FormalPara Key Points for Decision Makers

Individuals prioritized vaccine safety over vaccine effectiveness in both policy making and personal decisions.

Individuals were less likely to choose a vaccine and were more risk-averse to vaccine-related deaths when making policy decisions compared with personal decisions.

Policymakers and health authorities should prioritize clear and transparent communication about the safety profiles of vaccines, particularly when addressing concerns related to serious adverse events.

1 Introduction

During an infectious disease pandemic, governments play an important role in ensuring the availability and acceptability of vaccines [1]. Part of this consists of making value judgments that weigh the expected positive and negative effects for their constituents. In addition, successfully accounting for public preferences would allow health officials to develop strategies directed at successfully promoting vaccines. This is particularly important in develo** countries (e.g., Indonesia, Vietnam) where limited healthcare infrastructure and resources increase population vulnerability to pandemic-related infection and death.

Studies conducted among the general population have highlighted several important vaccine-related concerns, namely with regard to their effectiveness in preventing infection and their safety (e.g., vaccine-related adverse events) [2,3,4]. However, these studies focused on the personal preferences of individuals. It remains unclear if an individual’s personal preferences are reflective of how they would make decisions for others when they are placed in positions of responsibility (i.e., when they make decisions in the position of a policymaker).

While there is little research directly examining this, there is indirect evidence that individuals and agents in positions of responsibility may be pressured by social norms to recommend more risk-averse health-related decisions for others than they would otherwise choose for themselves [5,6,7]. For example, when surrogates and physicians were faced with hypothetical scenarios where they had to recommend treatments for patients, research found that they accepted less risk for patients (compared with themselves) and opted instead for safer treatments [7, 8]. It has been suggested that these self-other differences may be shaped by the social values theory [9], which suggests that decision makers are likely to employ greater caution if they think they will be held accountable for these decisions. In addition, policymakers are confronted with the challenge of making decisions that impact a large number of individuals. In situations involving probabilistic scenarios, such as the risk of death from vaccine-related severe adverse events, while the risk is just a probability at the individual level, a number of individuals in the community are highly likely to die as a result of these adverse events. This further complicates the decision-making process for policymakers.

The primary aim of this study was to investigate how individuals’ trade-offs between vaccine effectiveness and vaccine safety deviate if participants are asked to consider the perspective of a policymaker making decisions for others compared with the decisions they would make for themselves. Based on the literature, we would expect individuals evaluating vaccines for others as a policymaker (compared with decisions they would make for personal use) to (1) be less likely to choose a vaccine (over not choosing a vaccine), and (2) focus more on safety than effectiveness. The second objective was to investigate how individual characteristics affect these preferences. We hypothesized that individuals with higher education [10], who were living with vulnerable individuals [11], who worked in essential services [12], and who reported having close friends and/or family who died because of coronavirus disease 2019 (COVID-19) [13, 14] would be more likely to choose a vaccine, while those who believed that the COVID-19 pandemic was a hoax [15] would be less likely to choose a vaccine.

These objectives were examined using a discrete choice experiment (DCE), a health-economics method that has successfully been used in assessing preferences for pharmaceutical and non-pharmaceutical COVID-19 interventions [16,17,18,19,20,21] and other healthcare products and services [22, 23].

2 Methods

2.1 Study Setting and Participants

Recruitment for the survey occurred in two countries (Indonesia and Vietnam) between April and May 2022 as part of a larger study. These two countries were chosen as one country (Vietnam: 10 average daily cases) had a low number of COVID-19 cases, while the other (Indonesia: 5176 average daily cases) had a high number of cases at the time of the survey conceptualization [24]. However, both countries experienced a spike in the number of daily cases at the beginning of 2022, followed by a decline during the survey administration period (Vietnam: 18,931 average daily cases; Indonesia: 691 average daily cases) [24].

The survey instrument was first developed in English and then translated into Vietnamese and Indonesian. A web-enabled survey was administered by a market research vendor. Participants in each country were recruited to be representative in terms of age, sex, income, and geographic location. To be eligible for the study, panelists had to be at least 21 years of age and be able to read Vietnamese or Indonesian. All respondents provided informed consent. All activities were approved by the National University of Singapore Institutional Review Board (NUS-IRB-2021-401).

Based on Johnson and Orme’s suggestion [25, 26], the minimum required sample size was 250 per country. To ensure an adequate number of individuals in terms of age and income, and to sufficiently analyze the expected heterogeneity of the population, the survey was administrated online to approximately 500 individuals in each country.

2.2 Discrete Choice Experiment Survey Development

DCEs are a survey research method used to elicit individuals’ preferences for healthcare goods and services [27]. Individuals are asked to select their preferred alternative (e.g., vaccine) from two or more alternatives in a series of choice tasks. The alternatives are defined by a list of selected attributes (e.g., vaccine efficacy, vaccine safety, etc.) and vary from each other by the levels of these attributes.

The design of the experiment allows the analysis of the relationship between at least one independent variable, i.e. the attributes that can be precisely manipulated, and the dependent variable, the choice behavior that can be precisely measured. The design of the study also allows for the control of further variables, such as information about the decision makers, the decision-making process, and assumptions about the decision context. This allows researchers to quantify how individuals trade-off different levels of attributes and how important each attribute is relative to other attributes [28].

The three vaccine-related attributes used in this study were (1) effectiveness of the vaccine in reducing infection rate (50%, 70%, 90%, 99%); (2) effectiveness of the vaccine in reducing hospitalizations among those infected (50%, 70%, 90%, 99%); and (3) risk of death from vaccine-related serious adverse events (1, 10, 50, 200 out of 1 million). The effectiveness in reducing infection rate and safety attributes were selected as they were the most prevalent concerns observed in the extant literature and pretest interviews [2,3,4]. Effectiveness in reducing hospitalizations was included to reflect the evolving focus of governments from reducing the spread of infection to reducing the number of serious cases that required hospitalizations [29, 30]. The highest levels of effectiveness attributes were selected to account for the maximum potential vaccine effectiveness, while the range of death from serious adverse events was selected to encompass the observed real-world data [31,32,33]. The lowest levels for effectiveness attributes and the highest level for the vaccine-related serious adverse event attribute were determined based on findings from the pretest interviews, particularly the bidding game used to assess whether individuals were making trade-offs between the attributes.

The survey instrument was first developed in English and then professionally translated into the respective languages by a translation company (see the electronic supplementary material [ESM] for the English survey instrument). The translated versions were reviewed by native-speaking team members to ensure accuracy and appropriateness. The survey instruments were pretested with 10 eligible participants from each country who were quota sampled based on age, sex, and income using convenient sampling. Native-speaking interviewers followed a ‘think-aloud protocol’ [34], where participants were encouraged to verbalize their thoughts while answering the questions. The aim of these interviews was to evaluate the understandability and appropriateness of the attributes and levels and whether participants could answer the questions as a policymaker. Based on the feedback from the pretest interviews, revisions were primarily made to improve translational and syntactical aspects in order to enhance the understandability of the survey in the relevant languages. In addition, similar questions related to participants’ experience with the COVID-19 pandemic were removed to shorten the survey instrument.

To design the choice tasks, we created an experimental design using optimal D-efficiency measures in SAS 9.4 software (SAS Institute, Inc., Cary, NC, USA) [35]. The fractional factorial design resulted in 12 choice tasks, which were then divided into four blocks of three tasks each. Participants were randomly assigned to one of the blocks. Before the DCE choice tasks, two attention-test questions were asked to determine whether individuals paid attention to the survey and if they sufficiently understood the attributes. The first attention-test question provided information on the effectiveness rates of two vaccines and asked participants to identify the vaccine with the higher effectiveness in reducing the infection rate. The second question presented two vaccines similar to the DCE tasks, but with one vaccine superior to the other across all attributes (i.e., dominant-pair test). Participants who answered these questions incorrectly were provided with an explanation to improve clarity and understanding.

Before the broader distribution of the survey, we conducted a soft launch with 50 participants in each country. This step allowed us to evaluate the various aspects of the survey, including the functionality of the survey platform, and participant response patterns. Specifically, we investigated whether the number of those who fail the attention-test questions is reasonable and whether individuals dominated on any of the attributes (indicating lack of trade-offs between attributes) or an alternative (e.g., always choosing Vaccine A). We proceeded with the final launch as no concerns arose during the soft launch phase.

Participants were randomly assigned to one of two versions of the DCE. In the first version, individuals were asked to assume the role of a policymaker and were asked if they would approve the free distribution of one of two vaccine alternatives for use throughout the country (‘policy decision’ henceforth). They were also given the choice of a ‘Do not approve either vaccine’ option. In the second version, participants were presented with the same vaccine alternatives but were asked to assume that they had not yet been vaccinated (if they were already vaccinated) and were asked to choose a vaccine for themselves (‘personal decision’ henceforth). They were presented with the options of ‘I would get vaccinated with Vaccine A/B’ and an option of ‘I would not get vaccinated with either vaccine’. Henceforth, not choosing a vaccine in a choice task will be labeled as the ‘No Vaccine’ option. Figure 1 presents an example choice task.

Fig. 1
figure 1

Sample DCE choice task. DCE discrete choice experiment, COVID-19 coronavirus disease 2019

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2.3 Statistical Analysis

The attribute levels were effects coded. We also included an alternative specific constant (ASC) for choosing the ‘No Vaccine’ option, indicating the utility associated with choosing this option. Since there were two versions of the survey, interaction effects were created between the attribute levels and a dummy variable indicating the ‘personal decision’ version. We ran separate models for each country.

To analyze the data, we used a mixed logit model, which allows for heterogeneous preferences. Initially, all attributes were assumed to be random and normally distributed. The attributes without significant standard deviations (SDs) in the random parameters were then assumed to be non-random. We used 1000 Halton draws for the final model estimations.

We also allowed preference heterogeneity by creating interaction effects between preference weights and attitudes towards COVID-19 beliefs (believing that the COVID-19 pandemic was a hoax) and individual characteristics (education, living with vulnerable individuals [i.e., at least 65 years of age, with chronic health conditions or poor health], having worked in essential services, having close friends and/or family who died because of COVID-19). We first included all the interaction effects in the model but those that were not significant were excluded from the final model.

We calculated the relative importance of vaccine attributes and the ‘No Vaccine’ option by taking the difference between the best and worst levels of an attribute and scaling this difference by the sum of all attribute/covariate differences [36]. These calculations were conducted at the individual level, utilizing individual-specific coefficients. For policy decisions, we computed the average of the individual-level relative attribute importance among respondents assigned to the policy version. For personal decisions, we calculated the average of the individual-level relative attribute importance among respondents assigned to this version. These calculations were carried out separately for each country. This method accounts for varying interaction effects within the models.

We also calculated the probability of approving (policy decision) or choosing (personal decision) a vaccine compared with not choosing one. Similar to the process for calculating relative attribute importance, these probabilities were estimated at the individual level and were subsequently averaged across policy and personal decisions. Furthermore, we calculated how the probabilities of approving/choosing a vaccine changed in response to variations in vaccine attributes.

3 Results

3.1 Sample Characteristics

The mean age of the individuals was 39 years (SD 11) for Vietnamese participants and 41 years (SD 12) for Indonesian participants. Half of the participants were female. Most were married (71%), had a university degree (90%/75% for Vietnam/Indonesia), and were employed (88%/86% for Vietnam/Indonesia). 59%/49% of Vietnamese/Indonesian participants reported living with vulnerable people; 28%/33% of Vietnamese/Indonesian participants in the sample reported working in essential services, and 98%/97% of Vietnamese/Indonesian participants received the COVID-19 vaccine. 19% of Vietnamese and 43% of Indonesian participants reported having family or friends who died because of COVID-19, whereas 16%/6% of Vietnamese/Indonesian participants agreed that COVID-19 was a hoax perpetuated by those in power for profit. The Vietnamese sample was younger, more educated, had a higher rate of COVID-19 vaccination, had more people living with vulnerable individuals, and had fewer people whose family/friends died of COVID-19 (p ≤ 0.05) (Table 1). None of the personal characteristics were significantly different between the two versions of the survey (policy vs. personal decisions) in each country (ESM Table S1).

Table 1 Sample characteristics
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Regarding respondents’ attention, 20%/21% of individuals failed to provide the correct answer for one of the questions in the Vietnamese/Indonesian samples, and 4%/2% failed to provide the correct answer for both questions. These rates are within the range of the findings from other DCE studies [37]. Participants who failed these tests were not excluded from the analytical sample but received additional explanations to improve understanding.

3.2 Preferences for Coronavirus Disease 2019 (COVID-19) Vaccines

Table 2 displays the preference weights. The weights assigned to vaccine attributes were centered on zero (i.e., effects coded). The p values associated with the levels of vaccine attributes indicate the difference compared with the worst level of each attribute (i.e., omitted category). Higher weights indicated a higher preference for that outcome. On average, participants from both countries preferred higher effectiveness in reducing infection rate, higher effectiveness in reducing hospitalizations, and lower risk of death from vaccine-related adverse events. Participants from both countries also generally preferred choosing a vaccine to not choosing one.

Table 2 Preferences for COVID-19 vaccines: mixed logit estimates
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In the Vietnamese sample, although individuals in both versions preferred choosing a vaccine over not choosing one, the ‘No Vaccine’ option resulted in a bigger utility decline when making personal decisions compared with when making policy decisions (p < 0.05). In the Indonesian sample, compared with personal decisions, policy decisions were much more sensitive to the changes in the risk of death from adverse events (p < 0.05). In other words, an increase in the risk of adverse events resulted in a bigger utility decline when making policy decisions compared with when making personal decisions (Table 2).

Figure 2 presents the mean relative importance of the three vaccine attributes and the ‘No Vaccine’ option. For both countries, the factor that contributed the most to utility was the ‘No vaccine’ option. Among the three attributes, the risk of death from adverse events was the most important attribute, followed by effectiveness in reducing infection rate and reducing hospitalizations. For both samples, the risk of death from serious adverse events was more important for policy decisions than it was for personal decisions. In addition, the ‘No Vaccine’ option was more important in personal decisions such that individuals were much less likely to choose this option in personal decisions compared with policy decisions.

Fig. 2
figure 2

Relative importance of vaccine attributes and the ‘No Vaccine’ option (sums to 100% for each subsample)

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Regardless of making personal or policy decisions, essential workers in the Vietnamese sample were more likely to choose a vaccine (p < 0.05), and individuals who thought the COVID-19 pandemic was a hoax in the Indonesian sample were less likely to choose a vaccine (p < 0.05). In addition, those who lived with vulnerable people in Indonesia were more likely to choose a vaccine as a personal decision (p < 0.05) (Table 2).

3.3 Probabilities of Approving/Choosing a COVID-19 Vaccine

Consistent with our hypothesis, the mean probability of approving/choosing any vaccine was lower for policy decisions than it was for personal decisions for all comparisons except one. These probabilities were significantly different for 16 of 22 comparisons (p ≤ 0.05) [ESM Table S2]. The mean probability of approving the worst vaccine profile in the design as a policymaker was 76% (95% confidence interval [CI] 73–80%) for both Vietnam and Indonesia. In contrast, the mean probability of choosing the same vaccine in a personal capacity was 95% (95% CI 93–97%) for Vietnam and 90% (95% CI 87–93%) for Indonesia (Fig. 3).

Fig. 3
figure 3

Changes in the probability of approving vaccine as a policymaker or choosing vaccine personally based on changes in vaccine attribute levels

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For both policy and personal decisions, improving the safety of the vaccine resulted in the largest increase in the mean probability of approving/choosing a vaccine. Consistent with our hypothesis, individuals were more sensitive to the changes in the risk of adverse events when making policy decisions compared with when making personal decisions (p ≤ 0.05). In policy decisions, reducing the risk of death from adverse events from 200 to 1 out of 1 million increased the mean probability of vaccine approval by 18/19 percentage points in Vietnam/Indonesia. In personal decisions, the same improvement in vaccine safety resulted in 3.3/6.5 percentage-point increases in the mean probability of choosing a vaccine for Vietnam/Indonesia (Fig. 3).

Similarly, improving the effectiveness in reducing the risk of infection rate from 50 to 99% increased the probabilities by 13/12 percentage points for policy decisions for Vietnam/Indonesia as compared with 2/4 percentage points for personal decisions. Improving the effectiveness in reducing the risk of hospitalizations from 50 to 99% increased the probabilities by 11 and 9 percentage points for policy decisions for Vietnam and Indonesia, respectively, but only 1–3 percentage points for personal decisions (ESM Tables S2A and S2B present the CIs).

4 Discussion

The primary aim of this study was to investigate how individuals traded-off between COVID-19 vaccine effectiveness and vaccine safety in two different capacities—as a policymaker compared with a personal decision. In both decision-making capacities, individuals preferred vaccines that were more effective in reducing the infection rate and hospitalizations and had a lower risk of death from vaccine-related adverse events. However, contrasting some past studies, our findings indicated that participants considered vaccine safety more important than its effectiveness [4]. While it is important to note that a direct comparison between studies cannot be made as results are dependent on the attributes and levels used in each study, our findings suggest that there remain substantial concerns regarding vaccine safety among the public.

However, the level of risk aversion differed when decisions were made in a policy-making capacity compared with in a personal capacity. Consistent with our hypotheses and in line with the social values theory [7,8,9], we found that individuals were less likely to approve a vaccine for others when acting as policymakers than they would choose for personal use. Supporting these findings, decisions made in the capacity of a policymaker were more sensitive to the changes in the risk of death from vaccine-related adverse events compared with personal decisions. This heightened sensitivity could be attributed to the fact that in a society individuals will die, with certainty, as a result of vaccine-related adverse events as long as the risk pool is large and the probability is non-zero. This represents a concrete and tangible risk that policymakers face when making decisions on behalf of the community. In contrast, at the individual level, the risk of death is perceived as a probability that may or may not materialize after a choice is made. If the risk is low, individuals may down-weight this probability toward zero and thus believe there may be no adverse consequences from their decision.

This propensity towards risk averseness at the societal level can be seen in the handling of the AstraZeneca and Johnson & Johnson COVID-19 vaccines. For example, although the European Medicines Agency (EMA) suggested that the benefits of the vaccines outweighed their risks (i.e., severe blood clots) [38], several countries such as Denmark and Norway suspended their use [39].

Analyses also highlighted participant characteristics that were significantly associated with vaccine preferences. Vietnamese essential workers, regardless of decision-making type, were more likely to choose a vaccine. It is understandable that this subgroup of workers would want to take additional steps (e.g., vaccination) to protect not only themselves and their loved ones but also the community given the increased risks of exposure faced by them during a pandemic [40]. Supporting past research [41], we also found that Indonesian individuals who thought the COVID-19 pandemic was a hoax were less likely to choose a vaccine for themselves and others. These results were unsurprising as these individuals would also not believe in the necessity and benefits of vaccination. Indonesian individuals who lived with vulnerable people were also more likely to choose a vaccine for personal use. Participants living with individuals who are especially vulnerable to the virus would naturally want to take steps to protect their loved ones.

Our study findings should be interpreted within its limitations. First, individuals in these countries are not directly involved in making policy decisions as was required of them in our study. However, participants reported having no trouble answering the policy question during the pretest interviews. Second, since we directly asked respondents how they would make decisions as a policymaker, our findings may or may not reflect how the public would like their policymakers to make decisions. Third, in order to manage the cognitive burden on respondents, we did not include several other vaccine-related attributes such as non-fatal adverse effects, which may have influenced participants’ decisions. Fourth, the sample used in our study overrepresented individuals with a university degree, which is not representative of the broader population in the countries under study. This discrepancy can be attributed to the use of a web panel for data collection. Other studies found that individuals with lower education were less likely to get vaccinated compared with those with higher education [42]. Fifth, our findings may not be generalizable to other countries, especially those with lower vaccination rates, higher vaccine hesitancy, or with different political and sociocultural systems. Sixth, the absence of long-term effectiveness data for COVID-19 vaccines during the study period led us to omit the duration of vaccine effectiveness as an attribute. Seventh, the survey did not provide detailed information about the policymaker and their role, which might have resulted in varying interpretations among survey participants. However, the instructions clearly stated that respondents, as policymakers, would be making decisions regarding the approval of free vaccine distribution throughout the country. Last, as with any survey, there is a possibility of strategic bias, where individuals may misrepresent their preferences to influence the decision-making process when answering the DCE questions. However, we have no a priori expectation regarding in which direction individuals might have misrepresented their preferences; therefore, we believe this is not a major concern for this study.

5 Conclusion

By investigating how the public differently trade-off vaccine effectiveness and safety when evaluating COVID-19 vaccines, our findings demonstrated that individuals exhibited greater aversion to serious adverse events when making decisions for others as a policymaker compared with when making the same decision in a personal capacity. This finding may stem from the fact that policymakers are confronted with a tangible number of deaths at the population level, whereas the risk may or may not materialize at the individual level, especially for small probabilities. These findings might suggest that the public would like their policymakers to carefully evaluate the safety and effectiveness of vaccines when making vaccine-related decisions. These can be important deliberations for governments to consider when enacting vaccine policies or develo** media communication campaigns regarding vaccines. Policymakers and health authorities should prioritize clear and transparent communication about the safety profiles of vaccines, particularly when addressing concerns related to serious adverse events. Public health campaigns promoting vaccination should consider the nuanced risk perceptions associated with decision-making roles.