This study examines resilience among a sample of American Indian adolescents living on or near reservations in the upper Midwest. Data are from a baseline survey of 212 youth (115 boys and 97 girls) who were enrolled in the fifth through eighth grades. Based upon the definition of resilience, latent class analyses were conducted to identify youth who displayed pro‐social outcomes (60.5%) as opposed to problem behavior outcomes. A measure of family adversity was also developed that indicated only 38.4% of the youth lived in low‐adversity households. Defining resilience in the context of positive outcomes in the face of adversity, logistic regression was used to examine the predictors of pro‐social outcomes among youth who lived in moderate‐ to high‐adversity households. The analyses identified key risk and protective factors. A primary risk factor appeared to be perceived discrimination. Protective factors were from multiple contexts: family, community, and culture. Having a warm and supportive mother, perceiving community support, and exhibiting higher levels of enculturation were each associated with increased likelihood of pro‐social outcomes. © 2006 Wiley Periodicals, Inc.

Background: There is increasing interest in examining the influence of the built environment on physical activity. High-resolution data in a geographic information system is increasingly being used to measure salient aspects of the built environment and studies often use circular or road network buffers to measure land use around an individual's home address. However, little research has examined the extent to which the selection of circular or road network buffers influences the results of analysis.

Area-based deprivation indices (ABDIs) have become a common tool with which to investigate the patterns and magnitude of socioeconomic inequalities in health. ABDIs are also used as a proxy for individual socioeconomic status. Despite their widespread use, comparably less attention has been focused on their geographic variability and practical concerns surrounding the Modifiable Area Unit Problem (MAUP) than on the individual attributes that make up the indices. Although scale is increasingly recognized as an important factor in interpreting mapped results among population health researchers, less attention has been paid specifically to ABDI and scale. In this paper, we highlight the effect of scale on indices by mapping ABDIs at multiple census scales in an urban area. In addition, we compare self-rated health data from the Canadian Community Health Survey with ABDIs at two census scales. The results of our analysis confirm the influence of spatial extent and scale on mapping population health-with potential implications for health policy implementation and resource distribution.KEYWORDS Deprivation indices, MAUP, Population health, Scale. A BRIEF BIOGRAPHY OF POPULATION HEALTH INDICES COMMONLY USED IN CANADAThe use of census data to quantify socioeconomic deprivation is a generally wellaccepted method of identifying populations with poorer health outcomes.1-5 The history of census-based area deprivation indices dates back to at least until 1971, when the Department of the Environment (DOE) in the United Kingdom used data taken from the census to identify localities where a high proportion of households were exposed to adverse social and economic conditions. 6 The indices were developed to more effectively identify areas in need of resources to improve quality of life. Publications stemming from The Black Report, 7 the Whitehall, 8 and Acheson studies 9 launched additional public scrutiny of the relationship between socioeconomic gradients and health status. These studies have spurred a relatively new yet increasingly popular framework that uses socioeconomic data taken from the census to quantify deprivation and demonstrate its relationship with population health. 2,[10][11][12][13][14] Schuurman, Bell, and Oliver are with the

Background:The purposes of this study are to determine (i) if neighbourhood socioeconomic status (SES) is systematically related to the prevalence of overweight children and youth in Canada, (ii) if the factors accounting for the apparent relationship have face validity, and (iii) if neighbourhood SES has an independent influence on this distribution.Methods: Cross-sectional data from Cycle 4 (2000/2001) of the National Longitudinal Survey of Children and Youth were used. Children and youth aged 5 to 17 were included. Overweight was established using age and sex cut-off points. Neighbourhood socioeconomic data were obtained from the Statistics Canada 2001 Dissemination Area databases and SES quartiles constructed using a composite of socio-economic variables. Hierarchical non-linear modelling was used to test for independent neighbourhood effects.Results: A gradient of increasing overweight prevalence by decreasing neighbourhood SES quartiles was observed (24% high SES, 30% mid-high SES, 33% mid-low SES, 35% low SES). Controlling for individual age, gender, family income and education hierarchical analysis found that a child's odds of being overweight increases if living in a low versus a high SES neighbourhood (OR=1.29, 95% CI=1.14-1.46).La traduction du résumé se trouve à la fin de l'article.

The aim of this study was to examine spatial clustering of obesity and/or moderate physical activity and their relationship to a neighborhood's built environment. Data on levels of obesity and moderate physical activity were derived from the results of a telephone survey conducted in 2006, with 1,863 survey respondents in the study sample. This sample was spread across eight suburban neighborhoods in Metro Vancouver. These areas were selected to contrast residential density and income and do not constitute a random sample, but within each area, respondents were selected randomly. Obesity and moderate physical activity were mapped to determine levels of global and local spatial autocorrelation within the neighborhoods. Clustering was measured using Moran's I at the global level, Anselin's Local Moran's I at the local level, and geographically weighted regression (GWR). The global‐level spatial analysis reveals no significant clustering for the attributes of obesity or moderate physical activity. Within individual neighborhoods, there is moderate clustering of obesity and/or physical activity but these clusters do not achieve statistical significance. In some neighborhoods, local clustering is restricted to a single pair of respondents with moderate physical activity. In other neighborhoods, any moderate local clustering is offset by negative local spatial autocorrelation. Importantly, there is no evidence of significant clustering for the attribute of obesity at either the global or local level of analysis. The GWR analysis fails to improve significantly upon the global model—thus reinforcing the negative results. Overall, the study indicates that the relationship between the urban environment and obesity is not direct.