Objective: Previous studies on the relationship of dietary intake to the neighbourhood food environment have focused on access to supermarkets, quantified by geographic distance or store concentration measures. However, in-store food availability may also be an important determinant, particularly for urban neighbourhoods with a greater concentration of small food stores. This study synthesises both types of information -store access and in-store availability -to determine their potential relationship to fruit and vegetable consumption. Design: Residents in four census tracts were surveyed in 2001 about their fruit and vegetable intake. Household distances to food stores in these and surrounding tracts were obtained using geographical information system mapping techniques. In-store fruit and vegetable availability was measured by linear shelf space. Multivariate linear regression models were used to measure the association of these neighbourhood availability measures with consumption. Setting: Four contiguous census tracts in central-city New Orleans. Subjects: A random sample of 102 households. Results: Greater fresh vegetable availability within 100 m of a residence was a positive predictor of vegetable intake; each additional metre of shelf space was associated with 0.35 servings per day of increased intake. Fresh fruit availability was not associated with intake, although having a small food store within this same distance was a marginal predictor of fruit consumption. Conclusions: The findings suggest the possible importance of small neighbourhood food stores and their fresh produce availability in affecting fruit and vegetable intake.
A multitemporal, land use land cover (LULC) classification dataset incorporating distributions of mosquito larval habitats was produced in ERDAS Imagine using the combined images from the Multispectral Thermal Imager (MTI) at 5 m spatial resolution from 2001 with Thematic Mapper-classification data at 28.5 m spatial resolution from 1987 and 1989 for Kisumu and Malindi, Kenya. Total LULC change for Kisumu over 14 yr was 30.2%. Total LULC change for Malindi over 12 yr was 30.6%. Of those areas in which change was detected, the LULC change for Kisumu was 72.5% for nonurban to urban, 21.7% urban to nonurban, 0.4% urban to water, 4.5% water to urban, and 0.9% water to nonurban. The proportion of LULC change for Malindi was 93.5% for nonurban to urban, 5.9% urban to nonurban, 0.2% urban to water, 0.3% nonurban to water, and 0.1% water to urban. A grid (270 m x 270 m cells) was overlaid over the maps stratifying grid cells based on drainage and planning. Of 84 aquatic habitats in Kisumu, 32.1% were located in LULC change sites and 67.9% were located in LULC nonchange sites. Of 170 aquatic habitats in Malindi, 26.5% were located in LULC change sites and 73.5% were located in LULC nonchange sites. The most abundant LULC change per strata with anopheline habitats was unplanned and poorly drained. Ditches and puddles in Kisumu and car tracks in Malindi displayed the highest number of anopheline larval habitats for all LULC change sites. The proportion of site positive aquatic habitats for anopheline larvae was higher in LULC change sites than for LULC nonchange sites for Kisumu. This evidence suggests LULC change can influence anopheline larval habitat distribution.
The Environmental Public Health Tracking Network (EPHTN) proposes to link environmental hazards and exposures to health outcomes. Statistical methods used in case–control and cohort studies to link health outcomes to individual exposure estimates are well developed. However, reliable exposure estimates for many contaminants are not available at the individual level. In these cases, exposure/hazard data are often aggregated over a geographic area, and ecologic models are used to relate health outcome and exposure/hazard. Ecologic models are not without limitations in interpretation. EPHTN data are characteristic of much information currently being collected—they are multivariate, with many predictors and response variables, often aggregated over geographic regions (small and large) and correlated in space and/or time. The methods to model trends in space and time, handle correlation structures in the data, estimate effects, test hypotheses, and predict future outcomes are relatively new and without extensive application in environmental public health. In this article we outline a tiered approach to data analysis for EPHTN and review the use of standard methods for relating exposure/hazards, disease mapping and clustering techniques, Bayesian approaches, Markov chain Monte Carlo methods for estimation of posterior parameters, and geostatistical methods. The advantages and limitations of these methods are discussed.
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