Advertising ultra-processed foods around urban and rural schools in Kenya
Bibliographic record
Abstract
Abstract Background Marketing of ultra-processed foods (UPFs) can influence purchase intentions and consumption of such foods, especially among children. There is limited evidence on the extent to which UPFs are marketed around schools in low-and middle-income countries (LMICs), including Kenya. We assessed the extent, content, and type of advertising of ultra-processed/unhealthy foods around schools in urban and rural settings in Kenya. Methods This cross-sectional study assessed advertising of food and beverages within a 250m radius around schools in Kenya between June to July 2021. We conducted the study in three counties (Nairobi-urban, Mombasa-coastal urban city, and Baringo-rural). Each county was stratified into high and low socioeconomic status (SES) sub-counties. Within each, we randomly selected schools and collected detailed information on advertising around the schools. The information gathered included the location, type of food and beverage product advertised, and the promotional techniques used. We used the NOVA classification and International Network for Food and Obesity/NCDs Research Monitoring and Action Support (INFORMAS) methods to categorize the food and beverages advertised based on processing level and core (healthy)/non-core(unhealthy) groups. We determined the extent of advertisements using descriptive analysis frequencies, and median (interquartile ranges). Poisson regression was used to determine the factors associated with UPF advertisements. Results In total, 2300 food and beverage advertisements were mapped around the 500 schools. There was a higher median number of advertisements in urban areas (median=25, Interquartile range (IQR)=25,160) compared to rural areas (median=10, IQR= 4, 13). Of these advertisements, approximately 48.0% were UPFs. The most common promotional strategy used was cartoon and company-owned characters, while the most frequent premium offer was price discounts. In the multivariate analysis, there was a significantly higher rate of advertisements of UPFs in Baringo County (prevalence rate ratios (PRR): 1.17, 95% CI: 1.01-1.36) compared to the urban Nairobi County, and in lower compared to higher SES settings (PRR: 1.10, 95% CI: 1.01-1.20). Conclusion UPFs are frequently advertised around schools with promotional strategies that may be appealing to children. There is a need to restrict the marketing of unhealthy foods around schools to children in Kenya.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".