for guiding antimicrobial use practices. Therefore, the objective of this study was to identify predictors of MDR and methicillin resistance among Staphylococcus spp. commonly isolated from canine clinical specimens. Methods: This retrospective study used records of canine specimens submitted to the University of Tennessee College of Veterinary Medicine Clinical Bacteriology Laboratory for bacterial culture and antimicrobial susceptibility testing between 2006 and 2017. Records from 7,805 specimens positive for the following Staphylococcus species were included for analysis: Staphylococcus pseudintermedius, Staphylococcus aureus, Staphylococcus coagulans (formerly Staphylococcus schleiferi subspecies coagulans), and Staphylococcus schleiferi (formerly S. schleiferi subsp. schleiferi). Generalized linear regression models were fit using generalized estimating equations (GEE) to identify predictors of MDR (defined as resistance to three or more antimicrobial classes) and methicillin resistance among these isolates. Results: Multidrug resistance (42.1%) and methicillin resistance (31.8%) were relatively common. Isolates from skeletal (joint and bone) specimens had the highest levels of MDR (51.3%) and methicillin resistance (43.6%), followed by cutaneous specimens (45.8% multidrug-resistant, 37.1% methicillin resistant). Staphylococcus species, specimen site, and clinical setting were significant (p < 0.01) predictors of both outcomes. Compared to S. pseudintermedius, S. schleiferi had higher odds of methicillin resistance, while S. coagulans and S. schleiferi had lower odds of MDR. The odds of both MDR and methicillin resistance for isolates from hospital patient specimens were significantly higher than those from referral patients for urine/bladder and otic specimens. Odds of MDR among isolates from skeletal specimens of hospital patients were also higher than those of referral patients. Conclusions: Staphylococcus isolates in this study had substantial levels of MDR and methicillin resistance. Differences in the odds of these outcomes between referral and hospital patient isolates did not persist for all specimen sites, which may reflect differences in diagnostic testing and antimicrobial use practices with respect to body site or system. Judicious antimicrobial use, informed by culture and susceptibility testing, is important to limit treatment failures and curb selection pressure.

Geographic disparities in COVID-19 testing and outcomes in Florida.

BACKGROUND: Understanding geographic disparities in Coronavirus Disease 2019 (COVID-19) testing and outcomes at the local level during the early stages of the pandemic can guide policies, inform allocation of control and prevention resources, and provide valuable baseline data to evaluate the effectiveness of interventions for mitigating health, economic and social impacts. Therefore, the objective of this study was to identify geographic disparities in COVID-19 testing, incidence, hospitalizations, and deaths during the first five months of the pandemic in Florida.  METHODS: Florida county-level COVID-19 data for the time period March-July 2020 were used to compute various COVID-19 metrics including testing rates, positivity rates, incidence risks, percent of hospitalized cases, hospitalization risks, case-fatality rates, and mortality risks. High or low risk clusters were identified using either Kulldorff's circular spatial scan statistics or Tango's flexible spatial scan statistics and their locations were visually displayed using QGIS. RESULTS: Visual examination of spatial patterns showed high estimates of all COVID-19 metrics for Southern Florida. Similar to the spatial patterns, high-risk clusters for testing and positivity rates and all COVID-19 outcomes (i.e. hospitalizations and deaths) were concentrated in Southern Florida. The distributions of these metrics in the other parts of Florida were more heterogeneous. For instance, testing rates for parts of Northwest Florida were well below the state median (11,697 tests/100,000 persons) but they were above the state median for North Central Florida. The incidence risks for Northwest Florida were equal to or above the state median incidence risk (878 cases/100,000 persons), but the converse was true for parts of North Central Florida. Consequently, a cluster of high testing rates was identified in North Central Florida, while a cluster of low testing rate and 1-3 clusters of high incidence risks, percent of hospitalized cases, hospitalization risks, and case fatality rates were identified in Northwest Florida. Central Florida had low-rate clusters of testing and positivity rates but it had a high-risk cluster of percent of hospitalized cases. CONCLUSIONS: Substantial disparities in the spatial distribution of COVID-19 outcomes and testing and positivity rates exist in Florida, with Southern Florida counties generally having higher testing and positivity rates and more severe outcomes (i.e. hospitalizations and deaths) compared to Northern Florida. These findings provide valuable baseline data that is useful for assessing the effectiveness of preventive interventions, such as vaccinations, in various geographic locations in the state. Future studies will need to assess changes in spatial patterns over time at lower geographical scales and determinants of any identified patterns.

Geographically persistent clusters of La Crosse virus disease in the Appalachian region of the United States from 2003 to 2021.

La Crosse virus (LACV) is a mosquito-borne pathogen that causes more pediatric neuroinvasive disease than any other arbovirus in the United States. The geographic focus of reported LACV neuroinvasive disease (LACV-ND) expanded from the Midwest into Appalachia in the 1990s, and most cases have been reported from a few high-risk foci since then. Here, we used publicly available human disease data to investigate changes in the distribution of geographic LACV-ND clusters between 2003 and 2021 and to investigate socioeconomic and demographic predictors of county-level disease risk in states with persistent clusters. We used spatial scan statistics to identify high-risk clusters from 2003-2021 and a generalized linear mixed model to identify socioeconomic and demographic predictors of disease risk. The distribution of LACV-ND clusters was consistent during the study period, with an intermittent cluster in the upper Midwest and three persistent clusters in Appalachia that included counties in east Tennessee / western North Carolina, West Virginia, and Ohio. In those states, county-level cumulative incidence was higher when more of the population was white and when median household income was lower. Public health officials should target efforts to reduce LACV-ND incidence in areas with consistent high risks.

Antimicrobial resistance among Streptococcus equi subspecies zooepidemicus and Rhodococcus equi isolated from equine specimens submitted to a diagnostic laboratory in Kentucky, USA.

Background: Surveillance of antimicrobial resistance (AMR) among veterinary pathogens is necessary to identify clinically relevant patterns of AMR and to inform antimicrobial use practices. Streptococcus equi subsp. zooepidemicus and Rhodococcus equi are bacterial pathogens of major clinical importance in horses and are frequently implicated in respiratory tract infections. The objectives of this study were to describe antimicrobial resistance patterns and identify predictors of AMR and multidrug resistance (MDR) (resistance to three or more antimicrobial classes) among equine S. zooepidemicus and R. equi isolates. Methods: Antimicrobial susceptibility data from equine specimens submitted to the University of Kentucky Veterinary Diagnostic Laboratory between 2012 and 2017 were used in the study. Temporal trends in AMR and MDR were assessed using the Cochran-Armitage test. Logistic regression was used to identify associations between patient characteristics and the following outcomes: (a) MDR among S. zooepidemicus isolates, and (b) resistance to macrolides and ansamycins (rifampin) among R. equi isolates. Logistic regression was also used to investigate whether resistance of S. zooepidemicus and R. equi isolates to an antimicrobial class could be predicted by resistance to other drug classes. Results: The vast majority of S. zooepidemicus (99.6%) and R. equi isolates (83%) were resistant to at least one antimicrobial agent, but no significant temporal trends in AMR were observed. Approximately half (53.3%) of the S. zooepidemicus isolates were multidrug-resistant, and there was a significant (p < 0.001) increasing temporal trend of MDR among S. zooepidemicus isolates. Resistance to penicillin, which is typically recommended for treatment of suspected S. zooepidemicus infections, also increased during the study period, from 3.3% to 9.5%. Among R. equi isolates, 19.2% were resistant to one or more macrolide antibiotics, 24% were resistant to rifampin, and 15.6% were resistant to both macrolide(s) and rifampin. For both organisms, resistance to an antimicrobial class could be predicted based on resistance profiles to other drug classes. For instance, significant (p < 0.01) predictors of ß-lactam resistance among S. zooepidemicus isolates included resistance to macrolides (Odds Ratio (OR) = 14.7) and ansamycins (OR = 9.3). Resistance to phenicols (OR = 3.7) and ansamycins (OR = 19.9) were associated with higher odds of macrolide resistance among R. equi isolates. Conclusions: The increase in MDR among S. zooepidemicus isolates is concerning. The observed levels of resistance to macrolides and rifampin among R. equi are also worrisome given the limited number of antimicrobials available for treatment of this organism. The findings of this study highlight the importance of ongoing surveillance of AMR to guide treatment decisions and directions for future research.

Geographic disparities and determinants of COVID-19 incidence risk in the greater St. Louis Area, Missouri (United States).

BACKGROUND: Evidence seems to suggest that the risk of Coronavirus Disease 2019 (COVID-19) might vary across communities due to differences in population characteristics and movement patterns. However, little is known about these differences in the greater St Louis Area of Missouri and yet this information is useful for targeting control efforts. Therefore, the objectives of this study were to investigate (a) geographic disparities of COVID-19 risk and (b) associations between COVID-19 risk and socioeconomic, demographic, movement and chronic disease factors in the Greater St. Louis Area of Missouri, USA. METHODS: Data on COVID-19 incidence and chronic disease hospitalizations were obtained from the Department of Health and Missouri Hospital Association, respectively. Socioeconomic and demographic data were obtained from the 2018 American Community Survey while population mobility data were obtained from the SafeGraph website. Choropleth maps were used to identify geographic disparities of COVID-19 risk and several sociodemographic and chronic disease factors at the ZIP Code Tabulation Area (ZCTA) spatial scale. Global negative binomial and local geographically weighted negative binomial models were used to investigate associations between ZCTA-level COVID-19 risk and socioeconomic, demographic and chronic disease factors. RESULTS: There were geographic disparities found in COVID-19 risk. Risks tended to be higher in ZCTAs with high percentages of the population with a bachelor's degree (p<0.0001) and obesity hospitalizations (p<0.0001). Conversely, risks tended to be lower in ZCTAs with high percentages of the population working in agriculture (p<0.0001). However, the association between agricultural occupation and COVID-19 risk was modified by per capita between ZCTA visits. Areas that had both high per capita between ZCTA visits and high percentages of the population employed in agriculture had high COVID-19 risks. The strength of association between agricultural occupation and COVID-19 risk varied by geographic location. CONCLUSIONS: Geographic disparities of COVID-19 risk exist in the St. Louis area and are associated with sociodemographic factors, population movements, and obesity hospitalization risks. The latter is particularly concerning due to the growing prevalence of obesity and the known immunological impairments among obese individuals. Therefore, future studies need to focus on improving our understanding of the relationships between COVID-19 vaccination efficacy, obesity and waning of immunity among obese individuals so as to better guide vaccination regimens and reduce disparities.