Prevalence of and factors associated with non-communicable diseases among Bangladeshi adults: investigation from nationally surveyed data
Bibliographic record
Abstract
Abstract Background Chronic non-communicable diseases, owing to their increasing prevalence, are the greatest constraint to disease burden reduction in Bangladesh. As a result, we concentrated on determining the prevalence and risk factors for major chronic non-communicable diseases (NCDs) among adult Bangladeshis. Methods Data from Bangladesh Demographic and Health Survey (BDHS) 2017-18 were analyzed. If a participant had diabetes or hypertension, it was classified as NCD. Whereas comorbidity is defined as a subject having both diabetes and hypertension. Both the unadjusted and adjusted log-binomial regression models considering the survey weights were employed to identify the factors associated with NCDs and comorbidity. Results The overall prevalence (age-adjusted) of NCDs (40.43% (95% CI: 40.29-40.56) diabetes and hypertension was 11.55% (95% CI: 11.46-11.64) and 35.04% (95% CI: 34.91-35.17), respectively, while 6.16% (95% CI: 6.09-6.23) of participants had comorbidity. The adjusted regression model shows that being aged >34 years, and overweight or obese were significant risk factors of all NCDs, where being involved in work and from rich households were found as risk factors of diabetes and comorbidity. Smoker participants and females were more likely to have hypertension compared to their counterparts. Contrary, being underweight was a protective factor of having NCDs, similarly, engage in work was found as protective factors of diabetes and co-morbidity. Conclusion A growing prevalence of diabetes, hypertension, and comorbidity was discovered in this study. To reduce the burden of these NCDs, it is necessary to take the necessary steps.
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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 | 0.000 | 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 | 0.001 |
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".