Willingness to Pay for the COVID-19 Vaccine and Its Correlates in Bangladesh: Cross-Sectional Study
Bibliographic record
Abstract
Background: The Government of Bangladesh offers COVID-19 vaccines at no cost; however, sustaining this free vaccination program for a large population poses significant challenges. Thus, assessing the willingness to pay (WTP) for the COVID-19 vaccine is essential for understanding potential pricing strategies, subsidy requirements, and vaccine demand. Objective: This study aimed to assess the prevalence of WTP for the COVID-19 vaccine and its correlates. Methods: A cross-sectional design was used to collect data from 1497 respondents through web-based platform and face-to-face interviews. Multivariable logistic regression was used to analyze the correlates of the WTP. Results: The results showed that 772 of 1497 (51.6%) participants were willing to pay for the COVID-19 vaccine, with a median of 300 BDT (IQR 150-500 BDT; a currency exchange rate of 1 BDT=US $0.008 is applicable). The WTP was significantly higher among individuals with a graduate degree (adjusted odds ratio [aOR] 1.98, 95% CI 1.14-3.45) or master's and MPhil or PhD-level education (aOR 1.93, 95% CI 1.07-3.48) and those with higher knowledge about the vaccine (aOR 1.09, 95% CI 1.02-1.15), positive behavioral practices (aOR 1.11, 95% CI 1.06-1.17), stronger subjective norms regarding COVID-19 vaccine (aOR 1.25, 95% CI 1.08-1.46), and higher anticipated regret of getting infected with COVID-19 (aOR 1.17, 95% CI 1.04-1.32). Conversely, WTP was lower among participants with negative attitudes toward vaccines (aOR 0.91, 95% CI 0.88-0.95) and high perceived behavioral control regarding COVID-19 vaccination (aOR 0.86, 95% CI 0.76-0.96; P=.006). Conclusions: With nearly half of the respondents unwilling to pay, this study highlights the need to improve vaccine-related knowledge and enhance income-based affordability to increase WTP. Health promotion efforts should focus on disseminating knowledge about vaccines and addressing negative perceptions. Additionally, a subsidized program for low-income groups can help mitigate financial barriers and promote equitable access to vaccines.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".