Rationing of a scarce life‐saving resource: Public preferences for prioritizing COVID‐19 vaccination

1 INTRODUCTION

From November 2020 onwards, several vaccines that protect against the SARS-CoV-2 virus have become available (Bloom et al., 2020; Mahase, 2020; Mallapaty & Ledford, 2020). However, the initial supply was insufficient to vaccinate all (Wouters, Shadlen, Salcher-Konrad, et al., 2021) and throughout most of 2021 strict rationing has been required worldwide. First, there were problems of fairly distributing the vaccine internationally, across countries and continents (Emanuel, Persad, Kern, et al., 2020). Second, and this is the focus of this paper, at national levels, priority groups for vaccination needed to be designated (Emanuel, Persad, Upshur, et al., 2020; Persad et al., 2020; Schmidt, 2020; Subbaraman, 2020).

Almost unanimously, policy makers and expert groups selected the same groups for priority access: the highest risk categories – the elderly, those with pre-existing conditions, and essential workers, which include front-line health care professionals (CDC, 2020; European Commission, 2020; Gayle et al., 2020; JCVI, 2020; World Health Organization, 2020). Nonetheless, there could have been “reasonable disagreement” about ethical prioritization of a COVID-19 vaccine. As already illustrated earlier during the pandemic with scarcity of mechanical ventilation in intensive care units, how to ration a life-saving resource is never obvious (Emanuel, Persad, Upshur, et al., 2020; Liu et al., 2020; Persad et al., 2020; Roope et al., 2020). In the context of vaccines, fair rationing is even less straightforward because vaccines usually serve two separate functions: to prevent death and illness within the vaccinated individuals but also to reduce transmission toward others.

In this study, we investigated several allocative mechanisms to set vaccination priorities and their acceptability toward the general public. This is in the first place interesting from a scientific perspective. The circumstances of the pandemic present a unique research opportunity to investigate how people want to share a life-saving resource across the population. Their views are not elicited from an artificial, abstract context of scarcity, but from a concrete reality in which they are all directly involved parties. At the time of the survey, the circumstances allowed us to consider a sufficient level of abstraction; it was still unclear whether vaccines would become available at all, and if available, which properties and effectiveness they would have. This made it easier to focus on broad distributive principles regarding how to ration a critical resource, abstracting from issues such as side effects related to specific vaccines. Second, understanding the public's opinion is important for policy reasons as public involvement has already been highly instrumental in the COVID-19 pandemic for measures such as physical distancing, face masks or lockdowns to be effective (Chernozhukov et al., 2021; Mitze et al., 2020). In general, greater public and patient involvement in health care decisions, especially those with large stakes and a substantial ethical component, is increasingly considered important (Florin & Dixon, 2004).

Our first study objective was to ask a representative sample of the general population in Belgium to rank eight alternatives to distributing the first COVID-19 vaccines in their preferred order. Our second objective was to study further the respondents' preferences by letting them choose whom they would vaccinate over multiple pairs of concrete individuals competing for a vaccine. We finally summarize the overall preferences in a choice model that allowed us to calculate a vaccine priority score for specific population subgroups. What we found is that, when asked directly, people confirmed the three subgroups that policy makers eventually selected of highest priority: those with pre-existing conditions, essential workers and the elderly. However, when we elicited their priorities through observing actual priority setting choices between individuals, high virus spreaders were given higher priority, while elderly received lower priority. We also identified two clusters of respondents: one that wanted to target those individuals who spread the virus, and the other that wanted to target those who are worst-off through pre-existing conditions.

The paper proceeds as follows. Section 2 provides a summary of the previous literature. Section 3 describes the design of the survey and the two experiments and presents the methods for data analysis. Section 4 displays the results. Finally, we provide some concluding remarks.

2 BACKGROUND

Empirical evidence on public preferences toward COVID-19 vaccines was inexistent at the time of our survey and remains scarce. While Borriello et al. (2021) collected the preferences of Australians regarding hypothetical COVID-19 vaccines, their study did not focus on vaccine allocation but described vaccines according to seven attributes (i.e., incidence of mild and major side effects, effectiveness, mode of administration, location of administration, time to availability and cost). Public preferences in COVID-19 vaccine allocation strategies were examined in Gollust et al. (2020) where a sample of 1004 adults representative of the US population were asked to indicate among eight alternative groups based on age, health risk and employment type whom should receive high, medium, or low priority to vaccination. They found that respondents had a high willingness to allocate vaccines to front-line medical workers, essential non-medical workers, high-risk children, and older adults.

More recently, preferences of US adults' regarding vaccine prioritization were analyzed as part of two surveys (Persad et al., 2021); they both showed that people would prioritize health care workers and adults of any age with serious comorbidity among their top four priority groups. Healthy older adults were however not ranked within highest priority groups to vaccination, especially among older respondents. Most respondents were in agreement with the phased allocation strategy proposed by the National Academies of Science, Engineering, and Medicine (CDC, 2020) but placed a lower priority on vaccinating healthy older adults. Finally, an online conjoint experiment in 13 countries was carried out to identify preferences for different vaccine prioritization schemes based on five attributes (occupation, age, coronavirus transmission status, risk of death from COVID-19 and income) and between three and eight levels (Duch et al., 2021). This large-scale study showed that most countries favored access to vaccines to individuals at higher risk of COVID-19 death and higher risk of COVID-19 transmission, to essential workers and non-essential workers unable to work from home, to older individuals and to individuals in low-income categories.

Our study adds to this literature. It provides a unique ranking exercise of allocation strategies including priority groups along with standard strategies used in the context of scarce resources allocation. It also provides a discrete choice experiment (DCE) for COVID-19 vaccine allocation at national level comparing hypothetical individuals described on five key attributes.

3 METHODS 3.1 Sample and survey

We used a nationally representative panel of the market research agency Dynata to complete a survey between October 6, 2020 and October 16, 2020.1 A sample of 2698 respondents drawn from a panel of 5500 selected members who mirror the Belgian population (aged 18–80 years) as well as possible,2 were invited to participate in the survey. Of these, 494 did not complete the survey and 144 were excluded because they did not meet the company's internal quality controls (e.g., they completed the survey unreasonably fast: below a third of the median time to completion). This left us with a sample of 2060 respondents, which fulfilled pre-determined Belgium quota for age, gender, level of education and province.

The survey3 first asked respondents for a range of sociodemographic characteristics along with their financial situation, general health status, attitudes toward vaccination and toward the government's handling of the corona crisis, whether they had had COVID-19, whether someone they knew had had it, had been hospitalized or died because of it. Respondents were also asked whether their profession was among the “essential professions” (i.e., those that were obliged to keep working during the first “lockdown” in March/April 2020) and whether they considered themselves to be part of a risk group for COVID-19 and if so, which group they belonged to (i.e., old age, chronic illness, obesity, or other). The questionnaire was then followed with an explanation of the background to the study where we explicitly asked the respondents to think about what they considered fairest to society when allocating the limited first supply of COVID-19 vaccines, and not to choose simply what would be most advantageous to themselves. After the ranking exercise and the choice experiment, respondents were asked about whom should decide who gets the COVID-19 vaccine first (government, scientists or the population), whether they would choose to be vaccinated themselves once a vaccine becomes available, and how easy they found answering the survey.

3.2 Ranking exercise

We presented the respondents with eight alternative strategies to distribute the COVID-19 vaccines summarized in Table 1. Each strategy was presented one after the other using successive new screens that respondents were only able to progress from every 10 s. The eight strategies were then summarized as a list in their short version (with the possibility to go back to the full explanation if needed) and respondents were asked to rank all of them from “most suitable” to “least suitable” according to their opinion. They were told that the vaccine was equally safe and effective in all people and that they should think about what would be the best allocation not for their self-interest but for the society as a whole.

TABLE 1. Eight strategies to distribute a COVID-19 vaccine Strategy (in short) Full explanation as presented in the experiment Prioritizing chronically ill We could first give the vaccine to people who are medically most at risk of serious illness and death because they have another underlying condition: Cancer patients, people with lung disease, heart disease, kidney disease, severe obesity, etc. By vaccinating them first, we would protect the people most vulnerable to the virus. Prioritizing the elderly We could first give the vaccine to people over 60 years old. We know that, on average, these people run a much higher risk of serious illness or death from a corona infection. By vaccinating them first, we would protect the people most vulnerable to the virus. Prioritizing spreaders We could first give the vaccine to the people who spread the virus the most because they have a lot of social contacts in their daily life (at work, at school, in their neighborhood, in public transport, etc.). These people themselves are not at high risk of serious illness or death from COVID-19, but they can infect many others. By vaccinating them first, we would slow down the spread of the virus as much as possible. Prioritizing workers People who work will cause a greater economic cost when they become ill than those who do not work. By first vaccinating working people, we would ensure that the virus does as little further damage as possible to the economy. Prioritizing essential professions Some professions are more “essential” to society than others. During the pandemic, health workers, hospital staff, police and garbage services had to continue working as usual, while others had to work from home or were temporarily unemployed. By prioritizing workers from these vital sectors, we would protect the normal functioning of society. Lottery We could distribute the available vaccines randomly among the population, for example through a lottery. Therefore, each individual would have the same chance to be vaccinated, regardless of their health risk or the social impact of an infection. First-come, first-served We could distribute the available vaccines to the population according to the principle “first-come, first-served.” People who present themselves the fastest for vaccination at the doctor, pharmacy or government would be given priority from the moment there is a vaccine. Market We could sell the available vaccines to the highest bidder. The people who want to pay the most money for a vaccine would be given priority. 3.3 Discrete choice experiment

We then subjected respondents to a DCE. This is a widely used survey method to study individuals' preferences, especially in health care settings (Louviere et al., 2000; Ryan et al., 2008) including patients prioritization (Bryan & Dolan, 2004; Diederich et al., 2012; Luyten et al., 2015, 2019; Ratcliffe et al., 2009). Participants are presented with a series of choice sets, consisting of two or more products or services that are described by the same attributes with differing attribute levels. By observing a large number of choices, researchers can infer how attributes and levels implicitly determine the value of the good under evaluation. Here, we asked respondents to choose whom they would vaccinate from two hypothetical people candidates to the COVID-19 vaccine. Both candidates were described with identical attributes, but they differed in the levels of these attributes so that we could infer how important these attributes were to the respondents when prioritizing one or the other candidate for vaccination.

3.3.1 Attributes and levels

The DCE focused on the five attributes of people that are considered most relevant by experts (Liu et al., 2020; Persad et al., 2020; Roope et al., 2020) as well as policy institutions (European Commission, 2020; Gayle et al., 2020; World Health Organization, 2020) to claim to priority: (1) their age, (2) whether they belonged to a medically vulnerable group due to pre-existing conditions (e.g., diabetes, cancer, HIV, cardiovascular disease, obesity, etc.), (3) their cost to the economy if COVID-19 infected, (4) whether their profession is considered “essential” (e.g., health care workers, policemen, firemen, etc.), and (5) whether they would spread the virus to many or few other people in case of infection (see Table 2). The remaining strategies used in the ranking exercise (lottery, market, first-come first-served) were excluded from the DCE.

TABLE 2. Attributes and levels used in the DCE Attribute Levels Medical risk group ⁃ Someone who has no underlying conditions ⁃ Someone who has higher risk through chronic illness Age ⁃ Someone who is younger than 60 years ⁃ Someone who is at least 60 Virus spreader ⁃ In case of infection, someone who is expected to contaminate 1 other person ⁃ In case of infection, someone who is expected to contaminate 10 other persons Cost to society ⁃ In case of infection, someone who is expected to cost society 0 € per day ⁃ In case of infection, someone who is expected to cost society 100 € per day ⁃ In case of infection, someone who is expected to cost society 1000 € per day Essential profession ⁃ Someone who has a profession that is considered “essential” ⁃ Someone who has a profession that is considered not “essential” Abbreviation: DCE, discrete choice experiment. 3.3.2 Design

We designed the DCE using “partial profiles”, that is, we kept the levels of two attributes constant between the two candidate profiles and only varied the levels of three attributes (Kessels, Jones, & Goos, 2011, 2015). We colored the varying levels of each profile to make them stand out in the choice sets (Jonker et al., 2019). An example of a choice set appears in Figure 1. Varying the levels of only three attributes and highlighting them made the choice tasks easier to perform and therefore respondents' choices more consistent and valid for the analysis.4 Respondents even testified that despite the choice problem had been quite difficult, it had been doable thanks to the design strategy. Because the varying attributes differed between choice sets, the partial profile design also helped prevent respondents from using lexicographic decision rules, by which profile alternatives are first compared on the most important attribute, then on the second most important attribute, and so forth, until one profile remains. If one or more dominant attributes are held constant, respondents can trade off the remaining attributes more easily, and not divert to non-compensatory decision-making. The statistical efficiency of a partial profile design is, however, reduced compared to a full profile design, in which all attributes can vary in the choice sets, but this is generally offset by more consistent choices (Louviere et al., 2008).

image

The statistical design or the specific composition of the choice profiles we generated was “D-optimal” within a Bayesian framework (Kessels, Jones, Goos, & Vandebroek, 2011). A D-optimal design makes it possible to examine the importance of the attributes and their levels with maximum precision. The Bayesian addition means that prior information is taken into account in the design generating process so that choice sets with a dominant profile are largely avoided (Crabbe & Vandebroek, 2012). The complete design of the DCE consisted of 30 choice sets that we split into three different blocks of 10 choice sets and was efficiently constructed to estimate all two-way interaction effects between the attributes (see Appendix B for the design and the design generating process). A representative sample of respondents were assigned in three similar groups to each of the three blocks. The 10 choice sets of each survey were presented in a random order to counteract a possible order effect of the choice sets. At the start of the DCE, we presented the respondents with a mock choice set that was identical to the last choice set in their survey and allowed us to analyze consistency in their choices.

We first tested various visualizations among a convenience sample (N = 10) and then carried out a pilot study of the full survey in 174 respondents. After correcting for a few minor issues, we went ahead with the full launch of the study in 2060 respondents.

3.4 Statistical analysis

We analyzed the choice data by estimating a panel mixed logit (PML) model using the hierarchical Bayes technique in the JMP Pro 16 Choice platform (based on 10,000 iterations, with the last 5000 used for estimation; SAS Institute Inc.). This model assumes normally distributed utility parameters over the respondents to accommodate unobserved heterogeneity in the respondents' preferences. The mean utility function is thereby the sum of the mean attribute effects (Train, 2009).

We first estimated a PML model for the entire sample and then investigated the heterogeneity in the individual utility estimates by comparing the subject standard deviations to the mean attribute effects. These subject standard deviations were of the same size or even larger than the mean estimates, indicating the need to identify respondent segments. We therefore clustered the individual utility estimates from the PML model using Ward's hierarchical cluster analysis and estimated separate PML models for each cluster. This second-stage PML analysis for every cluster allows revealing differing and even opposing preferences between clusters (if there are). This procedure with a post-estimation cluster analysis has already shown its merits in a DCE measuring public preferences for vaccination programs (Luyten et al., 2019) and a DCE predicting the uptake of the COVID-19 digital contact-tracing app (Mouter et al., 2021).

To verify the cluster formation, we estimated latent class models with different numbers of classes using the lclogit2 package in Stata 17 (Yoo, 2020) as a more direct alternative to the two-step PML procedure. A latent class model assumes a discrete distribution for the heterogeneous utility parameters instead of the normal distribution underlying the PML analysis. By relaxing the normality assumption, a latent class model allows capturing multimodal utility distributions directly in the event of diverging or opposing preferences between respondents. This model is therefore particularly suited in the context of segmented samples of respondents (Goossens et al., 2014). Louviere (2006) recommended to use latent class models more frequently because they would often fit the data at least as good as PML models and are easy to interpret.

Once we distinguished clear and meaningful respondent segments, we characterized them through bivariate chi-square analyses on the respondents' covariates and multiple logistic regression with the cluster membership as response variable and the respondents' covariates as explanatory variables. In all our analyses, we used a significance threshold of 5%.

4 RESULTS

On average, the 2060 respondents took 29 min to complete the survey. The median completion time was 15 min, with the interquartile range between 13 and 20 min. When asked how difficult completion of the survey was, only 21 respondents (1%) indicated it was “too difficult” whereas 1154 (56%) found it “easy” and 43% “difficult but doable.” A sample of 1577 respondents (77%) gave the same answer twice to the repeated choice set, however differing answers do not point at invalid answers as the strength of preferences can be weak in this context. We observed that 116 respondents (6%) gave the same answer throughout the DCE and are therefore called “straightliners.” As their number is considerable and their answers unlikely to match their choices, we followed standard practice in excluding these straightliners as a way of caution not to lower the quality of the data (Johnson et al., 2019; Sandorf, 2019). This left us with 1944 respondents for the analysis.

Overall, the analysis sample included 39% of respondents considering themselves part of a specific COVID-19 risk group. A minority (<20%) of the sample experienced a COVID-19 infection themselves or in their immediate proximity. A majority (59%) reported being dissatisfied with the government's approach to the crisis. A large majority of respondents (78%) thought that the vaccine allocation decision should ultimately be determined by scientists; 10% thought the government should decide and 12% thought that it should be the population only. When asked whether they would become vaccinated with a COVID-19 vaccine, 74% responded affirmatively (see Table 3).

TABLE 3. Sample characteristics Variables Categories N Percentage (%) Respondents' general background Gender Female 993 51 Male 951 49 Age 18–24 194 10 25–34 330 17 35–44 331 17 45–54 379 19 55–64 321 17 65–80 389 20 Language Dutch 1112 57 French 832 43 Province Vlaams-Brabant 191 10 Brabant Wallon 129 7 Brussels Capital 176 9 Antwerpen 288 15 Limburg 157 8 East Flanders 249 13 West Flanders 200 10 Hainaut 115 6 Liège 186 10 Luxembourg 102 5 Namur 151 8 Education None 7 0 Primary school 61 3 First degree secondary school 187 10 Second degree secondary school 247 13 Third degree secondary school 684 35 Higher education (non-university) 468 24 University or post-university education 268 14 PhD 14 1 Other 8 0 Have children Yes 1213 62 No 731 38 Profession Working 978 50 Homemaker 80 4 Student 158 8 Unemployed 129 7 Disabled 127 7 Retired 472 24 Difficulties with monthly expenses Never 802 41 Once a year 422 22 Once every 3 months 391 20 Every month 329 17 Self-assessed health Very good 248 14 Good 741 41 Rather good 602 34 Bad 167 9 Very bad 22 1 Don't know/don't want to say 14 1 Respondents' COVID-19 related background Self-reported membership of a COVID-19 risk group No 1183 61 Yes, elderly 366 19 Yes, chronically ill 400 21 Yes, severe obesity 124 6 Yes, other 68 3 Self-reported profession is labeled as “essential” Yes 367 19 No 1577 81 Has had a COVID-19 infection Yes, confirmed with a test 57 3 Probably, but not confirmed with a test 160 8 No 1727 89 Know personally someone who has had COVID-19 Yes, confirmed with a test 293 15 Probably, but not confirmed with a test 175 9 No 1476 76 Know personally someone who was hospitalized for COVID-19 Yes 118 6 No 1826 94 Know personally someone who died of COVID-19 Yes 83 4 No 1861 96 Satisfaction with government's approach to COVID-19 pandemic Very satisfied 58 3 Rather satisfied 729 38 Rather dissatisfied 787 40 Very dissatisfied 370 19 Determination of the vaccine prioritization strategy Population 221 12 Government 175 10 Scientists 1398 78 COVID-19 vaccine acceptance once the vaccine is available and considered safe and effective by the authorities Yes, sure 624 35 Yes, probably 698 39 No, probably not 322 18 No, sure not 150 8 4.1 Ranking exercise results

The ranking exercise results are summarized in Figures 2 and 3. Figure 2 uses cumulative distribution functions to synthesize how each strategy was ordered by the respondents. There was not one single strategy that dominated and was considered as best by a large majority. The eight strategies were clearly divided into three groups: three dominant strategies, two strategies ranked somewhere in the middle, and three strategies ranked in the three worst strategies. Prioritizing essential workers, chronically ill and elderly were found to be the three most supported strategies. On the other hand, market, lottery or “first-come, first-served” strategies were clearly the least preferred strategies with at least 80% of the respondents ranking them at the bottom of the ranking. Finally, targeting spreaders or protecting the economy were strategies ranked in the middle.

image

Cumulative distribution functions of alternative COVID-19 vaccine allocation strategies ranked from “most suitable” (rank of 1) to “least suitable” (rank of 8)

image

Scatterplot of the ranks of prioritization strategies along with their relationship to age summarized by a regression spline. The graph plots the ranking of each prioritization strategy according to age. Dots toward the left- and right-hand side are rankings of younger and older respondents, respectively. A darker zone around a rank shows the most observed ranking of that strategy. Note that the dots have been uniformly shifted up and down within each rank to avoid over-plotting (uniform jitter). The red lines summarize for each strategy the relationship between the ranking and the age of the respondent. For example, younger respondents ranked essential professions lower than old

留言 (0)

沒有登入
gif