Effects of Annual Influenza Vaccination on Winter Mortality in Elderly People With Chronic Heart Disease

Cinta de Diego; Angel Vila-Córcoles; Olga Ochoa; Teresa Rodriguez-Blanco; Elisabeth Salsench; Imma Hospital; Ferran Bejarano; M. del Puy Muniain; Mercé Fortin; Montserrat Canals and EPIVAC Study Group


Eur Heart J. 2009;30(2):209-216. 

In This Article


Design, Setting, and Study Population

We conducted a cohort study that included all community-dwelling individuals 65 years or older assigned to eight Primary Health Care Centres (PHCCs) in the region of Tarragona (Catalonia, Spain) who had a diagnosis of chronic heart disease (including heart failure or coronary artery disease) registered in their clinical record at the start of the study.

When the study started, the Health District of Tarragona had 12 PHCCs with an overall assigned population of 134 232 all-age inhabitants. The selection of the eight participating PHCCs was not randomized and they were chosen taking into account the existence of electronic clinical registries working since 1998 or before. The other four PHCCs in the Health District were not included because they had only computerized the clinical records more recently.

The 1340 cohort members were followed from the beginning of the study (1 January 2002) until enrolment from the PHCC ceased, the occurrence of death, or until the end of the study (30 April 2005). The study was approved by the Ethical Committee of the Catalonian Health Institute and conducted in accordance with the general principles for observational studies.

Sources of Data

All participating PHCCs have an institutional computerized clinical record system which contains registries of immunizations, laboratory tests, medication prescription, diagnoses associated with outpatient visits, and chronic diseases coded according to the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9). The electronic records of each cohort member were used to identify whether the individual had received or not the influenza vaccine in each influenza vaccination campaign, and it was also used to identify the presence of chronic heart disease (heart failure: ICD-9 codes 428, 428.0 and 428.1; coronary artery disease: ICD-9 codes 410–414), co-morbidities, and other medical conditions.

Outcome Measure and Definitions

The influenza period was defined as the period during which influenza-like illnesses were frequently reported in the study area, from 1 January to 30 April for each year of the study.[15]

Primary outcome was all-cause death. Deaths were initially identified in the Institutional Demographic Database (which is updated monthly with administrative data about deaths, patients who have moved or new patients assigned to a PHCC). Afterwards, a review of the reference Civil Registry Offices of the eight PHCCs was used to identify those deaths that had occurred in cohort patients who had not been registered in the Institutional Database. This review was also used to validate the exact date of death in all cases. Finally, deaths were classified as occurring within the influenza period (January–April) or within a reference control summer period (June–September).

Exposure to Influenza Vaccination

For each year, information on the influenza vaccination status of the subjects was determined by a review of the PHCCs' clinical records, which contain specially designated fields for annual influenza vaccinations. We assumed that information in clinical records was complete, so a subject was considered as non-vaccinated when data on vaccination was missing or vaccination was not recorded (in other words, a patient was considered as non-vaccinated when the specific field for annual vaccination was empty).

Influenza vaccine status was considered as a dichotomous (vaccinated or non-vaccinated) time-varying condition throughout the study period (i.e. in the analysis covering the overall study period, the same person could be considered non-vaccinated in 2002, vaccinated in 2003, and non-vaccinated in 2004 according to the reception or not of the influenza vaccine in the prior autumn).


Covariates included dichotomous variables for sex, chronic lung disease (including asthma, emphysema, or chronic bronchitis), diabetes mellitus, hypertension, obesity, current smoking, and immunocompromised status. Age and the number of outpatient visits in the previous 2 years were considered as continuous covariates. Immunocompromise was a composite variable defined by the presence of any one of the following: cancer (solid organ or haematological neoplasia), chronic severe nephropathy (nephrotic syndrome, renal failure, dialysis, or transplantation), chronic severe liver disease (cirrhosis), anatomical or functional asplenia, AIDS, and long-term corticosteroid therapy (20 mg/day of prednisone) or another immunosuppressive medication. The presence of co-morbid conditions was determined by a review of the diagnosis codes in the electronic clinical record of each cohort member.

Statistical Analysis

Incidence rates (IR) of death were calculated as person-years and person-weeks. For the numerator we used number of deaths. The denominator was the total number of person-years/person-weeks of observation for each study period considered. So, for each individual we determine the amount of observation time contributed to that period and to add up those contributions for all cohort members. Attributable risk (AR) was the difference between IR among vaccinated and non-vaccinated subjects (AR=IR exposed – IR non-exposed). Numbers needed to be vaccinated (NNV) to save one death were estimated for influenza periods (January–April = 17.1 weeks) and were calculated as the inverse of the AR (NNV=1/AR).[16]

The differences between groups were evaluated by means of the χ2 test for categorical variables and Student's t-test for continuous variables.

Multivariate Cox proportional-hazards models were used to evaluate the association between receiving influenza vaccine and the time to death during the study period. We performed stratified analysis by influenza period (defined from January to April) and a reference non-influenza period (from June to September) of the overall study period and four supplementary analyses of the influenza season of each year. Influenza vaccine status was a time-varying covariate in the stratified analysis by influenza period and a dichotomous fixed condition (vaccinated/non-vaccinated in the previous autumn) in the analysis of each year.

The variables that have been considered in all the initial models are: age, sex, number of outpatient visits in the previous 2 years, chronic lung disease, diabetes, hypertension, obesity, smoking, and immunocompetence. The method to select a subset of covariates to include in the final proportional-hazards regression model is the purposeful selection.[17] Age and sex have been judged epidemiologically relevant variables, being included in all the final models. The authors checked for confounders (change-in-estimate ≥ 20%), interactions, and multicolinearity among the independent variables. In addition, all the models have been compared by the partial likelihood ratio test and the Akaike's information criterion (AIC). The proportional-hazard assumptions were assessed, adding the covariate by time interactions to the model and plotting the scaled and smoothed Schöenfeld residuals obtained from the main effects model. All results were expressed with 95% confidence intervals (CIs). Statistical significance was set at P < 0.05 (two-tailed). The analyses were performed using Stata/SE version 9.1 (Stata Corp.).


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