Evaluating the importance of policy amenable factors in explaining influenza vaccination

17 Pages·2017·0.42 MB·English

Open Access Research
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Evaluating the importance of policy M
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amenable factors in explaining p
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influenza vaccination: a cross-sectional : firs
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multinational study lis
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Ana Wheelock,1 Marisa Miraldo,2 Angus Thomson,3 Charles Vincent,4 0.1
Nick Sevdalis5 13
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To cite: Wheelock A, Miraldo M, AbstrAct Strengths and limitations of this study -2
Thomson A, et al. Evaluating the Objectives Despite continuous efforts to improve 01
importance of policy amenable 6
factors in explaining influenza irnisflku genrozuap vsa rcecminaaitniosn s ucobvoeprtaimgea,l .u Wptea akiem aemd otnog id heingthif-y ► We generated regression models comprised of a -01
vmauclctiinnaattiioonna: la s cturodsys. -BsMecJt iOonpaeln policy amenable factors associated with vaccination blinrokaedd tsoe tv aocfc ivnaartiiaobnl ebse, hmavoiostu ro.f which have been 4668
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communication strategies and interventions to improve 2
► Prepublication history and vaccination rates. countries (the USA, the UK and France). Ju
additional material for this ► The employed survey measures concerned the ly
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journal online (http://d x. doi. across the three countries. ► Our research may have suffered from respondent- . D
org/1 0. 1136/b mjopen- 2016- Outcome measures Self-reported influenza vaccination. related biases. For example, people for whom ow
014668). Methods Between March and April 2014, a stratified vaccination issues are particularly salient may have n
random sampling strategy was employed with the lo
been more prone to participate. a
Received 11 October 2016 aim of obtaining nationally representative samples in de
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about vaccination practices, perceptions and feelings. In most developed economies, an annual h
Multivariable logistic regression was used to identify influenza vaccine is recommended and ttp
factors associated with past influenza vaccination. offered free of charge to those at higher ://b
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Results The models were able to explain 64%–80%
risk of death from influenza complications, jo
of the variance in vaccination behaviour. Overall, p
including pregnant women, individuals with e
sociopsychological variables, which are inherently n
eligible chronic illnesses and people aged 65 .b
amenable to policy, were better at explaining past m
vaccination behaviour than demographic, socioeconomic years and older. The vaccine is also available j.c
at a cost—usually in pharmacies or private o
and health variables. Explanatory variables included m
social influence (physician), influenza and vaccine risk healthcare facilities—to those who do not o/
1National Institute for Health perceptions and traumatic childhood experiences. belong to a risk group, but wish to protect n J
Research Imperial Patient Conclusions Our results indicate that evidence-based themselves. In the USA, for example, where an
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Safety Translational Research sociopsychological items should be considered for the vaccine is recommended to all adults, a
Centre (PSTRC), Imperial College inclusion into national immunisation surveys to gauge approximately one-third of healthy adults ry 1
2LIomnpdeornia, lL Conoldleogne, UBKusiness tphreo apcutbivleicly’s avniedw osp,p iodretnutnifeyl ye madedrrgeisnsg pcootnecnetrianls b aanrdri etrhsu s uevnedreyr y 6e5a ry.4ears old vaccinate against influenza 8, 20
School, London, UK 2
3Sanofi Pasteur, Lyon, France and harness vaccination drivers. Despite continuous efforts to improve influ- 3 b
4Department of Experimental enza vaccination coverage, uptake among y g
Psychology, University of Oxford, Background high-risk groups remains low. In 2013/2014, ue
Oxford, UK s
5Centre for Implementation Upper respiratory tract infections are a for example, 65% of older adults (≥65s) and t. P
Science, Health Service leading cause of mortality and morbidity 46% of younger adults with eligible health ro
and Population Research in high-income countries, mostly among conditions were vaccinated against influenza te
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Department, King’s College adults.1 Influenza is a major contributor to in the USA.4 In the same season, vaccination te
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London, London, UK this burden of disease; estimates show that up rates in the UK, one of the highest in Europe, b
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to 49 000 people die every year in the USA2 were 73% in ≥65s and 53% in eligible under c
Correspondence to o
Dr Ana Wheelock; a . wheelock@ and 40 000 in the European Union3 from 65s, both below the minimum 75% coverage py
imperial.a c. uk influenza-related illness. recommended by the WHO.5 6 Worryingly, rig
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Wheelock A, et al. BMJ Open 2017;7:e014668. doi:10.1136/bmjopen-2016-014668 1
Open Access
a 151% rise in excess winter deaths in England and drawback is that most of the evidence in this area is B
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Wales in 2014/2015, partly attributed to the circulation produced in the USA, thus important contextual issues J
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of a mutated A(H3N2) influenza strain which made the remain unexplored. Furthermore, vaccination coverage p
vaccine significantly less effective,7 alongside unseason- and factors underpinning uptake among healthy adults en
able warm weather in 2015/2016, resulted in the lowest are often unknown. : firs
vaccination uptake in more than a decade.8 Vaccina- We sought to address these limitations by generating t p
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tion decisions are shaped by a myriad factors, including regression models comprised of a broad set of variables, b
demographic, socioeconomic and sociopsychological most of which have been linked to vaccination behaviour, lish
factors.9–12 The latter are of particular interest, given that by employing measures that gauge individuals’ own ed
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they are inherently amenable to policy and interventions perceived risk (eg, ‘The flu could make me severely ill’) s
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to change behaviour. Yet, few countries routinely collect and condition their perceptions upon having or not 0
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data on people’s beliefs and perceptions towards vaccina- having received the vaccine (eg, ‘With no flu vaccine, I 1
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tion, and those that do often use one open question (eg, would feel very vulnerable to the flu’),11 and aiming to 6
‘Why didn't you get a flu shot last winter?’).13 Although use representative samples of the population of interest /bm
cheaper and easier to administer, this form of enquiry in three different developed countries: the USA, the UK jop
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does not take into account people’s tendency to fall back and France. In order to assist in the monitoring of vacci- n
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on readily available information (eg, the first thought nation sentiment and the prioritisation and design of 0
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that comes to mind) or report postdecisional rationalisa- communication strategies and interventions to increase 6
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tions of their behaviours (eg, ‘I did not vaccinate, hence influenza vaccination across different contexts, this study 1
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it must not be necessary’) rather than actual drivers.14 15 aimed to answer three research questions: (1) What are 6
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Moreover, these data do not allow comparative analyses the variables that consistently explain recent influenza o
between vaccinated and unvaccinated people. vaccination uptake? (2) What is the importance of policy n 1
Multilateral efforts to measure and improve confidence amenable factors in relation to demographic, socio- 2 J
in vaccines are gathering pace,16 17 yet they are built economic and health characteristics in explaining past uly
upon a body of evidence which, although extensive and vaccination behaviour? (3) Are the factors associated with 2
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insightful, has a number of gaps. One key limitation is influenza vaccination comparable across countries? 17
that many studies evaluating the link between sociopsy- . D
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chological factors and influenza vaccination do not use w
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multivariable analysis, thus the importance of a given vari- MeThods lo
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able in relation to others often remains unknown. Studies study sample d
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number of variables, which can result in omitted-vari- UK and France, about vaccination between March and h
able bias, where the model compensates for the missing April 2014. Interlocking quotas based on gender, age and ttp
variables by over or underestimating the effect of the income were set. In addition, to ensure national repre- ://b
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included variables.9 18 19 For example, omitted-variable sentativeness, regional, settlement type (rural/urban) jo
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bias could explain why the model developed by Weinstein and ethnicity non-interlocking quotas were put in place. e
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et al—comprised of seven variables—showed that antici- Since some of the included variables had not been previ- .b
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pated regret of not vaccinating was more important than ously tested and others were not consistently correlated j.c
other established influenza perceptions or why they did with vaccination in previous studies, we assumed that the o
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not find an association between vaccine effectiveness and correlation coefficient between dependent and indepen- o/
vaccination uptake in this US sample.18 Moreover, these dent variables was 0.1 (a small effect size), the minimum n J
studies frequently include proxies of vaccination uptake, sample was calculated to be 782 subjects per country an
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such as historical vaccination (ie, vaccination in previous (α=0.05; 1-β=80%) with PASS V.11. a
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seasons not including the most recent) in the case of The American Institutes for Research (USA) and the 1
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retrospective studies or intention to vaccinate in the case Imperial College Research Ethics Committee (UK) , 2
of prospective studies, as independent variables,9 19 20 granted research ethics approval. The French Commis- 0
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thereby artificially boosting the explanatory ability of the sion nationale de l'informatique et des libertés and 3 b
model—because most people who vaccinate against influ- Comités de protection des personnes granted waivers to y g
enza do so periodically—without necessarily explaining approval. Participants were informed about the nature of ue
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vaccination behaviour (eg, people vaccinate because they the study and provided consent. t. P
feel vulnerable and/or receive a reminder from their ro
general practitioner (GP) every winter). As Brewer and Procedure te
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colleagues note, other important methodological short- A market research company (Double Helix) was respon- te
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comings are the prevalent use of weak survey measures sible for piloting, programming the online survey and b
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(eg, generic risk perceptions rather than own perceived conducting the telephone interviews. Ten pilot inter- c
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risk) and small convenience samples, which may affect views (seven face-to-face and three telephone interviews) p
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the validity and generalisability of findings.11 A related were conducted with purposively selected participants in rig
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2 Wheelock A, et al. BMJ Open 2017;7:e014668. doi:10.1136/bmjopen-2016-014668
Open Access
the UK to test the survey’s face and content validity, and the respondent’s perception of their own personal risk B
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ease of completion. Additionally, 10 pilot interviews were rather than their views on risk of illness in the wider J
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conducted over the phone in the USA and 10 in France population. Thus, we asked how likely it is that they p
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with the aid of a screen sharing platform. Interviews were might become ill rather than how likely people generally n
conducted by a trained researcher while the rest of the are to get influenza. We also wished to specifically focus : firs
team observed via live broadcast. The pilot showed the their attention on the risk of influenza in the presence t p
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survey was easy to complete and understand, and lasted or absence of vaccination, as people may feel more or b
approximately 20 min. The refinements to the study mate- less protected depending on their vaccination status. lish
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rials were related to wording and format. Self-completion The questions were therefore in the form of ‘Without d
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online surveys were then sent to a non-probability online a vaccine, it is likely I will get the flu’ rather than simply s
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panel and random-digit dialling was employed to recruit assessing their views on the likelihood of getting influ- 0
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a proportion of the 65+ age category and those belonging enza. Finally, when thematic hierarchy (eg, from general 1
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to D/E socioeconomic groups, due to their limited access to specific) was not important, items were rotated to 6
to or lack of familiarity with internet-based applications21 minimise response bias. /bm
(see online supplementary box S1 for more details about jop
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non-probability online panels). data analysis n
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As a quality control measure, participants classified as We used the following formula to calculate response rates: 0
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‘speeders’ (completed the survey in half of the average number of surveys completed divided by sent emails or 6
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length—16 min) and ‘flat-liners’ (gave homogenous interviews attempted minus ineligible individuals, multi- 1
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responses and completed the survey in less than half of plied by 100. Descriptive statistics, Pearson’s χ2 test and 6
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the optimum survey length—20 min) were removed and t-tests were computed to explore the relationships between o
replaced.22 the assessed variables and self-reported past vaccination n 1
behaviour. Point-biserial correlations were calculated and 2 J
Instrument χ2 statistics were converted into correlation coefficients uly
The measures reported here are a subset of a larger vacci- to explore whether the relationship between the depen- 2
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nation survey (available from the authors upon request). dent and independent variables matched or exceeded a 17
Our analyses included 32–34 items (see online supple- coefficient of 0.1—the assumption employed to calculate . D
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mentary table S1). We selected sociopsychological items the sample size. Cronbach’s alpha was used to explore the w
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that had been linked to influenza vaccination based on reliability of the proposed measures across countries. The lo
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existing evidence. These comprised adapted constructs outcome measure was receiving an influenza vaccine in d
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fvraotimon thTeh eHoerayl2t4h— Bneoltiaebf lyM, oindfellu2e3 naznad aPnrdo tevcatcicoinn eM roistik- thGe ilvaestn 6 t hmaot ntthhes d(2e0p1e3n/d2e0n1t4 v ianrfilaubelen zwaa sse absionnar)y., logistic d fro
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perceptions, vaccine effectiveness and self-efficacy,9–12 25 regression analysis was conducted to identify the vari- h
perceived knowledge of the vaccine10 and items assessing ables associated with past influenza vaccination. Four ttp
trust in key vaccination stakeholders.26 Additional policy continuous variables with ‘I don’t know/not applicable’ ://b
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amenable factors which had infrequently been used in responses were dichotomised as follows: values expressing jo
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the context of vaccination, but were considered potential agreement with a given statement (6–10) were coded as e
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explanatory variables, were also tested. These were worry 1=‘yes’ and the rest (0–5 and ‘I don’t know/not appli- .b
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of infecting other people (if unvaccinated)27—a measure cable’) were coded as 0=‘other than yes’ (see online j.c
aimed at evaluating the extent to which people vaccinate supplementary tables S2–S4). o
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to protect others, perceived control over influenza,28 29 Although a software-based stepwise approach is widely o/
regret of contracting influenza,30 childhood traumatic used in logistic regression, in recent years the purposeful n J
health experiences31—to evaluate their influence on selection of variables has been favoured over determin- an
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adult vaccination behaviour, and health decision-making istic model-building methods. This is because the latter a
preferences32 33—to further explore the effect of the tend to rely on automatic selection of variables based only ry 1
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doctor–patient relationship on vaccination acceptance. on mathematical criteria, which can lead to overfitting or , 2
Participants’ socioeconomic, demographic and health underfitting models. Therefore, we used a manual step- 0
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characteristics previously associated with influenza vacci- wise, hierarchical approach, where blocks of variables were 3 b
nation were prioritised.9 34 entered in a sequence based on previous evidence and our y g
We used 11-point Likert scales (0–10) for the majority aim of assessing the importance of policy amenable factors ue
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ple-choice items and alternate-choice items when Two goodness-of-fit tests—χ2 and Nagelkerke R²—were te
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appropriate. Except for trust, health decision-making used to assess the overall model (M1) and each of the te
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preferences and childhood traumatic health experi- seven models (blocks) generated using the hierarchical b
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ences items, sociopsychological measures were disease or approach. Employing a classification cut-off point of 0.5, c
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vaccine specific to avoid misinterpretation. As illustrated a final model with a Nagelkerke R² value close to 1, which p
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in the introduction, our questions also aimed to capture indicates optimal model fit, was sought. rig
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Wheelock A, et al. BMJ Open 2017;7:e014668. doi:10.1136/bmjopen-2016-014668 3
Open Access
Thorough checks to ensure the robustness of the and comparable across countries (see online supplemen- B
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models were conducted, including variance inflation tary tables S2–S4). Those who had received an influenza J
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factor (VIF) to assess collinearity, standardised residuals vaccine were older, reported having an eligible health p
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to detect and evaluate outliers and Cook’s distance to condition, had a private or public health insurance, lived n
identify influential cases. Separate analyses entering the with a partner (USA/France), were wealthier (USA/ : firs
blocks of variables in reverse order were also performed France) and more educated (USA). They were also t p
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(ie, from block 7 to block 1) to evaluate whether the order less constrained by practical barriers and more likely to b
in which variables were entered significantly modified our report that their physician and relatives thought they lish
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results. Data were analysed using IBM SPSS Statistics V.22. should vaccinate than those who had not received a d
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vaccine. Vaccinated participants were more concerned s
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about the risks of influenza, less worried about the risks of 0
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resulTs the vaccine and more trusting of vaccine manufacturers 1
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Participants and providers than unvaccinated participants. Vaccinees 6
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The online survey was completed by 814 partici- reported possessing a better understanding of the influ- m
pants in the USA, 791 in the UK and 787 in France. enza vaccine and were more prone to let physicians make jop
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Online response rates were low (USA=1%; UK=1.7%; decisions about their health (USA/UK) than those who n
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France=2.4%), although consistent with research on had not vaccinated. Lastly, vaccinated participants were 0
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non-probability online panels showing that, in recent less likely to have had a bad vaccine or injection-related 6
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years, response rates have fallen to a point where in experience (UK) and more likely to have had a scary 1
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many cases they are 10% or less.37 Eighty participants health-related experience in childhood than unvacci- 6
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were interviewed via the telephone in the USA, 100 in nated participants. o
the UK and 100 in France. Telephone response rates n 1
were 6%–9%. Telephone interviews targeted older Factors associated with past influenza vaccination in 2 J
people and those belonging to low socioeconomic regression analyses uly
strata, two populations with particularly low response When all variables were assessed concurrently, the models 2
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rates.38 Recruitment flow diagrams for the online and that best fitted the data (M6–M8) explained 73% of the 17
telephone samples are presented in online supplemen- variance in past vaccination behaviour in the USA, 80% in . D
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tary figures S1A–S3A and S1B–S3B, respectively. the UK and 64% in France (Nagelkerke R²=0.644–0.795) w
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Except for education in the USA—the sample was (tables 2–4). The first models (M1) included all the vari- lo
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more educated than the general population, there were ables, thus were less parsimonious than M6–M8, yet they d
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sample n=2412) and those of the general population, models (M2), which included demographic, socioeco- h
when available (table 1). To facilitate survey completion nomic and health variables, fitted the data poorly to ttp
and improve data accuracy, household income data for moderately and accounted for 22% of the variance in ://b
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this study were collected using a limited number of past vaccination behaviour in the USA, 38% in the UK jo
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bands relevant to each country. Therefore, it cannot be and 19% in France. Practical barriers only explained 3% e
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directly compared against census data, which collects of the variance in the USA (M3) and were not significant .b
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more granular income information per household. in the UK and France. Social influence explained 14% j.c
However, we have used as a reference the band that of the variance in the USA (M4), 21% in UK and 25% o
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is most approximate to the census median household in France (M3). Influenza perceptions accounted for o/
income (table 1). In the USA and the UK, roughly half 30% of past vaccination behaviour in the USA (M5), 17% n J
of the sample was below the reference band and the in the UK and 18% in France (M4), whereas influenza an
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other half was above; whereas in France, the number of vaccine perceptions only explained 1% of this behaviour a
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participants who reported a household income below in the USA (M6), 2% in the UK and 1% in France (M5). 1
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the median was substantially higher than those over the Finally, trust items explained less than 1% of the variance , 2
median (table 1). in the USA, while decision-making preferences and child- 0
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Healthcare professionals were excluded from the final hood experiences explained 2% of the variance in the UK 3 b
samples as their decision-making processes are influenced and 1% in France. y g
by those they care for or regulated by healthcare authori- When blocks were entered in reverse order, ue
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confirmed these differences (available upon request). behaviour—3% (USA), 1% (UK) and 0% (France). te
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This is not surprising, since people’s characteristics te
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differences between vaccinated and non-vaccinated have an effect on their perceptions, thus they explain b
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participants some of the same variance. This result further proves c
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Overall, the responses of vaccinated and unvaccinated that poorly specified models—which are not evidence p
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participants were significantly different (p<0.05–0.001) based—lead to biased estimates (the detailed results rig
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4 Wheelock A, et al. BMJ Open 2017;7:e014668. doi:10.1136/bmjopen-2016-014668
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Participant characteristicsTable 1 USA (n=801)*UK (n=806)†France (n=805)‡ SamplePopulationSamplePopulationSamplePopulationCharacteristicCategories GenderFemale50%51%52%51%53%52% Age18–6480%80%77%77%76%76% ≥6520%20%23%23%24%24% EthnicityWhite69%78%88%87%–– Other30%22%11%13%–– Prefer not to say1%–1%––– a<$50 000/£20 000/€26 00043%–46%–53%–Annual household income ≥$50 000/£20 000/€26 00049%–46%–34%– Prefer not to say8%–9%–13%– bc56%58%54%UnavailableMarital statusLiving as a couple60%Unavailable bcNot living as a couple39%Unavailable44%42%45%Unavailable Prefer not to say1%–1%–1%– EducationNo university degree49%71%60%73%64%76% University degree45%29%37%27%29%24% Prefer not to say5%–3%–7%– Settlement typeUrban76%81%77%81%78%78% Rural24%19%23%19%22%22% deVaccination status16%Unavailable<65 vaccinated43%37%27%Unavailable ≥65 vaccinated66%65%75%73%50%53% 59*Population estimates for gender, age, ethnicity, income, marital status, education and settlement type are 2012/2013 estimates from the US Census Bureau. Influenza vaccination status is 4a59bfrom the 2013/2014 season. The reference income band was the closest to the US 2012/2013 median household income ($53 046). Census data only include persons who are married with spouse present, married with spouse absent and separated (42%).60 61†Population estimates for gender, age, ethnicity, marital status, education and settlement type are 2011 and 2012/2013 estimates from the UK Office for National Statistics. Influenza 5davaccination status is from the 2013/2014 season. Available data for <65s include children. The reference income band was the closest to the UK 2012/2013 median household income (£22 62880).‡Population estimates for gender, age, income, marital status, education and settlement type are 2011 and 2012/2013 estimates from France’s National Institute of Statistics and Economic 6340eStudies. Ethnicity was not collected due to country-specific data protection restrictions. Influenza vaccination status is from the 2013/2014 season. Available data for <65s include a63cchildren.The reference income band was the closest to France’s 2012/2013 median household income (€29 330). Census data only include people who are legally married (49%).Note: Differences between samples and populations were evaluated using Fisher's exact test. Except for education in the USA (p<0.001), we found no significant differences. Percentages may not total 100 due to rounding. on January 18, 2023 by guest. Protected by copyrighttp://bmjopen.bmj.com/MJ Open: first published as 10.1136/bmjopen-2016-014668 on 12 July 2017. Downloaded from
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Wheelock A, et al. BMJ Open 2017;7:e014668. doi:10.1136/bmjopen-2016-014668 5
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SE 0.446 0.323 0.377 0.397 0.259 0.286 0.070 0.093 0.271 0.331 0.321 0.060 0.056 0.055 0.052 0.050 0.367 0.064 0.047 0.310 0.091 0.450 ntinue MJ Op
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OR 1.057 2.531** 3.374*** 4.273*** 0.898 0.728 1.145* 1.035 0.693 2.432** 4.285*** 1.290*** 1.216*** 0.903** 0.744*** 1.122** 0.388** 1.225*** 0.836*** 1.558 0.809** 2.153* : first p
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SE 0.441 0.320 0.372 0.391 0.257 0.283 0.070 0.093 0.270 0.329 0.322 0.059 0.056 0.055 0.052 0.049 0.366 0.064 0.046 0.309 0.090 lished
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OR 1.093 2.531** 3.269*** 4.158*** 0.867 0.759 1.130* 1.042 0.695 2.535** 4.260*** 1.284*** 1.226*** 0.909* 0.748*** 1.126** 0.368** 1.222** 0.835*** 1.585 0.796** 10.113
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OR 1.006 2.549** 3.062** 4.137*** 0.931 0.743 1.143* 1.046 0.677 2.194** 3.700*** 1.291*** 1.238*** 0.902* 0.757*** 1.117** 0.368** 1.188** 0.827*** 1.712* -2016-0
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SE 0.396 0.296 0.337 0.362 0.240 0.266 0.067 0.088 0.254 0.303 0.276 0.056 0.049 0.051 0.047 0.043 668 o
M5 OR 1.230 2.145** 2.394** 3.163*** 0.907 0.890 1.145** 1.025 0.681 2.417** 2.946*** 1.359*** 1.238*** 0.911* 0.741*** 1.112** n 12 July
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—USA OR 1.116 2.469*** 2.197** 2.542*** 0.859 1.032 1.140** 0.983 0.665** 2.565*** 6.909*** nloade
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st influenza vaccinatio 1M2 SEORS 0.4581.3690.2 0.3293.050***0.2 0.3862.833***0.2 0.4153.4610.2 0.2690.9130.1 0.2941.0930.1 0.0741.198***0.0 0.0950.7400.1 0.2871.369*0.2 0.384 0.356 0.345 0.312 0.069 0.056 0.060 0.126 0.061 0.063 0.056 0.056 0.054 0.390 0.066 0.054 0.055 0.329 0.053 0.096 0.081 0.086 0.147 0.417 0.464 on January 18mj.com/
d with pa M OR 1.019 2.528** 3.463*** 4.507*** 0.916 0.672 1.146* 1.052 0.664 1.277 2.182** 4.361*** 0.866 1.335*** 1.013 1.235*** 1.121 0.908* 0.973 0.932 0.752*** 1.165** 0.406** 1.249*** 0.848** 0.961 1.775* 1.010 0.836* 0.895 1.013 0.953 1.449 2.126* , 2023 by
Factors associateTable 2 Variables Age Eligible health condition Private insurance Public insurance Gender Marital status Income Education Ethnicity Vaccine access Time to vaccinate Physician’s opinion Relatives’ opinion Vulnerable to influenza Susceptible to influenza Likelihood of catching influenza Severity of influenza (bed-days) Severity of influenza Fear of influenza Worry of transmitting influenza Perceived control over influenza Regret of catching influenza Perceived knowledge of vaccine Effectiveness of vaccine Vaccine transmits influenza Vaccine contents are dangerous Vaccine is painful Vaccine-related self-efficacy Trust in physician (scale) Trust in manufacturers Trust in health authorities Shared decision-making doctor Bad experience vaccines—child Scary health experience—child guest. Protected by copyrig
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6 Wheelock A, et al. BMJ Open 2017;7:e014668. doi:10.1136/bmjopen-2016-014668
Open Access
of these analyses are available from the corresponding B
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ContinuedTable 2 Variables Number of participants Nagelkerke R The change in model fit inin the final model (M8). vaSouhtrattuaiaeagClltundnshlo ies deealftslooarrb ib snrr o.tidem edhvLlieaoeas er ennwcs2idatst y i5 s3atf edh,ry,n se iiad.tcnas4nhaig 4dd nu sn5ieuCcos%os aa o sttmle thosioixn cekofewg sr’rtrs e shee tth hir ednwtoe eihcg aswraat saeeaesn l ednxsni sos cc1ot n hehe% es oaansxs td iecth avaa easeamltdtul dia ivs isn ntnaetaiofrdc eblfi sudaoase reo brwld nitllcmuoeeicosrs teein eehin dd coaav eeerndatvernel anVsut tlhiio.Ieudf4Feyrs3-- t. Protected by copyrig
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Wheelock A, et al. BMJ Open 2017;7:e014668. doi:10.1136/bmjopen-2016-014668 7
Open Access
d B
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with past influenza v M1 ORSE 2.0440.437 4.088***0.413 3.115**0.472 0.6290.321 2.018**0.337 0.9180.105 0.9620.103 1.7680.478 1.3800.457 1.2950.427 3.447***0.371 2.205**0.355 1.183**0.081 0.889*0.066 1.355***0.078 1.317**0.130 1.0620.073 0.9700.068 0.872**0.066 0.832**0.064 1.324***0.064 2.098*0.410 1.1120.077 0.9010.066 0.8960.080 1.7320.412 1.164*0.082 0.8990.107 0.8680.088 0.9860.098 n January 18, 2023
Factors associated Table 3 Variables Age Eligible health condition Private insurance Gender Marital status Income Education Ethnicity Vaccine access Time to vaccinate Physician’s opinion Relatives’ opinion Vulnerable to influenza Susceptible to influenza Likelihood of catching influenza Severity of influenza (bed-days) Severity of influenza Fear of influenza Worry of transmitting influenza Perceived control over influenza Regret of catching influenza Perceived knowledge of vaccine Effectiveness of vaccine Vaccine transmits influenza Vaccine contents are dangerous Vaccine is painful Vaccine-related self-efficacy Trust in physician (scale) Trust in manufacturers Trust in health authorities by guest. Protected by copyrig
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8 Wheelock A, et al. BMJ Open 2017;7:e014668. doi:10.1136/bmjopen-2016-014668
Open Access
model. No values were higher than 1, which shows that no B
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M3 ORSE 728 0.589 significant (p<0.1). *p<0.1; **pn the final model (M6). vftotaeetohihadlnfoic reiugzvnriu aaaidlaibst cnn)ls licl ,fcenoy iulew, nun nc)htaeo ,hhdt nituilreehozerocr ea no nUos rt mevienhbSca seleA mcuyfh cac la1ouacti(gns6vntno eoi%edivso rc heoiuse atrdo.fixrt oiow p6Saoan 5ul csnastotr r,h ihyo p neiaiisernseratsi fd d srftvFaiso)h anr, a r ceagsa btc invslntzoiy ehcne,dtr erahsa ype.hbt t eiehwsolne mrecuigc to lhae pdUualp rl,ang o Ktpniatpo rtirdoh(ninoe uresswpsttn r.io i eaodtAirhfbnnetodooifr rodl uuoueniattf,-- h 2017. Downloaded from
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ContinuedTable 3 M1 VariablesORSE Shared decision-making 0.642**0.165doctor Bad experience vaccines—0.252**0.557child Scary health experience—child3.434**0.496 Number of participants728 Nagelkerke R0.798 2The change in model fit indicated by test for each block χIn bold are the variables significantly associated with influe mUsaweswviaaaaryncnioiueon lnnnabhiesKocmtallcdddtzonlihnlorir,eocae o ecseoemts hw bucr,hen r aru evt eihnedeafhea pimatl eoraetb i n acouwraoatisnrplcsemdyypnboteete .io e lhh 4ui—dnnaroiplsr hwgeeas5liea f toat ie i rdifmart1ytbastttro in2hpn hh tiostla orowtoecasietPe nh nor nel nfo m rtaae ao nA,chsthi st trcvp— ioith(ath eailaanoihapnraHntanetatusge eraeoefl ducg1g l tndans stuotth cN earginth h mtehnwu rnlioeee1aeynni tsic tdea gn) rtot hozoadish p oap aFnn,ecpsnsait vdr rcfcrtbp iu vdeifvaaimsuaydvoaaetbrei.inoalsr3tcsensrte0l i4trctuyhieiccooe i9 mce e4isvnna fwdna n6ode,aodvhc pg b snafd cTaueeem i faeotc w cnashn ricaiwuic oaniha hntlidriFitht tsynn epnlio a h tr ycotta cethbnea h shhhmi looi cnecoe tene aismhnaopht nnoce v wsUrctneletade a uareeiiea2f.dSss mneoyr sfre0toOA ade lva enwni 0entnceifn n vhno9si eatarcat osstiFee/tneshlre vryu fy rle2veieids, U pa aan tacdx0nm adaelnnlcSfetp1aaeennmlhecccnAauut0essdddieeeaysst,.,---- on January 18, 2023 by guest. Protected by copyrig/
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Wheelock A, et al. BMJ Open 2017;7:e014668. doi:10.1136/bmjopen-2016-014668 9
Open Access
d B
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past influenza vac M1 ORSE 2.772**0.342 1.1960.332 2.423*0.493 1.2810.292 1.935**0.316 1.1060.121 1.1510.092 0.501*0.387 0.8620.401 7.464***0.352 0.8060.347 1.1000.065 0.9220.064 1.231**0.069 1.0770.137 0.9990.067 0.9860.058 1.0770.064 0.846**0.054 1.319***0.063 1.3190.356 1.0670.076 0.9580.063 0.871**0.058 0.8690.465 1.0060.065 1.0050.105 0.9550.086 0.9000.089 0.9970.164 0.8540.448 4.139***0.447 699 on January 18, 2/
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Factors associated Table 4 Variables Age Eligible health condition Private insurance Gender Marital status Income Education Vaccine access Time to vaccinate Physician’s opinion Relatives’ opinion Vulnerable to influenza Susceptible to influenza Likelihood of catching influenza Severity of influenza (bed-days) Severity of influenza Fear of influenza Worry of transmitting influenza Perceived control over influenza Regret of catching influenza Perceived knowledge of vaccine Effectiveness of vaccine Vaccine transmits influenza Vaccine contents are dangerous Vaccine is painful Vaccine-related self-efficacy Trust in physician (scale) Trust in manufacturers Trust in health authorities Shared decision-making doctor Bad experience vaccines—child Scary health experience—child Number of participants by guest. Protected by copyrig
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10 Wheelock A, et al. BMJ Open 2017;7:e014668. doi:10.1136/bmjopen-2016-014668