Effectiveness of Inactivated Influenza Vaccines in
Preventing Influenza-Associated Deaths and
Hospitalizations among Ontario Residents Aged $ $65
Years: Estimates with Generalized Linear Models
Accounting for Healthy Vaccinee Effects
Benjamin J. Ridenhour1,2*, Michael A. Campitelli3, Jeffrey C. Kwong3,4,5,6, Laura C. Rosella4,5,
Ben G. Armstrong7, Punam Mangtani7,8, Andrew J. Calzavara3, David K. Shay1
1Influenza Division, United States Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America, 2Eck Institute for Global Health, Department of
Biological Sciences, University of Notre Dame, South Bend, Indiana, United States of America, 3Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada,
4Ontario Agency for Health Protection and Promotion, Toronto, Ontario, Canada, 5Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada,
6Department of Family and Community Medicine, University of Toronto, Toronto, Ontario, Canada, 7Department of Social and Environmental Health Research, London
School of Hygiene and Tropical Medicine, London, United Kingdom, 8Department of Epidemiology and Population Health, London School of Hygiene and Tropical
Medicine, London, United Kingdom
Background: Estimates of the effectiveness of influenza vaccines in older adults may be biased because of difficulties
identifying and adjusting for confounders of the vaccine-outcome association. We estimated vaccine effectiveness for
prevention of serious influenza complications among older persons by using methods to account for underlying differences
in risk for these complications.
Methods: We conducted a retrospective cohort study among Ontario residents aged $65 years from September 1993
through September 2008. We linked weekly vaccination, hospitalization, and death records for 1.4 million community-
dwelling persons aged $65 years. Vaccine effectiveness was estimated by comparing ratios of outcome rates during weeks
of high versus low influenza activity (defined by viral surveillance data) among vaccinated and unvaccinated subjects by
using log-linear regression models that accounted for temperature and time trends with natural spline functions.
Effectiveness was estimated for three influenza-associated outcomes: all-cause deaths, deaths occurring within 30 days of
pneumonia/influenza hospitalizations, and pneumonia/influenza hospitalizations.
Results: During weeks when 5% of respiratory specimens tested positive for influenza A, vaccine effectiveness among
persons aged $65 years was 22% (95% confidence interval [CI], 26%–42%) for all influenza-associated deaths, 25% (95% CI,
13%–37%) for deaths occurring within 30 days after an influenza-associated pneumonia/influenza hospitalization, and 19%
(95% CI, 4%–31%) for influenza-associated pneumonia/influenza hospitalizations. Because small proportions of deaths,
deaths after pneumonia/influenza hospitalizations, and pneumonia/influenza hospitalizations were associated with
influenza virus circulation, we estimated that vaccination prevented 1.6%, 4.8%, and 4.1% of these outcomes, respectively.
Conclusions: By using confounding-reducing techniques with 15 years of provincial-level data including vaccination and
health outcomes, we estimated that influenza vaccination prevented ,4% of influenza-associated hospitalizations and
deaths occurring after hospitalizations among older adults in Ontario.
Citation: Ridenhour BJ, Campitelli MA, Kwong JC, Rosella LC, Armstrong BG, et al. (2013) Effectiveness of Inactivated Influenza Vaccines in Preventing Influenza-
Associated Deaths and Hospitalizations among Ontario Residents Aged $65 Years: Estimates with Generalized Linear Models Accounting for Healthy Vaccinee
Effects. PLoS ONE 8(10): e76318. doi:10.1371/journal.pone.0076318
Editor: Suryaprakash Sambhara, Centers for Disease Control and Prevention, United States of America
Received April 29, 2013; Accepted August 23, 2013; Published October 16, 2013
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for
any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Funding: These authors have no support or funding to report.
Competing Interests: Dr. Shay is a member of the PLOS ONE Editorial Board. This does not alter our adherence to all the PLOS ONE policies on sharing data and
* E-mail: firstname.lastname@example.org
Influenza viruses are associated with substantial morbidity
annually, and persons aged $65 years are among those at highest
risk of serious outcomes following influenza infection [1–3]. Annual
influenza vaccination is recommended for older adults in Canada,
the United States, and many other developed countries [4,5].
However, the effectiveness of vaccination among older adults is a
subject of considerable debate. The only large randomized placebo-
controlled clinical trial of inactivated influenza vaccine in adults
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aged $60 years was conducted during a single season; efficacy was
58% for prevention of serologically-confirmed influenza in symp-
tomatic subjects [6,7]. All other large studies of influenza vaccine
effects among older persons have used observational data, typically
from retrospective cohort studies. For example, retrospective cohort
studies conducted with US health plan data reported that influenza
vaccine effectiveness (VE) in community-dwelling elderly persons
was 47% for preventing all-cause mortality and 27% for preventing
pneumonia/influenza hospitalizations [8,9].
Of course, the results from any observational study are
susceptible to bias [8–10]. It has been suggested that VE estimates
from cohort studies using electronic health records were suscep-
tible specifically to ‘‘healthy vaccinee’’ bias [10–12], and previous
work has shown that statistical adjustment for covariates available
in electronic health records, including ICD-9-CM-based diagnos-
tic codes, do not control for this bias . However, there is no
alternative to using observational data for assessing vaccine effects
against serious outcomes of influenza infections, given the rarity of
these outcomes. Clearly, methods more advanced than those
commonly used in ‘‘standard’’ cohort studies are needed to control
for unmeasured confounders in vaccine studies conducted among
Observational studies of vaccine effects are plagued not only by
the potential for confounding, but also by the non-specific nature
of the outcomes commonly used in these studies, such as
community-acquired pneumonia. A recent simulation study
demonstrated that if an influenza vaccine had a true VE against
influenza infection of 55%, attaining vaccine coverage of 38% in a
population would lead to a VE against pneumonia of just 7%
(95% confidence interval [CI] 0%–25%), given assumptions about
attack rates and risk of pneumonia following influenza infection
based on recent data . Most VE studies conducted among
older persons have estimated effectiveness by comparing rates of
serious, but not influenza-specific, outcomes among vaccinated
and unvaccinated persons during weeks when influenza viruses
circulated, with adjustment for potential confounders. However,
even during the discrete influenza seasons found in temperate
regions, most of these outcomes (e.g., all ICD-9-CM-coded
pneumonia/influenza hospitalizations) are not associated with
influenza infections. For example, among adults hospitalized with
lower respiratory tract infections during winter seasons, only 4%–
20% have evidence of influenza infection [15–18]. Thus, use of a
more specific outcome than pneumonia, for example, could
improve the precision of influenza VE estimates, and perhaps
decrease the likelihood of confounding as well. It is well known
that influenza epidemics are associated with increases in pneu-
monia/influenza hospitalizations and deaths above expected,
smoothed seasonal baselines [3,15]. We propose that using these
‘excess’ or influenza-associated outcomes in observational cohort
studies of influenza VE, rather than all the outcomes occurring
while influenza is circulating, could lead to more precise and less-
biased VE estimates.
Some studies have sought to reduce the likelihood of bias by
comparing adjusted VE for non-specific outcomes during weeks
when influenza is circulating and weeks when it is not. For
example, a UK study observed VE against respiratory hospital-
izations of 21% (95% CI, 17%–26%) and against respiratory-
coded deaths of 12% (95% CI, 8%–16%) during weeks when
influenza circulated, but not in weeks when it did not . Jackson
et al. conducted a case-control study estimating VE for preventing
community-acquired pneumonia. They reviewed individual med-
ical records to obtain information on possible confounders not
contained in traditional electronic health records (e.g., smoking
history, frailty, severity of lung and heart disease) and sought to
identify a set of covariates that resulted in a null VE during the
pre-influenza season period in their US health plan population,
and used the same covariates to estimate VE during the influenza
season . The estimated VE in this study was 8% (95% CI,
210%–23%), very similar to the estimate of 7% (95% CI, 0%–
12%) found in the simulation study for VE against a non-specific
pneumonia, given a VE against influenza infection of 55% .
Other studies have used novel statistical approaches in an
attempt to calculate less-biased VE estimates. For example,
Armstrong et al. estimated VE by comparing mortality rate ratios
among vaccinated and unvaccinated subjects as influenza activity
increased, thus adjusting for baseline differences between these two
groups that affect mortality risk [18,19]. In that study, when
influenza virus circulation was at its peak, defined as at or above
the 90th percentile of laboratory detections for influenza, VE for
the prevention of influenza-associated deaths was 85% (95% CI,
13%–100%). As mortality is a rare outcome even in elderly
subjects and this method is data-intensive, the confidence limits
covered the entire meaningful range, even though the study
included ,25,000 subjects. Fireman et al. sought to reduce healthy
vaccinee bias by estimating influenza VE with another novel
method they described as a ‘‘case-centered logistic regression.’’
Their analysis was similar conceptually to a finely stratified case-
cohort study, with the expected odds of vaccination in the
underlying population stratified by age, sex, and day, and
compared with the actual odds of vaccination in cases in the
same strata . Fireman estimated VE to be 4.6% (95% CI,
0.7%–8.3%) for preventing all deaths during influenza seasons.
Based on the authors’ calculations, this VE for prevention of all
deaths implied a VE of 47% against influenza-associated deaths.
This study used data from a single US managed heath care plan,
and thus the ability to generalize its findings to broader
populations is unknown.
In this study, we sought to provide robust and generalizable
estimates of influenza VE by using confounding-reducing methods
with the entire community-dwelling population of Ontario aged
$65 years during 15 influenza seasons. Our outcome measures
represent influenza-associated events, rather than all events
occurring during defined periods of influenza circulation.
Design, setting, and participants
We conducted a population-based retrospective cohort study
using Ontario data from 1993–1994 through 2007–2008. At each
season’s index date (the Sunday before 1 September), a study
population was established with Ontario residents aged $65 years
who were eligible to receive universal, publicly insured health care
services. These subjects had free access to hospital care, physician
services, and influenza vaccines. The study cohort was restricted to
non-institutionalized persons who had been in contact with the
health care system within 3 years of the index date, to exclude
individuals who may have moved, resided in the province rarely,
or died but had not yet been classified as deceased in provincial
We linked administrative health datasets for each study subject
by using encrypted health card numbers as unique identifiers.
Only de-identified, aggregated data were used for data analyses.
Ethics approval was obtained from the Research Ethics Board
of Sunnybrook Health Sciences Centre, Toronto, Canada. This
study used routinely collected health information from the
province of Ontario that was aggregated into weekly counts.
Influenza VE among Persons Aged $65 Years
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The mortality, hospitalization and physician services data used
contained no personal identifiers. The use of aggregate data and
data without personal identifiers precluded the need to obtain
informed consent. Additionally, the Institute of Clinical Evaluative
Sciences (ICES) is named as a prescribed entity under section 45 of
the Personal Health Information Protection Act (Ontario Regulation
329/04, Section 18). Under this designation, ICES can receive
and use health information without consent for purposes of
analysis and compiling statistical information about the health care
system of Ontario.
The study included all community-dwelling individuals aged
$65 years in Ontario from 1993 through 2008 who met the
inclusion criteria (Table 1).
Influenza vaccination status
Vaccination status was ascertained from influenza-specific and
generic vaccination codes for physician billing claims submitted to
the Ontario Health Insurance Plan, a universal insurance plan
which covers all residents of Ontario. This database contains
claims for outpatient visits from approximately 98% of Ontario
physicians . Because influenza-specific vaccination codes were
introduced in 1998, and their use gradually increased after
introduction, we also used generic vaccination codes billed during
weeks when the total number of these claims exceeded a summer
baseline rate (typically between late September and late Decem-
ber). Although these generic vaccination claims included those for
other vaccines, based on previous analyses and our study data,
,96% represented influenza vaccines (Figure S1) . Compared
with self-reported influenza vaccination, the combination of
influenza-specific and generic vaccination codes had sensitivity
of 75%, specificity of 90%, positive predictive value of 96%, and
negative predictive value of 54% among adults aged $65 years
. Cohort members were classified as unvaccinated at each
season’s index date; we defined an individual as ‘‘immunized’’ two
weeks after the billing claim service date to account for the delay
from vaccination to development of specific humoral immunity to
vaccine strains .
Ontario’s Registered Persons Database has key demographic
information about each person who has ever received an Ontario
health card . It was used to ascertain vital status and date of
death, if deceased, for individuals in the cohort. Specific cause of
death information was not available for this analysis, so we used as
outcomes all-cause mortality and mortality within 30 days of a
pneumonia/influenza hospitalization (see below) to provide a
more specific outcome for deaths in which pneumonia/influenza
likely played a role (hereafter referred to as ‘30-day pneumonia/
influenza death’) .
Hospitalizations for pneumonia/influenza (ICD-9-CM codes
480-487; ICD-10-CM codes J10-J18) were ascertained from the
Canadian Institute of Health Information’s Discharge Abstract
Database; this database contains detailed information on diagno-
ses and procedures for admissions to all acute-care hospitals in
Canada . We included hospitalizations for which any of the
listed codes were found in the discharge abstract.
To confirm the specificity of the VE estimates for pneumonia/
influenza hospitalizations, we also examined hospitalizations for
urinary tract infections (ICD-9-CM codes 590, 595, 599.0; ICD-
10-CM codes N10, N12, N13.6, N15.1, N30, N39.0) as a ‘negative
control’ outcome for which influenza vaccination is not expected
to have an effect .
We obtained weekly influenza viral surveillance data from a
provincial network of sentinel laboratories that submit weekly
reports of numbers of tests performed (using predominantly viral
culture or direct antigen detection methods) and numbers of
positive tests for influenza A and B to the Public Health Agency of
Canada. For each virus, we used the weekly percentage of tests
positive as our measure of viral circulation.
Rates of serious illness and mortality are associated with
fluctuations in temperature , as is influenza activity in
temperate regions. Thus, we sought consistently collected temper-
ature data to adjust for seasonal temperature variations. Daily
measures of mean, maximum, and minimum temperature at the
weather station located at Lester B. Pearson International airport
in Toronto were obtained from the Ontario Climate Centre.
Seventy-five percent of Ontario’s population resides within 150
kilometers of this station . Because we sought to adjust for
weekly associations between temperature and mortality — rather
than estimate potentially causal daily associations between
temperature and mortality — we concluded that data from this
station was sufficient to control for the possible confounding effects
of temperature trends in Ontario on VE.
Vaccine effectiveness calculation
We used Poisson models to regress weekly outcomes against the
weekly proportion of influenza tests that were positive for influenza
A or B. Similar methods have been used in time-series analyses of
air pollution and mortality . Our statistical approach was
similar to the methods described by Armstrong, et al  for
estimating VE against influenza-associated events by using log-
linear generalized linear models. VE was modeled as the ratio in
outcome rates during periods of varying influenza activity among
vaccinated and unvaccinated cohort members. Because the
vaccinated population was compared with the vaccinated popu-
lation at previous time points, and likewise for the unvaccinated
population, this approach adjusted implicitly for baseline health
status indicators like mobility, frailty, and dementia.
Vaccine effectiveness was represented as: VE= (RR(u) 2
RR(v))/(RR(u) 2 1)=1 2 (1 2 RR(v))/(1 2 RR(u)) where RR
was the incidence of an outcome during an influenza period
divided by the incidence outside an influenza period among the
vaccinated (v) and unvaccinated (u) groups . The estimator for
VE can be expressed by using terms from a generalized linear
model as VE=(1 2 exp(bx*vx))/(1 2 exp(2bxx)) where bxwas the
estimated regression coefficient for the influenza circulation
variable (i.e., the proportion of specimens testing positive for
influenza A or B), and bx*vwas the estimated regression coefficient
for the influenza circulation multiplied by vaccination status
variable (i.e., the interaction term between vaccination status and
influenza circulation). Note that our estimate of VE is actually a
continuous function that depends on the level of influenza
circulation. We present results for hypothetical weeks when 5%
or 10% of tests submitted were positive for influenza viruses, and
defined these weeks as those with moderate and high levels of
influenza circulation, respectively.
We regressed each of the outcomes each week against
respiratory specimens that tested positive for influenza. The ratios
in outcome rates during periods of varying influenza activity in
vaccinated and unvaccinated individuals were then compared.
Influenza VE among Persons Aged $65 Years
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Table 1. Number of individuals included in the study, influenza vaccinations, and outcomes for each study year.
30-day pneumonia/influenza deaths
Influenza VE among Persons Aged $65 Years
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Influenza VE among Persons Aged $65 Years
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Outcome rates were estimated using log-linear regression models.
Because the data were found to be overdispersed, we used a quasi-
poisson error distribution. An offset term was included to account
for the appropriate population denominator for each of the
outcome variables. The model can be stated as y,Quasi-
poisson(m, h); m m=N exp (X Xb b+e) where y is the outcome, log(N) is
the offset, X Xb b is the linear predictor, and the error term e is
distributed to allow for overdispersion h. The offset used in our
models was the logarithm of the number of person-days of
observation in a particular stratum for a given week. All analyses
were performed at the weekly level.
Our modeling strategy was as follows. First, a ‘baseline’ model
was constructed that consisted of nothing but an intercept and the
offset term, i.e.
Outcome = Intercept + log(Exposure)
and each of our outcomes was modeled separately. Additional
terms were added via forward selection to our baseline model
using analysis of deviance. Upon inclusion into the model, terms
could be subsequently dropped via backwards elimination.
Alternating forward and reverse selection steps were performed
until either no more potential variables were available or no
statistically significant (p,0.05) additions could be made.
Potential variables for inclusion into models included: vaccine
status (vaccinated/unvaccinated), influenza A circulation, influen-
za B circulation, sex (male, female), age group (ages 65–74 years or
$75 years), week of the year (i.e., 1…52), week of the study period
(i.e., 1…783), weekly mean temperature, and a dichotomous
variable to account for introduction of Ontario’s Universal
Influenza Immunization Program (UIIP) on 1 September 2000.
After UIIP began, the entire population of Ontario aged $6
months, regardless of age or underlying medical condition,
became eligible to receive free seasonal influenza vaccination.
Main effects of any variable had to be present before interaction
effects could enter the model. Only interactions with vaccine status
were considered. For example, a vaccine status and age group
interaction could be included in the model once the main effects of
vaccine status and age group had been included. Inclusion of any
vaccine status and time variables in our model indicated the
presence of time-varying biases of vaccination status and allowed
us to control for these effects appropriately. Vaccine effectiveness
was calculated only if a vaccine status and influenza circulation
interaction effect was included in a regression model.
Natural cubic spline functions were used to model the
association of three covariates on influenza-associated events:
week of the year, week of the study, and weekly mean temperature.
Natural splines are cubic functions ‘‘tied’’ together at n ‘‘knots’’
and have n +1 degrees of freedom (i.e., if d.f. = 1 then there are no
knots, and the spline is simply a cubic function). Splines for these
three variables with 1 d.f. to 6 d.f. were evaluated for possible
inclusion into our models. Once a spline for a given variable
entered a statistical model, no other spline for that variable could
enter the model. In other words, if a spline for mean temperature
with 2 d.f. was used based on analysis of deviance, then splines for
mean temperature with 1, 3, 4, 5, and 6 d.f. were not considered.
If the spline with 2 d.f. was subsequently dropped, then any of the
spline representations of temperature could be evaluated for
inclusion again. The use of natural splines for mean temperature
and week of the year allowed the model to control for the strong
seasonality of our outcomes in a more logical and flexible manner
than the sine/cosine harmomic variables often used in studies
seeking to identify influenza-associated outcomes [1–3,32]. Sim-
ilarly, using a natural spline to represent calendar week in models
was more flexible in capturing long-term time trends in outcomes
than simple polynomials.
A list of the predictors included in the final models (Table S1)
and an enumeration of those predictors from the model of each
outcome (Table S2) are provided in the Supplemental Materials.
Because of autocorrelation present in our time-series, we applied
Newey-West estimators to the parameter variance-covariance
matrix to correct for the observed autocorrelation and thereby
provide consistent estimates of the regression parameters .
Estimates of cases averted and numbers needed to
We estimated the absolute number of outcomes averted by
influenza vaccination in Ontario by using the predicted number of
cases ( y ˆ) in the vaccinated group and the vaccine effect on the
estimate of excess cases while influenza virus circulation varied
(bx*v). By setting the effect of vaccination to zero (bx*v=0), we
estimated the number of cases (y9) that would have occurred in the
vaccinated population if vaccine had no effect or if it was never
delivered. The number of cases averted was defined as (y9 – y ˆ). We
calculated the proportion of outcomes prevented in the vaccinated
group (i.e., the proportion averted among vaccinees) by dividing
the number of cases averted by the total number of cases in the
vaccinated group during weeks of influenza virus circulation with
the number of cases averted added back into the denominator.
This proportion can be interpreted as an estimate of VE against all
occurrences of these outcomes during periods of influenza virus
circulation. Furthermore, for each outcome we calculated the
number needed to vaccinate (NNV) to prevent one outcome by
dividing the mean annual number of influenza vaccines admin-
istered by the mean annual number of cases averted per season.
All analyses were conducted using R software (R Development
Core Team, R Foundation for Statistical Computing, Vienna,
During the 15 study seasons, an annual average of 48,402 all-
cause deaths, 4,552 30-day pneumonia/influenza deaths, and
22,839 pneumonia/influenza hospitalizations occurred among
community-dwelling adults aged $65 years in Ontario (Table 1;
outcomes stratified by age group in Table S3). During this period,
334,797 influenza tests were performed, with 26,753 positive for
influenza A viruses and 6,774 positive for influenza B viruses (8%
and 2% positive, respectively). Influenza viruses were detected
during 479 weeks (61% of study weeks). Greater than or equal to
5% or $10% of tests were positive for influenza during 218 weeks
(28%) and 132 weeks (17%), respectively. During each 52-week
period, $5% and $10% of tests were positive for influenza during
a median of 14 and 10 weeks, respectively. Each of the outcomes
Figure 1. Weekly trends of influenza viral surveillance outcome rates among individuals aged $ $65 years. In the top three panels,
weekly outcome rates are indicated by red symbols for unvaccinated individuals and blue symbols for vaccinated individuals; the size of the symbol
reflects the number of individuals in a category. The panels show all-cause mortality, 30-day pneumonia/influenza mortality, and pneumonia/
influenza hospitalization from top to bottom, respectively. In the bottom panel, weekly percentages of specimens testing positive for either influenza
A or B are represented by gray and black bars (respectively). The sum of the black bar and gray bar shows the total percent positive for the week (i.e.,
the data for influenza A and B are not overlaid).
Influenza VE among Persons Aged $65 Years
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demonstrated weekly seasonality, with spikes coinciding with
periods of influenza activity among both vaccinated and unvac-
cinated individuals (Figure 1).
In the statistical models for each of the outcomes we examined,
the presence of a vaccine status and influenza circulation
interaction variable (determined by using analysis of deviance)
indicated evidence of an effect of vaccination on influenza-
associated events. When we used hospitalizations for UTIs as a
negative-control outcome, no vaccine status or influenza circula-
tion interactions entered the model, demonstrating that influenza
vaccination had no effect on the observed rate of UTI admissions.
For each of our three outcomes of interest, a vaccine status and
influenza A circulation interaction variable was included in the
models, indicating an effect of vaccination. No vaccine status and
influenza B interaction was found for pneumonia/influenza
hospitalization or 30-day pneumonia/influenza deaths, indicating
no evidence for a vaccine effect for influenza B-related outcomes.
For influenza-associated all-cause deaths, the main effect of
influenza B was negative (Table S2), suggesting that increasing
circulation of influenza B was associated with lower outcome rates
in the study population. Thus, it was not logical to estimate VE for
prevention of influenza B-associated all-cause deaths.
During weeks when 5% of respiratory specimens tested positive
for influenza A (weeks with moderate influenza activity), VE
against influenza-associated all-cause deaths, 30-day pneumonia/
influenza deaths, and pneumonia/influenza hospitalizations was
22% (95% CI, 26%–42%), 25% (95% CI, 13%–37%), and 19%
(95% CI, 4%–31%), respectively (Table 2). VE was similar during
weeks when 10% of specimens tested positive for influenza, a
threshold often used to define peak weeks of activity. A plot of
estimated VE by values of the proportion of specimens testing
positive for influenza A from 0.01% to 25% is provided in the
online supplement (Figure S2).
A limited proportion of each outcome represented excess events
occurring during weeks of influenza A circulation: a mean of 6.0%
of all-cause deaths, 15.1% of 30-day pneumonia/influenza deaths,
and 16.6% of pneumonia/influenza hospitalizations (Figure 2).
These proportions varied considerably by season, as would be
expected, given the considerable season-to-season differences in
influenza circulation and relative intensity.
We estimated the annual numbers of each of the three outcomes
potentially averted by vaccination with VE point estimates and
predicted outcomes with or without a vaccine program (Table 3).
During weeks when influenza was circulating, we estimated that
influenza vaccination prevented 1.6% of all-cause deaths, 4.8% of
30-day pneumonia/influenza deaths, and 4.1% pneumonia/
influenza hospitalizations among vaccinated individuals. Based
on weekly vaccine coverage data, these results suggested that an
average of 139 deaths (with a minimum of 43 in 1993/1994 and a
maximum of 227 in 2004/2005) and 235 hospitalizations (with a
minimum and maximum of 65 and 433 occurring in 2000/2001
and 2003/2004, respectively) were averted annually in Ontario by
the influenza vaccination program. The numbers of elderly
individuals needed to vaccinate to prevent one outcome were
5,124, 14,105, and 3,039 for all-cause deaths, 30-day pneumonia/
influenza deaths, and pneumonia/influenza hospitalizations,
Linear models for all outcomes included interaction terms for
the week of the year with vaccination status and mean temperature
with vaccination status (Table S2). These terms controlled for
time-varying biases within a season. Examination of the partial
effects plots (not shown) for these factors show that substantial
(upward) bias in VE would be found using traditional models
during both the earlier part of the influenza season or for colder
periods of the season (i.e. the pre- and post-influenza time periods).
Thus our model, and specifically the aforementioned interaction
terms, removed biases that resulted from observations taken
during various periods of the year that have been observed in
other vaccine effectiveness studies, such as that by Jackson et al.
In this large population-based study, we applied a ‘ratio-of-
ratios’ modeling approach to reduce the influence of difficult-to-
measure individual-level confounders on the association between
vaccination and three outcomes among older community-dwelling
Ontario residents. The unmeasured confounders of greatest
concern include physical frailty and dementia, which are
incompletely captured in administrative health records and death
certificates, but are likely associated with high mortality risks and
low vaccination rates. During weeks of moderate-to-high influenza
activity, influenza vaccination was associated with a (non-
significant) 22% reduction in influenza-associated deaths (i.e.
those deaths in the vaccinated population that would exceed an
expected value in the absence of influenza circulation). Excess
deaths occurring within 30 days of a pneumonia/influenza
hospitalization, and excess pneumonia/influenza hospitalizations
were significantly reduced, by 25% and 19%, respectively. As
expected, no benefit from influenza vaccination was observed for
UTI hospitalizations. Despite demonstrating a moderate level of
VE for the three primary outcomes, the predicted mean annual
numbers of events prevented in Ontario were small (139 all-cause
deaths, 51 deaths in the 30 days following a pneumonia/influenza
hospitalization, and 235 pneumonia/influenza hospitalizations)
because the proportions of these deaths and hospitalizations that
were associated with influenza activity were small. It is important
to highlight that the protective effects of large-scale vaccination
campaigns might be greater than these estimates because of
indirect protective effects of such campaigns (i.e., herd-immunity)
[34,35]. However, the quantification of indirect effects is extremely
difficult for diseases that cannot be definitely diagnosed without
Table 2. Estimates of vaccine effectiveness (VE) for the prevention of influenza A-associated outcomes in community-dwelling
Ontario residents aged $65 years (95% confidence interval [CI]) during weeks when 5% or 10% of respiratory specimens tested
positive for influenza viruses.
Excess influenza-associated outcome5% Circulation VE (95% CI) 10% Circulation VE (95% CI)
All-cause deaths22% (26, 42)23% (25, 42)
30-day pneumonia/influenza deaths 25% (13, 37)26% (13, 38)
Pneumonia/influenza hospitalizations19% (4, 31) 20% (6, 33)
Influenza VE among Persons Aged $65 Years
PLOS ONE | www.plosone.org7 October 2013 | Volume 8 | Issue 10 | e76318
specific laboratory testing, including influenza infections .
Thus, we have focused on the direct and more conservative
benefits of influenza vaccination in this study.
We estimated the effectiveness of influenza vaccination for
prevention of outcomes that: 1) occurred during weeks when
specific laboratory testing for influenza revealed that influenza
viruses were circulating at pre-specified levels in Ontario; and 2)
were above a seasonally adjusted baseline of counts for each
outcome. These outcomes are in contrast to those used in many
other cohort studies, in which all events occurring during periods
of any influenza virus circulation were used as outcomes measures.
The importance of this difference is easily demonstrated: because
only 6.0% of all deaths among Ontarians aged $65 years
occurred during weeks when influenza circulated were above a
seasonal baseline (and thus were categorized as influenza-
associated), a VE of 22% for this outcome represented a 1.6%
reduction in deaths among vaccinated individuals. This 1.6%
reduction can be interpreted as a population-based estimate of
vaccine effects on all deaths occurring during periods of influenza
virus circulation. A meta-analysis of cohort studies calculated a VE
of 47% for the prevention of all-cause mortality among
community dwelling elderly during influenza seasons . Studies
by Jackson et al. and Mangtani et al., demonstrated the bias
inherent in cohort analyses by detecting putative vaccine benefits
for non-specific outcomes when influenza activity is nil [10,16].
Thus, we confirm with data from the most populous province in
Canada that VE estimates not accounting for individual-level
baseline risks for mortality are unrealistically optimistic. Because
greater proportions of 30-day pneumonia/influenza deaths and
pneumonia/influenza hospitalizations were attributed to influen-
za, greater percentages of these more specific events were averted
by influenza vaccination, and thus the VE estimates of 25% and
19%, respectively, against these outcomes are likely more robust.
Our results can be compared directly with those from a few
other studies that explicitly sought to adjust for unmeasured
confounding, including healthy vaccinee effects. Using medical
chart review to collect data on covariates not traditionally
available in administrative data, Jackson et al. estimated a VE of
8% (95% CI, 210%–23%) against community-acquired pneu-
monia during influenza seasons , consistent with our estimate
of 19% VE (CI 4%–31%) and a 4.1% reduction of all pneumonia/
influenza hospitalizations occurring during periods of influenza
virus circulation among vaccinated individuals. The Armstrong et
al. study estimate of VE for influenza-associated all-cause mortality
was 85% during 1996–2000. The cohort size of ,25,000 in that
study meant that its 95% CI of 13%–100% covered essentially the
entire possible range of vaccine benefits; thus their CI does include
our point estimate [18,19]. Fireman et al. estimated that influenza
vaccination was associated with a 47% reduction (95% CI not
provided) in influenza-associated (as opposed to all) deaths
between 1996 and 2005 in a single U.S. managed care plan
. The same group also reported a 28% reduction (95% CI not
provided) in influenza-associated pneumonia/influenza hospital-
izations , which is similar to our VE estimate for pneumonia/
influenza hospitalizations. Another recent study used an instru-
mental variable approach to estimate influenza VE. The use of an
instrumental variable to adjust for unmeasured confounding is
common in econometric analyses of observational data [38,39]. In
that Ontario study conducted during the 2000-2009 influenza
seasons, VE among adults aged $65 years was 6% (95% CI, 0%–
16%) for all-cause deaths and 14% (95% CI, 8%–21%) for a
composite outcome of a pneumonia/influenza hospitalization or
death ; these results are comparable to our results on the
Figure 2. Proportion of outcomes that were influenza A-associated per season and overall. Percentage of all-cause mortality, 30-day
pneumonia/influenza mortality, and pneumonia/influenza hospitalizations that were estimated to be influenza-associated during periods of influenza
A circulation are indicated in the green, blue, and red columns, respectively.
Influenza VE among Persons Aged $65 Years
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Table 3. Predicted number of cases averted by influenza vaccination, by study outcome.
No. weeks with
influenza A detected
Cases among the vaccinated during
weeks with influenza A detected
Cases averted by
% averted among
1994/199525 503261 1.2%
1996/199728 525279 1.5%
1998/199939 9870 1441.4%
1999/2000 3310519207 1.9%
2000/200126 7795 460.6%
2004/20054112204 227 1.8%
2005/2006 3511867136 1.1%
2006/2007 35 11087 2181.9%
2007/2008 44 133111561.2%
Mean32 8858139 1.6%
30-day pneumonia/influenza deaths
1993/1994 17398 163.9%
1994/1995 25 526 203.7%
1996/1997 28 65630 4.4%
1997/199824 93478 7.7%
1998/1999391215 47 3.7%
1999/2000 331368 865.9%
2000/200126 837 141.6%
2001/2002 33972 605.8%
2003/2004 361159 897.1%
2004/2005 411387 815.5%
2006/200735 1277 785.8%
2007/2008 441447 513.4%
Mean 321012 514.8%
1998/199939 6427 210 3.2%
1999/2000 337525 3975.0%
2001/2002 335532 281 4.8%
2003/2004 366454 4336.3%
2005/2006 356695 1962.8%
Influenza VE among Persons Aged $65 Years
PLOS ONE | www.plosone.org9 October 2013 | Volume 8 | Issue 10 | e76318
reduction in deaths due to all-cause deaths or pneumonia/
influenza hospitalization. The instrumental variable method
depends on finding a covariate closely associated with vaccination,
but unrelated to outcome; it is often difficult to find a good
instrument. Hence, an advantage of the regression methods we
used is that they are more broadly applicable and likely more
generalizable for use in other populations.
This study has a number of strengths. We studied influenza
vaccine effectiveness during 15 influenza seasons, representing
,21 million person-years of observation. We included tempera-
ture in our analyses, a variable that both affects winter mortality
among older persons and is associated with influenza circulation,
in temperate regions [31,41], which may have improved the
precision of our VE estimates. By further developing methods
pioneered by Armstrong et al., and using three outcomes of varying
specificity, we estimated the effectiveness of influenza vaccination
in preventing serious influenza-associated events in individuals
aged $65 years to a greater level of precision than previously. We
also carefully specified whether a specific VE estimate applied to
all events occurring during an influenza season, or to excess events
occurring during periods of a specific level of influenza activity
(e.g., when 5% of specimens submitted for influenza testing were
positive). Finally, our analyses use a generalized linear model
framework, and therefore are easy to implement using standard
statistical analysis software packages.
Our study also has a number of limitations. First, although most
Ontario residents aged $65 years receive influenza vaccination in
physician offices, some are vaccinated in settings where billing
claims are not submitted (e.g., clinics organized by public health
departments). Billing claims were found to be 75% sensitive and
90% specific compared with self-report of influenza vaccination in
one study . Misclassification resulting from use of billing data
would bias our results towards the null as the unvaccinated group’s
risk would be falsely lowered because of the inclusion of
misclassified vaccinated individuals. Second, cause-specific mor-
tality data were not available. We used excess mortality within the
30 days following a pneumonia/influenza hospitalization to
provide an outcome more specific for influenza than all-cause
deaths. Third, measures of influenza virus circulation are a key
data element in our analyses and the influenza surveillance data
we used were potentially susceptible to ascertainment and testing
biases over time. However, there were no major changes in data
collection or laboratory methods during the study, and the weekly
proportion of tests positive for influenza is a robust measure of
viral activity . Fourth, because only a small number (2%) of
influenza tests were positive for influenza B viruses, we could not
provide a specific estimate of VE for influenza B-associated events,
so VE estimates could be made only for influenza A-related
outcomes. Finally, in common with all population-based retro-
spective cohort studies, we did not have data on laboratory-
confirmed influenza infections from a per-protocol prospective
testing scheme. It is unlikely that such data will ever be collected
on a community- or province-wide scale because of the obvious
logistical and resource requirements.
Based on our results and those from other studies, influenza
vaccines that are more effective in preventing serious complica-
tions of influenza infections are clearly needed, particularly for
older persons. Several strategies offering potentially more effective
vaccines are being pursued. For example, a high-dose inactivated
vaccine was licensed recently in the United States based on
superior immunogenicity data . It will be important to assess
whether new influenza vaccines prevent more serious albeit rare
complications of influenza infections than the decades-old
standard inactivated vaccines. Large observational studies using
bias-reducing methods likely represent the only possible option to
study the relative effectiveness of new versus standard influenza
vaccines for the serious outcomes of greatest interest, including
mortality. In addition, the methods we used may also be suitable
for evaluating other large-scale public health interventions in
populations in which unmeasured individual-level characteristics
like frailty and dementia, for example, may be important
with influenza-specific and generic vaccination codes.
Physician billing claims for generic vaccination codes are
represented by gray bars and influenza-specific vaccination codes
are represented by black bars. The bars are stacked. Prior to the
introduction of influenza-specific codes in 1998, physicians used
generic codes when billing for influenza vaccination, which are
evident as substantial spikes above a fixed baseline. There was a
gradual increase in the use of the influenza-specific codes, and a
corresponding gradual reduction in the use of the generic codes.
We estimated that only 4% of the combined influenza-specific and
generic vaccination claims during weeks of the annual influenza
vaccination campaigns are not for influenza vaccination.
Weekly physician billing claims submitted
of circulating influenza levels. Vaccine effectiveness is a
continuous function dependent on the level of influenza circulation
within the population. Black lines represent the expected VE at
any level of circulation and the dashed red lines indicate the upper
and lower 95% confidence bands. Note that VE changes relatively
little across circulation values and in a linear manner.
Change in vaccine effectiveness as a function
linear regression model.
Variables potentially included in the log-
final log-linear regression models for each outcome.
Enumeration of all predictors included in the
Table 3. Cont.
No. weeks with
influenza A detected
Cases among the vaccinated during
weeks with influenza A detected
Cases averted by
% averted among
2007/200844 7824 2372.9%
Mean32 5548 2354.1%
aPercent averted among vaccinees is calculated as (cases averted / [total cases in the vaccinated population during weeks of influenza virus circulation + cases averted])
Influenza VE among Persons Aged $65 Years
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outcomes for each study year, stratified by age group
(65–74 years and $ $75 years).
Number of individuals, vaccinations and
Conceived and designed the experiments: BJR DKS JCK LCR MAC PM
BGA. Analyzed the data: BJR. Contributed reagents/materials/analysis
tools: AJC. Wrote the paper: BJR MAC JCK DKS.
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