Characterization of Food Safety Knowledge, Attitudes, and Behaviors of Adolescents in East Tennessee

Educational research suggests that middle school is an ideal time to teach food safety since adolescents are in the process of setting life-long behaviors and are, therefore, more likely to synthesize new food safety knowledge into positive behaviors. The objectives of this study were to: 1) Describe the baseline food safety knowledge and attitudes/ behaviors of 7th grade students in East Tennessee 2) determine the relationship with geographic location, socioeconomic status, race, and gender; and 3) compare the current data (Study 2) to a previous study (Study 1) that pre-tested 7th grade students prior to an education intervention. A 40-item survey was administered to 232 students in 12 schools chosen using a weighted, stratified random sample. A hierarchical model was used to obtain least squares means at the school and student levels. To compare Studies 1 and 2, independent sample t-tests and chi-square analysis were applied to determine significant differences in food safety knowledge or attitudes/behaviors between the populations. Study 2 results showed that 63% knew the importance of hand-washing, but only 50% reported ‘always’ washing their hands before eating or preparing food; 50% reported ‘always’ following temperature directions, but 85% did not know how to determine if a hamburger was cooked properly. No statistical difference was found in food safety knowledge for all variables except race, where Asian/Pacific students scored lower (p=0.0005). Males (p=0.0133) and Asian/Pacific students (p=0.0033) reported riskier food handling behaviors. No significant differences (p\u3c0.05) were found between Study 1 and 2 in food safety knowledge or attitudes/behaviors. Hand-washing and use of proper temperatures, as well as differences in behavior within gender and some ethnic groups should be focal points in adolescent food safety education. These results suggest that some differences in knowledge and behaviors are less pronounced in adolescents than those found in similar studies with adults. The results of the comparison between adolescent studies suggest that the food safety curriculum targeted to adolescents of Study 1 would likely be effective at raising student knowledge and improving students’ food handling behaviors in a larger population of 7th grade students

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

Identifying Unique Neighborhood Characteristics to Guide Health Planning for Stroke and Heart Attack: Fuzzy Cluster and Discriminant Analyses Approaches

Socioeconomic, demographic, and geographic factors are known determinants of stroke and myocardial infarction (MI) risk. Clustering of these factors in neighborhoods needs to be taken into consideration during planning, prioritization and implementation of health programs intended to reduce disparities. Given the complex and multidimensional nature of these factors, multivariate methods are needed to identify neighborhood clusters of these determinants so as to better understand the unique neighborhood profiles. This information is critical for evidence-based health planning and service provision. Therefore, this study used a robust multivariate approach to classify neighborhoods and identify their socio-demographic characteristics so as to provide information for evidence-based neighborhood health planning for stroke and MI.The study was performed in East Tennessee Appalachia, an area with one of the highest stroke and MI risks in USA. Robust principal component analysis was performed on neighborhood (census tract) socioeconomic and demographic characteristics, obtained from the US Census, to reduce the dimensionality and influence of outliers in the data. Fuzzy cluster analysis was used to classify neighborhoods into Peer Neighborhoods (PNs) based on their socioeconomic and demographic characteristics. Nearest neighbor discriminant analysis and decision trees were used to validate PNs and determine the characteristics important for discrimination. Stroke and MI mortality risks were compared across PNs. Four distinct PNs were identified and their unique characteristics and potential health needs described. The highest risk of stroke and MI mortality tended to occur in less affluent PNs located in urban areas, while the suburban most affluent PNs had the lowest risk.Implementation of this multivariate strategy provides health planners useful information to better understand and effectively plan for the unique neighborhood health needs and is important in guiding resource allocation, service provision, and policy decisions to address neighborhood health disparities and improve population health

Neighborhood disparities in stroke and myocardial infarction mortality: a GIS and spatial scan statistics approach

<p>Abstract</p> <p>Background</p> <p>Stroke and myocardial infarction (MI) are serious public health burdens in the US. These burdens vary by geographic location with the highest mortality risks reported in the southeastern US. While these disparities have been investigated at state and county levels, little is known regarding disparities in risk at lower levels of geography, such as neighborhoods. Therefore, the objective of this study was to investigate spatial patterns of stroke and MI mortality risks in the East Tennessee Appalachian Region so as to identify neighborhoods with the highest risks.</p> <p>Methods</p> <p>Stroke and MI mortality data for the period 1999-2007, obtained free of charge upon request from the Tennessee Department of Health, were aggregated to the census tract (neighborhood) level. Mortality risks were age-standardized by the direct method. To adjust for spatial autocorrelation, population heterogeneity, and variance instability, standardized risks were smoothed using Spatial Empirical Bayesian technique. Spatial clusters of high risks were identified using spatial scan statistics, with a discrete Poisson model adjusted for age and using a 5% scanning window. Significance testing was performed using 999 Monte Carlo permutations. Logistic models were used to investigate neighborhood level socioeconomic and demographic predictors of the identified spatial clusters.</p> <p>Results</p> <p>There were 3,824 stroke deaths and 5,018 MI deaths. Neighborhoods with significantly high mortality risks were identified. Annual stroke mortality risks ranged from 0 to 182 per 100,000 population (median: 55.6), while annual MI mortality risks ranged from 0 to 243 per 100,000 population (median: 65.5). Stroke and MI mortality risks exceeded the state risks of 67.5 and 85.5 in 28% and 32% of the neighborhoods, respectively. Six and ten significant (p < 0.001) spatial clusters of high risk of stroke and MI mortality were identified, respectively. Neighborhoods belonging to high risk clusters of stroke and MI mortality tended to have high proportions of the population with low education attainment.</p> <p>Conclusions</p> <p>These methods for identifying disparities in mortality risks across neighborhoods are useful for identifying high risk communities and for guiding population health programs aimed at addressing health disparities and improving population health.</p

Geographic Disparities Associated with Stroke and Myocardial Infarction in East Tennessee

Stroke and myocardial infarction (MI) are serious conditions whose burdens vary by socio-demographic and geographic factors. Although several studies have investigated and identified disparities in burdens of these conditions at the county and state levels, little is known regarding their geographic epidemiology at the neighborhood level. Both conditions require emergency treatments and therefore timely geographic accessibility to appropriate care is critical. Investigation of disparities in geographic accessibility to stroke and MI care and the role of Emergency Medical Services (EMS) in reducing treatment delays are vital in improving health outcomes. Therefore, the objectives of this work were to: (i) classify neighborhoods based on socio-demographic and geographic characteristics; (ii) investigate spatial patterns of neighborhood level mortality; (iii) identify disparities in geographic accessibility to stroke and MI care; and (iv) identify disparities in EMS transport times for stroke and MI patients in East Tennessee. Fuzzy cluster analysis was used to classify neighborhoods into peer neighborhoods (PNs) based on their socio-demographic and geographic factors. Neighborhood level spatial patterns of stroke and MI mortality risks were investigated using Spatial Empirical Bayesian smoothing techniques and neighborhoods with high mortality risks identified using spatial scan statistics. Travel times to stroke and cardiac care facilities were computed using network analysis to investigate geographic accessibility. Records of over 3,900 suspected stroke and MI patients, from two EMS providers, were used to investigate disparities in EMS transport delays. Four distinct PNs were identified. The highest stroke/MI mortality risks were observed in less affluent, urban PNs, and lowest risks in more affluent, suburban PNs. Several significant (p\u3c0.0001) stroke and MI high mortality risk spatial clusters were identified. Approximately 8% and 15% of the population did not have timely accessibility to appropriate stroke and MI care, respectively. The disparity was greatest for populations in rural areas. Important disparities in EMS transport delays were identified, with the travel time to a hospital contributing the longest delay. The identified disparities in neighborhood characteristics, mortality risks, geographic accessibility, and EMS transport delays are invaluable in guiding resource allocation, service provision, and policy decisions to support evidence-based population health planning and policy

Temporal Changes in Geographic Disparities in Access to Emergency Heart Attack and Stroke Care: Are We Any Better Today?

The objective of this study was to investigate temporal changes in geographic access to emergency heart attack and stroke care. Network analysis was used to compute travel time to the nearest emergency room (ER), cardiac, and stroke centers in Middle Tennessee. Populations within 30, 60, and 90 min driving time to the nearest ER, cardiac and stroke centers were identified. There were improvements in timely access to cardiac and stroke centers over the study period (1999–2010). There were significant (p \u3c 0.0001) increases in the proportion of the population with access to cardiac centers within 30 min from 29.4% (1999) to 62.4% (2009) while that for stroke changed from 5.4% (2004) to 46.1% (2010). Most (96%) of the population had access to an ER within 30 min from 1999 to 2010. Access to care has improved in the last decade but more still needs to be done to address disparities in rural communities. Highlights ► Temporal changes in timely access to heart attack and stroke care are investigated. ► Timeliness of access to cardiac and stroke care improved from 1999 to 2010. ► Ninety six percent of the population had access to an ER within 30 min in both 1999 and 2010. ► Neighborhoods with poor access had the lowest median income and housing values. ► Areas with access within 30 min had lower mortality risks than those within 60 min

Identified peer neighborhoods (PN) in East Tennessee based on socioeconomic and demographic population characteristics using K-means clustering algorithm.

<p>Identified peer neighborhoods (PN) in East Tennessee based on socioeconomic and demographic population characteristics using K-means clustering algorithm.</p

Sensitivity Analysis of Fuzzy Cluster Analysis Results for Peer Neighborhoods Based on Socioeconomic and Demographic Population Characteristics.

<p>DPU = Normalized average square error, values close to 1 are hard solutions; FPU = Dunn's normalized partition coefficient , values close to 1 are fuzzy solutions; PN = Peer Neighborhood.</p><p>*One wants to identify a solution that has a high FPU index and low DPU index without being too close to a completely fuzzy solution (where FPU = 1 and DPU = 0).</p

Nearest Neighbor Discriminant Analysis Results of Classification of East Tennessee Peer Neighborhoods Based on Socioeconomic & Demographic Characteristics.

<p>Nearest Neighbor Discriminant Analysis Results of Classification of East Tennessee Peer Neighborhoods Based on Socioeconomic & Demographic Characteristics.</p