PLoS ONE
Public Library of Science
image
From trivalent to quadrivalent influenza vaccines: Public health and economic burden for different immunization strategies in Spain
DOI 10.1371/journal.pone.0233526 , Volume: 15 , Issue: 5

Table of Contents

Highlights

Notes

Abstract

PurposeQuadrivalent influenza vaccine (QIV) includes the same strains as trivalent influenza vaccine (TIV) plus an additional B strain of the other B lineage. The aim of the study was to analyse the public health and economic impact of replacing TIV with QIV in different scenarios in Spain.MethodsA dynamic transmission model was developed to estimate the number of influenza B cases prevented under TIV and QIV strategies (<65 years (high risk) and ≥65 years). This model considers cross-protective immunity induced by different lineages of influenza B. The output of the transmission model was used as input for a decision tree model that estimated the economic impact of switching TIV to QIV. The models were populated with Spanish data whenever possible. Deterministic univariate and probabilistic multivariate sensitivity analyses were performed.ResultsReplacing TIV with QIV in all eligible patients with current vaccine coverage in Spain may have prevented 138,707 influenza B cases per season and, therefore avoided 10,748 outpatient visits, 3,179 hospitalizations and 192 deaths. The replacement could save €532,768 in outpatient visit costs, €13 million in hospitalization costs, and €3 million in costs of influenza-related deaths per year. An additional €5 million costs associated with productivity loss could be saved per year, from the societal perspective. The budget impact from societal perspective would be €6.5 million, and the incremental cost-effectiveness ratio (ICER) €1,527 per quality-adjusted life year (QALY). Sensitivity analyses showed robust results. In additional scenarios, QIV also showed an impact at public health level reducing influenza B related cases, outpatient visits, hospitalizations and deaths.ConclusionsOur results show public health and economic benefits for influenza prevention with QIV. It would be an efficient intervention for the Spanish National Health Service with major health benefits especially in the population ≥65-year.

Crépey, Redondo, Díez-Domingo, Ortiz de Lejarazu, Martinón-Torres, Gil de Miguel, López-Belmonte, Alvarez, Bricout, Solozabal, and Tsuzuki: From trivalent to quadrivalent influenza vaccines: Public health and economic burden for different immunization strategies in Spain

Introduction

Influenza is an infectious viral illness that occurs in seasonal epidemics every year [1]. It is mainly caused by influenza type A virus (A/H1N1 and A/H3N2 subtypes) and type B (B/Victoria and B/Yamagata lineages), or any combination of these. Influenza B is more stable than influenza A, with less antigenic drift and consequent immunological stability, although genetic distance of influenza B lineages has been increasing over the years [2].

At global level, the annual incidence rate of influenza is 5–10% in adults and 20–30% in children [1]. In Spain, influenza also constitutes a substantial clinical and socioeconomic burden for society. According to data of the Instituto de Salud Carlos III (ISCIII), there are around 600,000 confirmed cases of influenza in adults and 300,000 in children annually [3]. It has been estimated that influenza causes 1.3 million medical consultations every year, more than 140,000 emergency visits [4], 51,000 hospitalizations [5] and between 8,000 and 14,000 deaths in Spain [6, 7]. The total impact of seasonal influenza in Spain costs up to 145–1,000 million euros per year [8]. Hospitalization cost is the key driver, whereas for outpatient care indirect costs, due to absenteeism in the workplace, may be 3.5 times higher than direct costs.

The cases attributable to influenza B virus are distributed across all age groups, particularly in children and young adults [9]. Furthermore, since 2001, surveillance data documented the co-circulation of the two lineages [10, 11] and the unpredictability of predominant B lineage in each season. Whereas in Europe, B strains represent on average 20–25% of all circulating strains [12, 13], in Spain, the circulation of B virus has reached 27.6% between 2000–2001 and 2015–2016 seasons [3, 14]. Another recently published study has confirmed this result, showing that the median proportion of influenza B cases between 2007 and 2017 was 27.2% [15].

Annual vaccination is the most effective way to prevent influenza infections. In 2003 the World Health Organization (WHO) recommended a 75% vaccination coverage rate (VCR) for the older age groups, although by 2010, this objective has not been reached yet in Spain. Specifically, VCR in ≥65-year-old population was 55.7% in the 2017–2018 season [16]. Even more, VCR of healthcare professionals, considered as a group with higher risk for spreading the disease, was only 31% [17].

Twice each year, the WHO recommends the strains of influenza viruses that should be included in the influenza vaccine for the following epidemic season. Quadrivalent influenza vaccine (QIV), which includes two influenza A subtypes (H1N1 and H3N2) and two influenza B lineages (B/Victoria and B/Yamagata), have been included in these recommendations since the 2013–2014 season, together with the trivalent influenza vaccine (TIV) [18]. In the 2018–2019 season, for the first time the WHO defined the recommendation of QIV as first option [18]. In addition, since 2017 the European Centre for Disease Prevention and Control (ECDC) recommends QIV for influenza prevention [19]. Other European countries have also recognized the value of QIV [2022]. In Spain, between 2007 and 2017, when the WHO still recommended TIV [18], there has been a mismatch between the circulating B lineage and the one present in the vaccine, in four out of the ten seasons [15]. Overall, an estimated 53.9% of influenza B samples during this period were the influenza B strain not included in the vaccine [3]. As a result, vaccine effectiveness of TIV was reduced [23], which illustrates the need of an influenza vaccine with broader protection against B lineages.

Economic evaluation of health interventions is a useful tool to inform the decision-making process in a resource constrained health system [24]. A previous research on five European countries estimated on the basis of a static health economic model, that the use of QIV instead of TIV throughout 10 seasons (from 2002–2003 to 2012–2013, 2009–10 pandemic excluded) could have prevented up to 150,964 cases of influenza, 13,181 primary care visits, 4,042 hospitalizations and 1,511 deaths, as well as an absenteeism of 18,546 workdays. Altogether, more than 24 million € (21 million € of direct costs and 3 million € of indirect costs) could have been saved [25]. For communicable infectious diseases like influenza, as vaccination may also impact disease transmission, a dynamic model provides a more realistic simulation of disease transmission, capturing changes in the probability of infection over time [2629]. Hence, dynamic modeling represents the most appropriate approach to quantify the health and economic impact of QIV against seasonal influenza [26, 30]. The aim of this study was to understand and quantify retrospectively the potential public health and economic impact of influenza vaccination with QIV compared to TIV (current standard of care), between 2011 and 2018 seasons in Spain, according to different vaccination scenarios.

Materials and methods

Overview

To estimate age-stratified numbers of symptomatic influenza B cases under TIV and QIV strategies in Spain, we adapted a dynamic transmission model developed by Crépey et al. 2015 previously used in the United States (US) setting [31, 32].

Model structure

The model is a variation on the compartmental SEIR epidemiological model: susceptible to infection (S), exposed but not infectious (E), infectious (I), and recovered (R) (and therefore immune for a certain time period). In the present model a vaccination compartment was added to include individuals protected by the vaccine. The model considered B virus cross-protection, which accounts for people vaccinated or infected with one B lineage that are partially protected against the opposite B lineage. It also uses a population contact matrix based on data from Italy [33], as no contact matrix directly estimated on Spanish population was available at the time of the study, and population contact characteristics are assumed to be similar in both countries. We considered a latent period of one day and a contagious period of 4.8 days [34]. We assumed that the vaccination campaign was completed ahead of the start of the epidemic with a progressively increasing coverage starting on week 44 and ending on week 50 each year. Asymptomatic infections are also accounted for and assumed to represent a third of all infections [34].

The economic model is an age-structured decision tree model developed in Excel (Fig 1). The age-stratified symptomatic influenza cases (output of the dynamic model described above) were used as inputs to the economic model. Based on available economic data, the age groups were re-categorized (0–1 years, 2–4 years, 5–14 years, 15–19 years, 20–49 years, 50–64 years 65–69 years, 70–74 years and 75+ years), using age-distribution data of the Spanish population [35]. Influenza cases were stratified between non-high-risk (NHR) and high-risk (HR) patients, based on the presence of underlying medical conditions [36], and were subsequently divided into four categories based on medical outcomes (no medical attention, outpatient visit, hospital admission, and death), with different complication probabilities for each category [37].

Flow diagram of the economic decision tree model.
Fig 1
Flow diagram of the economic decision tree model.

Model calibration

Probabilities of influenza infection–bh1 and bh3, respectively for A/H1N1 and A/H3N2 and bv and by , respectively for B/Victoria and B/Yamagata, were calibrated two by two simultaneously (A together and B together, but not A and B together) for all seasons (2011 to 2018) (Fig 2). In addition, to replicate yearly variations in influenza peaks and dominance of one lineage or subtype over the other, first it was calibrated, for example, bvi and byi for the first year i = 2011. The final state of year i was the initial state of year i + 1. Then it was calibrated bvi + 1 and byi + 1 for year i + 1, taking into account the population immune status acquired in previous years. Each year, the population was vaccinated with a TIV containing the B lineage used the respective year.

Calibration results for B Victoria and B Yamagata.
Fig 2
Calibration results for B Victoria and B Yamagata.

Other influenza modelling approaches, including static models, compute the expected probability of infection without vaccination. In contrast, this dynamic model approach allows the impact of vaccination to be directly accounted for, as the estimates are computed under a given vaccination coverage, vaccine composition, and vaccine efficacy (in this case, TIV). The calibration uses a Nelder–Mead simplex algorithm with a least square fitness function. The model and calibration process are implemented in R and take approximately 45 minutes to compute on a 2.9 GHz microprocessing quadricore. The calibration results depicted in Fig 2 show the robustness of the dynamic model.

Vaccination scenarios

In Spain, influenza vaccination campaigns in most Autonomous Communities are focused on the ≥65-year-old population and on the <65-years-old population but at higher risk of complications (HR population). Vaccination scenarios in the present study were defined taking into account both target populations (S1 Fig).

The scenario 1 compared the current vaccination strategy, where only TIV vaccine is used, versus an alternative vaccination strategy, where all eligible population used QIV at current vaccination coverage rates (Table 1). The following additional scenarios were analysed:

Table 1
Model inputs.
VariableBase caseReference
Vaccine efficacy per strain[38] and CDC unpublished data
AH1N1
    0–0.5 y0
    0.5–5 y0.5085
    5–10 y0.473
    10–15 y0.41
    15–20 y0.41
    20–40 y0.4165
    40–60 y0.6665
    60–100 y0.5
AH3N2
    0–0.5 y0
    0.5–5 y0.5085
    5–10 y0.473
    10–15 y0.41
    15–20 y0.41
    20–40 y0.4165
    40–60 y0.6665
    60–100 y0.5
B Victoria
    0–0.5 y0
    0.5–5 y0.6102
    5–10 y0.5676
    10–15 y0.492
    15–20 y0.492
    20–40 y0.4998
    40–60 y0.7998
    60–100 y0.6
B Yamagata
    0–0.5 y0
    0.5–5 y0.6102
    5–10 y0.5676
    10–15 y0.492
    15–20 y0.492
    20–40 y0.4998
    40–60 y0.7998
    60–100 y0.6
Vaccine cross-protection ratio (B strains) (%)70Estimated from [38]
Vaccine coverage (%)
    0–4 y1.68[39]
    5–14 y1.68
    15–44 y5.22[40]
    45–64 y15.67
    65+ y58.16
Proportion of HR individuals (%)
    0–1 y7.0[39]
    2–4 y7.0
    5–14 y7.0
    15–19 y11.2[40]
    20–49 y12.1
    50–64 y27.3
    65–69 y37.0
    70–74 y44.3
    75–79 y50.1
    80+ y56.1
Probability of outpatient visit/flu infection (%)
    0–1 y9.60[41], [35]
    2–4 y9.60
    5–14 y13.66
    15–19 y7.52
    20–49 y7.09
    50–64 y8.99
    65–69 y7,92
    70–74 y6,81
    75–79 y5,40
    80+ y4,04
Probability of otitis media / influenza-related outpatient visit (%)
    0–6 m5.56[42]
    6–59 m5.56
    5–9 y5.56
    10–14 y5.56
Probability of pneumonia or other complications / influenza-related outpatient visit (%)
    0–6 m14.05[43]
    6–59 m14.05
    5–9 y14.05
    10–14 y14.05
Probability of hospitalization / flu case (%)
    0–1 y8.12[5, 41]
    2–4 y5.85
    5–14 y0.46
    15–19 y0.15
    20–49 y0.47
    50–64 y1.76
    65–69 y4.65
    70–74 y3.80
    75–79 y5.16
    80+ y3.98
Probability of death / flu case (%)
    0–1 y0.49[5, 41]
    2–4 y0.60
    5–14 y1.46
    15–19 y2.74
    20–49 y6.22
    50–64 y7.84
    65–69 y6.74
    70–74 y5.63
    75–79 y6.63
    80+ y6.47
Costs parameters (2017, €)
Outpatient visit without complication
    All ages54.06[44]
Outpatient visit otitis media
    0–14 y208.98[44]
Outpatient visit pneumonia or other complications
    0–14 y208.39[44]
Hospitalization
    0–4 y2,882.31[41]
    5–14 y2,981.36
    15–44 y4,775.83
    45–64 y5,786.75
    65+ y4,485.89
Medical cost per death
    0–4 y240.12[41]
    5–14 y1,212.72
    15–44 y9,394.25
    45–64 y16,191.23
    65+ y4,795.73
Medication
    All ages22.52[45]
Lost workdays: Outpatient visit (N days)
    15–44 y9[46]
    45–64 y9
Lost workdays: Hospitalization (N days)
    15–44 y30.50[46]
    45–64 y30.50
Daily earnings for productivity losses
    15–44 y84.66[47]
    45–64 y84.66
Vaccine price
    TIV7.15[45]
    QIV9.50
Health effects
Baseline utility
    0–4 y0.9900[48]
    5–14 y0.9900
    15–44 y0.9683[40]
    45–64 y0.9140
    65+ y0.7769
QALY loss per inpatient influenza episode
    0–18 y0.031068493[49]
    19–49 y0.034232877
    50–64 y0.033369863
    65+ y0.032219178
QALY loss per outpatient influenza episode
    0–18 y0.007863014[49]
    19–49 y0.008821918
    50–64 y0.00690411
    65+ y0.006136986
Life expectancy
    0–4 y81.18[50]
    5–14 y73.78
    15–44 y54.02
    45–64 y30.20
    65+ y9.46
Costs and health effects discounted at 3% [51].
    QIV in < 65 years old (HR population) and TIV for ≥ 65 years old, both at current vaccination coverage rates (scenario 2).
    TIV in < 65 years old (HR population) and QIV for ≥ 65 years old, both at current vaccination coverage rates (scenario 3).

Probabilities

Table 1 lists all relevant probabilities included in the model.

Health status

The proportion of adults (≥18 years) and young adults (15 to 17 years) with high risk of being infected by influenza were obtained from the Spanish National Health Survey [40]. For children this information was obtained from González et al. [39].

Primary care

The probability of an outpatient visit per influenza case was estimated from the total number of cases in Primary Care visits (International Classification of Primary Care, Second edition code: R80 influenza) [52] and the total population in Spain [35], between 2011 and 2014. An equal probability was assumed for NHR and HR patients due to the lack of a reliable Spanish source. For children, outpatient visits were further divided between uncomplicated and complicated cases, which were defined as cases that present otitis media [42] and pneumonia or other complications [43].

Hospitalizations and mortality

Probabilities of hospitalization were estimated from the hospitalized cases of laboratory-confirmed influenza of the Instituto de Salud Carlos III report [5], and the value was adjusted to the pre-specified age groups through Minimum Basic Hospital Data Set (MBDS) [41]; the International Classification of Disease-9 (ICD-9) codes for influenza were 487 and 488. Additionally, an equal probability for NHR and HR patients was assumed. The probability of death within hospitalized individuals was also estimated from the Instituto de Salud Carlos III report [5] and adjusted to age groups through MBDS [41].

Costs

As it is recommended in Spain, a discount rate of 3% was used for costs and health outcomes [51].

Medical costs

The cost of outpatient visits in adults was obtained from the eSalud database [44], as well as the cost of outpatient visits in children (<18 years), which also include the cost of physician visits for otitis media and the cost of physician visits for pneumonia or other complications [44]. Hospitalization cost and cost per death were obtained by age group from the National Health System hospital admission’s registry (ICD-9 487 and 488) [41]. Patients who died during an episode of hospitalization have an extra cost, based on hospital admission’s registry (ICD-9 487 and 488) with an exitus discharge; the average cost of all those patients was calculated. Medication cost was calculated as the average cost of influenza antiviral products, obtained from the Spanish official webpage BotPlus 2.0 [45].

Vaccination costs

The total yearly number of administered vaccinations was calculated by multiplying the age-specific coverage rates with the corresponding population sizes, as already present in the underlying dynamic model. Vaccine prices of TIV and QIV for the public and private markets were obtained from the Spanish official database BotPlus 2.0 [45]. No administration costs were included in this analysis because these were assumed to be equal in both alternatives.

Indirect costs

Days of productivity loss due to outpatient visit or hospitalization were only assigned to adults (≥18 years) and were obtained from Galante et al. [46]. We used the friction costs method approach to count productivity losses [53] and labor elasticity adjustment factor [54], as detailed in a previous publication [32]. The friction cost limits productivity losses of long-term absence to a friction period [55], and the labor elasticity adjustment quantifies the proportion of reduction in effective labor time due to absence. The friction period was set at 40 days [56, 57] and the elasticity of labor was 0.8 [58]. Days of productivity loss were multiplied by daily earnings [59], assumed equal across all age groups.

Health effects

Baseline utilities for adults (≥18 years) and young adults (15 to 17 years) were obtained from the Spanish National Health Survey [40]. For children these were derived from García et al. [48]. Lost quality-adjusted life years (QALY) due to influenza were calculated from Hollmann et al. [49], based on utility loss for inpatient and outpatient settings. Life expectancy data was extracted from Spanish projected mortality tables 2016–2065 [50] and was used to calculate the number of LYs lost due to influenza-related deaths.

Vaccine efficacy

Vaccine efficacy against influenza A and B was calculated by strain (A/H1N1, A/H3N2, B/Victoria and B/Yamagata). In all cases, it was estimated on the basis of Díaz-Granados et al. [38] and by age (CDC unpublished data). The same relative efficacy by age was assumed between different strains and between NHR and HR individuals. The model considered cross-protection between B lineages, estimated from mismatched efficacy data of Díaz-Granados et al. [38]. Potential heterosubtypic immunity between A/H1N1 and A/H3N2 has not been documented.

Outcomes

The epidemiological model produced weekly symptomatic influenza incidence, incidence per age group and per season, number of influenza cases per subtype and lineage for all years of the study period.

The model enables the estimation of the number of influenza cases by strain and by age groups for all defined scenarios (public health impact). Results are displayed annually and in an aggregated way (for 8 influenza seasons). Burden of influenza avoided due to the replacement of TIV with QIV in different scenarios is also estimated.

Analysis

Analysis were conducted from both payer and societal perspective. The payer perspective considered the costs assumed by healthcare system [51, 60]. In Spain there is a Public Healthcare System which covers all the health services included in the model but antiviral drugs indicated for influenza. Societal perspective included also the costs of antiviral drugs and indirect cost due to productivity losses [51, 60].

Sensitivity analysis

A deterministic sensitivity analysis was performed to assess the individual impact of each parameter on the results, using 95% confidence intervals of the parameters when available. Results of the deterministic sensitivity analysis were presented in a Tornado diagram. A probabilistic sensitivity analysis (PSA) was performed to assess the robustness of the results using the commonly accepted distributions: Beta for probabilities, Lognormal for costs and Gamma for utilities and lost workdays. A total of 1,000 iterations were calculated and the results were presented as 95% credibility intervals for the main model outputs.

Results

A total of 138,707 influenza B cases would have been avoided per season replacing TIV by QIV in all eligible populations (scenario 1) (1,109,654 cases avoided during 8 influenza seasons), with an impact in all age groups (Table 2). Consequently, related to influenza B, 10,748 outpatient visits, 3,179 hospitalizations and 192 deaths would have been avoided per season (85,982, 25,429 and 1,538 during the whole period), a reduction of 15%, 20% and 21%, respectively (Table 3). Regarding discounted costs, the replacement from TIV to QIV would have led to an increase per year of €27 million in vaccine costs (€219 million for 8 seasons); although it would have saved €0.5 million in outpatient visit costs, €13 million in hospitalization costs, and €3 million in costs of influenza-related deaths (€4 million, €107 million and €20 million during all seasons, respectively). An additional €5 million of productivity losses would have been saved from the societal perspective (€37 million for all 8 seasons) (Table 4).

Table 2
Reduction of influenza B cases achieved by use of QIV vs TIV, by group of age (over the 8 seasons; 2011–2018).
TIVScenario 1 (QIV in eligible groups at current VCR)Scenario 2 (QIV in <65y at current VCR, TIV in > = 65y at current VCR)Scenario 3 (QIV in > = 65y at current VCR, TIV in <65y at current VCR)
Absolute differenceAbsolute differenceAbsolute difference
Age groups (years)Current situationNew situationCasesRate*Relative differenceNew situationCasesRate*Relative differenceNew situationCasesRate*Relative difference
0–135,47831,138-4,340-579-12.2%34,720-758-101-2.1%31,578-3,900-520-11.0%
2–4105,38692,614-12,772-1,110-12.1%103,194-2,192-190-2.1%93,899-11,487-998-10.9%
5–14992,130873,200-118,930-2,817-12.0%971,289-20,841-494-2.1%885,930-106,200-2,516-10.7%
15–19689,546612,093-77,453-3,527-11.2%675,819-13,727-625-2.0%621,033-68,513-3,120-9.9%
20–492,876,0282,513,312-362,716-2,122-12.6%2,810,320-65,708-384-2.3%2,552,559-323,469-1,893-11.2%
50–641,022,743865,327-157,416-1,733-15.4%981,888-40,855-450-4.0%888,675-134,068-1,476-13.1%
65–69195,966148,505-47,461-1,575-24.2%190,207-5,759-191-2.9%150,659-45,307-1,504-23.1%
70–74277,914214,001-63,913-2,222-23.0%270,283-7,631-265-2.7%217,052-60,862-2,115-21.9%
75+1,048,624783,971-264,653-2,355-25.2%1,019,038-29,586-263-2.8%795,750-252,874-2,250-24.1%
Total7,243,8156,134,161-1,109,654-2,150-15.3%7,056,758-187,057-362-2.6%6,237,135-1,006,680-1,950-13.9%
QIV: quadrivalent influenza vaccine; TIV: trivalent influenza vaccine; VCR: vaccination coverage rate.
*Rate per 100,000
Table 3
Health outcomes of influenza B avoided with the replacement of TIV by QIV in Spain in different scenarios.
Yearly averageTotal over the 8 seasons (2011–2018)
OutcomesCurrent situationNew situationDifferenceCurrent situationNew situationDifferenceRelative difference
Scenario 1: (QIV in eligible groups at current VCR)
Number of cases905,477766,770-138,7077,243,8156,134,161-1,109,654-15.3%
Number of outpatient visits73,47362,725-10,748587,784501,801-85,982-14.6%
Number of hospitalizations15,90712,728-3,179127,254101,825-25,429-20.0%
Number of deaths905713-1927,2425,704-1,538-21.2%
Scenario 2: (QIV in <65y at current VCR, TIV in ≥65y at current VCR)
Number of cases905,477882,095-23,3827,243,8157,056,758-187,057-2.6%
Number of outpatient visits73,47371,590-1,883587,784572,717-15,067-2.6%
Number of hospitalizations15,90715,460-447127,254123,678-3,576-2.8%
Number of deaths905878-277,2427,026-215-3.0%
Scenario 3: (QIV in ≥65y at current VCR, TIV in <65y at current VCR)
Number of cases905,477779,642-125,8357,243,8156,237,135-1,006,680-13.9%
Number of outpatient visits73,47363,784-9,689587,784510,274-77,510-13.2%
Number of hospitalizations15,90712,940-2,967127,254103,518-23,736-18.7%
Number of deaths905725-1807,2425,804-1,438-19.9%
Table 4
Economic impact of influenza B avoided with the replacement of TIV by QIV in Spain in different scenarios.
Yearly averageTotal over the 8 seasons (2011–2018)
Current situation (TIV)
    Outpatient visits3,710,639 €-29,685,115 €-
    Hospitalizations65,627,471 €-525,019,766 €-
    Deaths11,888,318 €-95,106,546 €-
    Productivity losses34,410,751 €-275,286,007 €-
New situationDifferenceNew situationDifference
Scenario 1 (QIV in eligible groups at current VCR)
    Outpatient visits3,177,871 €-532,768 €25,422,972 €-4,262,143 €
    Hospitalizations52,247,448 €-13,380,022 €417,979,588 €-107,040,178 €
    Deaths9,347,429 €-2,540,889 €74,779,434 €-20,327,112 €
    Productivity losses29,749,405 €-4,661,346 €237,995,237 €-37,290,770 €
Scenario 2 (QIV in <65y at current VCR, TIV in > = 65y at current VCR)
    Outpatient visits3,615,048 €-95,591 €28,920,386 €-764,729 €
    Hospitalizations63,693,607 €-1,933,864 €509,548,854 €-15,470,912 €
    Deaths11,524,231 €-364,087 €92,193,851 €-2,912,695 €
    Productivity losses33,390,188 €-1,020,563 €267,121,503 €-8,164,504 €
Scenario 3 (QIV in > = 65y at current VCR, TIV in <65y at current VCR)
    Outpatient visits3,229,757 €-480,882 €25,838,058 €-3,847,057 €
    Hospitalizations53,118,653 €-12,508,818 €424,949,222 €-100,070,544 €
    Deaths9,509,338 €-2,378,980 €76,074,704 €-19,031,842 €
    Productivity losses30,325,479 €-4,085,272 €242,603,834 €-32,682,173 €

Taking into account the above-mentioned results (scenario 1), the incremental direct costs would have been €11.7 million (payer perspective) and €6.5 million from societal perspective (discounted savings per year). In terms of health-related outcomes, a total of 4,286 QALYs would have been saved per year. This results in an ICER of €2,751 per QALY gained from a payer perspective and €1,527 from a societal perspective. These results were robust as shown in Fig 3 for the deterministic sensitivity analysis and in Table 5 for the PSA.

Deterministic sensitivity analysis on incremental cost-effectiveness ratio in scenario 1.
Fig 3
Parameters were varied within their 95% confidence intervals.Deterministic sensitivity analysis on incremental cost-effectiveness ratio in scenario 1.
Table 5
Results of the probabilistic sensitivity analysis.
Current situation95% CINew situation95% CI
Scenario 1: QIV all eligible groups
    TOTAL costs (direct & societal, disc.) (€)462,963,000[410,462,000; 526,877,000]469,429,000[420,261,000; 528,359,000]
    Total number of hospitalizations50,630[44,800; 57,400]47,480[42,000; 53,700]
    Total number of deaths2,920[2,530; 3,320]2,730[2,370; 3,100]
Scenario 2: QIV <65y
    TOTAL costs (direct & societal, disc.) (€)462,508,000[414,574,000; 532,754,000]461,826,000[414,302,000; 531,372,000]
    Total number of hospitalizations50,790[44,900; 57,700]50,340[44,500; 57,200]
    Total number of deaths2,920[2,540; 3,380]2,900[2,510; 3,350]
Scenario 3: QIV +65y
    TOTAL costs (direct & societal, disc.) (€)459,760,000[415,006,000; 527,887,000]465,658,000[423,355,000; 530,595,000]
    Total number of hospitalizations50,880[44,900; 56,900]47,920[42,300; 53,500]
    Total number of deaths2,920[2,530; 3,310]2,740[2,380; 3,110]
CI: credibility intervals; QIV: quadrivalent influenza vaccine.

In both additional scenarios QIV showed reductions of influenza B cases. Specifically, in scenario 2, where <65-year-old patients were vaccinated with QIV, 23,382 cases would have been avoided per season (187,057 during the 8 influenza seasons), which would have led to avoid 1,883 outpatient visits, 447 hospitalizations and 27 deaths (a reduction of 3% in all cases) (Table 2 and Table 3). The replacement to QIV for part of the population would have implied additional €3 million in vaccine costs, whereas €95,591 would have been saved in outpatient visit costs, €2 million in hospitalization costs and €364,087 million in influenza-related deaths costs. From a societal perspective, €1 million of productivity losses would have been saved (Table 4).

In the last scenario, where patients ≥65 years old were vaccinated with QIV, 125,835 cases would have been avoided per influenza season (1,006,680 during the whole period), together with 9,689 outpatient visits, 2,967 hospitalizations and 180 deaths, a reduction of 13%, 19% and 20%, respectively (Table 2 and Table 3). Due to the switch from TIV to QIV, vaccination costs would have been incremented by €25 million, although €480,882 would have been saved in outpatient visits, €13 million in hospitalizations and €2 million in influenza-related deaths. €4 million of productivity losses would have been saved additionally from a societal perspective (Table 4).

Discussion

Our study suggests that the replacement of TIV by QIV in all eligible populations at current vaccination coverage rates would prevent 138,707 influenza B cases per year in Spain. In detail, 23,382 cases would be prevented if the population <65 years switched from TIV to QIV and 125,835 cases if the population ≥65 years was vaccinated with QIV. Consequently, outpatient visits, hospitalizations and deaths would be avoided in all scenarios. Although the complete switch from TIV to QIV to all age groups implies an increase in vaccination cost, the reduction of healthcare costs would compensate, showing that QIV is a highly cost-effective alternative both from payer and societal perspective, with an ICER clearly below the usually mentioned Spanish threshold of €25,000 per QALY [61]. Whether budget impact restrictions do not allow complete switching in a single influenza season, population ≥65 years will obtain the greater benefit due to its higher vaccination coverage rates and burden of illness related with hospitalization.

The results of this study show that the population ≥65 years is the population that most benefit from the switch from TIV to QIV. In scenario 1, when all eligible groups were vaccinated with QIV, relative difference (compared with TIV) in avoided cases with ≥65-years and <65 years were 25% and 13%, respectively; and in scenario 3, when only ≥65-year population was vaccinated with QIV, 24% and 11% additional cases were avoided. The higher number of avoided cases led to avoid a higher number of outpatient visits, hospitalizations and deaths in ≥65-year-old patients. These results were as expected, since in Spain, influenza vaccination is recommended to all population ≥65 years and <65 years at high risk. In addition, vaccination coverage of ≥65-year-old population is the highest. Even more, nowadays 10 of the 17 Autonomous Communities have started to include QIV in their influenza vaccination campaigns. Pérez-Rubio et al . [8] also published that, although accumulated incidence rate in population >65 years was the lowest, this age group was the most affected in terms of number of influenza-related hospitalizations and deaths.

In Spain, a cost-effectiveness model of QIV versus TIV, based on a static, lifetime, multi-cohort state transition model has been published previously [48]. This analysis, using a one-year scenario, showed that QIV (compared with TIV) would have prevented 18,565 influenza cases, 407 influenza-related hospitalizations and 181 deaths [48]. The reduction of influenza B cases and hospitalizations in our dynamic model was higher than the results of the static model, which can be partially explained by the risk reduction of virus transmission (indirect effect). In both static and dynamic models, the cost of QIV was the same (€9.50) and the cost of TIV was similar (€7.00 and €7.15, respectively), as well as most of the inputs used to populate both models. The costs from the payer perspective were higher with QIV due to a higher vaccine price in both cases; and the difference was offset due to lower indirect costs associated with QIV (societal perspective). Although in both analyses the results showed a QALY gain with QIV, the ICER of QIV over TIV from a societal perspective was €8,748/QALY gained with the static model [48], whereas it was more cost-effective with the dynamic model.

In England, a cost-effectiveness analysis of QIV found that it could be cost-effective for all targeted groups, but especially for children aged 2–11 years [62]. Conversely, population ≥65 year is the most benefited in our analysis. The rationale could be differences in vaccination programs, in England all children attending primary schools are vaccinated, while in Spain only high-risk children are vaccinated and the coverage is low, then herd protection of vaccinating children to protect other age groups would be greater in England than in Spain. Secondly, no GP consultations or hospitalisations for those individuals aged 65 years and older are attributed to influenza B on average each season, while in Spain this age group, as well as children < 5 years, has the higher rates hospitalisations related with influenza.

Until now, the most common approach to economic evaluations of vaccines are cost-effectiveness and cost-utility analyses, although they do not capture all vaccine externalities [63]. Influenza vaccines give indirect protection to non-vaccinated individuals by reducing the transmission of influenza from the vaccinated population. These broader benefits have been increasingly incorporated into economic evaluations [63]. Therefore, the benefits of dynamic models for economic analyses of vaccines have been demonstrated in many studies, such as in Pradas-Velasco et al [64]. This Spanish study evaluated the efficiency of seasonal influenza vaccination using both a static and dynamic model approach [64]. Study results showed that influenza vaccination was not efficient when using static models whereas it was efficient through a dynamic model. This difference was caused by indirect effects on the non-vaccinated population (herd protection) which were not reflected in the static model, although they could be greater than the direct effect [64].

Recently, two guidelines have been published with the objective of providing a consensus on how to apply economic evaluation to infectious diseases vaccines [65, 66]. Both studies recommend that infectious disease models should be dynamic, in order to reflect that vaccination programs can change the infectious disease dynamics. Therefore, vaccination programmes for infectious diseases have an indirect benefit on non-vaccinated individuals by preventing onward transmission in vaccinated people, which cannot be easily captured by static models. Even so, as indicated by the WHO, in some specific circumstances a static approach could be considered; for example when vaccinated groups are unlikely to change population disease transmission substantially (like ≥ 65-year-old population) [67].

This study has limitations given that different assumptions were necessary. Probability of flu complications and QALY loss for influenza B were not available, so we used data for influenza in general, although no significant differences between influenza A and B have been shown in clinical burden [14]. Because inter-individual contact data for the Spanish population were not available, we used an Italian contact matrix [33] assuming that population contact characteristics between Spain and Italy are similar. We also used data related to the natural history of influenza from other countries, however it is widely accepted that influenza natural history is similar among countries. Finally, despite the use of a dynamic model, which represents the recommended method for infectious disease vaccination programs [65], our approach did not capture other vaccination benefits like e.g. the reduction of antimicrobial drugs utilization that reduce antimicrobial resistance [68] or seasonal collapse of hospital services due to influenza outbreaks [1]. These variables would have likely increased the burden of influenza to the healthcare system.

Conclusions

Using a dynamic model as recommended by most recent vaccine evaluation guidelines, our study shows that QIV could be an efficient intervention for the National Health Service (from a payer perspective), being even more efficient from a societal perspective. This analysis also shows that most health benefits of QIV are obtained replacing TIV in the ≥65-year-old population.

References

1 

2 

    W Paul Glezen, JK Schmier, CM Kuehn, KJ Ryan, J Oxford. . The burden of influenza B: a structured literature review.American journal of public health. 2013;103(3):, pp.e43–51. Epub 2013/01/19. , doi: 10.2105/AJPH.2012.301137

3 

4 

5 

6 

    I Leon-Gomez, C Delgado-Sanz, S Jimenez-Jorge, V Flores, F Simon, D Gomez-Barroso, et al. [Excess mortality associated with influenza in Spain in winter 2012].Gac Sanit. 2015;29(4):, pp.258–65. Epub 2015/03/17. , doi: 10.1016/j.gaceta.2015.01.011 .

7 

    J Ryan, Y Zoellner, B Gradl, B Palache, J Medema. . Establishing the health and economic impact of influenza vaccination within the European Union 25 countries. Vaccine. 2006;24(47–48):, pp.6812–22. , doi: 10.1016/j.vaccine.2006.07.042

8 

    A Perez-Rubio, L Platero, JM Eiros Bouza. . Seasonal influenza in Spain: Clinical and economic burden and vaccination programmes.Med Clin (Barc).2019;153(1):, pp.16–27. Epub 2019/01/10. , doi: 10.1016/j.medcli.2018.11.014 .

9 

    S Caini, P Spreeuwenberg, GF Kusznierz, JM Rudi, R Owen, K Pennington, et al. Distribution of influenza virus types by age using case-based global surveillance data from twenty-nine countries, 1999–2014.BMC Infect Dis. 2018;18(1):, pp.269 Epub 2018/06/10. , doi: 10.1186/s12879-018-3181-y

10 

    C. Hannoun. The evolving history of influenza viruses and influenza vaccines. Expert Rev Vaccines. 2013;12(9):, pp.1085–94. Epub 2013/09/13. , doi: 10.1586/14760584.2013.824709 .

11 

    S Caini, WJ Alonso, CE Seblain, F Schellevis, J Paget. . The spatiotemporal characteristics of influenza A and B in the WHO European Region: can one define influenza transmission zones in Europe?Euro Surveill.2017;22(35). Epub 2017/09/08. , doi: 10.2807/1560-7917.ES.2017.22.35.30606

12 

    C Ambrose, M Levin. . The rationale for quadrivalent influenza vaccines. Human Vaccines & Immunotherapeutics.2012;8(1):, pp.81–8.

13 

    IG Barr, LL Jelley. . The coming era of quadrivalent human influenza vaccines: who will benefit?Drugs. 2012;72(17):, pp.2177–85. , doi: 10.2165/11641110-000000000-00000 .

14 

    JM Eiros-Bouza, A Perez-Rubio. . [Burden of influenza virus type B and mismatch with the flu vaccine in Spain].Revista espanola de quimioterapia: publicacion oficial de la Sociedad Espanola de Quimioterapia.2015;28(1):, pp.39–46. PubMed .

15 

    R Ortiz de Lejarazu, J Diez Domingo, A Gil de Miguel, F Martinon Torres, C Guzman Quilo, JM Guillen, et al. [Description of Influenza B in seasonal epidemics in Spain].Revista espanola de quimioterapia: publicacion oficial de la Sociedad Espanola de Quimioterapia.2018;31(6):, pp.511–9. Epub 2018/11/14. PubMed

16 

17 

18 

19 

20 

21 

22 

23 

    EA Belongia, BA Kieke, JG Donahue, RT Greenlee, A Balish, A Foust, et al. Effectiveness of inactivated influenza vaccines varied substantially with antigenic match from the 2004–2005 season to the 2006–2007 season. The Journal of infectious diseases. 2009;199(2):, pp.159–67. Epub 2008/12/18. , doi: 10.1086/595861 .

24 

    M Drummond, MJ Sculpher, K Claxton, GL Stoddart, GW Torrance. Methods for the economic evaluation of health care programmes. 4th edOxford: Oxford University Press; 2015.

25 

    M Uhart, H Bricout, E Clay, N Largeron. . Public health and economic impact of seasonal influenza vaccination with quadrivalent influenza vaccines compared to trivalent influenza vaccines in Europe.Hum Vaccin Immunother. 2016;12(9):, pp.2259–68. , doi: 10.1080/21645515.2016.1180490 .

26 

    R Pitman, D Fisman, GS Zaric, M Postma, M Kretzschmar, J Edmunds, et al. Dynamic transmission modeling: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force—5. Value in health: the journal of the International Society for Pharmacoeconomics and Outcomes Research. 2012;15(6):, pp.828–34. Epub 2012/09/25. , doi: 10.1016/j.jval.2012.06.011 .

27 

    AK Lugner, SD Mylius, J Wallinga. . Dynamic versus static models in cost-effectiveness analyses of anti-viral drug therapy to mitigate an influenza pandemic. Health economics. 2010;19(5):, pp.518–31. Epub 2009/04/22. , doi: 10.1002/hec.1485 .

28 

29 

    BM Van Maanen, PT de Boer, FC Dolk, AW Friedrich, JC Wilschut, R Pitman, et al. Dynamic modelling approaches for the analysis of the cost-effectiveness of seasonal influenza control.Expert Rev Vaccines. 2017;16(1):, pp.1–4. Epub 2016/08/20. , doi: 10.1080/14760584.2016.1221347 .

30 

    PT de Boer, BM van Maanen, O Damm, B Ultsch, FCK Dolk, P Crepey, et al. A systematic review of the health economic consequences of quadrivalent influenza vaccination.Expert review of pharmacoeconomics & outcomes research.2017;17(3):, pp.249–65. Epub 2017/06/15. , doi: 10.1080/14737167.2017.1343145 .

31 

    P Crepey, PT de Boer, MJ Postma, R Pitman. . Retrospective public health impact of a quadrivalent influenza vaccine in the United States. Influenza Other Respir Viruses. 2015;9Suppl 1:, pp.39–46. Epub 2015/08/11. , doi: 10.1111/irv.12318

32 

    PT de Boer, P Crepey, RJ Pitman, B Macabeo, A Chit, MJ Postma. . Cost-Effectiveness of Quadrivalent versus Trivalent Influenza Vaccine in the United States.Value in health: the journal of the International Society for Pharmacoeconomics and Outcomes Research. 2016;19(8):, pp.964–75. Epub 2016/12/19. , doi: 10.1016/j.jval.2016.05.012 .

33 

    J Mossong, N Hens, M Jit, P Beutels, K Auranen, R Mikolajczyk, et al. Social contacts and mixing patterns relevant to the spread of infectious diseases.PLoS Med.2008;5(3):, pp.e74 Epub 2008/03/28. , doi: 10.1371/journal.pmed.0050074

34 

    F Carrat, E Vergu, NM Ferguson, M Lemaitre, S Cauchemez, S Leach, et al. Time lines of infection and disease in human influenza: a review of volunteer challenge studies. American journal of epidemiology. 2008;167(7):, pp.775–85. , doi: 10.1093/aje/kwm375

35 

36 

    NM Smith, JS Bresee, DK Shay, TM Uyeki, NJ Cox, RA Strikas. . Prevention and Control of Influenza: recommendations of the Advisory Committee on Immunization Practices (ACIP).MMWR Recommendations and reports: Morbidity and mortality weekly report Recommendations and reports. 2006;55(Rr-10):, pp.1–42. Epub 2006/07/29. PubMed .

37 

    NA Molinari, IR Ortega-Sanchez, ML Messonnier. . The annual impact of seasonal influenza in the US: measuring disease burden and costs. Vaccine. 2007;27(25):, pp.5086–96.

38 

    CA DiazGranados, M Denis, S Plotkin. . Seasonal influenza vaccine efficacy and its determinants in children and non-elderly adults: a systematic review with meta-analyses of controlled trials. Vaccine. 2012;31(1):, pp.49–57. Epub 2012/11/13. , doi: 10.1016/j.vaccine.2012.10.084 .

39 

    R Gonzalez, M Campins, JA Rodrigo, S Uriona, LM Vilca. . [Influenza vaccination coverage in children with risk conditions in Catalonia].Enferm Infecc Microbiol Clin. 2015;33(1):, pp.22–6. Epub 2014/02/22. , doi: 10.1016/j.eimc.2013.12.010 .

40 

41 

42 

    A Bengoa, M San Martín, D Vicente, M Larrañaga. . Temporada gripal 2001–02 en una consulta de Pediatría en Atención Primaria.Bol S Vasco-Nav Pediatr. 2002;36:, pp.5–8.

43 

    CR Meier, PN Napalkov, Y Wegmuller, T Jefferson, H Jick. . Population-based study on incidence, risk factors, clinical complications and drug utilisation associated with influenza in the United Kingdom. Eur J Clin Microbiol Infect Dis. 2000;19(11):, pp.834–42. Epub 2001/01/11. , doi: 10.1007/s100960000376 .

44 

45 

46 

    M Galante, O Garin, E Sicuri, F Cots, A Garcia-Altes, M Ferrer, et al. Health services utilization, work absenteeism and costs of pandemic influenza A (H1N1) 2009 in Spain: a multicenter-longitudinal study. PLoS ONE.2012;7(2):, pp.e31696, doi: 10.1371/journal.pone.0031696 .

47 

48 

    A Garcia, R Ortiz de Lejarazu, J Reina, D Callejo, J Cuervo, R Morano Larragueta. . Cost-effectiveness analysis of quadrivalent influenza vaccine in Spain.Hum Vaccin Immunother. 2016;12(9):, pp.2269–77. , doi: 10.1080/21645515.2016.1182275 .

49 

    M Hollmann, O Garin, M Galante, M Ferrer, A Dominguez, J Alonso. . Impact of influenza on health-related quality of life among confirmed (H1N1)2009 patients. PLoS One. 2013;8(3):, pp.e60477, doi: 10.1371/journal.pone.0060477 .

50 

51 

    J Lopez-Bastida, J Oliva, F Antonanzas, A Garcia-Altes, R Gisbert, J Mar, et al. Spanish recommendations on economic evaluation of health technologies. The European journal of health economics: HEPAC: health economics in prevention and care. 2010;11(5):, pp.513–20. Epub 2010/04/21. , doi: 10.1007/s10198-010-0244-4 .

52 

53 

    MC Weinstein, JE Siegel, MR Gold, MS Kamlet, LB Russell. . Recommendations of the Panel on Cost-effectiveness in Health and Medicine. Jama. 1996;276(15):, pp.1253–8. Epub 1996/10/16. PubMed .

54 

    MA Koopmanschap, BM van Ineveld. . Towards a new approach for estimating indirect costs of disease. Social science & medicine (1982). 1992;34(9):, pp.1005–10. Epub 1992/05/01. , doi: 10.1016/0277-9536(92)90131-9 .

55 

    MA Koopmanschap, FF Rutten. . A practical guide for calculating indirect costs of disease.PharmacoEconomics.1996;10(5):, pp.460–6. Epub 1996/10/04. , doi: 10.2165/00019053-199610050-00003 .

56 

    SJ Davis, J Faberman, JC Haltiwanger. . The establishment-level behaviour of vacancies and hiring. Q J Econ. 2013;128:, pp.581–622.

57 

    TA Kanters, CAM Bouwmans, N van der Linden, SS Tan, L Hakkaart-van Roijen. . Update of the Dutch manual for costing studies in health care.PLoS One.2017;12(11):, pp.e0187477 Epub 2017/11/10. , doi: 10.1371/journal.pone.0187477

58 

    MA Koopmanschap, FF Rutten, BM van Ineveld, L van Roijen. . The friction cost method for measuring indirect costs of disease.J Health Econ. 1995;14(2):, pp.171–89. Epub 1995/05/08. , doi: 10.1016/0167-6296(94)00044-5 .

59 

60 

    J Puig-Junoy, J Oliva-Moreno, M Trapero-Bertran, J Abellán-Perpiñán, M Brosa-Riestra. Guía y recomendaciones para la realización y presentación de evaluaciones económicas y análisis de impacto presupuestario de medicamentos en el ámbito del CatSalut. Barcelona: Servei Català de la Salut (CatSalut)2014.

61 

62 

    D Thorrington, E van Leeuwen, M Ramsay, R Pebody, M Baguelin. . Cost-effectiveness analysis of quadrivalent seasonal influenza vaccines in England.BMC medicine.2017;15(1):, pp.166, doi: 10.1186/s12916-017-0932-3

63 

    M Jit, R Hutubessy. . Methodological Challenges to Economic Evaluations of Vaccines: Is a Common Approach Still Possible?Applied health economics and health policy. 2016;14(3):, pp.245–52. Epub 2016/02/03. , doi: 10.1007/s40258-016-0224-7

64 

    R Pradas-Velasco, F Antonanzas-Villar, MP Martinez-Zarate. . Dynamic modelling of infectious diseases: an application to the economic evaluation of influenza vaccination.PharmacoEconomics.2008;26(1):, pp.45–56. Epub 2007/12/20. , doi: 10.2165/00019053-200826010-00005 .

65 

    B Ultsch, O Damm, P Beutels, J Bilcke, B Bruggenjurgen, A Gerber-Grote, et al. Methods for Health Economic Evaluation of Vaccines and Immunization Decision Frameworks: A Consensus Framework from a European Vaccine Economics Community.PharmacoEconomics. 2016;34(3):, pp.227–44. Epub 2015/10/20. , doi: 10.1007/s40273-015-0335-2

66 

    J Mauskopf, B Standaert, M Connolly, A Culyer, L Garrison, R Hutubessy, et al. Economic Analysis of Vaccination Programs: An ISPOR Good Practices for Outcomes ResearchTask Force Report.Value in Health.2018;21(10):, pp.1133–49. , doi: 10.1016/j.jval.2018.08.005

67 

    AT Newall, N Chaiyakunapruk, P Lambach, RCW Hutubessy. . WHO guide on the economic evaluation of influenza vaccination.Influenza Other Respir Viruses. 2018;12(2):, pp.211–9. Epub 2017/10/13. , doi: 10.1111/irv.12510

68 


6 Mar 2020

PONE-D-20-03263

From Trivalent to Quadrivalent Influenza Vaccines: Public Health and Economic Burden for Different Immunization Strategies in Spain

PLOS ONE

Dear Dr Crepey,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

We would appreciate receiving your revised manuscript by 2nd April 2020. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter.

To enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

Please include the following items when submitting your revised manuscript:

    A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). This letter should be uploaded as separate file and labeled 'Response to Reviewers'.
    A marked-up copy of your manuscript that highlights changes made to the original version. This file should be uploaded as separate file and labeled 'Revised Manuscript with Track Changes'.
    An unmarked version of your revised paper without tracked changes. This file should be uploaded as separate file and labeled 'Manuscript'.

Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

We look forward to receiving your revised manuscript.

Kind regards,

Shinya Tsuzuki, MD, MSc

Academic Editor

PLOS ONE

Journal Requirements:

1. When submitting your revision, we need you to address these additional requirements.

 

Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

http://www.journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and http://www.journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. PLOS requires an ORCID iD for the corresponding author in Editorial Manager on papers submitted after December 6th, 2016. Please ensure that you have an ORCID iD and that it is validated in Editorial Manager. To do this, go to ‘Update my Information’ (in the upper left-hand corner of the main menu), and click on the Fetch/Validate link next to the ORCID field. This will take you to the ORCID site and allow you to create a new iD or authenticate a pre-existing iD in Editorial Manager. Please see the following video for instructions on linking an ORCID iD to your Editorial Manager account: https://www.youtube.com/watch?v=_xcclfuvtxQ

3. Thank you for stating the following in the Competing Interests section:

"I have read the journal's policy and the authors of this manuscript have the following competing interests:

PC has received scientific consultancy from Sanofi Pasteur. ER has received funding for scientific consultancy and speaker fees, as well as congress attendance grants from Sanofi Pasteur, GlaxoSmithKline, Merck Sharp and Dohme and Pfizer. JD has received funding for scientific consultancy and speaker fees from Sanofi Pasteur and Seqirus. RO has received funding for scientific consultancy and speaker fees from Sanofi Pasteur, GlaxoSmithKline and Seqirus. FM and his institution have received funding for consultancy and research and speaker fees from Sanofi Pasteur, GlaxoSmithKline, Merck Sharp and Dohme, Pfizer, Astrazeneca, Janssen, Seqirus and Ablynx. AG has received funding for scientific consultancy and speaker fees from Sanofi Pasteur, GlaxoSmithKline, Merck Sharp and Dohme y Pfizer. JLLB, FPA and HB are Sanofi Pasteur employees. MS is an IQVIA employee and works in consultancy projects with other Pharmaceutical Companies.".

We note that one or more of the authors are employed by a commercial company: 'IQVIA, and Sanofi Pasteur'

1.     Please provide an amended Funding Statement declaring this commercial affiliation, as well as a statement regarding the Role of Funders in your study. If the funding organization did not play a role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript and only provided financial support in the form of authors' salaries and/or research materials, please review your statements relating to the author contributions, and ensure you have specifically and accurately indicated the role(s) that these authors had in your study. You can update author roles in the Author Contributions section of the online submission form.

Please also include the following statement within your amended Funding Statement.

“The funder provided support in the form of salaries for authors [insert relevant initials], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.”

If your commercial affiliation did play a role in your study, please state and explain this role within your updated Funding Statement.

2. Please also provide an updated Competing Interests Statement declaring this commercial affiliation along with any other relevant declarations relating to employment, consultancy, patents, products in development, or marketed products, etc.  

Within your Competing Interests Statement, please confirm that this commercial affiliation does not alter your adherence to all PLOS ONE policies on sharing data and materials by including the following statement: "This does not alter our adherence to  PLOS ONE policies on sharing data and materials.” (as detailed online in our guide for authors http://journals.plos.org/plosone/s/competing-interests) . If this adherence statement is not accurate and  there are restrictions on sharing of data and/or materials, please state these. Please note that we cannot proceed with consideration of your article until this information has been declared.

* Please include both an updated Funding Statement and Competing Interests Statement in your cover letter. We will change the online submission form on your behalf.

Please know it is PLOS ONE policy for corresponding authors to declare, on behalf of all authors, all potential competing interests for the purposes of transparency. PLOS defines a competing interest as anything that interferes with, or could reasonably be perceived as interfering with, the full and objective presentation, peer review, editorial decision-making, or publication of research or non-research articles submitted to one of the journals. Competing interests can be financial or non-financial, professional, or personal. Competing interests can arise in relationship to an organization or another person. Please follow this link to our website for more details on competing interests: http://journals.plos.org/plosone/s/competing-interests

Additional Editor Comments (if provided):

I believe the manuscript would be of great value for publication, however, both reviewers raised reasonable concerns. Please clarify what they pointed out, especially model structure and parameters, perspective used in economic evaluation, comparison with other countries, and sensitivity analysis about contact matrix.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This is a very interesting and important piece of work for Spanish vaccination policy against seasonal influenza. I highly recommend that this study is published following some minor revisions to the manuscript.

The most important comment that I can make is that the Discussion section fails to mention the context of this analysis by comparing the results presented here to other cost-effectiveness analyses on quadrivalent influenza vaccines in other countries. This analysis found that the QIV would be highly cost-effective for all target populations, whereas other studies (notably one conducted by Public Health England) found the opposite - that is, QIV was cost-effective for the elderly population, but it was less likely to be cost-effective for younger populations, mainly due to the existing QLAIV vaccination programme in schools. Other countries have found different results driven the local epidemiology of influenza B. It would therefore be really helpful to this manuscript if reference could be made to other countries and their CEAs in this domain.

There are several minor comments to make in addition to the main one above:

- Line 12 needs re-writing grammatically. I would suggest "The total impact of seasonal influenza in Spain costs up to 145-1,000 million euros per year

- Line 14 : define indirect costs

- Line 15 : Re-write as "The cases attributable to influenza B..." or "The cases caused by influenza B..."

- Line 24 : Citation 16 is using data that is now 10 years old, and a later estimate of vaccine coverage in the region is presented later in the paragraph. Is citation 16 therefore necessary? Why refer to achieved vaccine coverage of 10 years ago?

Finally, I would also suggest that the authors consider adding to their sensitivity analysis by estimating the impact on their results of the assumption that the Italian contact matrix can be substituted for the Spanish population. Contact patterns can be a key determinant for cost-effectiveness of some vaccination programmes and making assumptions here can be problematic. Even countries that participated in the POLYMOD study still like to perform sensitivity analyses on their contact matrices now and again.

Reviewer #2: 1. Dynamic model

The analysis uses an age-structured SEIR model with an age structure to consider indirect benefits of vaccination. I would like to see more details on the model, and model parameters (i.e., infection rate and recovery rate). Do the authors estimate the model parameters? How vaccination is incorporated in the model? In which parameter does age matters? All of these details are important to understand the procedure and to interpret the results.

2. Perspectives

The authors conduct an analysis from two perspectives: societal and payer perspectives. Please explain what the analysis from each perspective considers. It is common to use the word “perspectives” to describe who pays the cost, but authors seem to use societal perspective to indicate productivity losses. It is confusing and need some clarifications.

3. Comparison across three scenarios

There are three scenarios, and I wonder how the three scenarios are selected in a relation to healthcare policy in Spain. What is the current recommendation and is there any argument to change the recommendation? Also I would like to see some discussion of the results. At the end, which scenario is favored in terms of cost-effectiveness?

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step.


14 Apr 2020

We really appreciate journal, editor and reviewers’ comments which will allow to improve the manuscript quality. Below you can find our response and actions taken to incorporate your comments in the manuscript.

Journal Requirements:

1. When submitting your revision, we need you to address these additional requirements.

Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

http://www.journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

http://www.journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

Response: Done

2. PLOS requires an ORCID iD for the corresponding author in Editorial Manager on papers submitted after December 6th, 2016. Please ensure that you have an ORCID iD and that it is validated in Editorial Manager. To do this, go to ‘Update my Information’ (in the upper left-hand corner of the main menu), and click on the Fetch/Validate link next to the ORCID field.

This will take you to the ORCID site and allow you to create a new iD or authenticate a pre-existing iD in Editorial Manager. Please see the following video for instructions on linking an ORCID iD to your Editorial Manager account: https://www.youtube.com/watch?v=_xcclfuvtxQ

Response: Done

3. Thank you for stating the following in the Competing Interests section:

"I have read the journal's policy and the authors of this manuscript have the following competing interests: PC has received scientific consultancy from Sanofi Pasteur. ER has received funding for scientific consultancy and speaker fees, as well as congress attendance grants from Sanofi Pasteur, GlaxoSmithKline, Merck Sharp and Dohme and Pfizer. JD has received funding for scientific consultancy and speaker fees from Sanofi Pasteur and Seqirus. RO has received funding for scientific consultancy and speaker fees from Sanofi Pasteur, GlaxoSmithKline and Seqirus. FM and his institution have received funding for consultancy and research and speaker fees from Sanofi Pasteur, GlaxoSmithKline, Merck Sharp and Dohme, Pfizer, Astrazeneca, Janssen, Seqirus and Ablynx. AG has received funding for scientific consultancy and speaker fees from Sanofi Pasteur, GlaxoSmithKline, Merck Sharp and Dohme y Pfizer. JLLB, FPA and HB are Sanofi Pasteur employees. MS is an IQVIA employee and works in consultancy projects with other Pharmaceutical Companies.".

We note that one or more of the authors are employed by a commercial company: 'IQVIA, and Sanofi Pasteur'

1. Please provide an amended Funding Statement declaring this commercial affiliation, as well as a statement regarding the Role of Funders in your study. If the funding organization did not play a role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript and only provided financial support in the form of authors' salaries and/or research materials, please review your statements relating to the author contributions, and ensure you have specifically and accurately indicated the role(s) that these authors had in your study. You can update author roles in the Author Contributions section of the online submission form.

Please also include the following statement within your amended Funding Statement.

“The funder provided support in the form of salaries for authors [insert relevant initials], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.”

If your commercial affiliation did play a role in your study, please state and explain this role within your updated Funding Statement.

2. Please also provide an updated Competing Interests Statement declaring this commercial affiliation along with any other relevant declarations relating to employment, consultancy, patents, products in development, or marketed products, etc.

Within your Competing Interests Statement, please confirm that this commercial affiliation does not alter your adherence to all PLOS ONE policies on sharing data and materials by including the following statement: "This does not alter our adherence to PLOS ONE policies on sharing data and materials.” (as detailed online in our guide for authors

http://journals.plos.org/plosone/s/competing-interests) . If this adherence statement is not accurate and there are restrictions on sharing of data and/or materials, please state these. Please note that we cannot proceed with consideration of your article until this information has been declared.

* Please include both an updated Funding Statement and Competing Interests Statement in your cover letter. We will change the online submission form on your behalf.

Please know it is PLOS ONE policy for corresponding authors to declare, on behalf of all authors, all potential competing interests for the purposes of transparency. PLOS defines a competing interest as anything that interferes with, or could reasonably be perceived as interfering with, the full and objective presentation, peer review, editorial decision-making, or publication of research or non-research articles submitted to one of the journals. Competing interests can be financial or non-financial, professional, or personal. Competing interests can arise in relationship to an organization or another person.

Please follow this link to our website for more details on competing interests:

http://journals.plos.org/plosone/s/competing-interests

Response: Modified. Since we could not find the adequate field to provide the competing interests statement during the submission process, we include this declaration in the Respond to Reviewers field and the Cover Letter :

I have read the journal's policy and the authors of this manuscript have the following competing interests:

PC has received scientific consultancy from Sanofi Pasteur. ER has received funding for scientific consultancy and speaker fees, as well as congress attendance grants from Sanofi Pasteur, GlaxoSmithKline, Merck Sharp and Dohme and Pfizer. JD has received funding for scientific consultancy and speaker fees from Sanofi Pasteur and Seqirus. RO has received funding for scientific consultancy and speaker fees from Sanofi Pasteur, GlaxoSmithKline and Seqirus. FM and his institution have received funding for consultancy and research and speaker fees from Sanofi Pasteur, GlaxoSmithKline, Merck Sharp and Dohme, Pfizer, Astrazeneca, Janssen, Seqirus and Ablynx. AG has received funding for scientific consultancy and speaker fees from Sanofi Pasteur, GlaxoSmithKline, Merck Sharp and Dohme y Pfizer. JLLB, FPA and HB are employed by the commercial Sanofi Pasteur. MS is employed by the commercial IQVIA and works in consultancy projects with Sanofi Pasteur and other Pharmaceutical Companies. JLLB, FPA, HB and MS were involved in the design of the study, data collection and the decision to publish the results. The manuscript was reviewed by the study sponsor prior to submission.

This does not alter our adherence to PLOS ONE policies on sharing data and materials. There are no patents, products in development or marketed products to declare.

This study was supported by Sanofi Pasteur. IQVIA was funded by Sanofi Pasteur for data collection and preparation of the manuscript. MS is an IQVIA employee. The funder provided support in the form of salaries for authors JLLB, FPA and HB. They played a role in the study design, data collection, and manuscript preparation.

Comments to the Author:

3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Response: We are not completely sure what is the additional information required. In the results section when variance measures are estimated they are included, see Table 5.

Reviewer #1: This is a very interesting and important piece of work for Spanish vaccination policy against seasonal influenza. I highly recommend that this study is published following some minor revisions to the manuscript.

The most important comment that I can make is that the Discussion section fails to mention the context of this analysis by comparing the results presented here to other cost-effectiveness analyses on quadrivalent influenza vaccines in other countries. This analysis found that the QIV would be highly cost-effective for all target populations, whereas other studies (notably one conducted by Public Health England) found the opposite - that is, QIV was cost-effective for the elderly population, but it was less likely to be cost-effective for younger populations, mainly due to the existing QLAIV vaccination programme in schools. Other countries have found different results driven the local epidemiology of influenza B. It would therefore be really helpful to this manuscript if reference could be made to other countries and their CEAs in this domain.

Response: A new paragraph was included in the Discussion section, comparing our results with the study conducted by Public Health England

There are several minor comments to make in addition to the main one above:

- Line 12 needs re-writing grammatically. I would suggest "The total impact of seasonal influenza in Spain costs up to 145- 1,000 million euros per year

Response: Modified

- Line 14 : define indirect costs

Response: Modified

- Line 15 : Re-write as "The cases attributable to influenza B..." or "The cases caused by influenza B..."

Response: Modified

- Line 24 : Citation 16 is using data that is now 10 years old, and a later estimate of vaccine coverage in the region is presented later in the paragraph. Is citation 16 therefore necessary? Why refer to achieved vaccine coverage of 10 years ago?

Response: Modified

Finally, I would also suggest that the authors consider adding to their sensitivity analysis by estimating the impact on their results of the assumption that the Italian contact matrix can be substituted for the Spanish population. Contact patterns can be a key determinant for cost-effectiveness of some vaccination programmes and making assumptions here can be problematic. Even countries that participated in the POLYMOD study still like to perform sensitivity analyses on their contact matrices now and again.

Response: To our knowledge, there is no contact matrix directly estimated on the Spanish population. All European inter-individual matrices display similar features, in particular the assortativeness of contacts among similar age-groups, hence we do not expect any qualitatively significant variations. Nevertheless, we are aware of this potential limitation of the study and we acknowledge it in the discussion.

Reviewer #2: 1. Dynamic model

The analysis uses an age-structured SEIR model with an age structure to consider indirect benefits of vaccination. I would like to see more details on the model, and model parameters (i.e., infection rate and recovery rate). Do the authors estimate the model parameters? How vaccination is incorporated in the model? In which parameter does age matters? All of these details are important to understand the procedure and to interpret the results.

Response: We agree with the reviewer that the model was not described in large details as it was previously published in another publication. Following the reviewer’s advice, we have added more information regarding the latent and contagious period considered. We have also added information regarding the timing of influenza vaccination (coverage rates by age-group were already in the text). The details of the calibration procedure and its results are already in the method section of the manuscript. However, we chose not to show the estimated probabilities of infection as they are model-dependent and not meaningful for the reader, but they are available upon request.

2. Perspectives

The authors conduct an analysis from two perspectives: societal and payer perspectives. Please explain what the analysis from each perspective considers. It is common to use the word “perspectives” to describe who pays the cost, but authors seem to use societal perspective to indicate productivity losses. It is confusing and need some clarifications.

Response: A new paragraph explaining perspectives analyses was included in the Methods section.

3. Comparison across three scenarios

There are three scenarios, and I wonder how the three scenarios are selected in a relation to healthcare policy in Spain. What is the current recommendation and is there any argument to change the recommendation? Also I would like to see some discussion of the results. At the end, which scenario is favored in terms of cost-effectiveness?

Response: It is explained in the Vaccination scenarios subsection of Methods. To support it a new figure to be included in Supplementary Material has been developed. In the discussion additional sentences were added regarding different scenarios analyzed

Submitted filename: Response to reviewers_Vaxigrip Dynamic Model Spain.docx

28 Apr 2020

PONE-D-20-03263R1

From Trivalent to Quadrivalent Influenza Vaccines: Public Health and Economic Burden for Different Immunization Strategies in Spain

PLOS ONE

Dear Dr Crepey,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

I feel that this manuscript is almost ready for publication, however, please insert further references as one reviewer suggested.

==============================

We would appreciate receiving your revised manuscript by 11th May 2020. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter.

To enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

Please include the following items when submitting your revised manuscript:

    A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). This letter should be uploaded as separate file and labeled 'Response to Reviewers'.
    A marked-up copy of your manuscript that highlights changes made to the original version. This file should be uploaded as separate file and labeled 'Revised Manuscript with Track Changes'.
    An unmarked version of your revised paper without tracked changes. This file should be uploaded as separate file and labeled 'Manuscript'.

Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

We look forward to receiving your revised manuscript.

Kind regards,

Shinya Tsuzuki, MD, MSc

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Thank you for addressing my comments. I agree with your comments on the difference between the PHE study and yours. A key difference between the two was the impact of vaccination of English school children, making any vaccination of other population groups less impactful. I can now recommend this manuscript for publication.

Reviewer #2: The authors have adequately responded to my previous comments except for one thing. They now added a new subsection "Analyses" to describe perspectives following my previous comment. Please insert references for the definition of the two perspectives they provided as I wonder if societal perspectives should include productivity losses.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step.


4 May 2020

Following your recommendation, we have added the reference, specified hereafter, to the manuscript (ref. 51). This reference details the recommendations for economic evaluations of health technologies in Spain. We also now mention in the text the scope of indirect costs.


7 May 2020

From Trivalent to Quadrivalent Influenza Vaccines: Public Health and Economic Burden for Different Immunization Strategies in Spain

PONE-D-20-03263R2

Dear Dr. Crepey,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.

Shortly after the formal acceptance letter is sent, an invoice for payment will follow. To ensure an efficient production and billing process, please log into Editorial Manager at https://www.editorialmanager.com/pone/, click the "Update My Information" link at the top of the page, and update your user information. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, you must inform our press team as soon as possible and no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

With kind regards,

Shinya Tsuzuki, MD, MSc

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:


11 May 2020

PONE-D-20-03263R2

From Trivalent to Quadrivalent Influenza Vaccines: Public Health and Economic Burden for Different Immunization Strategies in Spain

Dear Dr. Crépey:

I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

For any other questions or concerns, please email plosone@plos.org.

Thank you for submitting your work to PLOS ONE.

With kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Shinya Tsuzuki

Academic Editor

PLOS ONE

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

https://www.researchpad.co/tools/openurl?pubtype=article&doi=10.1371/journal.pone.0233526&title=From trivalent to quadrivalent influenza vaccines: Public health and economic burden for different immunization strategies in Spain&author=&keyword=&subject=Research Article,Medicine and Health Sciences,Infectious Diseases,Viral Diseases,Influenza,Biology and Life Sciences,Immunology,Vaccination and Immunization,Medicine and Health Sciences,Immunology,Vaccination and Immunization,Medicine and Health Sciences,Public and Occupational Health,Preventive Medicine,Vaccination and Immunization,Medicine and Health Sciences,Health Care,Patients,Outpatients,Medicine and Health Sciences,Infectious Diseases,Infectious Disease Control,Vaccines,People and places,Geographical locations,Europe,European Union,Spain,People and Places,Population Groupings,Age Groups,Social Sciences,Economics,Health Economics,Medicine and Health Sciences,Health Care,Health Economics,Medicine and Health Sciences,Epidemiology,Disease Dynamics,