The data were collected as part of the first wave of the CANDOUR study.Footnote 5 Overall, this first wave involved 15,536 individuals from 13 countries. Respondents completed an anonymous survey between 24 November and 28 December 2020 using Qualtrics web-based software. Quota sampling (and in five countries additional weighting) was used to obtain a sample that reflected the distribution of age, education, gender and region in each country.Footnote 6
As in Clarke et al. , the present study used information from 8,209 individuals aged 18 years or more from the subset of HIC in the sample (Australia, Canada, France, Italy, Spain, the UK and the USA).Footnote 7 As described in , we adapted a question previously used in the context of H1N1  and asked whether respondents supported donating some COVID-19 vaccine doses for distribution to poor countries with insufficient resources to buy their own vaccines. The exact wording was:
Some people feel that the [HIC name] government should donate some of the COVID-19 vaccine doses it has purchased for distribution to poor countries that do not have the resources to buy their own vaccine.
Which of the following statements most closely matches your view:
The [HIC name] government should not donate any vaccine it has purchased
The [HIC name] government should donate less than 10% of its purchased vaccine
The [HIC name] government should donate 10% of its purchased vaccine
The [HIC name] government should donate more than 10% of its purchased amount of vaccine
Do not know
Prefer not to say
Overall proportions of each response category for each country, including 95% confidence intervals (CIs), were reported by Clarke et al. . In this study, we hypothesised that, in each country, the responses favouring a greater emphasis on vaccine provision for poorer countries would vary by levels of altruism, income, political ideology and education, and be greater among:
The more altruistic half of the sample;
The half of the sample with higher equivalised household income;
Those who identify as left/centre politically versus those who identify as right politically;
Those with at least a university degree.
In exploratory analyses, we also aimed to test whether the responses were heterogenous in subgroups defined by:
Willingness to take risks with health.
The more ‘altruistic’ half of the sample were identified according to responses to a question that asked:
Imagine the following situation: Today you unexpectedly received
$700 [US version: amendments by country to currency and amount]
. How much of this amount would you donate to a good cause?
Do not know
Prefer not to say
There were some missing data (including “do not know” and “prefer not to say”) for education (1.5%), income (24.3%), political ideology (15.9%), altruism (39.9%) and willingness to take risks (7.0%). We wished to avoid excluding these responses from the analysis, particularly in light of the fact that unanswered responses to these questions might group people with similar attitudes to donating vaccines. To avoid excluding these responses, we included ‘unanswered’ as a separate category.
The household income data were equivalised for household composition using the Modified OECD Equivalence scale. A three-category variable was created for “high income” (on or above the median), “low income” (below the median) and “unanswered”.
We created a three-category variable for altruism based on the donation amount. The first category combined “unanswered”, “do not know” and “prefer not to answer”. The next two categories were “meagre”, which was below the median donation amount for that country, and “generous”, which was on or above the median donation amount for that country. Stratifying by country for this variable was necessary because each country had different amounts and units of currency.
Political ideology was identified by asking respondents to indicate where their political views lay on a scale from 0 (“left”) to 10 (“right”). The centre of the distribution on this scale varied greatly between countries. To make a more comparable variable for this multi-country analysis we split each country’s results into tertiles to give a relative measure of political leaning within countries. So this variable had four categories: left, centre, right and unanswered.
For education we created a variable with four categories: primary education or less, secondary, university degree and unanswered.
Willingness to take risks with health was assessed based on a widely used risk-preference indicator originating from the German Socio-Economic Panel (SOEP) survey. Respondents were again asked to use a scale from 0 (“completely unwilling to take risks”) to 10 (“very willing to take risks”). We divided this into two groups defining those with a score of 0–5 as “unwilling” and those with a score of 6–10 as “willing”. We added a third group of “unanswered”.
For gender, we looked at male gender versus all other responses. For age, the categories were up to age 39, 40–59 and 60+ years.
In our main analysis, we used a Bayesian ordered logistic model with the following four ordered categories:
Answers of “Prefer not to say” or “Do not know” were excluded for this analysis.
Our ordinal logistic regression model assumed a latent normal distribution for donation willingness with three cut-points that defined the observed ordinal response . The model was formulated as a cumulative logit model and we assumed that the odds ratio for a one unit change in the predictors was the same across the ordinal responses. The odds ratios are interpreted as the odds of moving to a higher category (greater willingness to donate). The ordered cut-points can be unequally spaced to allow for differences in the proportions of responses in the four categories, and we allowed the cut-points to vary by country, permitting differences between countries in the proportions supporting donation.
We examined the association with this ordinal outcome and the independent variables of age (up to 39, 40–59, 60+ years), gender (male, not male), education (primary or less, secondary, university degree, unanswered), altruism (meagre, generous, unanswered), income (high, low, unanswered), political ideology (left, centre, right, unanswered) and willingness to take risks with health (unwilling, willing, unanswered). We fitted each variable independently and then used all seven in a multiple variable model.
Each model was fitted as a Bayesian ordinal logistic regression model using random effects by country. We allowed the effect of the independent variables to vary by country as we had a strong expectation of differences between countries. For example, the education level “university” is not fixed over countries but instead varies by country around an overall effect. Estimates were therefore made both at country level and overall. We estimated the mean odds ratio and a Bayesian 95% credible interval.
To test whether the independent variables did vary by country, we fitted an alternative fixed-effects model and compared the model fit using the deviance information criterion (DIC), which balances model complexity and fit [17, 18]. We compared the more complex model containing a random effect in each country with the simpler model containing a fixed effect. All models used a random intercept in each country to account for overall differences between countries in the willingness to donate vaccines.
The Bayesian models were fitted in WinBUGS (version 1.4.3) and the plots were made using R (version 4.1.1). We used two chains thinned by three with a burn-in and sample of 4,000. We visually checked the convergence and mixing of the chains. We used vague normal priors for the mean parameters in the ordinal regression model and vague gamma priors for the inverse variances. The Bayesian models and R code are freely available on github (https://github.com/agbarnett/donate).
We used Bayesian posterior probabilities (BPPs) to compare the groups. The BPPs examine the odds ratios for a group relative to the reference category, for example, oldest age group relative to youngest. The BPP is the estimated probability that the odds ratio is equal to one (the null hypothesis).
It could be argued that there is a case for interpreting responses of “do not know” or “prefer not to say” as being less supportive of donation than a response that clearly expresses some support. As a sensitivity analysis, we therefore repeated the above analysis including the unanswered donation responses, with the following five ordered categories:
Should donate more than 10%;
Should donate 10%;
Should donate less than 10%;
Prefer not to say/Do not know;
Should not donate.
As above, we used an ordinal logistic regression model assuming a latent normal distribution for donation willingness, this time with four cut-points that defined the observed ordinal response .