Advertising ultra-processed foods around urban and rural schools in Kenya
Bibliographic record
Abstract
Marketing of ultra-processed foods (UPFs) can influence children's food preferences and consumption patterns. However, limited data exist on the extent and nature of UPF marketing around schools in low- and middle-income countries, including Kenya. This study assessed the extent, type, and content of food and beverage advertising near schools in urban and rural settings in Kenya. We conducted a cross-sectional study in June-July 2021 across three Kenyan counties-Nairobi (urban), Mombasa (coastal urban), and Baringo (rural). Each county was stratified by socioeconomic status (SES), and schools were randomly selected. Food and beverage advertisements within a 250-meter radius of schools were documented. Data collected included the type of product, location, and promotional techniques used. Advertised products were categorized using the NOVA classification and the INFORMAS framework. Descriptive statistics were used to summarize advertisement patterns, and Poisson regression was applied to identify factors associated with UPF advertising. A total of 2,300 food and beverage advertisements were documented around 500 schools. Urban areas had a higher median number of advertisements (median = 25, IQR: 25-160) compared to rural areas (median = 10, IQR: 4-13). Nearly 48% of all advertisements featured UPFs. The most frequent promotional strategy involved cartoon and company-owned characters, while price discounts were the most common premium offers. In multivariate analysis, Baringo County showed a higher rate of UPF advertisements compared to Nairobi (PRR: 1.17, 95% CI: 1.01-1.36), as did lower versus higher SES areas (PRR: 1.10, 95% CI: 1.01-1.20). UPFs are commonly advertised around schools in Kenya, often using strategies that appeal to children. Regulatory efforts are needed to limit the marketing of unhealthy foods in school environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".