•• When people don’t #vaccinate because of compla-cency, communicate the risk of disease
•• When people don’t #vaccinate because it’s inconve-nient, remove barriers and add incentives
•• When people don’t #vaccinate because they lack con-fidence in vaccines, it’s important to correct myths: #vaccineswork
•• When people don’t #vaccinate because the calculated risks outweigh benefits, emphasize the social good: #protecttheherd
Key Points
•• Non-vaccination can result from complacency, conve-nience, a lack of confidence, and utility calculation (the Four C Model).
•• Depending on the reason for non-vaccination, inter-ventions should be targeted to the active determinants that impede vaccination.
•• When people do not vaccinate because of compla-cency, use informational interventions to explain dis-ease risks and to stress social benefits of vaccination.
•• When people do not vaccinate because it is inconvenient, remove barriers, support self-control, and add incentives.
•• When people do not vaccinate because they lack con-fidence in vaccines, it is important to correct myths.
•• When people do not vaccinate because they calculate that risks outweigh benefits, emphasize the social benefit of vaccination and add incentives.
Vaccination’s Success and Vaccine Hesitancy
Vaccination has greatly reduced the burden of infectious diseases. Only clean water, also considered to be a basic human right, performs better. . . . The benefits of vaccination extend beyond prevention of specific diseases in individuals. They enable a rich, multifaceted harvest for societies and nations. . . . A comprehensive vaccination programme is a cornerstone of good public health and will reduce inequities and poverty.
—Andre et al. (2008, p. 140; 143; 144)
Vaccinations save an estimated number of 2 to 3 million lives per year (World Health Organization [WHO], 2012). For
600716 BBSXXX10.1177/2372732215600716Policy Insights from the Behavioral and Brain SciencesBetsch et al.research-article2015
1University of Erfurt, Germany2RWTH Aachen University, Germany3Rutgers University, Piscataway, NJ, USA
Corresponding Author:Cornelia Betsch, Department of Psychology and Center for Empirical Research in Economics and Behavioral Sciences (CEREB), University of Erfurt, Nordhäuser Str. 63, 99089 Erfurt, Germany. Email: [email protected]
Using Behavioral Insights to Increase Vaccination Policy Effectiveness
Cornelia Betsch1, Robert Böhm2, and Gretchen B. Chapman3
AbstractEven though there are policies in place, and safe and effective vaccines available, almost every country struggles with vaccine hesitancy, that is, a delay in acceptance or refusal of vaccination. Consequently, it is important to understand the determinants of individual vaccination decisions to establish effective strategies to support the success of country-specific public health policies. Vaccine refusal can result from complacency, inconvenience, a lack of confidence, and a rational calculation of pros and cons. Interventions should, therefore, be carefully targeted to focus on the reason for non-vaccination. We suggest that there are several interventions that may be effective for complacent, convenient, and calculating individuals whereas interventions that might be effective for those who lack confidence are scarce. Thus, efforts should be concentrated on motivating the complacent, removing barriers for those for whom vaccination is inconvenient, and adding incentives and additional utility for the calculating. These strategies might be more promising, economic, and effective than convincing those who lack confidence in vaccination.
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instance, smallpox has been eliminated from the landscape of diseases due to concentrated efforts—and partially with policies that mandated vaccination. Diseases such as polio, measles, and rubella are the next on the list for planned elim-ination. This dramatic success of vaccinations is recognized globally and mirrored in country-specific vaccination poli-cies. In most countries, vaccination is voluntary and based on expert recommendations from a National Immunization Technical Advisory Group (Duclos, 2010; WHO, 2015).
Almost every country struggles with vaccine hesitancy, that is, “the delay in acceptance or refusal of vaccination despite availability of vaccination services. Vaccine hesi-tancy is complex and context specific, varying across time, place and vaccines” (MacDonald & the Strategic Advisory Group of Experts [SAGE] working group, 2015, p. 1). As a consequence, there are repeated outbreaks of vaccine- preventable diseases that consume resources and cost lives. In addition, the resulting sub-optimal vaccine uptake often fails to reach thresholds of uptake that are necessary to reach elimination of certain diseases (e.g., 95% uptake is required to reach herd immunity to eliminate measles; Fine, Eams, & Heymann, 2011).
It is, therefore, important to understand the determinants of individual vaccination decisions to establish effective interventions that support or complement the success of country-specific public health policies. Ideally, interven-tions are directed at the factors that impede vaccination (Butler & MacDonald, 2015). Among the reasons that are usually mentioned first in discussions about vaccine refusal are the anti-vaccination movements and negative attitudes toward vaccination. However, as we will show, there are other reasons with a much broader scope and a potentially more effective set of interventions to increase vaccine uptake. For example, it can sometimes be more successful to assist people in the implementation of their decision for vaccination rather than to run an educational campaign to change risk perceptions. In general, the current article does not aim at suggesting policies based on insights from epide-miology or public health, such as the vaccination of certain age groups or conducting immunization activities in schools. Other sources provide valuable insights on this (such as Briss et al., 2000; Sadaf, Richards, Glanz, Salmon, & Omer, 2013; Shefer et al., 1999). Rather, this contribu-tion summarizes strategies that can be used to overcome vaccine hesitancy and increase vaccine uptake building on evidence from social and behavioral sciences, particularly from psychology.
Overview
In this article, we will first outline major psychological deter-minants of individual-level decision making about vaccina-tion. In relation to these determinants, we will suggest psychological profiles of four types of decision makers who
have different reasons to decline vaccination, namely, (a) those who are complacent and do not care about immuni-zation, (b) those who do not vaccinate because it is inconve-nient, (c) those who have a lack of confidence in the vaccine and the health system, and (d) those who engage in some reasoning process by weighting pros and cons (utility calcu-lation). Calculation can lead to non-vaccination either due to free-riding, that is, the perception that vaccination is unnec-essary as long as enough other people vaccinate, or due to fence-sitting, that is, the refusal or inability to make a deci-sion when pros and cons are weighted equally. We will finally discuss evidence for the effectiveness of several strat-egies and outline which determinants of the vaccination decision each strategy targets. Based on this, we will propose which strategies might be successful to address compla-cency, convenience, lack of confidence, and free-riding or conflict issues, respectively. Table 1 gives an overview of this analysis.
Factors Influencing Vaccination Behavior
To change vaccination behavior, it is important to understand which factors determine the decision to vaccinate or to omit vaccination. Figure 1 visualizes selected determinants of vaccine decision making. These determinants proved partic-ularly relevant in predicting the vaccination decision. The central idea is that individuals take up protective behaviors (vaccination) when they feel threatened or at risk (Norman, Boer, & Seydel, 2005), that is, if they perceive high levels of risk of disease, they will be more likely to vaccinate; if they perceive high levels of risk of vaccination, they will become less likely to vaccinate. Perceived risk is often channeled via anticipated emotions, such as anticipated regret and worry (e.g., Betsch & Schmid, 2013; Chapman & Coups, 2006; Loewenstein, Weber, Hsee, & Welch, 2001). Feeling at risk is usually more strongly related to behavior than knowing about risks (Loewenstein et al., 2001). Any information, for example, from campaigns, conversations, information from anti-vaccination websites, and so on, will be translated to a subjective representation of risk. As humans are not perfect information processors, this will not result in a 1:1 represen-tation of the encoded information. Individual differences, such as numeracy (ability to process and use numbers; Peters et al., 2006) or health literacy (Berkman, Sheridan, Donahue, Halpern, & Cotty, 2011), can influence the translation from objective to subjective representations. Moreover, the format in which information is displayed can influence the percep-tion of risk (Hawley et al., 2008; Slovic, Finucane, Peters, & MacGregor, 2012) as can a range of cognitive biases (nega-tivity bias, Siegrist & Cvetkovich, 2001; narrative bias, Betsch, Renkewitz, Betsch, & Ulshöfer, 2010; Betsch, Renkewitz, & Haase, 2013; availability bias, Tversky & Kahnemann, 1973). It is also important to note that the encoded information is not always correct, as several myths
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about vaccination circulate (e.g., about vaccine safety; Kata, 2012).
In addition, there are several factors that directly affect the vaccination decision or that modify the effect of subjec-tive risk perception on vaccination intention and behavior. Importantly, attitudes toward vaccination are a strong predic-tor of vaccination and also moderate the effect of risk percep-tions on vaccine uptake (Godin, Vézina-Im, & Naccache, 2010; Herzog et al., 2013; Hollmeyer, Hayden, Poland, & Buchholz, 2009; LaVail & Kennedy, 2012). Strong attitudes against vaccination often come out of a particular identity (e.g., anthroposophy; Sobo, 2015) and can override rational thought and decision processes (Fazio & Olson, 2014). Furthermore, social norms affect a decision maker’s vaccina-tion intention and behavior in various ways (Allen et al., 2009). Social norms are shared rules within a group that determine behavior (Cialdini et al., 2006). The so-called “injunctive” norm expresses the way in which people should behave. Non-adherence can be sanctioned, which makes injunctive norms effective (Oraby, Thampi, & Bauch, 2014). The descriptive norm reflects what people usually do, such as the current level of vaccine uptake. When the majority is vaccinated, individuals are likely to conform to the majoity’s behavior and will get vaccinated, too (bandwagoning; Hershey, Asch, Thumasathit, Meszaros, & Waters, 1994). However, there is recent evidence that high population
uptake also induces free-riding behavior (Böhm, Betsch, & Korn, under review; Ibuka, Li, Vietri, Chapman, & Galvani, 2014; Vietri, Li, Galvani, & Chapman, 2012). That is, some individuals do not vaccinate if they are aware that there is sufficient uptake in the population to keep infection risks low (due to herd immunity; Fine et al., 2011). Norms can also be counter-productive if the social norm is to refuse vaccination (e.g., Sobo, 2015). Moreover, a new decision may not be prompted each time a vaccination is due. In line with the psychological saying, “Past behavior is the best predictor of future behavior” (Mischel, 1968, p. 139), past vaccinations (e.g., against influenza) predict future vaccination behavior very well (e.g., Lin et al., 2010; Nowalk et al., 2010). Last, even if an individual is generally willing to vaccinate, struc-tural barriers can hinder eventual implementation of the vac-cination (Gerend, Shepherd, & Shepherd, 2013; Rosenstock, 1974). Greater barriers are therefore associated with less behavioral change (Abraham & Sheeran, 2005; Harrison, Mullen, & Green, 1992) and less vaccination (Kimmel, Burns, Wolfe, & Zimmerman, 2007). Such barriers include the ease of accessibility and the resulting effort, and inconve-nience or potential financial costs.
In sum, information about objective risks leads to a sub-jective representation of risks related to vaccination and non-vaccination. Other variables such as vaccination attitude, the salience of social norms, vaccine uptake in the population,
Figure 1. Determinants of vaccine decision making.Note. The summary of determinants follows theoretical and empirical work by Janz and Becker (1984); Rosenstock (1974); Betsch and Wicker (2012); Ajzen (1991); Betsch, Böhm, and Korn (2013); Roberto, Krieger, Katz, Goei, and Jain (2011); Gerend and Shepherd (2012); Milne, Sheeran, and Orbell (2000); Payaprom, Bennett, Alabaster, and Tantipong (2011). Interventions should address these determinants to increase vaccine uptake. Table 1 shows which interventions are suitable for changing the major active determinants that impede vaccination clustered by type of non-vaccinator.
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and barriers influence vaccination decision making. In the next section, we will refer to these determinants when we describe four prototypical types of non-vaccinators.
Why People Do Not Vaccinate: The Four C Model
Global efforts to understand vaccine hesitancy were concen-trated in the WHO–SAGE vaccine hesitancy working group. The main outcome is the “Three C model,” claiming that complacency, a lack in confidence, and convenience issues impede vaccination (e.g., Dubé, Gagnon, Nickels, Jeram, & Schuster, 2014; MacDonald & the SAGE Working Group, 2015). Based on the determinants reported above, we will try to sketch a more psychological profile of these types and suggest constellations of determinants that match and com-plement the description of the SAGE working group. In addition, we would like to propose a fourth “C,” namely, the degree of rational calculation that individuals engage in before deciding, as there is converging evidence that strate-gic behavior also plays a role in vaccination decision making (e.g., Bauch & Bhattacharyya, 2012; Bauch & Earn, 2004; Betsch, Böhm, & Korn, 2013; Chapman et al., 2012; Cohen, Brezis, Block, Diederich, & Chinitz, 2013; Galvani, Reluga, & Chapman, 2007; Ibuka et al., 2014).
Complacency “exists where perceived risks of vaccine-preventable diseases are low and vaccination is not deemed a necessary preventive action” (MacDonald & the SAGE Working Group, 2015, p. 2). Thus, general involvement in the decision is low because complacent individuals do not feel threatened by infectious diseases. And if individuls do not feel at least a minimum level of threat, they will not engage in protective behavior (Schwarzer & Fuchs, 1996). It can be assumed that knowledge, awareness, and the level of active information search are also low (Fischer et al., 2011). The attitude toward vaccination is weak, which means that it does not predict behavior very well (Glasman & Albarracín, 2006). The preventive behavior is not seen as the descriptive or injunctive norm in the society. It is likely that there is no habit to vaccinate. Thus, complacent people passively omit vaccination rather than actively decide against it.
Convenience is an issue when “physical availability, affordability and willingness-to-pay, geographical accessi-bility, ability to understand (language and health literacy) and appeal of immunization service affect uptake” (MacDonald & the SAGE Working Group, 2015, p. 3). In other words, even if there is a positive intention to vaccinate, structural barriers such as difficult access or a lack of self-control (will power) block the implementation of the vaccination decision. Barriers can be seen as “gate-keepers” as, for instance, although most people agree that vaccination is important, other personal issues seem more important or urgent—and then making the vaccination appointment gets subordinated under other obligations. This type relates to what is known as
the intention-behavior gap in the psychological literature (Sheeran, 2002). Attitudes are not strongly against or in favor of vaccination in this case, which means that vaccination is not important enough to actively overcome barriers. Consequently, when decision makers face barriers such as lack of access, cost, or travel time, they decline vaccination to avoid these barriers.
The term confidence “is defined as trust in (i) the effec-tiveness and safety of vaccines, (ii) the system that delivers them, including the reliability and competence of the health services and health professionals, and (iii) the motivations of policy-makers who decide on the need of vaccines” (MacDonald & the SAGE Working Group, 2015, p. 2).
Lack of confidence can lead to a failure to vaccinate (LaVail & Kennedy, 2012). This type of non-vaccinators usually holds strong negative attitudes toward vaccination (in contrast to the complacency and convenience types). Vaccination knowledge is likely to be distorted by misinfor-mation about risks posed by vaccination (Zingg & Siegrist, 2012) or by affiliation to certain social groups close to the anti-vaccination movement (Sobo, 2015). If there is a per-ception of vaccination as a norm or coercion, reactance will lead to consciously counteracting this norm (Betsch & Böhm, in press; Brehm, 1966). Thus, this type of vaccine hesitancy results from a plethora of circumstances that can cause a strong negative vaccination attitude that stops any deliberate decision process and leads directly to behavior determined by the negative attitude (Fazio & Olsen, 2014; Glasman & Albarracín, 2006).
The involvement in the vaccination decision is high for parents who engage in subjective expected utility calcula-tion. They engage in an extensive information search for pros and cons of vaccination. These individuals do not have a strong pre-existing attitude toward vaccination but base their decisions on utility maximization, which leads to vaccination or non-vaccination, depending on the subjective evaluations of risks. If the risk of infection is perceived to be lower than the risk of vaccination, the decision will be against vaccina-tion. One reason for this perception is that usually many others in the population are vaccinated, which leads to a low overall risk of infection (Fine et al., 2011). This can result in free-rid-ing, that is, the idea that it is selfish-rational to omit vaccina-tion as long as enough other individuals are vaccinated to keep the infection risk low. In addition, high involvement and cal-culating can also lead to an abundance of contradictory infor-mation. Facing conflicting information, for example, when doctors present different information than friends or vaccine-critical websites, will lead to indifference, that is, the same expected utility from vaccination and non-vaccination, which leads to fence-sitting. Fence-sitting refers to a state of indeci-sion or refusal of decision making, which means that there is no clear preference for or against vaccination (Leask, 2011)—similar to someone sitting on a fence who cannot decide on which side he or she should jump down. Any additional
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information about costs or (social) benefits will influence the decision because it is included in and updates the utility calculation.
The next section presents strategies demonstrated in the literature to increase vaccination behavior that have been proven effective in the domain of vaccinations or other areas of health decision making. In the final “Policy Implications” section, we will link the Four C Model to specific interven-tions (cf. also Table 1).
Strategies to Increase Vaccination
Strategies to increase vaccination uptake can be directed at different aspects of the decision process and at different determinants of the decision. The decision process starts with information that the decision maker translates into sub-jective representations. Thus, informational interventions can be used to influence risk perceptions, correct myths, raise awareness, and strengthen the positive attitude toward vaccination. Structural interventions can increase the proba-bility of vaccination by changing parts of the vaccination policy that are related to known biases and behavioral habits (e.g., changing from opt-in systems to opt-out systems). Finally, interventions can aim at minimizing the intention-behavior gap by fostering the implementation of existing positive intentions to vaccinate, for example, via increasing individuals’ self-control or creating external reminders that facilitate vaccination.
Informational Interventions: Providing Necessary Information
Informational interventions aim at improving the data base, i.e., the available and accessibe information that an individual decision maker uses to make a decision. These interventions can improve knowledge about risks of diseases, explain the social value of vaccinations, or correct misperceptions (e.g., about risks of vaccination) and myths about vaccination.
Campaigns to change risk perceptions. Fear appeals can be used to increase the perceived risk of infectious diseases. A meta-analysis from the year 2000 demonstrated that health messages creating strong fear in the receiver and, at the same time, providing advice that increases self-efficacy were most successful in changing behavior (Witte & Allen, 2000). Another overview concludes that self-efficacy information is more important than the actual arousal of fear (Ruiter, Kes-sels, Peters, & Kok, 2014). In addition, a recent study showed that presenting pictures of sick children or dramatic narra-tives about a child in danger from vaccine-preventable dis-ease did not lead to the intended change in vaccination intention (Nyhan, Reifler, Richey, & Freed, 2014). Thus, although it is important that public health agencies explain the risks that are related to vaccine-preventable diseases as
well as to increase self-efficacy to overcome this threat by vaccination, creating fear for persuasive reasons is not advisable.
Campaigns with appeal to social motives. Vaccinations provide direct protection to the vaccinating individual. Additionally, they also have an indirect effect on other non-vaccinated individuals by increasing herd immunity (Fine et al., 2011). This is important because some individuals are too young or ill to vaccinate themselves. It has been shown that the com-munication of such social (rather than individual) benefits from vaccinations increases the vaccination intention, par-ticularly when the risk associated with vaccination is low and vaccination comes with low effort (Betsch, Böhm, & Korn, 2013; Shim, Chapman, Townsend, & Galvani, 2012). Hence, such appeals to social motives should increase vaccination intentions among individuals with social preferences regard-ing the welfare of others. This type of intervention is effec-tive when it is directed at pregnant women, as a recent study shows (Wiley, Cooper, Wood, & Leask, 2015), where moth-ers expressed a higher intention to get vaccinated against pertussis when the vaccine was described as a protection for their baby rather than for themselves.
Campaigns debunking vaccination myths. Parents who decide against vaccination often hold misperceptions about vaccina-tion (Zingg & Siegrist, 2012). Therefore, it is necessary to find interventions that effectively correct the misinformation and myths. Interventions that provide an alternative account of the myth have been proven successful in eliminating misinforma-tion (H. M. Johnson & Seifert, 1994; Tenney, Cleary, & Spell-man, 2009). However, debunking attempts can also backfire or have only partial positive effects. For instance, Nyhan et al. (2014) presented parents with one of four messages about the measles, mumps, and rubella (MMR) vaccine or a control mes-sage. The messages corrected misinformation about the link between vaccination and autism, conveyed information on the risk of the diseases prevented by the MMR vaccine, presented a narrative about the dangers of measles, or presented pictures of children with measles, mumps, and rubella. None of the messages increased the intention to vaccinate. Some of these messages even had counter-predicted effects. Whereas the message correcting misinformation about the link with autism did decrease parents’ stated belief that vaccines cause autism, it also decreased intention to vaccinate one’s next child. Further-more, the pictures of sick children actually increased parents’ belief in the link between vaccination and autism. The narrative about the dangers of measles increased parents’ concern about MMR side effects. In another study, debunking was combined with an intervention that increased the individual’s self-worth (Nyhan & Reifler, under review). This led to more effective debunking, showing that holding on to misinformation can also be related to keeping up a certain identity (Sobo, 2015). Thus, as messages debunking vaccination myths do not always
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have the desired effect, they should be created carefully, pre-tested for their effectiveness, and potentially combined with other interventions.
A recent publication by the European Centre for Disease Prevention and Control (ECDC) provides several examples of how to debunk myths about the measles, mumps, and rubella vaccine (ECDC, 2014), based on extensive psycho-logical research on how to create effective debunking materi-als (Lewandowsky, Ecker, Seifert, Schwarz, & Cook, 2012). The basic building blocks are to emphasize the facts, not the myth. The introduction should start with the facts to make them easy to memorize and use a limited number of key facts to keep it simple. Explicit warnings should precede any men-tion of a myth. Any gaps in the mental model left by the debunking need to be filled, for example, by providing an alternative explanation. Core facts should be displayed graphically if possible. Language should be careful as strong risk negations can backfire (ECDC, 2014).
Campaigns to foster vaccine acceptance. It is not only impor-tant which information is given, but also how to provide it. For many health behaviors, framing information in terms of gains versus losses has differential effects on behaviors (Gal-lagher & Updegraff, 2012; Updegraff & Rothman, 2013). Ferguson and Gallagher (2007) found that for participants at high perceived risk of the flu, a gain-framed message (flu shot is effective in 80% of cases) was more effective than a loss-framed message (flu shot is ineffective in 20% of cases). In addition, a negatively framed goal message (if you don’t get a flu shot, you fail to take advantage of an 80% chance of preventing flu) was more effective than a positively framed goal message (if you get a flu shot, you reduce your risk of the flu by 80%). Participants at low perceived risk showed no framing effects.
Structural Interventions: Changing the Structure of the Decision
Making changes in the structure of the vaccination decision can affect the likelihood of deciding in favor of vaccination. If incentives are introduced (by giving rewards or reducing costs), the utility of vaccination increases, which makes vaccination more likely. Moreover, it is crucial what the default option is, that is, the option that is implemented if one does nothing. Finally, a drastic change in the decision structure is to make vaccination mandatory, as this eliminates nearly all choice. The following discusses these structural interventions.
Changing incentives. Incentives are rewards and/or fines asso-ciated with vaccination decisions that have been shown to significantly increase vaccine uptake (for a meta-analysis see, for example, Stone et al., 2002). For instance, college students were more likely to get a flu shot when offered a US$20 reward (19% vs. 9%; Bronchetti, Huffman, &
Magenheim, 2015). Furthermore, a study by Moran, Nelson, Wofford, Velez, and Case (1996) showed that a US$50 gift certificate for groceries offered for vaccination increased vaccine uptake from 20% in the baseline to 29%. Interest-ingly, in combination with mailed client reminders, the incentive increased uptake only by 6%. This is a good exam-ple of how incentives can increase vaccine uptake; however, it also points to potential motivational crowding-out effects, that is, the phenomenon that voluntary behavior may decrease when it is rewarded (see also, for example, Gneezy & Rus-tichini, 2000). Another possibility to change incentives is to reduce costs: A review concludes that there is convincing evidence that reducing out-of-pocket costs increases vaccine uptake (Briss et al., 2000).
Changing defaults. People pre-scheduled for a flu shot appointment (which they can cancel if they do not want it) are more likely to get vaccinated than those who are not pre-scheduled but who can make an appointment if they want one (Chapman, Li, Colby, & Yoon, 2010). This intervention capitalizes on the default effect or the tendency for people to stick with the option they will get automatically if they do not take explicit action. For the pre-scheduled group, the default is having a flu shot appointment, whereas for the comparison group, the default is not having an appointment. Whereas both groups have a choice to have a flu shot appoint-ment or not, most people tend to stick with their default sta-tus. Furthermore, having a flu shot appointment is a strong predictor of actually getting a flu shot (in part, because those with an appointment received a reminder email prior to the appointment date, in line with standard clinic practices).
Mandatory/compulsory vaccination. Vaccination mandates for health care workers are met with high compliance rates (Pitts, Maruthur, Millar, Perl, & Segal, 2014; Rakita, Hagar, Crome, & Lammert, 2010). In the United States, public school districts and private schools routinely mandate that children be current on vaccinations as a precondition for school registration. However, families are permitted to opt-out of the mandatory vaccinations for medical, religious, or personal reasons, and so the mandate acts as a type of opt-out default. The ease of opting out varies by State, and the States where opting out of the mandate is easier have lower vacci-nation rates and higher rates of pertussis disease (Omer et al., 2006). The January 2015 measles outbreak at the Disneyland theme park was made possible because of the number of unvaccinated children visiting the park (Centers for Disease Control and Prevention [CDC], 2015). In response to this incident, California passed a law in June 2015 removing all non-medical exemptions to vaccine requirements for school entry, making California the strictest State in the United States in terms of vaccination policy other than Mississippi and West Virginia (Haelle, 2015). Indeed, making the opt-out more difficult proved effective in increasing vaccine uptake
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in previous studies (Omer et al., 2006; Omer, Richards, Ward, & Bednarczyk, 2012).
The (negative) consequences in case of an individual’s non-vaccination, that is, punishment, are either rather direct (e.g., enforced vaccination by administrative bodies) or indi-rect by excluding the individual from certain activities (e.g., unvaccinated children are excluded from child care facili-ties). Mandatory/compulsory vaccination applies either to the whole vaccine program of a country (e.g., Czech Republic) or as a method to partly force people to some par-ticularly important vaccinations (e.g., Belgian mandate for polio vaccination). Whether to make vaccinations mandatory is certainly a matter of ethics and ideology. Beyond ethical concerns, recent evidence suggests that partial compulsory vaccination may also backfire as it leads to reactance among those with a negative vaccination attitude, decreasing, in turn, their vaccine uptake in other voluntary vaccinations (Betsch & Böhm, in press).
Interventions Supporting Self-Control and Implementation
Although the intention to vaccinate should, theoretically, lead to vaccination (Ajzen, 2012), there is ample evidence that the relation between intention and behavior is imperfect (Sheeran, 2002). Several interventions have been proposed to translate the intention into behavior by means of fostering self-control or by reducing external barriers that impede vac-cination. The following provides an overview of the relevant interventions.
Implementation intentions. Implementation intentions (Goll-witzer & Sheeran, 2006) are specific plans about when and how one will carry out an action. In one study, employees who received a postcard about available workplace flu shots were more likely to vaccinate if they were prompted to write down when they planned to come for the vaccination (Milk-man, Beshears, Choi, Laibson, & Madrian, 2011). The effect was most pronounced if the flu shots were only available on-site for 1 day.
Pre-commitment. Several strategies targeting self-control have never been evaluated with vaccination but have proved effective with other health behaviors. Pre-commitment entails binding oneself to a course of action such that one cannot later reverse course without incurring a cost (Rogers, Milkman, & Volpp, 2014). For example, Schwartz et al. (2014) invited grocery store shoppers to pre-commit to buy-ing more healthy foods such as fresh produce. The shoppers received a discount for the healthy foods they purchased. Those who pre-committed agreed to relinquish that discount unless they met their goal of increasing their purchase of health foods by a specified percentage. The shoppers who were invited to make an actual binding pre-commitment increased their healthy food purchase relative to a group who
merely indicated hypothetically whether they would pre-commit. This idea could be applied to vaccination if patients were asked to pre-commit to the recommended vaccination plan far in advance. For example, when scheduling one’s child’s wellness exam for next year, the parent could be asked to pre-commit to approving the scheduled vaccines for the child and to put down a deposit that would only be returned (with perhaps a bonus) once the vaccine had been administered.
Reminders and recall systems. There is large support for the effectiveness of reminders/recall on vaccine uptake (for meta-analysis/review, see Briss et al., 2000; Groom et al., 2014; Stone et al., 2002; Szilagyi et al., 2000). To just name a few examples, influenza immunization in children increased when parents received a reminder in a U.S. field experiment (42% compared with 25% in the control group; Daley et al., 2004). Recalls have been shown to be a particu-larly cost-effective tool to increase vaccine coverage among adults and children (Kempe et al., 2013; Suh et al., 2012). Text-message reminders via telephones or smartphone, which are suitable to also reach a low-income, urban popula-tion (Stockwell et al., 2012), complement classic approaches that build on emails or phone calls.
Recommendations. A lack of physician recommendations is among the most common reasons for non-vaccination (D. R. Johnson, Nichol, & Lipczynski, 2008). Studies show that recommendations increase uptake (Bovier, Chamot, Gallac-chi, & Loutan, 2001; Gargano et al., 2013), especially if they are strong: Women who received a strong recommendation were 4 times more likely to get vaccinated against human papillomavirus (HPV) than women who received one that was not strong (Rosenthal et al., 2011).
Conclusions and Policy Implications: How to Enhance a Vaccine Policy’s Success by Targeting Reasons for Vaccine Hesitancy
We suggest that different types of non-vaccinators have dif-ferent sets of “active determinants” that influence their deci-sions. Furthermore, we propose that interventions should be targeted to these differences (see also Butler & MacDonald, 2015). This section discusses which interventions can be promising for which type of non-vaccination based on the evidence currently available. Table 1 summarizes the discus-sion. Further research is needed to test whether these inter-ventions and combinations of interventions are especially suitable for the suggested types of non-vaccinators.
Strategies directed at complacent individuals should raise the perceived risk of disease, stress the social benefit, and highlight that vaccination is the norm. Furthermore, a posi-tive attitude toward vaccination should be strengthened.
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Informational interventions to correct misinformation, raise awareness, and increase the visibility of the topic should lead to a stronger positive attitude (Fazio, 2007). Furthermore, structural interventions help to provide (external) reasons or motivation to vaccinate. Interventions that are directed at the implementation of the decision require an already formed intention, which does not exist in complacent individuals. Only strong recommendations should increase uptake: Physicians can anchor strong recommendations in explicat-ing the risk of diseases and make clear that vaccination is important to prevent them.
For individuals heavily influenced by convenience issues, interventions should aim at eliminating structural barriers to vaccination if they are known (Kimmel et al., 2007). In addi-tion, it is important to strengthen a positive attitude toward vaccination. The latter can be achieved by informational inter-ventions. Structural interventions such as changing the default option should also be effective, because here the implementa-tion of the decision is the major problem. For the same reason, all interventions that strengthen self-control or the implemen-tation should have a positive impact in this group.
Individuals with a lack of confidence usually possess a considerable amount of incorrect knowledge that distorts risk perceptions and undermines the general trust in vaccina-tion. It is important that interventions aim at debunking these myths and that trustworthy sources, such as doctors, do so. We assume that all other strategies will have no positive effect and could even have negative effects. Other informa-tional interventions seem too weak to change strong anti-vaccination attitudes. Their persuasive appeal (such as fear appeals or gain-framed messages) is likely to be devaluated and leads to reactance. Structural interventions such as changing defaults should not work in this group, as there is usually high involvement in the decision process (cf. large opt-out rates in States where opting out is easily attainable; Omer et al., 2012). Similarly, incentives would likely need to be very high to convert those principally opposed to vaccina-tion. In a similar vein, all interventions directed at self-con-trol or the implementation of the decision are not suitable for this type of non-vaccinators. This analysis reveals that those who lack confidence and have a negative attitude are the hardest to convert.
Table 1. Suggestions for Interventions According to the Reason for Non-Vaccination.
Complacency Convenience Confidence Calculation
Goals:•• Raise perceived risk of
infection•• Stress social benefit•• Stress that vaccination is
the norm•• Strengthen positive
attitude toward vaccination
Goals:•• Strengthen positive
attitude toward vaccination
•• Change structure to facilitate vaccination
•• Strengthen self-control and the implementation
Goal:•• Debunk myths
Goals:•• Raise perceived risk
of infection•• Debunk myths•• Stress social benefit•• Add incentives
Informational interventions Campaigns to raise risk
perceptionsX X X
Campaigns with appeal to social motives
X X X
Debunking vaccination myths X X X X Campaigns to foster vaccine
acceptance: framingX X
Structural interventions Incentives X X X Default = Opt-out X X Mandatory/compulsory
vaccinationX X X
Interventions to support self-control and implementation Implementation intentions X Pre-commitment X Reminders X Making strong
recommendationsX X
Note. The table gives suggestions regarding which strategy can address potential determinants of vaccine refusal that we see as predominant in certain types of non-vaccinators. There are not enough studies to make evidence-based recommendations. Moreover, it has not been tested whether these interventions are especially suitable for the suggested types of non-vaccinators. Future research should address this question.
Betsch et al. 69
For individuals that rationally calculate by weighting potential gains and losses of vaccination, it is important to provide a basis of correct knowledge and to increase the cor-rect translation of the objective information into subjective representations. Informational interventions help to correct misinformation or add information in favor of vaccination, which is particularly important when calculation results in fence-sitting, that is, inaction due to contradictory informa-tion. Moreover, structural interventions that increase the utility of vaccination are well targeted at calculating indi-viduals. If we assume that calculating individuals are per-fectly rational decision makers, framing and changes of the default should be fairly ineffective. In a similar vein, all interventions that aim to improve the implementation of the decision should not be necessary, as a rational decision maker should implement the option with the highest expected utility.
To make such a targeted approach successful, it is crucial to regularly monitor a societies’ attitudes toward vaccination and to assess the distribution of confidence, complacency, convenience, and calculation within a society before inter-ventions are planned and rolled out. Social media such as Twitter can serve as a valuable tool to learn more about a societies’ thinking about vaccination (e.g., Infoveillance, Eysenbach, 2009; Vaccine Confidence Project, Larson et al., 2013). Other approaches, such as the “Tailoring Immunization Programs” tool by the WHO/Euro (Butler & MacDonald, 2015), work toward targeted solutions by applying a social-science research approach to the population in question (e.g., health care workers, asylum seekers, etc.).
The usual first guess is not correct, which assumes that vaccine hesitancy occurs because anti-vaccination move-ments infect the public with a lack of confidence and mis-trust (see also Dubé et al., 2014). Instead, complacency, convenience, and calculation can also play a role. As the above analysis has shown, there are a lot more interventions that promise to be effective for complacent, convenient, and calculating individuals whereas there is only one method that might be effective for those who lack confidence. Thus, efforts should be concentrated on motivating the complacent, removing barriers for those for whom vaccination is incon-venient, and adding incentives and additional utility for the calculating. These strategies might be more promising, eco-nomic, and effective than convincing those who lack confi-dence in vaccination.
Acknowledgments
The authors gratefully acknowledge the valuable support by Cindy Holtmann and Lars Korn. The authors also thank Mandeep K. Dhami for her helpful feedback on an earlier version of this article.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, author-ship, and/or publication of this article.
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