Street Connectivity is Negatively Associated with Physical Activity in Canadian Youth

Street connectivity, defined as how well streets connect to one and other and the density of intersections, is positively associated with active transportation in adults. Our objective was to study the relation between street connectivity and physical activity in youth. Study participants consisted of 8,535 students in grades 6–10 from 180 schools across Canada who completed the 2006 Health Behaviour in School-aged Children (HBSC) survey. Street connectivity was measured in a 5 km circular buffer around these schools using established geographic information system measures. Physical activity performed outside of school hours was assessed by questionnaire, and multi-level regression analyses were used to estimate associations with street connectivity after controlling for several covariates. Compared to students living in the highest street connectivity quartile, those in the second (relative risk = 1.22, 95% confidence interval = 1.10–1.35), third (1.25, 1.13–1.37), and fourth (1.21, 1.09–1.34) quartiles were more likely to be physically active outside of school. In conclusion, youth in neighbourhoods with the most highly connected streets reported less physical activity outside of school than youth from neighbourhoods with less connected streets. Relationships between street connectivity and physical activity reported in this national study are in the opposite direction to those previously observed for active transportation in adult populations

Forestry pesticide spraying and cancer incidence in New Brunswick : an ecological study

The human health risk associated with exposure to pesticide formulations applied to New Brunswick forests was examined for 31 sites of cancer, using measures of exposure based on the proximity of non-city population centers to spray areas.Two organochlorine and two organophosphate exposure indices were developed by using maps of areas sprayed each year during the period 1952 to 1976. These data were analyzed in relation to cancer incidence rates during the period 1977-l980 for 254 New Brunswick municipalities.Follow-up case-control studies of the cancer sites considered does not seem to be a matter of high priority at present. However, continued surveillance and data analysis involving more recent data is needed, particularly in the case of organophosphate formulations, due to the relatively short interval between exposure and outcome ascertainment for this exposure

Using Principal Component Analysis to Identify Priority Neighbourhoods for Health Services Delivery by Ranking Socioeconomic Status

Objectives. Socioeconomic status (SES) is a comprehensive indicator of health status and is useful in area-level health research and informing public health resource allocation. Principal component analysis (PCA) is a useful tool for developing SES indices to identify area-level disparities in SES within communities. While SES research in Canada has relied on census data, the voluntary nature of the 2011 National Household Survey challenges the validity of its data, especially income variables. This study sought to determine the appropriateness of replacing census income information with tax filer data in neighbourhood SES index development. Methods. Census and taxfiler data for Guelph, Ontario were retrieved for the years 2005, 2006, and 2011. Data were extracted for eleven income and non-income SES variables. PCA was employed to identify significant principal components from each dataset and weights of each contributing variable. Variable-specific factor scores were applied to standardized census and taxfiler data values to produce SES scores. Results. The substitution of taxfiler income variables for census income variables yielded SES score distributions and neighbourhood SES classifications that were similar to SES scores calculated using entirely census variables. Combining taxfiler income variables with census non-income variables also produced clearer SES level distinctions. Internal validation procedures indicated that utilizing multiple principal components produced clearer SES level distinctions than using only the first principal component. Conclusion. Identifying socioeconomic disparities between neighbourhoods is an important step in assessing the level of disadvantage of communities. The ability to replace census income information with taxfiler data to develop SES indices expands the versatility of public health research and planning in Canada, as more data sources can be explored. The apparent usefulness of PCA also contributes to the improvement of SES measurement and calculation methods, and the freedom to input area-specific data allows the present method to be adapted to other locales

Using Principal Component Analysis to Identify Priority Neighbourhoods for Health Services Delivery by Ranking Socioeconomic Status

Objectives. Changes to the Canadian Census in 2010 led to the creation of the National Household Survey (NHS). The voluntary nature of the NHS has important implications to health research in Canada, as the validity of its data used for socioeconomic status (SES) index creation, especially income variables, is questionable. This study sought to determine the appropriateness of replacing census income information with tax filer data to produce SES neighbourhood indices.Methods. Census and taxfiler data for Guelph, Ontario were retrieved for the years 2005, 2006, and 2011. Data were extracted for eleven income and non-income SES variables. Principal component analysis was utilized to identify significant principal components from each dataset and weights of each contributing variable. Variable-specific factor scores were applied to standardized census and taxfiler data values to produce SES scores.Results. The substitution of taxfiler income variables for census income variables yielded SES score distributions and neighbourhood SES classifications that were similar to SES scores calculated using entirely census variables. Combining taxfiler income variables with census non-income variables also produced clearer SES level distinctions.Conclusion. Identifying socioeconomic disparities between neighbourhoods is an important step in assessing the level of disadvantage of communities, and the presented method can be adapted to other locales for such a purpose. The ability to replace census income information with taxfiler data to develop SES indices will increase the versatility of public health research and planning in Canada, and contribute to the improvement of SES measurement and calculation methods

Estimating micro area behavioural risk factor prevalence from large population-based surveys: a full Bayesian approach

Abstract Background An important public health goal is to decrease the prevalence of key behavioural risk factors, such as tobacco use and obesity. Survey information is often available at the regional level, but heterogeneity within large geographic regions cannot be assessed. Advanced spatial analysis techniques are demonstrated to produce sensible micro area estimates of behavioural risk factors that enable identification of areas with high prevalence. Methods A spatial Bayesian hierarchical model was used to estimate the micro area prevalence of current smoking and excess bodyweight for the Erie-St. Clair region in southwestern Ontario. Estimates were mapped for male and female respondents of five cycles of the Canadian Community Health Survey (CCHS). The micro areas were 2006 Census Dissemination Areas, with an average population of 400–700 people. Two individual-level models were specified: one controlled for survey cycle and age group (model 1), and one controlled for survey cycle, age group and micro area median household income (model 2). Post-stratification was used to derive micro area behavioural risk factor estimates weighted to the population structure. SaTScan analyses were conducted on the granular, postal-code level CCHS data to corroborate findings of elevated prevalence. Results Current smoking was elevated in two urban areas for both sexes (Sarnia and Windsor), and an additional small community (Chatham) for males only. Areas of excess bodyweight were prevalent in an urban core (Windsor) among males, but not females. Precision of the posterior post-stratified current smoking estimates was improved in model 2, as indicated by narrower credible intervals and a lower coefficient of variation. For excess bodyweight, both models had similar precision. Aggregation of the micro area estimates to CCHS design-based estimates validated the findings. Conclusions This is among the first studies to apply a full Bayesian model to complex sample survey data to identify micro areas with variation in risk factor prevalence, accounting for spatial correlation and other covariates. Application of micro area analysis techniques helps define areas for public health planning, and may be informative to surveillance and research modeling of relevant chronic disease outcomes