No abstract

BackgroundSocioeconomic, 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.Methods and FindingsThe 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.ConclusionsImplementation 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.

Two bottom trawl surveys of fish were undertaken during the seasonal sea ice retreat in 2006 and 2007 in the northern Bering Sea. For each trawl, we calculated catch per unit area (CPUA) for all fish taxa. Arctic cod Boreogadus saida, Bering flounder Hippoglossoides robustus and snailfish (Liparidae) were the dominant species south of St. Lawrence Island (SLI), whereas Arctic alligatorfish Ulcina olrikii, Arctic staghorn sculpin Gymnocanthus tricuspis and shorthorn sculpin Myoxocephalus scorpius were the dominant fishes north of SLI. Cluster analysis and multidimensional scaling were used to investigate relationships between environmental conditions and fish community structure in the northern Bering Sea. One goal of the study was to assess the importance of environmental variables on groundfish assemblages. The results showed that sediment grain size (an indicator of current speed) was the most important environmental factor explaining fish community structure in both years of the study. Bottom water nutrients (nitrate + nitrite), bottom water chlorophyll a (chl a) (with similar results for total chl a in the water column), sediment grain size, and sediment C/N ratios had stronger relationships with fish distribution in 2006 (cold, pre-bloom conditions), whereas bottom water temperature and sediment grain size were more important in 2007 (warm, bloom conditions) among a total of 14 environmental variables that were analyzed. These findings indicate strong linkages between physical conditions (e.g. water temperature and hydrography as it affects sediment grain size) and biological conditions (e.g. bloom status) in structuring fish communities in the northern Bering Sea.

Traffic volumes on local roads have not received much attention from highway planners and researchers, although local roads constitute the majority of road mileage in a state. In recent years the need for reliable estimates of vehicle-miles of travel on local roads has been recognized for the analysis of air quality and highway safety issues. To provide a better understanding of traffic volumes on local roads and to explore alternative methods for estimation, data from Georgia were analyzed by using different statistical procedures. The findings of this analysis are presented, along with the results of an attempt to develop a mathematical model for estimation of local road traffic volumes.

No abstract