Influenza is an acute respiratory illness caused by infection with influenza viruses. The illness affects the upper and/or lower respiratory tract and is often accompanied by systemic signs. There are three types of seasonal influenza viruses; A, B and C. Human influenza A and B viruses cause seasonal epidemics of disease almost every winter. Influenza C causes mild respiratory illness and occurs much less frequently than A and B. Type A influenza viruses are further typed into subtypes according to different kinds and combinations of virus surface glycoproteins: hemagglutinin (HA) and neuraminidase (NA). Influenza B viruses are not divided into subtypes (UpToDate 2010).
Symptoms of influenza include fever, myalgia, headache, cough, chills, nasal congestion and sore throat (Kamps 2006). The major complication of influenza is pneumonia, with secondary bacterial pneumonia being the most common form. Primary influenza pneumonia is a rare condition, but the most severe. Other complications include otitis media, bronchiolitis in infants and young children and exacerbations of chronic respiratory disease. There are also non-respiratory complications, including febrile convulsions, Reye’s syndrome (A rare, acute encephalopathy characterized by fever, vomiting, fatty infiltration of the liver, disorientation, and coma, occurring mainly in children and usually following a viral infection), neurological sequele and myocarditis (Angelo 2004; Wiselka 1994). The major morbidity associated with influenza is probably worsening of chronic health problems. Complications occur most frequently in certain groups of patients with underlying chronic illnesses who are classified as "high risk" for this infection (WHO Influenza 2009; Glezen 2008). These high-risk groups include patients with illnesses that involve the cardiovascular or pulmonary systems; patients with diabetes mellitus, renal disease or immunosuppression; residents of nursing homes or chronic care facilities; healthy individuals over age 65, children aged 6 to 23 months and pregnant women. Pneumonia and influenza-related death rates range from fewer than ten per 100,000 healthy people up to more than 600 per 100,000 chronically ill adults WHO Surveillance 2009. However these estimated rates have wide variability dependent on case definitions, the statistical models used for estimation and the cause for deaths categories considered (CDC MMWR 2010). Typically, there are between three and five million cases of severe illness and up to 500,000 deaths yearly worldwide related to influenza (WHO Surveillance 2009).
Description of the condition
Influenza among cancer patients
Patients with haematological or solid cancers undergoing chemotherapy and bone marrow transplant recipients are at increased risk of influenza-related complications (Kunisaki 2009; CDC Cancer prevention 2010). Patients at highest risk include those with impaired cell-mediated and antibody-mediated immunity, as reflected by a decrease in the number or function of T and B lymphocytes, respectively (Pirofski 1998). The risk probably relates to the degree of immune suppression. Highest-risk patients include those following allogeneic bone marrow transplant (or haematopoietic stem-cell transplant, thereafter referred to collectively as HSCT) recipients, especially during episodes of graft-versus-host disease (GVHD). Lower-risk patients with impaired lymphocyte function include haematological cancer patients with chronic lymphocytes leukaemia (CLL), multiple myeloma and probably those treated with specific anti-lymphocyte antibodies such as rituximab, alemtuzumab and others (Issa 2009). The main immune deficit affecting other cancer patients is neutropenia that is associated mainly with higher risk for bacterial infections rather than viral infections. However, influenza-related complications are more common among these patients compared to the general population. Different studies report wide range of influenza rates and influenza related pneumonia and deaths rates among patients with malignancies admitted to hospital with respiratory symptoms ( Table 1). Overall, comparing with the general population, influenza-related hospitalization rates are 4 times higher and mortality 10 times higher among cancer patients (Cooksley 2005; Yousuf 1997).
Due to paucity of data, it is hard to conclude on differences in influenza and influenza-related complications rates for specific risk-groups among all cancer patients. Although data are limited, allogeneic HSCT recipients seem to be more susceptible to influenza than autologous HSCT recipients. Solid cancer patients were included in one study only, thus results cannot be generalized to all cancer patients. One of the studies Chemaly 2006, found absolute lymphocyte count < 200 cells/mL (indicating severe immune dysfunction) an independent predictor of progression to influenza-related pneumonia.
Description of the intervention
Influenza vaccines contain antigens of the circulating influenza viruses and are intended to trigger antibody-mediated protection. Influenza A viruses undergo gradual, continuous change in the HA and NA proteins. These antigenic changes necessitate annual updating of the influenza vaccine components (Glezen 2008). Current influenza vaccines are available as trivalent inactivated vaccine (TIV, three strains; usually A/H1N1, A/H3N2, and B) or as nasal spray of live attenuated influenza vaccine (LAIV) (CDC Flu activity 2010). There are three types of TIV: (1) whole virion vaccines which consist of complete viruses which have been inactivated, so that they are not infectious but retain their strain specific antigenic properties; (2) subunit virion vaccines which are made of surface antigens (HA and NA) only; (3) split virion vaccines in which the viral structure is broken up by a disrupting agent. These vaccines contain both surface and internal antigens. The “subunit” or “split” vaccines are those used routinely for seasonal vaccination in adults. Currently, both TIV and the nasal spray are manufactured using chicken eggs. TIV is indicated to all individuals six months and older. LAIV has been approved by the United States Food and Drug Administration (FDA) for healthy persons aged 2 to 49 years of age and is contra-indicated in immune-suppressed individuals (Fiore 2009). The protective efficacy of the vaccine is largely determined by the relationship (closeness of "fit" or "match") between the strains in the vaccine and viruses that circulate in the season. If this "fit" is close, rates of protection of 70% to 90% reduction in laboratory-confirmed influenza are expected. Vaccine effectiveness may also be lower among persons with chronic medical conditions and among the elderly, as compared to healthy young adults and children (CDC Seasonal Influenza 2009).
A number of previous Cochrane systematic reviews examined influenza vaccine efficacy and effectiveness in different populations (Jefferson 2010a; Goossen 2009; Jefferson 2010). Among healthy adults inactivated parenteral vaccines were 30% effective against influenza-like illness (ILI), and 80% efficacious against proven influenza, in randomized controlled trials (RCTS), when the vaccine matched the circulating strain and circulation was high (Jefferson 2010a). Efficiency against influenza decreased to 50% when the vaccine did not match the seasonal strains. There was insufficient evidence to draw conclusions on hospital admissions or complication in healthy individuals. Nearly all studies included in the systematic review on elderly people (Jefferson 2010) were non-randomized (cohort or case-control studies); the authors of this review stated that the trials were at high risk for bias and that any conclusions drawn from these trials may be potentially misleading. The authors performed separate analyses by study design, vaccine match and level of viral circulation. In an analysis of four RCTs, inactivated influenza vaccines reduced ILI and influenza among elderly people living in the community, when circulation was high. In cohort studies assessing elderly people living in the community, vaccines reduced hospitalization rates for influenza or pneumonia and mortality when the match was good. In long-term care facilities, ILI and pneumonia were reduced, and matching was important when circulation level was high. Hospitalizations and deaths were reduced only when circulation was high and matching was good. Case-control studies showed that influenza vaccines reduced hospitalization rates and death from pneumonia or influenza for elderly in the community in adjusted analyses. For all these results, the authors cautioned against concluding on the effect of the vaccine from non-RCTs.
Immune-suppression might attenuate the response to influenza vaccine (Kunisaki 2009). Cancer patients with cell-mediated immune dysfunction are likely to have least response to influenza vaccination. Some degree of lymphopenia and cellular dysfunction accompany also the neutropenia that follows chemotherapy and thus most cancer patients are at risk for poor response to the vaccine. However, the vaccine might still have some protective efficacy among immune-compromised patients justifying its administration. A meta-analysis of four studies (two controlled cohorts, one case control and one RCT) assessed the efficacy of influenza vaccine in HIV-positive patients (Anema 2008). Two of the studies defined influenza cases as both ILI and laboratory confirmed influenza (one case control and one cohort study). The outcome of the other two studies was influenza symptoms or ILI. Meta analysis of the three prospective studies resulted in a 66% relative risk (RR) reduction in symptomatic influenza, although the one RCT yielded a 41% reduction. A Cochrane review assessed the efficacy of influenza vaccine among children with cancer (Goossen 2009). One RCT and eight controlled clinical trials (CCTs) were included. The intervention was TIV or bivalent inactivated vaccine (BIV). In five CCTs, immune response to TIV and BIV in children receiving chemotherapy was weaker than in children off chemotherapy, but not for all influenza virus strains tested. A four-fold rise of in antibody titer was observed in 38% to 65% of children receiving chemotherapy compared to 71% to 89% of healthy children (3 CCTs). One CCT reported lower vaccine response in children with ALL on chemotherapy than in children with asthma. None of the studies evaluated clinical influenza or laboratory-confirmed influenza as outcomes. The authors concluded that children with cancer receiving chemotherapy are able to generate an immune response to influenza vaccine. However, Immune responses are weaker in children receiving chemotherapy (four-fold rise of 25% to 52%) than in those children who are off chemotherapy for at least one month (50% to 86%) and in healthy children (71% to 89%).
Why it is important to do this review
No systematic review tried to compile the evidence on influenza vaccine effects among adults with cancer. Previous narrative reviews tried to summarize the evidence (Alistair 2002; Arrowood 2002; Kunisaki 2009; Melcher 2005). Nonetheless, data on vaccine efficacy and effectiveness are lacking. Furthermore, patient groups are heterogeneous with respect to chemotherapy regimens and underlying malignant disease. No consensus guidelines on influenza immunization for patients with malignancies exist. Two review articles recommend vaccination every year timed to occur either more than two weeks before receiving chemotherapy or between chemotherapy cycles (Arrowood 2002; Melcher 2005). An accurate assessment of existing evidence on influenza vaccine effects (with regard to serological response, clinical outcome and adverse effects) in adult cancer patients is essential to allow comprehensive, rational decision concerning influenza vaccination. Therefore we will systematically review all data including immunological response and clinical consequences.
To assess the efficacy and effectiveness of influenza vaccine in immune-suppressed adult patients with malignancies. The primary review outcome will be influenza-like illness.
Criteria for considering studies for this review
Types of studies
We will consider randomized controlled trials (RCTs), cohort studies and case control studies. Non-RCTS will be included since we expect that most evidence of influenza vaccine efficacy and effectiveness in cancer patients is unlikely to be studied in randomized trials. We will separate analyses for RCTs and non-RCTs throughout the review.
Types of participants
Adults (>16 years) with cancer. Including:
- Solid malignancies treated with chemotherapy
- Haematological cancer patients treated or not treated with chemotherapy (since patients might be immune-suppressed even without chemotherapy)
- Cancer patients post autologous (up to six months after transplantation) or allogeneic (at any time) hematopoietic stem cell transplantation.
Studies considering more than 25% of individuals younger than 16 years of age will be excluded. Studies recruiting patients with solid malignancies both treated and not treated with chemotherapy will be considered for inclusion; we will try to extract data only for treated patients and if not available will include the complete study cohort if more than 50% of patients were treated with chemotherapy.
Types of interventions
We will include studies comparing inactivated influenza vaccines versus placebo, no vaccination or a different vaccine. We will include inactivated influenza vaccine of any type, any dose and any schedule:
- Trivalent or other
- Whole, subunit or split virion vaccine
Vaccines may be matched or unmatched to circulating strains. We will exclude comparisons of the same or different vaccines given during different influenza seasons or to different cancer patient populations.
Types of outcome measures
Clinical outcomes will be collected for a maximal follow-up period until end of the influenza season following vaccination. We will document the duration of follow-up in individual studies. Immunological response will assessed up to three months after vaccination, as defined in each study. Adverse events will be assessed up to two weeks after vaccination. We will include studies if reporting on at least one of the review-defined (primary or secondary) outcomes.
Influenza-like illness defined as: ILI definition in study or Pneumonia of any cause or influenza-related death
- Immunological: seroconversion or rise in titre as defined in study
- Confirmed influenza using the methods defined in study
- Hospitalizations for any respiratory disease and hospital days
- Influenza-related pneumonia
- Chemotherapy interruptions
- Number of febrile episodes of any cause
- Mortality: we will collect data on all-cause mortality. However we expect bias in non-randomized studies and thus collect data also on influenza-related mortality
- Adverse events (AE): local events on injection site (tenderness/soreness, erythema, arm stiffness), systemic events (myalgia, fever, headache, fatigue, rash)
Search methods for identification of studies
We will search PubMed, The Cochrane Central Register of Controlled Trials (CENTRAL), EMBASE and LILACS databases. We will use the search strategy detailed in Appendix 1. Search terms in PubMed will be combined with a highly sensitive search filter for identifying randomized controlled trials as recommended in the Cochrane Handbook (Higgins 2009) and with the SIGN search strategy for identifying observational studies (SIGN 2010).
Searching other resources
We will search the following conference proceedings: ICAAC, ECCMID, IDSA (infectious disease conferences), ASH, ASBMT, EBMT (hematological), and ASCO (oncological) between the years 2006 to 2010. In addition we will scan the references of all identified studies and pertinent reviews. We will search the website of the manufacturers of influenza vaccine. Finally, we will search for ongoing or unpublished trial in clinical trial registry databases using the http://www.controlled-trials.com/mrct/ website.
Data collection and analysis
Selection of studies
We will include all trials fulfilling the above-define eligibility criteria for design, participants and intervention. We will not restrict inclusion by outcomes reported in abstract, but obtain the full-text and attempt to obtain at least one of the review-defined outcomes from text or author correspondence. Two review authors will independently apply inclusion criteria to all identified and retrieved articles.
Data extraction and management
Two review authors will independently perform data extraction using a data extraction form. We will extract data on the characteristics of:
Length of the follow up
Dates of study
Location of study
Risk of bias
Description of vaccines (content, timing of vaccination and antigenic match)
Description of viral circulation degree
Description of outcomes, we defined above.
Characteristics of participants; (age, sex, type of malignancy, bone marrow transplantation, anti-cancer treatment, expected baseline immune suppression: primarily cellular immune dysfunction, severe; primarily cellular immune dysfunction, moderate; primarily neutropenia, severe)
Assessment of risk of bias in included studies
The two review authors will independently assess trials’ risk of bias. We will contact authors for additional information where necessary.
Assessment of the methodological quality of the RCTs will be according to the guidelines of the Cochrane Collaboration’s tool for assessing risk of bias (Higgins 2009) (Appendix 2). Studies will be classified according to the following criteria: allocation sequence generation, allocation concealment, blinding, incomplete outcome data addressing,selective outcome reporting, other source of bias.
For quality assessment of Cohort studies we will use the Newcastle-Ottawa Scale adapted for our review (NOS 2010) (Appendix 3). The following items will be assessed: representativeness of the exposed cohort, selection of the non exposed cohort, ascertainment of exposure, demonstration that outcome of interest was not present at start of study, comparability of cohorts on the basis of the following items, assessment of outcome, was follow-up long enough for outcomes to occur, adequacy of follow up of cohorts.
Measures of treatment effect
We will measure outcomes using odds ratio (OR) (using a 95% confidence interval CI). Unadjusted ORs will be calculated from RCTs and non-RCTs (cohort and case control studies). For non-RCTs, we will preferentially extract and use adjusted ORs with 95% CI.
Unit of analysis issues
We expect that all studies will include patients only once. Influenza is not expected to occur more than once per season. The studies might report influenza-like illness as episodes that occur more than once per patient. In this case, we will try to extract the number of patient experiencing at least one event.
Dealing with missing data
Whenever data will be missing, we will attempt to contact the authors of the study and request the missing information.
Assessment of heterogeneity
The statistic I
Assessment of reporting biases
Given the paucity of studies in each analysis we do not expect to be able to formally assess reporting biases.
We will pool ORs with 95% CI using random-effect models throughout to take account of the between-study variance in our findings (DerSimonian 1986). Using the pooled OR we will calculate vaccine efficacy (VE). VE is expressed as a proportion, using the formula VE
In cases when more than one influenza season is included in a study, we will create different data sets, and consider each season separately.
Aggregation of studies will be according to:
- Patient's type of malignancy and expected degree of immune dysfunction
- Degree of viral circulation
- Vaccine matching with the relevant year's WHO recommended content.
Subgroup analysis and investigation of heterogeneity
To compare vaccinated with unvaccinated cancer patients, subgroup analyses will be carried out according to malignancy type and/or expected immune suppression degree as described above. We will also consider the degree of matching with that year’s WHO recommended content and with circulating viruses. (“WHO recommendations and matching” when known - published since 1973). Subgroups will be pooled only if there will be no significant heterogeneity.
Participants will be sub grouped according to expected severity of immune suppression. All analyses will be stratified by these subgroups and pooled only if there will be no significant heterogeneity:
- Primarily cellular immune dysfunction, severe: patients post allogeneic hematopoietic stem cell transplantation (HSCT);
- Primarily cellular immune dysfunction, moderate: Chronic lymphocytic leukemia (CLL) treated with alkylating agents, multiple myeloma (MM) treated with monoclonal antibodies
- Primarily neutropenia, severe: Patients with severe neutrophil dysfunction: administration of vaccine during neutropenia. (e.g. acute leukemias, autologous HSCT, sarcoma).
In addition, we will analyse results for patients treated with monoclonal antibodies separately.
We will carry out sensitivity analysis based on trials’ risk of bias assessment for the primary outcome. We will restrict the analysis to RCTs with adequate allocation generation and concealment methods and cohort studies at low risk of bias according to the Newcastle-Ottawa Scale adapted for our review (NOS 2010). We will not use the numerical score for each study in the meta-analysis, since this is discouraged (Emerson 1990; Higgins 2009; Schulz 1995).
Appendix 1. MEDLINE search strategy
Appendix 2. Assessment of risk of bias in RCTs
Studies will be classified according to the following criteria:
Allocation sequence generation:
A=adequate (e.g. a random number table; a computer random number generator)
B= inadequate (e.g. sequence generated by date)
C= unclear or not described
A= adequate (e.g. numbered drug containers of identical appearance)
B= inadequate (e.g. using an open random allocation schedule)
C= unclear or not described
A= adequate (e.g. outcome measurement are not likely to be influenced by blinding approach)
B= inadequate (e.g. outcome measurement are likely to be influenced by blinding approach)
C= unclear or not described
Incomplete outcome data addressing (separately for each outcome defined in the study)
A= adequate (e.g. no missing outcome data)
B= inadequate (e.g. reason for missing outcome data likely to be related to true outcome)
C= unclear or not described
Lack of selective outcome reporting (separately for each outcome defined in the study)
A= adequate (e.g. published reports include all expected outcomes)
B= inadequate (e.g. not all of the study’s pre-specified primary outcomes have been reported)
C= unclear or not described
Absence of other source of bias
A= adequate (e.g. the study appears to be free of other sources of bias)
B= inadequate (e.g. the study had a potential source of bias related to the specific study design)
Duration of follow up and percent of participants lost to follow up.
Appendix 3. Assessment of risk of bias in cohort studies
Adapted Newcastle-Ottawa Scale:
We will classify studies as low risk of bias (up to one inadequate item in the NOS), medium risk of bias (up to three inadequate items) and high risk of bias (more than three inadequate or no description of methods. The following items will be assessed:
1) Representativeness of the exposed cohort (active cancer patients, undergoing chemotherapy, whose immune status is well characterized)
a) truly representative of the exposed cohort ✶
b) somewhat representative of the exposed cohort ✶
c) selected group of exposed cancer patients. eg 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
a) secure record ✶
b) structured interview ✶
c) written self report
d) no description
4) Demonstration that outcome of interest was not present at start of study
a) yes ✶
1) Comparability of cohorts on the basis of the the following items
a) study controls for severity of immune suppression / chemotherapy regimen / total duration of therapy ✶
b) study controls for timing of vaccination in relation of chemotherapy ✶
1) Assessment of outcome
a) independent blind assessment ✶
b) record linkage ✶
c) self report
d) no description
2) Was follow-up long enough for outcomes to occur (the end of influenza season)
a) yes (select an adequate follow up period for outcome of interest) ✶
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 - > 80% (select an adequate %) follow up, or description provided of those lost) ✶
c) follow up rate < 80% (select an adequate %) and no description of those lost
d) no statement
Last assessed as up-to-date: 24 January 2011.
Protocol first published: Issue 2, 2011
Declarations of interest
Sources of support
- New Source of support, Not specified.
- None, Not specified.
- No sources of support supplied