Obesogenic Environments: Access to and Advertising of Sugar-Sweetened Beverages in Soweto, South Africa, 2013
Bibliographic record
Abstract
INTRODUCTION: Rates of obesity and overweight among South Africans are increasing. Food marketing has a profound impact on children and affects their lifelong eating patterns; in urban areas of South Africa, disposable incomes are growing and ultra-processed food is increasingly available at low cost. The combination of these factors will strain an already fragile health system. Our aim was to investigate the density of outdoor sugar sweetened beverage (SSB) advertising and the number of formal and informal vendors selling SSBs in a transforming, historically disadvantaged urban setting of South Africa. METHODS: A digital camera and global positioning system navigation system were used to record the location of SSB advertisements and food vendors in a demarcated area in Soweto. Data were collected by walking or driving through each street; a food inventory was completed for every food vendor. Spatial analyses were conducted using a geographic information system. RESULTS: A total of 145 advertisements for SSBs were found over a driven or walked distance of 111.9 km. The density of advertisements was 3.6 per km(2) in relation to schools, and 50% of schools had branded advertising of SSBs on their school property. Most (n = 104; 58%) of the 180 vendors in the study sold SSBs. CONCLUSION: This is the first study in South Africa to document the location of billboard advertisements and vendors in relation to schools. Marketing of products that contribute to obesity is common in urban Soweto. Our findings have implications for policies that regulate SSB advertising, especially in the proximity of schools.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".