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Eurosurveillance, Volume 22, Issue 30, 27 July 2017
Research article
Rondy, Larrauri, Casado, Alfonsi, Pitigoi, Launay, Syrjänen, Gefenaite, Machado, Vučina, Horváth, Paradowska-Stankiewicz, Marbus, Gherasim, Díaz-González, Rizzo, Ivanciuc, Galtier, Ikonen, Mickiene, Gomez, Kurečić Filipović, Ferenczi, Korcinska, van Gageldonk-Lafeber, I-MOVE+ hospital working group, and Valenciano: 2015/16 seasonal vaccine effectiveness against hospitalisation with influenza A(H1N1)pdm09 and B among elderly people in Europe: results from the I-MOVE+ project

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Citation style for this article: Rondy M, Larrauri A, Casado I, Alfonsi V, Pitigoi D, Launay O, Syrjänen RK, Gefenaite G, Machado A, Vučina VV, Horváth JK, Paradowska-Stankiewicz I, Marbus SD, Gherasim A, Díaz-González JA, Rizzo C, Ivanciuc AE, Galtier F, Ikonen N, Mickiene A, Gomez V, Kurečić Filipović S, Ferenczi A, Korcinska MR, van Gageldonk-Lafeber R, I-MOVE+ hospital working group, Valenciano M. 2015/16 seasonal vaccine effectiveness against hospitalisation with influenza A(H1N1)pdm09 and B among elderly people in Europe: results from the I-MOVE+ project. Euro Surveill. 2017;22(30):pii=30580. DOI: http://dx.doi.org/10.2807/1560-7917.ES.2017.22.30.30580

Received:13 October 2016; Accepted:08 February 2017


Background

Elderly populations, defined as those aged 65 years and above, and, more specifically, elderly people with underlying conditions, are at increased risk for hospitalisation due to influenza [1]. Influenza may also increase the severity of underlying chronic lung diseases, probably through inflammatory processes [2]. Viral pneumonia due to influenza seems to predispose to myocardial infarction, and congestive heart failures are more common during influenza seasons [3]. Patients with cancer treated with chemotherapy [4] and diabetic patients are more vulnerable to influenza. Their impaired immune response [5] could also affect host response to vaccination [6,7]. Evidence of the effectiveness of influenza vaccination in preventing severe clinical outcomes was recently described as low or very low among elderly people [8], and among patients with cancer [9], diabetes mellitus [10], lung diseases [11] [12], or cardiovascular diseases [13].

Despite the Council of the European Union and the World Health Organization’s (WHO) recommendations to annually vaccinate elderly people [14,15], influenza vaccine coverage among elderly people remains below the 75% target in most European countries [16].

In this context, post-marketing studies to estimate the influenza vaccine effectiveness (IVE) among elderly people are needed to inform about vaccination benefits for vaccinees, detect subgroups in which the vaccine performs less well and identify vaccine types that perform best. In 2015, to address this issue, the Integrated Monitoring of Vaccines in Europe plus (I‑MOVE+) consortium initiated a network of hospitals across Europe to measure IVE against laboratory-confirmed hospitalised influenza among elderly people.

The WHO recommended to include in the 2015/16 trivalent influenza vaccine for the northern hemisphere an A/California/7/2009 (H1N1)pdm09-like virus, an A/Switzerland/9715293/2013 (H3N2)-like virus and a B/Phuket/3073/2013-like virus (Yamagata lineage) [17].

In the 2015/16 influenza season in Europe, influenza A(H1N1)pdm09 and influenza B (mainly Victoria lineage) viruses predominated [18]. We conducted a multicentre hospital-based test-negative design (TND) case–control study to measure the 2015/16 seasonal IVE against hospitalisation with influenza A(H1N1)pdm09 and influenza B among elderly people in Europe, by risk groups and for specific vaccine types.

Methods

Study sites and design

We set up a European network of 27 hospitals in 11 countries (Croatia, Finland, France, Hungary, Italy, Lithuania, the Netherlands, Poland, Portugal, Romania and Spain) (Figure 1), organised in 12 study sites (in Spain, Navarre region hospitals had their own coordination centre). Each study site adapted a generic protocol to their local setting [19,20]. Monitoring visits were organised to ensure the study was done similarly across hospitals. We conducted a multicentre TND case–control study.

Figure 1

Location of the hospitals participating in the I-MOVE + study, Europe, influenza season 2015/16 (n = 27 hospitals)

/images/dynamic/articles/22842/16-00685-f1

I MOVE+: Integrated Monitoring of Vaccines in Europe plus.

Each dot represents one location and there may be more than one hospital in one location.

Study period

In each study site, the study period started at least two weeks after the beginning of the vaccination campaign in the respective countries and lasted from the week of the first detection of a laboratory-confirmed case of influenza to the week of the last laboratory-confirmed case of influenza. We defined different study periods for influenza A(H1N1)pdm09 and B.

Study population

Our study population included all community dwelling patients aged 65 years and above who had no contraindication for influenza vaccination or previous laboratory-confirmed influenza in the season and agreed to participate. In the participating services of each hospital, patients admitted for clinical conditions that could be related to influenza were screened for eligibility. Study physicians, nurses or collaborating medical staff asked patients about onset of symptoms compatible with the definition of a severe acute respiratory infection (SARI) in the previous 7 days.

We defined a SARI case as a hospitalised patient with at least one systemic (fever or feverishness, malaise, headache, myalgia or deterioration of general or functional condition) and at least one respiratory sign or symptom (cough, sore throat or shortness of breath) at admission, or within 48 hours after admission.

Data collection

The hospital study teams swabbed patients meeting the SARI case definition. Specimens were tested by RT-PCR and patients classified as influenza A(H1N1)pdm09 cases, influenza B cases, other influenza cases or controls if their specimens tested negative for any influenza virus.

The hospital study teams collected information on patients’ age and sex, influenza vaccination status including date and brand of the 2015/16 vaccine and the status in two previous seasons and underlying conditions listed for clinical risk groups recommended for influenza vaccination [21]. The underlying conditions included diabetes mellitus, obesity (defined as body mass index ≥ 30 kg/m2), cardiovascular conditions (such as congenital heart disease, congestive heart failure and coronary artery disease), lung diseases (such as chronic obstructive pulmonary disease, cystic fibrosis, asthma), renal and rheumatologic diseases, cancer, stroke, dementia and cirrhosis. Information on number of hospitalisations for underlying conditions in the previous 12 months, number of general practitioners (GP) visits in the previous three months, smoking status and functional impairment (based on Barthel index score [22]) was also collected.

Information on demographics and underlying conditions were collected from interviews with patients (or their relatives) and hospital and/or primary care databases. In study sites with no vaccination register, vaccination status was collected through interview with patients. For patients vaccinated or unable to provide their vaccination status, study sites called patients’ GP or pharmacists to retrieve vaccination status, date and brand (Table 1).

Table 1

Vaccine types used and source of information for vaccination status by study site, I-MOVE + study, Europe, influenza season, 2015/16

Study site Number of hospitals Vaccines used Data sources
Source of information
for vaccination status
Source of information
for underlying conditions
Croatia 1 Inactivated subunit I I; H
Finland 2 Inactivated split R; I; GP I; GP; H
France 3 Inactivated subunit; inactivated split I; P I; H
Hungary 2 Adjuvanted I; GP I; H
Italy 3 Inactivated subunit; inactivated split; adjuvanted I; GP I; H
Lithuania 2 Inactivated subunit I; GP I; H
Navarre 3 Inactivated split R I; GP; H
The Netherlands 1 Inactivated subunit; inactivated split I I; H
Poland 3 Unknown I; GP I; H
Portugal 2 Inactivated subunit; inactivated split R; I; GP I; H
Romania 3 Inactivated subunit I; GP I; H
Spain 2 Inactivated subunit; inactivated split R; I; GP I; H

GP: general practitioner/primary care database; H: hospital database/medical charts; I: interview with patient; I-MOVE+: Integrated Monitoring of Vaccines in Europe plus; P: pharmacist; R: register.

We defined patients as vaccinated with the 2015/16 influenza vaccine if they had been vaccinated at least 14 days before symptoms onset. Otherwise, they were considered as unvaccinated.

Data analysis

We computed the IVE as (1 minus the odds ratio (OR) of vaccination between cases and controls) x 100. We performed a pooled one-stage analysis using the study site as a fixed effect and estimated IVE stratified on the presence of underlying conditions. All IVE estimates were adjusted for study site, date of SARI symptom onset and age modelled as restricted cubic splines with four knots (initial model). To adjust for additional potential confounders (sex, each group of underlying conditions, hospitalisation in the past year, more than one GP visit in the past 3 months, functional impairment, current smoking), we performed a multivariable analysis using an onward step by step modelling and analysing them as dichotomous variables. Patients with missing covariates were excluded from the analyses adjusted for these covariates. We retained in the model (full model) all covariates that changed the IVE estimate by 10% of more (relative change).

We grouped the vaccine brands in split virion, subunit or adjuvanted vaccines. To compute vaccine type-specific effectiveness, we restricted our analyses to countries with at least one patient vaccinated with a specific type.

We also computed a pooled IVE with a two-stage model, adjusting study site-specific IVE for study site-specific confounders (same as listed above) when sample size allowed. We quantified the heterogeneity between site estimates using the I-square [23].

To minimise the inclusion of false influenza-negatives in the control group, we carried out sensitivity analyses by restricting population to (i) patients swabbed up to three days after symptom onset and (ii) patients not treated with antivirals until the day before swabbing.

Results

A total of 2,077 patients meeting the inclusion criteria were recruited in the study. We excluded 472 controls (23%) recruited outside of the study period and 65 patients (4%) with missing information on vaccination status. We included 1,274 controls and 528 cases, of which 353 (67%) were influenza A(H1N1)pdm09 positive, 105 (20%) were influenza B-positive, 41 (8%) were influenza A(H3N2)-positive, 24 (5%) were influenza A (non-subtyped)-positive, two (<1%) were co-infected by influenza A(H1N1)pdm09 and B, one (<1%) was co-infected by influenza A(H3N2) and B and two (<1%) were co-infected by influenza A (non-subtyped) and B. Of the 52 cases of influenza B with a known lineage, 47 (90%) were Victoria and 5 (10%) were Yamagata. The 42 cases positive for influenza A(H3N2) did not allow us to compute IVE against this subtype.

The maximum number of influenza A(H1N1)pdm09 cases were recruited in weeks 5 to 8 of 2016 and the maximum number of influenza B and A(H3N2) cases in week 10 (Figure 2).

Figure 2

Cases of severe acute respiratory infection with influenza A(H3N2), A(H1N1)pdm09, and B, and negative controls, I-MOVE+ study, Europe, influenza season 2015/16 (n = 504 casesa; n = 1,274 controls)

/images/dynamic/articles/22842/16-00685-f2

I-MOVE+: Integrated Monitoring of Vaccines in Europe plus; ISO: International Organisation for Standardisation; SARI: severe acute respiratory infection.

a Including two influenza A(H1N1)pdm09 and B co-infections and one influenza A(H3N2) and B co-infection.

Overall, 216/528 cases (41%) and 694/1,274 controls (54%) had received trivalent inactivated vaccines. Among those vaccinated, 51 (6%) received adjuvanted vaccines, 338 (37%) inactivated subunit vaccines,513 (56%) inactivated split virion vaccines and the information on vaccine type was missing for 8 (1%) vaccinated patients. Age and time adjusted IVE against any influenza was 39% (95 % confidence interval (CI): 22 to 53).

Vaccine effectiveness against hospitalised influenza A(H1N1)pdm09

We included in this analysis 355 cases of influenza A(H1N1)pdm09, of whom 138 (39%) were vaccinated, and 976 controls, of whom 543 (56%) vaccinated. The median age of A(H1N1)pdm09 cases and controls was 76 (Interquartile range (IQR) = 12 years) and 78 (IQR = 12 years) years respectively (p = 0.001). The proportion of patients with underlying conditions was similar among cases and controls except for renal (16% among cases vs 23% among controls, p = 0.003) and rheumatologic diseases (6% among cases vs 11% among controls, p = 0.033). Ten percent of A(H1N1)pdm09 cases and 3% of controls had received antivirals before swabbing (p < 0.001) and 61% of cases vs 53% of controls were swabbed within 3 days after symptoms onset (p = 0.013) (Table 2).

Table 2

Characteristics of influenza A(H1N1)pdm09 and influenza B hospitalised cases and corresponding test-negative controls included in the I-MOVE +  study, Europe, influenza season 2015/16

Influenza A(H1N1)pdm09 Influenza B
Cases
(n = 355)
Controls
(n = 976)
Cases
(n = 110)
Controls
(n = 1,015)
          n           %           n           %           n           %           n           %
Median age in years (range) 76 (65–95) 78 (65–101) 76 (65–94) 78 (65–101)
Aged 65–79 years 235/355 66.2 535/976 54.8a 76/110 69.1 566/1,015 55.8a
Sex = male 194/351 55.3 512/975 52.5 57/110 51.8 520/1,014 51.3
2015/16 seasonal influenza vaccination 138/355 38.9 543/976 55.6a 50/110 45.5 588/1,015 57.9a
2014/15 seasonal influenza vaccination 136/347 39.2 537/958 56.1a 53/109 48.6 589/998 59.0a
Type of vaccine
Not vaccinated 217/353 61.5 433/970 44.6a 60/110 54.5 427/1012 42.2a
Inactivated subunit 77/353 21.8 209/970 21.5 20/110 18.2 207/1012 20.5
Inactivated split virion 59/353 16.7 312/970 32.2 30/110 27.3 332/1012 32.8
Adjuvanted 0/353 0.0 16/970 1.6 0/110 0.0 46/1012 4.5
Underlying conditions
Diabetes 99/347 28.5 277/954 29.0 31/104 29.8 284/992 28.6
Heart disease 215/351 61.3 590/967 61.0 63/107 58.9 631/1,006 62.7
Lung disease 141/351 40.2 438/965 45.4 46/108 42.6 484/996 48.6
Immunodeficiency 7/343 2.0 34/942 3.6 10/106 9.4 32/986 3.2a
Cancer 93/350 26.6 263/963 27.3 19/105 18.1 280/1,001 28.0a
Nutritional deficiency 16/239 6.7 65/723 9.0 9/84 10.7 51/753 6.8
Renal disease 54/349 15.5 221/960 23.0a 20/106 18.9 236/996 23.7
Dementia or stroke 46/346 13.3 160/956 16.7 17/104 16.3 156/991 15.7
Rheumatologic disease 15/246 6.1 80/737 10.9a 11/87 12.6 83/757 11.0
Obesityb 43/349 12.3 139/951 14.6 5/104 4.8 123/985 12.5a
Any underlying condition 325/350 92.9 908/976 93.0 99/110 90.0 955/1,015 94.1
≥ 2 underlying conditions 244/347 70.3 719/964 74.6 72/108 66.7 752/1,006 74.8
Functional impairmentc 116/347 33.4 347/948 36.6 20/109 18.3 359/988 36.3a
Hospitalisation in past 12 months 152/345 44.1 446/960 46.5 39/108 36.1 475/989 48.0a
Current smoking 79/340 23.2 183/901 20.3 36/102 35.3 210/927 22.7a
Study sites
Croatia 16/355 4.5 15/976 1.5 5/110 4.5 3/1,015 0.3
Finland 18/355 5.1 57/976 5.8 3/110 2.7 35/1,015 3.4
France 11/355 3.1 124/976 12.7 26/110 23.6 124/1,015 12.2
Hungary 0/355 0.0 0/976 0.0 1/110 0.9 5/1,015 0.5
Italy 3/355 0.8 102/976 10.5 10/110 9.1 249/1,015 24.5
Lithuania 17/355 4.8 41/976 4.2 3/110 2.7 31/1,015 3.1
Navarra 87/355 24.5 240/976 24.6 27/110 24.5 230/1,015 22.7
The Netherlands 5/355 1.4 12/976 1.2 3/110 2.7 6/1,015 0.6
Poland 17/355 4.8) 14/976 1.4 6/110 5.5 9/1,015 0.9
Portugal 14/355 3.9 35/976 3.6 1/110 0.9 1/1,015 0.1
Romania 58/355 16.3 101/976 10.3 2/110 1.8 70/1,015 6.9
Spain 109/355 30.7 235/976 24.1 23/110 20.9 252/1,015 24.8
Potential for misclassification
Antivirals before swabbing 36/353 10.2 32/972 3.3a 7/107 7.5 27/1,012 2.7a
Swabbing within  3 days of onset 216/355 60.8 518/976 53.1a 54/110 49.1 585/1,015 57.6

I MOVE+: Integrated Monitoring of Vaccines in Europe plus.

a Indicates a significant difference (p value < 0.05) between cases and controls.

b Defined as body-mass index ≥ 30 kg/m2.

c Defined as Barthel score < 100 [22].

One-stage pooled IVE against A(H1N1)pdm09 adjusted for onset time and age was 42% (95% CI: 22 to 57) and 39% (95% CI: 17 to 56) when further adjusted for a range of underlying conditions and hospitalisation in the previous year (Table 3). IVE against A(H1N1)pdm09 was 59% (95% CI: 23 to 78), 48% (95% CI: 5 to 71), 43% (95% CI: 8 to 65) and 39% (95% CI: 7 to 60) in patients with diabetes mellitus (n = 362), cancer (n = 346), lung (n = 573) and heart disease (n = 792), respectively (Table 3).

Table 3

Pooled adjusted seasonal influenza vaccine effectiveness against hospitalised influenza A(H1N1)pdm09 overall among elderly people, by risk groups and vaccine type, I-MOVE +  study, Europe, influenza season, 2015/16

Analyses Model used for adjustmenta Vaccinated
/cases
Vaccinated
/controls
Adjusted IVE    95% CI   
Overall
Initial 138/355 543/976 42.4 22.0 to 57.4
Full 131/336 509/923 39.4 16.6 to 55.9
By risk groups
At least one underlying condition Initial 130/317 499/892 35.7 11.4 to 53.3
Initial plus severity 35.6 11.2 to 53.3
Diabetes mellitus
No Initial 98/242 370/674 33.9 4.6 to 54.2
Yes Initial 33/96 150/266 58.5 22.8 to 77.7
Initial plus severity 58.5 22.7 to 77.8
Heart disease
No Initial 54/131 207/372 37.3 -1.2 to 61.1
Yes Initial 80/211 321/581 38.4 6.5 to 59.5
Initial plus severity 39.0 7.3 to 59.9
Lung disease
No Initial 61/203 250/515 39.7 8.0 to 60.4
Yes Initial 72/139 276/434 42.4 7.2 to 64.3
Initial plus severity 42.8 7.8 to 64.5
Cancer
No Initial 93/252 375/691 35.7 6.7 to 55.7
Yes Initial 41/90 150/256 47.7 4.8 to 71.3
Initial plus severity 47.8 4.8 to 71.4
Vaccine type
Inactivated subunit Initial 77/224 209/538 28.1 -8.6 to 52.4
Inactivated split virion Initial 59/178 312/588 54.7 30.7 to 70.4
Sensitivity analyses
Two-stage model two-stageb 132/329 527/932 49.0 13.5 to 70.0
Restricted to patients swabbed within 3 days Initial 85/216 313/518 49.1 23.8 to 66.0
Restricted to patients not receiving antivirals before swabbing Initial 126/317 531/940 42.2 20.8 to 57.8

CI: confidence interval; I MOVE+: Integrated Monitoring of Vaccines in Europe plus; IVE: influenza vaccine effectiveness.

a Initial: one-stage model adjusted for study site, date of symptom onset and age (modelled as restricted cubic splines). Full: one-stage model adjusted for study site, date of symptom onset, age (modelled as restricted cubic splines), lung, heart, renal disease, diabetes mellitus, cancer, obesity (body-mass index ≥ 30 kg/m2) and hospitalisation for underlying conditions in past year. Severity: hospitalisations for underlying conditions in the previous year.

b Poland and Hungary were excluded because there were no vaccinated controls in Poland and no cases in Hungary.

IVE against A(H1N1)pdm09 was 28% (95% CI: −9 to 52) for inactivated subunit vaccines (n = 762) and 55% (95% CI: 31 to 70) for inactivated split virion vaccines (n = 716).

Study site specific IVE ranged between −152% (95% CI: −3,081 to 80) in Italy (n = 105) and 95% (95% CI: 7 to 100) in the Netherlands (n = 17) (Table 4). The statistical heterogeneity between study site specific IVE estimates was moderate (I2 = 36%). The two-stage pooled analysis (n = 1,261) included Croatia, Finland, France, Italy, Lithuania, Navarre, the Netherlands, Portugal, Romania and Spain. IVE was 49% (95% CI: 14 to 70). In sensitivity analyses, IVE against influenza A(H1N1)pdm09 was 42% (95% CI: 21 to 58) when restricting to patients not having received antiviral treatment (n = 1,257) and 49% (95% CI: 24 to 66) among patients swabbed within 3 days of symptoms onset (n = 734) (Table 3).

Table 4

Study site specific and two-stagea pooled seasonal vaccine effectiveness against hospitalised influenza A(H1N1)pdm09 among elderly people, I- MOVE + study, Europe, influenza season 2015/16 (n = 1,261)

Study site Inclusion period Variables used for adjustmentb Vaccinated
/cases
Vaccinated
/controls
Adjusted IVE          95% CI          I-square
Croatia 2016w5–2016w13 Date 4/16 1/15 -122.0 −4,314.5 to 88.8
Finland 2015w50–2016w7 Date 5/18 38/57 85.0 43.7 to 96.0
France 2016w4–2016w14 Date 3/11 84/124 83.7 32.2 to 96.1
Italy 2016w5–2016w11 Date 2/3 47/102 -152.2 −3,081.1 to 80.0
Lithuania 2016w2–2016w10 Date 1/17 7/41 66.8 −210.4 to 96.4
Navarra 2015w46–2016w13 Date 46/87 169/240 45.9 5.3 to 69.1
The Netherlands 2015w50–2016w7 Date 1/5 10/12 94.8 6.9 to 99.7
Portugal 2015w51–2016w8 Date, cancer, obesity 3/14 14/35 11.9 −372.7 to 83.6
Romania 2016w3–2016w14 Date, cancer, renal disease 4/58 6/100 -22.6 −490.3 to 74.6
Spain 2016w1–2016w14 Date, age, heart disease, dependency 63/100 151/206 22.5 −39.6 to 56.9
two-stage pooled                 – 49.0 13.5 to 70.0 36.2%

CI: confidence interval; I MOVE+: Integrated Monitoring of Vaccines in Europe plus; IVE: influenza vaccine effectiveness; w: week (International Organisation for Standardisation (ISO) week).

a Poland and Hungary were excluded from the two-stage analyses because there were no vaccinated controls in Poland and no cases in Hungary.

b Date of symptom onset and age modelled as restricted cubic spline with four knots.

Vaccine effectiveness against hospitalised influenza B

We included in this analysis 110 cases of influenza B, of whom 50 (46%) were vaccinated and 1,015 controls, of whom 588 (58%) vaccinated. The median age of cases and controls were 76 (IQR: 12 years) and 78 years (IQR: 12 years) respectively (p = 0.056). A lower proportion of cases than controls had cancer (18% vs 28%, p = 0.037), a functional impairment (18% vs 36%, p < 0.001), and had been hospitalised in the previous 12 months (36% vs 48%, p = 0.02). The proportion of current smokers was higher among influenza B cases than among controls (35% vs 23%, p = 0.007) (Table 2).

One stage pooled IVE against influenza B adjusted for symptom onset time and age was 52% (95% CI: 24 to 70) and 47% (95% CI: 13 to 68) when further adjusted for a range of underlying conditions and hospitalisation in the previous year (Table 5). IVE was 62% (95% CI: 5 to 85), 60% (95% CI: 18 to 80) and 36% (95% CI: −23 to 67) in patients with diabetes mellitus (n = 302), lung (n = 520) and heart disease (n = 675), respectively.

Table 5

Pooled adjusted seasonal vaccine effectiveness against hospitalised influenza B among elderly people overall and by risk groups, I-MOVE + study, Europe, influenza season 2015/16

Model used for adjustmenta Vaccinated
/cases
Vaccinated
/controls
Adjusted IVE       95% CI      
Overall
Overall Initial 50/110 588/1,015 51.8 23.7 to 69.5
Overall Full 46/101 544/948 47.0 13.1 to 67.7
By risk groups
At least one underlying condition Initial 47/97 536/929 50.2 18.7 to 69.4
Initial plus severity 49.4 17.5 to 69.0
Diabetes mellitus
No Initial 33/72 404/696 40.9 −7.1 to 67.4
Yes Initial 13/30 152/272 62.1 5.8 to 84.7
Initial plus severity 62.0 5.3 to 84.8
Heart disease
No Initial 16/44 211/368 66.5 27.6 to 84.5
Yes Initial 32/61 354/614 36.3 −22.2 to 66.8
Initial plus severity 36.1 −22.9 to 66.7
Lung disease
No Initial 27/61 258/502 32.8 −28.6 to 64.8
Yes Initial 22/45 305/475 60.5 19.2 to 80.6
Initial plus severity 59.9 18.2 to 80.4
Vaccine type
Inactivated subunit Initial 20/61 207/542 49.0 13.5 to 70.0
Inactivated split virion Initial 30/74 332/652 54.1 18.9 to 74.0
Sensitivity analyses
two-stage model two-stageb 48/86 551/858 47.0 11.9 to 68.2
Restricted to patients swabbed within 3 days Initial 31/54 358/585 25.0 −50.5 to 62.6
Restricted to patients not receiving antivirals before swabbing Initial 46/99 577/985 52.3 22.8 to 70.5

CI: confidence interval; I MOVE+: Integrated Monitoring of Vaccines in Europe plus; IVE: influenza vaccine effectiveness.

a Initial: one-stage model adjusted for study site, date of onset and age (modelled as restricted cubic splines). Full: one-stage model adjusted for study site, date of symptom onset, age (modelled as restricted cubic splines), lung, heart, renal disease, diabetes mellitus, cancer, obesity and hospitalisation in the previous year Severity: hospitalisations for underlying conditions in the previous year.

b Croatia, Hungary, Italy, Lithuania, the Netherlands, Poland, Portugal and Romania were excluded from the two-stage analyses because there were no vaccinated controls and/or cases, respectively.

IVE against influenza B was 49% (95% CI: 14 to 70) for inactivated subunit vaccines (n = 603) and 54% (95% CI: 19 to 74) for inactivated split virion vaccines (n = 726).

Study site specific IVE ranged between 18% (95% CI: −106 to 67) in Finland (n = 38) and 76% (95% CI: −24 to 95) in Italy (n = 259) (Table 6). There was no statistical heterogeneity between study site specific IVE estimates (I2 = 0%). The two-stage pooled analysis (n = 944) included Finland, France, Italy, Navarre and Spain. IVE was 47% (95% CI: 12 to 68). In sensitivity analyses, IVE against influenza B was 52% (95% CI: 23 to 71) when restricting to patients not having received antiviral treatment (n = 1,084) and 25% (95%CI: −51 to 63) among patients swabbed within 3 days of symptoms onset (n = 639).

Table 6

Study site-specific and two-stagea pooled seasonal vaccine effectiveness against hospitalised influenza B among elderly people, I- MOVE + study, Europe, influenza season 2015/16

Study site       Inclusion period       Variables used for adjustmentb Vaccinated
/cases
Vaccinated
/controls
Adjusted IVE         95% CI         I-square
Finland 2016w8–2016w15 Date 2/3 22/35 23.3 -1,785.9 to 96.9
France 2016w4–2016w14 Date, age, functional impairment 17/26 87/120 18.1 -105.6 to 67.4
Italy 2016w1–2016w12 Date 2/10 121/249 75.5 -23.7 to 95.1
Navarre 2015w53–2016w17 Date 17/27 165/230 59.4 -2.5 to 83.9
Spain 2016w2–2016w16 Date, lung disease, dependency 11/20 159/220 44.3 -48.9 to 79.1
two-stage pooled (n = 944) 47.0 11.9 to 68.2 0.0%

CI: confidence interval; I MOVE+: Integrated Monitoring of Vaccines in Europe plus; IVE: influenza vaccine effectiveness; w: week (International Organisation for Standardisation (ISO) week).

a Croatia, Hungary, Italy, Lithuania, the Netherlands, Poland, Portugal and Romania were excluded from the two-stage analyses because there were no vaccinated controls and/or cases.

b Date of onset and age modelled as restricted cubic spline with four knots.

Discussion

Our results suggest that the seasonal IVE against hospitalised influenza among elderly people was moderate during the 2015/16 influenza season in Europe for influenza: 39% overall, 42% against influenza A(H1N1)pdm09 and 52% against influenza B . These estimates did not vary between categories of underlying conditions.

Data from European virological surveillance reported that most of the characterised influenza A(H1N1)pdm09 viruses belonged to the emerging subclade 6B.1, defined by haemagglutinin amino acid substitutions S162N and I216T [18]. Despite these genetic evolutions, A(H1N1)pdm09 viruses were considered antigenically similar to the northern hemisphere vaccine component A/California/7/2009. IVE estimates against hospitalised A(H1N1)pdm09 was consistent with the results we reported in 2012/13 and 2013/14 among hospitalised elderly people [24,25].

In 2015/16, the circulating influenza B Victoria lineage was distinct from the Yamagata vaccine component [26] and there was no quadrivalent vaccine used in our study population. IVE against influenza B was close to what we reported, using the same generic protocol, in 2012/13 (66% in the 65–79 year-olds and 44% in the 80 year-olds and older) in a season with co-circulation of B Victoria and Yamagata lineages and a Yamagata vaccine component [24,27]. These results suggest some cross-lineage protection and they are in line with previously reported data in GP-based studies [28,29] and a meta-analysis of eight randomised controlled trials with mismatched B viruses resulting in a VE of 52% (95% CI: 19 to 72) among healthy adults [30]. Further studies are needed to increase the understanding of mechanisms of cross-lineage protection for influenza B and better guide policy makers in terms of recommendations for using trivalent or tetravalent seasonal vaccines.

We observed higher point estimates of IVE for the inactivated split virion vaccines compared with inactivated subunit vaccines, although the 95% CIs of the point estimates were widely overlapping. The completeness of data on vaccine type was high (1% of missing vaccine type among those vaccinated), thus these results, concurring with published data [31-33], could be due to differences in T-cell responses conferred by the two vaccine types [34]. However, they should be interpreted with caution as they may be due to random variation. Further evidence, and pooling of several years of data would be required to obtain precise vaccine type specific effectiveness. Higher adjuvant vaccine coverage would be needed to compute adjuvant vaccine specific IVE. This would be useful information to adapt influenza vaccination strategies among elderly people.

Recent reviews underlined the need for further evidence of seasonal IVE against laboratory-confirmed influenza in elderly people and patients with underlying conditions [8-13]. We were able to collect high quality data from 1,802 elderly patients hospitalised with SARI, making our study one of the largest hospital-based studies on IVE. The large number of participants, and a vaccine coverage close to 50%, enabled us to compute IVE against type/subtype-specific influenza among patients with specific underlying conditions. Our results suggest that, in 2015/16, the seasonal influenza vaccine provided protection against hospitalised influenza A(H1N1)pdm09 and B in the elderly with diabetes mellitus, heart and lung disease. We were unable to refine the underlying conditions categories further. To better guide vaccine recommendations, IVE among patients receiving specific treatment (e.g. statins [35,36], chemotherapy [9,37]) or with more specific conditions (e.g. asthma, chronic obstructive pulmonary disease) would be needed. A larger sample size would be required for such studies.

We collected information related to access to care, health conditions and smoking status. Recruited cases and controls were similar. We adjusted our estimates for study site, onset week and age. Further adjustment for potential confounders (underlying lung, heart, renal disease, diabetes mellitus, cancer, obesity and hospitalisations in the past year) did not change the estimates. However, as in any observational study, we cannot exclude unmeasured confounding leading to over- or under-estimation of the IVE.

The contribution to the pooled dataset was different between study sites. The two Spanish sites recruited 44% of the patients. The viruses circulating and vaccines used in Spain were similar to the other countries. Consequently we do not expect the over-representation of Spanish sites to have biased our overall estimates. Variations in the number of recruited individuals may be explained by differences in local influenza activity or number and size of participating hospitals/services. We believe that access to hospitalisation in case of severe influenza is similar across participating European countries. A common generic protocol and the monitoring of its implementation through on-site visits contributed to ensuring comparability of patients recruited and data collected across study sites. We measured low statistical heterogeneity based on I-square values. However, small number of estimates and large study-specific CIs may hinder adequate quantitative assessment of heterogeneity between studies [38]. True differences between study site specific IVE could be related to different vaccines used during this season or different immunological profiles of recruited patients including their past vaccination histories [39]. Larger study site-specific sample sizes are required to ensure that the differences in IVE across study sites are not due to chance. Currently, multicentre studies are necessary to obtain precise IVE estimates.

A recent publication by Foppa et al. suggested that measuring IVE against laboratory-confirmed influenza SARI hospitalisation using the TND was subject to biases if the test-negative controls were hospitalised because of an exacerbation of underlying lung disease unrelated to a respiratory infection [40]. In our study, cases and controls had similar prevalence of underlying lung disease. Underlying lung disease did not appear to confound IVE estimates, even when combined with a proxy of its severity (hospitalisation because of underlying conditions in the past 12 months). Cohort and TND-based IVE estimates against laboratory-confirmed hospitalised influenza in Navarre repeatedly showed similar estimates, reassuring on the appropriateness of TND at hospital level [41].

Several studies suggest that past influenza vaccinations may decrease or enhance current vaccine effectiveness depending on previous and current vaccine and circulating strains as well as past exposure to the virus [24,32,42-44]. A large proportion of our vaccinated population had been vaccinated in the previous season(s) but the very small number of patients with varying repeated vaccination status over the years did not allow us to measure the effect of previous vaccinations. To understand the effect of repeated vaccinations on IVE, large cohorts of individuals with different vaccination patterns and symptomatic (and asymptomatic) influenza infection status over the years would be needed.

Conclusion

Our multicentre test-negative case–control study estimated that in 2015/16 the seasonal influenza vaccination prevented approximately half of the cases of hospitalisation with laboratory-confirmed influenza among vaccinated elderly people. Our results suggest that vaccination provided similar protection to elderly patients with underlying diabetes mellitus, cancer, lung and heart diseases. Because vaccination remains the most effective preventive measure against severe influenza among elderly people, increasing the vaccine coverage in this group should be a priority. This pilot season of the hospital-based I-MOVE + project proved that obtaining precise estimates of IVE against a severe influenza outcome among elderly people was feasible. Enlarging our network and its sample size will enable us to better guide vaccination strategies against severe influenza cases by comparing the performance of different vaccine types and identifying risk groups for poor response to vaccination.


I-MOVE + hospital working group

EpiConcept: Alain Moren

Spain: Jone M. Altzibar, Ion García Arraras (Subdirección de Salud Pública de Gipuzkoa, CIBERESP, Basque Country, Spain), Gustavo Cilla (Donostia University Hospital, CIBERES, San Sebastian, Basque Country, Spain), Elisa Marco (Dirección General de Salud Pública, Aragón, Spain), Matxalen Vidal, Manuel Omeñaca (Miguel Servet University Hospital, Zaragoza, Spain)

Navarra: J. Castilla (Instituto de Salud Pública de Navarra, IdiSNA, CIBERESP, Pamplona), A Navascués, C Ezpeleta (Complejo Hospitalario de Navarra, IdiSNA, Pamplona, Spain), L Barrado (Hospital de Estella, Spain), MT Ortega (Hospital de Tudela, Spain)

Italy: A. Bella, M.R. Castrucci, S. Puzelli (Istituto Superiore di Sanità, Rome), M.Chironna, C. Germinario (Policlicnico Hospital, University of Bari); F. Ansaldi, A. Orsi (IRCCS AOU San Martino- IST, Genoa hospital); I. Manini, E. Montomoli (Department of Molecular and Developmental Medicine, University of Siena)

Romania: E.Lupulescu, M.Lazar, CM Cherciu, C. Tecu, ME Mihai (National Institute of Research Cantacuzino), M. Nitescu (INBI Matei Bals, Bucharest), D. Leca (UMF Gr. Popa Iasi), E. Ceausu (UMF Carol Davila Bucharest, Infectious Diseases Hospital V. Babes, Bucharest)

France: N. Lenzi, Z. Lesieur (Innovative clinical research network in vaccinology (I-REIVAC), P. Loulergue (Inserm, CIC Cochin-Pasteur, I-REIVAC, Paris), V. Foulongne, F. Letois, C. Merle (CHU de Montpellier, I-REIVAC), P. Vanhems (Hôpital Edouard Herriot, Lyon, I-REIVAC), B. Lina (Université Lyon 1, Centre National de Référence virus influenza France Sud, Lyon, France)

Finland: H. Nohynek, A. Haveri (National Institute for Health and Welfare)

Lithuania: M. Kuliese, D. Velyvyte (Department of Infectious Diseases of Lithuanian University of Health Sciences, Kaunas, Lithuania), R. Grimalauskaite, G. Damuleviciene, V. Lesauskaite (Department of Geriatrics, Lithuanian University of Health Sciences, Kaunas, Lithuania), L. Jancoriene, B. Zablockiene, A. Ambrozaitis (Clinic of Infectious, Chest Diseases, Dermatovenerology and Allergology, Vilnius University Faculty of Medicine, Vilnius, Lithuania)

Portugal: B. Nunes, A.P. Rodrigues (National Health Institute Doutor Ricardo Jorge, Lisbon), V. Gomes, R. Côrte-Real (Centro Hospitalar de Lisboa Central, Lisbon), J. Poças, M.J. Peres (Centro Hospitalar de Setúbal, Setúbal)

Croatia: B. Kaić (Croatian Institute of Public Health, Zagreb)

Hungary: B. Oroszi (Office of the Chief Medical Officer, Budapest)

Poland: L.B. Brydak, K. Cieślak, D. Kowalczyk, K. Szymański (National institute of Public Health - National Institute of Hygiene, Warsaw), A. Jakubik (Szpital Praski, Warsaw), G. Skolimowska (Mazovia Regional Hospital, Siedlce), D. Hulbój (Regional hospital, Bielsko-Biała)

The Netherlands: A. Meijer, W. van der Hoek (National Institute for Public Health and the Environment (RIVM), Bilthoven), P.M. Schneeberger (Jeroen Bosch Hospital, 's Hertogenbosch)

Acknowledgements

The I-MOVE+ project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 634446.

The Lithuanian I-MOVE+ study sites were supported by a grant from the Research Council of Lithuania (SEN-03/2015). We are grateful to all patients, medical staff, study nurses and epidemiologists from the 12 study sites who actively participated in the study.

EpiConcept: Esther Kissling

Italy: Anna Pina Palmieri, Stefania Giannitelli and Alessia Ranghiasci (Istituto Superiore di Sanità, Rome, Italy).

Romania: Rodica Bacruban, Delia Azamfire, Aura Dumitrescu, Elena Ianosik (INBI Matei Bals), Elena Duca, Codrina Bejan, Andra Teodor (Infectious Diseases Hospital Sf. Parascheva, Iasi), Simin-Aysel Florescu, Corneliu Popescu, Gratiela Tardei, (UMF Carol Davila Bucharest, Infectious Diseases Hospital V. Babes, Bucharest, Romania)

France: Julien Charpentier, Nathalie Marin (Service de réanimation médicale, Cochin-Pasteur, APHP, Université Paris Descartes, Sorbonne Paris Cité, Paris, France), Benoit Doumenc (Service des urgences, Cochin-Pasteur, APHP, Université Paris Descartes, Sorbonne Paris Cité, Paris, France), Claire Le jeunne (Service de médecine Interne, Cochin-Pasteur, APHP, Université Paris Descartes, Sorbonne Paris Cité, Paris, France), Anne Krivine (Service de virologie, Cochin-Pasteur, APHP, Université Paris Descartes, Sorbonne Paris Cité, Paris, France), Sonia Momcilovic: CIC Cochin-Pasteur, APHP, Université Paris Descartes, Sorbonne Paris Cité, Paris, France), Thomas BENET (Infection Control and Epidemiology Unit, Hôpital Edouard Herriot, Lyon), Sélilah AMOUR (Infection Control and Epidemiology Unit, Hôpital Edouard Herriot, Lyon), Laetitia HENAFF (Infection Control and Epidemiology Unit, Hôpital Edouard Herriot, Lyon).

Finland: Jukka Jokinen, Outi Lyytikäinen and Arto Palmu (study design, protocol writing), Päivi Sirén (clinical data collection), Esa Ruokokoski (data management), The laboratory staff in Viral Infections Unit of THL, Tampere University Hospital, Hatanpää Hospital (collaboration with the clinical work and data collection).

Portugal: Baltazar Nunes, Ana Paula Rodrigues, Raquel Guiomar (Infectious Diseases Department, National Health Institute Doutor Ricardo Jorge, Lisbon, Portugal), Victor Gomes, Filipa Quaresma, Luis Vale, Teresa Garcia, Teresa Bernardo, Liliana Dias, Paula Fonseca, Helena Amorim, João Rolo, Helena Pacheco, Paula Branquinho, Rita Côrte-Real (Centro Hospitalar de Lisboa Central, Lisbon, Portugal),José Poças, Paula Lopes, Maria João Peres, Rosa Ribeiro, Paula Duarte, Ermelinda Pedroso, Sara Rodrigues, Ana Rita Silvério, Diana Gomes Pedreira, Marta Ferreira Fonseca, (Centro Hospitalar de Setúbal, Setúbal, Portugal).

Croatia: Adriana Vince, Antea Topić, Neven Papić, Jelena Budimir Mihalić (all from the University Hospital for Infectious Diseases ‘Dr. Fran Mihaljević’, Zagreb), Iva Pem Novosel, Goranka Petrović, Martina Zajec, Vladimir Draženović (all from the Croatian Institute of Public Health, Zagreb).

Hungary: Éva Hercegh, Bálint Szalai (Influenza Virus Laboratory, National Center for Epidemiology, Budapest, Hungary), Katalin Antmann (Hospital Hygiene Department, Semmelweis University, Budapest, Hungary), Kamilla Nagy, (Hospital Hygiene Department, University of Szeged, Szeged, Hungary)

Conflict of interest

None declared.

Authors’ contributions

Marc Rondy was involved in the original methodological design of the study (generic protocol). He coordinated the European hospital IVE network, undertook the statistical analysis on which the research article is based and led the writing of the research article.

Marta Valenciano initiated the original methodological design of the study. She coordinated the European hospital IVE network and contributed to the writing of the research article.

Amparo Larrauri, Itziar Casado, Valeria Alfonsi, Daniela Pitigoi, Odile Launay, Ritva S. Syrjänen, Giedre Gefenaite, Ausenda Machado, Vesna Višekruna Vučina, Judith Krisztina Horváth, Iwona Paradowska-Stankiewicz, Sierk D. Marbus, Alin Gherasim, Jorge Alberto Díaz-González, Caterina Rizzo, Alina E. Ivanciuc, Florence Galtier, Niina Ikonen, Aukse Mickiene, Veronica Gomez, Sanja Kurečić Filipović, Annamária Ferenczi, Monika R. Korcinska and Rianne van Gageldonk-Lafeber were responsible for the coordination of the study at the local level. They were in charge of the data collection and management. They read, contributed and approved the manuscript final version.

The I-MOVE+ hospital working group contributors contributed to developing the study site specific protocol. They were in charge of supervising the study at the hospital level and collect the data published in this research article. They read, contributed and approved the manuscript final version.


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