Understanding The Determinants of Obesity In Urban Canada
Bibliographic record
Abstract
This research examined the geographic variability as well as the individual-and neighbourhood-level determinants of overweight and obesity in Canada. Overweight and obesity represent a significant public health problem with grave implications for individuals as well as populations. Over the past two decades, the prevalence of overweight and obesity has reached epidemic proportions with the most substantial increases observed in economically developed countries. The World Health Organization indicated that globally 1.6 billion adults (age 15+) are overweight and at least 400 million adults were obese. In a Canadian context, recent data from Statistics Canada confirms that over the past twenty-five years, adult obesity rates in Canada have doubled (23% ), while childhood obesity rates have nearly tripled. Until recently, research has focused on biological and behavioural determinants of obesity, and currently there is a great deal of knowledge regarding the relationships between weight status and various risk factors at the individual-level (e.g. age, sex, socioeconomic deprivation, diet, physical activity). However, the majority of existing research has ignored the potential role played by the environment in the development of these conditions, despite a growing consensus that environmental and/or societal constraints may be major influences on increasing prevalence rates. Using data from the Canadian Community Health Surveys and the Desktop Mapping Information Technologies Incorporated spatial database, this research addressed the following objectives: (I) to examine sex-specific spatial patterns of overweight/obesity in Canada as well as investigate the presence of spatial clusters (2) to investigate the prevalence and determinants of overweight and obesity in Canada using spatial analysis and geographical information systems (GIS) and (3) to identify heterogeneities associated with the relationships between individual and socioenvironmental determinants and overweight and obesity at the individual-and community-levels. Results revealed marked geographical variation in overweight/obesity prevalence with higher values in the Northern and Atlantic health-regions and lower values in the Southern and Western health-regions of Canada. Significant positive spatial autocorrelation was found for both males and females, with significant clusters of high values or 'hot spots' of obesity in the Atlantic and Northern health-regions of Alberta, Saskatchewan, Manitoba and Ontario. Results also demonstrate the important role of the built-environment after adjustment demographic, socio-economic and behavioural characteristics. With regard to the built environment measures, landuse mix and residential density were found to be significantly associated with BMI. This study also demonstrated significant differences at the area-level of analysis, supporting related research that has suggested that individual-level factors alone cannot explain variation in obesity rates across space. In particular, average dwelling value was related to BMI independently of individual-level characteristics. Ultimately, this research has demonstrated that Canadian urban environments play a small but significant role in shaping the distribution of BMI. Yet, reversing current trends will require a multifaceted public health approach where interventions are developed from the individual-to the neighbourhood-level, specifically focusing on altering obesogenic environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".