Modeling spatial accessibility to parks: a national study

<p>Abstract</p> <p>Background</p> <p>Parks provide ideal open spaces for leisure-time physical activity and important venues to promote physical activity. The spatial configuration of parks, the number of parks and their spatial distribution across neighborhood areas or local regions, represents the basic park access potential for their residential populations. A new measure of spatial access to parks, population-weighted distance (PWD) to parks, combines the advantages of current park access approaches and incorporates the information processing theory and probability access surface model to more accurately quantify residential population's potential spatial access to parks.</p> <p>Results</p> <p>The PWD was constructed at the basic level of US census geography - blocks - using US park and population data. This new measure of population park accessibility was aggregated to census tract, county, state and national levels. On average, US residential populations are expected to travel 6.7 miles to access their local neighborhood parks. There are significant differences in the PWD to local parks among states. The District of Columbia and Connecticut have the best access to local neighborhood parks with PWD of 0.6 miles and 1.8 miles, respectively. Alaska, Montana, and Wyoming have the largest PWDs of 62.0, 37.4, and 32.8 miles, respectively. Rural states in the western and Midwestern US have lower neighborhood park access, while urban states have relatively higher park access.</p> <p>Conclusions</p> <p>The PWD to parks provides a consistent platform for evaluating spatial equity of park access and linking with population health outcomes. It could be an informative evaluation tool for health professionals and policy makers. This new method could be applied to quantify geographic accessibility of other types of services or destinations, such as food, alcohol, and tobacco outlets.</p

Identification of contrastive and comparable school neighborhoods for childhood obesity and physical activity research

The neighborhood social and physical environments are considered significant factors contributing to children's inactive lifestyles, poor eating habits, and high levels of childhood obesity. Understanding of neighborhood environmental profiles is needed to facilitate community-based research and the development and implementation of community prevention and intervention programs. We sought to identify contrastive and comparable districts for childhood obesity and physical activity research studies. We have applied GIS technology to manipulate multiple data sources to generate objective and quantitative measures of school neighborhood-level characteristics for school-based studies. GIS technology integrated data from multiple sources (land use, traffic, crime, and census tract) and available social and built environment indicators theorized to be associated with childhood obesity and physical activity. We used network analysis and geoprocessing tools within a GIS environment to integrate these data and to generate objective social and physical environment measures for school districts. We applied hierarchical cluster analysis to categorize school district groups according to their neighborhood characteristics. We tested the utility of the area characterizations by using them to select comparable and contrastive schools for two specific studies. RESULTS: We generated school neighborhood-level social and built environment indicators for all 412 Chicago public elementary school districts. The combination of GIS and cluster analysis allowed us to identify eight school neighborhoods that were contrastive and comparable on parameters of interest (land use and safety) for a childhood obesity and physical activity study. CONCLUSION: The combination of GIS and cluster analysis makes it possible to objectively characterize urban neighborhoods and to select comparable and/or contrasting neighborhoods for community-based health studies

Improved fuzzy logic method to distinguish between meteorological and non-meteorological echoes using C-band polarimetric radar data

To obtain better performance of meteorological applications, it is necessary to distinguish radar echoes from meteorological and non-meteorological targets. After a comprehensive analysis of the computational efficiency and radar system characteristics, we propose a fuzzy logic method that is similar to the MetSignal algorithm; the performance of this method is improved significantly in weak-signal regions where polarimetric variables are severely affected by noise. In addition, post-processing is adjusted to prevent anomalous propagation at a far range from being misclassified as meteorological echo. Moreover, an additional fuzzy logic echo classifier is incorporated into post-processing to suppress misclassification in the melting layer. An independent test set is selected to evaluate algorithm performance, and the statistical results show an improvement in the algorithm performance, especially with respect to the classification of meteorological echoes in weak-signal regions

Do supplemental list frames for subpopulations increase subpopulation sampling efficiency? Evidence from the National Household Food Acquisition and Purchase Survey

Multiple-frame sampling has been regarded as a device for increasing efficiency in identifying small subpopulations. However, there has been a lack of empirical evidence in supporting the efficiency of the multiple-frame approach and in guiding best practices. The current study focuses on a special scenario in which two frames were used to recruit sample members. Using paradata from the U.S. National Household Food Acquisition and Purchase Survey (FoodAPS), the current analysis focuses on recruiting households that received Supplementary Nutrition Assistance Program (SNAP) as a sub-goal of the survey sampling. SNAP households account for around one-fifth of the general U.S. population, compared to a survey goal of 30 percent of responding households. Our findings were consistent with theoretical expectations. Having and using additional SNAP list frames improved the efficiency of identifying SNAP households as opposed to screening a general address-based sample frame. This efficiency remained even as the SNAP list frames aged

Estimating Population Exposure to Fine Particulate Matter in the Conterminous U.S. Using Shape Function-Based Spatiotemporal Interpolation Method: A County Level Analysis

This paper investigates spatiotemporal interpolation methods for the application of air pollution assessment. The air pollutant of interest in this paper is fine particulate matter PM2.5. The choice of the time scale is investigated when applying the shape function-based method. It is found that the measurement scale of the time dimension has an impact on the quality of interpolation results. Based upon the result of 10-fold cross validation, the most effective time scale out of four experimental ones was selected for the PM2.5 interpolation. The paper also estimates the population exposure to the ambient air pollution of PM2.5 at the county-level in the contiguous U.S. in 2009. The interpolated county-level PM2.5 has been linked to 2009 population data and the population with a risky PM2.5 exposure has been estimated. The risky PM2.5 exposure means the PM2.5 concentration exceeding the National Ambient Air Quality Standards. The geographic distribution of the counties with a risky PM2.5 exposure is visualized. This work is essential to understanding the associations between ambient air pollution exposure and population health outcomes

Estimating Population Exposure to Fine Particulate Matter in the Conterminous U.S. using Shape Function-based Spatiotemporal Interpolation Method

This paper investigates spatiotemporal interpolation methods for the application of air pollution assessment. The air pollutant of interest in this paper is fine particulate matter PM2.5. The choice of the time scale is investigated when applying the shape function-based method. It is found that the measurement scale of the time dimension has an impact on the quality of interpolation results. Based upon the result of 10-fold cross validation, the most effective time scale out of four experimental ones was selected for the PM2.5 interpolation. The paper also estimates the population exposure to the ambient air pollution of PM2.5 at the county-level in the contiguous U.S. in 2009. The interpolated county-level PM2.5 has been linked to 2009 population data and the population with a risky PM2.5 exposure has been estimated. The risky PM2.5 exposure means the PM2.5 concentration exceeding the National Ambient Air Quality Standards. The geographic distribution of the counties with a risky PM2.5 exposure is visualized. This work is essential to understanding the associations between ambient air pollution exposure and population health outcomes

Low usage of government healthcare facilities for acute respiratory infections in guatemala: implications for influenza surveillance

<p>Abstract</p> <p>Background</p> <p>Sentinel surveillance for severe acute respiratory infections in hospitals and influenza-like illness in ambulatory clinics is recommended to assist in global pandemic influenza preparedness. Healthcare utilization patterns will affect the generalizability of data from sentinel sites and the potential to use them to estimate burden of disease. The objective of this study was to measure healthcare utilization patterns in Guatemala to inform the establishment of a sentinel surveillance system for influenza and other respiratory infections, and allow estimation of disease burden.</p> <p>Methods</p> <p>We used a stratified, two-stage cluster survey sample to select 1200 households from the Department of Santa Rosa. Trained interviewers screened household residents for self-reported pneumonia in the last year and influenza-like illness (ILI) in the last month and asked about healthcare utilization for each illness episode.</p> <p>Results</p> <p>We surveyed 1131 (94%) households and 5449 residents between October and December 2006 and identified 323 (6%) cases of pneumonia and 628 (13%) cases of ILI. Treatment for pneumonia outside the home was sought by 92% of the children <5 years old and 73% of the persons aged five years and older. For both children <5 years old (53%) and persons aged five years and older (31%) who reported pneumonia, private clinics were the most frequently reported source of care. For ILI, treatment was sought outside the home by 81% of children <5 years old and 65% of persons aged five years and older. Government ambulatory clinics were the most frequently sought source of care for ILI both for children <5 years old (41%) and persons aged five years and older (36%).</p> <p>Conclusions</p> <p>Sentinel surveillance for influenza and other respiratory infections based in government health facilities in Guatemala will significantly underestimate the burden of disease. Adjustment for healthcare utilization practices will permit more accurate estimation of the incidence of influenza and other respiratory pathogens in the community.</p

Healthcare-seeking Behaviour for Common Infectious Disease-related Illnesses in Rural Kenya: A Community-based House-to-house Survey

Community surveys of healthcare-use determine the proportion of illness episodes not captured by health facility-based surveillance, the methodology used most commonly to estimate the burden of disease in Africa. A cross-sectional survey of households with children aged less than five years was conducted in 35 of 686 census enumeration areas in rural Bondo district, western Kenya. Healthcare sought for acute episodes of diarrhoea or fever in the past two weeks or pneumonia in the past year was evaluated. Factors associa-ted with healthcare-seeking were analyzed by logistic regression accounting for sample design. In total, 6,223 residents of 981 households were interviewed. Of 1,679 children aged less than five years, 233 (14%) had diarrhoea, and 736 (44%) had fever during the past two weeks; care at health facilities was sought for one-third of these episodes. Pneumonia in the past year was reported for 64 (4%) children aged less than five years; 88% sought healthcare at any health facility and 48% at hospitals. Seeking healthcare at health facilities was more likely for children from households with higher socioeconomic status and with more symptoms of severe illness. Health facility and hospital-based surveillance would underestimate the burden of disease substantially in rural western Kenya. Seeking healthcare at health facilities and hospitals varied by syndrome, severity of illness, and characteristics of the patient