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BMC Public Health
Published in: BMC Public Health 1/2024

Open Access 01-12-2024 | Vaccination | Research

Vaccine decision making in New Zealand: a discrete choice experiment

Authors: Amy Hai Yan Chan, Marvin Tao, Samantha Marsh, Helen Petousis-Harris

Published in: BMC Public Health | Issue 1/2024

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Abstract

Background

Vaccine hesitancy is a significant threat to global health. A key part of addressing hesitancy is to ensure that public health messaging prioritises information that is considered important to the public. This study aimed to examine how different vaccine characteristics affect public preferences for vaccines in New Zealand, what trade-offs they are willing to make between different vaccine characteristics, and how their preferences are affected by their vaccine-related conspiracy beliefs and COVID-19 vaccination status.

Methods

An online discrete choice experiment (DCE) was designed to elicit individual preferences about vaccines using the 1000minds platform. Members of the general population of New Zealand aged ≥ 18 years were invited to complete the DCE. Participants were asked to indicate their preference between two options showing different combinations of vaccine characteristics. Data on sociodemographic characteristics were collected. Beliefs were measured using the vaccine conspiracy beliefs scale (VCBS) with scores ≥ 19 indicating strong vaccine-related conspiracy beliefs. The DCE was analysed using the PAPRIKA method (Potentially All Pairwise RanKings of all possible Alternatives) and preferences compared between respondents with high versus low VCBS scores and vaccinated versus unvaccinated respondents for COVID-19.

Results

A total of 611 respondents from 15 regions completed the DCE. Mean (SD) age was 45.9 (14.7) years with most having had 2 or more doses of the coronavirus vaccine (86%). Mean (SD) VCBS score was 18.5 (12.4) indicating moderate vaccine-related conspiracy beliefs. Risk of severe adverse effects was the most highly valued vaccine characteristic, followed by vaccine effectiveness and duration of protection. Vaccine origin and route of administration were ranked least important. Respondents scoring high on the VCBS placed less value on the effectiveness of vaccines but greater value on development time and total number of doses (p < 0.001). COVID-19 unvaccinated respondents ranked development time and total number of doses more highly than those vaccinated respondents (p < 0.001).

Conclusions

Risk of severe adverse effects, vaccine effectiveness and duration of protection were rated by the New Zealand public as the top three most important vaccine characteristics. This information is important for informing public health messaging to promote vaccine uptake and inform vaccine decision-making.
Appendix
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Literature
1.
Siddiqui M, Salmon DA, Omer SB. Epidemiology of vaccine hesitancy in the United States. Hum Vaccines Immunotherapeutics. 2013;9(12):2643–8.CrossRef
2.
Larson HJ, de Figueiredo A, Xiahong Z, Schulz WS, Verger P, Johnston IG, et al. The state of Vaccine confidence 2016: global insights through a 67-Country survey. EBioMedicine. 2016;12:295–301.PubMedPubMedCentralCrossRef
3.
Gowda C, Dempsey AF. The rise (and fall?) Of parental vaccine hesitancy. Hum Vaccines Immunotherapeutics. 2013;9(8):1755–62.CrossRef
4.
Bertoncello C, Ferro A, Fonzo M, Link to external site this link will open in a new window, Zanovello S, Napoletano G et al. Socioeconomic Determinants in Vaccine Hesitancy and Vaccine Refusal in Italy. Vaccines. 2020;8(2):276.
5.
6.
Cooper S, Betsch C, Sambala EZ, Mchiza N, Wiysonge CS. Vaccine hesitancy– a potential threat to the achievements of vaccination programmes in Africa. Hum Vaccines Immunotherapeutics. 2018;14(10):2355–7.CrossRef
7.
Prickett KC, Habibi H, Carr PA. COVID-19 Vaccine Hesitancy and Acceptance in a cohort of Diverse New zealanders. Lancet Reg Health West Pac. 2021;14:100241.PubMedPubMedCentralCrossRef
8.
9.
Thaker J. The persistence of Vaccine Hesitancy: COVID-19 vaccination intention in New Zealand. J Health Communication. 2021;26(2):104–11.PubMedCrossRef
10.
11.
12.
Turner N. A measles epidemic in New Zealand: why did this occur and how can we prevent it occurring again? NZMJ. 2019;132(1504).
13.
Parent du Châtelet I, Antona D, Freymuth F, Muscat M, Halftermeyer-Zhou F, Maine C, et al. Spotlight on measles 2010: update on the ongoing measles outbreak in France, 2008–2010. Euro Surveill. 2010;15(36):19656.PubMedCrossRef
14.
Phadke VK, Bednarczyk RA, Salmon DA, Omer SB. Association between Vaccine Refusal and Vaccine-Preventable diseases in the United States. JAMA. 2016;315(11):1149–58.PubMedPubMedCentralCrossRef
15.
Winter K, Harriman K, Zipprich J, Schechter R, Talarico J, Watt J, et al. California pertussis epidemic, 2010. J Pediatr. 2012;161(6):1091–6.PubMedCrossRef
16.
Winter K, Glaser C, Watt J, Harriman K. Centers for Disease Control and Prevention (CDC). Pertussis epidemic–California, 2014. MMWR Morb Mortal Wkly Rep. 2014;63(48):1129–32.PubMedPubMedCentral
17.
Oostvogel PM, van Wijngaarden JK, van der Avoort HG, Mulders MN, Conyn-van Spaendonck MA, Rümke HC, et al. Poliomyelitis outbreak in an unvaccinated community in the Netherlands, 1992-93. Lancet. 1994;344(8923):665–70.PubMedCrossRef
18.
Foot H, La Caze A, Gujral G, Cottrell N. The necessity-concerns framework predicts adherence to medication in multiple illness conditions: a meta-analysis. Patient Educ Couns. 2016;99(5):706–17.PubMedCrossRef
19.
Horne R, Chapman SCE, Parham R, Freemantle N, Forbes A, Cooper V. Understanding patients’ adherence-related beliefs about medicines prescribed for long-term conditions: a meta-analytic review of the necessity-concerns Framework. PLoS ONE. 2013;8(12):e80633.PubMedPubMedCentralCrossRef
20.
Chan AHY, Aspden T, Brackley K, Ashmore-Price H, Honey M. What information do patients want about their medicines? An exploration of the perspectives of general medicine inpatients. BMC Health Serv Res. 2020;20(1):1131.PubMedPubMedCentralCrossRef
21.
Sheed L, Haua R, Honey MLL, Aspden TJ, Brackley KE, Chan AHY. Exploring medicines information needs of mental health service users at a New Zealand hospital. Psychol Health Med. 2020;1–13.
22.
Ewing G, Sarigöllü E. Assessing Consumer preferences for Clean-Fuel vehicles: a Discrete Choice Experiment. J Public Policy Mark. 2000;19(1):106–18.CrossRef
23.
Kim D, Park BJ (Robert), editors. The moderating role of context in the effects of choice attributes on hotel choice: A discrete choice experiment. Tourism Management. 2017;63:439–51.
24.
Cheraghi-Sohi S, Hole AR, Mead N, McDonald R, Whalley D, Bower P, et al. What patients want from primary care consultations: a Discrete Choice experiment to identify patients’ priorities. The Annals of Family Medicine. 2008;6(2):107–15.PubMedCrossRef
25.
Gerard K, Salisbury C, Street D, Pope C, Baxter H. Is fast access to general practice all that should matter? A discrete choice experiment of patients’ preferences. J Health Serv Res Policy. 2008;13(Suppl 2):3–10.PubMedCrossRef
26.
King MT, Hall J, Lancsar E, Fiebig D, Hossain I, Louviere J, et al. Patient preferences for managing asthma: results from a discrete choice experiment. Health Econ. 2007;16(7):703–17.PubMedCrossRef
27.
Sculpher M, Bryan S, Fry P, de Winter P, Payne H, Emberton M. Patients’ preferences for the management of non-metastatic prostate cancer: discrete choice experiment. BMJ. 2004;328(7436):382.PubMedPubMedCentralCrossRef
28.
Diks ME, Hiligsmann M, van der Putten IM. Vaccine preferences driving vaccine-decision making of different target groups: a systematic review of choice-based experiments. BMC Infect Dis. 2021;21(1):879.PubMedPubMedCentralCrossRef
29.
Lack A, Hiligsmann M, Bloem P, Tünneßen M, Hutubessy R. Parent, provider and vaccinee preferences for HPV vaccination: a systematic review of discrete choice experiments. Vaccine. 2020;38(46):7226–38.PubMedCrossRef
30.
Michaels-Igbokwe C, MacDonald S, Currie GR. Individual Preferences for Child and adolescent vaccine attributes: a systematic review of the stated preference literature. The Patient. 2017;10(6):687–700.PubMedCrossRef
31.
McPhedran R, Toombs B. Efficacy or delivery? An online Discrete Choice experiment to explore preferences for COVID-19 vaccines in the UK. Econ Lett. 2021;200:109747.PubMedPubMedCentralCrossRef
32.
Wang K, Wong ELY, Cheung AWL, Yau PSY, Chung VCH, Wong CHL, et al. Influence of vaccination characteristics on COVID-19 Vaccine Acceptance among Working-Age people in Hong Kong, China: a Discrete Choice Experiment. Front Public Health. 2021;9:793533.PubMedPubMedCentralCrossRef
33.
Hess S, Lancsar E, Mariel P, Meyerhoff J, Song F, van den Broek-Altenburg E, et al. The path towards herd immunity: Predicting COVID-19 vaccination uptake through results from a stated choice study across six continents. Soc Sci Med. 2022;298:114800.PubMedPubMedCentralCrossRef
34.
Liao Q, Ng TWY, Cowling BJ. What influenza vaccination programmes are preferred by healthcare personnel? A discrete choice experiment. Vaccine. 2020;38(29):4557–63.PubMedPubMedCentralCrossRef
35.
Dong D, Xu RH, Wong EL, yi, Hung CT, Feng D, Feng Z, et al. Public preference for COVID-19 vaccines in China: a discrete choice experiment. Health Expect. 2020;23(6):1543–78.PubMedPubMedCentralCrossRef
36.
Wang B, Chen G, Ratcliffe J, Afzali HHA, Giles L, Marshall H. Adolescent values for immunisation programs in Australia: a discrete choice experiment. PLoS ONE. 2017;12(7):e0181073–3.PubMedPubMedCentralCrossRef
37.
Shono A, Kondo M. Parents’ preferences for seasonal influenza vaccine for their children in Japan. Vaccine. 2014;32(39):5071–6.PubMedCrossRef
38.
Sampson R, Wong L, Macvicar R. Parental reasons for non-uptake of influenza vaccination in young at-risk groups: a qualitative study. Br J Gen Pract. 2011;61(588):e386–391.PubMedPubMedCentralCrossRef
39.
Pal M, Goodyear-Smith F, Exeter D. Factors contributing to high immunisation coverage among New Zealand asians. J Prim Health Care. 2014;6(4):304–11.PubMedCrossRef
40.
Gauld N, Martin S, Sinclair O, Petousis-Harris H, Dumble F, Grant CC. A qualitative study of views and experiences of women and Health Care professionals about free maternal vaccinations administered at Community pharmacies. Vaccines (Basel). 2020;8(2):152.PubMedCrossRef
41.
Mills E, Jadad AR, Ross C, Wilson K. Systematic review of qualitative studies exploring parental beliefs and attitudes toward childhood vaccination identifies common barriers to vaccination. J Clin Epidemiol. 2005;58(11):1081–8.PubMedCrossRef
42.
Lee CHJ, Sibley CG. Attitudes toward vaccinations are becoming more polarized in New Zealand: findings from a longitudinal survey. EClinicalMedicine. 2020;23:100387.PubMedPubMedCentralCrossRef
43.
Watson PB, Yarwood J, Chenery K. Meningococcal B: tell me everything you know and everything you don’t know. New zealanders’ decision-making regarding an immunisation programme. N Z Med J. 2007;120(1263):U2751.PubMed
44.
Hensher DA, Rose JM, Greene WH. Applied Choice Analysis. 2nd ed. Cambridge: Cambridge University Press; 2015.CrossRef
45.
de Bekker-Grob EW, Veldwijk J, Jonker M, Donkers B, Huisman J, Buis S, et al. The impact of vaccination and patient characteristics on influenza vaccination uptake of elderly people: a discrete choice experiment. Vaccine. 2018;36(11):1467–76.PubMedCrossRef
46.
Gong T, Chen G, Liu P, Lai X, Rong H, Ma X, et al. Parental vaccine preferences for their children in China: a Discrete Choice Experiment. Vaccines. 2020;8(4):687.PubMedPubMedCentralCrossRef
47.
Verelst F, Willem L, Kessels R, Beutels P. Individual decisions to vaccinate one’s child or oneself: a discrete choice experiment rejecting free-riding motives. Soc Sci Med. 2018;207:106–16.PubMedCrossRef
48.
Veldwijk J, Lambooij MS, Bruijning-Verhagen PCJ, Smit HA, de Wit GA. Parental preferences for rotavirus vaccination in young children: a discrete choice experiment. Vaccine. 2014;32(47):6277–83.PubMedCrossRef
49.
Cameron MP, Newman PA, Roungprakhon S, Scarpa R. The marginal willingness-to-pay for attributes of a hypothetical HIV vaccine. Vaccine. 2013;31(36):3712–7.PubMedCrossRef
50.
Marshall HS, Chen G, Clarke M, Ratcliffe J. Adolescent, parent and societal preferences and willingness to pay for meningococcal B vaccine: a Discrete Choice Experiment. Vaccine. 2016;34(5):671–7.PubMedCrossRef
51.
Hall J, Kenny P, King M, Louviere J, Viney R, Yeoh A. Using stated preference discrete choice modelling to evaluate the introduction of varicella vaccination. Health Econ. 2002;11(5):457–65.PubMedCrossRef
52.
Seanehia J, Treibich C, Holmberg C, Müller-Nordhorn J, Casin V, Raude J, et al. Quantifying population preferences around vaccination against severe but rare diseases: a conjoint analysis among French university students, 2016. Vaccine. 2017;35(20):2676–84.PubMedCrossRef
53.
Sullivan T, Hansen P, Ombler F, Derrett S, Devlin N. A new tool for creating personal and social EQ-5D-5L value sets, including valuing ‘dead’. Soc Sci Med. 2020;246:112707.PubMedCrossRef
54.
Hansen P, Ombler F. A new method for scoring additive multi-attribute value models using pairwise rankings of alternatives. J Multi-Criteria Decis Anal. 2008;15(3–4):87–107.CrossRef
55.
Baggott C, Hansen P, Hancox RJ, Hardy JK, Sparks J, Holliday M, et al. What matters most to patients when choosing treatment for mild–moderate asthma? Results from a discrete choice experiment. Thorax. 2020;75(10):842–8.PubMedCrossRef
56.
Golan O, Hansen P, Kaplan G, Tal O. Health technology prioritization: which criteria for prioritizing new technologies and what are their relative weights? Health Policy. 2011;102(2–3):126–35.PubMedCrossRef
57.
Golan O, Hansen P. Which health technologies should be funded? A prioritization framework based explicitly on value for money. Isr J Health Policy Res. 2012;1(1):44.PubMedPubMedCentralCrossRef
58.
Sullivan T, Hansen P. Determining Criteria and Weights for Prioritizing Health Technologies based on the preferences of the General Population: a New Zealand pilot study. Value in Health. 2017;20(4):679–86.PubMedCrossRef
59.
Taylor WJ, Laking G. Value for money - recasting the problem in terms of dynamic access prioritisation. Disabil Rehabil. 2010;32(12):1020–7.PubMedCrossRef
60.
Chrzan K. Using partial Profile choice experiments to handle large numbers of attributes. Int J Market Res. 2010;52(6):827–40.CrossRef
61.
Meyerhoff J, Oehlmann M. The performance of full versus partial profile choice set designs in environmental valuation. Ecol Econ. 2023;204:107665.CrossRef
62.
Al-Sanafi M, Sallam M. Psychological determinants of COVID-19 Vaccine Acceptance among Healthcare Workers in Kuwait: a cross-sectional study using the 5 C and vaccine conspiracy beliefs scales. Vaccines. 2021;9(7):701.PubMedPubMedCentralCrossRef
63.
Sallam M, Dababseh D, Eid H, Al-Mahzoum K, Al-Haidar A, Taim D, et al. High rates of COVID-19 vaccine hesitancy and its Association with Conspiracy beliefs: a study in Jordan and Kuwait among other Arab countries. Vaccines. 2021;9(1):42.PubMedPubMedCentralCrossRef
64.
65.
Johnson FR, Yang JC, Reed SD. The Internal Validity of Discrete Choice Experiment Data: a Testing Tool for quantitative assessments. Value in Health. 2019;22(2):157–60.PubMedCrossRef
66.
Zimet GD, Blythe MJ, Fortenberry JD. Vaccine characteristics and acceptability of HIV immunization among adolescents. Int J STD AIDS. 2000;11(3):143.PubMedCrossRef
67.
Metadata
Title
Vaccine decision making in New Zealand: a discrete choice experiment
Authors
Amy Hai Yan Chan
Marvin Tao
Samantha Marsh
Helen Petousis-Harris
Publication date
01-12-2024
Publisher
BioMed Central
Published in
BMC Public Health / Issue 1/2024
Electronic ISSN: 1471-2458
DOI
https://doi.org/10.1186/s12889-024-17865-8

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