Longitudinal geo-referenced field evidence for the heightened BMI responsiveness of obese women to price discounts on carbonated soft drinks
Bibliographic record
Abstract
There is increasing interest in the effect that food environments may have on obesity, particularly through mechanisms related to the marketing and consumption of calorie-dense, nutrient-poor foods and sugary beverages. Price promotions, such as temporary price discounts, have been particularly effective in the marketing of carbonated soft drinks (CSDs) among consumers. Research has also suggested that the purchasing behavior of consumer groups may be differentially sensitive to price discounts on CSDs, with obese women particularly sensitive. In addition, the intensity of price discount in a person's food environment may also vary across geography and over time. This study examines whether the weight change of obese women, compared to overweight or normal BMI women, is more sensitive to the intensity of price discounts on CSDs in the food environment. This study used longitudinal survey data from 1622 women in the Montreal Neighborhood Networks and Health Aging (MoNNET-HA) Panel. Women were asked to report their height and weight in 2008, 2010 and 2013 in order to calculate women's BMI in 2008 and their change of weight between 2008 and 2013. Women's exposure to an unhealthy food environment was based on the frequency in which their neighborhood food stores placed price discounts on CSDs in 2008. The price discount frequency on CSDs within women's neighborhoods was calculated from Nielsen point-of sales transaction data in 2008 and geocoded to participant's forward sortation area. The prevalence of obesity and overweight among MoNNET-HA female participants was 18.3% in 2008, 19.9% in 2010 and 20.7% in 2013 respectively. Results showed that among obese women, exposure to unhealthy food environments was associated with a 3.25 kilogram (SE = 1.35, p-value = 0.02) weight gain over the five-year study period. Exposure to price discounts on CSDs may disproportionately affect and reinforce weight gain in women who are already obese.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".