Exploratory investigation of region level risk factors of Ebola Virus Disease in West Africa

Background. Ebola Virus Disease (EVD) is a highly infectious disease that has produced over 25,000 cases in the past 50 years. While many past outbreaks resulted in relatively few cases, the 2014 outbreak in West Africa was the most deadly occurrence of EVD to date, producing over 15,000 confirmed cases. Objective. In this study, we investigated population level predictors of EVD risk at the regional level in Sierra Leone, Liberia, and Guinea. Methods. Spatial and descriptive analyses were conducted to assess distribution of EVD cases. Choropleth maps showing the spatial distribution of EVD risk across the study area were generated in ArcGIS. Poisson and negative binomial models were then used to investigate population and regional predictors of EVD risk. Results. Results indicated that the risk of EVD was significantly lower in areas with higher proportions of: (a) the population living in urban areas, (b) households with a low quality or no toilets, and (c) married men working in blue collar jobs. However, risk of EVD was significantly higher in areas with high mean years of education. Conclusions. The identified significant predictors of high risk were associated with ar- eas with higher levels of urbanization. This may be due to higher population densities in the more urban centers and hence higher potential of infectious contact. However, there is need to better understand the role of urbanization and individual contact structure in an Ebola outbreak. We discuss shortcomings in available data and emphasize the need to consider spatial scale in future data collection and epidemiological studies

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

Prevalence and Predictors of Pre-Diabetes and Diabetes among Adults 18 Years or Older in Florida: A Multinomial Logistic Modeling Approach

Background: Individuals with pre-diabetes and diabetes have increased risks of developing macro-vascular complications including heart disease and stroke; which are the leading causes of death globally. The objective of this study was to estimate the prevalence of pre-diabetes and diabetes, and to investigate their predictors among adults ≥18 years in Florida. Methods: Data covering the time period January-December 2013, were obtained from Florida’s Behavioral Risk Factor Surveillance System (BRFSS). Survey design of the study was declared using SVYSET statement of STATA 13.1. Descriptive analyses were performed to estimate the prevalence of pre-diabetes and diabetes. Predictors of pre-diabetes and diabetes were investigated using multinomial logistic regression model. Model goodness-of-fit was evaluated using both the multinomial goodness-of-fit test proposed by Fagerland, Hosmer, and Bofin, as well as, the Hosmer-Lemeshow’s goodness of fit test. Results: There were approximately 2,983 (7.3%) and 5,189 (12.1%) adults in Florida diagnosed with pre-diabetes and diabetes, respectively. Over half of the study respondents were white, married and over the age of 45 years while 36.4% reported being physically inactive, overweight (36.4%) or obese (26.4%), hypertensive (34.6%), hypercholesteremic (40.3%), and 26% were arthritic. Based on the final multivariable multinomial model, only being overweight (Relative Risk Ratio [RRR] = 1.85, 95% Confidence Interval [95% CI] = 1.41, 2.42), obese (RRR = 3.41, 95% CI = 2.61, 4.45), hypertensive (RRR = 1.69, 95% CI = 1.33, 2.15), hypercholesterolemic (RRR = 1.94, 95% CI = 1.55, 2.43), and arthritic (RRR = 1.24, 95% CI = 1.00, 1.55) had significant associations with pre-diabetes. However, more predictors had significant associations with diabetes and the strengths of associations tended to be higher than for the association with pre-diabetes. For instance, the relative risk ratios for the association between diabetes and being overweight (RRR = 2.00, 95% CI = 1.55, 2.57), or obese (RRR = 4.04, 95% CI = 3.22, 5.07), hypertensive (RRR = 2.66, 95% CI = 2.08, 3.41), hypercholesterolemic (RRR = 1.98, 95% CI = 1.61, 2.45) and arthritic (RRR = 1.28, 95% CI = 1.04, 1.58) were all further away from the null than their associations with pre-diabetes. Moreover, a number of variables such as age, income level, sex, and level of physical activity had significant association with diabetes but not pre-diabetes. The risk of diabetes increased with increasing age, lower income, in males, and with physical inactivity. Insufficient physical activity had no significant association with the risk of diabetes or pre-diabetes. Conclusions: There is evidence of differences in the strength of association of the predictors across levels of diabetes status (pre-diabetes and diabetes) among adults ≥18 years in Florida. It is important to monitor populations at high risk for pre-diabetes and diabetes, so as to help guide health programming decisions and resource allocations to control the condition

The Incidence Risk, Clustering, and Clinical Presentation of La Crosse Virus Infections in the Eastern United States, 2003–2007

BACKGROUND:Although La Crosse virus (LACV) is one of the most common causes of pediatric arboviral infections in the United States, little has been done to assess its geographic distribution, identify areas of higher risk of disease, and to provide a national picture of its clinical presentation. Therefore, the objective of this study was to investigate the geographic distribution of LACV infections reported in the United States, to identify hot-spots of infection, and to present its clinical picture. METHODS AND FINDINGS:Descriptive and cluster analyses were performed on probable and confirmed cases of LACV infections reported to the Centers for Disease Control and Prevention from 2003-2007. A total of 282 patients had reported confirmed LACV infections during the study period. Of these cases the majority (81 percent) presented during the summer, occurred in children 15 years and younger (83.3 percent), and were found in male children (64.9 percent). Clinically, the infections presented as meningioencephalitis (56.3 percent), encephalitis (20.7 percent), meningitis (17.2 percent), or uncomplicated fever (5 percent). Deaths occurred in 1.9 percent of confirmed cases, and in 8.6 percent of patients suffering from encephalitis. The majority of these deaths were in patients 15 years and younger. The county-level incidence risk among counties (n = 136) reporting both probable and confirmed cases for children 15 years and younger (n = 355) ranged from 0.2 to 228.7 per 100,000 persons. The southern United States experienced a significantly higher (p<0.05) incidence risk during the months of June, July, August, and October then the northern United States. There was significant (p<0.05) clustering of high risk in several geographic regions with three deaths attributed to complications from LAC encephalitis occurring in two of these hot-spots of infections. CONCLUSIONS:Both the incidence risk and case fatality rates were found to be higher than previously reported. We detected clustering in four geographic regions, a shift from the prior geographic distributions, and developed maps identifying high-risk areas. These findings are useful for raising awareness among health care providers regarding areas at a high risk of infections and for guiding targeted multifaceted interventions by public health officials

An exploratory investigation of geographic disparities of stroke prevalence in Florida using circular and flexible spatial scan statistics

Background Stroke is a major public health concern due to the morbidity and mortality associated with it. Identifying geographic areas with high stroke prevalence is important for informing public health interventions. Therefore, the objective of this study was to investigate geographic disparities and identify geographic hotspots of stroke prevalence in Florida. Materials and methods County-level stroke prevalence data for 2013 were obtained from the Florida Department of Health’s Behavioral Risk Factor Surveillance System (BRFSS). Geographic clusters of stroke prevalence were investigated using the Kulldorff’s circular spatial scan statistics (CSSS) and Tango’s flexible spatial scan statistics (FSSS) under Poisson model assumption. Exact McNemar’s test was used to compare the proportion of cluster counties identified by each of the two methods. Both Cohen’s Kappa and bias adjusted Kappa were computed to assess the level of agreement between CSSS and FSSS methods of cluster detection. Goodness-of-fit of the models were compared using Cluster Information Criterion. Identified clusters and selected stroke risk factors were mapped. Results Overall, 3.7% of adults in Florida reported that they had been told by a healthcare professional that they had suffered a stroke. Both CSSS and FSSS methods identified significant high prevalence stroke spatial clusters. However, clusters identified using CSSS tended to be larger than those identified using FSSS. The FSSS had a better fit than the CSSS. Most of the identified clusters are explainable by the prevalence distributions of the known risk factors assessed. Conclusions Geographic disparities of stroke risk exists in Florida with some counties having significant hotspots of high stroke prevalence. This information is important in guiding future research and control efforts to address the problem. Kulldorff’s CSSS and Tango’s FSSS are complementary to each other and should be used together to provide a more complete picture of the distributions of spatial clusters of health outcome

Geographic distribution of Staphylococcus spp. infections and antimicrobial resistance among dogs from Gauteng Province presented at a veterinary teaching hospital in South Africa.

The objective of this study was to investigate spatial patterns of staphylococcal infections and resistance patterns of clinical isolates from dogs from Gauteng province in South Africa. Data from records of 1,497 dog clinical samples submitted to a veterinary teaching hospital between 2007 and 2012 were used in the study. Spatial empirical Bayesian smoothed risk maps were used to investigate spatial patterns of staphylococcal infections, antimicrobial resistance (AMR), and multidrug resistance (MDR). Moran’s I and spatial scan statistics were used to investigate spatial clusters at municipal and town spatial scales. Significant clusters of staphylococcal infections were identified at both the municipal (Relative Risk [RR]=1.71, p=0.003) and town (RR=1.65, p=0.039) scales. However, significant clusters of AMR (p=0.003) and MDR (p=0.007) were observed only at the town scale. Future larger studies will need to investigate local determinants of geographical distribution of the clusters so as to guide targeted control efforts

Does Place of Residence or Time of Year Affect the Risk of Stroke Hospitalization and Death? A Descriptive Spatial and Temporal Epidemiologic Study

Background: Identifying geographic areas with significantly high risks of stroke is important for informing public health prevention and control efforts. The objective of this study was to investigate geographic and temporal patterns of stroke hospitalization and mortality risks so as to identify areas and seasons with significantly high burden of the disease in Florida. The information obtained will be useful for resource allocation for disease prevention and control. Methods: Stroke hospitalization and mortality data from 1992 to 2012 were obtained from the Florida Agency for Health Care Administration. Age-adjusted stroke hospitalization and mortality risks for time periods 1992–94, 1995–97, 1998–2000, 2001–03, 2004–06, 2007–09 and 2010–12 were computed at the county spatial scale. Global Moran’s I statistics were computed for each of the time periods to test for evidence of global spatial clustering. Local Moran indicators of spatial association (LISA) were also computed to identify local areas with significantly high risks. Results: There were approximately 1.5 million stroke hospitalizations and over 196,000 stroke deaths during the study period. Based on global Moran’s I tests, there was evidence of significant (p\u3c0.05) global spatial clustering of stroke mortality risks but no evidence (p\u3e0.05) of significant global clustering of stroke hospitalization risks. However, LISA showed evidence of local spatial clusters of both hospitalization and mortality risks with significantly high risks being observed in the north while the south had significantly low risks of stroke deaths. There were decreasing temporal trends and seasonal patterns of both hospitalization and mortality risks with peaks in the winter. Conclusions: Although stroke hospitalization and mortality risks have declined in the past two decades, disparities continue to exist across Florida and it is evident from the results of this study that north Florida may, in fact, be part of the stroke belt despite not being in any of the traditional stroke belt states. These findings are useful for guiding public health efforts to reduce/eliminate inequities in stroke outcomes and inform policy decisions. There is need to continually identify populations with significantly high risks of stroke to better guide the targeting of limited resources to the highest risk populations

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 \u3c 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 \u3c 0.001) more S. aureus isolates (72.5%) were AMR than S. pseudintermedius isolates (38.9%). Similarly, significantly (p \u3c 0.001) more S. aureus (52.9%) exhibited MDR than S. pseudintermedius (16.7%). The highest levels of AMR were towards β-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

Antibiotic Prescription Practices and Opinions Regarding Antimicrobial Resistance among Veterinarians in Kentucky, USA

Background Inappropriate antimicrobial use (AMU) is a global concern. Opinions of veterinarians regarding AMU and its role in the development of antimicrobial resistance (AMR) may influence their prescription practices. It is important to understand these opinions, prescription practices and their potential impact on the development of AMR in order to guide efforts to curb the problem. Therefore, the objective of this study was to investigate the antimicrobial prescription practices and opinions of veterinarians in Kentucky regarding AMU and AMR. Methods This cross-sectional study used a 30-question survey questionnaire administered to veterinarians who were members of the Kentucky Veterinary Medical Association. Survey responses from 101 participants were included in the study. Descriptive statistics were computed and associations between categorical variables assessed using Chi-square or Fisher’s exact tests. Firth logistic models were used to investigate predictors of “Compliance with prescription policies” and “Cost of antimicrobial affects prescription decisions”. Results Almost all (93%) respondents indicated that improper AMU contributed to selection for AMR. A total of 52% of the respondents believed that antimicrobials were appropriately prescribed, while the remaining 48% believed that antimicrobials were inappropriately prescribed. Significant predictors of compliance with prescription policies were availability of prescription policy at the veterinary facility (Odds Ratio (OR) = 4.2; p\u3c 0.001) and over-prescription (OR = 0.35; p = 0.025). Similarly, significant predictors of cost of antimicrobials affecting prescription decisions were lack of post-graduate training (OR = 8.3; p = 0.008) and practice type, with large animal practices having significantly lower odds of the outcome (OR = 0.09; p = 0.004) than small animal practices. Conclusion Most veterinarians indicated that improper AMU contributed to selection for AMR. Since the odds of compliance with prescription policies were 4-times higher among veterinarians working at facilities that had prescription policies compared to those at facilities that didn’t, more veterinary facilities should be encouraged to adopt prescription policies to help improve compliance and reduce AMR. Veterinarians would also benefit from continued professional education to help improve prescription practices, antimicrobial stewardship and curb AMR

Socioeconomic determinants of geographic disparities in campylobacteriosis risk: a comparison of global and local modeling approaches

BACKGROUND: Socioeconomic factors play a complex role in determining the risk of campylobacteriosis. Understanding the spatial interplay between these factors and disease risk can guide disease control programs. Historically, Poisson and negative binomial models have been used to investigate determinants of geographic disparities in risk. Spatial regression models, which allow modeling of spatial effects, have been used to improve these modeling efforts. Geographically weighted regression (GWR) takes this a step further by estimating local regression coefficients, thereby allowing estimations of associations that vary in space. These recent approaches increase our understanding of how geography influences the associations between determinants and disease. Therefore the objectives of this study were to: (i) identify socioeconomic determinants of the geographic disparities of campylobacteriosis risk (ii) investigate if regression coefficients for the associations between socioeconomic factors and campylobacteriosis risk demonstrate spatial variability and (iii) compare the performance of four modeling approaches: negative binomial, spatial lag, global and local Poisson GWR. METHODS: Negative binomial, spatial lag, global and local Poisson GWR modeling techniques were used to investigate associations between socioeconomic factors and geographic disparities in campylobacteriosis risk. The best fitting models were identified and compared. RESULTS: Two competing four variable models (Models 1 & 2) were identified. Significant variables included race, unemployment rate, education attainment, urbanicity, and divorce rate. Local Poisson GWR had the best fit and showed evidence of spatially varying regression coefficients. CONCLUSIONS: The international significance of this work is that it highlights the inadequacy of global regression strategies that estimate one parameter per independent variable, and therefore mask the true relationships between dependent and independent variables. Since local GWR estimate a regression coefficient for each location, it reveals the geographic differences in the associations. This implies that a factor may be an important determinant in some locations and not others. Incorporating this into health planning ensures that a needs-based, rather than a “one-size-fits-all”, approach is used. Thus, adding local GWR to the epidemiologists’ toolbox would allow them to assess how the impacts of different determinants vary by geography. This knowledge is critical for resource allocation in disease control programs