The promotion of ultra-processed foods in modern retail food outlets in rural and urban areas in Kenya
Bibliographic record
Abstract
Abstract Objective: To assess the availability and marketing of ultra-processed foods (UPF) in modern retail food outlets (supermarkets and minimarts) in Kenya and associated factors. Design: This cross-sectional study was conducted in Kenya from August 2021 to October 2021. Variables included the geographic location and the socio-economic status (SES) levels, the food items displayed for sale and advertised in the stores, and locations in the stores such as the entrance. Setting: Three counties in Kenya (Nairobi – urban, Mombasa – coastal tourist and Baringo – rural). Each county was stratified into high and low SES using national poverty indices. Participants: Food outlets that offered a self-service, had at least one checkout and had a minimum of two stocked aisles were assessed. Results: Of 115 outlets assessed, UPF occupied 33 % of the cumulative shelf space. UPF were the most advertised foods (60 %) and constituted 40 % of foods available for sale. The most commonly used promotional characters were cartoon characters (18 %). UPF were significantly more available for sale in Mombasa (urban) compared to Baringo (rural) (adjusted prevalence rate ratios (APRR): 1·13, 95 % CI 1·00, 1·26, P = 0·005). UPF advertisements were significantly higher in Mombasa ((APRR): 2·18: 1·26, 3·79, P = 0·005) compared to Baringo and Nairobi counties. There was a significantly higher rate of advertisement of UPF in larger outlets ((APRR): 1·68: 1·06, 2·67 P = 0·001) compared to smaller outlets. Conclusions: The high marketing and availability of UPF in modern retail outlets in Kenya calls for policies regulating unhealthy food advertisements in different settings in the country.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".