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.

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.

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.

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.

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.

Patterns of antimicrobial, multidrug and methicillin resistance among Staphylococcus spp. isolated from canine specimens submitted to a diagnostic laboratory in Tennessee, USA: a descriptive study.

BACKGROUND: Multidrug- and methicillin-resistant staphylococci are both veterinary and public health concerns due to their zoonotic potential. Therefore, the objective of this study was to investigate patterns of antimicrobial, multidrug, and methicillin resistance among four Staphylococcus spp. commonly isolated from canine clinical specimens submitted to the Clinical Bacteriology Laboratory at the University of Tennessee College of Veterinary Medicine (UTCVM). METHODS: Results of antimicrobial susceptibility testing and mecA polymerase chain reaction (PCR) for isolates of four common Staphylococcus spp. isolates were obtained from the Bacteriology Laboratory at the UTCVM between 01/01/2006 and 12/31/2017. Cochran-Armitage trend test was used to assess temporal trends of antimicrobial resistance (AMR), multidrug resistance (MDR), and methicillin resistance. Kappa test of agreement was used to assess agreement between the results of PCR and disk diffusion tests. RESULTS: Most of the 7805 isolates were S. pseudintermedius (6453 isolates), followed by S. coagulans (860), S. aureus (330), and S. schleiferi (162). Among S. pseudintermedius isolates, 45.5% were MDR, and 30.8% were methicillin-resistant (MRSP). There was a significant temporal increase in MRSP (p = 0.017). Chloramphenicol resistance increased among both MRSP and methicillin-susceptible (MSSP) isolates (p <  0.0001). Among S. aureus isolates, 40.9% were MDR, 37.4% were methicillin-resistant (MRSA), and the proportion of MRSA isolates increased significantly (p = 0.0480) over time. There was an increasing temporal trend in the proportion of MDR isolates among MSSP (p = 0.0022), but a decrease among MRSP (p <  0.0001) and MRSA (p = 0.0298). S. schleiferi had the highest percentage (56.9%) of methicillin-resistant isolates. Oxacillin disk diffusion was superior to cefoxitin for the detection of mecA-mediated resistance and had almost perfect agreement with mecA PCR assay for S. pseudintermedius (95.4% agreement, kappa (κ) = 0.904; p <  0.0001), S. coagulans (95.6%, κ = 0.913; p <  0.0001) and S. schleiferi (97.7%, κ = 0.945; p <  0.0001). However, cefoxitin disk diffusion was superior to oxacillin disk diffusion and had almost perfect agreement with mecA PCR assay for S. aureus (95.3%, κ = 0.834; p <  0.0001). CONCLUSIONS: The levels of resistance and increasing temporal trends are concerning. These findings have implications for treatment decisions and public health due to the zoonotic potential of staphylococci. Continued surveillance and use of antibiograms to guide clinical decisions will be critical.

Prevalence and predictors of stroke among individuals with prediabetes and diabetes in Florida.

BACKGROUND: The prevalence of both prediabetes and diabetes have been increasing in Florida. These increasing trends will likely result in increases of stroke burden since both conditions are major risk factors of stroke. However, not much is known about the prevalence and predictors of stroke among adults with prediabetes and diabetes and yet this information is critical for guiding health programs aimed at reducing stroke burden. Therefore, the objectives of this study were to estimate the prevalence and identify predictors of stroke among persons with either prediabetes or diabetes in Florida. METHODS: The 2019 Behavioral Risk Factor Surveillance System (BRFSS) survey data were obtained from the Florida Department of Health and used for the study. Weighted prevalence estimates of stroke and potential predictor variables as well as their 95% confidence intervals were computed for adults with prediabetes and diabetes. A conceptual model of predictors of stroke among adults with prediabetes and diabetes was constructed to guide statistical model building. Two multivariable logistic models were built to investigate predictors of stroke among adults with prediabetes and diabetes. RESULTS: The prevalence of stroke among respondents with prediabetes and diabetes were 7.8% and 11.2%, respectively. The odds of stroke were significantly (p ≤ 0.05) higher among respondents with prediabetes that were ≥ 45 years old (Odds ratio [OR] = 2.82; 95% Confidence Interval [CI] = 0.74, 10.69), had hypertension (OR = 5.86; CI = 2.90, 11.84) and hypercholesterolemia (OR = 3.93; CI = 1.84, 8.40). On the other hand, the odds of stroke among respondents with diabetes were significantly (p ≤ 0.05) higher if respondents were non-Hispanic Black (OR = 1.79; CI = 1.01, 3.19), hypertensive (OR = 3.56; CI = 1.87, 6.78) and had depression (OR = 2.02; CI = 1.14, 3.59). CONCLUSIONS: Stroke prevalence in Florida is higher among adults with prediabetes and diabetes than the general population of the state. There is evidence of differences in the importance of predictors of stroke among populations with prediabetes and those with diabetes. These findings are useful for guiding health programs geared towards reducing stroke burden among populations with prediabetes and diabetes.

Geographic disparities and predictors of COVID-19 hospitalization risks in the St. Louis Area, Missouri (USA).

BACKGROUND: There is evidence of geographic disparities in COVID-19 hospitalization risks that, if identified, could guide control efforts. Therefore, the objective of this study was to investigate Zip Code Tabulation Area (ZCTA)-level geographic disparities and identify predictors of COVID-19 hospitalization risks in the St. Louis area. METHODS: Hospitalization data for COVID-19 and several chronic diseases were obtained from the Missouri Hospital Association. ZCTA-level data on socioeconomic and demographic factors were obtained from the American Community Survey. Geographic disparities in distribution of COVID-19 age-adjusted hospitalization risks, socioeconomic and demographic factors as well as chronic disease risks were investigated using choropleth maps. Predictors of ZCTA-level COVID-19 hospitalization risks were investigated using global negative binomial and local geographically weighted negative binomial models. RESULTS: COVID-19 hospitalization risks were significantly higher in ZCTAs with high diabetes hospitalization risks (p < 0.0001), COVID-19 risks (p < 0.0001), black population (p = 0.0416), and populations with some college education (p = 0.0005). The associations between COVID-19 hospitalization risks and the first three predictors varied by geographic location. CONCLUSIONS: There is evidence of geographic disparities in COVID-19 hospitalization risks that are driven by differences in socioeconomic, demographic and health-related factors. The impacts of these factors vary by geographical location implying that a 'one-size-fits-all' approach may not be appropriate for management and control. Using both global and local models leads to a better understanding of geographic disparities. These findings are useful for informing health planning to identify geographic areas likely to have high numbers of individuals needing hospitalization as well as guiding vaccination efforts.

Investigation of geographic disparities of pre-diabetes and diabetes in Florida.

BACKGROUND: Diabetes is a leading cause of death and disability in the United States, and its precursor, pre-diabetes, is estimated to occur in one-third of American adults. Understanding the geographic disparities in the distribution of these conditions and identifying high-prevalence areas is critical to guiding control and prevention programs. Therefore, the objective of this study was to investigate clusters of pre-diabetes and diabetes risk in Florida and identify significant predictors of the conditions. METHODS: Data from the 2013 Behavioral Risk Factor Surveillance System were obtained from the Florida Department of Health. Spatial scan statistics were used to identify and locate significant high-prevalence local clusters. The county prevalence proportions of pre-diabetes and diabetes and the identified significant clusters were displayed in maps. Logistic regression was used to identify significant predictors of the two conditions for individuals living within and outside high-prevalence clusters. RESULTS: The study included a total of 34,186 respondents. The overall prevalence of pre-diabetes and diabetes were 8.2 and 11.5%, respectively. Three significant (p < 0.05) local, high-prevalence spatial clusters were detected for pre-diabetes, while five were detected for diabetes. The counties within the high-prevalence clusters had prevalence ratios ranging from 1.29 to 1.85. There were differences in the predictors of the conditions based on whether respondents lived within or outside high-prevalence clusters. Predictors of both pre-diabetes and diabetes regardless of region or place of residence were obesity/overweight, hypertension, and hypercholesterolemia. Income and physical activity level were significant predictors of diabetes but not pre-diabetes. Arthritis, sex, and marital status were significant predictors of diabetes only among residents of high-prevalence clusters, while educational attainment and smoking were significant predictors of diabetes only among residents of non-cluster counties. CONCLUSIONS: Geographic disparities of pre-diabetes and diabetes exist in Florida. Information from this study is useful for guiding resource allocation and targeting of intervention programs focusing on identified modifiable predictors of pre-diabetes and diabetes so as to reduce health disparities and improve the health of all Floridians.

Investigation of Predictors of Newborn Screening Refusal in a Large Birth Cohort in North Dakota, USA.

Objectives The objective of this study was to identify maternal and provider predictors of newborn screening (NBS) refusal in North Dakota between 2011 and 2014. Methods Records of 40,440 live resident births occurring in North Dakota between 2011 and 2014 were obtained from the North Dakota Department of Health and included in the study. Factor-specific percentages of NBS refusals and 95% confidence intervals were computed for each predictor. Since the outcome is rare, multivariable Firth logistic regression was used to investigate maternal and provider predictors of NBS refusal. Model goodness-of-fit test was evaluated using the Hosmer-Lemeshow test. All analyses were conducted in SAS 9.4. Results Of the 40,440 live births, 135 (0.33%) were NBS refusals. 97% of the refusals were to white women, 94% were homebirths, and 93% utilized state non-credentialed birth attendants. The odds of NBS refusals were significantly higher among non-credentialed birth attendants (p < 0.0001), homebirths (p < 0.0001), and among those that refused Hepatitis B vaccination (HBV) at birth (p = 0.047). On the other hand, odds of NBS refusals were significantly (p < 0.0001) lower among women that had more prenatal visits. Conclusions for Practice This study provides preliminary evidence of association between NBS refusal and provider type, home births, and HBV refusal. Additional studies of obstetric providers, home births and women are needed to improve our understanding of the reasons for NBS refusal to better deliver preventive services to newborns.

Antimicrobial resistance among Escherichia coli isolates from dogs presented with urinary tract infections at a veterinary teaching hospital in South Africa.

BACKGROUND: This study investigated the burden and predictors of canine E. coli urinary tract infections (UTI) and antimicrobial resistance among dogs presented at a veterinary teaching hospital in South Africa, 2007-2012. METHODS: The Cochran-Armitage trend test was used to investigate temporal trends while logistic regression models were used to investigate predictors (age, sex, breed, year) of E. coli infections and antimicrobial resistance (AMR). RESULTS: A total of 22.3% (168/755) of the urinary specimens tested positive for E. coli. A significant (p = 0.0004) decreasing temporal trend in the percentage of E. coli positive isolates was observed over the study period. There were high levels of AMR to penicillin-G (99%), clindamycin (100%), tylosine (95%), cephalothin (84%) but relatively low levels of resistance to enrofloxacin (16%), orbifloxacin (21%). Almost all (98%, 164/167) the isolates exhibited multidrug resistance (MDR), while only 11% (19/167) and 2% (4/167) exhibited extensive drug resistance (XDR) and pan-drug resistance (PDR), respectively. CONCLUSIONS: Although, the risk of E. coli UTI declined during the study period, the risk of AMR increased. The high levels of AMR and MDR as well as the presence of XDR and PDR is concerning as these have the potential of affecting prognosis of UTI treatments.

An epidemiologic study of antimicrobial resistance of Staphylococcus species isolated from equine samples submitted to a diagnostic laboratory.

BACKGROUND: Antimicrobial resistance limits traditional treatment options and increases costs. It is therefore important to estimate the magnitude of the problem so as to provide empirical data to guide control efforts. The aim of this study was to investigate the burden and patterns of antimicrobial resistance (AMR) among equine Staphylococcus samples submitted to the University of Kentucky Veterinary Diagnostic Laboratory (UKVDL) from 1993 to 2009. Retrospective data of 1711 equine Staphylococcus samples submitted to the UKVDL during the time period 1993 to 2009 were included in the study. Antimicrobial susceptibility testing, that included 16 drugs, were performed using cultures followed by the Kirby-Bauer disk diffusion susceptibility test. The proportion of resistant isolates by animal breed, species of organism, sample source, and time period were computed. Chi-square and Cochran-Armitage trend tests were used to identify significant associations and temporal trends, respectively. Logistic regression models were used to investigate predictors of AMR and multidrug resistance (MDR). RESULTS: A total of 66.3% of the isolates were resistant to at least one antimicrobial, most of which were Staphylococcus aureus (77.1%), while 25.0% were MDR. The highest level of resistance was to penicillins (52.9%). Among drug classes, isolates had the highest rate of AMR to at least one type of ß-lactams (49.2%), followed by aminoglycosides (30.2%). Significant (p < 0.05) associations were observed between odds of AMR and horse breed, species of organism and year. Similarly, significant (p < 0.05) associations were identified between odds of MDR and breed and age. While some isolates had resistance to up to 12 antimicrobials, AMR profiles featuring single antimicrobials such as penicillin were more common than those with multiple antimicrobials. CONCLUSION: Demographic factors were significant predictors of AMR and MDR. The fact that some isolates had resistance to up to 12 of the 16 antimicrobials assessed is quite concerning. To address the high levels of AMR and MDR observed in this study, future studies will need to focus on antimicrobial prescription practices and education of both practitioners and animal owners on judicious use of antimicrobials to slow down the development of resistance.

Patterns and predictors of antimicrobial resistance among Staphylococcus spp. from canine clinical cases presented at a veterinary academic hospital in South Africa.

BACKGROUND: Antimicrobial resistance in staphylococci, often associated with treatment failure, is increasingly reported in veterinary medicine. The aim of this study was to investigate patterns and predictors of antimicrobial resistance among Staphylococcus spp. isolates from canine samples submitted to the bacteriology laboratory at the University of Pretoria academic veterinary hospital between 2007 and 2012. Retrospective data of 334 Staphylococcus isolates were used to calculate the proportion of samples resistant to 15 antimicrobial agents. The Cochran-Armitage trend test was used to investigate temporal trends and logistic regression models were used to investigate predictors of antimicrobial resistance in Staphylococcus aureus and Staphylococcus pseudintermedius. RESULTS: Results show that 98.2% (55/56) of the S. aureus isolates were resistant to at least one drug while 42.9% were multidrug resistant. Seventy-seven percent (214/278) of the S. pseudintermedius isolates were resistant to at least one drug and 25.9% (72/278) were multidrug resistant. Resistance to lincospectin was more common among S. aureus (64.3%) than S. pseudintermedius (38.9%). Similarly, resistance to clindamycin was higher in S. aureus (51.8%) than S. pseudintermedius (31.7%) isolates. There was a significant (p = 0.005) increase in S. aureus resistance to enrofloxacin over the study period. Similarly, S. pseudintermedius exhibited significant increasing temporal trend in resistance to trimethoprim-sulphamethoxazole (p = 0.004), clindamycin (p = 0.022) and orbifloxacin (p = 0.042). However, there was a significant decreasing temporal trend in the proportion of isolates resistant to doxycycline (p = 0.041), tylosin (p = 0.008), kanamycin (p = 0.017) and amoxicillin/clavulanic acid (p = 0.032). CONCLUSIONS: High levels of multidrug resistance and the increasing levels of resistance to sulphonamides, lincosamides and fluoroquinolones among Staphylococcus spp. isolates in this study are concerning. Future studies will need to investigate local drivers of antimicrobial resistance to better guide control efforts to address the problem.

An Exploratory Descriptive Study of Antimicrobial Resistance Patterns of Staphylococcus Spp. Isolated from Horses Presented at a Veterinary Teaching Hospital.

BACKGROUND: Antimicrobial resistant Staphylococcus are becoming increasingly important in horses because of the zoonotic nature of the pathogens and the associated risks to caregivers and owners. Knowledge of the burden and their antimicrobial resistance patterns are important to inform control strategies. This study is an exploratory descriptive investigation of the burden and antimicrobial drug resistance patterns of Staphylococcus isolates from horses presented at a veterinary teaching hospital in South Africa. METHODS: Retrospective laboratory clinical records of 1027 horses presented at the University of Pretoria veterinary teaching hospital between 2007 and 2012 were included in the study. Crude and factor-specific percentages of Staphylococcus positive samples, antimicrobial resistant (AMR) and multidrug resistant (MDR) isolates were computed and compared across Staphylococcus spp., geographic locations, seasons, years, breed and sex using Chi-square and Fisher's exact tests. RESULTS: Of the 1027 processed clinical samples, 12.0% were Staphylococcus positive. The majority of the isolates were S. aureus (41.5%) followed by S. pseudintermedius (14.6%). Fifty-two percent of the Staphylococcus positive isolates were AMR while 28.5% were MDR. Significant (p < 0.05) differences in the percentage of samples with isolates that were AMR or MDR was observed across seasons, horse breeds and Staphylococcus spp. Summer season had the highest (64.3%) and autumn the lowest (29.6%) percentages of AMR isolates. Highest percentage of AMR samples were observed among the Boerperds (85.7%) followed by the American saddler (75%) and the European warm blood (73.9%). Significantly (p < 0.001) more S. aureus isolates (72.5%) were AMR than S. pseudintermedius isolates (38.9%). Similarly, significantly (p < 0.001) more S. aureus (52.9%) exhibited MDR than S. pseudintermedius (16.7%). The highest levels of AMR were towards ß-lactams (84.5%) followed by trimethoprim/sulfamethoxazole (folate pathway inhibitors) (60.9%) while the lowest levels of resistance were towards amikacin (14.%). CONCLUSIONS: This exploratory study provides useful information to guide future studies that will be critical for guiding treatment decisions and control efforts. There is a need to implement appropriate infection control, and judicious use of antimicrobials to arrest development of antimicrobial resistance. A better understanding of the status of the problem is a first step towards that goal.

Antimicrobial resistance patterns of Staphylococcus species isolated from cats presented at a veterinary academic hospital in South Africa.

BACKGROUND: Antimicrobial resistance is becoming increasingly important in both human and veterinary medicine. This study investigated the proportion of antimicrobial resistant samples and resistance patterns of Staphylococcus isolates from cats presented at a veterinary teaching hospital in South Africa. Records of 216 samples from cats that were submitted to the bacteriology laboratory of the University of Pretoria academic veterinary hospital between 2007 and 2012 were evaluated. Isolates were subjected to antimicrobial susceptibility testing against a panel of 15 drugs using the disc diffusion method. Chi square and Fisher's exact tests were used to assess simple associations between antimicrobial resistance and age group, sex, breed and specimen type. Additionally, associations between Staphylococcus infection and age group, breed, sex and specimen type were assessed using logistic regression. RESULTS: Staphylococcus spp. isolates were identified in 17.6% (38/216) of the samples submitted and 4.6% (10/216) of these were unspeciated. The majority (61.1%,11/18) of the isolates were from skin samples, followed by otitis media (34.5%, 10/29). Coagulase Positive Staphylococcus (CoPS) comprised 11.1% (24/216) of the samples of which 7.9% (17/216) were S. intermedius group and 3.2% (7/216) were S. aureus. Among the Coagulase Negative Staphylococcus (CoNS) (1.9%, 4/216), S. felis and S. simulans each constituted 0.9% (2/216). There was a significant association between Staphylococcus spp. infection and specimen type with odds of infection being higher for ear canal and skin compared to urine specimens. There were higher proportions of samples resistant to clindamycin 34.2% (13/25), ampicillin 32.4% (2/26), lincospectin 31.6% (12/26) and penicillin-G 29.0% (11/27). Sixty three percent (24/38) of Staphylococcus spp. were resistant to one antimicrobial agent and 15.8% were multidrug resistant (MDR). MDR was more common among S. aureus 28.6% (2/7) than S. intermedius group isolates 11.8% (2/17). One S. intermedius group isolate was resistant to all ß-lactam antimicrobial agents tested. CONCLUSION: S. intermedius group was the most common cause of skin infections and antimicrobial resistance was not wide spread among cats presented at the veterinary academic hospital in South Africa. However, the presence of MDR-Staphylococcus spp. and isolates resistant to all ß-lactams is of both public health and animal health concern.

Emergency medical services transport delays for suspected stroke and myocardial infarction patients.

BACKGROUND: Prehospital delays in receiving emergency care for suspected stroke and myocardial infarction (MI) patients have significant impacts on health outcomes. Use of Emergency Medical Services (EMS) has been shown to reduce these delays. However, disparities in EMS transport delays are thought to exist. Therefore the objective of this study was to investigate and identify disparities in EMS transport times for suspected stroke and MI patients. METHODS: Over 3,900 records of suspected stroke and MI patients, reported during 2006-2009, were obtained from two EMS agencies (EMS 1 & EMS 2) in Tennessee. Summary statistics of transport time intervals were computed. Multivariable logistic models were used to identify predictors of time intervals exceeding EMS guidelines. RESULTS: Only 66 and 10 % of suspected stroke patients were taken to stroke centers by EMS 1 and 2, respectively. Most (80-83 %) emergency calls had response times within the recommended 10 min. However, over 1/3 of the calls had on-scene times exceeding the recommended 15 min. Predictors of time intervals exceeding EMS guidelines were EMS agency, patient age, season and whether or not patients were taken to a specialty center. The odds of total transport time exceeding EMS guidelines were significantly lower for patients not taken to specialty centers. Noteworthy was the 72 % lower odds of total time exceeding guidelines for stroke patients served by EMS 1 compared to those served by EMS 2. Additionally, for every decade increase in age of the patient, the odds of on-scene time exceeding guidelines increased by 15 and 19 % for stroke and MI patients, respectively. CONCLUSION: In this study, prehospital delays, as measured by total transport time exceeding guideline was influenced by season, EMS agency responsible, patient age and whether or not the patient is transported to a specialty center. The magnitude of the delays associated with some of the factors are large enough to be clinically important although others, though statistically significant, may not be large enough to be clinically important. These findings should be useful for guiding future studies and local health initiatives that seek to reduce disparities in prehospital delays so as to improve health services and outcomes for stroke and MI patients.

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.

Spatial patterns and sociodemographic predictors of chronic obstructive pulmonary disease in Florida.

Background: Chronic obstructive pulmonary disease (COPD) is a chronic, inflammatory respiratory disease that obstructs airflow and decreases lung function and is a leading cause death globally. In the United States (US), the prevalence among adults is 6.2%, but increases with age to 12.8% among those 65 years or older. Florida has one of the largest populations of older adults in the US, accounting for 4.5 million adults 65 years or older. This makes Florida an ideal geographic location for investigating COPD as disease prevalence increases with age. Understanding the geographic disparities in COPD and potential associations between its disparities and environmental factors as well as population characteristics is useful in guiding intervention strategies. Thus, the objectives of this study are to investigate county-level geographic disparities of COPD prevalence in Florida and identify county-level socio-demographic predictors of COPD prevalence. Methods: This ecological study was performed in Florida using data obtained from the US Census Bureau, Florida Health CHARTS, and County Health Rankings and Roadmaps. County-level COPD prevalence for 2019 was age-standardized using the direct method and 2020 US population as the standard population. High-prevalence spatial clusters of COPD were identified using Tango's flexible spatial scan statistics. Predictors of county-level COPD prevalence were investigated using multivariable ordinary least squares model built using backwards elimination approach. Multicollinearity of regression coefficients was assessed using variance inflation factor. Shapiro-Wilks, Breusch Pagan, and robust Lagrange Multiplier tests were used to assess for normality, homoskedasticity, and spatial autocorrelation of model residuals, respectively. Results: County-level age-adjusted COPD prevalence ranged from 4.7% (Miami-Dade) to 16.9% (Baker and Bradford) with a median prevalence of 9.6%. A total of 6 high-prevalence clusters with prevalence ratios >1.2 were identified. The primary cluster, which was also the largest geographic cluster that included 13 counties, stretched from Nassau County in north-central Florida to Charlotte County in south-central Florida. However, cluster 2 had the highest prevalence ratio (1.68) and included 10 counties in north-central Florida. Together, the primary cluster and cluster 2 covered most of the counties in north-central Florida. Significant predictors of county-level COPD prevalence were county-level percentage of residents with asthma and the percentage of current smokers. Conclusions: There is evidence of spatial clusters of COPD prevalence in Florida. These patterns are explained, in part, by differences in distribution of some health behaviors (smoking) and co-morbidities (asthma). This information is important for guiding intervention efforts to address the condition, reduce health disparities, and improve population health.

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.

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.