Description of the condition
Healthcare workers, such as doctors, nurses, other health professionals, cleaners and porters (and also family visitors), may have substantial rates of clinical and sub-clinical influenza during influenza seasons (Elder 1996; Ruel 2002) but there are no reliable data on rates of laboratory-proven influenza in healthcare workers and whether they differ from those of the general population (Jefferson 2009). Laboratory-proven influenza in the general population on average accounts for a small proportion of 'influenza-like illnesses'. Data from the control arms of randomised controlled trials (RCTs) could provide data on laboratory-proven influenza rates.
Healthcare workers often continue to work when infected with influenza, increasing the likelihood of transmitting influenza to those in their care (Coles 1992; Weingarten 1989; Yassi 1993). Those aged 60 or older in institutions such as long-stay hospital wards and nursing homes are at risk of influenza and its complications, especially if affected with multiple pathologies (Fune 1999; Jackson 1992; Muder 1998; Nicolle 1984).
Description of the intervention
One way to prevent the spread of influenza to those aged 60 years or older resident in long-term care institutions (LTCIs) may be to vaccinate healthcare workers. The Centers for Disease Control (CDC) Advisory Committee on Immunisation Practices (ACIP) recommends vaccination of all healthcare workers (Harper 2004). However, only 36% of healthcare workers in the US were vaccinated in 2003 (CDC 2003), 35% of staff in LTCIs in Canada were vaccinated in 1999 (Stevenson 2001) and 34% to 44% after a randomised controlled trial (RCT) in 43 geriatric healthcare settings in France to increase vaccination rates (Rothan-Tondeur 2010). Nurses and (in some institutions) physicians, tend to have lower influenza vaccination rates than other healthcare workers. This relatively low uptake may partly be a reflection of doubts as to the vaccine's efficacy (its ability to prevent influenza) (Ballada 1994; Campos 2002-3; Ludwig-Beymer 2002; Martinello 2003; Quereshi 2004). The design and execution of campaigns to increase vaccination rates are also important (Doebbeling 1997; NFID 2004; Russell 2003a; Russell 2003b), in order to provide an intervention at minimal risk of bias from inadequate randomisation, concealment of allocation, blinding, attrition, incomplete reporting and inappropriate statistical analysis.
How the intervention might work
Healthcare workers are the key group who enter nursing and LTCIs on a daily basis. Immune systems of the elderly are less responsive to vaccination and vaccinating healthcare workers could reduce the exposure of those aged 60 years or older to influenza.
Why it is important to do this review
Previous systematic reviews of the effects of influenza vaccines in those aged 60 years or older are now out of date or do not include all relevant studies. The Gross 1995 review is 17 years old and its conclusions are affected by the exclusion of recent evidence. The Vu 2002 review has methodological weaknesses (excluding studies with denominators smaller than 30 and quantitative pooling of studies with different designs), which are likely to undermine the conclusions. A systematic review by Jordan 2004 of the effects of vaccinating healthcare workers against influenza on high-risk individual elderly reports significantly lower mortality in the elderly (13.6% versus 22.4%, odds ratio (OR) 0.58, 95% confidence interval (CI) 0.4 to 0.84) but does not include the latest studies. The Burls 2006 systematic review of effects on elderly people only identified the RCTs by Potter 1997 and Carman 2000, and Anikeeva 2009 does not include the study by Lemaitre 2009. It is important to provide accurate information for policy makers and highlight the need for high-quality trials to test combinations of interventions, including healthcare worker vaccination.
There are Cochrane systematic reviews assessing the effects of influenza vaccines in children (Jefferson 2012), the elderly (Jefferson 2010b), healthy adults (Jefferson 2010a), people affected with chronic obstructive pulmonary disease (Poole 2010) and cystic fibrosis (Dharmaraj 2009).
To identify all randomised controlled trials (RCTs) and non-RCTs assessing the effects of vaccinating healthcare workers on the incidence of laboratory-proven influenza, pneumonia, death from pneumonia and admission to hospital for respiratory illness in those aged 60 years or older residing in LTCIs.
Criteria for considering studies for this review
Types of studies
RCTs and non-RCTs (cohort or case-control studies) reporting exposure and outcomes by vaccine status.
Types of participants
Healthcare workers (nurses, doctors, nursing and medical students, other health professionals, cleaners, porters and volunteers who have regular contact with those aged 60 years or older) of all ages, caring for those aged 60 years or older in institutions such as nursing homes, LTCIs or hospital wards.
Types of interventions
Vaccination of healthcare workers with any influenza vaccine given alone or with other vaccines, in any dose, preparation, or time schedule, compared with placebo or with no intervention. Studies on vaccinated elderly are included in reviews looking at the effects of influenza vaccines in the elderly (Jefferson 2010b). The Jefferson 2010a review looked at the effects of vaccination in healthy adults such as healthcare workers.
Types of outcome measures
Outcomes in those aged 60 years or older in LTCIs.
- Cases of influenza in those aged 60 years or older confirmed by viral isolation or serological supporting evidence (or both), plus a list of likely respiratory symptoms.
- Lower respiratory tract infection.
- Admission to hospital for respiratory illness.
- Deaths caused by respiratory illness.
We excluded studies reporting only serological outcomes in the absence of symptoms. Outcomes for healthcare workers were not considered.
We did not select any secondary outcomes. We did not choose influenza-like illness (ILI) (Appendix 1) or all-cause mortality (Appendix 2) because these are not the effects the vaccines were produced to address. Influenza vaccines were designed and produced to prevent a specific disease caused by two specific viruses, not a syndrome such as ILI which includes a wide variety of viruses and studies of such cases often cannot document a specific cause of infection. ILI may be useful to some health officials for rough estimates of upcoming workload during the viral season. We did not use all-cause mortality because of deficiencies in laboratory-proven ascertainment of cause of death due to influenza, and thus under-reporting of influenza on death certificates.
Search methods for identification of studies
For this update we searched the Cochrane Central Register of Controlled Trials (CENTRAL) 2013, Issue 2, part of The Cochrane Library, www.thecochranelibrary.com (accessed 28 March 2013) which contains the Cochrane Acute Respiratory Infections Group's Specialised Register, MEDLINE (September 2009 to March week 3, 2013), EMBASE (September 2009 to March 2013) and Web of Science (2009 to March 2013). See Appendix 3 for details of previous searches. There were no language restrictions.
We searched MEDLINE and CENTRAL using the following search strategy. We combined the MEDLINE search with the Cochrane Highly Sensitive Search Strategy for identifying randomised trials in MEDLINE: sensitivity-maximising version (2008 revision); Ovid format (Lefebvre 2011). We adapted the search strategy to search EMBASE (Appendix 4) and Web of Science (Appendix 5).
We also combined the following search strategy with the SIGN filter (SIGN 2009) for identifying observational studies and ran the searches in MEDLINE and adapted them for EMBASE and Web of Science (see Appendix 6).
1 Influenza Vaccines/
2 Influenza, Human/
3 exp Influenzavirus A/
4 exp Influenzavirus B/
8 exp Vaccines/
11 exp Immunization/
12 (immuniz* or immunis*).tw.
14 7 and 13
15 1 or 14
16 exp Health Personnel/
17 ((health or health care or healthcare) adj2 (personnel or worker* or provider* or employee* or staff or professional*)).tw.
18 ((medical or hospital) adj2 (staff or employee* or personnel or worker*)).tw.
19 (doctor* or physician* or clinician*).tw.
20 (allied health adj2 (staff or personnel or worker*)).tw.
23 (nursing adj2 (staff or personnel or auxiliar*)).tw.
24 exp Hospitals/
25 Long-Term Care/
26 exp Residential Facilities/
27 Health Services for the Aged/
28 nursing home*.tw.
29 (institution* adj3 elderly).tw.
30 aged care.tw.
32 ((long stay or long term) adj3 (ward* or facilit* or hospital*)).tw.
33 old people* home*.tw.
37 15 and 36
Searching other resources
We searched the WHO International Clinical Trials Registry Platform (ICTRP) and US National Institutes of Health trials registry (latest search 2 November 2012). We also searched the Database of Abstracts of Reviews of Effects (DARE) 2013 part of The Cochrane Library and reviewed the references for further possible studies. We searched bibliographies of retrieved articles and contacted trial authors for further details, if required.
Data collection and analysis
Selection of studies
Two review authors (TJL, RET) independently reviewed the abstracts by using the following inclusion criteria.
- People 60 years or older.
- LTCIs or hospitals.
- Healthcare workers.
- Influenza vaccination.
- Morbidity and mortality of residents.
Disagreements were resolved by a third review author (TOJ).
Data extraction and management
Two review authors (RET, TJL) applied the inclusion criteria to all identified and retrieved articles and extracted data from included studies into standard Cochrane Vaccines Field forms. We extracted the following data in duplicate.
- Methods: purpose; design; period study conducted and statistics.
- Participants: country or countries of study; setting; eligible participants; age and gender.
- Interventions and exposure: in intervention group and control group.
- Outcomes in those aged 60 years or older residing in LTCIs:
- Cases of influenza confirmed by viral isolation or serological supporting evidence (or both) plus a list of likely respiratory symptoms.
- Lower respiratory tract infection.
- Hospitalisation for respiratory illness.
- Death from respiratory illness.
Two review authors (RET, TJL) independently checked data extraction and disagreements were resolved by third review author (TOJ).
Assessment of risk of bias in included studies
We carried out assessment of methodological quality for RCTs using The Cochrane Collaboration's 'Risk of bias' tool (Higgins 2011). We assessed the quality of non-RCTs in relation to the presence of potential confounders using the appropriate Newcastle-Ottawa Scales (NOS) (Wells 2005). The NOS asks whether all possible precautions against confounding have been taken by the study designers and links study quality to the answer. We translated the number of inadequately reported or conducted items into categories of risk of bias. We used quality at the analysis stage as a means of interpreting the results. The review authors resolved disagreements on inclusion or methodological quality of studies by discussion. Two review authors (RET, TOJ) checked quality assessment.
We looked for details of formal ethics approval and informed consent of participants.
Measures of treatment effect
We assessed efficacy against laboratory-proven influenza, pneumonia, deaths from pneumonia and hospitalisation using risk differences (RD) with 95% confidence intervals (CI). The number needed to vaccinate (NNV) was computed as 1/RD.
Unit of analysis issues
All three RCTs that provided outcome data that met our criteria were cluster-RCTs. Carman 2000 did not control for clustering and we were not able to adjust his data to do so. We adjusted the precision of the study estimates for the cluster-RCTs based on standard Cochrane Handbook for Systematic Reviews of Interventions advice (Higgins 2011). We contacted trial authors to ascertain the intra-cluster correlation coefficient (ICC) and to confirm statistical analyses.
Dealing with missing data
We did not use any strategies to impute missing outcome data and recorded missing data in the 'Risk of bias' table. We attributed an ICC to two studies (Carman 2000; Potter 1997) from an assumed intra-cluster variance of 2.3% from a larger study we did not include (Hayward 2006).
Assessment of heterogeneity
We used the Chi
Assessment of reporting biases
We reviewed an additional 554 abstracts for potential RCTs and 251 for non-RCTs and 312 citations from the systematic review by Jefferson 2010b. We identified only three cluster-RCTs that met our criteria for outcome data and so we could not create a funnel plot to assess publication bias due to the small number of included studies.
We meta-analysed RCTs when the I
Subgroup analysis and investigation of heterogeneity
Whenever data presented in the study allowed it, we carried out subgroup analysis according to the vaccination status of residents aged 60 years or older. We assessed the following outcomes which arose during the influenza season.
- Laboratory-proven influenza infections (by paired serology, nasal swabs, reverse-transcriptase polymerase chain reaction (RT-PCR) or tissue culture).
- Lower respiratory tract infection.
- Hospitalisation for respiratory illness.
- Death from respiratory tract illness.
With only three cluster-RCTs that met our criteria for outcome data, a sensitivity analysis was not feasible.
Description of studies
Results of the search
This updated 2013 search retrieved 268 records with the RCT filter and 479 records with the observational filter.
The 2009 search had retrieved a total of 554 records in the search for RCTs and 251 records in the search for observational studies and in the first publication of this review we also examined 312 reports for detailed assessment from the review on the effects of influenza vaccines in the elderly (Jefferson 2010b). Due to the comprehensive nature of the Cochrane review on the effects of influenza vaccines in the elderly (Jefferson 2010b) we carried out a review with a very focused study question and benefited from extensive searches which generated a large number of 'hits' but a relatively low yield of studies to include.
We found three cluster-RCTs that met our criteria for outcome data. For the third publication of this review we have excluded outcome data relating to influenza-like illness (Appendix 1) and all-cause mortality (Appendix 2) as outcome measures. Two studies that were in the second publication no longer contribute outcome data to this review: one cluster-RCT (Hayward 2006) as the main outcome measure was all-cause mortality and the secondary measure was ILI; and a cohort study (Oshitani 2000) which used ILI as the outcome measure.
Five studies met the inclusion criteria (see Characteristics of included studies table). Three studies contribute data to the outcomes of interest to this review, recruiting a total of (5896 participants) (Carman 2000; Lemaitre 2009; Potter 1997). These three studies were comparable in study populations, intervention and outcome measures. The studies did not report on adverse events.
All 747 new citations identified in the 2013 search for this third publication of this review were excluded because they either did not have influenza vaccination outcome data for those aged 60 years or older or healthcare workers, or both, or did not report the outcome data we specified, or reported only influenza antibody levels.
Risk of bias in included studies
|Figure 1. Methodological quality graph: review authors' judgements about each methodological quality item presented as percentages across all included studies.|
|Figure 2. Methodological quality summary: review authors' judgements about each methodological quality item for each included study.|
There was adequate sequence generation in three studies. One used a random-number table (Carman 2000) and one used a centralised random-number generator (Lemaitre 2009), and for the third study we considered that the process was likely to have been carried out reliably (Hayward 2006). However, there was uncertainty in one study (Potter 1997): "Hospital sites were stratified by unit policy for vaccination, then randomised for their healthcare workers to be routinely offered either influenza vaccination and patients unvaccinated...".
No RCTs explicitly stated that they had appropriate means of blinding participants or study personnel to vaccination. In Carman 2000 and Potter 1997 there is no statement that any researcher, assessor, data analyst, healthcare worker or participant was blinded. In Carman 2000 the study nurses "took additional opportunistic nose and throat swabs from non-randomised patients who the ward nurses thought had an influenza-like illness". In Potter 1997 ward nurses paged the research nurses "if any patients under their care developed clinical symptoms suggestive of upper respiratory tract viral illness, influenza, or lower respiratory tract infection," and in Lemaitre 2009 "Influenza vaccination was further recommended during face-to-face interviews with each member of staff ... The study team individually met all administrative staff, technicians and caregivers to invite them to participate and volunteers were vaccinated at the end of the interview."
Incomplete outcome data
No study appeared to report results selectively.
Other potential sources of bias
For Potter 1997 potential sources of bias were as follows.
- Selection bias: the total number of long-term care hospitals in West and Central Scotland is not stated. There were inconsistencies in outcome gradients ( Table 1). In the population under observation, Potter 1997 reported 216 cases of suspected viral illness, 64 cases of influenza-like illness, 55 cases of pneumonia, 72 deaths from pneumonia and 148 deaths from all causes; in the sub-population of both vaccinated staff and patients, Potter 1997 reported 24 cases of suspected viral illness, two cases of influenza-like illness, seven cases of pneumonia, 10 deaths from pneumonia and 25 deaths from all causes. As these gradients are not plausible (one would expect a greater proportion of cases of influenza-like illness to be caused by influenza during a period of high viral activity), the effect on all-cause mortality is likely to reflect a selection bias rather than a real effect of vaccination.
- Performance bias: 67% of staff in active arm one and 43% in active arm two were vaccinated.
- There is no description of the vaccines administered, vaccine matching or background influenza epidemiology.
For Carman 2000 potential sources of bias were as follows.
- Selection bias: the total number of long-term care hospitals in West and Central Scotland is not stated. In the long-term care hospitals in which healthcare workers were offered vaccination, residents had higher Barthel scores.
- Performance bias: only 51% of healthcare workers in the Lemaitre 2009 arm received vaccine in the long-term care hospitals where vaccine was offered and 4.8% where it was not; 48% of patients received vaccine in the arm where healthcare workers were offered vaccination and 33% in the arm where healthcare workers were not.
- Statistical bias: the analysis was not corrected for clustering, unlike the Potter 1997 pilot; in the long-term care hospitals where healthcare workers were offered vaccination, the patients had significantly higher Barthel scores and were more likely to receive influenza vaccine (no significance level stated) and due to missing data these differences could not be adjusted for other than by estimation. Statistical power may also have been a problem as the detection rate of 6.7% was lower than the estimated rate of 25% used in the power calculation.
The Potter 1997 and Carman 2000 cluster-RCTs can be regarded as investigations in the same geographical area with a modest possible but unknown overlap of staff and residents. Only three of the long-term care hospitals in the Potter 1997 study were included in the Carman 2000 cluster-RCT because some of the homes were closed down (e-mail communication from Dr. Stott) but the continuity of staff between the institutions is unknown.
Ethics approval: Carman 2000, Lemaitre 2009 and Potter 1997 received formal ethics approval. Carman 2000 and Potter 1997 obtained written informed consent from healthcare workers and witnessed verbal consent from participants for nose swabs to be taken and Potter 1997 for blood samples. The LTCIs already had policies for opting in or opting out of influenza vaccination. Lemaitre 2009 obtained face-to-face informed consent from healthcare workers.
Effects of interventions
1. Cases of influenza in those aged 60 years or older confirmed by viral isolation or serological supporting evidence (or both), plus a list of likely respiratory symptoms
Potter 1997 reported outcomes only for unvaccinated patients. We computed a risk difference (RD) of 0.01, 95% confidence interval (CI) -0.03 to 0.05, P = 0.73). Carman 2000 reported data on influenza cases among vaccinated and unvaccinated patients combined. We computed a RD of -0.01, 95% CI -0.05 to 0.03, P = 0.54). We were able to pool the results for Carman 2000 and Potter 1997 and we computed an overall RD of -0.00, 95% CI -0.03 to 0.03, P = 0.45, I
2. Lower respiratory tract infection
Only Potter 1997 reported data for lower respiratory tract infection. He reported results separately for vaccinated and unvaccinated patients. For vaccinated patients we computed a RD of -0.02, 95% CI -0.05 to 0.01, P = 0.21. For unvaccinated we computed a RD of -0.02, 95% CI -0.06 to 0.03, P = 0.47. For the vaccinated and unvaccinated patients combined we computed a RD of -0.02, 95% CI -0.04 to 0.01, P = 0.15, I
3. Admission to hospital for respiratory illness
Only Lemaitre 2009 provided data for "admissions to hospital for respiratory illness" and we computed a RD of 0.00, 95% CI -0.02 to 0.02, P = 0.84 ( Analysis 1.3). The pooled RD based on adjusted study effect estimates was RD 0.00, 95% CI -0.02 to 0.03 ( Analysis 2.3).
4. Deaths caused by respiratory illness
Potter 1997 reported data for deaths from pneumonia separately for vaccinated patients and unvaccinated patients. For vaccinated patients we computed a RD of -0.03, 95% CI -0.07 to 0.01, P = 0.09 and for unvaccinated we computed a RD of -0.03, 95% CI -0.07 to 0.01, P = 0.18. Lemaitre 2009 reported results for "deaths from respiratory illness" (not further defined) for vaccinated and unvaccinated patients combined and we computed a RD of 0.00 (95% CI -0.00 to 0.01, P = 0.23) ( Analysis 1.4). We computed a pooled result for Potter 1997 and Lemaitre 2009 of -0.02, 95% CI -0.06 to 0.02, P = 0.40 but the I
We identified three cluster-RCTs which met our criteria for outcome data to answer the question of whether vaccinating healthcare workers against influenza protects those aged 60 years or older residing in LTCIs. For the three cluster-RCTs we assessed the risk of bias for sequence generation as low in two and unclear in one; concealment of allocation as unclear in all three; blinding as unclear in all three; incomplete data as low risk in one and high in two; selective reporting as low in all three and for other biases (performance bias due to incomplete influenza vaccination of the healthcare workers in the intervention arms) we assessed all three as high risk of bias. Carman 2000 did not adjust results for the effect of clustering.
Pooled data showed no effect on specific outcomes: laboratory-proven influenza (Carman 2000; Potter 1997), lower respiratory tract infections (Potter 1997), admissions to hospital for lower respiratory tract illness (Lemaitre 2009) and deaths from lower respiratory tract illness (Lemaitre 2009; Potter 1997), with the 95% CI in each case including unity.
One question is what is the maximum contribution that influenza vaccination of people aged 60 years or older could make in reducing total annual mortality? A population study by Simonsen 2006 used data from the US national multiple-cause-of-death databases from 1968 to 2001 and found that for those aged 65 years or older, the mortality attributable to pneumonia or influenza never exceeded 10% of all deaths during those winters. The study by Vila-Córcoles 2007 of 11,240 Spanish community-dwelling elderly, conducted between January 2002 and April 2005, found the attributable mortality risk in individuals not vaccinated against influenza was 24 deaths/100,000 person-weeks within influenza periods. Vaccination prevented 14% of these deaths for the population and one death was prevented for every 239 annual vaccinations (ranging from 144 in winter 2005 to 1748 in winter 2002). It should be noted that these data are not for residents of LTCIs. A mathematical model predicted that for a 30-bed unit, an increase in healthcare worker vaccination rates from 0% to 100% would decrease resident influenza infections by 60% (van den Dool 2008).
Summary of main results
We identified three cluster-RCTs that provided outcome data that met our criteria. Pooled data showed that there was no effect on laboratory-proven influenza, lower respiratory tract infections, admissions to hospital for respiratory illness or deaths from respiratory illness.
Overall completeness and applicability of evidence
Three cluster-RCTs focused directly on the question of the effect of healthcare worker vaccination on the mortality and morbidity of long-term care facility residents aged 60 years or older. The three cluster-RCTs contributed data from a total of 5896 residents in LTCIs. These three (and Hayward 2006) cluster RCTs have certain common features: they are all underpowered to detect any difference in influenza mortality which is a rare event. All participants, were they residents or carers, were unblinded to their intervention status. All trials showed no reduction in influenza or its complications (the registered indication for the vaccines), and all reached conclusions which were not based on the data presented. As influenza is not even in the top 10 causes of death in the elderly, none of the trialists seemed to reflect on the sheer implausibility of no effect on influenza but apparent effect on a syndrome (ILI) which is caused only in part by influenza viruses.
Quality of the evidence
The estimated effects for the outcomes we assessed in this review are imprecise and we considered the studies that contributed outcome data to have been at a high risk of bias. The analysis of both adjusted and unadjusted study results for the four outcomes of interest were consistent with each other. The high I
Potential biases in the review process
We imposed no language restrictions on the search and all studies were independently assessed by two review authors. The intra-cluster correlation coefficients (ICCs) we used for one of the studies were based on the estimate provided by Hayward 2006. Although the recalculation of the effective sample size was done in accordance with recommended procedures (Higgins 2011), we have assumed that the adjustment required is the same across the outcomes extracted for each study. Rather than increase uncertainty around the pooled effect size, adjustment of the standard errors for the studies by our method reduced the statistical heterogeneity between the study effect estimates. If the ICCs we used as the basis for these calculations were too large, our adjusted analyses may underestimate the true amount of variation between the study results.
Agreements and disagreements with other studies or reviews
Other reviews addressing similar study questions do not include all the studies that we found.
Implications for practice
All three cluster-randomised controlled trials (RCTs) contributing outcome data to our review are at high risk of bias and pooled data found no effect on the outcomes of direct interest, namely laboratory-proven influenza, lower respiratory tract infections and admissions to hospital and deaths from lower respiratory fract illness, with the 95% confidence interval (CI) in each case including unity. We conclude that there is an absence of high-quality evidence that vaccinating healthcare workers against influenza protects people aged 60 years or older in their care and thus there is little evidence to justify medical care and public health practitioners mandating influenza vaccination for healthcare workers who care for the elderly in long-term care institutions (LTCIs).
Implications for research
There are currently only three cluster-RCTS which provide outcome data that meet our criteria to evaluate the impact on residents aged 60 years or older of vaccinating their healthcare workers against influenza. All of these studies are at high risk of bias. RCTs are needed with minimal risk of bias from sequence generation, failure to conceal allocation, performance, attrition and detection and these should be adequately powered for the key outcomes of laboratory-proven influenza, hospitalisation for pneumonia and death from pneumonia. They should carefully define and measure outcomes including laboratory-proven influenza, lower respiratory tract infection, cause of hospitalisation and deaths from pneumonia. They should carefully consider the degree to which they must, to adequately assess outcomes, obtain proof of diagnosis for all participants by laboratory testing all participants with appropriate symptoms for influenza and all other likely viruses, performing blood cultures, white blood cell counts and other laboratory investigations and chest X-rays if pneumonia is suspected, and following the course of all hospitalised patients by scrutinising individual records so that they can definitively assess all outcomes and co-morbidities. A particular issue in the analysis of data from studies with a cluster design is the provision and use of an ICC. It is a major limitation with the analysis of data in our review that we have not had available a reliable estimate of this quantity for each of the outcomes of interest.
The area of interest is those aged 60 years or older in LTCIs. Therefore, if the existing LTCIs' organisational structure is to be used to implement the interventions, these will need to be given to clusters of residents aged 60 years or older and healthcare workers, which will make blinding difficult. An important ethical issue is informed consent by those aged 60 years or older and healthcare workers. It is not ethical to blind participants or healthcare workers but the researchers, data assessors and statisticians could all be blinded.
The elderly are much keener to be vaccinated than healthcare workers and there is extensive literature about the group of healthcare workers who say they do not feel vulnerable to influenza, do not believe the vaccine is effective and are afraid of side effects, and some of these do not perceive risk for their patients. Persistence of these beliefs may limit uptake by healthcare workers and make it difficult to test conclusively the effect of very high levels of healthcare worker influenza vaccination.
A large publicly funded trial is needed to test combinations of interventions to reduce influenza and mortality from influenza in those aged 60 years or older in LTCIs with thorough delivery of each intervention: vaccinating residents and healthcare workers, hand-washing, face masks, early detection of laboratory-proven influenza in individuals with influenza-like illness by using nasal swabs, quarantine of floors and entire LTCIs during outbreaks, avoiding new admissions, prompt use of antivirals and asking healthcare workers with an influenza-like illness not to present for work.
Professor David J. Stott, Academic Section of Geriatric Medicine, Glasgow Royal Infirmary, UK provided supplementary information on the Potter 1997 and Carman 2000 studies. Dr. Magali Lemaitre confirmed the ICC for Lemaitre 2009 and Dr. Andrew Hayward provided information regarding the analysis of data for Hayward 2006.
We acknowledge the contributions of Vittorio Demicheli (previously responsible for design of the review and responsible for the final draft of a previous version); Daniela Rivetti who was responsible for the previous searches; and Sarah Thorning, who conducted the searches for these review updates.
The authors wish to thank the following people for commenting on the draft of the second publication: Amy Zelmer, Laila Tata, Amir Shroufi, Rob Ware and John Holden.
Data and analyses
- Top of page
- Authors' conclusions
- Data and analyses
- What's new
- Contributions of authors
- Declarations of interest
- Sources of support
- Index terms
Appendix 1. Reasons not to use influenza-like illness in assessing the effectiveness of influenza vaccines
Influenza-like illness (ILI)
There are six reasons not to use ILI as an outcome.
1. There are multiple definitions. The Centers for Disease Control and Prevention (CDC) definition is a temperature ≥ 38°C, cough or sore throat or both and the absence of a known cause other than influenza (CDC 2006). Health Canada's Flu Watch uses fever, cough and ≥ one of sore throat, arthralgia, myalgia or prostration (www.phac-aspc.gc.ca/fluwatch).
2. The percentage of ILI cases that are laboratory-proven influenza cases is low. During the 2009 H1N1p pandemic in Marseille, GPs assessed 660 patients as ILI cases: 158 were positive for A/H1N1p. Of the 502 reverse-transcriptase polymerase chain reaction (RT-PCR) influenza-negative patients 296 were randomly selected for further testing: 82 were positive for at least one other virus (58 human rhinovirus, nine parainfluenza viruses 1-4, nine human coronavirus OC43, five enterovirus, four adenovirus and two human metapneumovirus) and 204 were negative for all 18 viruses tested (Thiberville 2012). A RCT in 46 Hutterite colonies in Canada defined ILI as fever ≥ 38°C, cough, runny nose, sore throat, headaches, sinus problems, muscle ache, fatigue, ear ache and chills but only 37 (26%) of 142 tested were PCR positive (Barbara 2012). A study in India defined ILI as sudden onset of fever > 38°C or a history of sudden onset of fever in the recent past (< three days), cough or sore throat and/or rhinorrhoea and SARI (severe acute respiratory infections) as an ILI with breathlessness or difficulty in breathing/tachypnoea or clinically suspected pneumonia (in children) with increased respiratory rates. They isolated influenza from only 617 (4.43%) of 13,928 throat or nasal swabs (Chadha 2011). A study in Taiwan of 26,601 ILI cases found influenza in only 25% by viral culture or RT-PCR (Chuang 2012).
3. There is a remarkable similarity between the symptoms of influenza A/H1N1p and human rhinovirus. Of the 660 patients in Marseille, 85% had a fever (91% H1N1p, 79% HRV), 83% had a cough (97%, 86%), 75% had ILI symptoms (89%, 74%), 65% a sore throat (65%, 69%), 93% asthenia (96%, 88%), 80% myalgia (80%, 74%), 63% rhinorrhoea (74%, 81%), 77% headache (78%, 69%), 65% chills (74%, 52%), 40% arthralgia (41%, 31%) and 35% nausea (39%, 23%) (Thiberville 2012).
4. Some studies use ILI in circular definitions resulting in unclear outcomes. The Australian Flutracking programme defined ILI as the proportion of participants in their programme who had both fever and cough during the peak influenza period for each year 2007 to 2009. In a completely circular manner the peak influenza period was defined as the four consecutive weeks with the highest Flutracking ILI rates. No analysis was performed of whether any symptom correlated with laboratory-proven influenza (Dalton 2011). A study of “influenza activity” in Hong Kong also achieved circularity by confounding together the laboratory proven influenza rate and the ILI rate: “The product of the laboratory influenza detection rate and the GP ILI consultation rate was used as the reference standard indicator of influenza virus activity, rather than the laboratory data alone which suffer from denominator dilution during periods of non-influenza epidemics and the GP ILI data alone which suffer from numerator dilution because not all ILI episodes are associated with influenza” (Lau 2012).
5. Some studies argue that multiple viral activity databases which may have peaks at similar times measure the same phenomenon. A study in Singapore during the 2009 pandemic defined ILI by the WHO criteria, plus new onset respiratory symptoms and temperature > 38°C and multiplied the rate of ILI cases diagnosed by 23 sentinel GPs (n of patients not stated) by the “relative proportion of ILI seen by the average GP” and thereby estimated the ILI rate at 15% (Bayesian credible intervals 10%, 25%). A separate serological study of samples from 727 adult patients four weeks before, four weeks after the epidemic peak and four weeks after the epidemic subsided estimated the influenza rate at 17% (BCI 14%, 20%) and the two rates were presented as confirming each other. There was no relationship between the two samples, which were merely used to attempt estimates of the rate of ILI and influenza activity during the epidemic and no assessment was made of the utility of the ILI definition (Lee 2011). A study of “influenza activity” during 166 weeks in the US 2003-8 compared the CDC Outpatient ILI Surveillance Network (which uses the CDC ILI definition and a network of “health care providers”), Google Flu Trends (weekly percentage of persons seeking health care with ILI) and the CDC Influenza Virologic Surveillance System. The Pearson correlation coefficient between Google Flu trends and CDC Virus surveillance was 0.72 (95% CI 0.64 to 0.79) and with CDC ILI surveillance was 0.94 (95% CI 0.92 to 0.96) and between the two CDC databases 0.85 (0.81 to 0.89). There was no attempt to identify individuals across all three databases and no assessment of the utility of symptoms (Ortiz 2011).
6. Influenza rapid diagnostic tests have low sensitivity. There is an increasing tendency to use rapid influenza diagnostic tests for ILI cases. A review of 159 studies evaluating 26 rapid influenza diagnostic tests found the pooled sensitivity was 62.3% (95% CI 57.9% to 66.6%) and specificity 98% (97.5% to 98.7%), with lower sensitivity in adults 53.9% (47.9% to 59.8%). If these are used to assess the effectiveness of influenza vaccines further inaccuracy will be introduced (Chartrand 2012).
Appendix 2. Reasons not to use all-cause mortality as an outcome measure in assessing the effectiveness of influenza vaccines
There are three reasons not use all-cause mortality to assess the effectiveness of influenza vaccine.
1. Mortality attributable to influenza is a small proportion of all deaths. For those aged ≥ 65 in the US national multiple-cause-of-death databases 1968 to 2001 mortality attributable to pneumonia or influenza never exceeded 10% of all winter deaths (Simonsen 2006). All-cause deaths could be subject to considerable bias and fluctuations as an estimate of influenza mortality.
2. The number of nursing home residents with proven respiratory infections is low. A unique inclusive prospective study in France of 44,869 nursing home residents aged ≥ 65 found < 4.5% of the nursing home residents studied had an upper or lower respiratory tract infection, with 1.31% definite (95% CI 1.09 to 1.68) (using McGeer’s consensus definition, which is a physician diagnosis (McGeer 1991)) and 3.34% probable (2.88 to 3.87). Influenza vaccine had been received by 93.4% of the patients and pneumococcal vaccine by 13% (Chami 2011).
3. Many cases of "influenza" are not laboratory-proven and deaths are not recorded on death certificates. Three statistical approaches have attempted to use existing databases to predict mortality due to influenza.
a. The first assumes all differences in mortality comparing virus and non-virus seasons are due to influenza. A study in France using data from a sentinel network of GPs estimated only 3.35% (176,053) of all 5,295,480 deaths from 1998 to 2007 were due to ILI and 2.14% (113,240) due to cold spells. Mortality in the four winter months correlated with reported ILI (r = 0.75, P = 0.02) (Pin 2012). A study in the US and Japan had similar findings (Charu 2011). Mortality due to influenza cannot be estimated from these ILI data.
b. The second distributes “influenza related deaths” among co-morbidities. A study of weekly mortality data from the US National Health Statistics database 1997 to 2007 attributed an average of 11.92 (95% CI 10.1 to 13.6) deaths/100,000 to influenza, with 9.41 (8.3 to 10.5) to A/H3N2 years and 2.51 to influenza B years. These 11.92 deaths/100,000 ascribed to influenza were then further partitioned into: all circulatory causes 4.6 (3.79 to 5.39), all respiratory 3.58 (3.04 to 4.14), cancer 0.87 (0.68 to 1.05), diabetes 0.33 (0.26 to 0.39), renal disease 0.19 (0.14 to 0.24), CNS 0.42 (0.31 to 0.53) and Alzheimer’s 0.41 (0.3 to 0.52) and the authors concluded that 69% of the “influenza associated mortality” was attributable to circulatory and respiratory causes (Goldstein 2012). A study in the US and South Africa estimated excess deaths over baseline winter deaths as 16% for South Africa and 6% for the US. Within co-morbidity diagnoses the percentage of excess deaths over baseline winter deaths for all respiratory causes was estimated as 25% and 14%, for pneumonia and influenza as 29% and 20%, for cerebrovascular events as 16% and 4%, for diabetes as 13% and 5% and for ischaemic heart disease as 9% and 6% (Cohen 2010). A study in Canada assumed that recorded influenza-certified deaths substantially underrepresented influenza activity and estimated there were 3834 (1.9%) “influenza-attributable deaths” and allocated 877 to ischaemic heart disease, 563 to pneumonia, 529 to chronic obstructive pulmonary disease (COPD), 349 to other heart disease, 295 to cancer and 249 to stroke and then assessed whether their statistical model provided good predictions of these allocated deaths (Schanzer 2007).The precise number of deaths due to influenza cannot be known from these data and statistical methods.
c. The third computes a moving mortality average for the 13-month period centred on each month, assuming that these 13 month periods would be “unaffected by preceding or following epidemics.” For each disease class and month the 13-month moving average is then subtracted from the observed mortality. For unpublished data 1959 to 1999 from the public use data files of the US National Center for Health Statistics the authors created a time series for each class with a common metric by converting data into z scores with a mean of zero and standard deviation of 1. The peak months for pneumonia and influenza coincided with those for ischaemic heart disease 34 of 40 times, with cerebrovascular disease 33 of 40 times and with diabetes 30 of 40 times. However, midwinter peaks for pneumonia and influenza, ischaemic heart disease, cerebrovascular disease and diabetes varied in size and differed widely in mean values and seasonal variation. As expected, the tallest peaks in mortality curves occurred during the A(H3N2) 1968/9 pandemic and were lower in years where influenza A(H1N1) and B predominated and categorisation by influenza type correctly sorted winter seasons as having low or high mortality, without requiring additional information (Reichert 2004). The precise number of deaths due to influenza cannot be known from these data and statistical methods.
Appendix 3. Previous search
For our original search in 2006 we searched the Cochrane Central Register of Controlled Trials (CENTRAL), the Cochrane Database of Systematic Reviews and the NHS Database of Abstracts of Reviews of Effects (DARE) (The Cochrane Library 2006, Issue 1); MEDLINE (January 1966 to Week 1, February 2006); EMBASE (1974 to March 2006); Biological Abstracts (1969 to December 2005) and Science Citation Index-Expanded (1974 to March 2006).
MEDLINE was searched using the following search terms in combination with stages I, II and III of the highly sensitive search strategy defined by The Cochrane Collaboration and detailed in Appendix 5b of the Cochrane Handbook for Systematic Reviews of Interventions (Deeks 2005).
1 exp INFLUENZA/
4 exp VACCINES/
5 exp VACCINATION/
6 (immuniz$ or immunis$).mp.
9 3 and 8
10 exp Influenza Vaccine/
11 (influenz$ adj (vaccin$ or immun$)).mp.
13 9 or 12
14 exp Health Personnel/
15 (health personnel or healthcare personnel or health care personnel).mp.
16 (health worker$ or healthcare worker$ or health care worker$).mp.
17 (healthcare provider$ or health care provider$).mp.
18 (health practitioner$ or healthcare practitioner$ or health care practitioner$).mp.
19 health employee$.mp.
20 medical staff.mp.
21 (doctor$ or physician$).mp.
22 (allied health adj (staff or personnel)).mp.
24 nursing staff.mp.
26 nursing auxiliar$.mp.
27 hospital personnel.mp.
28 hospital staff.mp.
29 hospital worker$.mp.
30 exp HOSPITALS/
31 exp Long-Term Care/
32 exp Residential Facilities/
33 nursing home$.mp.
34 (institution$ adj3 elderly).mp.
36 13 and 35
This strategy was adapted to search the other electronic databases. See below for the EMBASE search strategy. There were no language or publication restrictions. The search of CENTRAL included trial reports identified in the systematic search by hand of the journal Vaccine. To identify additional published and unpublished studies the Science Citation Index-Expanded was used to identify articles that cite the relevant studies. The relevant studies were also keyed into PubMed and the Related Articles feature used.
#1 explode 'influenza-' / all subheadings in DEM,DER,DRM,DRR
#2 (influenza in ti) or (influenza in ab)
#3 #1 or #2
#4 explode 'vaccine-' / all subheadings in DEM,DER,DRM,DRR
#5 explode 'vaccination-' / all subheadings in DEM,DER,DRM,DRR
#6 (immuniz* in ti) or (immuniz* in ab)
#7 (immunis* in ti) or (immunis* in ab)
#8 (vaccin* in ti) or (vaccin* in ab)
#9 #4 or #5 or #6 or #7 or #8
#10 #3 and #9
#11 explode 'influenza-vaccine' / all subheadings in DEM,DER,DRM,DRR
#12 explode 'influenza-vaccination' / all subheadings in DEM,DER,DRM,DRR
#13 (influenz* adj (vaccin* or immun*)) in ti
#14 (influenz* adj (vaccin* or immun*)) in ab
#15 #10 or #11 or #12 or #13 or #14
#16 explode 'health-care-personnel' / all subheadings in DEM,DER,DRM,DRR
#17 (health personnel or healthcare personnel or health care personnel) in ti
#18 (health personnel or healthcare personnel or health care personnel) in ab
#19 (health worker* or healthcare worker* or health care worker*) in ti
#20 (healthcare provider* or health care provider*) in ti
#21 (healthcare provider* or health care provider*) in ab
#22 (health practitioner* or healthcare practitioner* or health care practitioner*) in ti
#23 (health practitioner* or healthcare practitioner* or health care practitioner*) in ab
#24 (health employee* in ti) or (health employee* in ab)
#25 explode 'hospital-personnel' / all subheadings in DEM,DER,DRM,DRR
#26 explode 'hospital-physician' / all subheadings in DEM,DER,DRM,DRR
#27 explode 'medical-personnel' / all subheadings in DEM,DER,DRM,DRR
#28 (medical staff in ti) or (medical staff in ab)
#29 explode 'physician-' / all subheadings in DEM,DER,DRM,DRR
#30 (doctor* or physician*) in ti
#31 (doctor* or physician*) in ab
#32 (allied health adj (staff or personnel)) in ti
#33 explode 'paramedical-personnel' / all subheadings in DEM,DER,DRM,DRR
#34 ( paramedic* in ti) or ( paramedic* in ab)
#35 explode 'nursing-staff' / all subheadings in DEM,DER,DRM,DRR
#36 ( nursing staff in ti) or ( nursing staff in ab)
#37 ( nurse* in ti) or ( nurse* in ab)
#38 ( nursing auxiliar* in ti) or ( nursing auxiliar* in ab)
#39 (hospital staff in ti) or (hospital staff in ab)
#40 (hospital worker* in ti) or (hospital worker* in ab)
#41 explode 'hospital-' / all subheadings in DEM,DER,DRM,DRR
#42 explode 'long-term-care' / all subheadings in DEM,DER,DRM,DRR
#43 explode 'residential-care' / all subheadings in DEM,DER,DRM,DRR
#44 explode 'residential-home' / all subheadings in DEM,DER,DRM,DRR
#45 (nursing home* in ti) or (nursing home* in ab)
#46 (institution* adj elderly) in ti
#47 (institution* adj elderly) in ab
#48 #16 or #17 or #18 or #19 or #20 or #21 or #22 or #23 or #24 or #25 or #26 or #27 or #28 or #29 or #30 or #31 or #32 or #33 or #34 or #35 or #36 or #37 or #38 or #39 or #40 or #41 or #42 or #43 or #44 or #45 or #46 or #47
#49 #15 and #48
Bibliographies of all relevant articles were obtained and any published review and proceedings from relevant conferences were assessed for additional studies. We explored Internet sources in December 2005: NHS National Research Register (http://www.update-software.com/national/); the metaRegister of Clinical Trials (http://www.controlled-trials.com/) and the digital dissertations website (http://wwwlib.umi.com/dissertations). The Vaccine Adverse Event Reporting System website was searched (http://www.vaers.org). We contacted first or corresponding authors of relevant studies to identify further published or unpublished trials.
For the update search in September 2009 we searched the Cochrane Central Register of Controlled Trials (CENTRAL) (The Cochrane Library 2009, Issue 3), which contains the Cochrane Acute Respiratory Infections Group's Specialised Register and the Database of Abstracts of Reviews of Effects (DARE); MEDLINE (January 1966 to Week 3, September 2009); EMBASE (1974 to September 2009); Biological Abstracts (1969 to December 2005) and Science Citation Index-Expanded (1974 to September 2009), which included Science Citation Index-Expanded, Biosis Previews and Current Contents. There were no language restrictions.
Appendix 4. EMBASE search strategy
#23 #11 AND #22
#22 #12 OR #13 OR #14 OR #15 OR #16 OR #17 OR #18 OR #19 OR #20 OR #21
#21 (('long stay' OR 'long term') NEAR/2 (ward* OR facilit* OR hospital*)):ab,ti
#20 'nursing home':ab,ti OR 'nursing homes':ab,ti OR 'aged care':ab,ti OR hospice*:ab,ti OR (institution* NEAR/3 elderly):ab,ti OR 'old peoples homes':ab,ti OR 'old peoples home':ab,ti
#19 'health care facility'/de OR 'hospice'/de OR 'nursing home'/de OR 'residential home'/de OR 'geriatric hospital'/de OR 'hospital'/de OR 'public hospital'/de OR 'private hospital'/de
#18 (nursing NEAR/2 (staff OR personnel OR auxiliar* OR assistan*)):ab,ti
#17 paramedic*:ab,ti OR nurse*:ab,ti
#16 ('allied health' NEAR/2 (personnel OR staff OR employee* OR worker* OR professional*)):ab,ti
#15 doctor*:ab,ti OR physician*:ab,ti OR clinician*:ab,ti
#14 ((medical OR hospital) NEAR/2 (staff OR employee* OR personnel OR worker*)):ab,ti
#13 ((health OR 'health care' OR healthcare) NEAR/2 (personnel OR worker* OR provider* OR employee* OR staff OR professional*)):ab,ti
#12 'health care personnel'/exp
#11 #1 OR #10
#10 #5 AND #9
#9 #6 OR #7 OR #8
#8 immuniz*:ab,ti OR immunis*:ab,ti
#6 'vaccine'/exp OR 'vaccination'/de
#5 #2 OR #3 OR #4
#4 'influenza virus a'/exp OR 'influenza virus b'/de
#3 influenza*:ab,ti OR flu:ab,ti
#1 'influenza vaccine'/de
Appendix 5. Web of Science search strategy
Appendix 6. SIGN search strategy for observational studies
1 epidemiologic studies/
2 exp case-control studies/
3 exp Cohort Studies/
4 case control.tw.
5 (cohort adj (study or studies)).tw.
6 cohort analy*.tw.
7 (follow up adj (study or studies)).tw.
8 (observational adj (study or studies)).tw.
11 cross sectional.tw.
12 Cross-Sectional Studies/
Appendix 7. Assessment of Oshitani 2000 using the Newcastle-Ottawa Scale for non-RCTs (Wells 2005)New Appendix
1. Representativeness of the exposed cohort:
a. truly representative of the average Long Term Care Facilities in Niigata Prefecture and City (mandatory surveys of influenza vaccination status and influenza-like illness occurrence every 2 weeks January to March 1999) in the community
b. somewhat representative of the average ___________ in the community
c. selected group of users (e.g. nurses, volunteers)
d. no description of the derivation of the cohort
2. Selection of the non-exposed cohort:
a. drawn from the same community as the exposed cohort
b. drawn from a different source
c. no description of the derivation of the non-exposed cohort
3. Ascertainment of exposure to influenza vaccine:
a. secure record (e.g. surgical records)
b. structured interview. "Mandatory survey." "Influenza vaccine had been given to 3933 residents (30.8%). No resident had received vaccine in 75 facilities (50.3%). Vaccines had also been given to 1532 of 7459 staff and10 or more staff had been vaccinated in 47 facilities (31.5%)." No description of survey or how administered or how completeness ascertained.
c. written self report
d. no description
4. Demonstration that outcome of interest was not present at start of study:
a. yes "An influenza outbreak was defined when the number of ILI per week exceeded 10% of the residents"
1. Comparability of cohorts on the basis of the design or analysis:
a. study controls for differences in demographic characteristics and co-morbidities of residents who were vaccinated and characteristics of homes where residents received vaccination (select the most important factor) No
b. study controls for any additional factor: geriatric health services facilities compared to special nursing homes for those with more severe conditions (this criteria could be modified to indicate specific control for a second important factor) No
1. Assessment of outcome:
a. independent blind assessment
b. record linkage
c. self report "Mandatory survey every 2 weeks January to March 1999"
d. no description
2. Was follow-up long enough for outcomes to occur (select an adequate follow-up period for outcome of interest):
a. yes - January to March 1999
3. Adequacy of follow-up of cohorts:
a. complete follow-up - all subjects accounted for
b. subjects lost to follow-up unlikely to introduce bias - small number lost (> ___ % (select an adequate %) to follow-up, or description of those lost))
c. follow-up rate < ___% (select an adequate %) and no description of those lost
d. no statement. No statement of admissions, deaths or separations from homes during study period. Total number of residents in Table 2 in homes where < 10 staff vaccinated is listed as 8699 but subcategories add to 8669 andin homes where >= 10 staff vaccinated listed as 4085 but subcategories add to 4073
Influenza vaccination for healthcare workers who work with the elderly, 5 May 2008
Feedback: The below is not an article in Journal of Infectious Diseases 1997; 175 (1) as cited. Indeed I've not been able to locate the the study in any other journal, though the study has been cited many times in other studies as well.
Potter J, Stott DJ, Roberts MA, Elder AG, O'Donnell B, Knight PV, et al. Influenza vaccination of health care workers in long-term-care hospitals reduces the mortality of elderly patients. Journal of Infectious Diseases 1997;175(1):1-6
Submitter agrees with default conflict of interest statement:
I certify that I have no affiliations with or involvement in any organization or entity with a financial interest in the subject matter of my feedback.
We thank Thomas Kristiansen for his comment. The article was in fact published in the Journal of Infectious Diseases (volume 175), issue 1 in 1997. It is available for purchase or download at: http://www.jstor.org/pss/30129986.
Thomas Birk Kristiansen
Feedback comment added 21 June 2008
Influenza vaccination for healthcare workers who work with the elderly, 1 December 2009
In the table and list of included studies, you have reported Hayward 2006 (BMJ Des 2006) but this study is not included in the analyses or mentioned in the text. The outcomes of this study do not seem to be adequately reported in the table.
Submitter agrees with default conflict of interest statement: I certify that I have no affiliations with or involvement in any organization or entity with a financial interest in the subject matter of my feedback.
We thank Signe Flottorp for his comment, which we received as we were updating the review. His comment has now been addressed.
Last assessed as up-to-date: 31 March 2013.
Protocol first published: Issue 2, 2005
Review first published: Issue 3, 2006
Contributions of authors
Responsible for the design of the review: Roger Thomas (RET), Tom Jefferson (TOJ).
Responsible for data extraction: all authors.
Responsible for the assessment of study quality and outcomes: RET and TJL (Toby Lasserson).
Responsible for the first draft: RET.
Responsible for the final draft: RET, TOJ, TJL
Declarations of interest
Dr. Jefferson receives royalties from his books published by Blackwell and Il Pensiero Scientifico Editore, Rome. Dr Jefferson is co-recipient of a UK National Institute for Health Research grant to carry out a Cochrane review of neuraminidase inhibitors (http://www.hta.ac.uk/2352). In 1997-99 Dr Jefferson acted as consultant for Roche, in 2001-2 for GSK and in 2003 for Sanofi. In 2011-2012, Dr Jefferson acted as an expert witness in a litigation case related to an antiviral (oseltamivir phosphate; Tamiflu (Roche)). Dr Jefferson is on a legal retainer for expert advice on litigation for influenza vaccines in healthcare workers. No declarations of interest for Dr. Roger Thomas or Toby Lasserson.
Sources of support
- No sources of support supplied
- National Institute for Health Research (NIHR), UK.Competitive grant awarded through The Cochrane Collaboration
- National Health and Medical Research Council (NHMRC), Australia.Competitive grant to Chris Del Mar and Tom Jefferson, 2009
Medical Subject Headings (MeSH)
*Health Personnel; Homes for the Aged; Infectious Disease Transmission, Professional-to-Patient [*prevention & control]; Influenza Vaccines [*administration & dosage]; Influenza, Human [prevention & control; *transmission]; Randomized Controlled Trials as Topic; Vaccines, Inactivated [administration & dosage]
MeSH check words
Adult; Aged; Humans; Middle Aged