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.

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.

Background Antimicrobial resistance among pathogens of public health importance is an emerging problem in sub-Saharan Africa. Unfortunately, published information on the burden and patterns of antimicrobial resistance (AMR) in this region is sparse. There is evidence that the burden and patterns of AMR vary by geography and facility. Knowledge of local epidemiology of AMR is thus important for guiding clinical decisions and mitigation strategies. Therefore, the objective of this study was to determine the burden and predictors of AMR and multidrug resistance (MDR) among bacterial pathogens isolated from specimens submitted to the diagnostic laboratory of a hospital in Nairobi, Kenya. Methods This retrospective study used laboratory records of 1,217 clinical specimens submitted for bacterial culture and sensitivity testing at the diagnostic laboratory of The Karen Hospital in Nairobi, Kenya between 2012 and 2016. Records from specimens positive for Enterobacteriaceae, Staphylococcus aureus, or Pseudomonas spp. isolates were included for analysis. Firth logistic models, which minimize small sample bias, were used to investigate determinants of AMR and MDR of the isolates. Results A total of 222 specimens had bacterial growth. Most Enterobacteriaceae isolates were resistant to commonly used drugs such as penicillin/β-lactamase inhibitor combinations (91.2%) and folate pathway inhibitors (83.7%). Resistance to extended-spectrum cephalosporins was also high (52.9%). Levels of AMR and MDR for Enterobacteriaceae were 88.5% and 51%, respectively. Among S. aureus isolates, 57.1% were AMR, while 16.7% were MDR. As many as 42.1% of the Pseudomonas spp. isolates were aminoglycoside-resistant and 15% were fluoroquinolone-resistant, but none exhibited resistance to antipseudomonal carbapenems. Half of Pseudomonas spp. isolates were AMR but none were MDR. Significant predictors of MDR among Enterobacteriaceae were organism species (p = 0.002) and patient gender (p = 0.024). Conclusions The high levels of extended-spectrum cephalosporin resistance and MDR among Enterobacteriaceae isolates are concerning. However, the relatively low levels of MDR S. aureus, and an absence of carbapenem resistance among Pseudomonas isolates, suggests that last-line drugs are still effective against S. aureus and Pseudomonas infections. These findings are relevant for guiding evidence-based treatment decisions as well as surveillance efforts and directions for future research, and contribute to the sparse literature on AMR in sub-Saharan Africa.

Background Left unchecked, pre-diabetes progresses to diabetes and its complications that are important health burdens in the United States. There is evidence of geographic disparities in the condition with some areas having a significantly high risks of the condition and its risk factors. Identifying these disparities, their determinants, and changes in burden are useful for guiding control programs and stopping the progression of pre-diabetes to diabetes. Therefore, the objectives of this study were to investigate geographic disparities of pre-diabetes prevalence in Florida, identify predictors of the observed spatial patterns, as well as changes in disease burden between 2013 and 2016. Methods The 2013 and 2016 Behavioral Risk Factor Surveillance System data were obtained from the Florida Department of Health. Counties with significant changes in the prevalence of the condition between 2013 and 2016 were identified using tests for equality of proportions adjusted for multiple comparisons using the Simes method. Flexible scan statistics were used to identify significant high prevalence geographic clusters. Multivariable regression models were used to identify determinants of county-level pre-diabetes prevalence. Results The state-wide age-adjusted prevalence of pre-diabetes increased significantly (p ≤ 0.05) from 8.0% in 2013 to 10.5% in 2016 with 72% (48/67) of the counties reporting statistically significant increases. Significant local geographic hotspots were identified. High prevalence of pre-diabetes tended to occur in counties with high proportions of non-Hispanic black population, low median household income, and low proportion of the population without health insurance coverage. Conclusions Geographic disparities of pre-diabetes continues to exist in Florida with most counties reporting significant increases in prevalence between 2013 and 2016. These findings are critical for guiding health planning, resource allocation and intervention programs.

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.

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.

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 β-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.

Background: Diabetes and its complications represent a significant public health burden in the United States, with evidence of geographic disparities. Identifying these disparities and their determinants is useful for guiding control programs. Therefore, this study investigated geographic disparities of pre-diabetes and diabetes prevalence in Florida in 2016, and identified predictors of the observed spatial patterns. Additionally, we investigated changes in geographic distribution of the two conditions between 2013 and 2016. Methods: The 2013 and 2016 Behavioral Risk Factor Surveillance System data were obtained from the Florida Department of Health. Flexible scan statistics were used to identify significant high prevalence geographic clusters. Global ordinary least squares regression and local Poisson geographically weighted generalized linear models were used to investigate predictors of the identified spatial patterns. Counties with significant changes in prevalence of the two conditions between 2013 and 2016 were identified using tests for equality of proportions adjusted for multiple comparisons using Simes method. Results: The state-wide diabetes prevalence was 11.2% in 2013, and 11.8% in 2016. Statistically significant ( p ≤0.05) increases in prevalence were identified in 73% (49/67) of the counties. Similarly, the state-wide prevalence of pre-diabetes was 7.1% in 2013 and 9.2% in 2016 with 76% (51/67) of the counties reporting statistically significant increases. Significant local hotspots were identified for both conditions. Predictors of county-level diabetes prevalence were: proportion of the obese population, number of physicians per 1000 persons, proportion of the population living below the poverty level, and proportion of the population with arthritis. Predictors of pre-diabetes prevalence included proportion of the population with arthritis and proportion of the population that identified as non-Hispanic black. There was evidence of geographical variability of all regression coefficients for both the pre-diabetes and diabetes models indicating that the strength of association of the relationships between the predictors and outcomes varied by geographic area. Conclusions: Geographic disparities of both conditions continue to exist in Florida. Moreover, there was a state-wide increase in the burden of both conditions between 2013 and 2016. The fact that the strength of association of the relationships between the predictors and outcomes varied across the counties implies that some predictors may be more important in some counties than others. These findings imply that local models provide useful information to guide public health decision-making and resource allocation. Identifying high-risk geographic areas and location-specific determinants of chronic disease prevalence should be used to inform targeted intervention programs.