Cost-effectiveness of seasonal influenza vaccination in WHO-defined high-risk populations in Bangladesh
Bibliographic record
Abstract
Background: Bangladesh carries a substantial health and economic burden of seasonal influenza, particularly among the World Health Organization (WHO)-defined high-risk populations. We implemented a modelling study to determine the cost-effectiveness of influenza vaccination in each of five high-risk groups (pregnant women, children under five years of age, adults with underlying health conditions, older adults (≥60 years), and healthcare personnel) to inform policy decisions on risk group prioritisation for influenza vaccination in Bangladesh. Methods: We implemented a Markov decision-analytic model to estimate the impact of influenza vaccination for each target risk group. We obtained model inputs from hospital-based influenza surveillance data, unpublished surveys, and published literature (preferentially from studies in Bangladesh, followed by regional and global ones). We used quality-adjusted life years (QALY) as the health outcome of interest. We also estimated incremental cost-effectiveness ratios (ICERs) for each risk group by comparing the costs and QALY of vaccinating compared to not vaccinating each group, where the ICER represents the additional cost needed to achieve one year of additional QALY from a given intervention. We considered a willingness-to-pay threshold (ICER) of less than one gross domestic product (GDP) per capita as highly cost-effective and of one to three times GDP per capita as cost-effective (per WHO standard). For Bangladesh, this threshold ranges between USD 2462 and USD 7386. Results: The estimated ICERs were USD -99, USD -87, USD -4, USD 792, and USD 229 per QALY gained for healthcare personnel, older adults (≥60), children aged less than five years, adults with comorbid conditions, and pregnant women, respectively. For all risk groups, ICERs were below the WHO willingness-to-pay threshold for Bangladesh. Vaccinating pregnant women and adults with comorbid conditions was highly cost-effective per additional life year gained, while vaccinating healthcare personnel, older adults (≥60), and children under five years were cost-saving per additional life year gained. Conclusions: Influenza vaccination to all target risk groups in Bangladesh would be either cost-saving or cost-effective, per WHO guidelines of GDP-based thresholds.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".