Ultra-Processed Food Consumption and its Association with Nutritional Status and Diet-Related Non-Communicable Diseases among School-Aged Children in Lilongwe City, Malawi
Bibliographic record
Abstract
In underdeveloped nations like Malawi, Non-Communicable Diseases (NCDs) have received less attention despite increasing NCDs morbidity and incidence rates. NCDs are responsible for 68% of all deaths worldwide each year. Dietary problems are the most common causes of these deaths. In underdeveloped countries, NCDs are responsible for two-thirds of all fatalities. In addition, developing countries account for two out of every three overweight and obese individuals worldwide. Lifestyle factors such as Ultra-Processed Foods (UPFs) consumption is among the causes. Purpose of the Study: To investigate ultra-processed food consumption and its association with nutritional status and diet-related NCDs among school-aged children. Methods: The research was conducted from March-April, 2021, using cross-sectional quantitative methods. A systematic random sample of 382 school-aged children was drawn to collect data. Data were analyzed using the R software package by frequency tables, means, and Chi-Square. Study Findings: Findings suggest high consumption (95.6%) of UPFs, which included flitters, carbonated drinks, processed juice, French fries, and sweets. The study further attributed the high consumption of UPFs to age, residence, price, and availability. In addition, in children who consume high amounts of UPFs, dental problems and high mid-upper arm circumference were common. Conclusion: Prevalence of UPFs is high among school-aged children in Lilongwe city. Although there are reported differences between these two locations, the locations are generally similar. However, age, residence, price, and availability seem to influence UPFs consumption behaviors. In later years, this may predispose children to be cardiovascular and metabolic conditions.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".