Risk of disease and willingness to vaccinate in the United States: A population-based survey

Bert Baumgaertner; Benjamin J Ridenhour; Florian Justwan; Juliet E Carlisle; Craig R Miller

doi:10.1371/journal.pmed.1003354

. 2020 Oct 15;17(10):e1003354. doi: 10.1371/journal.pmed.1003354

Risk of disease and willingness to vaccinate in the United States: A population-based survey

Bert Baumgaertner ^1,^*, Benjamin J Ridenhour ², Florian Justwan ¹, Juliet E Carlisle ³, Craig R Miller ⁴

Editor: Sanjay Basu⁵

PMCID: PMC7561115 PMID: 33057373

Abstract

Background

Vaccination complacency occurs when perceived risks of vaccine-preventable diseases are sufficiently low so that vaccination is no longer perceived as a necessary precaution. Disease outbreaks can once again increase perceptions of risk, thereby decrease vaccine complacency, and in turn decrease vaccine hesitancy. It is not well understood, however, how change in perceived risk translates into change in vaccine hesitancy.

We advance the concept of vaccine propensity, which relates a change in willingness to vaccinate with a change in perceived risk of infection—holding fixed other considerations such as vaccine confidence and convenience.

Methods and findings

We used an original survey instrument that presents 7 vaccine-preventable “new” diseases to gather demographically diverse sample data from the United States in 2018 (N = 2,411). Our survey was conducted online between January 25, 2018, and February 2, 2018, and was structured in 3 parts. First, we collected information concerning the places participants live and visit in a typical week. Second, participants were presented with one of 7 hypothetical disease outbreaks and asked how they would respond. Third, we collected sociodemographic information. The survey was designed to match population parameters in the US on 5 major dimensions: age, sex, income, race, and census region. We also were able to closely match education. The aggregate demographic details for study participants were a mean age of 43.80 years, 47% male and 53% female, 38.5% with a college degree, and 24% nonwhite. We found an overall change of at least 30% in proportion willing to vaccinate as risk of infection increases. When considering morbidity information, the proportion willing to vaccinate went from 0.476 (0.449–0.503) at 0 local cases of disease to 0.871 (0.852–0.888) at 100 local cases (upper and lower 95% confidence intervals). When considering mortality information, the proportion went from 0.526 (0.494–0.557) at 0 local cases of disease to 0.916 (0.897–0.931) at 100 local cases. In addition, we ffound that the risk of mortality invokes a larger proportion willing to vaccinate than mere morbidity (P = 0.0002), that older populations are more willing than younger (P<0.0001), that the highest income bracket (>$90,000) is more willing than all others (P = 0.0001), that men are more willing than women (P = 0.0011), and that the proportion willing to vaccinate is related to both ideology and the level of risk (P = 0.004). Limitations of this study include that it does not consider how other factors (such as social influence) interact with local case counts in people’s vaccine decision-making, it cannot determine whether different degrees of severity in morbidity or mortality failed to be statistically significant because of survey design or because participants use heuristically driven decision-making that glosses over degrees, and the study does not capture the part of the US that is not online.

Conclusions

In this study, we found that different degrees of risk (in terms of local cases of disease) correspond with different proportions of populations willing to vaccinate. We also identified several sociodemographic aspects of vaccine propensity.

Understanding how vaccine propensity is affected by sociodemographic factors is invaluable for predicting where outbreaks are more likely to occur and their expected size, even with the resulting cascade of changing vaccination rates and the respective feedback on potential outbreaks.

In a population-based survey, Bert Baumgaertner and colleagues investigate factors associated with willingness of US adults to vaccinate for a hypothetical vaccine-preventable disease.

Author summary

Why was this study done?

In the US, vaccine-preventable diseases have gone down (~1970–2000s), followed by a rise in vaccine hesitancy (~2000–2018).
In places where disease outbreaks have occurred, vaccination rates have gone back up (~2000–2018).
We conducted this survey to better understand how local cases of disease can influence people to vaccinate.

What did the researchers do and find?

We conducted a survey that presented participants with one of 7 new disease outbreaks, each with a different degree of morbidity or mortality.
We asked participants how many local case counts it would take for them to vaccinate against that disease.
The risk of mortality was associated with greater willingness to vaccinate in the presence of fewer case counts compared to the risk of morbidity.
Likewise, older populations were more willing than younger, people with high incomes were more willing than all income levels, men were more willing than women, and our findings suggest a relationship between willingness to vaccinate and political ideology.

What do these findings mean?

Part of people’s decision to vaccinate is their risk of contracting the disease, and this assessment can vary across different populations.
This information can be helpful for campaigns that aim to reduce vaccine hesitancy and is useful for modeling feedback between human decision-making and the spread of disease.

Introduction

In order to understand the recent decline in vaccination rates and the increase of nonmedical vaccine exemptions, research on the formation of vaccine attitudes has been on the rise [1]. “Vaccine hesitancy” is an important concept that has emerged out of this research and unifies several considerations. Vaccine hesitancy is understood as the delay in acceptance or refusal of vaccination despite the availability of vaccination services [2]. More specifically, it encompasses 3 factors that contribute to the complex decision-making process for vaccination: complacency, confidence, and convenience.

Vaccination complacency occurs when the perceived risks of vaccine-preventable diseases are sufficiently low so that vaccination is no longer perceived as a necessary precaution. For example, after the first measles vaccine was licensed in 1963, the number of reported cases in the US dwindled from the hundreds of thousands per year to about 1,000–10,000 per year by the 1980s, to fewer than 1,000 per year by the year 2000, when it was declared eliminated [3]. Without the threat of contracting measles, and similarly other vaccine-preventable diseases, the success of vaccination programs decreases the incentive to vaccinate, thereby providing room for complacency and, consequently, vaccine hesitancy. Furthermore, a decrease in vaccine confidence and lack of convenience can further exacerbate vaccine hesitancy.

Evidence of increased vaccine hesitancy in the US can be seen in the rise of nonmedical vaccine exemptions and, relatedly, a decrease in vaccination rates [4]. In order to combat dropping vaccination, some states, e.g., California and Washington, have instituted policies that prohibit nonmedical exemptions. It is expected that such changes in policies will help increase vaccination rates. Of course, such policies may change in the future. Furthermore, many states in the US have not adopted such a strategy and continue to allow for nonmedical exemptions. Currently, 18 states allow nonmedical exemptions [4].

Even without institutional changes, however, there is evidence that suggests that disease outbreaks can once again increase perceptions of risk, thereby decrease vaccine complacency, and in turn decrease vaccine hesitancy. For example, following a measles outbreak of more than 16,000 cases and 75 deaths in California from 1988 to 1990, Dales and colleagues [5] found that the strongest vaccination response occurred where media coverage was highest and that the response decayed with both time and distance [6–8]. Similarly for pertussis: when a county in the US experienced a large pertussis outbreak, the proportion of unvaccinated children there decreased significantly [9]. In a poll conducted by the Harvard School of Public Health in September 2009, of the adults who said that they did not intend to get the pandemic influenza vaccine for themselves or their children, 60% also said that they would change their mind if other members of the community were sick or dying from A(H1N1)pdm2009 [10]. Finally, in a meta-analysis of 34 studies, Brewer and colleagues [11] found that both the perceived likelihood of being harmed by a disease and the perceived severity of that harm are significant predictors of vaccine behavior.

In sum, complacency is a major contributing factor to vaccine hesitancy, but hesitancy can be overcome by increasing perceived risk (which decreases complacency). What is not well understood, however, is how change in perceived risk translates into change in vaccine hesitancy. In other words, we do not yet have a sufficiently rich understanding of “vaccine propensity,” a concept introduced by Justwan and colleagues [12] that we further advance here.

We define vaccine propensity as a mapping from perceived risk of infection to reported willingness to vaccinate, holding fixed other considerations, such as vaccine confidence and convenience. By “perceived risk of infection,” we mean the combination of the probability of contraction and the severity of the disease, as understood in terms of morbidity and mortality. We see the concept of vaccine propensity as enriching the concept of vaccine complacency by providing a dynamic mechanism of how hesitancy may change in response to changes in the landscape of risk.

Strictly speaking, vaccine propensity is an individual attribute and is expected to vary across individuals. That is, given some information about disease prevalence and severity, individuals subjectively determine (or “perceive”) their risk of infection. Individuals can then report their (un)willingness to vaccinate if the perceived risk is above (below) some subjective threshold. Alternatively, the threshold itself can be probed by asking how much risk someone is willing to accept (e.g., in terms of disease prevalence) before they are willing to vaccinate (holding fixed other concrete information about, e.g., disease severity). While vaccine propensity is individual or subjective, it is helpful to translate it to the aggregate level. This can be done by estimating a function that outputs the cumulative proportion of a sampled population that reports a willingness to vaccinate for a given level of disease prevalence as input. In this way, the concept of vaccine propensity can also be understood and illustrated at a population level. This is particularly helpful for epidemiological considerations, wherein we may want to identify subpopulations that are more or less responsive to disease risk. That is, we can look at the increase of the cumulative proportion of a population that is willing to vaccinate as disease prevalence increases. In light of these considerations, and given that our analysis emphasizes the population level, we will use the phrase “vaccine propensity” to denote the aggregate version.

This paper has 2 goals. The first is to enrich our understanding of the role of complacency in vaccine hesitancy by determining how changes in vaccine hesitancy are associated with changes in risk (complacency). We do this by using the concept of vaccine propensity (see Fig 1). To make “risk” more concrete here, we use “number of local cases” as the primary determinant of probability of infection. The second goal of this paper is to determine how vaccine propensity is associated with sociodemographic factors. It is known that a variety of variables predict vaccine status or attitude. For example, research shows that vaccination rates vary with sociodemographic factors such as income, marital status, and age [13–15]. It has also been shown that, while the average rate of nonmedical exemption has increased from 1.5% to 3% across more than 6,000 schools in California from 2007 to 2013, there are many schools and regions with rates between 10% and 20%, and that white children attending private schools from families with higher incomes tend to have much higher exemption rates [16, 17]. In Texas, exemptions statewide rose from 10,000 to 45,000 in the 2007–2015 span [18], and of the 14 schools with exemption rates between 15% and 40%, 6 are clustered in the Austin area [19]. Similar vaccine refusal clustering has been documented in Washington State [20] and Michigan [21]. In fact, as of 2018, several “hotspots” of nonmedical exemptions have been documented across the US and appear to continue to grow [4]. While the true reason for variation in nonmedical exemptions and vaccination rates is expected to be nuanced and involve cultural dimensions, we can reasonably expect that sociodemographic factors at least roughly track some of the sources of variation.

Fig 1 — The source of vaccine hesitancy has 3 components: complacency, confidence, and convenience. We are interested in understanding how a change in complacency due to a shift in risk—e.g., an increase in disease prevalence—maps onto a change in vaccine acceptance.

Both theory and empirical evidence indicate that the clustering of susceptible individuals makes outbreaks more probable [20–25]. It is not known, however, whether sociodemographic variables are also predictive of changes in vaccine behavior with respect to changes in risk of infection. If there are differences in vaccine propensity due to differences in sociodemographic makeup, we do not expect outbreaks to be equally probable, nor equally severe: some regions may be more responsive to changes in risk than others and thereby decrease the chances of a regional outbreak and/or decrease the size of an outbreak. Understanding how vaccine propensity is affected by sociodemographic factors is thus invaluable for predicting where outbreaks are more likely to occur and their expected size, even with the resulting cascade of changing vaccination rates and the respective feedback on potential outbreaks.

Furthermore, understanding sociodemographic factors of vaccine propensity would be useful for intervention strategies that make use of targeted messaging. It has been recognized that the “information deficit model” approach, which proceeds on the basis that vaccine hesitancy is simply a matter of lack of knowledge, is not sufficient for changing vaccine behavior [26]. One way to improve communication strategies is to incorporate our understanding of the sociodemographic factors that influence hesitancy.

To help fill these knowledge gaps, we sought to better understand how vaccine hesitancy is associated with changes in risk (which we call vaccine propensity) and, in turn, how vaccine propensity is associated with common sociodemographic factors.

Methods

This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 STROBE Checklist). Our study did not have a protocol or prespecified analysis plan with respect to empirically advancing the concept of vaccine propensity. The details of our analyses were decided upon after conducting the survey. Analyses were not data driven or exploratory—we opted for a model selection approach constrained by all our explanatory variables. Our knowledge-based model analysis was conducted in response to a request from a reviewer.

Data collection and sample characteristics

For our statistical analysis, we rely on original micro-level data from a demographically diverse online survey programmed on Qualtrics (https://www.qualtrics.com). Participants for our online survey were collected via Survey Sampling International (SSI) (please note that SSI rebranded as Dynata after our study [https://www.dynata.com]). SSI is a US-based market research firm that maintains panels of persons used only for research. Panelists voluntarily join an SSI panel by responding to an online SSI advertisement (e.g., a banner advertisement on a website). SSI uses invitations of all types, including email invitations, phone alerts, and banners and messaging on panel community sites to include people with diverse motivations to take part in research. At the time of enrollment, new panelists are asked to join an online market research panel. At this point, it is made clear that it is not part of a sales process. Our survey invitations provide only basic links and information that is nonleading. Panelists are rewarded for taking part in surveys according to a structured incentive scheme, with the incentive amount offered for a survey determined by the length and content of the survey, the type of data being collected, the nature of the task, and the sample characteristics. Panelists are supported by a dedicated team and have the option to unsubscribe at any time. SSI’s panel management is compliant with market research industry standards, data protection, and privacy laws.

Our quota sample (rather than a probability sample) includes individuals who were selected based on several demographic characteristics and resemble the US population according to 5 major dimensions: age, income, sex, race, and census region. Our sample was not designed to match population characteristics for education, but it still turned out to be relatively close in that regard. A detailed comparison of sample characteristics and cell percentages from the 2010 census can be found in Table 1.

Table 1. Sample characteristics (compared to 2010 census).

Variable	Population (2010 Census)	Sample % (N)
Age, y
18–24	13.08%	16.0% (481)
25–34	17.51%	18.2% (547)
35–44	17.51%	18.1% (543)
45–54	19.19%	18.4% (553)
55–64	15.55%	14.5% (435)
65 or older	17.17%	14.9% (446)
Sex
Male	48.53%	47.1% (1,404)
Female	51.47%	52.9% (1,574)
Income (in USD)
Less than $30,000	29.00%	26.4% (792)
$30,000–$49,999	19.00%	20.2% (607)
$50,000–$100,000	30.00%	34.00% (1,023)
$100,000+	22.00%	19.4% (583)
Race/Ethnicity
Hispanic or Latino	16.30%	14.1% (423)
White	63.70%	76% (2,335)
African American	12.20%	12.2% (395)
Asian	4.70%	4.4% (149)
Region
Northeast	18.00%	18.0% (541)
Midwest	22.00%	22.0% (661)
South	37.00%	37% (1,113)
West	23.00%	23.0% (691)
Education
Less than high school	4.8%	0.4% (13)
High school incomplete	8.9%	2.9% (87)
High school graduate	31.0%	19.9% (598)
Some college, no degree	19.3%	27.0% (812)
Two-year associate’s degree	8.6%	11.2% (337)
Four-year college degree	18.0%	26.5% (798)
Postgraduate/professional degree	9.3%	12.0% (360)

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Our survey was designed to match population characteristics for age, sex, income, ethinicity, and region to data from the 2010 census. We include a comparison for education as well.

Survey data were collected from January 25, 2018, to February 2, 2018. A total of 6,597 survey invitations were sent out by SSI. We had a total of 5,140 respondents that began the survey; 229 respondents discontinued the survey by the first “quality control” check, and another 32 discontinued by the second. The 2 “quality control” checks were the following 2 questions, respectively. Question 1 read as follows: “For quality control purposes, please select the number five with the letter ‘G’ next to it.” Answer options were 5A, 5B, 5C, G, 5D, 5E, 5F, 5G, and 5H. Question 2 read as follows: “Research shows that people, when making decisions and answering questions, prefer not to pay attention and minimize their effort as much as possible. Some studies show that over 50% of people don’t carefully read questions. If you are reading this question and have read all the other questions, please select the box marked ‘other.’ Thank you for participating and taking the time to read through the questions carefully! What is this study about?” Answer options were (1) Health, (2) Diseases, (3) Vaccines, and (4) Other. After deleting individuals who did not pass quality control checks, there were 3,007 valid responses. The completion rate (CR) for our survey is 58.5% and is based on the following calculation: the number of respondents who completed the survey divided by the number of those who began the survey, or CR = 3,007/5,140 = 58.5%.

The survey was structured as follows. First, respondents provided information about the size of their social network and the places they live in and visit during a typical week. Second, participants read a brief description of a hypothetical infectious disease and then were asked a series of questions about how they would respond to this disease if it broke out in the US. The survey concluded with a third section in which respondents answered a series of questions about their political beliefs and basic demographic attributes. The survey questions for the variables in this paper are provided in S1 Text.

Ethics statement

Before launching the survey, we obtained Institutional Review Board (IRB) exemption from the University of Idaho IRB (Project Number: 18–017; exemption granted under category 2 at 45 Code of Federal Regulations [CFR] 46.101[b][2]). Informed consent was obtained electronically from study participants before they began the online survey.

Dependent variable measurement

Our dependent variable is “vaccine propensity,” which we defined as the extent to which infection risk translates to a respondent's self-reported willingness to vaccinate. In order to measure this concept, we need to ascertain at what level of risk a given respondent would be willing to get vaccinated against a particular disease, holding other parameters constant. Given that individuals are likely to vary substantially in their knowledge about and experiences with any “real world” disease, we opted to rely on a hypothetical scenario.

Survey respondents were told that a new “disease has been discovered within the United States.” We explained that any person who comes in contact with an infected individual has a 25% chance of contracting the disease. Providing this piece of information allowed us to hold the perceived infectivity of the condition constant. Next, we described that the disease causes a number of different symptoms: fever, diarrhea, vomiting, severe stomach pain, headaches and dizziness. Finally, in order to explore whether the severity of the disease influences vaccine propensity, we randomized the last bit of information across respondents: we split our sample into 7 even-sized groups and informed participants about the specific dangers associated with the infection. One group read that “people who contract the disease are sick for 1–3 days—too sick to work, go to school, care for others or leave the house.” Three other groups read the same statement with the exception that the condition would last “4–7 days,” “8–14 days,” or “more than 15 days.” The final 3 groups were informed that the illness could potentially be lethal and that it would kill approximately “1 in 1,000 people (0.1%)” who contract it, “1 in 100 people,” or “1 in 10.”

After reading one of these 7 different severity cues, respondents learned that a “highly protective vaccine (with minimal chances of side effects) is recommended and available” to them locally at no cost. Subsequently, all respondents answered a survey question that directly taps into vaccine propensity. In particular, we asked each respondent how many people in his/her local community would have to get infected with this disease for him/her to get vaccinated. Answer options were as follows: (1) no one, (2) 1, (3) 10, (4) 100, or (5) other [write in], (6) I will not vaccinate for this disease, and (7) I do not know. Only 45 answered “other,” 395 answered “I do not know,” and 160 did not answer the question. These individuals were omitted from the following analyses.

Thus, our final dependent variable has 5 distinct categories. Individuals who would vaccinate even if “no one” is infected and respondents who would not vaccinate for this disease at all are both unaffected by infection risk, but they occupy different end points on a spectrum. Individuals in our middle categories (1, 10, 100) are those survey respondents who display (varying levels of) sensitivity to infection risk—some of them requiring high numbers of locally reported cases before they would get vaccinated and some of them reacting to fairly low case counts. Our statistical analyses assess the sociodemographic and risk factors that are associated with when individuals will seek vaccination.

Independent variables, data processing, and analysis

All processing and analysis of the data was performed in R version 3.4.4 [27]. In order to predict vaccine propensity, we consider sociodemographic characteristics, including race, age, sex, income, education, population size of a respondent’s hometown, population sizes of all cities commuted to during a typical week, number of children, age of youngest child (if applicable), political ideology, religious affiliation, religiosity (importance of religion and frequency of attending religious services), and the self-reported health of the respondent. All of the above variables were treated as factors with the exception of age, which was treated as a continuous variable. Some of the factors were treated as ordinal if an obvious ordering relationship exists, such as for income ranges and population sizes. We also controlled for whether the disease scenario presented to a given survey respondent involved mortality or morbidity. Only respondents who answered all of the questions pertaining to the variables above were included in our analyses (i.e., if any of the questions was not answered or answered as “unknown,” then the observation was removed from the sample). The final sample size for analysis was 2,411.

Some of our independent variables were recoded prior to conducting the analyses. This was done since the case counts in some response categories were very low. The population sizes of cities commuted to during a week were reduced to the largest city commuted to, which was categorized (in order) as follows: does not commute, smaller than 1,000, 1,000–50,000, 50,000–250,000, 250,000–1,000,000, or more than 1,000,000. We used number of children and the age of the youngest child to compute a new variable that reflected whether the respondent had either no children, children at home (age of youngest child less than 19 years), or adult children. Political ideology is measured via 7-point Likert scale ranging from very liberal to very conservative. We omitted from our analyses respondents who identified as either “Libertarian” or “other.” Respondents who identified themselves as either Jewish or Mormon were coded as “other,” thereby providing us with the following categories for analysis: Protestant, Catholic, other, and not religious. Income level (originally recorded in US$10,000 intervals) was recoded as follows: less than $30,000, $30,000–$59,999, $60,000–$90,000, and more than $90,000. Respondents who answered that they had 1–8 years of education or did not complete high school were categorized as “no high school diploma”; those who graduated high school but either did not attend college or did not graduate from college were categorized as having “high school diploma”; those who either received a bachelor’s degree, an associate’s degree, or did not complete a graduate/professional degree were treated as “college graduates”; those who did complete a graduate/professional degree were treated as having “graduate education.” Race was reduced to 3 categories: black, white, and other. The final number of respondents used for analysis was 2,411. Descriptive statistics are provided in Table 2.

Table 2. Frequency of responses for survey questions that were used for analysis (information for age frequency can be found using S1 Code).

Question	Response	Percentage
Local cases before vaccination
	Always	50
	1	18
	10	14
	100	7
	Never	11
Sex
	Male	49
	Female	51
Race
	White	77
	Black	12
	Other	11
Income (in USD)
	<30,000	24
	30,000–60,000	29
	60,000–90,000	20
	>90,000	27
Child status
	No children	37
	At home	34
	Adult children	29
Hometown population size
	<1,000	8
	1 000–50 000	33
	50,000–250,000	31
	250,000–1,000 000	18
	>1,000,000	11
Largest city population size commuted to
	Do not commute	53
	<1,000	1
	1,000–50,000	11
	50,000–250,000	18
	250,000–1,000,000	12
	>1,000,000	5
Education
	No high school diploma	2
	High school diploma	45
	College graduate	39
	Graduate education	13
Political leaning
	Very liberal	10
	Liberal	15
	Slightly liberal	10
	Moderate	28
	Slightly conservative	10
	Conservative	18
	Very conservative	8
Religion
	Protestant	27
	Catholic	22
	Not religious	24
	Other	27
Frequency of religious service attendance
	Never	27
	Seldom	24
	A few times per year	15
	Once or twice per month	9
	Once per week	20
	More than once per week	7
Importance of religion
	Unimportant	20
	Not too important	17
	Somewhat important	28
	Very important	34
Respondent health
	Poor	4
	Fair	21
	Good	56
	Excellent	18

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Of the retained surveys, 58% (N = 2,411) presented mortality as a consequence of infection.

Analysis of the survey data was conducted in the following manner. Our dependent variable was whether an individual would choose to get vaccinated (binary yes/no) at a particular risk level (0, 1, 10, 100 cases, or never). Thus our response variable was binary, and each individual had 4 repeated observations (one for each risk level less the “never” category). To control for the repeated observations, we utilized generalized estimating equations (GEEs) to fit a binomial model. We performed model selection in 2 phases: First, step-wise selection based on quasi-information criterion (QIC) was performed on all explanatory variables. Second, with the reduced set of explanatory variables, we then searched the model space where all pairwise interactions were considered (again, based on QIC). Model selection was halted after all remaining terms had a significance level ≤0.1. After model selection was completed, P values for independent variables were determined by ANOVA. In order to test the robustness of our analysis, we also used a knowledge-driven approach to building models. Our results are similar across our automatic model selection approach and the knowledge-driven approach. Details for the latter can be found in S2 Text.

Results

The results of the analysis are presented in Tables 3 and 4. The step-wise model selection procedure determined that the best model (in terms of QIC) to predict vaccine propensity from our data used age, sex, political ideology, income, and the consequences of the disease (mortality versus morbidity); multiple interaction terms—all involving either age or risk (number of local cases)—were statistically significant.

Table 3. The resulting GEE model for vaccination propensity by respondent characteristics (N = 2,411).

	Degrees of Freedom	χ²	P(>\|χ\|)
Political Leaning	6	19.27	0.0037
Scenario	1	13.52	0.0002
Age	1	21.14	<0.0001
Sex	1	10.72	0.0011
Income	3	22.29	0.0001
Local Cases	3	1,190.05	<0.0001
*Political Leaning Local Cases**	18	37.93	0.0040
*Scenario Local Cases**	3	6.25	0.1000
*Age Sex**	1	3.43	0.0639
*Age Local Cases**	3	23.69	<0.0001
*Age Scenario**	1	3.56	0.0591

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Model selection was done based on QIC, and sandwich error variances were calculated to correct for individual effects. For the resulting model, pseudo-R² = 0.135 (see [41]).

Abbreviations: GEE, generalized estimating equation; QIC, quasi-information criterion

Table 4. Pairwise comparisons of very liberal, moderate, and very conservative across our 4 levels of risk in terms of local cases (N = 2,411).

Contrast	Estimate	Standard Error	z	P(>\|z\|)
Local Cases = 0
Very liberal–moderate	0.4343	0.1547	2.808	0.0050
Very liberal–very conservative	0.4803	0.1988	2.415	0.0157
Moderate–very conservative	0.0460	0.1667	0.276	0.7827
Local Cases = 1
Very liberal–moderate	0.2611	0.1661	1.572	0.1161
Very liberal–very conservative	0.5855	0.2092	2.798	0.0051
Moderate–very conservative	0.3244	0.1731	1.875	0.0609
Local Cases = 10
Very liberal–moderate	0.2985	0.2028	1.472	0.1411
Very liberal–very conservative	0.8897	0.2410	3.691	0.0002
Moderate–very conservative	0.5912	0.1913	3.091	0.0020
Local Cases = 100
Very liberal–moderate	0.0648	0.2419	0.268	0.7887
Very liberal–very conservative	0.9144	0.2752	3.323	0.0009
Moderate–very conservative	0.8496	0.2189	3.882	0.0001

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Results are given on the log odds ratio (not the response) scale.

We did not find significant relationships between vaccine propensity and the following variables (P values reported from knowledge-driven approach, P<0.01 indicates statistical significance, see S2 Text for more details): religion (P = 0.21), religious importance (P = 0.82), frequency of attending religious service (P = 0.8152), hometown size (P = 0.9397), the size of the largest city commuted to (P = 0.2909), race (P = 0.0209), whether individuals had children or children at home (P = 0.4771), an individual's health (P = 0.7275), and the individual's educational attainment (P = 0.0168).

Fig 2 illustrates our concept of vaccine propensity and compares the morbidity and mortality scenarios. We can visually see how a change in vaccine propensity at the aggregate level (understood here as the proportion of a population willing to seek vaccination, or “proportion seeking vaccination” for short) responds to a change in risk in terms of local cases. As risk increases in terms of the number of local cases of disease, so does the proportion seeking vaccination. In addition, the proportion seeking vaccination tends to be higher in the mortality scenario than the morbidity scenario for each risk level. In both scenarios, it is approximately 40%, a shift from 50% to 90%. That is, the difference between the proportion of the population willing to seek vaccination when risk is high (100 local cases) is around a 40-percentage-point increase from when risk is lowest (zero local cases).

Moreover, vaccine propensity appears to be gradual. That is, we do not observe that the 40-percentage-point increase occurs from one level of risk to the next, say from zero to 1 local case. Rather, we observe a more gradual increase over the 4 levels. This suggests that respondents are doing some, albeit rough, mental calculations regarding the probability of getting infected. And, given that the mortality scenario is higher than the morbidity scenario, this calculation is also taking into consideration the consequences of the disease.

One of the reasons for using the concept of vaccine propensity is to identify whether there are differential responses to risk levels for different types of populations. That is, we expect that some populations are more responsive to risk than others and that we would see this by comparing their changes in proportion willing to seek vaccination. In terms of our model, this means identifying sociodemographic variables that significantly interact with the variable we use to represent risk: number of local cases.

The variable for number of local cases had 2 interactions with sociodemographic variables (our scenario variable does not count as sociodemographic). The first is with political leaning (see Fig 3). Results suggest that a smaller proportion of respondents on the conservative end of the ideology spectrum are willing to seek vaccination than respondents who report being liberal. Specifically, at 0 local cases the proportion of “very conservative” respondents willing to vaccinate was 0.457 (0.386–0.528), and the proportion of “very liberal” respondents was 0.576 (0.511–0.638). At 100 local cases the proportion of “very conservative” respondents willing to vaccinate was 0.786 (0.721–0.840), and the proportion of “very liberal” respondents was 0.902 (0.860–0.932). Pairwise comparisons of very liberal, moderate, and very conservative respondents at each risk level can be found in Table 4.

Fig 3 — Visual inspection suggests a rough trend that the conservative end of the ideology spectrum has a lower proportion willing to seek vaccination than the liberal end. Exact values can be found in Table 4. It is noteworthy to compare the very liberal, the very conservative, and the moderate. When risk is lowest at zero local cases, moderate respondents are similar to conservative and very conservative respondents. When risk is highest at 100 local cases, moderate respondents are similar to the liberal ends of the spectrum. The moderate population thereby exhibits a higher responsiveness to changes in risk. The points are offset from 0, 1, 10, and 100 as a visual aid. Whiskers from the points are the 95% confidence intervals.

One group that is particularly interesting to notice is “moderate.” When the number of local cases is zero, we see that the proportion of moderates willing to seek vaccination is 0.468 (0.430–0.507), about as low as those that are conservative 0.430 (0.383–0.479) or very conservative 0.457 (0.386–0.528). When the number of local cases reaches 100, the proportion of moderates is 0.896 (0.871–0.917), just as high as the others, with the exception of respondents that are very conservative. In short, when risk is low, moderates respond much like very conservatives, but as risk increases moderates respond more and more like very liberals.

The other sociodemographic variable that risk interacted with is age (P<.0001). Fig 4 illustrates that risk assessments are being done differently across age groups for different risk levels. The proportion of younger respondents willing to vaccinate has the most variation, starting from 0.397 (0.360–0.436), when risk is at its lowest (0 local cases), going up to 0.900 (0.876–0.919), when risk is highest (100 local cases). As we consider older respondents, this variation decreases, with 0.653 (0.599–0.704) of the oldest respondents willing to seek vaccination at the lowest risk level and 0.878 (0.835–0.910) willing at the highest.

The proportion willing to vaccinate in the mortality scenario is nominally higher and less variable across ages than the morbidity scenario; however, the interaction between age and scenario did not reach statistical significance (P = 0.0591). Fig 5 illustrates the observed relationship between age and proportion willing to vaccinate for each scenario. The proportion willing to vaccinate appears relatively stable across age groups in the mortality scenario. Moreover, older people seem to treat the mortality and morbidity scenarios similarly, as the proportion willing to vaccinate in both are nearly identical. In the morbidity scenario, however, fewer young people are willing to vaccinate than older people. However, it is important to note that these differences did not reach statistical significance.

Fig 5 — The mortality scenario is relatively stable across ages, while the morbidity scenario changes. Younger populations are less motivated by morbidity than mortality, but older populations respond similarly to both scenarios. The solid lines are the trend lines, and the shaded area is the 95% prediction interval.

Although the interaction between age and sex approached but did not reach statistical significance (P = 0.0639), Fig 6 illustrates our observation that for younger populations, similar proportions of men and women are willing to vaccinate (0.716 [0.671–0.757] and 0.716 [0.673–0.755], respectively, at 95% CI.) As age increases, a larger proportion of men (0.837 [0.792–0.874]) are willing to seek vaccination than women (0.739 [0.667–0.800]), although with overlapping confidence intervals. We provide a possible interpretation in the discussion section.

Finally, family incomes above $90,000 are willing to vaccinate in higher proportion at 0.801 (0.774–0.825) than those with incomes below that (P = 0.0001) (see S2 Text for comparisons in our knowledge-driven approach). Specifically, the proportion of families with incomes in the $60,000–$90,000 range was 0.718 (0.682–0.752), in the $30,000–$60,000 range was 0.735 (0.705–0.762), and in the below $30,000 range was 0.717 (0.683–0.750) (see Fig 7).

Fig 7 — Populations with family incomes above $90,000 are significantly greater in their proportion to seek vaccination than others, particularly in the 50-year-old and 60-year-old age groups. Whiskers from the points are the 95% confidence intervals.

Discussion

Our study objective was to better understand how vaccine hesitancy is associated with changes in risk (vaccine propensity) and, in turn, how vaccine propensity is associated with common sociodemographic factors. To do this, we collected and analyzed original micro-level data from a demographically diverse online survey.

In brief, we find an overall change of at least 30% in proportion willing to vaccinate as risk of infection increases, where risk is understood in terms of number of local cases. In addition, we find that the risk of mortality invokes a larger proportion willing to vaccinate than mere morbidity, that older populations are more willing than younger, that the highest income bracket (>$90,000) is more willing than all others, that men are more willing than women, and that the proportion willing to vaccinate can depend on both ideology and the level of risk.

It is known that changes in risk correspond with changes in rates of vaccination and nonmedical exemptions: decreases in risk can lead to increases in nonmedical exemptions and lower vaccination rates [16–18, 20, 21], while increases in risk can lead to decreases in nonmedical exemptions and higher vaccination rates [5–11]. Moreover, it is known that vaccination rates vary with sociodemographic factors such as income, marital status, and age [13–15] and that vaccine hesitancy also has other sociodemographic determinants [2]. It is not known, however, how much of a change in risk is required to produce a change in vaccine hesitancy, nor do we know the sociodemographic variables that moderate these changes. Thus, the goals of this paper were to empirically advance the concept of vaccine propensity to enrich our understanding of complacency in vaccine hesitancy and to study the sociodemographic factors that are associated with vaccine propensity.

With respect to the first goal, we studied several scenarios of severity associated with disease, 4 ranging in different levels of morbidity and 3 in chances of mortality. For each scenario, we measured people’s acceptable risk of infection in terms of the number of local cases of infections that it would take for someone to get vaccinated. We were able to estimate a vaccine propensity relationship that started from the number of people that would vaccinate given zero cases of local infections. As the number of local cases of infection increased from zero to 1 to 10 to 100, the cumulative number of people that would vaccinate increased as well, with more people being added for lower increases of infection risk (e.g., from zero to 1) than for higher increases (e.g., from 10 to 100). Somewhat surprisingly, we did not find significant predictors within either morbidity or mortality individually, i.e., respondents did not seem to be motivated by, e.g., increases in how long symptoms last or how likely the infection is to cause mortality. However, we did find a significant difference between the morbidity scenarios and the mortality scenarios. In the morbidity scenario specifically, we estimated that around 48% would vaccinate at zero cases, 17% would vaccinate at 1 case, 15% would at 10 cases, and 7% at 100. Moreover, we found that respondents are more motivated by risk of mortality than risk of morbidity to vaccinate, which we intuitively expect—death is scarier than symptoms of disease. More specifically, we see the same rising trend in response to increasing risk in the mortality scenario as we saw in the morbidity scenario, but with an addition of about 5% more at each risk level.

These results give us a first estimate of vaccine propensity that provides a more detailed understanding of complacency. We can distinguish, e.g., not only between the heights of 2 vaccine propensity functions, which tells us which scenario or subpopulation has higher rates of reported willingness to vaccinate, but also between their slopes, which tells us about responsiveness to changes in disease prevalence. The vast majority of people would vaccinate for higher levels of infection risk (e.g., 100 or 10 cases), but as infection risk declines, so does intention to vaccinate, with the largest decrease in motivation seen when risk of infection goes from 1 to zero local cases. Put differently, we expect the largest increase in proportion willing to vaccinate to be when a single case of the disease first arrives locally, with a diminishing return as the number of local cases increases.

With respect to the second goal, we were able to identify several sociodemographic factors that are associated with vaccine propensity, including age, sex, income, and ideology. Given the extant empirical evidence on the relationship between age and risk [28, 29], we expected to see that younger populations would be less risk averse than older populations, with older respondents being more willing to vaccinate. Consistent with the literature, we found that older populations were more willing to vaccinate in order to mitigate risk of infection. Potential explanations for this include that older respondents are more likely to be concerned about mortality from infections and, additionally, are more likely than younger respondents to have experienced or witnessed diseases. However, the difference between younger and older respondents is not merely because the young are generally less averse to risk, since at high risk the young are just as averse. Rather, it seems that the young are more actively assessing risk against other factors—perhaps convenience—and making a calculated decision.

In terms of sex differences, the literature suggests women to be more risk averse than men [30]. However, contrary to the general literature on sex and risk, the association between vaccine propensity and sex was in the opposite direction in our study, with men being more likely to vaccinate than women. The only exception is for the youngest respondents, with similarly lower proportions willing to vaccinate in the 70%–75% range. One possible explanation for the difference between men and women is as follows. In a disease context, there are 2 risks being weighed: risk of infection and risk of vaccine side effects. In our study, respondents were told that there were minimal chances of side effects. However, it is possible that the perceived severity of side effects is different across sex, with lower perceptions of severity among men and higher perceptions of severity among women. This explanation is consistent with a recent study in which women report more adverse effects of vaccination than do men [31]. It is also possible that men become more risk averse as they get older because their overall mortality risk is higher than women of the same age and this influences their decision-making.

With respect to income, Sakai [32] found that childhood vaccination rates at the country level rise and then fall with increases in income, with vaccination rates peaking around a per capita income of $30,000–$40,000. For the US counties specifically, 4 of the 7 vaccines examined also showed a peak in vaccination rates for middle-range incomes, with vaccination rates decreasing as incomes moved toward low and high. Similar results were obtained at the individual level, with the probability of a child being up to date on vaccination lower on both low- and high-income ends for many vaccines. We did not observe such a pattern when it comes to vaccine propensity: higher levels of income are associated with higher vaccine propensity. The difference could be that, in our case, we asked respondents to make a decision for themselves, while Sakai [32] focused on parents making decisions concerning their children. One purported explanation for why high-income parents have lower vaccination rates for their children is that high-income parents feel that they can protect their children through avoidance measures, thereby limiting their children’s exposure to the threat of disease by reducing risk of infection [16, 33]. Moreover, higher-income respondents may have perceived the vaccination option described in our survey to be less costly than engaging in avoidance measures that would disrupt the routine places they visit (e.g., staying home from work). A second purported explanation for why childhood vaccination rates decline in high-income populations is that high-income parents may believe that they have better access to medical technology to treat or mitigate disease symptoms. Our survey instrument again does not present this as a viable possibility, since the only technological solution we present to respondents for the new disease is vaccination. Thus, while we cannot definitely rule out that respondents cannot “buy” mitigation, we did not encourage such thinking. Both of these purported explanations highlight health-related affordances made possible by higher levels of income that are alternatives to the option of getting a vaccination. Since our survey question includes vaccination as the only possible technological option, it may be tapping into a different pattern. However, since we cannot effectively test the range of technological options with our survey instrument, we refrain from drawing a specific explanation.

Given the college wage premium, it is reasonable to expect that the higher vaccine propensity of the >$90 income bracket would also mean a higher vaccine propensity in our education variable. Education was included in our survey instrument, but we found no significant relationship between vaccine propensity and education in our analysis. We therefore have reason to believe that whatever explanations are offered for our finding that the >$90,000 income bracket has the highest vaccine propensity are unlikely to be related to education.

Arguably our most interesting result pertains to political ideology. Strong conservatives make up a larger fraction of the vaccine-hesitant population than liberals [34]. Our results are consistent with these previous findings: the vaccine propensity of very conservative respondents is lower than that of very liberal respondents. Interestingly, however, if we focus on the slope of the propensity functions, we see a symmetric pattern related to ideology. While there are more very liberal than very conservative respondents who will vaccinate when there are zero local cases, increases in infection risk were associated with increased vaccine propensity for both groups. By contrast, visually the slope was greater among respondents who are moderate or less extreme ideologically, suggesting that they could be more motivated by increasing infection risk. This is suggested by noting that when risk is lowest, those who are “middle of the road” ideologically are among the lowest proportion of those willing to vaccinate, but as risk increases to the highest level, they are among the highest willing to seek vaccination. This is consistent with findings that suggest that people with stronger or more extreme ideological views are less responsive to changes in the world than those with more moderate views [35]. In brief, the more entrenched or strong a person’s ideological leaning, the more steadfast s/he will be in their (un)willingness to vaccinate in response to changes in risk. Moderates may be more pragmatic in their decision-making by focusing more on the risk of contracting the disease and focusing less on how that decision fits into other aspects of their worldview.

Limitations of our study are as follows. Evidence discussed in the introduction suggests that an increase in case counts can decrease vaccine hesitancy. Consequently, we focused on how local case counts were associated with vaccine decisions, holding fixed other considerations. Nevertheless, it is possible that case counts interact with or differentially weight other considerations that are not captured by our focus on local case counts alone (such as social influence). Moreover, our survey instrument captured differences between the morbidity and mortality scenarios but failed to detect significant differences within these categories. We are unable to say whether this is an artifact of our survey design or whether people’s decision-making is heuristically driven and glosses over degrees in the mortality and morbidity categories. Other limitations concern sample size and representativeness. Our survey design ensured demographic diversity by matching the US census on age, sex, income, race/ethnicity, and region. After data collection, we also found that our sample closely matched for the education variable. However, it is possible that our sample differs from the population in other respects. For example, because our survey is online, it is unable to capture the part of the US that does not have access to the internet. We are also not able to capture those who did not complete the survey or failed our attention checks. Finally, as with any survey, it is possible that responses do not perfectly reflect actual traits or behaviors. Specifically to us, our survey asked participants to consider a counterfactual scenario, and it is possible that actual reactions to disease outbreaks would differ from the predictions that individuals made of themselves.

Our findings have important connections to epidemiological modeling and public health interventions. A wealth of work is being done in recognition that there is feedback between changes in human behavior in response to disease and the spread of disease [36–40]. By improving our understanding of how a change in risk of contracting a disease relates to a change in willingness to seek vaccination, we can improve epidemiological models often used to inform public health interventions. More specifically, we have advanced our understanding by (i) quantifying how different degrees of risk (in terms of local cases of disease) correspond with different proportions of populations willing to vaccinate and (ii) uncovering how those proportions will reflect populations that differ across sociodemographic variables, particularly those related to age, sex, income, and political ideology.

Supporting information

S1 STROBE Checklist. Completed STROBE Checklist.

(PDF)

Click here for additional data file.^{(134.4KB, pdf)}

S1 Text. Survey questions document.

(PDF)

Click here for additional data file.^{(97.9KB, pdf)}

S2 Text. Robustness check using knowledge-driven approach.

(PDF)

Click here for additional data file.^{(39.6KB, pdf)}

S1 Data. Collected data.

(CSV)

Click here for additional data file.^{(1.2MB, csv)}

S1 Code. R script for data analysis.

(R)

Click here for additional data file.^{(24.5KB, R)}

Acknowledgments

We thank the Institute for Modeling Complex Interactions at the University of Idaho for its continued support of interdisciplinary endeavors.

Disclosure: The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Abbreviations

CFR: Code of Federal Regulations
CR: completion rate
GEE: generalized estimating equation
IRB: Institutional Review Board
QIC: quasi-information criterion
SSI: Survey Sampling International (Dynata)
STROBE: Strengthening the Reporting of Observational Studies in Epidemiology

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

Research reported in this publication was supported by the National Institute Of General Medical Sciences of the National Institutes of Health under Award Number P20GM104420 (BB, BJR, CRM). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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PLoS Med. doi: 10.1371/journal.pmed.1003354.r001

Decision Letter 0

Caitlin Moyer

11 Mar 2020

Dear Dr. Baumgaertner,

Thank you very much for submitting your manuscript "Vaccine Propensity: a representative qualitative survey measuring how changing risk of disease affects willingness to vaccinate in the United States." (PMEDICINE-D-19-03203) for consideration at PLOS Medicine.

Your paper was discussed with an academic editor with relevant expertise and sent to independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

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Associate Editor, PLOS Medicine

rturner@plos.org

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Requests from the editors:

You mention that you aim to "introduce" vaccine propensity in the present paper, but this has been mooted previously (PMID: 31461443). Please amend the presentation as appropriate.

Please adapt the title to better match journal style. We suggest: "Risk of disease and willingness to vaccinate in the United States: a population-based survey".

Please expand the "methods and findings" subsection of your abstract, aiming to describe the methods and approaches used in additional detail in the early part of the subsection.

We ask you to quote aggregate demographic details for study participants in the abstract.

Please add a new final sentence to the "methods and findings" subsection of your abstract, which should summarize the study's main limitations.

Please begin the "Conclusions" subsection with "In this study, we found that ..." or similar.

After your abstract, we ask you to add a new and accessible "author summary" section in non-identical prose. You may find it helpful to consult one or two recent research papers in PLOS Medicine to get a sense of the preferred style.

Please streamline the "Introduction" section of your main text to reduce discussion of the Wakefield saga, for example.

Early in the "Methods" section, please state whether the study had a protocol or prespecified analysis plan, and if so attach the relevant document(s) as a supplementary file. Please highlight analyses that were not prespecified.

Noting the "table sample vs census" in the supplementary material, we suggest that you include a table presenting the characteristics of the study participants, preferably including absolute numbers of participants, early in the "Results" section. Please rephrase "White Alone".

Please restructure the early part of the "Discussion" section of your main text: the first paragraph should consist mainly of a summary of the paper's findings.

Also, we ask you add a discrete discussion paragraph on study limitations.

Please substitute "sex" for "gender", where appropriate, throughout your paper.

Please avoid using italics for emphasis.

In the reference list, please abbreviate journal names consistently (e.g., "PLoS Med." for reference 4).

Noting reference 11, please ensure that all references have full access information.

Noting "S3 file", we suggest removing the IP addresses and any other information which could be used to identify study participants. Please let me know if it would be helpful to discuss this further.

Please adapt your attached SRQR checklist so that individual items are referred to by section (e.g., "Methods") and paragraph number rather than by page or line numbers, as the latter generally change in the event of publication.

Comments from the reviewers:

*** Reviewer #1:

The authors report the results from a single online experimental study examining willingness to vaccinate for a novel, hypothetical disease. The design allows in principle for exploration of both variations across demographics and political attitudes and variation over severity of disease (through a between-subjects manipulation of the description in to 7 groups, 3 with differing degrees of mortality and 4 with varying duration of non-life threatening illness) and likelihood of exposure (through a response question that incorporated information about local cases). The authors frame their inquiry as introducing a new concept ("vaccine propensity"). While there is some value to further explorations of correlates to vaccination intention / attitudes, I have both conceptual concerns about what is being measured here and substantial uncertainty about the significance of the findings.

To start, I don't really see how "vaccine propensity" is a novel construct. Many authors (including some cited in this paper) have explored likelihood of vaccination as various individual and situational factors are changed (e.g., in discrete choice experimental structures). The present authors take a novel approach to measurement, but it remains unclear how readers should think about what is being measured here and how it will relate to real world behaviors.

Second, I have a conceptual problem with the design of this study. The authors "we asked each respondent how many people in his/her local community would have to get infected with this disease for him/her to get vaccinated" and then provided response options that listed different numbers of local cases. The structure of the question not only assumes that the proportion of people affected in their local community is a variable that ordinarily affects people's decisions, it implies directly that it should be. Thus, I see strong potential for a demand effect, in which respondents use the local case information because they are told to do so. There is also a huge potential for bias based on the set of response options provided (consistent with the literature on option set biases in survey design.) In other words, I am not confident that changes in the response option set would not change the results. Thus, what is measured is which respondents react to a particular set of local case count options, and the graphics are showing the increase in cumulative willingness to vaccinate as we add each group of responses.

The authors collapse over their 7 scenarios into either mortality or morbidity, thereby ignoring the variance in degree of either outcome. This appears to be because the degree did not affect results (per the second paragraph of the discussion but never discussed or demonstrated in the results). Nonetheless that should be shown especially since it is interesting to consider how long duration morbidity compares to rare mortality.

In terms of results, I'm left with very unclear takeaways. Mortality evokes slightly stronger reactions than morbidity (surprising no one, since a disease that kills is clearly more severe than one that does not). However we have no sense whether we should see this difference as larger or smaller than appropriate. More local cases evokes stronger vaccination interest, which is expected because local prevalence increases chances of exposure and because it serves as an indirect measure of disease scope. (I suspect many people are using this value as a measure of infectiousness, in replacement of the provided 25% rate.) The slope of this effect, however, is dependent on the hypothetical scenario context, the parameters used, and the option set presented, which limits how much readers can generalize from it to the real world. The age gradient suggests that older adults are just generally more likely to want to vaccinate regardless whereas younger adults are more situation dependent. This is a mildly interesting finding, consistent with the literature on differences in processing styles and decision making over the life course.

I agree that the political ideology information probably the most interesting part of these findings, and a paper that focused primarily in this domain might be of more interest to me.

Minor points:

The authors change their Y axis scaling from graphic to graphic, thereby distorting readers' perceptions of the magnitude of the changes shown. This is poor communication practice in general, but it is particularly pernicious in this context where the effect sizes are (a) only involving changes of roughly .1 and (b) variable across analyses.

I'm also concerned by the authors' reframing of the local case information as "risk." While there is a relationship between local cases and likelihood of exposure, "risk" is a broader construct that this. The authors should remain concrete in their descriptions: this is a manipulation of local cases.

There is little to no discussion of limitations, which is a significant omission given the many challenges in trying to generalize these findings beyond this particular survey.

*** Reviewer #2:

This paper investigates population willingness to consider vaccination using a set of vignette questions administered to an online sample of adults in the US, and variations across demographic subgroups. The paper is well written and well-reasoned. In this review, I focus on methodological aspects of the study and leave substantive concerns to vaccination experts. Specific questions for the authors include the following:

* How many survey respondents (and what %) failed the three quality control checks?

* What was the survey response rate, and how was it calculated? Most journals now require that the specific formula be reported, such as those provided by the American Association for Public Opinion Research.

* Was the final sample weighted and were those weights used for all or some of the analyses reported?

* How confident are the authors that their sample will respond to hypothetical threat questions in a manner similar to how they would respond to a real threat? Many survey research experts frown on hypothetical questions because they have been found not to be predictive of subsequent behavior. This is, at a minimum, a limitation that should be acknowledged if not more aggressively addressed.

* Actually, the paper does not include a discussion of study limitations. This needs to be corrected.

* The number of respondents answering that they did not know what the likelihood was that they would vaccinate (n=395) seems sufficiently large that it might be informative to examine the demographic variables associated with that uncertainty as well. This could probably easily be accomplished with the data at hand.

* Sample sizes should be provided for the models shown in Tables 2 & 3.

* The final supporting table, that compares the sample with benchmark data, does not include education, and this seems to be a serious omission.

* Relatedly, another important limitation, not acknowledged, is that the study sample purports to be nationally representative, but provides no information about the sample frame for the online survey that was conducted. Given that many Americans do not access the internet, the burden of proof is on investigators to make a convincing case that the sample is indeed nationally representative, as the paper's title implies.

*** Reviewer #3:

I confine my remarks to statistical aspects of this paper. The general approach is fine but I have several issues to resolve before I can recommend publication.

Line 199 -- How were ordinal values treated? (they are tricky)

Line 205 - How much data was "missing"? Just deleting it is not usually good, unless there is very little missing

Lines 217-219 Don't further clump income this way. This increases both type I and type II error.

Lines 225-227 Can it be right to treat others as having graduate education? Maybe it is, but this needs some explanation.

Line 236-238 Do not use stepwise selection. All the results will be wrong. P values are too low, standard errors are too small, parameter estimates are biased away from 0.... See e.g. Harrell *Regression Modeling Strategies*. Instead, substantive knowledge should be used to build models. If the authors insist on an automatic method, LASSO is much better than stepwise (but not that good).

Table 2 There is no real R^2 for logistic regression. There are various alternative measures, none of which are perfect. Which was used?

Peter Flom

***

Any attachments provided with reviews can be seen via the following link:

[LINK]

PLoS Med. 2020 Oct 15;17(10):e1003354. doi: 10.1371/journal.pmed.1003354.r002

Author response to Decision Letter 0

15 Apr 2020

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Submitted filename: Response to editors and reviewers.docx

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PLoS Med. doi: 10.1371/journal.pmed.1003354.r003

Decision Letter 1

Caitlin Moyer

24 Apr 2020

Dear Dr. Baumgaertner,

Thank you very much for submitting your revised manuscript "Shifting risk of disease and willingness to vaccinate in the United States: a population-based survey." (PMEDICINE-D-19-03203R1) for consideration at PLOS Medicine.

Your paper was re-evaluated by a senior editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and re-reviewed by three reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a further revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

In particular, please be sure to address the reviewer concerns regarding the limitations of the SSI dataset and the lack of response rates. If it is not possible to provide the response rates, we request that you please devote considerable space in the discussion to this limitation and the implications, in addition to addressing the points raised by all the reviewers.

In your rebuttal letter you should indicate your response to the reviewers' and editors' comments, the changes you have made in the manuscript, and include either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please submit a clean version of the paper as the main article file; a version with changes marked should be uploaded as a marked up manuscript.

We expect to receive your revised manuscript by May 08 2020 11:59PM. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

Please use the following link to submit the revised manuscript:

https://www.editorialmanager.com/pmedicine/

Your article can be found in the "Submissions Needing Revision" folder.

We look forward to receiving your revised manuscript.

Sincerely,

Caitlin Moyer, Ph.D.

Associate Editor

PLOS Medicine

plosmedicine.org

-----------------------------------------------------------

Requests from the editors:

1.Title: Thank you for revising your title. However as your survey was cross-sectional and none of your results speak to any time course of changing attitudes, we feel that the use of the word “shifting” is not congruent with the study. Again, we suggest: "Risk of disease and willingness to vaccinate in the United States: a population-based survey".

2.Abstract: Methods and Findings: Please present the study methods before the study findings. In the limitations sentence, please clarify what is meant: “...it does not consider how other considerations interact with local case counts in people's vaccine decision making…” as it is not clear what is meant by ‘other considerations’

3.Author Summary: Thank you for including an author summary. Please structure the author summary using bullet points for separate ideas within the three sections, rather than writing in paragraph format. Please see our author guidelines for more information: https://journals.plos.org/plosmedicine/s/revising-your-manuscript#loc-author-summary

4.Methods: Line 118-119: It is not satisfactory to state that: “With respect representativeness, we outsourced the distribution of our survey to Survey Sampling International which utilizes proprietary methods.”- please provide details or a reference to the methods relating to the survey administration including response rates, and also see the comments of reviewer 2. If response rates are not available due to the limitations of SSI, please address the concerns of reviewer 2 and devote substantial space to describing how this impacts the interpretation of the study’s findings as a paragraph of the limitations section of the Discussion.

5.Discussion: Line 333-336 (and similarly at line 369): Please remove the suggestion of a causal relationship from this sentence, and throughout, we suggest the following change: “Thus, the goals of this paper were: to empirically advance the concept of vaccine propensity to enrich our understanding of complacency in vaccine hesitancy, and to study the sociodemographic factors that are associated with vaccine propensity."

6.References: Please use the "Vancouver" style for reference formatting, and see our website for other reference guidelines https://journals.plos.org/plosmedicine/s/submission-guidelines#loc-references

(for example, journal title is inconsistent in #4 and #18).

7. Table 1: The “Income” measure requires units.

8. Table 2: Income, hometown size, commute city size need units associated with the values presented.

9. Table 3: Please provide the abbreviation for “df” in the legend.

10. Table 4: Please provide the abbreviation for “SE” in the legend.

11. Figure 2: Please see reviewer 1’s comments regarding the axes of graphs, and please begin the y-axis at zero (in this case, the axis should display from 0 - 1.0). Please explain why the points are offset from the “1” “10” “100” lines, etc. Please describe the purpose of the dotted line connecting the points in the legend.

12. Figure 3: Please see reviewer 1’s comments regarding the graph axes (in this case, the axis should display from 0 - 1.0). Please explain why the points are offset from the “1” “10” “100” lines, etc. In the legend, please define the whiskers extending from the points.

13. Figure 4: Please see reviewer 1’s comments regarding the graph axes (in this case, the axis should display from 0 - 1.0). Please explain the nature of the solid lines in the legend- are these trendlines? Please explain the shaded regions in the figure legends. As these are difficult to see, we suggest using different colors.

14. Figure 5: Please see reviewer 1’s comments regarding the graph axes (in this case, the axis should display from 0 - 1.0). Please explain the nature of the solid lines in the legend- are these trendlines? Please explain the shaded regions in the figure legends. As these are difficult to see, we suggest using different colors.

15. Figure 6: Please see reviewer 1’s comments regarding the graph axes (in this case, the axis should display from 0 - 1.0). Please explain the nature of the solid lines in the legend- are these trendlines? Please explain the shaded regions in the figure legends. As these are difficult to see, we suggest using different colors.

16. Figure 7: Please see reviewer 1’s comments regarding the graph axes (in this case, the axis should display from 0 - 1.0). In the legend, please define the whiskers extending from the points.

17. Supporting information file S3: It appears that you have included data that may breach participant confidentiality. Please remove potentially identifying information from the file (e.g. IP address, county and zip codes, latitudes and longitudes, etc.)

Comments from the reviewers:

Reviewer #1: This manuscript is a revision of a paper I reviewed previously. The authors have addressed a number of the concerns raised previously about the framing of their work and its implications. I still find the paper less focused than I would prefer, but this is an editorial choice.

One issue that I believe was insufficiently addressed, however, is axis scaling in their figures. The authors state: "We have now rescaled the axes on figure 2-4 to be the same; we have done likewise for figures 5-7. We have updated the figure descriptions to make clear the two scales we have selected.." Figures 2-4 are scaled from roughly .3 to 1.0 despite the fact that it is reporting a propensity. There is no logical reason to use this scale (vs. the full 0-1.0 range that a propensity can vary over) except to magnify slope, which is amplifying readers' perceptions of the effect. Figures 5-7 are scaled from roughly 0.6 to 0.95. Same issue, same effect, same problem, except even more so. Worse, Figure 4 is of the same type as Figures 5-7, but it is on a different scale. Thus, the slope represented in Figure 5 for morbidity (a range of roughly 0.15 over the age distribution) looks significantly steeper than the slope in Figure 4 for 1 local cases (a range of roughly 0.12-0.15) despite the fact they show numerically very similar levels of variation. This is clearly distorting perceptions of results. Let me be clearer than I was previously. In reporting propensity, I find it hard to justify any Y axis scaling other than 0-1.0.

I also have a few minor but important issues that need resolution:

1) Kudos to the other reviewer for flagging an issue I should have caught the last time around: the presentation of this sample from SSI as "nationally representative". I acknowledge that the authors have now put a note in their limitations on this issue. However, speaking as a frequent user of online samples, I _never_ describe an SSI sample or any other online sample (except maybe GfK) as "representative" of anything. It is demographically diverse in ways that mirror US Census demographics, yes, and the authors took specific steps to achieve this (with clear success). But it is not "representative" not only because of limitations on access to the internet but because it is an opt-in panel: SSI recruits people who want to take surveys, which is not everyone, and this is a characteristic that plausibly might relate to survey responses on this type of scenario. Thus, this is a minor but to me important point: I ask the authors to remove all descriptions of their sample as "nationally representative" and frame it instead as "demographically diverse" or similar.

2) It's not just that survey providers like SSI don't report response rates, it's that their methodology doesn't align to the concept. I believe SSI is no longer sending links to participants (which participants would either reply to or not) but instead basically encourages panel members to sign on to their account and then surveys are dynamically assigned to them moment by moment. This approach (or any like it) does not have a true denominator for use in determining a response rate. I would reword this to clarify more details of the sampling process in place at that time.

3) What does matter for online survey quality, however, is completion. The authors report that over 40% of their respondents failed quality control checks and were omitted from analyses. That is a huge number, and hence readers deserve a bit more detail on the character of those checks so as to judge the implications. In addition, it represents one more way in which the analytical sample is not representative (per comment above), since a representative sample would include at least some people who fail those attention checks.

Reviewer #2: Thank you for continuing to work on this paper, which has been considerably improved. There are several methodological questions regarding data collection, though, that remain inadequately addressed. These problems largely stem from the sample employed for the survey, how it was selected, what proportion of sampled respondents participated, and how the sample was weighted for analysis. Below, these concerns are summarized.

* Inability to report response rates suggests a poor methodology that more than anything seriously challenges any claims that the sample is representative of anything. I would encourage the authors to push back on the survey group they worked with to obtain and report a convincing rationale for this complete lack of transparency. My guess is that the only potential reason for being unable to provide a response rate is the use of a very poor convenience sampling methodology. My personal opinion is also that failure to provide response rate information is grounds for rejection. Appreciate that the sample's demographics have been compared with Census data. However, one key measure is not included in those comparisons: the percent of the population that does and does not have internet access. There is no question that the sample will be unrepresentative of this characteristic.

* Providing a discussion of limitations is appreciated and improves the paper. Lack of a response rate, which leaves the authors unable to even begin to address nonresponse bias concerns, is missing from this discussion, however.

* Failure to properly weight the sample also detracts from claims that findings are representative. Even the most carefully designed nationwide probability surveys include statistical weights. That a nonprobability sample does not need to use weights because it is already "representative" is laughable.

* Another relevant topic that remains unaddressed is the question of the frame from which the sample for the survey was selected. Exactly how do individuals get recruited into the SSI sample frame? It is almost certainly not a random process and the reader is left to guess or search the internet for relevant information. I did look at the SSI (now Dynata) web site and was unable to locate anything helpful.

Reviewer #3: I am checking "Proceed without recommendation" because I am a little confused.

The authors have addressed most of my concerns. Remaining issues:

I wrote: Lines 217-219 Don't further clump income this way. This increases both type I and type II error.

The authors responded: If we understand what the reviewer is saying here, we agree with them but are not sure what action to take. Aggregating responses changes the variance structure of the data. However, categorical response for things like income are already a substitute for a continuous variable; so getting at some sort of "true" relationship with income level isn't possible. We are therefore already limited with what we can infer from the data. By aggregating and changing the variance structure of the income classification (say into 10k increments), we are changing the hypothesis being tested. In other words, the variance structure is no longer appropriate for testing

hypotheses about the 10-classes of income, but it IS appropriate for testing hypotheses about the new 4-classes of income. Also, it is important to recognize that income is a continuous

variable and not one where people are forced to make subjective decisions about which category something should be in; i.e., if we had originally presented individuals with the 4

choices of income level, their responses should (in theory) exactly match the aggregated version of the more finely divided data.

My further response. Certainly changing the variable changes the variance structure. But .... I'm confused as to why that means the authors have to use income as a 4 category variable rather than a continuous one or a 10 category one. How was income recorded in the original data? Was it continuous (that would be unusual) or was it 10 category (in which case, I still don't see why they changed it). I feel like I am missing something here. Clearly the authors are proficient at statistics, but I don't quite understand their reasoning.

-----

Regarding model selection:

Sorry, but while it is true that model selection is valuable and that there are many ways to do it, the debate about stepwise is pretty much over: It doesn't work. The output is wrong.

I favor using substantive knowledge. The authors know more about the data and the models than any automatic algorithm does. But if they insist on using an algorithm, they should use one that attempts to adjust for the problems of stepwise. I like LASSO but I would be fine with other penaized methods (there IS debate about which of these methods is best).

They are correct that stepwise is used a lot. But ... that's not really a reason to keep using it.

Peter Flom

Any attachments provided with reviews can be seen via the following link:

[LINK]

PLoS Med. 2020 Oct 15;17(10):e1003354. doi: 10.1371/journal.pmed.1003354.r004

Author response to Decision Letter 1

16 Jun 2020

Attachment

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PLoS Med. doi: 10.1371/journal.pmed.1003354.r005

Decision Letter 2

Caitlin Moyer

6 Aug 2020

Dear Dr. Baumgaertner,

Thank you very much for re-submitting your manuscript "Risk of disease and willingness to vaccinate in the United States: a population-based survey." (PMEDICINE-D-19-03203R2) for review by PLOS Medicine.

I apologize for the delay in getting back to you with our decision. I have discussed the paper with my colleagues and the academic editor and it was also seen again by three reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

Our publications team (plosmedicine@plos.org) will be in touch shortly about the production requirements for your paper, and the link and deadline for resubmission. DO NOT RESUBMIT BEFORE YOU'VE RECEIVED THE PRODUCTION REQUIREMENTS.

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract.

We expect to receive your revised manuscript within 1 week. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

If you have any questions in the meantime, please contact me or the journal staff on plosmedicine@plos.org.

We look forward to receiving the revised manuscript by Aug 13 2020 11:59PM.

Sincerely,

Caitlin Moyer, Ph.D.

Associate Editor

PLOS Medicine

plosmedicine.org

------------------------------------------------------------

Requests from Editors:

1.Please completely respond to the remaining points of reviewer 1; specifically:

-Explicitly clarify whether or not individual study-specific email invitations were sent to potential respondents

- Describe the quality control checks used by SSI/Dynata, such as attention check questions, timing algorithms, so that the reader may judge the potential for bias from screen-out processes.

-Separate those who voluntarily discontinue the survey vs. those who are screened out of the survey.

Please note that the editors feel that it is appropriate for the knowledge-based model to remain in the supporting information given that this was a secondary analysis conducted in response to feedback during peer review.

2.Throughout manuscript: Your study is observational and therefore causality cannot be inferred. Please remove language that implies causality, such as “effect of” and similar. Refer to associations instead.

3.Abstract: Methods and Findings: Please quantify the main outcomes presented in the abstract, and include p values and 95% CIs associated with the result for: “We find an overall change in proportion willing to vaccinate of at least 30% as risk of infection increases.In addition, we find that the risk of mortality invokes a larger proportion willing to vaccinate than mere morbidity, that older populations are more willing than younger, that the highest income bracket (> $90k) is more willing than all others, that men are more willing than women, and that the proportion willing to vaccinate can depend on both ideology and the level of risk.”

4. Author summary: “Why was this study done?”: In the first bullet point, or where most appropriate, please clarify points relevant to your study such as the setting (United States) and the time frames (i.e. vaccine hesitancy on the rise, vaccination rates have gone down, preventable diseases have gone up, etc, please provide some sense of the relevant time period)

5.Author summary: “What did the researchers do and find?”: Please revise the third bullet point to:

• Likewise, older populations are more willing than younger, people with high incomes are more willing than all income levels, men are more willing than women, and our findings suggest a relationship between willingness to vaccinate and political ideology.

6.Introduction: Line 72-74: Please revise to: “The first is to enrich our understanding of the role of complacency in vaccine hesitancy by determining how changes in vaccine hesitancy are associated with changes in risk (complacency).”

7.Introduction: Line 76-77: Please revise to: “The second goal of this paper is to determine how vaccine propensity is associated with sociodemographic factors.”

8.Introduction: Line 111-112: Please remove “Specifically, we find that factors such as age, sex, income, and ideological leanings influence reported vaccine responsiveness to changes in risk.” as this statement implies causality. Please remove any study results and findings from the Introduction and conclude the Introduction with a clear description of the study question or hypothesis (please re-organize the Introduction if necessary).

9. Introduction: Lines 110-112: Please delete the sentences mentioning the findings from the Introduction, and instead conclude the Introduction with a clear description of the study question or objective. “In this paper, we provide relevant insights on factors that shape vaccine propensity. Specifically, we find that factors such as age, sex, income, and ideological leanings influence reported vaccine responsiveness to changes in risk.”

10.Methods: Please update where appropriate to indicate that SSI is now called Dynata.

11.Methods: Line 114: Please spell out “Standards for Reporting Qualitative Research (SRQR) checklist” at first use. However, we do not feel that this is the most appropriate checklist for your study. If you agree, please instead add the following statement, or similar, to the Methods: "This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Checklist)."

12. Methods: Line 115-117: You state that there was no prespecified analysis plan, but please explicitly state when the analyses were decided upon; e.g., which analyses were determined before seeing the data and if any were data-driven or conducted in response to a reviewer request.

13.Methods: Line 122: Please replace "subject" with participant, respondent, individual, or person.

14.Methods 132-133: As requested by the reviewer, please describe the quality control selection process.

15.Methods: 136-38: In a subsection of the Methods, please note your exemption from IRB approval and add a description of how informed consent was obtained from study participants, whether written or oral.

16.Methods: Line 150 and Line 183: Please replace "subject" with participant, respondent, individual, or person.

17.Methods: 186-188: Please revise to: “Our statistical analyses assess the socio-demographic and risk factors that are associated with when individuals will seek vaccination.”

18.Methods: Line 234: Please define the abbreviation QIC at first use

19.Results: Throughout, please present findings in the text with 95% CIs and p values.

20.Results: Lines 248-252: Please revise to: “We did not find significant relationships between vaccine propensity and the following variables: religion, religious importance, frequency of attending religious service, hometown size, the size of the largest city commuted to, race, whether individuals had children (or children at home), an individual's health, and the individual's educational attainment.”

21.Results: Line 260-261: Please revise to “In both scenarios, it is approximately 40%, a shift from 50% to 90%.”

22.Results: Line 279-282: Please provide the proportions, 95% CI and p values to accompany this result: “Results suggest that a smaller proportion of respondents on the conservative end of the ideology spectrum are willing to seek vaccination than respondents who report being liberal|compare specifically \\very liberal" and \\very conservative." Exact values can be found in Table 4.”

23.Results: Line 292: Please present this result with 95% CIs and p values “The other sociodemographic variable that risk interacted with is age.”

24.Results: Line 315: Please present this result with 95% CIs and p values: “Our age variable also interacted with sex.”

25.Results: Line 320-321: Please present this result with 95% CIs and p values: “Finally, we had one main effect that did not significantly interact with other variables: income. Family incomes above $90 000 are willing to vaccinate in higher proportion than those with incomes below that”

26.Discussion: Please begin the Discussion with 1-2 sentences briefly summarizing what was done to address the study objectives.

27.Discussion: Line 388-389: Please revise to: “However, contrary to the general literature on sex and risk, the association between vaccine propensity and sex was in the opposite direction in our study, with men being more likely to vaccinate than women.”

28.Discussion: Line 432-433: Please revise to: “Education was included in our survey instrument, but we found no significant relationship between vaccine propensity and education in our analysis.”

29.Discussion: Line 436: - Please make sure that there are separate references explicitly supporting the statements that “liberals are less likely to vaccinate as part of their emphasis on natural lifestyles” and that “strong conservatives make up a larger fraction of the vaccine hesitant population” unless reference 34 supports all aspects of the claims of this sentence:

“While it may be the case that some liberals are less likely to vaccinate as part of their emphasis on “natural” lifestyles, strong conservatives make up a larger fraction of the vaccine hesitant population [34].”

30.Discussion: Line 442-446: Please revise to: “While there are more very liberal than very conservative respondents who will vaccinate when there are zero local cases, increases in infection risk were associated with greater vaccine propensity for both groups. By contrast, the slope was greater among respondents who are moderate or less extreme ideologically, suggesting they could be more motivated by increasing infection risk.” or similar.

31.Discussion: Line 457-458: Please revise to: “Consequently, we focused on how local case counts were associated with vaccine decisions, holding fixed other considerations.”

32.Discussion: Line 474: Please replace "subject" with participant, respondent, individual, or person.

33.Supporting information S1 file: SPQR Checklist: This is not the most appropriate checklist for your study, as your study is not qualitative (please report your study according to the relevant guideline, which can be found here: http://www.equator-network.org/); we suggest you report your study according to the STROBE guideline, and include the completed STROBE checklist as Supporting Information. When completing the checklist, please use section and paragraph numbers, rather than page numbers. Please add the following statement, or similar, to the Methods: "This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 Checklist)."

34.Table 1: Please indicate that the right-most column represents % (number)

35.Table 2: Under the importance of religion measure, there may be a typo in the category of “No too important”

36.Table 3: Please fully define abbreviations for GEE and QIC in the legend.

37.Figure 1: Figure 1 is missing. We will need to evaluate the final version of the figure.

38.Figure 3: We suggest changing the colors of some of the markers to make them easier to distinguish from each other.

Comments from Reviewers:

Reviewer #1: The authors have responded appropriately to my previous concerns about the figures.

The main issues remaining from my perspective concern (a) the back and forth about the sample process and characteristics and (b) the modeling issues raised by Reviewer 3.

Re modeling. Stepwise is indeed problematic. Saying that results are very similar regardless of method used is good. But if so, why prioritize stepwise or frankly any algorithmic approach in the paper? I'm wondering whether the best choice is to essentially flip positions: make the main presentation the knowledge-driven approach, move the algorithmic model to the supplementary file. But I'll defer to others on this.

Re: sample. First, the authors should note that SSI is now Dynata. Second, I am somewhat confused and concerned, since the additional methods text both disagrees with what i thought SSI/Dynata is doing at present and is incomplete in some important ways. The authors need to confirm:

- Individual study-specific email invitations were sent to potential respondents? While this was their method many years ago, I had been under the impression that Dynata was moving to inviting people to log on to the survey portal to take surveys in general and that the specific survey assignment was not done until login. I'm skeptical of straight email recruitment, because response rates to email invitations for surveys have never been good (often <10%), yet these data suggest a very high response rate. But, regardless, confirming this is key.

- What are the quality control checks? These remain undefined, and they are screening out a huge fraction of people. SSI/Dynata needs to disclose to the authors, and the authors need to clearly summarize, what attention check questions, timing algorithms, etc. are being used to screen people out. Whatever sampling biases may exist between SSI/Dynata's population and the general population pale in comparison to the degree of potential bias risk from a screenout process that removes almost half of respondents. We need to know what is done in detail to assess the risk of bias. I think the results are of interest almost regardless of the answer, but we need the answer.

- The authors need to separate voluntary failures to complete from failing control checks. At the moment, we cannot tell how many people are choosing to stop taking the survey themselves vs. are being screened out.

Reviewer #2: Thank you to the authors for continuing to work on this manuscript. I am satisfied that they have addressed, as much as is possible, my earlier concerns, and have no additional recommendations to make.

Reviewer #3: The authors have addressed my concerns and I now recommend publication

Peter Flom

Any attachments provided with reviews can be seen via the following link:

[LINK]

PLoS Med. doi: 10.1371/journal.pmed.1003354.r006

Decision Letter 3

Caitlin Moyer

11 Sep 2020

Dear Dr. Baumgaertner,

On behalf of my colleagues and the academic editor, Dr. Sanjay Basu, I am delighted to inform you that your manuscript entitled "Risk of disease and willingness to vaccinate in the United States: a population-based survey." (PMEDICINE-D-19-03203R3) has been accepted for publication in PLOS Medicine.

PRODUCTION PROCESS

Before publication you will see the copyedited word document (in around 1-2 weeks from now) and a PDF galley proof shortly after that. The copyeditor will be in touch shortly before sending you the copyedited Word document. We will make some revisions at the copyediting stage to conform to our general style, and for clarification. When you receive this version you should check and revise it very carefully, including figures, tables, references, and supporting information, because corrections at the next stage (proofs) will be strictly limited to (1) errors in author names or affiliations, (2) errors of scientific fact that would cause misunderstandings to readers, and (3) printer's (introduced) errors.

If you are likely to be away when either this document or the proof is sent, please ensure we have contact information of a second person, as we will need you to respond quickly at each point.

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PROFILE INFORMATION

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Thank you again for submitting the manuscript to PLOS Medicine. We look forward to publishing it.

Best wishes,

Caitlin Moyer, Ph.D.

Associate Editor

PLOS Medicine

plosmedicine.org

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 STROBE Checklist. Completed STROBE Checklist.

(PDF)

Click here for additional data file.^{(134.4KB, pdf)}

S1 Text. Survey questions document.

(PDF)

Click here for additional data file.^{(97.9KB, pdf)}

S2 Text. Robustness check using knowledge-driven approach.

(PDF)

Click here for additional data file.^{(39.6KB, pdf)}

S1 Data. Collected data.

(CSV)

Click here for additional data file.^{(1.2MB, csv)}

S1 Code. R script for data analysis.

(R)

Click here for additional data file.^{(24.5KB, R)}

Attachment

Submitted filename: Response to editors and reviewers.docx

Click here for additional data file.^{(28.6KB, docx)}

Attachment

Submitted filename: Response to editors and reviewers.docx

Click here for additional data file.^{(26.2KB, docx)}

Data Availability Statement

All relevant data are within the manuscript and its Supporting Information files.

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PERMALINK

Risk of disease and willingness to vaccinate in the United States: A population-based survey

Bert Baumgaertner

Benjamin J Ridenhour

Florian Justwan

Juliet E Carlisle

Craig R Miller

Roles

Abstract

Background

Methods and findings

Conclusions

Author summary

Why was this study done?

What did the researchers do and find?

What do these findings mean?

Introduction

Fig 1. Illustration of vaccine propensity.

Methods

Data collection and sample characteristics

Table 1. Sample characteristics (compared to 2010 census).

Ethics statement

Dependent variable measurement

Independent variables, data processing, and analysis

Table 2. Frequency of responses for survey questions that were used for analysis (information for age frequency can be found using S1 Code).

Results

Table 3. The resulting GEE model for vaccination propensity by respondent characteristics (N = 2,411).

Table 4. Pairwise comparisons of very liberal, moderate, and very conservative across our 4 levels of risk in terms of local cases (N = 2,411).

Fig 2. The proportion of respondents willing to seek vaccination given number of local cases of disease for the morbidity and mortality scenarios.

Fig 3. Comparing ideologies in proportion willing to seek vaccination across 4 levels of risk.

Fig 4. For each level of risk, we plot the proportion of respondents willing to seek vaccination across ages.

Fig 5. Comparing the morbidity and mortality scenarios across ages.

Fig 6. Proportion of men versus women willing to seek vaccination across age groups in the morbidity scenario.

Fig 7. Comparing different levels of income in their proportion to seek vaccination across ages.

Discussion

Supporting information

Acknowledgments

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Caitlin Moyer

Roles

Author response to Decision Letter 0

Decision Letter 1

Caitlin Moyer

Roles

Author response to Decision Letter 1

Decision Letter 2

Caitlin Moyer

Roles

Decision Letter 3

Caitlin Moyer

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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