Comparative Analysis of Preferences and Willingness to Pay for COVID‐19 Vaccine Among Individuals With and Without Chronic Diseases: A Discrete Choice Experiment Approach
Bibliographic record
Abstract
Background and Aims: The rapid development and deployment of COVID-19 vaccines have been pivotal in the global battle against the pandemic. However, vaccine acceptance remains a critical determinant of their effectiveness. While numerous studies have explored factors influencing vaccine acceptance, there is a notable dearth of research regarding preferences and willingness to pay (WTP) among individuals with chronic diseases (ChD) who are at heightened risk of severe COVID-19 outcomes. This study addresses this gap by investigating COVID-19 vaccine preferences and WTP among individuals with and without chronic diseases. Methods: Employing a discrete choice experiment (DCE) approach, we interviewed 843 participants to assess their preference for COVID-19 vaccine attributes such as origin, efficacy level, protection duration, and provider. We used a mixed logit model to analyze DCE data and estimated WTP by taking the negative ratio of preference and price parameters. Furthermore, we performed a stratified analysis according to the household head's ChD status. Results: As expected, vaccines with higher efficacy and longer protection are most preferred. Interestingly, our research shows a preference for European and American vaccines over Chinese vaccines. Additionally, this study reveals a preference for private facilities over government facilities for vaccinations among vaccine seekers. Patients with ChD exhibit higher WTP than those without such conditions, demonstrating the group's urgency in becoming vaccinated. Conclusion: Vaccine procurement and administration decisions by health authorities should be evidence-based and reflect public preferences regarding acceptance and WTP. Health authorities should prioritize both high efficacy and long-lasting protection in vaccine procurement, ensuring that neither is compromised. This approach addresses the top concerns of both groups-vaccine effectiveness for the ChD group and extended immunity for the NChD group. This study also informs policy decisions regarding vaccine origin and suggests incorporating private facilities as vaccine administration points to reduce pressure on government facilities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".