Prevalence and determinants of non-communicable diseases risk factors among reproductive-aged women of Bangladesh: Evidence from Bangladesh Demographic Health Survey 2017-2018
Bibliographic record
Abstract
Abstract Introduction Knowing the risk factors like hypertension, overweight/obesity, and smoking status among women of reproductive age could allow the development of an effective strategy for reducing the burden of non-noncommunicable diseases (NCD). We sought to determine the prevalence and determinants of NCD risk factors among Bangladeshi women of reproductive age. Methods This study utilized the Bangladesh Demographic and Health Survey (BDHS) data from 2017-2018 and analyzed 5,624 women of reproductive age. This nationally representative cross-sectional survey utilized a stratified, two-stage sample of households. Mixed-effects Poisson regression models were fitted to find the adjusted prevalence ratio for smoking, overweight and obesity, and hypertension. Results The average age of 5,624 participants was 31 years (SD=9.07). The prevalence of smoking, overweight/obesity, and hypertension was 9.55%, 31.57%, and 20.27%, respectively. More than one-third of the participants (34.55%) had one NCD risk factor, and 12.51% of participants had two NCD risk factors. Women between 40–49 years had more NCD risk factors than 18–29 years aged women (APR: 2.44; 95% CI: 2.22-2.68). Women with no education (APR: 1.15; 95% CI: 1.00-1.33), married (APR: 2.32; 95% CI: 1.78–3.04), and widowed/divorced (APR: 2.14; 95% CI: 1.59–2.89) were more prevalent in NCD risk factors. Individuals in the Barishal (APR: 1.44; 95% CI: 1.28–1.63) division were living with higher risk factors for NCD. Women who belonged to the richest (APR: 1.82; 95% CI: 1.60–2.07) wealth quintile were more likely to have the risk factors of NCD. Conclusions This study revealed that older women, currently married and widowed/divorced women, women from the wealthiest socio-economic group, and women with a lower level of education were more likely to have NCD risk factors. Furthermore, there was a significant relationship between the geographical division and NCD risk factors. To reduce the future prevalence of NCD, it is necessary to implement effective prevention and control programs that target women with a higher risk of having the disease.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".