Public Preference and Priorities for Including Vaccines in China’s National Immunization Program: Discrete Choice Experiment
Bibliographic record
Abstract
Background: Several important vaccines, such as the Haemophilus influenzae type b vaccine, rotavirus vaccine, pneumococcal conjugate vaccine, and influenza vaccine, have not been included in China's National Immunization Program (NIP) due to a prolonged absence of updates and limited resources. Public engagement could identify concerns that require attention and foster trust to ensure continuous support for immunization. Objective: This study aimed to identify public preferences for vaccine inclusion in the NIP and to determine the desired vaccine funding priorities in the Chinese population. Methods: A dual-response discrete choice experiment was utilized to estimate the relative importance of 6 attributes, including incidence of vaccine-preventable diseases, mortality of vaccine-preventable diseases, vaccine effectiveness, vaccine cost, vaccinated group, and vaccine coverage. Participants were recruited through the Wenjuanxing platform using a census-based quota sample of the nationwide population aged 18 years and older. A mixed logit model was used to estimate the coefficient of attribute preferences and predict the selection probability. Subgroups and interaction effects were analyzed to examine the heterogeneity in preferences. Results: In total, 1258 participants completed the survey, of which 880 were involved in the main analysis and 1166 in the sensitivity analysis. The relative importance and model estimates of 2 attributes, vaccine cost and vaccination group, varied between the unforced- and forced-choice settings. All 6 vaccine attributes significantly influenced the preferences for vaccine inclusion, with vaccine effectiveness and coverage as the most important factors, followed by the vaccinated group and mortality of vaccine-preventable diseases in the unforced-choice settings. The top vaccines recommended for China's NIP included the varicella vaccine, Haemophilus influenzae type b vaccine, enterovirus 71 vaccine, and influenza vaccine for preschoolers and school-aged children. The current analysis also revealed distinct preference patterns among different subgroups, such as gender, age, education, and income. The interaction analysis indicated that the region and health status of participants contribute to preference heterogeneity. Conclusions: Public preferences for including vaccines in the NIP were primarily influenced by vaccine effectiveness and coverage. The varicella vaccine should be prioritized for inclusion in the NIP. The public preferences could provide valuable insights when incorporating new vaccines in the NIP.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".