Neighbourhood fruit and vegetable availability and consumption: the role of small food stores in an urban environment.

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.

Hierarchical modeling and other spatial analyses in prostate cancer incidence data.

BACKGROUND: State central cancer registries are often asked to respond to questions about the spatial distribution of cancer cases. Spatial analysis methods and technology are evolving rapidly, and can be a considerable challenge to registries that do not have staff with training in this area. The purpose of this article is to describe a general methodological approach that potentially might be a starting point for many cancer registry spatial analyses at the county level. METHODS: Prostate cancer incident cases (N=31,159) from the Louisiana Tumor Registry from 1988 to 1999 were used for illustrative purposes. To explore spatio-temporal patterns, analyses focused on four time periods, each 3 years in length: 1998-1990, 1991-1993, 1994-1996, and 1997-1999. For each time period, race-specific (white and black), direct age-adjusted incidence rates and indirect standardized incidence ratios (SIRs) were calculated, smoothed using Bayesian methods, and assessed for evidence of spatial autocorrelation using global and local Moran's I. Hierarchical generalized linear models (HGLM) were fitted to identify significant covariates. Clusters of elevated and lower rates were identified using a spatial scan statistic (SaTScan). RESULTS: Temporal trends in SIRs in both race groups were consistent with the introduction of prostate specific antigen (PSA) testing in Louisiana during the late 1980s and early 1990s, but possibly with a lag in black males. Clusters of lower than expected values were observed for white males in the central (p=0.001) and southeastern coastal areas (p=0.001), and to a greater extent for black males in the central (p=0.001), southwestern and southeastern coastal parishes (p=0.001). CONCLUSIONS: Mapping disease occurrence by time period is an effective way to explore spatio-temporal patterns. HGLM models and software are available to control for covariates and for unstructured and spatially structured variability that may confound spatial variability patterns.

Statistical methods for linking health, exposure, and hazards.

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.

Occurrence and distribution of Anopheles (Diptera: Culicidae) larval habitats on land cover change sites in urban Kisumu and urban Malindi, Kenya.

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.