Dietary risk factors for non-communicable diseases in Kenya: findings of the STEPS survey, 2015
Bibliographic record
Abstract
BACKGROUND: Burden of non-communicable diseases (NCD) is increasing worldwide. Risk factor surveillance informs public health interventions in NCD control. This study describes the dietary risk factors for NCD found in the Kenya STEPS survey, 2015. METHODS: We performed secondary analysis of the STEPS dataset to determine prevalence of dietary NCD risk factors and their determinants. New variables were created; high dietary salt, defined as addition of salt while eating or intake of processed foods high in salt and high dietary sugar, defined as intake of processed foods or drinks high in sugar in most meals or addition of sugar to beverages already with sugar, on a daily basis. We used the World Health Organization definition of minimum required intake of fruits and vegetables as consumption of less than five servings of fruits and vegetables per day. Perceptions of respondents on diet and health were also assessed. Accounting for complex survey sampling, we calculated prevalence of the various dietary modifiable determinants and adjusted odds ratios (AOR) to identify factors independently associated with dietary NCD risk factors. RESULTS: Of the 4484 individuals surveyed; mean age was 40.5 years (39.9-41.1 years), 60% were female. Prevalence of high reported dietary salt intake was 18.3% (95% CI 17.2%, 19.5%) and sugar 13.7% (95% CI 11.7-15.8%). Awareness of health risk from dietary salt was 88% and 91% for dietary sugar. Approximately 56% of the respondents were implementing strategies to reduce dietary salt and 54% were doing the same for dietary sugar. Only 6.0% (95% CI 4.3-7.6%) of the respondents reported intake of a minimum of five servings of both fruits and vegetables daily. Unhealthy diet was associated with being male (AOR 1.33, 95% CI 1.04, 1.70,), age below 46 years (AOR 1.78, 95% CI 1.42, 2.12) and being a student (AOR 15.6, 95% CI 2.44, 99.39). CONCLUSION: Dietary risk communication should be targeted to males and people under 45 years of age, especially students. Further research is necessary to understand the knowledge: practice mismatch on unhealthy diets.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| 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".