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COVID-19 vaccine preferences among university students in Hong Kong: a discrete choice experiment
Xue Li1,2, Man Yui Chong3, Ching Yui Chan3
1Department of Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People's Republic of China. sxueli@hku.hk.
Objective:
To promote public health and resume university activities, COVID-19 vaccination has been mandated from an increasing number of universities worldwide. The objective of the study is to understand the factors that impact preference and willingness to take the vaccine among university students in Hong Kong universities utilizing an online questionnaire. The findings will be imperative for health education and the success of the vaccination program.
Results:
We conducted a discrete choice experiment survey among university students in Hong Kong and applied conditional logit regression to estimate their vaccine preference and the weight of each attribute. Regression results showed adverse reactions, efficacy, origin of the vaccine, required number of doses and out-of-pocket price are significant determinants for the choice of vaccine, ranked from the most to least important. Similar preference weighting results were observed after adjusting age, sex, monthly household income, studying medical-related subjects and recent influenza vaccination. Safety, efficacy and origin of the vaccine are key drivers for vaccination decisions among young adults in Hong Kong. Health education and communication focused on these factors are urgently needed to overcome vaccine hesitancy and improve the vaccine uptake.
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Testing a Claim about Population Proportion
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...

