Attribute nonattendance in COVID-19 vaccine choice: A discrete choice experiment based on Chinese public preference.

objectivesThe global coronavirus disease 2019 (COVID-19) pandemic has not been well controlled, and vaccination could be an effective way to prevent this pandemic. By accommodating attribute nonattendance (ANA) in a discrete choice experiment (DCE), this paper aimed to examine Chinese public preferences and willingness to pay (WTP) for COVID-19 vaccine attributes, especially the influence of ANA on the estimated results.

methodsA DCE was designed with four attributes: effectiveness, protection period, adverse reactions and price. A random parameter logit model with an error component (RPL-EC) was used to analyse the heterogeneity of respondents' preferences for COVID-19 vaccine attributes. Two equality constraint latent class (ECLC) models were used to consider the influence of ANA on the estimated results in which the ECLC-homogeneity model considered only ANA and the ECLC-heterogeneity model considered both ANA and preference heterogeneity.

resultsData from 1,576 samples were included in the analyses. Effectiveness had the highest relative importance, followed by adverse reactions and protection period, which were determined by the attributes and levels presented in this study. The ECLC-heterogeneity model improved the goodness of fit of the model and obtained a lower probability of ANA. In the ECLC-heterogeneity model, only a small number of respondents (29.09%) considered all attributes, and price was the most easily ignored attribute (64.23%). Compared with the RPL-EC model, the ECLC-homogeneity model obtained lower WTPs for COVID-19 vaccine attributes, and the ECLC-heterogeneity model obtained mixed WTP results. In the ECLC-heterogeneity model, preference group 1 obtained higher WTPs, and preference groups 2 and 3 obtained lower WTPs.

conclusionsThe RPL-EC, ECLC-homogeneity and ECLC-heterogeneity models obtained inconsistent WTPs for COVID-19 vaccine attributes. The study found that the results of the ECLC-heterogeneity model considering both ANA and preference heterogeneity may be more plausible because ANA and low preference may be confused in the ECLC-homogeneity model and the RPL-EC model. The results showed that the probability of ANA was still high in the ECLC-heterogeneity model, although it was lower than that in the ECLC-homogeneity model. Therefore, in future research on DCE (such as the field of vaccines), ANA should be considered as an essential issue. PUBLIC CONTRIBUTION: Chinese adults from 31 provinces in mainland China participated in the study. All participants completed the COVID-19 vaccine choice questions generated through the DCE design.