Australian preferences for influenza vaccine attributes and cost: A discrete choice experiment

ABSTRACT

People in Australia have access to different influenza vaccines, but may be unaware of their options and features. Preference studies for differentiated influenza vaccines including cell-based vaccines are limited, particularly in Australia. This study investigated which influenza vaccine attributes people in Australia value using a discrete choice experiment (DCE). Adults in Australia ineligible for free influenza vaccines had been vaccinated in the last 5 years and intended to be vaccinated again completed an online survey. Participants (N = 1203) were presented three influenza vaccine profiles described by eight attributes. Half the DCE scenarios described influenza season severity to be the same as last year, and the other half as more severe. DCE data were analyzed using a mixed multinomial logit (MMNL) model. All eight attributes significantly predicted vaccine choice (p < .05). Regardless of influenza season severity, participants preferred a vaccine: with greater protection, designed to be an exact match to circulating strains (match), using modern technology, manufactured by an Australian company, available at pharmacies, preferred by health care professionals (HCP), government funded for high-risk individuals and having lower cost. The top three attributes by importance were protection, match and cost. Participants were willing to pay more for match and higher protection. The Marginal Willingness to Pay (MWTP) for the most important attributes, excluding cost, were AUD $1.61/$2.18 for each additional percent in protection (same/more severe season), AUD $25.37/$32.37 for match and AUD $4.06/$15.97 for HCP preference. Findings indicate that match, protection, cost and HCP preference are key to vaccine choice, highlighting the importance of shared decision-making.

Background/Introduction

Seasonal influenza (flu) is a highly prevalent acute respiratory illness caused by influenza viruses.¹ Infections can lead to mild to severe illness, sometimes resulting in death.¹ In Australia, an average of approximately 100 deaths and 5100 hospitalizations are caused by influenza each year.² Annual vaccination remains the strongest strategy to prevent influenza infection and related complications.³

Vaccine effectiveness varies each season as a result of multiple factors, including the potential for vaccine-virus mismatch. One example of this is egg adaptation.^4–7 Traditional egg-based manufacturing of influenza vaccines (a technology of over 70 years)⁸ introduces the opportunity for mutations during viral propagation within embryonic eggs. These changes, due to selection pressures, may alter the antigenicity resulting in a vaccine strain that differs from the World Health Organization (WHO) selected reference strain. This can contribute to vaccine-virus mismatch which may, subsequently, impact vaccine effectiveness.^9–12

A number of advances have been made with influenza vaccine technology providing different options to standard egg-based technology. One differentiated offering is the cell-based inactivated quadrivalent influenza vaccine (QIVc), which eliminates the potential for egg-adaptive mutations as the virus is propagated in mammalian cell culture, thus producing vaccine strains more antigenically similar to the seed-strain virus.^13–15 Cell-based influenza vaccines can provide greater certainty of match to circulating influenza strains, especially H3N2, improving effectiveness compared to standard egg-based vaccines.^12,16 Cell-based influenza vaccines have been listed as a current recommendation by the Australian Technical Advisory Group on Immunisation (ATAGI) alongside egg-based vaccines for their respective age-approved indications.¹⁷ Cell-based and standard dose egg-based influenza vaccines have a similar safety profile in children and adults.¹⁸ In Australia, cell-based influenza vaccines are priced higher in the non-Government-funded market. The Australian National Immunisation Program (NIP) provides funded influenza vaccines to children 6 months to less than 5 years, pregnant women, First Nations people aged 6 months and over, people aged 65 years and over and people with specific medical conditions that increase the risk of severe influenza.

Currently, influenza vaccinations can be administered by several providers; including a general practitioner or nurse at a clinic; or by a pharmacist or nurse at the pharmacy. It is an expectation that all health care professionals (HCPs) administering vaccines obtain valid informed consent from the individuals for the procedure. Shared decision-making is an increasingly recognized aspect of patient-centered care and should form part of the consent process through providing information about the risks and benefits of each vaccine.¹⁹ Despite the number of vaccine options available, there is limited evidence on influenza vaccine preferences including cell-based vaccines in the Australian context. Such studies are important to understand Australian preferences on influenza vaccines to inform and improve shared decision-making.

To address this gap, this study utilized a discrete choice experiment (DCE) with various influenza vaccine profiles representing standard (egg-based) and nonstandard (e.g., cell-based) to investigate which attributes people value that subsequently drive influenza vaccine choice. Additionally, the DCE allowed for the assessment of participants’ marginal willingness to pay (MWTP) for influenza vaccine attributes. MWTP measures how much people are willing to pay for an improvement in one attribute.

Assessing vaccine preferences through a discrete choice experiment

Grounded in psychology and economics,^20–22 DCEs are a prominent and reliable method used in many fields to understand and model trade-offs and preferences revealed by people’s choices.²³ In a DCE, participants respond to a series of hypothetical choice scenarios, each presenting competing alternatives made up of several attributes.^24,25 Levels of attributes vary across the series of choice scenarios based on experimental design. In each scenario, participants are asked to choose their preferred alternative – in this case, their preferred influenza vaccine option. Through this, participants trade-off features when selecting an option that maximizes their ‘utility.’ Participants’ change in responses across various choice scenarios are observed, and the importance of these attributes is then inferred via statistical modeling.

Material and methods

Participants

Adults across Australia were recruited via TEG an online panel company, and directed to the online survey link hosted on Forsta.²⁶ Genuine participants who passed data quality checks and completed the survey received a reimbursement for their time.

Inclusion criteria

Participants were included if they were at least 18 years old, likely or definitely intending to get vaccinated against influenza in 2023, had been vaccinated against influenza at least once in the past 5 years, and were currently living in Australia.

Exclusion criteria

Participants were excluded if they were i) eligible for a free influenza vaccine through work or employment, ii) eligible for a free influenza vaccine through the Australian National Immunisation Program (Aboriginal and Torres Strait Islander people, people aged 65 years and older, people at high risk of a serious medical condition), or iii) employed by a pharmaceutical or vaccine company. Additional data quality checks were used to clean the data (see Figure 2).

Figure 2.

Participant flow diagram.

Development of DCE attributes and levels

A targeted rapid review of previous influenza vaccine preference studies informed the creation of the DCE grid, including attributes such as protection, location of vaccination appointment and HCP endorsement.^27–30 Other attributes of interest were included, such as: vaccine technology, manufacturing company location, whether it was designed to be an exact match to circulating influenza strains (match), whether it was government funded for high-risk individuals, and total out-of-pocket costs. In the DCE, each scenario presented the standard influenza vaccine profile alongside two influenza vaccine alternatives. Each vaccine profile was described by eight attributes outlined in Table 1. Levels for the ‘standard vaccine’ profile (status quo) were fixed across scenarios to represent an estimation of the typical standard influenza vaccine (non-cell-based) available to adults <65 years old in Australia.^8,31 These hypothetical levels were 50% protection from illness, using traditional (egg-based) technology, manufactured in both Australia and overseas, preferred by HCPs for themselves and others, designed to be an exact match to circulating influenza strains, not government funded for high-risk individuals and a total out-of-pocket cost of $20. The fixed level for where to receive the vaccine was piped in from their response on where they received their most recent vaccine (see example scenario presented in Figure 1). A full list of attributes, their descriptions and range of levels is presented in Table 1. The term “influenza” was abbreviated to “flu” in the online survey for participant-friendly language. The survey was piloted (n = 130) before full study launch. Minor adjustments to the wording were made for clarity before the full launch.

Table 1.

DCE attributes, their descriptions and levels.

Attribute	Description (hover-over text)	Levels
Protection against the flu	Level of protection against getting sick from the flu	50% protection
		55% protection
		60% protection
		65% protection
Vaccine technology	Type of vaccine technology	Traditional (70+ year old technology)
		More modern technology (15+ year old technology)
Manufactured by an Australian company	Whether or not the vaccine is manufactured by an Australian company	Manufactured by an Australian company
		Manufactured by an overseas company
		Manufactured by both an Australian and overseas company
Where you receive your flu vaccination	Where you go to receive your flu vaccination. Please assume this is at your closest clinic or pharmacy, even if they do not currently offer the flu vaccine.	GP or Nurse in clinic
		Pharmacy
Preferred vaccine of HCPs	Whether or not your GP/Nurse/Pharmacist would prefer to use this vaccine for themselves and for you.	No
		Yes
Vaccine designed to be a match to circulating flu strains	Whether or not it was designed to be an exact match to circulating flu strains.	No
		Yes
Government funded for high-risk individuals	Whether or not the vaccine was government funded for high-risk individuals, for example, people with certain medical conditions	No
		Yes
Total out-of-pocket cost	The total out-of-pocket cost for a completed vaccination	$20
		$30
		$40
		$50

Figure 1.

Example DCE scenario with a more severe influenza season framing.

DCE experimental design

The DCE experimental design is a matrix of values used to determine which levels are shown in which choice scenarios. The levels are not randomly shown but rather placed in each scenario in a way to maximize the trade-off information provided by each respondent. The current study utilized Bayesian efficient design using naïve priors to account for the sign of the parameters and level order using Ngene version 1.3 software.^32,33

The experimental design consisted of 12 blocks of 6 choice scenarios (72 scenarios in total). Half the blocks described an influenza season with the same severity as last year, while the other half described the season as more severe than last year. Participants were randomly assigned to one block from each of the two season severity sets (i.e., completing 12 choice scenarios in total).

In each scenario, participants were told to assume vaccine options were all backed by robust clinical trials, recommended in the current vaccination guidelines, designed to last the influenza season, covered four influenza strains, and had no difference in side-effects from standard vaccines. An example DCE scenario is presented in Figure 1.

Measures of background variables

Participant sociodemographic information, influenza vaccine history and preferences around access to vaccine information were collected and analyzed descriptively (see Tables 2 and 3).

Table 2.

Participant demographic information.

Age group	n (%)
18–29 years	170 (14.1%)
30–39 years	261 (21.7%)
40–49 years	245 (20.4%)
50–59 years	319 (26.5%)
60–64 years	208 (17.3%)
Gender
Male	455 (37.8%)
Female	740 (61.5%)
Other	8 (0.7%)
State
NSW	389 (32.3%)
VIC	332 (27.6%)
QLD	216 (18%)
WA	138 (11.5%)
SA	102 (8.5%)
TAS	26 (2.2%)
Area
Metro/city	901 (74.9%)
Regional	229 (19%)
Rural	73 (6.1%)
Household composition
Couple with no children	288 (23.9%)
Couple family with children	519 (43.1%)
One parent family	75 (6.2%)
Single person household	171 (14.2%)
Group household (i.e., shared)	90 (7.5%)
Other	38 (3.2%)
Prefer not to answer	22 (1.8%)
Household income
$0–$20,799	46 (3.8%)
$20,800–$51,999	163 (13.6%)
$52,000–$103,999	341 (28.4%)
$104,000–$207,999	333 (27.7%)
$208,000–$415,999	75 (6.2%)
$416,000 and over	9 (0.7%)
Don’t know	30 (2.5%)
Prefer not to answer	206 (17.1%)
Occupation status
Working (full-time)	539 (44.8%)
Working (part-time or casual)	295 (24.5%)
Student	66 (5.5%)
Not working	51 (4.2%)
Home duties and/or caring responsibilities	102 (8.5%)
Retired	114 (9.5%)
Other	22 (1.8%)
Prefer not to answer	14 (1.2%)

Note. Dollar amounts are in Australian dollars.

Table 3.

Participants’ vaccine background and views on vaccine information.

Most recent influenza vaccine location
GP or Nurse in general practice clinic	597 (49.6%)
Pharmacy	606 (50.4%)
Most recent travel time to influenza vaccine appointment (minutes)
Median	30
Mean	62.8
Standard deviation	131.19
Most recent total influenza vaccine cost
Median	$20
Mean	$15.5
Standard deviation	13.41
Number of influenza vaccinations taken over the past 5 years
Median	4
Mean	3.6
Standard deviation	1.4
Intent to get influenza vaccination this year
Likely yes	431 (35.8%)
Definitely yes	772 (64.2%)
Needle fear (1 – I don’t mind them to 10 – I avoid them at all cost)
Median	2
Mean	3.2
Standard deviation	2.51
Expect HCP to inform them of available vaccines
Yes	990 (82.3%)
No	213 (17.7%)
Expect media to inform them of available vaccines
Yes	660 (54.9%)
No	543 (45.1%)
When they prefer to be informed of vaccine options
Before they arrive for their vaccination appointment	857 (71.2%)
At the time of vaccination appointment	346 (28.8%)

DCE analytical approach

DCE data was modeled using a mixed multinomial logit (MMNL) model for analysis.^33,34 A model was estimated for each of the influenza season severity types (see Additional file 1 for model utility functions). Attributes such as level of protection and cost were coded continuously while the remaining attributes were dummy-coded categories. The MMNL model estimated parameters for each level to determine whether and how much each level predicted vaccine choice. DCE data was modeled using the econometric software, Nlogit version 6.³⁵

The relative importance of each attribute was calculated by finding the maximum difference in utility between the attribute’s levels and expressing it as a percentage of the sum of all maximum differences.³⁶

MWTP is the value a person is willing to pay for a change in an attribute (level) that results in zero change in utility (i.e., they are indifferent between the change). With a linear cost parameter, MWTP is calculated by dividing the attribute level parameter by the cost parameter.³⁴

Results

Demographics and vaccine background information

The analysis sample included 1203 participants (see participant flow diagram in Figure 2). Data was collected in April 2024.

Most participants were female (n = 740, 61.5%). There was a spread across the age groups with around 44% over 50 years of age (n = 527, 43.8%) and the lowest representation among the 18–29-year-olds (n = 170, 14.1%). Representation across the states was reflective of the Australian population distribution. Almost three quarters (n = 901, 74.9%) lived in metro or city areas and the majority were part of a couple, either with (n = 519, 43.1%) or without (n = 288, 23.9%) children. Most were working, either full- (n = 539, 44.8%) or part-time (n = 295, 24.5%) and median reported household income range was $104,000–$207,999, with 17.1% (n = 206) preferring not to respond (see Table 2).

Similar numbers of participants received their last vaccine from a clinic (n = 597, 49.6%) or pharmacist (n = 606, 50.4%). Over 80% (990, 82.3%) of participants expected their HCP to inform them of available vaccines with a lower proportion (n = 660, 54.9%) expecting the media to do so. The majority (857, 71.2%) prefer to be informed about their options before their vaccination appointment (Table 3).

Influenza vaccine attributes predicting preferences

The effect of the constant for a new vaccine was lower compared to the standard vaccine, this means that participants were on average more likely to choose the standard vaccine holding everything else equal. All eight attributes significantly predicted influenza vaccine choice for both same severity and more severe influenza seasons (all p < .05). For both influenza season severity models, participants have a higher preference for a vaccine with higher level of protection against the flu, modern vaccine technology and manufactured by an Australian company. There is also greater preference if the vaccine can be administered at a pharmacy, and when the vaccine is preferred by HCPs for themselves and their patient. Participants also prefer a vaccine that is designed to be an exact match to circulating influenza strains and is government funded for high-risk individuals. However, participants’ vaccine preference is higher when the total out-of-pocket cost is lower. Model parameters for the same severity season (Table 4) and a more severe season (Table 5) are presented below.

Table 4.

Model parameters for DCE framed as having same season severity as last year. Note. Ref = reference category.

Attribute	Level	Coefficient	p-value	SE	T-Ratio
New vaccine constant	Constant for a new vaccine	−0.34	<0.001	0.10	−3.32
Protection	Protection against the influenza (continuous variable)	0.20	<0.001	0.01	18.38
Technology	Traditional technology	0	Ref
	Modern technology	0.52	<0.001	0.08	6.43
Manufacturer location	Overseas company	0	Ref
	Australian and overseas company	0.44	<0.001	0.08	5.27
	Australian company	0.98	<0.001	0.10	9.53
Where vaccine is received	GP or Nurse in clinic	0	Ref
	Pharmacy	0.24	<0.001	0.07	3.38
HCP preference	No	0	Ref
	Yes	1.59	<0.001	0.10	15.65
Match	No	0	Ref
	Yes	3.10	<0.001	0.14	21.53
Funded for high-risk individuals	No	0	Ref
	Yes	0.50	<0.001	0.09	5.74
Cost	Total out-of-pocket cost (continuous variable)	−0.12	<0.001	0.01	−20.77

Table 5.

Model parameters for DCE framed as having a more severe season than last year. Note. Ref = reference category.

Attribute	Parameter (levels)	Coefficient	p-value	SE	T-Ratio
New vaccine constant	Constant for a new vaccine	−0.21	0.046	0.11	−2
Protection	Protection against the influenza (continuous variable)	0.22	<0.001	0.01	21.43
Technology	Traditional technology	0	Ref
	Modern technology	0.39	<0.001	0.08	5.01
Manufacturer location	Overseas company	0	Ref
	Australian and overseas company	0.53	<0.001	0.08	6.32
	Australian company	1.06	<0.001	0.10	10.9
Where vaccine is received	GP or Nurse in clinic	0	Ref
	Pharmacy	0.29	<0.001	0.07	4.06
HCP preference	No	0	Ref
	Yes	1.63	<0.001	0.10	16.8
Match	No	0	Ref
	Yes	3.30	<0.001	0.14	23.39
Funded for high-risk individuals	No	0	Ref
	Yes	0.57	<0.001	0.08	6.83
Cost	Total out-of-pocket cost (continuous variable)	−0.10	<0.001	0.01	−19.86

Relative attribute importance

All eight attributes were ordered based on relative importance to participants. Relative attribute importance is calculated by finding the maximum difference in utility between the attribute levels and expressing this difference as a percentage of the sum of all maximum differences. The relative importance sums to 100%. The three most important attributes in both season severity scenarios were protection, match and cost, however, cost was of relatively higher importance in the “same severity” season than in the “higher severity” season where greater protection and match were relatively more valued. Figure 3 illustrates the relative importance of each attribute for both influenza season severity types.

Figure 3.

Relative attribute importance for both influenza season severity types. Blue bars represent influenza season that has the same severity as last year while dark blue bars represent a influenza season more severe than last year.

Preference shares

Model parameters can be used to simulate preference shares by mapping parameters to levels, for example, vaccine profiles, calculating the utility for each profile and generating probabilities using the standard logit formula. To understand preferences for recently developed influenza vaccines, preference shares were simulated for profiles representing an estimated cell-based vaccine and a standard (egg-based) vaccine in Australia based on expert opinion (see Figure 4), with levels presented in Table 6. Figure 4 includes a season when egg-adapted mutations may not be a concern, and both vaccines were assumed to match circulating influenza strains. Considering the small effect and relatively low importance of vaccine location, the levels of pharmacy and GP clinic were aggregated for a wider readership.

Figure 4.

Preference shares of estimated standard (egg-based) and cell-based vaccines. Blue bars indicate a preference for the standard vaccine and green bars indicate a preference for a cell-based vaccine.

Table 6.

Levels of estimated standard (egg-based) and cell-based influenza vaccine profiles simulated in figure 4.

Attribute	Standard (egg-based) influenza vaccine	Cell-based influenza vaccine
Level of protection (representing a 14% difference in relative vaccine effectiveness in protection)	50%	57%
Vaccine technology	Traditional	More modern
Manufacturing company location	Australian and overseas company	Australian company
Where vaccine is received	GP clinic or pharmacy	GP clinic or pharmacy
Preferred vaccine of HCPs	Yes	Yes
Match to circulating flu strains	No	Yes
Match to circulating flu strains (in a season when match is not a concern)	Yes	Yes
Government funded for high-risk individuals	No	No
Total out-of-pocket cost	$20	$35

Marginal willingness to pay (MWTP)

The average MWTP for the three most important attributes was calculated and presented in Table 7 (see Additional file 2 for all attributes). As “cost” is the denominator used in calculating MWTP and the parameter of interest is used as the numerator, the next most important attribute (HCP preference) was included. For each percentage increase in protection against the flu, participants were willing to pay an additional $1.61 in a season of the same severity and an additional $2.18 in a more severe season. For a vaccine designed to be an exact match, the MWTP was $25.37 and $32.37 in a season of the same or greater severity, respectively. HCP preference elicited a MWTP of $4.06 and $15.97, respectively, in seasons of the same or greater severity.

Table 7.

The average marginal willingness to pay (MWTP) for three most important attributes not including cost.

	Same severity as last year	More severe than last year
Protection against the flu
WTP for each additional % in protection	$1.61	$2.18
Vaccine designed to be an exact match
No	$0.00	$0.00
Yes	$25.37	$32.37
Preferred vaccine of HCPs
No	$0.00	$0.00
Yes	$4.06	$15.97

Note. Protection against influenza was coded continuously and hence the amount ($) represents the incremental amount participants would be willing to pay for an extra percentage of protection, above the standard protection of 50%. For all other attributes, a MWTP of $0 indicates this was the reference category. A MWTP greater than $0 indicates the incremental amount ($) participants were willing to pay for that level compared to the reference category.

Discussion

Vaccine attributes as predictors of choice

Findings of this study are consistent with earlier studies highlighting that protection, location where vaccine is offered and HCP preference are important considerations to influenza vaccine choice.^27,28 In addition, our findings suggest that vaccine technology, manufacturing company location, match to circulating influenza strains, whether it is government funded for high-risk individuals, and total out-of-pocket costs are also important considerations, and may in fact be more important, when selecting an influenza vaccine.

The directional relationships between attributes and choice are the same regardless of influenza season severity type. This too is consistent with previously tested attributes,^27,28 however, it is of note that the relative importance of the attributes varies based on the presented influenza season severity.

Relative attribute importance

In both influenza season severity types, protection, match to circulating influenza strains and cost were the three most important attributes when considering influenza vaccine choice. Although participants were more cost-sensitive relative to match and protection in the same influenza season severity type, protection and match were prioritized ahead of cost when the influenza season was framed as more severe than last year. Perhaps, participants are generally cost-sensitive when choosing an influenza vaccine, but attributes relating to vaccine efficacy become more salient in a more severe influenza season. The design of this study enabled teasing apart nuanced differences in attribute importance for different severities of influenza seasons. Findings highlight the importance of protection, match and cost in vaccine choice, and changes in prioritization depending on influenza season severity.

Preference shares

When preference shares were simulated from model estimates for a standard (egg-based) and cell-based vaccine (Figure 4), there was a distinctly large preference for a cell-based vaccine, even when cost was nearly doubled. When both vaccines have a match to circulating flu strains, a cell-based vaccine was still preferred over a standard vaccine, although to a lesser extent. In both scenarios, preference for a cell-based vaccine was more pronounced in a more severe influenza season. Taken together, the findings highlight the willingness to trade-off on cost for greater protection, match to circulating strains, more modern vaccine technology and manufactured by an Australian company when choosing an influenza vaccine.

Marginal willingness to pay (MWTP)

The MWTP was calculated for the three most important attributes, excluding cost. Similar to patterns found in relative attribute importance, the MWTP for vaccine attributes highlight the value participants ascribe to and are willing to pay more for, such as greater protection against the flu, match to circulating influenza strains and a vaccine preferred by HCPs for themselves and their patients, especially during a more severe influenza season.

Limitations

Findings should be interpreted alongside consideration of the study’s limitations. First, in completing the DCE, participants who opted in to participate (self-selection) had full information in the experiment about the current and new vaccine options when making a choice (i.e., vaccine attribute descriptions and levels were presented for consideration). If individuals are not presented with full information on vaccine options, which is a likely scenario in a real-world context, these results may not translate in the same way. To avoid assumptions on vaccine options, it is important to communicate vaccine information to individuals for an informed choice, promoting shared decision-making. Second, because the researchers wished to examine cost as an attribute, people eligible for a free influenza vaccine were excluded to avoid confounding the effect of vaccine cost, and to provide a more realistic trade-off. This meant Aboriginal and Torres Strait Islander people, pregnant women, people 65 years and above and people at high risk of a medical condition, were not represented in this sample. Further, people who had never been vaccinated against influenza or who were not intending to get vaccinated in the upcoming season were not included in the study. Results should be interpreted as representing a specific segment of the population, people who are considering getting vaccinated for the next season and who are not eligible for a free vaccine. Despite this, findings share valuable insight on people’s preferences for different influenza vaccine profiles, such as cell-based vaccines.

Practical implications

These findings extend the literature by examining the importance of protection and match to circulating influenza strains. In addition, findings highlight that, while people are cost-sensitive, they are willing to trade-off on cost for efficacy-related attributes such as protection and match to circulating influenza strains, especially in a more severe influenza season. Along the same vein, people value, and are willing to pay more for greater protection against the flu, a vaccine designed to be an exact match with circulating influenza strains, and HCP endorsement. Findings can help HCPs, vaccine manufacturers and policy-makers better understand people’s preference, particularly with more modern developments such as cell-based vaccines. Given that HCP recommendation is an important predictor of vaccine uptake,³⁷ insights from this study can assist HCPs in communicating vaccine information to patients. This includes supporting a patient to make an informed choice through a discussion of evidence and information on the vaccine options available, relative risks and benefits of vaccine options, and the individual preferences and circumstances of the patient, thus enabling an informed decision with shared responsibility in determining the most suitable vaccine.

Conclusion

This Australian study demonstrates the value people place on influenza vaccine attributes and that they are willing to pay more for those attributes important to them. Specifically, people are willing to pay more for greater protection against influenza and a vaccine designed to be an exact match to circulating influenza strains. This is more pronounced in seasons when circulating influenza is of greater severity. Findings highlight the importance of providing relevant information to enable shared decision-making in influenza vaccine choice.

Ethical approval and consent to participate

Informed consent was obtained from all participants. This study was conducted in accordance with the Declaration of Helsinki. According to the National Statement on Ethical Conduct in Human Research²⁶ in Australia, this non-interventional study was of negligible risk and could be exempted from ethical review. No ethics approval was deemed necessary.

Biography

Dr Simon Fifer is on the Advisory committee at the Patient Voice Initiative and is Director of Research and Innovation at CaPPRe. He is a ‘pracademic’ (practical academic), with a research focus directed at solving real-world problems by studying human decision-making using choice-based measurement. In healthcare, this translates to measuring patient preferences and values. Simon is on the editorial board for the journal The Patient, and regularly presents at national and international conferences and publishes papers in leading academic journals. Simon has a PhD in Choice modelling from the University of Sydney.

Funding Statement

The study was funded by CSL Seqirus Ltd who were not part of the overall study design. Members of the companies were involved in the final discussion of results and dissemination. Funding bodies had no specific role in data collection, or data analysis. DY, KY and JM are employees of CSL Seqirus. SF and LT are from CaPPRe who were contracted by CSL Seqirus to design the experiment, provide data management, conduct the analysis and write the manuscript. CaPPRe has consulted to AbbVie, Amgen, AstraZeneca, BMS, Celgene, CSL Behring, Edwards, Gilead, GSK, Ipsen, Janssen, MSD, Novo Nordisk, Roche, Sanofi, Shire, Takeda, UCB and Vertex, outside of the submitted work.

Disclosure statement

No potential conflict of interest was reported by the authors.

Authors’ contributions

SF: conceptualization, design and methodology, formal analysis, supervision, writing – review and editing. LT: project administration, design and methodology, data collection and curation oversight, preparation of the manuscript – original draft and editing DY: project administration, writing – review and editing. KY: writing – review and editing. JM: funding acquisition, conceptualization, writing – review and editing. All authors read and approved the final manuscript.

Supplementary material

Supplemental data for this article can be accessed on the publisher’s website at https://doi.org/10.1080/21645515.2024.2440164.