Patterns and determinants of eating healthy in Kenya
Bibliographic record
Abstract
Abstract Background The burden of non-communicable diseases (NCDs) is rising in low-and-middle-income countries (LMICs) with diet being a key risk factor. Policies to tackle diet-related NCDs require a broader understanding of patterns and drivers of healthy eating to inform interventions. This study assessed the patterns and determinants of eating healthy in Kenya. Methods This study used cross-sectional data from the 2015/16 Kenya Integrated Household Budget Survey (KIHBS). The outcome variable for this study was a continuous healthy diet index (HDI) developed using nine WHO/FAO healthy diet recommendations through principal component analysis (PCA), as a measure of eating healthy. The HDI score and the proportion of Kenyan households meeting WHO/ FAO healthy diet recommendations for the nine dietary components were summarized by gender of the household head, residence, and socioeconomic status. Multivariable linear regression was used to assess determinants of eating healthy in Kenya. Crude and adjusted marginal effects and 95% CI were used to assess the strength of association. Results A total of 21,512 households in Kenya were included in the sample of which 60% were rural and about two thirds headed by males. The HDI index ranged from − 1.13 to 1.70, with a higher score indicating healthier eating. The mean HDI score in Kenya was 0.24, which was considered moderate, with urban residents having a higher score (0.25) than rural residents (0.23). No Kenyan household met all the nine healthy diet recommendations with majority (84%) meeting four or less. Healthy eating was associated with higher socioeconomic status (0.28, 95% CI 0.27–0.30), living in a rural area (0.16, 95% CI 0.14–0.19), having children under five years (0.02, 95% CI 0.01–0.03) in the household, and the household head being female, having education, being employed or in union. Conclusion In conclusion, the majority of Kenyan households do not meet all the healthy dietary recommendations. Furthermore, eating healthy is associated with higher socioeconomic status, living in a rural area, having children under 5 years in the household, and the household head having education, being in employment and in union. The findings from this study can be used to inform policies that promote healthy eating and the prevention of diet-related NCDs among the Kenyan population.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".