Incidence and Viral Etiology of Acute Respiratory Infections and Pneumonia among Children Under Two Years: A Birth Cohort Study in Dhaka, Bangladesh
Bibliographic record
Abstract
Globally, acute respiratory infections (ARI), including pneumonia, remain the leading infectious causes of morbidity and mortality among children under-two years of age. We conducted this longitudinal birth cohort study in a low-income urban community in Dhaka, Bangladesh, to estimate the incidence of ARI and pneumonia and assess their viral etiology. From May 2015 to March 2016, 447 children were enrolled and followed till 2022. In this analysis, we included data from the first two years of children's lives, which contributed to a total observation of 778 child-years. Nasopharyngeal wash samples were collected during symptomatic episodes, which were tested using rRT-PCR for rhinovirus (RV), respiratory syncytial virus (RSV), human metapneumovirus (hMPV), influenza virus, human parainfluenza virus (HPIV), and adenovirus. We calculated incidence rates using Poisson-based methods with 95% confidence intervals (CI) and stratified age-specific rates into three groups: 0 to <6 months, 6 to <12 months, and 12 to 24 months. A total of 2,335 ARIs and 314 pneumonia episodes were documented. At least one respiratory virus was detected in 71% of ARI and 75% of pneumonia episodes. RV was the most frequently detected virus (54% in ARI, 40% in pneumonia), followed by RSV, HPIV, and influenza. The incidence of viral ARI was 212/100 child-years (95% CI: 202-223), and that of viral pneumonia was 30/100 child-years (95% CI: 27-35). The observed incidence of viral ARI and pneumonia during early childhood underscores the need for targeted interventions. Future research should examine environmental and socioeconomic influences, assess preventive strategies, and improve early detection and treatment.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".