Stroke Prevalence and Risk Factors in Rural Communities Within a Resource-Constrained South Asian Setting: Population-Based Study of 1.3 Million Individuals
Bibliographic record
Abstract
Background: Stroke is a leading cause of death and long-term disability worldwide, with an estimated 6.2 million deaths each year. In Bangladesh, data on stroke prevalence and risk factors in rural areas are limited, making it difficult to develop effective early prevention and intervention programs. Objective: This study aimed (1) to present the prevalence of stroke in a rural community in Bangladesh and (2) to identify and associate various stroke risk factors. Methods: Data collection was done by community health workers, as a part of the "Enriched Sastho" program of the Palli Karma Sahayak Foundation, Bangladesh. Community health workers received 2 weeks of training to ensure data quality. The presence of stroke was determined by a binary survey question, with a history of stroke=1 and absence=0. The prevalence of stroke per 1000 people was examined along with the 95% CI. In addition, the association of stroke risk predictors was calculated using multivariate logistic regression and presented in crude odds ratio (OR) and adjusted OR along with 95% CI. Results: The study analyzed data from 1,341,589 individuals, with an average (SD) age of 29.23 (19.05) years. The overall stroke prevalence was found to be 1.07 per 1000 people, with a higher prevalence in male participants and increasing with age. The highest stroke prevalence was observed in the Khulna division (OR 1.881, 95% CI 1.671-2.117), and the least in the Rangpur division (OR 0.677, 95% CI 0.576-0.795). Individuals aged 65-79 years were at a higher risk of having a stroke than other age groups (crude OR 9.883 and adjusted OR 9.728 [adjusted for sex]). In addition, male participants were at greater risk of having a stroke than female participants were (crude OR 1.565 and adjusted OR 1.469 [adjusted for age]). Conclusions: The study emphasizes the need for early prevention and intervention programs for stroke in rural Bangladesh and the importance of managing hypertension and diabetes to reduce stroke risk.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".