A spatial analysis of community level overweight and obesity
Bibliographic record
Abstract
BACKGROUND: Rates of overweight and obesity are now considered to be epidemic. Few studies have examined the spatial distribution of overweight and obesity at the community level, an area of geography recommended for prevention and intervention. Therefore, the present study aimed to examine the spatial variation of overweight and obesity using community geographic boundaries. METHODS: A cross-sectional secondary spatial data analysis was conducted using three combined cycles of Canadian Community Health Survey data for the province of Nova Scotia with community level boundaries. Descriptive rates were calculated using standardised incidence ratio values and spatial analysis was carried out using Global and Local Moran's I and the GetisOrdGi* statistic for cluster identification. RESULTS: Maps illustrating local cluster analysis showed a significant degree of similarity between neighbouring communities in urban areas more so than rural communities. Hot spot analysis maps showed communities clustering together in the urban centre tended to have lower incidence of overweight and obesity ('cool spots'), whereas clustered communities in a more rural area had a higher incidence of overweight and obesity ('hot spots). CONCLUSIONS: The present study showed that there was geographical variation in overweight and obesity between urban and rural communities, and also there was a tendency for communities to cluster based on the incidence of overweight and obesity. This highlights the importance of understanding community level obesity rates and associated behavioural determinants, such as diet and physical activity, as well as the role that urbanisation or rurality may play in intervention initiatives for these behavioural determinants. Specifically, public health nutrition efforts for community level food environments in rural areas should ensure an individualised approach is used, whereas urban areas may be amenable to more general approaches aiming to support healthy weight status among the broader population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".