Hypertension prevalence and its trend in Bangladesh: evidence from a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Hypertension, itself being a major chronic condition, is one of the most significant risk factors for premature cardiovascular diseases and mortality. Hypertension is responsible for 13% of global deaths and three-quarters of the world’s hypertensive population reside in low- and middle-income countries. Bangladesh is one of those countries that experiencing an epidemiological transition from communicable to non-communicable diseases, a nutritional transition from a traditional diet to process and fast food, and an increase in a sedentary lifestyle, resulting in increased hypertension prevalence. We carried out a systematic review and meta-analysis to identify existing research on hypertension prevalence in Bangladesh, summarize findings and assess its temporal change. Methods We searched MEDLINE, EMBASE and PubMed and relevant references to identify studies on the prevalence of hypertension in Bangladesh. We used Random-effects meta-analysis to pool the prevalence estimates and performed subgroup analyses. We assessed heterogeneity, a trend in prevalence of hypertension and publication bias in selected studies. Results Our search initially identified 735 articles and after removing duplicates, reviewing titles and abstracts, and screening full texts, 53 studies were finally selected. The studies comprised 305,432 subjects and reported overall, gender-specific, geographical location specific and criteria specific prevalence of hypertension. We identified the range of hypertension prevalence is from 1.10% to 75.0% and the overall weighted pooled prevalence of hypertension is 20.0%. An extremely high heterogeneity (I2 = 99.53%; Cochran Q-statistic p < 0.001) was observed in the prevalence of hypertension. Consequently, we performed subgroup analysis based on gender, age group and geographical location of the study participants, the cut-off level used to define hypertension, and the types of hypertension reported and presented our findings accordingly. An overall increasing trend of hypertension prevalence is also observed. Conclusions The prevalence of hypertension is high and rising in Bangladesh. Strategies targeting prevention are required to mitigate a further increase in the prevalence and reduce the morbidity and mortality associated with it.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".