Assessment of non-communicable disease related lifestyle risk factors among adult population in Bangladesh
Bibliographic record
Abstract
Abstract Non-communicable diseases (NCDs), which can largely be prevented by controlling avoidable lifestyle-related risk factors, are rapidly penetrating the entire world, including developing countries. The present study aimed to assess NCD lifestyle risk factors among the adult population in Bangladesh. The data used in the study were collected as part of a population-based cross-sectional survey covering rural and urban areas of Bangladesh conducted in 2015–16 (N=11,982 adults aged ≥35 years). The lifestyle factors considered were diet (daily fruit and vegetable consumption and extra salt intake with meals), sleeping patterns, smoking, smokeless tobacco consumption, and physical activity. The study found that approximately 18.5% of participants had a non-daily consumption of fruit or vegetables, 46.6% used extra salt with their meals, 11.8% reported sleeping <7 hours daily, 25.7% smoked tobacco, 60.9% used smokeless tobacco and 69.7% were less physically active. The prevalence of improper lifestyle practices relevant to NCDs, such as an inadequate diet, poor sleeping pattern, tobacco consumption, and low physical activity, was significantly higher among older adults, women, the uneducated, the unemployed, urban dwellers, and people from rich households. The study found that NCD-related lifestyle characteristics were poorly compliant with standard guidelines among many adult populations in Bangladesh. The findings can inform preventative strategies to control the overwhelming NCD burden in Bangladesh, such as the promotion of physical exercise, healthy eating, and the cessation of the use of tobacco products.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".