Under‐five children's acute respiratory infection dropped significantly in Bangladesh: An evidence from Bangladesh demographic and health survey, 1996–2018
Bibliographic record
Abstract
AIM: This study aims to systematically identify and review the most significant risk factors and the trends that follow acute respiratory infection (ARI) among children under five in Bangladesh. METHODS: A total of 6863 under-five children were eligible for our analysis, retrieved from Bangladesh Demographic and Health Survey (BDHS), 2014. ARI cases were defined if a child experienced coughing with short and rapid breathing at the chest that occurred during 2 weeks prior to the study. Logistic regression and systematic review methods were appraised to explore the various risk factors involving ARI in Bangladesh. Furthermore, a trend analysis was performed to overlook the historical trend of ARI prevalence and affiliated determinants from 1996/97 to 2017/18 in Bangladesh. RESULTS: Over the past two decades, Bangladesh experienced a significant drop in ARI prevalence from 12.8% in 1996 to only 3.0% in 2018. The cross-sectional findings revealed that boys (OR = 1.35, 95% CI: 1.03-1.78), stunted children (OR = 1.35, 95% CI: 1.03-1.78) and mothers with primary or no education (OR = 2.53, 95% CI: 1.43-4.90) and secondary education (OR = 1.77, 95% CI: 1.00-3.44) have the higher odds of ARI than their counterparts. CONCLUSION: Acute respiratory infection prevalence significantly declined in Bangladesh, while boys, stunted children and uneducated or primary educated mothers were identified as potential risk factors.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".