An Ounce of Prevention or a Pound of Cure?
The Effect Vaccination Coverage Changes on Influenza Hospitalizations and Work
Absences
Courtney J. Ward*
May 19, 2008
Abstract
Each year starting in November a new flu season leads to numerous infections of flu and can
impose significant costs on society in terms of lost work, doctor visits, hospitalization and
death. Although flu vaccination is an effective method of prevention and is associated with
positive consumption externalities, estimates of the return to vaccination may be biased due
to omitted variables. These include, for example, individual health status, which can affect
both the decision to vaccinate and outcomes. In order to address this, I use large upward shifts
in vaccination rates that resulted from policy changes providing coverage of the vaccine to
demographically identifiable groups in Ontario and Quebec. The coverage changes affected
ages under 65; those age 65 and older were already covered. I use variation in these policies in
conjunction with exogenous variation in the year-by-year effectiveness of the vaccine in
order to identify the effect of this program on weekly rates of flu infection, short-term work
absence and influenza hospitalizations and death. I find significant decreases in all of these
illness measures relative to the other provinces, in years when the vaccine was a good match
against circulating flu. While the results are strongest for those age 2 to 64, and in particular
those age 2 to 12, hospitalization rates for those greater than 65 fell significantly as well in
comparison to other provinces. This suggests that increased vaccination of prime age
individuals led to positive externality effects for the elderly.
*
I would like to acknowledge the invaluable discussions and comments provided by Mark Stabile and Dwayne
Benjamin.
1
1 Introduction
Each year starting in November a new flu season leads to numerous infections of flu
and can impose significant costs on society in terms of lost work hours, doctor visits,
hospitalization and death. This common respiratory disease is associated with a 10 to 20%
infection rate per year with symptoms lasting an average of 5 to 6 days (WHO 2003). In the
U.S., flu is estimated to be responsible for 100 million days of bed disability, 75 million days
of work absenteeism and 22 million health care provider visits per year for those aged 18 to
64 (Benson, et. al. 1998).
For at-risk groups with low health, flu and its related
complications account for between 100,000 to 300,000 excess hospitalizations and between
20,000 to 40,000 excess deaths (Thompson, et. al. 2003, 2004). Estimates put health care
costs of the average flu season in the billions (Nichol 2001).
Vaccination is the most effective protection against flu infection and is described as
one of the most important health advancements to date (Philipson 2000, PHAC 2008).
Before its development in the 1940s, severe flu epidemics were recurrent. A notable example
is the 1918 Spanish Flu epidemic, which caused a higher death toll in the first 25 days than
heart disease has in the last 25 years (CDC 2007, Knobler 2005). The 1918 epidemic was
caused by the H1N1 subtype of flu, a strain that is now commonly included in the yearly flu
vaccination. Since the development of the flu vaccine, positive yearly vaccination rates have
caused flu epidemics to become less persistent with diminished numbers of infection.
However, yearly vaccination rates still remain low enough that the flu is able to circulate and
cause infection in the unvaccinated population and high-risk groups each year 1.
Increasing the vaccination rate would further diminish the magnitude and burden of
yearly flu epidemics. However, the vaccination decision remains a private and not societal
one with individuals comparing the cost of vaccination to the private, and not societal,
benefit of vaccination. In the case of flu, rates of vaccination are often substantially lower
than that of other infectious disease since this vaccine is rarely mandatory for school or work
attendance as is typical for most childhood vaccines. Positive consumption externalities are
often the argument for mandates or subsidization of the vaccine cost since it is expected that
this will lead to increased rates of vaccination and hence decrease infection. These
externalities, however, are difficult to measure or quantify making it difficult to forecast the
benefit of such mandates or subsidies.
The presence of externalities can also present a
problem when estimating the private benefit to vaccination since downward bias can arise
1
These are groups for which vaccination does not generate the necessary immune response to protect against
infection.
2
when unvaccinated neighbours benefit, in terms of illness reduction, from the vaccination of
peers. Further, even estimates of the total marginal benefit, the sum of the private marginal
benefit and the externality associated with vaccination, may be biased when estimated in a
standard regression of illness on group vaccination rates. Here, bias would occur if unobserved
factors determining vaccination are correlated with illness outcomes. These factors include,
for example, an individual’s health status, which may be correlated with the decision to
vaccinate and outcomes associated with flu such as disease severity, probability of contraction
and other general illness measures.
In order to address this, I use large upward shifts in vaccination rates resulting from
policy changes that
reduced the cost of vaccination by providing coverage to
demographically identifiable groups. The policy changes occurred in the provinces of Ontario
and Quebec in October 2000. In Ontario, vaccination coverage was extended to those
between age 2 and 64 and for this age group, led to a 10% increase in the vaccination rate
relative to other provinces. This put the rate, in absolute terms, above the highest rate
achieved in the rest of Canada or other developed countries. In Quebec, vaccination coverage
was extended to those between age 60 and 64. This led to an 18% increase in the vaccination
rate for this age group.
I use variation in these policies over time in conjunction with exogenous variation in
the year-by-year protection of the vaccine in order to identify the effect of protective
vaccination on illness outcomes. My contribution is in; first, estimating the effect of cost
subsidization on vaccination take-up by using a policy changes that reduced the cost of
vaccination for broad and demographically identifiable groups and second by using an
estimation strategy that documents the effect that these policy changes, with a protective
vaccine, has on inflection of flu and other illness outcomes.
This paper is organized as follows; section 3 provides background information on the
flu virus, vaccination and provincial vaccination programs, section 4 outlines a model of
infection and vaccination demand and provides a direction for the empirical approach,
section 5 describes the data and empirical methodology, section 6 presents results and section
7 concludes.
3 Background: The Flu, Vaccination and Vaccination Programs in Ca nada
Influenza, or flu, is a respiratory virus that typically begins circulating in North
America in the fall and winter months and is usually the predominant cause of serious
3
respiratory disease in this time. 2 Each flu season results in infection of 10 to 20% of the
population (WHO 2003) with transmission occurring from an infected person to a susceptible
person by “droplet spread”3 . This can occur either through the air or through contact with
surfaces where respiratory droplets exist. An infected person remains an infection risk from 1
day before the onset of symptoms and up to 5 days after becoming sick (CDC 2008). In
addition, the virus can stay virulent on surfaces for a varying length of time. At body
temperature, the virus is usually inactivated in less than a week whereas in cool dry
temperatures the virus can last considerably longer (Zhang, et.al. 2006). This is, in part, the
reason why seasonal epidemics appear during winter months (WHO 2003).
Flu infection can cause mild to severe illness. Recovery usually occurs in a few days to
two weeks. In some cases, the flu can lead to death, particularly if infection develops into
pneumonia or is coupled with other complications such as asthma, heart disease or other
conditions associated with immunosuppression (PHAC 2007).
There are many strains of the flu virus; genetic composition or structure is what
differentiates them. Broadly, the flu virus can be divided into type A flu and type B flu. Type
A can be further sub typed by 2 types of antigens, called haemagglutinin (H) and
neuraminidase (N), which lie on the surface of the virus. The virus is genetically
differentiated or typed on the basis of these surface antigens. Antibodies to these antigens,
particularly to the H antigen, can protect an individual against a virus carrying the same
antigen. Since the flu virus undergoes continuous antigenic change, immunity to the virus,
either by infection or vaccination, is not permanent. This antigenic drift occurs rapidly
through point mutations of the virus and where the antigenic drift is great, the crossimmunity to the new strain that was conferred by the previously circulating virus is
diminished (PHAC 2007). Due to antigenic drift, new flu virus types evolve year to year and
the vaccine must be reformulated to account for these new strains (WHO 2003).
Prevention of flu through vaccination has traditionally been at the core of managing
flu and flu epidemics. The World Health Organization (WHO) closely monitors circulating flu
viruses and every year writes the annual vaccine recipe. This vaccine is constructed to target
the 3 most virulent strains in circulation in a given region (WHO 2006). Each year in
Canada, inactivated flu vaccine 4 is licensed for use by Health Canada. Once licensed, the
2
Two good sources of summary information on influenza and vaccination are the Center for Disease Control and
Prevention in the U.S. (http://www.cdc.gov/flu/about/disease/index.htm) or the Canadian Immunization Guide
published by the Public Health Agency of Canada (http://www.phac-aspc.gc.ca/publicat/cig-gci/index-eng.php).
3
Transmission by droplet spread results when respiratory droplets of an infected person come in contact with the
eyes, nose or mouth of a susceptible person.
4
As the name suggests, inactivated vaccine contains viruses that have been killed and hence are not associated
with adverse reactions or infection of flu that may result from a live attenuated vaccine. Alternatively, since the
4
Government of Canada, through Public Works and Government Services Canada (PWGSC),
purchases flu vaccines on behalf of the provinces and territories for distribution in late
October early November. The turn around period from the yearly WHO recommendation to
the date the vaccine is available is approximately 6 to 8 months depending on manufacturing
conditions (Health Canada 2007).
The effectiveness of the vaccine depends on the immunocompetence of the recipient
and the immunity or cross-immunity of the vaccine contained strains to strains in
circulation. Protection generally begins about 2 weeks after immunization and may last 6
months or longer. However, for those in low health, antibody levels may fall below
protective levels in 4 months or less. For maximum protection, the preferred time for
immunization is in October or November (PHAC 2007). Following this prescription, each
province holds a flu immunization week that occurs in late October and it is around this date
that most recipients are vaccinated.
Provincial governments make flu vaccine available at public health clinics and
doctor's offices in accordance with provincial influenza immunization programs.5 Beginning
in the early 1990s, all provinces developed similar programs; the vaccine was available to
customers at a cost and for specific groups this cost was covered by provincial governments.
The standard covered group is recipients less than 24 months or 65 and older, health care
support staff, and those in care homes or with specific chronic conditions.6 With two
exceptions, this standard group has been covered in all provinces since 1996 (Johansen
2004). Prince Edward Island and New Brunswick are the exceptions to this prescription. In
Prince Edward Island, although there is no charge for the vaccine, recipients are responsible
for the cost of administration and only health care workers and residents of nursing homes
are exempt from any fee. In New Brunswick, coverage for those 65 or older first began in
2002 (CPA 2007, 2003).
Beginning in 2000, two provinces expanded coverage outside the standard group. In
July of 2000, the Government of Ontario announced that it was extending vaccination
coverage to all residents of the province. Quebec, as part of projets spéciaux, extended
vaccine coverage to ages 60 to 64. Since the vaccine only became available in October, the
start date for both changes is the October 2000.
In Ontario, the expansion of coverage was only one part of a larger ten-point plan to
reduce emergency room wait times (Kurji 2004). Accordingly, the stated objective of the
virus has been killed, the immune response to an inactivated vaccine may be less than that of a live-attenuated
vaccine.
5
Flu vaccines are also available through private market contracts and can be found at local pharmacies.
6
Covered conditions include cardiac or pulmonary disease, asthma, diabetes, renal disease, liver disease, anaemia,
HIV, and cancer. These conditions can potentially cause complications in the event of flu infection.
5
program was to ease pressure on health facilities and providers, in particular emergency
rooms, by decreasing the impact of influenza during the flu season (MOHLC 2000). Even
though the program targeted, by default, healthy prime age individuals, it was expected that
this would afford protection for high-risk groups with already high rates of vaccination
through an externality effect. Secondary objectives of the program were to decrease the
economic impact of the flu during flu season and also to build infrastructure for delivery of flu
or other vaccines in the event of a pandemic (Kurji 2004). In its first year, the program cost
$31 million with 7.9 million vaccines administered (up from 2.1 million in the previous
season) (Kurji 2004). In Quebec, projets spéciaux were originally started to promote and
increase accessibility to flu vaccination. The program had similar objectives to the program
in Ontario, but it expanded coverage to a more targeted group of 60 to 64 year olds
(Johansen et. al. 2004).
It is clear that, in order to achieve these objectives, these programs anticipated both
that subsidization of the vaccine price would lead to increases in vaccination and also that
vaccination would protect against illness. It follows that the coverage changes in Canada can
provide a means to test two hypotheses; the first is that decreases in the cost of vaccination
will lead to increases in the demand for vaccination and the second is that these increases in
vaccination will lead to decreases in illness. This second effect is limited by the effectiveness
of the vaccine itself. Here, the question is not regarding the effect of vaccination alone
(there may be different conclusions about the effect of the vaccine injection status), but
rather regarding the effect of vaccination as it depends on the measured protection of the
vaccine.
4 Model of Infection and Vaccination
I will formalize these two hypotheses using a standard model of disease spread
modified to include choice over vaccination and variation in vaccine match. 7 This model is
illustrative. It is meant to describe the basic features and dynamics of disease spread while
providing a point of reference for the empirical approach.
Demand
Consider a population of individuals who are both identical and risk neutral and make
7
The model is based on the Kermack and McKendrick SIR model of disease epidemics. Jones and Sleeman (2003)
offer a good textbook exposition of the model. Several variations of the model are shown in: Kremer and Snyder
(2006), Geoffard and Philipson (1997), Francis (1997, 2004), Boulier, et. al. (2007) and Auld (2003). The model
shown here is most similar to Boulier, et. al. (2007).
6
the decision to vaccinate before the onset of the epidemic. 8 This decision is based on
comparing the expected marginal benefit of vaccination to the marginal cost of vaccination.
The benefit of being vaccinated is related to both the probability of contracting the flu and
the burden of infection. Suppose, the burden or cost of becoming sick is given by k and that
the probability of becoming infected given that v individuals are vaccinated is p(v). Here, the
expected cost of disease for an unvaccinated individual is p(v)k. Alternatively, if an individual
is vaccinated against flu with a vaccine of match or effectiveness m, then the expected cost
of disease falls to (1-m)p(v)k. Comparing these two costs, the benefit of being vaccinated as
opposed to remaining unvaccinated can be calculated as mp(v). If this benefit is greater than
the price of vaccination, P, then the individual will vaccinate.
Epidemiology of contagious disease and p(v)
The probability of infection, p(v), depends on the epidemiology of the disease. For
instance, it will depend on factors such as the number of infections, the transmission rate and
the rate of recovery. These dynamics will be summarized here using a standard SIR model. In
this model, at any point in time an individual can be in one of three states: susceptible (s)
where disease contraction is still possible, infective (I) where illness is present and contagion
is possible, and recovered (r) where immunity to the disease strain has been conferred and reinfection is not possible. By allowing for vaccination, a proportion m of vaccinated
individuals are able to leave the susceptible state and can neither catch nor transmit the
disease. If the match between the disease and the vaccine is perfect (m=1) then all v
individuals are protected against the disease.
Given this framework, the dynamics of the disease may be characterized by the
following system of differential equations:
s˙ = "asI
I˙ = asI " bI
r˙ = bI
(1)
(2)
(3)
! become infected at a rate proportional to the number
Equation 1 indicates that susceptibles
of contacts between s and I. This assumes that contact only depends on the numbers of each
8
This assumption may be relaxed within the context of the model, however this simplification is common (Francis
1997, Geoffard and Philipson 1997, Boulier, et. al. 2007). Additionally, with respect to flu vaccination, this is
likely an accurate assumption given the yearly timing of vaccine manufacture and the standard recommendations
for vaccination use. For instance, data from the 2006 National Health Interview Survey in the U.S. show that 92%
of vaccinated respondents had received the current flu vaccine by December of 2005. Administrative data from
Ontario OHIP physician billings show similar results (Kwong and Manuel 2007).
7
group. Here, the transmission rate is given by a. 9 Through transmission, the number of
infectives is increased by new infections each period. However, it is also decreased by those
who recover. The rate of recovery is a constant hazard rate b; 1/b is the average duration of
infection. Assuming constancy of the population with size normalized to 1 and using the fact
that an individual is either protected though vaccination or in one of the three states, then at
any point in time we must have:
s(t) + I(t) + r(t) + mv = 1
(4)
Consider the beginning of disease progression. We start with a positive number of
invectives, I(0) > 0 and initially
there is no one in the recovered state, r(0) = 0. The model
!
has interesting implications for the occurrence of an epidemic; an epidemic in this setting is
defined as an increase in the number of infectives above the initial number. In other words, an
epidemic will not occur if I˙ < 0 for all t. Using this and equation (2), this leads to the following
condition necessary for the prevention of an epidemic:
!
s(0) <
b
a
(5)
If Equation 5 is maintained, an epidemic will be prevented. If Equation 5 does not hold true,
!
then the infective population will rise over time. This rise will proceed until s(t) = b/a, at
which point the infected population will then begin to decline. After the epidemic, total
disease incidence is given by r(∞,v), which is the proportion (or number) of infectionrecoveries.
Within the context of the epidemiological model, we can return now to vaccination
demand and evaluate the probability of becoming infected. Here, we have:
p(v) =
r(",v) # I(0)
s(0)
(6)
which is the proportion of initial susceptibles that become infected and removed throughout
!
the progression of time. Following Boulier, et. al. (2007), I assume that individuals are
forward looking and can estimate p(v) for each level of v. Then for any price P, there is an
equilibrium level of vaccination, v*, where the number of vaccinations satisfying P =
mp(v*)k equals v*. The condition that P = mp(v*)k is simply the statement that the
9
To elaborate, if p is the rate of contact and q is the proportion of contacts that lead to infection, then there will be
pI contacts per each susceptible, each period of which qpI will be infective. The transmission rate, a, is then the
product of the contact rate and the contagiousness of the disease (a = qp)
8
marginal cost of vaccination equals the marginal benefit and hence this equation traces the
demand curve.
Implications
There are two testable implications of this model. The first is that the demand curve
is downward sloping. That is, a decrease in the price (the marginal cost of vaccination) will
result in an increase in vaccination. To see this, note that increasing the level of vaccination
reduces p(v) since it reduces directly the initial population of susceptibles. If the price of
vaccination falls, then the marginal benefit of vaccination outweighs the marginal cost and as
a result, there will be an increase in demand for vaccination. This increase in demand will
cause p(v) to decline and this will occur up until the point where p(v) has fallen sufficiently to
reduce the marginal benefit to once again equate with marginal cost.
The second implication is that the marginal effect of vaccination on the total
number of infection-recoveries will be negative. To see this, note that the number of ever
infected, r(∞,v), solves 10:
r(",v) = s(0) + I(0) # s(0) e
#a b
r(",v)
(7)
Differentiating Equation 7 with respect to v yields:
!
$ a r(#,v)
"r(#,v)
$m(1 $ e b
)
=
a
$
r(#,v)
"v
1 $ s(0) ab e b
(8)
Equation 8 implies two testable relationships. The first is that the marginal effect of
!
vaccination on the number of infection-recoveries is negative. 11 The negative effect of a
marginal vaccination arises because an additional vaccination reduces the susceptible
population by a magnitude of m, which both protects the additional vaccinated individual
with probability m (a private benefit) but also limits the probability of disease transmission to
others (an externality). The second implication of equation 8 is that the marginal effect of
vaccination is more negative the higher the match rate of the vaccine. 12 If the match rate is
ds
a
= " s(t)
b
From equation 1 and 3 we have dr
. Solving this for s(∞) yields s(") = s(0) e
that r(",v) = s(0) + I(0) # s(") provides the result.
10
11
!
# ab
To see the first result, note
that the numerator in equation 8 is negative since 1 > e
!
negative. The denominator is positive since s(") < b yielding a negative
expression.
!
a
12
This can be found by differentiating equation 8 with respect to m.
r(",v)
" ab
. Using the fact
r(#,v) and m is non-
!
!
9
zero, for instance, the marginal effect of vaccination is also zero.
5 Data and Methodology
Methodology
The purpose of the empirical work is to identify the effect of coverage expansion in
Ontario and Quebec on illness outcomes when vaccination is effective prevention against
circulating flu. Since the effectiveness of the program is constrained by the effectiveness of
the vaccine, this strategy will define the treated group as individuals in years when the
vaccine is a good match against circulating flu. As a simple illustration of this idea, consider
the following comparison of means for Ontario for the period of 1996 to 2006: in years
where the vaccine was a poor match against circulating flu, the per week average rate of
flu/pneumonia hospitalizations before the coverage changes was 12.07 per 100,000
population and it was 12.20 after the changes. Alternatively, for years where the vaccine was
a perfect match, the average rate before the program was 12.22 and after it was 10.92,
representing a statistically significant decrease of 1.30 (=12.22-10.92). To separate this
decrease from other possible trends in hospitalizations, I can calculate the relative difference
in average hospitalizations before and after the program in good and poor match years. This
difference represents a decrease of 1.42 hospitalizations per week per 100,000.
I extend this idea by using data for all provinces and a continuous measure of the vaccine
match rate each year. Identifying the preventative effect of vaccination requires that I
control for any systematic shocks to the treated group in Ontario and Quebec that are not
due to the coverage changes but that are correlated with it. To do this, I include region effects
to control for time invariant characteristics of regions, season effects to account for
common trends in illness, and the match rate to control for the province-time varying effect
of vaccine match. I also include second level interactions to control for changes over time in
the treated provinces, changes in the effect of the match rate over time and time invariant
effects of the match rate in each region. By using this variation in vaccine match I am able
to differentiate the effect of coverage changes from regional time trends; I identify this
effect by capturing the variation in illness specific to the treated regions (versus the untreated
regions) after the coverage changes (as opposed to before) in years where the flu vaccine was
100% matched (as opposed to a match of less than 100%).
Data
In order to document the change in vaccination resulting from the coverage change
for 2 to 64 year olds in Ontario and 60 to 64 year olds in Quebec, I use health survey data
from Statistics Canada. These data are summarized in Table 1. There are four health surveys
10
that contain questions relevant to flu vaccination: the National Population Health Survey
(NPHS), Cycle 2 1996/1997 and the Canadian Community Health Survey (CCHS), Cycles 1.1
(2000/2001), 2.1 (2003) and 3.1 (2005). These surveys are national, population-based
surveys conducted on persons 12 years of age or older. As well as collecting demographic,
socioeconomic and health information, these surveys also collect information on current
vaccination status and limited information on previous vaccination patterns. Given that the
yearly flu vaccine becomes available in late October, I define the flu season year to be the
year starting October to September of the following year. This follows the definition used by
the Public Health Agency of Canada and the Center for Disease Control in the U.S.. Using
this definition for survey respondents, I am able to determine coverage rates for each fluseason year.
Ideally, data including individual vaccination status could be matched with individual
influenza infection status. Unfortunately, broad scale data with these features is unavailable at
the individual level. However, data on flu infections alone can be obtained from surveillance
counts of laboratory confirmed flu. The Public Health Agency of Canada (PHAC) collects
this data through its respiratory surveillance program. The data is collected weekly from
appointed sentinels in each surveillance region and includes outbreak reports, estimated
physician visits for influenza-like-illness and laboratory tests. The most preferred method of
surveillance is weekly counts of laboratory confirmed flu tests. Each week these tests are
collected and sent to laboratories where they are tested for flu and other respiratory diseases.
In the flu off-season, sentinels are still encouraged to collect tests. I use these data for two
purposes; the first is to analyze the effect of coverage changes on the rate of laboratory
confirmed flu and the second is to define the flu season. I use the flu season as a conditioning
variable for other illness outcomes such as hospitalizations and work absenteeism and I define
it as the contained set of weeks from the first week the number of positive tests is greater
than 5% of the season total to the last week it falls below 5%13,
To get a measure of the vaccine match, I use strain isolation data from the PHAC
along with reports on the immunity of cross-immunity of the yearly vaccine. These reports
are published each year in the Canadian Communicable Disease Report (CCDR) and evaluate
the protection that the vaccine confers against the strains circulating during each flu season.
Additionally, I compare these findings with that of the U.S. Center of Disease Control and
the vaccine recipe from the World Health Organization and find that they correspond. In
order to get a measure of the match rate, I use information from a sub sample of strain
isolated flu tests in each province and compare it to the yearly CCDR report. The match rate
13
This definition is common (Izurieta, et. al. 2000) and I use it so results found here may be compared to results
from previous literature.
11
is calculated as the proportion of circulating strains that were matched with the vaccine.
These data are summarized in Table 2.
Since one of the main policy arguments for coverage expansion is to decrease strain
on health services, I analyze a dimension of these services: acute hospitalizations. I extract
data from the Hospital Morbidity Database (HMDB) holdings of the Canadian Institute for
Health Information. The HMDB data include a complete record of hospital inpatient
discharges for all hospitals in Canada. Since, hospitals in Quebec and non-Winnipeg Manitoba
only began submitting to the HMDB starting in 2001, these areas are excluded from the
analysis. Each discharge abstract consists of information on patient age, sex and home postal
code as well as detailed medical information such as date of hospital admittance, admittance
from care facility, date of discharge, hospital death and detailed diagnoses information. In
addition to the diagnosis labeled the most responsible diagnosis (MRD), up to 15 diagnoses
may be recorded for each hospitalization. Using this information, I am able to analyze
hospitalizations where influenza or pneumonia was the MRD diagnosis or one of the other 15
listed diagnoses. Since an influenza case is not always recorded with the influenza diagnosis
code but instead may be coded under the more general code of pneumonia, I will analyze both
influenza and pneumonia diagnoses.14 I also discuss results for known complications of the flu
such as: heart disease, COPD, acute respiratory disease and, as a specification check,
unintentional accidents.
I use diagnosis counts from the HMDB to construct weekly hospitalization rates for
regions in Canada. I use the definition of economic regions (ERs) defined by Statistics
Canada. Each ER is made up of a group of adjacent census divisions and here, hospitalizations
are assigned to regions based on the patient postal code. For each region, I am able to
construct rates for different age and sex groups. Population counts for each group, region and
year are used in the denominator of the weekly rate.
Since I am also able to observe whether the patient was admitted from a registered
care facility, I calculate flu and pneumonia hospitalization rates for care facility residents
using total provincial resident counts obtained from Statistics Canada as the denominator in
the rate. Since vaccination rates have been high for residents of care facilities15 and the cost
of vaccination for this population has been covered in provinces since the 1990s,16 finding a
negative effect of this program would operate through an externality. Here, since residents of
14
Keren et. al. 2006 reanalyzed hospital diagnosis codes by performing flu tests on a sample of patients and found
that only 65% of the tests that were positive for influenza had been coded with a diagnosis of influenza.
15
For instance the vaccination rate for care facility residents in Ontario was 93% in 1999/00 (before the UIIP
program) and 95% in 2000/01 (after the UIIP program) (Clement and D’Cunha 2002).
16
Prince Edward Island is the one exception where only the administration of the vaccine is covered by for care
facility residents as well as all other groups.
12
care facilities have lower health and hence lower immune responses to vaccination, infection
from flu will be related to the vaccination status of others. When the vaccination rate of
others is increased, it will afford care residents more protection from flu transmission where
they are only able to obtain a limited amount of protection from the vaccine.
While hospitalization data is able to capture acute outcomes of flu, the vaccination
program is expected to have an effect along other dimensions as well. Labour productivity
may be one of these dimensions. To analyze the effect that coverage changes have on labour
supply, I use the Labour Force Survey. This is a monthly survey that provides information on
illness absence and hours in a reference week.
6 Results
Vaccination and Coverage Changes
The coverage changes in Ontario and Quebec targeted the vaccination of a population less
than 65 years of age. The Ontario program began to cover the cost of vaccination to those
ages 2 to 64 and Quebec began to cover those ages 60 and 64. Table 3 gives a summary of
vaccination rates for various groups both pre and post October 2000, the date both
provincial programs came into effect. The table shows that for the full sample, vaccination
rates have increased for all provinces in the post period relative to previous rates. For
instance, in the post period, there was a 21% increase in the vaccination rate for Ontario, a
17% increase in Quebec and an average 12% increase in other provinces. The remainder of
the table shows that, for various sub groups, the absolute change in vaccination is also
positive. In order to provide a comparison of these changes for Ontario and Quebec relative
to the other provinces, Table 4 presents the relative change in vaccination rates for each of
these sub groups. The relative change is simply a summary difference-in-difference estimate
for each of Ontario and Quebec versus the other provinces before and after October 2000. As
shown in this table, a pattern becomes apparent among the sub groups. For instance, in
Ontario the relative change in vaccination rates is similar across all sub groups except for
separate age groups. Here, the youngest age group, 12 to 14 years, has a relative increase in
vaccination of 10.5% while the elder set of teenagers has, of all affected age groups, the
lowest relative increase in vaccination: 4.4%. This number increases with age until the ages of
60 to 64. This age group has an estimated relative increase of 13.0% and is the oldest age
group that was affected by the change in coverage. Those 65 or greater were not affected by
the coverage change and have a much smaller relative increase of 1.8%. For Quebec, no age
groups under 60 have a significant relative increase in vaccination, which corresponds with
the coverage changes in Quebec; only those 60 to 64 were affected. Alternatively, this
targeted group experienced a large increase of 20.3%. Those 65 and greater, who were already
13
covered, also had a statistically significant increase of 13.5%. While for Ontario there are no
major differences across other sub groups, in Quebec, the highest relative change in
vaccination occurs among those with low education, low household income, with chronic
conditions and who are not employed. These differences may be somewhat attributable to the
demographic group that the Quebec program targeted; the increase in vaccination occurred
mostly among an older population.
I formalize the empirical analysis presented in Table 3 and 4 by replacing the
summary comparisons of means with estimates obtained using a regression approach. This
approach allows me control for additional explanatory variables and in addition, allows me to
account for the age structure that is a feature of both the Ontario and Quebec program
changes. To do this, I regress individual vaccination status on a vector of individual
characteristics such as gender, education, income, student status, presence of children,
presence of chronic conditions and occupation (with non-workers entering as a separate
category). I include the following fixed effects: year, province and age effects for the groups
of 12 to 59, 60 to 64 and 65 or older and, in addition, I include all second level interactions:
time varying age effects, province varying age effects and time varying province effects.
Finally, I include an indicator variable for those observations that were of an eligible age
group in Ontario or Quebec after October 2000. In Table 5, I present results where I have
allowed two separate indicators for Ontario and Quebec. These regression results are
conceptually equivalent to a triple difference; in this case, I am comparing the vaccination of
treated individuals after the coverage change to the change in vaccination of all other
individuals in a particular province and comparing that to the same change in provinces that
did not change their coverage programs.
Table 5 shows that vaccination increased by 10% for the treated group in Ontario and
18% for the treated group in Quebec. I also present the results by self-rated health in order to
analyze whether the effect is strongest for those who perceive themselves to be in worse
health. For Ontario, the estimate is highest for those who rate themselves in fair health
(14%) but this is quite similar to the estimates for those who rate themselves in good or very
good health. Alternatively, those who rate themselves at the extremes, either excellent or
poor health, have the lowest increase in vaccination amongst the treated group (around 6%).
In Quebec, there is a clearer relationship between the effect of coverage changes and self
rated health; those with low self-perceived health are much more likely to become vaccinated
among the treated group after the coverage change.
These changes in vaccination can be put into context with the model shown in
section 4. Equation (5) gives the conditions under which an epidemic could be avoided; if the
proportion of initial susceptibles is less than b/a, then infections will not increase. The
14
inverse of b/a is known as the contact number. It is a summary measure of the average
number of susceptibles that would be infected by one infective when the population is
otherwise entirely susceptible. For instance, the contact number for flu is estimated to be 1.4
(Hethcote 2000). Using equation 5, this estimate implies that a proportion of initial
susceptibles less than 70% will prevent the onset on an influenza epidemic. This corresponds
to a vaccination rate that is able to protect more than 30% of individuals. Table 3 shows
that, for Ontario, the vaccination rate was increased from 20% to 42% pre versus post. With
a perfectly protective vaccine this increase, within the context of this model, is able to
prevent a flu epidemic. Therefore, in seasons where the match rate is high, coverage changes
are likely to be associated with large gains in illness prevention. I turn now to estimating the
magnitude of these gains.
Laboratory Confirmed Flu
I begin to document the effect that vaccination coverage changes have on illness
within the following regression framework:
y ijt = " + #x ijt + $ j + % t + &Match jt
+'1 (Post t * Treat j ) + ' 2 (Post t * Match jt ) + ' 3 (Treat j * Match jt )
(9)
+' 4 (Post t * Treat j * Match jt ) + (ijt
Here, i indexes the age group, j indexes the region and t indexes the time period (in weekly
!
increments). The variable on the left, y, is any measure of illness (flu surveillance counts,
hospitalizations, or worker absences), x is a vector of observable characteristics, Match is the
match rate between the yearly vaccine and circulating flu, λj is a vector of fixed region
effects, τt is a vector of fixed year effects, Post is a dummy variable for after October 2000,
and Treat is a dummy variable for treatment province (1 if in Ontario or Quebec, 0
otherwise).
In this regression, the fixed effects control for time series changes in illness and time
invariant characteristics of regions and Match controls for the province-time varying level
effect of the match rate. The second level interactions control for changes over time in the
treated provinces, changes in the effect of the match rate over time and time invariant
effects of the match rate in each region. The third level interaction (β4 ), captures the
variation in illness specific to the treated regions (versus the untreated regions) after the
coverage changes (as opposed to before) in years where the flu vaccine was 100% matched
(as opposed to a match of less than 100%).
The first panel of Table 6 presents the estimates of the second and third level
15
interactions from equation 9. Here, illness is defined as the laboratory confirmed flu rate
calculated from weekly respiratory surveillance tests. This data is available at the provinceweek level and thus each region is defined as a province and there is one age group defined as
all ages. The estimate of β4 using all weeks of each season is negative, small and insignificant
statistically. This is partially due to the fact that for a majority of the weeks throughout the
year, incidence of the flu is diminutive and surveillance counts are zero. This causes low
variability among regions and match rates. Put another way, during these off-season weeks,
vaccination and match will have little effect on illness from flu and, therefore, differences in
vaccination or match across regions will not generate large differences in illness. In order to
account for this, I condition on weeks when flu is circulating. Flu season weeks are defined as
consecutive weeks where surveillance counts of flu comprise more than 5% of the season
total 17 . Conditioning on flu season, the estimate of β4 is imprecisely measured but larger at 5%. This represents a 24% decrease from the average rate of laboratory confirmed flu. In the
off-season weeks, the estimate of β4 is small and insignificant from zero, which is the
expected effect and occurs in this data by definition of the conditioning variable. The
estimate of β1 is negative for all 3 specifications in Panel A and indicates that even where the
match rate is low (quantitatively zero) the coverage changes still have a negative effect on
the flu rate. In Panel B of Table 6, I parse these estimates by each treated province. The
estimate of the third level interaction is -7% for Ontario and -12% for Quebec.
The validity of these estimates require that surveillance testing behaviour be
uncorrelated with changes in the match and implementation of coverage adjustments in each
province. For instance, if sentinel physicians tested more in provinces with coverage
adjustments after these adjustments occurred and even more so in good match years it may be
the case that the estimated effect is biased downward. This would be the case if sentinels test
more when there is less flu and this causes a lower proportion of positives when testing is not
random. Although the extent of measurement error in the surveillance data cannot be known,
bias in estimation will only occur if the number of tests is correlated with both the average
rate of positives and also with changes in yearly match rates of treatment provinces versus
the untreated provinces, before and after the coverage changes. This is not likely the case;
even if sentinels are choosing to test in the order of those most likely to be infected with flu
(causing the flu rate to decline with each test), generally the match of the vaccine is unknown
at the beginning of the season and further to this, flu is only one of many respiratory
illnesses that sentinel physicians may come in contact with and only one of the respiratory
illnesses for which they collect surveillance tests.
17
Other definitions of the flu season such as consecutive weeks with any positive flu tests yield substantially the
same results.
16
Hospitalizations
Hospitalization is a measure of illness for which a complete diagnosis is required for each
discharge. I now present results for hospitalization rates of flu and pneumonia. Figure 1 shows
the hospitalization rate for Ontario and the average rate for other provinces. Unfortunately,
data for Quebec was not consistently collected throughout this period and hence this province
is dropped from this section of the analysis. This figure presents a weekly time series
including only 100% match seasons. Here, it is clear that for seasons after the program
change, the hospitalization rate for flu dropped for all provinces and most substantially for
Ontario. Figure 2 shows all seasons with a less than a 100% match rate. One feature of this
figure compared to Figure 1 is that the rates are higher (the epidemics are generally more
severe) in low match seasons. The second feature of Figure 2 is that rates for bad match years
fell after the coverage changes, and did so for all provinces. This is not unexpected;
vaccination rates rose for all provinces during this period and there is still some magnitude of
match between flu and the vaccine. However, in Figure 2, as opposed to Figure 1, there is not
a noticeable difference between Ontario and the other provinces after the program change. In
fact, in the 2003/2004 season, the epidemic in Ontario appears worse (if not shorter) than in
the other provinces. Here, this may be explained by the match rate; this season was also one
where the match rate in the other provinces was higher on average than in Ontario.
In order to disentangle these effects, I estimate equation 9 for these hospitalization data.
To do this, I define regions, j, as the ERs within Canada and age groups, i, as: less than 5, 5 to
12, 13 to 18, 19 to 24, 25 to 49, 50 to 64, 65 to 74, 75 to 84, and 85 or older. The results
are given in Table 7. Here, for hospitalizations with any diagnosis of flu the estimate of β4 is
an approximate decrease of 1 hospitalization per 100,000 people per week. This estimate is
statistically significant and represents the effect of the coverage changes in years when the
vaccine match was 100%. The decrease is a 36% decline relative to the average rate of 2.6
per week.
The result conditioning on flu season weeks is an estimated decrease of 6.7
hospitalizations per 100,000 per week representing a 63% decrease from the average. The
estimate is greatest at the season peak. Here, the peak is defined as the 3 consecutive weeks
where flu surveillance counts represented the highest proportion of the season total. The
effect on flu hospitalizations in off season was insignificantly different from zero. The results
for pneumonia hospitalizations demonstrate the same pattern although here the estimates are
imprecisely estimated. For all weeks, the effect is a positive 1.0 with a large standard error of
1.8. However, for weeks during flu season, the effect on pneumonia hospitalizations is -5.2
per 100,000 people per week. Again, the effect is strongest for the season peak and smallest
during the off-season.
Table 8 presents results for deaths and length of stay. The coverage changes reduce the
17
number of hospital days by 75 per 100,000 people per week in seasons that delivered an
effective vaccine match. Alternatively, there was no effect on the average length of a
hospital stay, which may indicate that there is little difference in the severity of flu
hospitalizations. This corresponds to the results for hospital mortality; when comparing the
decrease in hospital deaths to the decrease in hospitalizations, both estimates represent a
similar 63% decrease from the average rate. The point estimate for death indicates that
hospital mortality with diagnosis of flu is reduced by 0.41 per 100,000 people per week. The
results for Pneumonia are similar but less precise.
There are differences in the gain of the coverage changes across age groups for both flu
and pneumonia hospitalizations. Table 9 presents the results for flu hospitalizations for
different age and sex groups and Table 10 presents results for pneumonia hospitalizations.
The results are qualitatively similar. Comparing the effect for males and females in Table 9,
the point estimate for females is larger, which corresponds to the larger increases in
vaccination for this group. Looking at the different age groups, the table shows that the
estimate is highest for the youngest groups. Here, the incidence of flu hospitalizations has
been reduced almost entirely from the average rate for the sample period. There is a much
smaller effect for prime age individuals but quite large and significant effects on the
hospitalizations of those greater than 64. The effect is largest for the eldest age group of 85
or older. This group had very little relative increase in vaccination after the program and
thus this may indicate that the vaccination of other groups has afforded the elderly more
protection. These groups are particularly vulnerable since the vaccine may not sustain a
sufficient immune response to offer protection.
To explore this further, I focus on hospitalizations for residents of registered care
facilities. Care homes are unique in that they have enforced high rates of vaccination since
the early 1990s and residents are usually in poorer health, which makes them more vulnerable
to infection even with vaccination. In Table 11, I present these results. The estimates,
although insignificant statistically, are very similar in magnitude to results for the elderly. For
instance, the estimate for residents of care homes during flu season weeks is 35 less
hospitalizations per 100,000 residents and this is a relative decrease of 57%. This is very
similar for the relative decrease for those age groups 65 or greater and is evidence that
vaccination of younger groups has had an effect on these vulnerable populations.
Table 12 presents results for other disease diagnoses. The first panel shows the results for
different types of respiratory disease. The estimates for weeks during flu season are negative
and significant for all types of respiratory disease. The second panel shows negative results
for types of circulatory disease for which flu is a possible complication. As a specification
check, I include the results for unintentional accidents for which flu and vaccination should
18
have no effect. Here results do not differ in flu versus off-season, are small in magnitude, and
are insignificant from zero.
Worker Absenteeism
While hospitalization data captures acute outcomes of flu infection, I now focus on
absences from work. Since coverage changes occurred for ages under 65 and also led to large
increases in the vaccination of these groups, there is a unique opportunity to detect the effect
that this has on worker absences. Here the sample of individuals is from the employed
population under 65 years of age. In Table 13, I present results for absence incidence and
number of hours missed due to illness. The estimated effect of coverage changes in good
match years on illness absences is 0.5% per week during flu season weeks. This is a relative
decrease of 19% from the average rate. The average number of absence hours is decreased by
2.7 hours per week during the flu season. Table 14 explores how this effect differs over age
and sex groups. The results here are similar to results for hospitalizations; younger age groups
(with the exception of teenagers) have a larger gain from coverage changes in good match
years, the middle age groups have small gains and the eldest age groups have, again, large
gains.
From Table 4 it is evident that teenagers had the lowest relative increase in
vaccination while other age groups had more significant increases. A similar pattern emerges
for worker absences. The relative decrease for those 60 to 64 is the largest at 75% fewer
absences relative to the average rate. Females also have the largest gains from coverage
changes in good match years; the estimate here is -0.012, which is a 28% decrease from the
average. The effect for males is quantitatively zero.
7 Conclusion
I show that changes in vaccination coverage leads to increased take-up of
vaccination. For the program change in Ontario, there was an average increase of 10% in the
vaccination rate for the targeted population in the post period and relative to other
provinces. For Quebec this number was 18% for the targeted population. I then show that
these changes in coverage had significant and negative effects on illness measures in years
when the vaccine was a more effective method of flu prevention.
19
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21
Table 1 - Summary of Data Sources
Data Source
Time period Frequency
Provinces
Flu Surveillance
Laboratory confirmed
influenza
Public Health Agency of Canada
1996 to 2006
Weekly
All
Strain detection and
subtypes of influenza
Public Health Agency of Canada
1996 to 2006
Yearly
All
Antigenic match with
vaccine
Canadian Communicable Disease Report*
1996 to 2006
Yearly
All
Hospitalizations
Hospital Morbidity Database,
Canadian Institute for Health Information
1996 to 2006
Weekly
Incomplete
data: Quebec
and rural
Manitoba
Worker Absence
Labour Force Survey,
Statistics Canada
1996 to 2006
Monthly
All
National Population Health Survey Cycle 2,
Statistics Canada
1996/1997
Yearly
All
Canadian Community Health Survey Cycle 1.1,
Statistics Canada
2000/2001
Yearly
All
Canadian Community Health Survey Cycle 2.1,
Statistics Canada
2003
Yearly
All
Canadian Community Health Survey Cycle 3.1,
Statistics Canada
2005
Yearly
All
Vaccination Status
Population
Populations Counts
Population and Demography,
Statistics Canada
1996 to 2006
Yearly
All
Number of Residents
in Residential Care
Facilities
Residential Care Facilities Survey,
Statistics Canada
1996 to 2006
Yearly
All
* Confirmed using data from the Center for Disease Control in the U.S. and World Health Organization
22
Table 2 - Average Weekly Laboratory Confirmed Flu and Vaccine Match
Season
Positive Tests (%)
All weeks
Flu Season
Average Tests per
week
Vaccine Match Rate
Match
Standard Error
1995/96
0.045
0.159
102
1.00
0.000
1996/97
0.062
0.164
111
1.00
0.000
1997/98
0.050
0.241
249
0.17
0.008
1998/99
0.054
0.161
220
1.00
0.000
1999/00
0.063
0.238
276
0.91
0.005
2000/01
0.054
0.220
284
1.00
0.000
2001/02
0.058
0.232
263
0.84
0.007
2002/03
0.051
0.207
174
1.00
0.000
2003/04
0.058
0.263
543
0.05
0.003
2004/05
0.068
0.242
453
0.42
0.004
2005/06
0.054
0.176
307
0.64
0.010
Notes: The Flu Season period is defined as all consecutive weeks with more than 5% of the season's total number of
positive tests.
23
Table 3 - Influenza Vaccination Rates by Group (%)
Pre
All
20.6
Observations
Sex
Male
Ontario
Post Change
41.8
21.3***
37,453 120,324
Quebec
Post Change
Pre
8.6
25.3
2,456
60,767
16.7***
Other Provinces
Pre
Post Change
17
29.3
12.2***
30,665 143,570
18.3
37.3
18.9***
7.6
21.8
14.2***
14.2
25.5
11.2***
Female
22.4
45.6
23.2***
9.3
28.1
18.8***
19.5
32.4
13***
Age
12 to 14
12.1
31.2
19.1***
8.4
6.9
-1.5
5.5
14.1
8.6***
15 to 19
18.0
29.4
11.3***
1.7
6.8
5.1**
6.8
13.8
7***
20 to 24
6.7
20.2
13.6***
0.6
8.2
7.6***
4.9
10.2
5.4***
25 to 29
6.8
21
14.2***
1.9
10.1
8.2***
5.3
11.3
6***
30 to 49
8.3
28.6
20.3***
4.1
13.6
9.5***
7.4
17.1
9.6***
50 to 59
18.0
45.1
27.1***
6.6
23.1
16.6***
15.4
29.1
13.7***
60 to 64
30.5
58.1
27.5***
7.2
42
34.8***
24.9
39.4
14.5***
Ages 65+
60.4
74.8
14.4***
34.2
60.3
26.1***
52.1
64.7
12.6***
11.5
29.4
17.9***
3.4
14.3
10.8***
8.9
17.4
8.6***
40.5
60.7
20.2***
20.7
44.2
23.5***
35.6
48.2
12.6***
Chronic Condition1
None
At least one
Education
Less than secondary graduation
28.9
46.3
17.4***
13.2
30.8
17.5***
22.8
31.9
9.1***
Secondary graduation
19.6
39.5
19.9***
7.3
22
14.6***
13.4
24.4
11***
At least some post-secondary
16.4
35.9
19.5***
5
17.8
12.8***
14.5
23.5
8.9***
Post secondary graduation
16.8
40.8
24***
5.9
23.2
17.3***
14.4
30.1
15.8***
29.1
48.3
19.2***
12.4
31.8
19.3***
24.6
34
9.4***
$30,000 to $49,999
18.2
45.9
27.6***
5.3
24.4
19.1***
13.6
31.3
17.6***
Above $50,000
25.2
29.6
18.7***
26.3
25.3
15.8***
25.2
25.8
16.1***
11.4
32.7
21.3***
4.3
15.8
11.5***
10.7
21.7
11***
10.3
28
17.7***
4.6
11.1
6.6***
7.8
14.6
6.8***
Household Income
Less than $30,000
Occupation
Business, Sciences, Health & Education
Sales & Service
8.3
24.7
16.4***
2.4
9.7
7.3***
6.9
12
5.1***
Labour Force Status
Full time worker
9.7
30.5
20.8***
3.4
15.3
11.9***
8.4
19.3
10.8***
Part time worker
12.8
36.3
23.5***
5.4
16.8
11.4***
9.8
21.4
11.6***
Not in Labour Force
33.0
50.4
17.4***
15.7
30.6
14.9***
31.2
36.6
5.3***
Student Status
Full time student
12.7
28.3
15.6***
3.1
7.7
4.7**
6.4
13.3
6.9***
Part time student
11.5
29.6
18.1***
1.6
19.4
17.8***
13
21.8
8.8***
Non-student
21.9
44.2
22.3***
9.5
28.1
18.6***
18.3
31.7
13.4***
40.0
42.9
16.2***
40.5
39.5
14.3***
39.9
40.5
14.5***
Households with kids
27.0
29.5
14.4***
26.8
27.3
14***
* p<0.10, ** p<0.05, *** p<0.001
Source: NPHS cycle 2, CCHS cycles 1.1, 2.1, 3.1
1 Asthma, Heart Diesease, High Blood Pressure, Diabetes, Cancer, Emphysema/Chronic Bronchitis
26.8
28.3
13.8***
Trades, Processing, Manufacturing
Household Type
Households with no kids
24
Table 4 - Relative Change in Influenza Vaccination for Ontario and Quebec versus Other Provinces2
Relative Change
Ontario vs. Other Prov.
Quebec vs. Other Prov.
All
9.0***
4.5***
Sex
Male
7.7***
3.0**
Female
10.2***
5.9***
Age
12 to 14
10.5***
-10.2**
15 to 19
4.4**
-1.9
20 to 24
8.2***
2.2
25 to 29
8.2***
2.2
30 to 49
10.6***
-0.1
50 to 59
13.4***
60 to 64
13.0***
20.3***
Ages 65+
1.8*
13.5***
Chronic Condition1
None
At least one
Education
Less than secondary graduation
Secondary graduation
At least some post-secondary
Post secondary graduation
Household Income
Less than $30,000
$30,000 to $49,999
Above $50,000
Occupation
Business, Sciences, Health & Education
2.8
9.3***
2.3**
7.6***
11.0***
8.3***
8.4***
8.9***
3.6
10.5***
3.8**
8.3***
1.5
9.8***
9.9***
10.0***
1.5
9.5***
-2.9
10.3***
0.5
10.9***
11.3***
-0.2
2.2
Labour Force Status
Full time worker
10.0***
1.1
Part time worker
11.9***
-0.3
Not in Labour Force
12.1***
9.5***
Sales & Service
Trades, Processing, Manufacturing
Student Status
Full time student
8.7***
-2.2
Part time student
9.3***
9.0*
Non-student
8.9***
5.3***
9.2***
4.3**
Household Type
Households with no kids
Households with kids
10.2***
* p<0.10, ** p<0.05, *** p<0.001
Source: NPHS cycle 2, CCHS cycles 1.1, 2.1, 3.1
1 Asthma, Heart Diesease, High Blood Pressure, Diabetes, Cancer, Emphysema/Chronic Bronchitis
2
Other provinces are all provinces excluding Ontario and Quebec
1.5
25
Table 5 - OLS Regression Results: Vaccination Status and Vaccination Programs in Ontario and Quebec
All
Change in Coverage: ON
Change in Coverage: QC
Season Effects
Province Effects
Age Effects
Second Level Interactions
Mean Vaccination Rate
R-Square
Observations
0.103***
(0.014)
0.176***
(0.023)
Yes
Yes
Yes
Yes
0.305
0.225
325,958
Self Rated Health
Excellent
Very Good
Good
Fair
Poor
0.055**
(0.020)
0.084***
(0.019)
Yes
Yes
Yes
Yes
0.218
0.15
70,062
0.090***
(0.021)
0.073**
(0.030)
Yes
Yes
Yes
Yes
0.265
0.199
120,740
0.111***
(0.016)
0.240***
(0.039)
Yes
Yes
Yes
Yes
0.339
0.234
92,545
0.135***
(0.023)
0.231***
(0.020)
Yes
Yes
Yes
Yes
0.473
0.224
32,312
0.056
(0.036)
0.434***
(0.070)
Yes
Yes
Yes
Yes
0.528
0.164
10,151
Note: Regressions also control for sex, education, income, student status, household type, presence of chronic conditions
and occupation (with non-workers entering as a separate category). Robust standard errors are given in parentheses and are
clustered by season-province-age cells. * p<0.10, ** p<0.05, *** p<0.001
26
Table 6 - OLS Regression Results: Weekly Laboratory Confirmed Flu, Vaccine Match and Vaccination Coverage Changes
All Weeks
Flu Season Weeks
Off Season
-0.014
-0.054
-0.009
(0.030)
(0.079)
(0.031)
Post*Match
-0.111**
-0.138
-0.042
(0.035)
(0.124)
(0.026)
Treat*Match
-0.001
-0.034
0.008
(0.030)
(0.063)
(0.033)
-0.033**
-0.029
-0.037**
(0.016)
(0.062)
(0.012)
0.057
0.203
0.209
0.192
0.023
0.078
-0.001
-0.073
0.002
(0.018)
(0.068)
(0.012)
-0.021
-0.066
-0.007
(0.013)
(0.046)
(0.007)
Post*Ontario
-0.017
-0.033
-0.021
(0.019)
(0.082)
(0.013)
Post*Quebec*Match
-0.082
-0.124
-0.061
(0.119)
(0.174)
(0.135)
0.067
0.074
0.057
(0.119)
(0.166)
(0.135)
-0.005
Panel A - Treated group is Quebec and Ontario
Post*Treat*Match
Post*Treat
Mean Flu Rate
Adj. R-Squared
Panel B - Treated group is separated by Quebec and Ontario
Post*Ontario*Match
Ontario*Match
Quebec*Match
Post*Quebec
Post*Match
Mean Flu Rate
Adj. R-Squared
0.010
0.027
(0.062)
(0.105)
(0.071)
-0.110**
-0.153
-0.040
(0.038)
(0.128)
(0.036)
0.057
0.204
0.209
0.191
0.023
0.080
Season Effects
Yes
Yes
Yes
Province Effects
Yes
Yes
Yes
Week Effects
Yes
Yes
Yes
Observations
5,140
956
4,184
Note: The laboratory confirmed flu rate is defined as the proportion of positive tests out of tests performed that week.
Regressions also control for population size and surveillance counts of other respiratory disease (Respiratory Syncytial Virus,
Parainfluenza, and Adenovirus). Robust standard errors are given in parentheses and are clustered by season-province cells. *
p<0.10, ** p<0.05, *** p<0.001
27
28
Table 7 - Regression Results: Weekly Hospitalization Rate for any diagnosis of Flu and Pneumonia
Season
Start
Weeks During
Season
Season
Peak
End
Off
Season
-6.739***
(1.893)
10.658
0.266
-5.433**
(2.189)
10.994
0.285
-7.325**
(2.921)
11.800
0.321
-6.314**
(2.163)
8.721
0.229
-0.340
(0.216)
1.098
0.193
Mean of Dependent Var.
Adj. R-Squared
1.034
(1.797)
25.232
0.726
-5.165
(4.150)
33.748
0.755
-5.039
(5.343)
35.327
0.774
-9.173
(6.212)
37.074
0.784
-2.379
(4.911)
30.242
0.74
0.863
(1.679)
23.256
0.747
Year Effects
Region Effects
Age Effects
Month Effects
Second Level Interactions
Observations
Yes
Yes
Yes
Yes
Yes
110,925
Yes
Yes
Yes
Yes
Yes
20,889
Yes
Yes
Yes
Yes
Yes
5,418
Yes
Yes
Yes
Yes
Yes
6,687
Yes
Yes
Yes
Yes
Yes
8,784
Yes
Yes
Yes
Yes
Yes
90,036
All Weeks
Flu Season Weeks
Hospitalizations with any diagnosis of flu
Post*Treat*Match
Mean of Dependent Var.
Adj. R-Squared
-0.936**
(0.423)
2.610
0.171
Hospitalizations with any diagnosis of pneumonia
Post*Treat*Match
Note: Hospitalization rates are per 100,000 population. Regressions also control for surveillance counts of other respiratory
disease (Respiratory Syncytial Virus, Parainfluenza, and Adenovirus) and coding classifications changes (ICD10 versus
ICD9). Robust standard errors are given in parentheses and are clustered by season-region-age cells. * p<0.10, ** p<0.05,
*** p<0.001
29
30
Yes
Yes
Yes
Yes
Yes
-0.356
(1.361)
14.482
0.615
110,925
1.034
(1.797)
25.232
0.726
110,925
Yes
Yes
Yes
Yes
Yes
-5.851**
(2.961)
20.360
0.661
20,889
-0.599**
(0.303)
1.705
0.140
110,925
-0.936**
(0.423)
2.610
0.171
110,925
-5.165
(4.150)
33.748
0.755
20,889
-4.625***
(1.307)
7.076
0.226
20,889
-6.739***
(1.893)
10.658
0.266
20,889
Yes
Yes
Yes
Yes
Yes
0.306
(0.536)
4.496
0.528
110,925
-0.301
(1.322)
5.881
0.598
20,889
-0.113**
(0.047)
0.161
0.030
110,925
-0.410
(0.269)
0.643
0.082
20,889
Yes
Yes
Yes
Yes
Yes
24.061
(45.039)
338.277
0.468
110,925
-44.704
(73.926)
438.526
0.557
20,889
-9.222*
(4.768)
25.111
0.064
110,925
-74.985***
(22.134)
106.109
0.144
20,889
Length of Stay
(Days/100,000)
Yes
Yes
Yes
Yes
Yes
0.329
(0.592)
10.626
0.100
93,960
1.257
(0.834)
9.954
0.128
18,263
1.431
(1.928)
7.015
0.043
21,175
-0.667
(1.448)
7.590
0.093
9,054
Average Length of Stay
(Days/Hospitalization)
Note: Hospitalization rates are per 100,000 population. Regressions also control for surveillance counts of other respiratory disease (Respiratory Syncytial Virus,
Parainfluenza, and Adenovirus) and coding classifications changes (ICD10 versus ICD9). Robust standard errors are given in parentheses and are clustered by season yearregion cells. * p<0.10, ** p<0.05, *** p<0.001
Year Effects
Region Effects
Age Effects
Month Effects
Second Level Interactions
Mean of Dependent Var.
Adj. R-Squared
Observations
Mean of Dependent Var.
Adj. R-Squared
Observations
All Weeks
Post*Treat*Match
Weeks During Flu Season
Post*Treat*Match
Hospitalizations with diagnosis of pneumonia
Mean of Dependent Var.
Adj. R-Squared
Observations
Mean of Dependent Var.
Adj. R-Squared
Observations
All Weeks
Post*Treat*Match
Weeks During Flu Season
Post*Treat*Match
Hospitalizations with diagnosis of flu
Table 8 - Regression Results: Weekly Hospitalization Rate for MRD, Death and Length of Stay
Most Responsible
Any Diagnosis
Any Diagnosis with Death
Diagnosis
31
Yes
Yes
No
Yes
Yes
-0.662
(0.453)
1.469
0.258
12,325
-6.720**
(2.030)
6.185
0.354
2,321
Yes
Yes
No
Yes
Yes
-0.192**
(0.086)
0.461
0.354
12,325
-1.677***
(0.389)
1.695
0.352
2,321
5 to 12
Yes
Yes
No
Yes
Yes
-0.048
(0.076)
0.421
0.411
12,325
-0.884**
(0.291)
1.047
0.47
2,321
13 to 18
Yes
Yes
No
Yes
Yes
0.052
(0.067)
0.299
0.474
12,325
-0.259
(0.266)
0.846
0.52
2,321
19 to 24
Yes
Yes
No
Yes
Yes
-0.030
(0.046)
0.339
0.404
12,325
-0.367*
(0.195)
1.068
0.467
2,321
25 to 49
Yes
Yes
No
Yes
Yes
-0.042
(0.092)
0.695
0.38
12,325
-1.142**
(0.470)
2.618
0.427
2,321
50 to 64
Yes
Yes
No
Yes
Yes
-0.771**
(0.285)
2.112
0.331
12,325
-4.779**
(1.486)
8.322
0.394
2,321
65 to 74
Yes
Yes
No
Yes
Yes
-1.518*
(0.819)
5.686
0.316
12,325
-10.704**
(4.228)
23.365
0.393
2,321
75 to 84
Yes
Yes
No
Yes
Yes
-3.306*
(1.779)
12.005
0.339
12,325
-26.964**
(9.601)
50.780
0.404
2,321
85 or Older
Yes
Yes
No
Yes
Yes
-0.224**
(0.097)
0.800
0.285
12,325
-2.196***
(0.565)
3.131
0.387
2,321
Male
Female
Yes
Yes
No
Yes
Yes
-0.349**
(0.141)
1.110
0.289
12,325
-2.758***
(0.724)
4.213
0.42
2,321
Sex
Note: Regressions also control for surveillance counts of other respiratory disease (Respiratory Syncytial Virus, Parainfluenza, and Adenovirus) and coding classifications
changes (ICD10 versus ICD9). Robust standard errors are given in parentheses and are clustered by season-region cells. * p<0.10, ** p<0.05, *** p<0.001
Year Effects
Region Effects
Age Effects
Month Effects
Second Level Interactions
Mean of Dependent Var.
Adj. R-Squared
Observations
All Weeks
Post*Treat*Match
Mean of Dependent Var.
Adj. R-Squared
Observations
Flu Season Weeks
Post*Treat*Match
Less Than 5
Age Group
Table 9 - Regression Results: Weekly Hospitalization Rate for any Diagnosis of Flu by Age Group and Sex
32
Yes
Yes
No
Yes
Yes
-1.982
(1.362)
17.439
0.569
12,325
-12.037**
(4.906)
25.729
0.58
2,321
Yes
Yes
No
Yes
Yes
-1.055***
(0.313)
3.246
0.336
12,325
-2.714**
(0.862)
3.982
0.343
2,321
5 to 12
Yes
Yes
No
Yes
Yes
-0.277
(0.178)
1.317
0.361
12,325
-1.057**
(0.394)
1.640
0.373
2,321
13 to 18
Yes
Yes
No
Yes
Yes
-0.463**
(0.207)
1.371
0.378
12,325
-1.523**
(0.504)
1.671
0.411
2,321
19 to 24
Yes
Yes
No
Yes
Yes
-0.244
(0.171)
2.375
0.372
12,325
-1.008**
(0.350)
2.938
0.369
2,321
25 to 49
Yes
Yes
No
Yes
Yes
0.243
(0.433)
7.664
0.353
12,325
-2.614**
(1.127)
9.738
0.41
2,321
50 to 64
Yes
Yes
No
Yes
Yes
-0.289
(1.193)
24.257
0.349
12,325
-6.402**
(3.165)
30.262
0.395
2,321
65 to 74
Yes
Yes
No
Yes
Yes
2.609
(2.379)
55.7
0.355
12,325
-14.382**
(6.607)
72.147
0.391
2,321
75 to 84
Yes
Yes
No
Yes
Yes
7.399
(5.578)
113.719
0.333
12,325
-5.092
(15.180)
155.626
0.303
2,321
85 or Older
Yes
Yes
No
Yes
Yes
-0.165
(0.376)
10.135
0.550
12,325
-3.609***
(1.083)
12.839
0.524
2,321
Male
Sex
Yes
Yes
No
Yes
Yes
0.161
(0.374)
8.681
0.518
12,325
-2.031**
(0.944)
11.568
0.474
2,321
Female
Note: Regressions also control for surveillance counts of other respiratory disease (Respiratory Syncytial Virus, Parainfluenza, and Adenovirus) and coding classifications
changes (ICD10 versus ICD9). Robust standard errors are given in parentheses and are clustered by season-region cells. * p<0.10, ** p<0.05, *** p<0.001
Year Effects
Region Effects
Age Effects
Month Effects
Second Level Interactions
Mean of Dependent Var.
Adj. R-Squared
Observations
All Weeks
Post*Treat*Match
Mean of Dependent Var.
Adj. R-Squared
Observations
Flu Season Weeks
Post*Treat*Match
Less Than 5
Age Group
Table 10 - Regression Results: Weekly Hospitalization Rate for any Diagnosis of Pneumonia by Age Group and Sex
Table 11 - Regression Results: Weekly Hospitalization Rate for Residents of Care Facilities
Hospitalization rate for flu
(per 100,000 residents)
Flu Season Weeks
All Weeks
Hospitalization rate for pneumonia
(per 100,000 residents)
Flu Season Weeks
All Weeks
Post*Treat*Match
-34.493
-7.365
-98.204
-35.047
(32.889)
(5.832)
(75.540)
(22.468)
Year Effects
Yes
Yes
Yes
Yes
Province Effects
Yes
Yes
Yes
Yes
Month Effects
Yes
Yes
Yes
Yes
Second Level Interactions
Yes
Yes
Yes
Yes
Mean of Dependent Var.
60.62
13.46
136.67
99.92
Adj. R-Squared
0.261
0.174
0.267
0.283
Observations
785
4,320
785
4,320
Note: Regressions also control for surveillance counts of other respiratory disease (Respiratory Syncytial Virus,
Parainfluenza, and Adenovirus) and coding classifications changes (ICD10 versus ICD9). Robust standard errors are
given in parentheses and are clustered by season-region cells. * p<0.10, ** p<0.05, *** p<0.001
33
34
1.018
(4.872)
70.759
0.781
110,925
Yes
Yes
Yes
Yes
Yes
-1.186
(5.424)
43.853
0.793
20,889
Yes
Yes
Yes
Yes
Yes
2.098
(3.442)
35.088
0.78
110,925
All Circulatory Disease
-16.515*
(8.902)
90.911
0.787
20,889
All Respiratory
All Weeks
All Weeks
0.133
(1.902)
28.886
0.7
110,925
Yes
Yes
Yes
Yes
Yes
-0.079
(0.393)
1.567
0.362
20,889
Yes
Yes
Yes
Yes
Yes
-0.103
(0.279)
1.360
0.343
110,925
Pulmonary Disease
-12.519**
(5.026)
52.698
0.723
20,889
Flu and Pneumonia
Flu Season
All Weeks
-0.401
(1.197)
7.977
0.496
110,925
Yes
Yes
Yes
Yes
Yes
-0.637
(1.114)
4.802
0.548
20,889
Yes
Yes
Yes
Yes
Yes
-0.343
(0.667)
3.961
0.52
110,925
Cerebrovascular Disease
-4.398*
(2.525)
12.594
0.598
20,889
Acute Respiratory
Flu Season
All Weeks
0.306
(3.284)
34.194
0.735
110,925
Yes
Yes
Yes
Yes
Yes
0.019
(0.609)
3.120
0.444
20,889
Yes
Yes
Yes
Yes
Yes
-0.310
(0.406)
2.857
0.426
110,925
Unintentional Accidents
-5.085
(4.804)
42.073
0.75
20,889
Chronic Respiratory
Flu Season
Note: Regressions also control for surveillance counts of other respiratory disease (Respiratory Syncytial Virus, Parainfluenza, and Adenovirus) and coding
classifications changes (ICD10 versus ICD9). Robust standard errors are given in parentheses and are clustered by season-region-age cells.
Year Effects
Region Effects
Age Effects
Month Effects
Second Level Interactions
Mean of Dependent Var.
Adj. R-Squared
Observations
Post*Treat*Match
Panel B - Other Diseases
Mean of Dependent Var.
Adj. R-Squared
Observations
Post*Treat*Match
Panel A - Respiratory Dieases
Flu Season
Table 12 - Regression Results: Weekly Hospitalization Rate by Disease
Table 13 - Regression Results: Absence for Own Illness
Absence
Post*Treat*Match
Year Effects
Province Effects
Age Effects
Second Level Interactions
Mean of Dependent Var.
Adj. R-Squared
Observations
Hours Absent
Flu Season Weeks
All Weeks
Flu Season Weeks
All Weeks
-0.005*
(0.003)
Yes
Yes
Yes
Yes
0.026
0.018
982,084
-0.001
(0.001)
Yes
Yes
Yes
Yes
0.025
0.018
5,306,445
-2.710*
(1.416)
Yes
Yes
Yes
Yes
9.714
0.019
837,870
-3.123**
(0.964)
Yes
Yes
Yes
Yes
9.933
0.020
4,526,798
Note: Regressions also control for surveillance counts of other respiratory disease (Respiratory Syncytial Virus,
Parainfluenza, and Adenovirus), Education, Marital Status, Sex, Occupation and Union Status. Robust standard errors
are given in parentheses and are clustered by season-province-age cells. * p<0.10, ** p<0.05, *** p<0.001
35
36
Yes
Yes
Yes
Yes
0.007**
(0.003)
0.021
0.006
272,371
0.014
(0.010)
0.023
0.011
37,658
Yes
Yes
Yes
Yes
-0.008*
(0.005)
0.036
0.023
478,349
-0.016**
(0.007)
0.037
0.024
82,569
22 to 26
Yes
Yes
Yes
Yes
-0.001
(0.001)
0.028
0.021
3,370,654
-0.004
(0.003)
0.030
0.021
609,026
27 to 49
Age Group
Yes
Yes
Yes
Yes
-0.001
(0.001)
0.013
0.003
1,015,864
-0.006**
(0.003)
0.014
0.003
183,360
50 to 59
Yes
Yes
Yes
Yes
0.001
(0.003)
0.011
0.004
169,207
-0.009
(0.007)
0.012
0.005
29,768
60 to 64
Yes
Yes
Yes
Yes
0.000
(0.001)
0.014
0.004
3,109,723
0.001
(0.003)
0.015
0.004
550,247
Male
Sex
Yes
Yes
Yes
Yes
-0.004
(0.002)
0.041
0.025
2,196,722
-0.012**
(0.004)
0.043
0.024
392,134
Female
Note: Regressions also control for surveillance counts of other respiratory disease (Respiratory Syncytial Virus, Parainfluenza, and Adenovirus), Education, Marital Status,
Sex, Occupation and Union Status. Robust standard errors are given in parentheses and are clustered by season-province-age cells. * p<0.10, ** p<0.05, *** p<0.001
Year Effects
Province Effects
Age Effects
Second Level Interactions
Mean of Dependent Var.
Adj. R-Squared
Observations
All Weeks
Post*Treat*Match
Mean of Dependent Var.
Adj. R-Squared
Observations
Flu Season Weeks
Post*Treat*Match
15 to 21
Table 14 - Regression Results: Absence for Own Illness by Age and Sex