Cost of inaction on sugar-sweetened beverage consumption: implications for obesity in South Africa
Bibliographic record
Abstract
OBJECTIVE: To estimate the effect of increased sugar-sweetened beverage (SSB) consumption on future adult obesity prevalence in South Africa in the absence of preventive measures. DESIGN: A model was constructed to simulate the effect of a 2·4 % annual increase in SSB consumption on obesity prevalence. The model computed the change in energy intake assuming a compounding increase in SSB consumption. The population distribution of BMI by age and sex was modelled by fitting measured data from the 2012 South African National Income Dynamics Survey to the log-normal distribution and shifting the mean values. SETTING: Over the past decade the prevalence of obesity and related non-communicable diseases has increased in South Africa, as have the sales and availability of SSB. Soft drink sales in South Africa are projected to grow between 2012 and 2017 at an annual compounded growth rate of 2·4 % in the absence of preventive measures to curb consumption. RESULTS: A 2·4 % annual growth in SSB sales alongside population growth and ageing will result in an additional 1 287 000 obese adults in South Africa by 2017, 22 % of which will be due to increased SSB consumption. CONCLUSIONS: In order to meet the South African target of reducing the number of people who are obese and/or overweight by 10 % by 2020, the country cannot afford to delay implementing effective population-wide interventions. In the face of plans to increase growth of SSB, the country will soon face even greater challenges in overcoming obesity and related non-communicable diseases.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".