School-based nutrition programs produced a moderate increase in fruit and vegetable consumption: meta and pooling analyses from 7 studies.

OBJECTIVE: To evaluate, through study- and individual-level analyses of data from 7 studies, the effectiveness of school-based nutrition interventions on child fruit and vegetable (FV) consumption. DESIGN: To find original studies on school-based nutrition interventions, the authors searched electronic databases from 1990 to 2002. First authors of the 13 eligible studies were contacted to request their data. Data from 7 studies were received for inclusion in this pooled analysis. SETTING: Schools. PARTICIPANTS: 8156 children were matched from pretest to posttest. Participants were primarily elementary school-aged (75.5%) and white (66%), and 50.4% were males. MAIN OUTCOME MEASURES: Net FV difference and net FV relative change (%). ANALYSIS: Data were analyzed at both the study and individual levels. A fitted multivariable fixed-effects model was used to analyze the role of potential covariates on FV intake. Statistical significance was set at alpha = .05. RESULTS: At the individual level, the net difference in FV consumption was 0.45 (95% CI 0.33-0.59) servings; the net relative change was 19% (95% CI 0.15-0.23) servings. CONCLUSIONS AND IMPLICATIONS: School-based nutrition interventions produced a moderate increase in FV intake among children. These results may have implications for chronic disease prevention efforts, including cardiovascular disease and cancer.

Current practices in spatial analysis of cancer data: mapping health statistics to inform policymakers and the public.

BACKGROUND: To communicate population-based cancer statistics, cancer researchers have a long tradition of presenting data in a spatial representation, or map. Historically, health data were presented in printed atlases in which the map producer selected the content and format. The availability of geographic information systems (GIS) with comprehensive mapping and spatial analysis capability for desktop and Internet mapping has greatly expanded the number of producers and consumers of health maps, including policymakers and the public.Because health maps, particularly ones that show elevated cancer rates, historically have raised public concerns, it is essential that these maps be designed to be accurate, clear, and interpretable for the broad range of users who may view them. This article focuses on designing maps to communicate effectively. It is based on years of research into the use of health maps for communicating among public health researchers. RESULTS: The basics for designing maps that communicate effectively are similar to the basics for any mode of communication. Tasks include deciding on the purpose, knowing the audience and its characteristics, choosing a media suitable for both the purpose and the audience, and finally testing the map design to ensure that it suits the purpose with the intended audience, and communicates accurately and effectively. Special considerations for health maps include ensuring confidentiality and reflecting the uncertainty of small area statistics. Statistical maps need to be based on sound practices and principles developed by the statistical and cartographic communities. CONCLUSION: The biggest challenge is to ensure that maps of health statistics inform without misinforming. Advances in the sciences of cartography, statistics, and visualization of spatial data are constantly expanding the toolkit available to mapmakers to meet this challenge. Asking potential users to answer questions or to talk about what they see is still the best way to evaluate the effectiveness of a specific map design.

Spatial analysis of disease--applications.

The application of spatial statistical analysis to health data has reached adolescence. The theory and the software are both still maturing. We are drawing upon the experiences of the geostatisticians in modeling surfaces and the econometricians in modeling time series. "New and improved" computer algorithms are constantly being provided to implement the evolving theory or to improve the processing in terms of stability, reliability, and efficiency. We will come of age when we have the theory, the software, and the process to reliably produce "generalized spatio-temporal" models suitable for health data. In the meantime, biostatisticians need to acknowledge when their data is not independently distributed and to consider the spatial correlation in their analysis. This chapter provided examples using four available methods. The methods were spatial filtering, identifying clusters using the spatial scan statistic, hierarchical modeling, and conditional autoregression modeling.

Relationship between incidence of gastric dilatation-volvulus and biometeorologic events in a population of military working dogs.

OBJECTIVE: To calculate the monthly incidence of gastric dilatation-volvulus (GDV) in a population of military working dogs during a 5-year period and determine whether there was an association with synoptic climatologic indices. SAMPLE POPULATION: Medical records of all military working dogs housed at Lackland Air Force Base,Tex, from Jan 1, 1993 to Dec 31, 1997. PROCEDURE: Confirmed cases of GDV were identified from evaluation of medical records and used to calculate incidence of GDV. Factor analysis of local climatologic data was used to classify each day into 1 of 8 meteorologically homogeneous types of days for this location. Occurrence of GDV was compared with frequency of occurrence of synoptic climatologic days. RESULTS: 48 cases of GDV were identified from January 1993 through December 1997 Mean monthly incidence was 2.5 cases/1,000 dogs at risk (range, 0 to 18.5 cases/1,000 dogs; median, 2.5 cases/1,000 dogs). A seasonal increase in incidence of GDV was detected, because half of the episodes were during November, December, and January. An association with a specific synoptic climatologic day was not detected. CONCLUSIONS AND CLINICAL RELEVANCE: Seasonal fluctuations in incidence of GDV may be associated with external factors that precipitate physiologic changes resulting in GDV. Although a specific cause-effect relationship was not documented, clinicians must be alert for the potential of seasonal variation in incidence of GDV and accordingly heighten their index of suspicion for the condition, particularly in populations of dogs that are predisposed to development of GDV.

Geographic patterns of advanced breast cancer in Los Angeles: associations with biological and sociodemographic factors (United States).

OBJECTIVE: Examination of patterns of advanced breast cancer may provide evidence needed to direct health care resources to those communities or population groups in greatest need. We assessed to what degree biologic, ethnic, and sociodemographic factors could explain such patterns within Los Angeles County. METHODS: The proportion of cases of advanced disease among all breast cancer cases identified during 1992-1996 were analyzed using generalized linear mixed models with random census tract effects. Models included characteristics of the individual and her tumor, census tract of residence, and aggregated health districts. RESULTS: Approximately 6% of cases, ranging from 4% for Asian to 10% for Black women, were diagnosed as advanced, exhibiting striking geographic patterns. Tumor histology and hormone receptor status were most predictive of advanced disease. Sociodemographic variables such as marital status, median income, and distance to nearest mammography unit showed additional association with risk. CONCLUSIONS: These models explain most of the geographical patterns and eliminate differences between White and Hispanic but not Asian or Black women, identify subpopulations at high risk of advanced disease, and suggest cancer control opportunities.