COVID-19 Vaccination Preferences Among Non-Chinese Migrants in Hong Kong: Discrete Choice Experiment
Bibliographic record
Abstract
BACKGROUND: Studies have shown increasing COVID-19 vaccination hesitancy among migrant populations in certain settings compared to the general population. Hong Kong has a growing migrant population with diverse ethnic backgrounds. Apart from individual-level factors, little is known about the migrants' preference related to COVID-19 vaccines. OBJECTIVE: This study aims to investigate which COVID-19 vaccine-related attributes combined with individual factors may lead to vaccine acceptance or refusal among the migrant population in Hong Kong. METHODS: An online discrete choice experiment (DCE) was conducted among adults, including Chinese people, non-Chinese Asian migrants (South, Southeast and Northeast Asians), and non-Asian migrants (Europeans, Americans, and Africans) in Hong Kong from February 26 to April 26, 2021. The participants were recruited using quota sampling and sent a link to a web survey. The vaccination attributes included in 8 choice sets in each of the 4 blocks were vaccine brand, safety and efficacy, vaccine uptake by people around, professionals' recommendation, vaccination venue, and quarantine exemption for vaccinated travelers. A nested logistic model (NLM) and a latent-class logit (LCL) model were used for statistical analysis. RESULTS: A total of 208 (response rate 62.1%) migrant participants were included. Among the migrants, those with longer local residential years (n=31, 27.7%, for ≥10 years, n=7, 20.6%, for 7-9 years, n=2, 6.7%, for 4-6 years, and n=3, 9.7%, for ≤3 years; P=.03), lower education level (n=28, 28.3%, vs n=15, 13.9%, P=.01), and lower income (n=33, 25.2%, vs n=10, 13.2%, P=.04) were more likely to refuse COVID-19 vaccination irrespective of vaccination attributes. The BioNTech vaccine compared with Sinovac (adjusted odds ratio [AOR]=1.75, 95% CI 1.14-2.68), vaccine with 90% (AOR=1.44, 95% CI 1.09-1.91) and 70% efficacy (AOR=1.21, 95% CI 1.03-1.44) compared with 50% efficacy, vaccine with fewer serious adverse events (1/100,000 compared with 1/10,000; AOR=1.12, 95% CI 1.00-1.24), and quarantine exemption for cross-border travelers (AOR=1.14, 95% CI 1.01-1.30) were the vaccine attributes that could increase the likelihood of vaccination among migrants. For individual-level factors, full-time homemakers (AOR=0.44, 95% CI 0.29-0.66), those with chronic conditions (AOR=0.61, 95% CI 0.41-0.91) and more children, and those who frequently received vaccine-related information from the workplace (AOR=0.42, 95% CI 0.31-0.57) were found to be reluctant to accept the vaccine. Those with a higher income (AOR=1.79, 95% CI 1.26-2.52), those knowing anyone infected with COVID-19 (AOR=1.73, 95% CI 1.25-2.38), those having greater perceived susceptibility of COVID-19 infection (AOR=3.42, 95% CI 2.52-4.64), those who received the influenza vaccine (AOR=2.15, 95% CI 1.45-3.19), and those who frequently received information from social media (AOR=1.52, 95% CI 1.12-2.05) were more likely to accept the vaccine. CONCLUSIONS: This study implies that migrants have COVID-19 vaccination preference heterogeneity and that more targeted and tailored approaches are needed to promote vaccine acceptance for different subgroups of the migrant population in Hong Kong. Vaccination promotion strategies are needed for low-education and low-income migrant groups, migrants with chronic diseases, the working migrant population, homemakers, and parents.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".