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Influenza-related deaths - Available methods for estimating numbers and detecting patterns for seasonal and pandemic influenza in Europe

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Two methodologies are used for describing and estimating influenza-related mortality: Individual-based methods, which use death certification and laboratory diagnosis and predominately determine patterns and risk factors for mortality, and population-based methods, which use statistical and modelling techniques to estimate numbers of premature deaths. The total numbers of deaths generated from the two methods cannot be compared. The former are prone to underestimation, especially when identifying influenza-related deaths in older people. The latter are cruder and have to allow for confounding factors, notably other seasonal infections and climate effects. There is no routine system estimating overall European influenza-related premature mortality, apart from a pilot system EuroMOMO. It is not possible at present to estimate the overall influenza mortality due to the 2009 influenza pandemic in Europe, and the totals based on individual deaths are a minimum estimate. However, the pattern of mortality differed considerably between the 2009 pandemic in Europe and the interpandemic period 1970 to 2008, with pandemic deaths in 2009 occurring in younger and healthier persons. Common methods should be agreed to estimate influenza-related mortality at national level in Europe, and individual surveillance should be instituted for influenza-related deaths in key groups such as pregnant women and children.
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1www.eurosurveillance.org
R 
Inuenza-related deaths - available methods for
estimating numbers and detecting patterns for seasonal
and pandemic inuenza in Europe
A Nicoll (angus.nicoll@ecdc.europa.eu)1, B C Ciancio1, V Lopez Chavarrias1, K Mølbak2, R Pebody3, B Pedzinski4, P Penttinen1,
M van der Sande5 ,6, R Snacken1, M D Van Kerkhove7
1. European Centre for Disease Prevention and Control, Stockholm, Sweden
2. Statens Serum Institut, Copenhagen, Denmark
3. Health Protection Agency, Colindale, United Kingdom
4. Medical University of Białystok, Department of Public Health, Białystok, Poland
5. National Institute for Public Health and the Environment (RIVM), Centre for Infectious Disease Control Netherlands, Bilthoven,
the Netherlands
6. Julius Centre for Health Sciences and Primary Care, Utrecht University, Utrecht, the Netherlands
7. Medical Research Council Centre for Outbreak Analysis and Modelling, Imperial College London, London, United Kingdom
Citation style for this article:
Nicoll A , Ciancio BC, Lopez Chavarria s V, Møl bak K, Pebody R , Pedzinski B, Pen ttinen P, van der Sande M , Snacken R, Van Kerkhove MD. Influenza -related deaths -
available methods for es timating numbers and detec ting patterns for seasonal and p andemic influenza in Europe . Euro Sur veill. 2012;17(18):pii=20162. Available
online: http://www.eurosurveillance.org/ViewArticle.aspx?ArticleId= 20162
Article submi tted on 26 July 2011/ published on 3 May 2012
Two methodologies are used for describing and esti-
mating influenza-related mortality: Individual-based
methods, which use death certification and laboratory
diagnosis and predominately determine patterns and
risk factors for mortality, and population-based meth-
ods, which use statistical and modelling techniques to
estimate numbers of premature deaths. The total num-
bers of deaths generated from the two methods cannot
be compared. The former are prone to underestimation,
especially when identif ying influenza-related deaths
in older people. The latter are cruder and have to allow
for confounding factors, notably other seasonal infec-
tions and climate effects. There is no routine system
estimating overall European influenza-related prema-
ture mortality, apart from a pilot system EuroMOMO. It
is not possible at present to estimate the overall influ-
enza mortality due to the 2009 influenza pandemic in
Europe, and the totals based on individual deaths are
a minimum estimate. However, the pattern of mortal-
ity differed considerably between the 2009 pandemic
in Europe and the interpandemic period 1970 to 2008,
with pandemic deaths in 2009 occurring in younger
and healthier persons. Common methods should be
agreed to estimate influenza-related mortality at
national level in Europe, and individual surveillance
should be instituted for influenza-related deaths in
key groups such as pregnant women and children.
Introduction
The three influenza pandemics of the 20th century all
resulted in substantial premature mortality (referred to
as mortality in this review) which has been estimated
by various parameters (Table 1) [1,2].
Mortality rates during past pandemics have differed
considerably both between pandemics and within the
same pandemic [1,3,4]. For example, estimates for the
United States (US) varied from 30.5 premature deaths
per 105 population (1968 pandemic) through 53.4/105
(1957 pandemic) to 450.9/105 (1918 pandemic) com-
pared with an average of 16.9/105 for influenza A(H3N2)-
dominated seasons from 1979 to 2001 [4]. The pattern
of deaths (i.e. mortality rates by age, risk groups,
pathogenesis and disease presentation) probably also
differed between pandemics and seasonal epidemics,
but this is less well documented [5-8]. Viboud et al’s
analysis in 2010 estimated the mean ages of prema-
ture deaths during the 1918, 1957 and 1968 pandemics
as 27, 65 and, 62 years, respectively, and as 76 years
for seasonal influenza A(H3N2) from 1979 to 2001 [4].
Finally the annual mortality has differed between sea-
sonal epidemics [9-13]. All this variation is due to a
complex mix of factors of which some are real effects
on mortality, while others are related to the methodol-
ogies used to estimate mortality (Box 1). For example,
substantial variations in the estimates of influenza-
related premature mortality have been observed within
the same epidemic or pandemic depending on the data
sources, the analytic approach, and the geographi-
cal setting [14-21]. For these reasons, estimating the
extent of influenza-related mortality is complex.
The published rates of deaths for the 2009 pandemic
have varied nearly 70-fold from 0.02 to 1.46 per 105
population with a tendency to decline with the time
passed between the start of the pandemic and the esti-
mate, with more data being acquired and further analy-
ses undertaken [4, 13,14, 22-26]. There is no evidence
of changes in the virus that could be responsible for
this decline in the estimates [27].
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For policy formulation, simply counting numbers of
deaths attributable to influenza would be undesirable,
even if it were possible. Robust comparable mortal-
ity analyses for seasonal and pandemic influenza are
needed to determine risk groups, to guide and evalu-
ate distribution of resources, to communicate and
prepare the public and policy makers. These analy-
ses have to accommodate some of the complexities
mentioned above. The objectives of this review are
to summarise the methods for estimating seasonal
and pandemic influenza-related mortality, particularly
describing the systems in place in Europe, to document
and interpret the initial European mortality data for the
2009 pandemic, and to suggest how to develop bet-
ter approaches to influenza mortality surveillance and
estimates for Europe.
Methods for measuring influenza-
associated mortality
The history of estimating influenza-associated mortal-
ity is as old as formal death monitoring. William Farr
measured the impact of influenza in London in 1847 by
subtracting the number of deaths recorded in a rela-
tively influenza-free winter from the number recorded
during an epidemic season [28]. In the 20th and 21st
centuries, more sophisticated approaches to esti-
mate mortality were developed and applied, including
monitoring cause-specific mortality, statistical and
modelling approaches and incorporating virological
information into routine systems and special studies
[21,29,30] (Table 2).
In the United States (US) it is customary to monitor and
model trends in cause-coded death notifications due
to pneumonia and influenza or all respiratory, cardio-
vascular and cerebrovascular conditions, while moni-
toring all-cause mortality is generally the approach
in Europe. Since the 1957 pandemic, the US has had
a specific system in place using pneumonia and influ-
enza (P&I) death data from 122 US cities for estimating
influenza mortality [21,37,38]. Simpler approaches to
measure excess all-cause mortality have been applied
in at least eight European countries (Table 3) and else-
where [15,35,39-47]. In the following section we criti-
cally describe these various methods.
Methods based on individual death
certification or laboratory-confirmed cases
Using vital registration data and counting the number
of individuals who died with an influenza diagnosis
mentioned on their death certificate is straightforward
but it is also a method highly liable to result in under-
detection, especially for seasonal influenza and in
older people [50,51]. For example in England and Wales,
only 131 deaths were coded with an underlying cause
of influenza between 2005 and 2008 when statistical
techniques estimated there were over 12,700 prema-
ture influenza deaths [13,46,52]. In contrast, during
T 1
Definitions relating to influenza mortality
Parameter Definition Notes
Mortality impact Absolute numbers of deaths due to influenza
(seasonal or pandemic)
Needs to be converted to rates according to the population and
period of time.
Case fatality rate (CFR) Risk of death among those with clinical disease Often expressed as a percentage.
Infection fatality rate (IFR) Risk of death among those infected A measure using serolog y to estimate the number of infections.
Population fatality rate
(PFR)
Numbers of deaths due to influenza per unit
population Often expressed as per 100,000 resident population.
Years of potential life lost
(YPLL)
An estimate of the cumulative number of years
a person who died of inf luenza would have
lived against standard life expectancy
This is of ten expressed as a total for a population. An
alternative to death rates that gives more weight to deaths
occurring among younger people. It can be used as a measure
of the relative impact of various diseases and other lethal
forces on a population. Special care has to be taken when
applying this for influenza regarding deaths in people with
chronic conditions, many of whom would have shor ter than
standard life expectancy.
Premature mortality A death occurring earlier than it would have
done without the inter vention of influenza
Almost all influenza-related deaths are premature. However it
is important to emphasise this point with seasonal influenza
when many of the deaths are focused in older people and so
are less premature than they would be in younger people.
Influenza infection and
disease
Influenza is here defined as a laboratory-
confirmed human infection with an inf luenza
virus and influenza disease as the clinical
consequence
This should not be confused with influenza -like illness (ILI)
which has a European clinical case definition. A number
of other organisms and conditions can cause ILI. Equally,
influenza infection can be asymptomatic or cause symptoms
that do not meet the case definition or entirely dif ferent
symptoms.
Old and new seasonal
influenza
Old: the seasonal influenza circulating
between 1977 (when human inf luenza A(H1N1)
viruses re-emerged) and 2008
New: influenza circulating from 2010 onwards
It should not be assumed that the new (from 2010 onwards)
mix of seasonal viruses will have the same characteristics or
mortality as its predecessor.
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T 2
Methods of estimating influenza-related mortality
Method Description Limitations and biases Use in pandemic Use for seasonal
influenza Reference
1.
Vital registration data
Influenza mentioned on
death certificate.
Especially weak in
older people and
people with chronic
medical conditions; will
underestimate total.
Because of high prof ile
in pandemics may
become more sensitive
with increased testing
where facilities are
available.
High specificit y but can
be very insensitive; will
severely underestimate
total.
[13,21]
2.
Laboratory-confirmed
deaths
A death is only included
if there is laborator y
confirmation.
High specificit y but can
be very insensitive; will
always underestimate
totals, sometimes
severely.
Because of high prof ile
in pandemics may
become more sensitive
with increased testing;
but during intense
transmission there are
only clinical diagnoses
and so this approach
will lose sensitivity.
High specificit y but can
be very insensitive; will
severely underestimate
total.
[21, 31]
3.
Statistical and
modelling techniques
(see Table 4 for more
detail)
Estimates influenza-
attributed mortality
through comparing all-
cause or selected-cause
deaths during periods of
intense and no influenza
activitya; applies a
variet y of models
which may or may not
be streng thened by
surveillance data.
Without care can be
confounded by rises
in mortality due to
other causes; the best
approaches are further
informed by virological
surveillance and using
data on competing
causes (severe weather
and other infections).
Requires age-specific
analyses and often
cannot be applied until
a year or more af ter
the event; most often
used for predictions
or investigating
the likely ef fects of
interventions, but can
be used for estimations
(now-casting).
Without care results can
be confounded by rises
in mortality due to other
factor such as weather.
Method rarely used in
seasonal influenza in
Europe.
[21,32-35]
4.
Weighting deaths by
years of potential life
lost (YPLL)
Estimating and totalling
the numbers of years
of life that deaths
represent; can be
combined with other
methods such as 1-3.
Useful in comparing
impact of deaths
affecting dif ferent age-
groups; limitations are
difficulties in knowing
the life expectancies for
people with underlying
illness; does not allow
for disability and work
productivity; can be
especially difficult to
apply to estimated
numbers of deaths and
deaths from multiple
causes (influenza
and an underlying
condition).
Became more useful and
possible in the 2009
pandemic in Europe
because of more deaths
being diagnosed and
laboratory-confirmed
than in seasonal
influenza.
Can be ver y problematic
if the base is confirmed
deaths and only a few of
these are diagnosed.
Using
individual
deaths:
[36]; using
statistical
approach:
[4]
5.
Emerging infection
programme (US)
Community-based
surveys, notably the
US emerging infection
programme .
Especially helpful where
surveys are enduring
over years. May still
miss some cardiac and
cerebrovascular deaths
due to influenza.
More accurate than 1-3;
in the 2009 pandemic
with its young age
profile, missing cardiac
and cerebrovascular
deaths may be less
important.
More accurate method
than 1-3, but will miss
cardiac and circulatory
deaths; this has not
been applied in Europe
because considerable
financial investment
would be needed.
[29]
6.
Enhanced mor tality
analysis (US)
Laboratory-confirmed
deaths due to
pneumonia and
influenza from 122 US
cities.
Also used to
calculate Y PLL and
captures cardiac and
cerebrovascular deaths.
More accurate than 1-3
but may be subject to
biases from changes of
relationships during and
outside of pandemics.
More accurate than 1-3. [4]
US: United States.
a In the United States all age rapid mor tality monitoring systems usually only includes diagnoses for influenza and pneumonia or all
respiratory and circulator y diagnoses
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T 3
European assessments of the mortality burden due to seasonal influenza until 2009
Country Period
of study Method
Results (mortality per 100,000 population in a year or season) Investigated factors
unrelated to influenza aReference
Average Highest Lowest Other findings
Czech
Republic
1982-
2000
Statistical modelling for excess all-cause and
cardiovascular mortality in association with
surveillance of acute respiratory infections
26/105 all-cause
deaths and 17/105
cardiovascular
deaths
60. 4/105
(1995/9 6)
No
detectable
deaths
(negative
values)
Highest mortality
associated with inf luenza
A(H3N2) epidemics
Mentioned but
dismissed effects of low
temperatures; RSV not
investigated
[39]
Germany 1985-
2001
Time series analyses and cyclical regression
applied to time series data looking for
excess all-cause mortality in association
with influenza epidemics (virological and
syndromic data)
8.4 to 17/105
(depending on
assumptions)
40.5/105
(1995/9 6) 4 .5/105
Highest mortality
associated with inf luenza
A(H3N2)
Not investigated [40]
Italy 1969-
2001
Estimated excess deaths due to pneumonia
and influenza and deaths from causes
associated with inf luenza (cardio and
cerebrovascular disease including during the
pandemic winter of 1969/70
3/105 (range
0–38) for
pneumonia and
influenza and
18/105 for all
causes (range
0–107).
Influenza seasons
with higher excess
deaths tended to
be characterised by
a predominance of
influenza A(H3N2)
viruses.
Comparisons between
the seasonal epidemics
and the pandemic in
1969/70 showed that
in the pandemic, more
deaths occur red in those
under 70 years of age.
[15]
The
Nether-
lands
1967- 89
1970-89 Poisson regression analysis
8.4/105
By age group: >60
years: 82/105
>70 years: 143/105
>80 years:
280/105
In >60
year-olds:
44.1/105
(1972/73)
In >60
year-olds:
1.8/105
(1987/88)
Age distribution of
estimated deaths was
5%, 12%, 29% and 54%
in persons <60, 60-69,
70-79 and ≥80 years-old,
respectively
Not investigated [41, 42]
Norway 1975-
2004
Poisson regression analysis applied to time
series data looking for excess all-cause
mortality in association with influenza
epidemics (virological and syndromic data)
21. 2/10 5 41.5/105
(1993/9 4)
5.3 /105
(1976/77)
Highest mortality in
those 65 years and older
with some excess also in
under five year-olds
Temperature had little
effect on influenza
estimates; RSV not
investigated
[43]
Portugal 2008-09 Cyclical regression model 18.5/105 (2008/09 only) 2008-09 was an
influenza A(H3N2 season)
Ambient temperature
as a variable made
little difference to the
estimates
[44]
Swit zer-
land 1969-85
Regression model applied to time series
data looking for excess all-cause mor tality in
association with epidemics of influenza using
Fourier and Autoregregressive integrated
moving average (ARIMA) models modelling
271.6/105 excess mortality risk during
influenza epidemics in the 70-89 year-olds
was 1.7/105 1–59 year-olds
[45]
UK
(England) 2004-09
Statistical model based on the Serfling
method to establish a baseline of the
expected weekly number of registered deaths;
if the obser ved number is above the upper
limit of a 90% confidence interval around this
expected number for at least one week, an
excess is said to have occurred.
8.8/105 21.1/10.5
(20 08/09)
No
detectable
excess
deaths
Weather ef fects not
included [46]
UK
(England
and
Wales)
1975-79
Regression model applied to time series
data looking for excess all-cause mor tality in
association with influenza epidemics
19.0/105 4 4.9/105
(1975/76)
8.19/105
(1976/77)
88% estimated of deaths
in those 65 years and
older in 1975
All influenza A(H3N2)
Seasons, controlled for
air temperature
[35]
UK: United Kingdom.
a ambient temperature and other respiratory viruses such as respirator y syncytial virus (RSV )
Source of Population Denominators: Eurostat total population data accessed June 2009 [48] and for England and
Wales Office for National Statistics [49].
5www.eurosurveillance.org
the 2010/11 influenza season, there were over 600
deaths for which influenza was laboratory-confirmed
and appeared on the death certificate [46]. However,
interpretation of death certification data has to take
into account the various ways how influenza infection
results in premature death, how influenza infections
are diagnosed and hence how it may be suspected or
missed by clinicians (Box 1).
Some influenza deaths are due to primary viral infec-
tions, and in the 2009 pandemic, deaths were often
associated with acute respiratory distress syndrome
(ARDS), an exceedingly rare presentation of seasonal
influenza [7,53-55]. But seasonal influenza can often
result in secondary bacterial infections, which are dan-
gerous in the very young, in people older than 65 years
and those with chronic underlying conditions [56-58].
Influenza also precipitates death from cardiac and cer-
ebrovascular complications, usually in those with pre-
existing underlying medical conditions [59]. Similar
under-ascertainment can occur for laboratory diagno-
ses: Influenza infection is confirmed by the detection
of the virus or its antigens. However, the period of viral
shedding is usually short and frequently missed, espe-
cially by the time complications make the patient seek
care. Hence a preceding influenza infection causing the
complication is often unsuspected, or test-negative if a
swab is taken [58].
During a pandemic, awareness of influenza is higher
and diagnostic tests are more likely to be conducted,
if they are available. But as the predictive value of
clinical syndromes rises, clinicians are discouraged,
or choose not to, take diagnostic specimens. Hence
influenza cases may not be confirmed even when com-
plications ensue [60].The magnitude of missed influ-
enza cases and hence misdiagnosed deaths is hard to
determine and will vary from country to country and
over time [13,52]. This is also true for seasonal influ-
enza. In the Netherlands for example, it was estimated
that for every death registered in the period 1967–89
as due to seasonal influenza there were another 2.6
unrecognised influenza deaths [41]. While in a study
in Denmark during the 2009 pandemic that compared
laboratory-confirmed deaths with those estimated
from a regression model suggested a ratio of 10 deaths
for every one confirmed death [61]. It is likely that there
was less under-identification in death certification
and laboratory diagnosis during the 2009 pandemic
than for seasonal influenza in industrialised countries
because awareness of influenza among clinicians was
high, testing more readily available and more countries
used or developed enhanced surveillance systems
13,60]. There are some indications that since the 2009
pandemic, influenza diagnostic tests have been more
widely available and used in hospitals. This, in combi-
nation with pandemic patients typically being younger
than those dying from seasonal influenza, will prob-
ably result in influenza appearing more frequently on
death certificates [46,62].
A policy of reporting laboratory-confirmed deaths was
adopted early on in the 2009 pandemic in European
countries [8]. This resulted in high specificity and
quality, but low sensitivity, of data on risk factors.
This approach tends to miss influenza deaths espe-
cially in older people and those in whom influenza is
the trigger for a severe illness of a non-specific nature
(cerebrovascular and cardiovascular deaths) [59]. This
age effect may have been less important in the 2009
pandemic because older age groups had some pre-
existing immunity and were less likely to be infected
with the pandemic virus [63]. Also, since the criteria for
using laboratory tests changed as the 2009 pandemic
progressed, estimates relying on laboratory confirma-
tion represent minimum totals, in particular for periods
of intense transmission when a smaller proportion of
clinical cases were being tested [60].
Some countries, for example the US and Australia,
have special reporting systems developed for par-
ticular groups, notably children, to inform decisions
on vaccination policies. Such routine systems are
not found in Europe. Laboratory-confirmed influenza
deaths in children have been notifiable in the US since
the 2004/05 influenza season. This proved especially
helpful in contrasting the impact of seasonal influenza
epidemics with the 2009 pandemic [64]. Similarly, the
Australian Paediatric Surveillance Unit resumed winter
surveillance for any severe complication of influenza in
children during the pandemic [65].
Statistical and modelling approaches
Statistical and modelling approaches (Table 4) analyse
death data from vital registries, looking at multiple
codes that are expected to capture influenza-related
deaths, i.e. pneumonia and influenza or all conditions
coded as respiratory or cardiovascular [66,67]. There
are trends in clinicians’ preference for diagnosis and
B 1
Factors inf luencing observed inf luenza-related mortality
Factors leading to real dif ferences in influenza-associated
mortality
• Characteristics of the virus: virulence and transmissibility;
• Characteristics of the populations affected: demographics,
access to healthcare, health seeking behaviour, social
and economic circumstances, prevalence of risk factors;
• Levels of pre-existing immunit y in the population (due to
e.g. innate immunity, previous exposure to influenza
viruses, vaccination, genetic susceptibility);
• Prevalence of complicating co-infections and underlying
medical conditions in the affected populations.
Factors related to diagnosis and reporting of individual cases
• Different case definitions and methods of ascertainment;
• Different mortality reporting systems;
• Different routine and enhanced surveillance systems
established in pandemics;
• Changing awareness of clinicians and their testing
practices;
• Availability and quality of testing, testing policies;
• Different disease presentations.
6www.eurosurveillance.org
death classification, with influenza diagnosis being
more likely when epidemics are prominent while they
would at other times be classified as due to pneumonia
[13]. Authorities in the US look for surges in the com-
bined number of deaths due to influenza or pneumonia
as a percentage of all deaths, at the same time as labo-
ratory reports of influenza rise. However there will still
be misclassification when identifying absolute num-
bers of respiratory deaths since even in a pandemic not
all pneumonias are due to influenza and obviously car-
diac and vascular deaths will be missed. The latter was
probably less important in the 2009 pandemic with
the protective cross-immunity in older people among
whom cardiac and vascular deaths are most important
[59,63,68]. In Europe the preference has been to use
trends in all-cause mortality. Often deaths are con-
sidered by age group. The trends are then examined
using a range of statistical and modelling techniques
to look for excess deaths in association with influenza
epidemics and pandemics (Table 4) [9,32,33,37,69-74].
Various other modelling techniques have been used
(Table 4), including the original Serfling method to
develop a baseline and detect variations from that
[37,71]. More sophisticated multivariate regression
models have been employed to calculate the mortality
during periods of influenza activity in a given popula-
tion controlling for potential confounders (e.g. weather
or other circulating respiratory viruses), and estimate
the excess compared with the expected mortality in
the same period based on historical data (Tables 2 and
4). These models have used different death end points
ranging from all-cause, cardiac and respiratory to pneu-
monia and influenza. Each method has its advantages
and disadvantages (see Table 2 and 4). Methods that
include competing causes of deaths (confounders) are
preferable as they avoid overestimation of the attrib-
uted mortality. Excess mortality is then calculated with
confidence intervals for pneumonia and influenza or
for respiratory and circulatory causes or for all causes
[10]. Extrapolation from the US data to Europe was the
basis for estimate from the European Centre for Disease
Prevention and Control (ECDC) of influenza-attributable
deaths in seasonal influenza (1977/78 to 2008/09) of
up to 38,500 per year in the countries of the European
Union and European Free Trade Association in recent
years [10,75].
All-cause mortality attributable to influenza has been
estimated in at least eight European countries (Table
3), sometimes with age-specific results [41]. However
there are no routinely published outputs like those
in the weekly influenza surveillance report FluView
in the US [31,76] and therefore it is not possible to
state a European normal seasonal influenza range.
Estimating all cause mortality is also insensitive, as
large numbers of influenza deaths need to take place
before excess mortality is detectable across all age
groups [13]. Hence paradoxically in a mild influenza
season the best national estimate may appear as no
excess of deaths due to influenza, when at the same
time there are influenza related deaths that appear in
death certificates [13,46]. There is, however, the dan-
ger of overestimating deaths attributable to influenza
when important confounders are not considered such
as other respiratory infections (notably respiratory
syncytial virus) and ambient temperature.
T 4
Statistical and modelling methods of estimating influenza-related mortality
Method
Inclusion of
virological
surveillance data
Advantages Disadvantages References
Peri- and summer
season rate difference
models
No
Simple; can be undertaken with less
than five years worth of data;Does
not need virological data on t ype
and subtype; cannot be used where
seasonality of influenza is not clearly
known (aequatorial areas).
Tend to produce inf lated estimates
when compared to other methods;
Cannot be used to estimate
differences in viral type and subt ype;
Other seasonal factors are hard to
control for.
[67,68 ]
Serf ling least squares
cyclical regression
model
No
Does not need virological data on
type and subtype; cannot be used
where seasonalit y of influenza is not
clearly known (aequatorial areas).
Simple in comparison to other
regression approaches.
Cannot easily allow for other
variables such as other infections
(notably with respiratory syncytial
virus (RSV), severe winters etc.
[8,37,6 9]
Serfling-Poisson
regression model Yes
Produces estimates on virus type and
subtype; can allow for other variables
such as other infections (notably
RSV), severe winters etc.
Needs a number of years of data;
Needs a number of years of
virological data.
[33,70,71]
Autoregregressive
integrated moving
average (ARIM A)
models
No Easy to update as more information is
collected.
Complicated and can be difficult to
use; Provide few advantages over the
more simple linear models.
[32,65,7 2]
7www.eurosurveillance.org
Methods of measuring mortality
during a pandemic
Classical statistical approaches using historical influ-
enza data may not readily be applied for pandem-
ics because pandemic influenza activity often occurs
outside of the traditional influenza seasons and
baselines are hard to determine. More reliable data
may only become available some time after the event
and are subject to reanalysis even many years later
[4,16,19]. Capturing mortality is particularly difficult
in a pandemic, such as during the 2009 pandemic,
which caused a relatively small number of deaths. A
more sensitive approach is to look for age group-spe-
cific effects in younger people in whom background
deaths are less frequent than in the elderly so that
modest influenza-related signals may be detectable
[77]. Another approach is age-specific regression mod-
elling. Previously this has only been undertaken in
individual European countries. Combining data from
different EU countries and looking at age-specific
excess mortality is more sensitive. This is the cur-
rent approach used in the pilot European Mortality
Monitoring Project (EuroMOMO). EuroMOMO found
that overall all-cause mortality in the 2009 pandemic
was within the expected range for seasonal influenza,
but there was a short-term but significant increase
in child mortality in the age-group of 5–14 year-olds
[77]. A similar excess of deaths in children has been
observed through regression modelling and enhanced
surveillance and in the UK [78,79]. The latter indicated
that many of the deaths were in children with underly-
ing conditions. In addition, a disproportionate number
of excess deaths was observed in certain ethnic minor-
ity groups [77]. The EuroMOMO and the UK approaches
have an advantage over the US system in that they
provide a measure of population impact almost in real
time, and that sustained changes in mortality can be
expressed as a proportion of the expected number of
deaths. Individual case surveillance provides essential
information on the epidemiological characteristics of
the fatal cases which allows for the determination of
risk factors and estimates of years of potential life lost
(YPLL) [80]).
Another approach developed for pandemic planning is
to use predictive modelling, producing projections or
forecasts as ranges of deaths. This is useful for plan-
ning purposes, but is especially vulnerable to uncer-
tainty since these projections are usually based on
assumptions of the epidemiologic characteristics of
the virus gathered early in the pandemic or based on
the characteristics of past pandemic viruses. These
estimates are usually based on reasonable worst case
scenario assumptions (i.e. on a severe pandemic, but
one that countries can with preparation still cope with),
and as such tend to produce a range of estimates for
cases and deaths that are high in their upper bounds
[81]. This can easily confuse the general public as it
may be seen as a prediction for a pandemic. Hence,
mortality estimates generated using a worst case sce-
nario must be presented very carefully to policy makers
and the media who can seize on and misinterpret upper
estimates [82]. Accuracy in case and death estimates
greatly increases as more robust surveillance data
become available and are incorporated into the mod-
els [34]. Such revised estimates of possible numbers of
deaths, based on updated epidemiological and virolog-
ical data, have been called ‘now-casting’ [34,83,84].
Potentially the most accurate method for estimating
pandemic influenza-related mortality is using pre-
existing population-based surveillance to estimate
the absolute number of influenza-related deaths or
to detect excess premature mortality associated with
epidemics or pandemics. This has been done through
the Emerging Infections Program of the US Centers for
Disease Control and Prevention (CDC) which collects
exhaustive hospital-based surveillance data in spe-
cific geographical areas [29,85]. This allowed the US
CDC to estimate the number of influenza deaths by age
group, deriving an all-age estimated range for the US
in the first 12 months of the 2009 pandemic of between
8,870 and 18,300 deaths with a central estimate of
12,470, which is equivalent to a population rate of
4.14/105. These numbers compare with 2,125 reported
confirmed deaths (population rate 0.69/105) [85]. From
this it was possible to determine multipliers for the
US that could be applied in that country to its all age
or paediatric reported deaths to estimate true excess
deaths [29,85]. However the US multipliers must not be
applied in other countries.
A related US approach for estimating deaths caused by
the pandemic applies the relationship seen between
seasonal influenza and deaths coded as due to pneu-
monia and influenza and applying the observed age-
group distribution seen in virologically confirmed
deaths. This has been extended to calculate estimates
of deaths and YPLL using pneumonia and influenza
excess deaths as the lower bound and all-cause excess
deaths as the upper [4]. The YPLL approach incorpo-
rates important qualitative aspects of deaths in young
people in the 2009 pandemic and allows for more accu-
rate comparisons with seasonal influenza. To date in
Europe, only the Netherlands has published YPLL fig-
ures for confirmed 2009 pandemic deaths, estimating
that the minimum YPLL were similar to those from sea-
sonal influenza [36]. There are, however, difficulties
with the YPLL approach since individuals with chronic
conditions who die from influenza often have a shorter
expected life span and attributing their years of life
lost entirely to influenza will result in an overestima-
tion [11,80]. It is also possible that for the very elderly
and very ill, influenza infection only brings forward
death by a few weeks or months.
Deaths due to the 2009 pandemic
recorded on national websites
versus deaths reported to ECDC
Aside from the EuroMOMO project, there was no rou-
tine European system for monitoring mortality dur-
ing the 2009 pandemic using statistical or modelling
8www.eurosurveillance.org
rate of 0.56/105 with national rates varying eight- to
nine-fold from 0.18 to 1.51/105 [89]. National totals cited
will have changed somewhat since April 2010 due to
late reporting and data improvement. The official num-
ber of deaths reported to ECDC and WHO was lower.
This was due to a few large countries hardly reporting
any deaths (Figure). With the exception of age, com-
paring population rates of reported deaths yielded
no obvious patterns [88]. It is likely that much of the
differences in patterns are reflected by differences in
diagnosis and reporting between countries. The age
pattern of the cases reported was strikingly different
from that observed with the previously circulating sea-
sonal influenza (Table 5) [61,67]. Pandemic deaths were
more often in children and young adults. Approximately
20% of deaths were in people over 65 years of age
compared with the usual figure of around 90% for sea-
sonal influenza deaths [33,90,91]. This likely reflects
the underlying pre-existing immunity in the older
sections of the population due to exposure to earlier
similar influenza A(H1N1) viruses, which reduced their
risk of infection and death [63,68]. However, elderly
persons who were infected, had a significantly higher
risk of dying than younger persons [88,92]. A number
of national and international studies using individual
data added important details, notably concerning the
risk factors for deaths [79,91,93,94]. While these have
confirmed that chronic underlying disease was a risk
factor in adults and children, they found that between
18% and 30% of the deaths were in people without any
techniques [86]. Surveillance of individual severe influ-
enza illness and influenza-related deaths was insti-
tuted for Europe and globally after the 2009 pandemic
virus was first detected in North America [8]. Reports
from EU Member States were published in the ECDC
Weekly Influenza Surveillance Over view and reports
from the World Health Organization (WHO) [8,87,88]. In
addition active epidemic intelligence was undertaken
by ECDC, monitoring official websites of ministries of
health or other national authorities to collect informa-
tion on fatal cases [8,88]. Data collected from websites
were validated via the Early Warning and Response
System (EWRS) where EU/EEA Member States reported
additional information on fatal cases. The first fatal
cases in Europe were reported in June 2009 during
the spring/summer wave of the pandemic in the UK
[8]. Through the 2009 summer, 10 to 25 deaths were
announced weekly in the EU/EAA, with an increase in
numbers around week 43 (week beginning 19 October)
and continued to increase until week 50 (week begin-
ning 7 December) when the total peaked at over 300
deaths/week. The Figure illustrates how the differ-
ences between the announced versus reported pan-
demic deaths were principally due to some countries
reporting to ECDC and WHO only a small number of
the cases they had announced on websites (names of
countries have been removed).
The ECDC ceased active monitoring of websites in
April 2010. By then the 30 EU/EFTA Member States had
announced a total of 2,900 fatal cases, a population
34
244
19
26
44
23
18
3
122
181
56
5
134
19
62
29
21
2
8
102
40
474
254
40
122
29
344
33
141
271
34
1
19
26
44
23
0
3
0
148
56
5
134
0
62
24
19
2
0
102
40
296
255
0
122
29
312
0
149
4
0
50
100
150
200
250
300
350
400
450
500
No of deaths
Announced
Reported to TESSy
F
Cumulative confirmed fatalities due to influenza A(H1N1)pdm09, announced (n=2,900) versus reported (n=1,890), by
countrya, 15 April 2009–10 May 2010
a Each space on the x-axis represents an EU/EFTA Member State. The order has been
randomised so as not to follow alphabetical order.
9www.eurosurveillance.org
reflect national variation in diagnosis, testing, test
availability, awareness in clinicians, and access to
care. It would be interesting to investigate the reasons
for different death rates within the EU, since the pan-
demic virus did not change. The modelling approach
in Denmark has cautiously derived an estimate of
312 influenza deaths, whereas only 30 laboratory-
confirmed deaths were observed. Hence Denmark has
a multiplier of 10 and an estimated true death rate of
up to 5.7 per 100,000 population [61]. While it is likely
that many deaths were unreported, the magnitude of
the underestimate almost certainly differs by country.
There are likely to be unidentified pandemic deaths in
older adults but they cannot be many or there would
have been excesses in observed all-cause or older age
mortality.
chronic health condition [24,79,90,93,94]. A UK study
examined ethnic group effects and found that children
of southern Asian origin were at higher risk of death
than white children, a finding replicated for hospitali-
sations but not for perinatal mortality [79,95,96].
Interpretation of European 2009
pandemic mortality data
The 2,900 laboratory-confirmed deaths attributed to
the 2009 A(H1N1) pandemic reported by EU Member
States are a minimum number and a considerable
underestimate of the true mortality [87,88]. Given the
very different crude population death rates announced
by different countries it is likely that the multipliers to
estimate a more accurate figure of premature deaths
differ from country to country and no single multiplier
should be applied [89]. Differences in rates probably
T 5
Differences in the patterns of mortality during influenza seasons 2000/01 to 2008/09 and the 2009 inf luenza pandemic
Seasonal influenza 2000/01 to 2008/09 2009 pandemic influenza
Intensity of diagnostic
testing
Compared to the pandemic there was
less testing for influenza
More intensive testing than during seasonal epidemics, although to
varying extent between countries and over the period of the pandemic
When deaths occurred
In season - mostly starting after
Christmas in recent years, may have
coincided with extreme weather
Started out of season with a spring/summer wave, then
an early autumn/winter wave in Europe
Experiencing severe
disease
Those in clinical risk groups
and older people
Young children, pregnant women and those in clinical risk groups.
About 30% with severe disease were outside risk groups.
Many born before the mid-1950s were immune,
but those not experienced severe disease.
Premature deaths Around 90% are considered to occur in
people 65 years or older
In laboratory-conf irmed reported deaths around
80% were under 65 years-old
Increase in all-cause deaths in children detected across eight EU
countries by EuroMOMO system
Mortality and years of
potential life lost ( YPLL)
Few confirmed deaths reported each
year in official statistics
Estimated using statistical methods to be
up to 38,500 on average in the EU
Substantial numbers of confirmed deaths announced by EU/EFTA
Member States (n=2,900) but recognised to be an underestimate
Not estimated in any EU Member State but estimated in the US
Acute respiratory
distress syndrome Extremely rare
Uncommon but has been recorded in many
countries, even in young fit adults
Partially explained by the tropism of the pandemic virus for epithelial
receptors that predominate in the lung alveoli while the previous
seasonal viruses bind best to receptors found predominately in the
upper airways
Pathological f indings
Viral pneumonia rare, but
secondar y bacterial infections
more common in fatal cases
Fatal viral pneumonias relatively common with alveolar lining cells,
including type I and t ype II pneumocytes the primar y infected cells
More than 25% of fatalities also had bacterial infections
ECDC: European Centre for Disease Prevention and Control; EF TA: European Free Trade Association; EU: European Union; EuroMOMO: European
Mortality Monitoring Project; US: United States.
10 www.eurosurveillance.org
reporting in order to identify risk groups [102]. They
further agreed that YPLL should be estimated as well
as death totals, although such calculations need to
allow for differing life expectancy in those with and
without chronic conditions. In addition influenza infec-
tions should be suspected more readily as a potential
diagnosis and more diagnostic tests should be used
in hospitals. That will allow systematic investigation
of the patterns of influenza-related premature deaths
and their risk factors as these can indicate how these
deaths and severe cases can best be prevented. This
will require individual reporting of deaths particularly
for key groups for whom vaccination and early treat-
ment policy is uncertain, such as children, pregnant
women and young healthy adults.
References
1. Monto AS, Comanor L, Shay DK, Thompson W W. Epidemiology
of pandemic influenza: use of surveillance and modeling for
pandemic preparedness. J Infectious Dis. 2006;194 Suppl
2:S9 2-7.
2. Kilbourne ED. Inf luenza pandemics of the 20th century. Emerg
Infectious Dis. 2006;12(1):9 -14.
3. Henderson DA, Courtney B, Inglesby T V, Toner E, Nuzzo JB.
Public health and medical responses to the 1957-58 inf luenza
pandemic. Biosecur Bioterror. 2009;7(3):265-73.
4. Viboud C, Miller M, Olson D, Osterholm M, Simonsen L.
Preliminary Estimates of Mortality and Years of Life Lost
Associated with the 2009 A/H1N1 Pandemic in the US
and Comparison with Past Influenza Seasons. PLoS Curr.
2010:RRN1153.
5. Viboud C, Boelle PY, Pakdaman K, Carrat F, Valleron AJ,
Flahault A. Inf luenza epidemics in the United States, France,
and Australia, 1972-1997. Emerg Infect Dis. 2004;10(1):32-9.
6. Simonsen L, Clarke MJ, Schonberger LB, Arden NH, Cox NJ,
Fukuda K. Pandemic versus epidemic influenza mor talit y:
a pattern of changing age distribution. J Infect Dis.
1998;178(1):53-60.
7. Shieh WJ, Blau DM, Denison AM, Deleon-Carnes M, Adem P,
Bhatnager J, et al. 2009 Pandemic influenza A(H1N1) Pathology
and pathogenesis of 100 fatal cases in the United States. Am J
Pathol. 2010;177(1):166-75.
8. European Centre for Disease Prevention and Control (ECDC).
The 2009 A(H1N1) pandemic in Europe - A review of the
experience. Stockholm: ECDC; 2010. Available from: http://
www.ecdc.europa.eu/en/publications/Publications/101108_
SPR_ pandemic_experience.pdf .
9. Simonsen L , Clarke MJ, Williamson GD, Stroup DF, Arden
NH, Schonberger LB. The impact of inf luenza epidemics on
mortality: introducing a severity index. Am J Public Health.
1997;87(12):1944-50.
10. Centers for Disease Control and Prevention (CDC). Estimates of
deaths associated with seasonal influenza --- United States,
1976-2007. MMWR Morb Mortal Wkly Rep. 2010;59(33):1057-
62. MMWR Morb Mortal Wkly Rep.
11. Eickhoff TC, Sherman IL, Serfling RE. Obser vations on excess
mortality associated with epidemic influenza. Md State Med J.
1962;11:104-110.
12. Sprenger MJ, Van Naelten MA , Mulder PG, Masurel N. Influenza
mortality and excess deaths in the elderly, 1967-82. Epidemiol
Infect. 1989;103(3):633-41.
13. Donaldson LJ, Rutter PD, Ellis BM, Greaves FE, My tton OT,
Pebody RG, et al. English mor tality from A/H1N1. Comparisons
with recent flu mor tality. BMJ. 2010;340:c612.
14. Garske T, Legrand J, Donnelly CA, Ward H, Cauchemez S, Fraser
C, et al. A ssessing the severity of the novel influenza A/H1N1
pandemic. BMJ. 2009;339:b2840.
15. Rizzo C, Bella A, Viboud C, Simonsen L , Miller MA, Rota MC,
et al. Trends for influenza-related deaths during pandemic
and epidemic seasons, Italy, 1969-2001. Emerg Infect Dis.
2007;13( 5):694-9.
16. Johnson NP, Mueller J. Updating the accounts: global mortality
of the 1918-1920 “Spanish” influenza pandemic. Bull Hist Med.
2002 Spring;76(1):105-15.
17. Dauer CC. Mor tality in the 1957-58 influenza epidemic. Public
Health Rep. 1958;73(9):803-10.
There is more certainty in the characteristics of the
fatalities. In contrast to seasonal influenza, global
deaths from the 2009 pandemic occurred more often
among children and young adults, and a substan-
tial proportion of fatal cases did not have underlying
chronic health conditions (Table 5). As in the 1918 and
1957 pandemics, the 2009 influenza A(H1N1) pandemic
affected mainly younger members of society with many
but not all older people (born before 1960) possessing
some levels of cross-protective immunity [63,68,97,98].
Qualitatively and quantitatively, the deaths in Europe
also reflected this pattern (Table 5). Cautious applica-
tion of the YPLL approach shown by the Dutch investi-
gators is a better way to proceed [36].
It is instructive to note how misleadingly high the
early estimates of case fatality rates in Mexico were,
although they were at the time based on the best avail-
able data [23,26]. The early broad clinical experience
in New York City (US), Melbourne (Australia) and the
UK were more instructive for judging the mortality and
severity of this pandemic than the initial impressions
and numerical analyses from Mexico [99]. Due to the
mild symptoms of many of the cases the true case fatal-
ity rates were impossible to measure. Infection fatality
rates are more reliable because they are less affected
by differing definitions of mild cases. If accurate case
fatality rates are to be derived in a timely manner in
future pandemics and provide population-based fatal-
ity rates for comparisons between countries prior prep-
aration for early rapid seroepidemiological studies.will
be needed [24,100-102].
Recommendations for practice,
surveillance and study
Influenza epidemics and pandemics are important
public health events with a significant impact at least
on healthcare systems. What is needed are national
routine systems for monitoring the annual numbers
of influenza-associated deaths. Preferably methods
should be consistent and corrected for confounding
to avoid systematic overestimation. Monitoring inter-
national all-cause winter deaths during the influenza
season through international surveillance building on
the example of EuroMOMO is desirable. However, it is
important to add data on cause. The EuroMOMO pro-
ject has made an important start and includes more
than ten EU countries. EuroMOMO now needs to grow
and introduce analyses that can provide standard
timely estimates of mortality attributable to influenza
(both seasonal and pandemic), including cause-spe-
cific data. Regression modelling can provide a comple-
mentary approach to estimate the burden of influenza
retrospectively and allows the opportunity to control
for potential confounding factors. Participants of the
annual meeting of the European Influenza Surveillance
Network in 2011 (held jointly with WHO Regional Office
for Europe) agreed there should be agreement on
one or more preferred European methods for statisti-
cal national estimates of excess influenza deaths as
well as preferred methods of formal individual death
11www.eurosurveillance.org
and underlying disease, 1967-1989. Int J Epidemiol.
1993;22(2):334-40.
42. Sprenger MJ, Mulder PG, Beyer WE, Masurel N. Influenza:
relation of mortality to morbidity parameters--Netherlands,
1970-1989. Int J Epidemiol. 1991;20(4):1118-24
43. Gran JM, Iversen B, Hungnes O, Aalen OO. Estimating
influenza-related excess mortality and reproduction numbers
for seasonal influenza in Norway, 1975-2004. Epidemiol Infect.
2010;138(11):1559-68.
44. Nogueira PJ, Nunes B, Machado A, Rodrigues E, Gomez
V, Sousa L, et al. Early estimates of the excess mor talit y
associated with the 2008-9 influenza season in Por tugal. Euro
Surveill. 2009 May 7;14(18):pii=19194. Available from: http://
www.eurosur veillance.org/ViewArticle.aspx?ArticleId=19194
45. Egger MJ, S. Spuhler, T. Zimmremann, H.P. Paccaud, F. Somaini,
B. [Mortality in influenza epidemics in Switzerland 1969-1985].
Schweiz Med Wochenschr. 1989;119(13-14):434- 9. [German].
46. Health Protection Agency (HPA). Surveillance of influenza
and other respirator y viruses in the UK: 2010-2011. London:
HPA; May 2011 . Available from: http://www.hpa.org.uk/
Publications/InfectiousDiseases/Influenza/1105influenzarepo
rt/
47. Wong CM, Chan KP, Hedley AJ, Peiris JS. Inf luenza-associated
mortality in Hong Kong. Clin Infect Dis. 2004;39(11):1611-7.
48. Eurostat. Statistics. Luxembourg: European Commission;
[Accessed: November 2010] http://epp.eurostat.ec.europa.eu/
portal/page/portal/statistics/search_database
49. UK National Statistics. Population. Newport: Off ice for
National Statistics; [Accessed: November 2010] . Population
http://www.statistics.gov.uk/hub/population/index.html
50. Doshi P. Trends in recorded influenza mortality: United States,
1900-2004. Am J Public Health. 2008;98(5):939-45.
51. Noymer A. Influenza analysis should include pneumonia. Am J
Public Health. 2008;98(11):1927-8.
52. McLean E , Murdoch H, Reynolds A, Begum F, Thomas D,
Smyth B, et al. Sur veillance of influenza and other respirator y
viruses in the United Kingdom: October 2008 to April 2009.
Health Protection Report. 2009:3(39). Available from: http://
www.hpa.org.uk/hpr/archives/Infections/2009/respiratory09.
htm#supp09
53. Patel M, Dennis A, Flutter C, Khan Z. Pandemic (H1N1) 2009
influenza. Br J Anaesth. 2010;104(2):128-42.
54. Writing Commit tee of the WHO Consultation on Clinical
Aspects of Pandemic (H1N1) 2009 Inf luenza, Bautista E,
Chotpitayasunondh T, Gao Z, Harper SA , Shaw M, et al. Clinical
aspects of pandemic 2009 inf luenza A (H1N1) virus infec tion. N
Engl J Med. 2010;362(18):1708-19.
55. World Health Org anization (WHO). Comparing deaths from
pandemic and seasonal influenza. Pandemic (H1N1) 2009
briefing note 20. Geneva: WHO; 22 Dec 2009. Available
from: http://www.who.int/csr/disease/swineflu/notes/
briefing_20091222/en/index.html
56. Thompson W W, Shay DK, Weintraub E, Brammer L, Bridges
CB, Cox NJ, et al. Influenza-associated hospitalizations in the
United States. JAMA. 2004;292(11):1333-40.
57. Centers for Disease Control and Prevention (CDC). Bacterial
coinfections in lung tissue specimens from f atal cases of 2009
pandemic inf luenza A (H1N1) - United States, May-August 2009.
MMWR Morb Mortal Wkly Rep. 2009;58(38):1071-4.
58. McGeer A, Green KA, Plevneshi A , Shigayeva A, Siddiqi N,
Raboud J, et al. Antiviral therapy and outcomes of influenza
requiring hospitalization in Ontario, Canada. Clin Infect Dis.
2007;45(12):1568-75 .
59. Warren-Gash C, Smeeth L , Hayward AC. Influenza as
a trigger for acute myocardial infarction or death f rom
cardiovascular disease: a systematic review. Lancet Infect Dis.
2009;9(10):601-10.
60. Lim MB, Bermingham A, Edmunds J, Fragasz y J, Har vey G,
Johnson A , et al.Flu Watch. Community burden of Influenza
during three influenza seasons and the summer wave of
the 2009 H1N1 pandemic in England – implications for
interpretation of surveillance data. Options for the Control of
Influenza VII Options Conference; 3-7 Sep 2010; Hong Kong,
China. P-321.
61. Mølbak K, Widgren K, Jensen KS, Ethelberg S, Andersen PH,
Christiansen AH, et al. Burden of illness of the 2009 pandemic
of influenza A (H1N1) in Denmark. Vaccine. 2011;29 Suppl
2:B63-B9.
62. van Gageldonk-Lafeber AB, Riesmeijer RM, Friesema IHM,
Meijer A, Isken LD, Timen A, et al. Case-based reported
mortality associated with laboratory-confirmed influenza
A(H1N1) 2009 virus infection in the Netherlands: the 2009-2010
pandemic season versus the 2010-2011 influenza season. BMC
Public Health. 2011;11:758.
18. Alling DW, Blackwelder WC, Stuart-Harris CH. A study of excess
mortality during influenza epidemics in the United States,
1968-1976. Am J Epidemiol. 1981;113(1):30-43.
19. Ansart S, Pelat C, Boelle PY, Carrat F, Flahault A, Valleron
AJ. Mor tality burden of the 1918-1919 influenza pandemic in
Europe. Influenza Other Respi Viruses. 2009;3( 3):99-106.
20. Murray CJ, Lopez AD, Chin B, Feehan D, Hill KH. Estimation of
potential global pandemic influenza mortality on the basis of
vital registr y data f rom the 1918-20 pandemic: a quantitative
analysis. L ancet. 2006;368(9554):2211-8.
21. Thompson WW, Moore MR, Weintraub E, Cheng PY, Jin X,
Bridges CB, et al. Estimating influenza-associated deaths
in the United States. Am J Public Health. 2009,99 Suppl
2:S225-30.
22. Pebody RG, McLean E, Zhao H, Clear y P, Bracebridge S, Foster
K, et al. Pandemic Influenza A (H1N1) 2009 and mor talit y in the
United Kingdom: risk factors for death, April 2009 to March
2010. Euro Sur veill. 15(20):pii=19571. Available f rom: http://
www.eurosur veillance.org/ViewArticle.aspx?ArticleId=19571
23. Transmission dynamics and impact of pandemic influenza A
(H1N1) 2009 virus. Wkly Epidemiol Rec. 2009;84(46):481-4.
24. Wu JT, Ma ES, Lee CK, Chu DK, Ho PL, Shen AL, et al. The
infection attack rate and severit y of 2009 pandemic H1N1
influenza in Hong Kong. Clin Infect Dis. 2010;51(10):1184-91.
25. van den Wijngaard CC, van Asten L, Meijer A, van Pelt W,
Nagelkerke NJ, Donker GA, et al. Detection of excess influenza
severity: associating respiratory hospitalization and mortality
data with reports of influenza-like illness by primary care
physicians. Am J Public Health. 2010;100(11):2248-54.
26. Fraser C, Donnelly CA, Cauchemez S, Hanage WP, Van
Kerkhove MD, Hollingsworth TD, et al. Pandemic potential
of a strain of influenza A (H1N1): early f indings. Science.
2009;324(5934):1557-61.
27. World Health Organization (WHO). Recommended composition
of influenza virus vaccines for use in the 2011-2012 nor thern
hemisphere influenza season. Geneva: WHO; Feb 2011.
Available from: http://www.who.int/entity/influenza/
vaccines/2011_02_recommendation.pdf
28. Farr W. Tenth annual repor t of Registrar-General xii 1847.
London: H.M. Stationery Office; 1847.
29. Reed C, Angulo FJ, Swerdlow DL, Lipsitch M, Meltzer MI,
Jernigan D, et al. Estimates of the prevalence of pandemic
(H1N1) 2009, United States, April-July 2009. Emerg Infect Dis.
2009;15(12):2004-7.
30. Collins SD. Influenza-pneumonia mor talit y in a group of
about 95 cities in the United States, 1920-29. Pub Health Rep.
1930;45:361-406.
31. Centers for Diseaase Prevention and Control (CDC). Overview
of Influenza Sur veillance in the United States. Atlanta: CDC; 2
Oct 2011. Available from: ht tp://www.cdc.gov/flu/weekly/pdf/
overview.pdf
32. Choi K, Thacker SB. Mortalit y during influenza epidemics
in the United States, 1967-1978. Am J Public Health.
1982;72(11):1280 -3.
33. Thompson W W, Shay DK , Weintraub E, Brammer L, Cox
N, Anderson LJ, et al. Mor talit y associated with influenza
and respiratory syncytial virus in the United States. JAMA.
2003;289(2):179-86.
34. Swine Flu UK. Planning Assumptions. London: Department of
Health; 3 Sep 2009. Available from: http://www.dh.gov.uk/dr_
consum_dh/groups/dh_digitalassets/documents/digitalasset/
dh_104843.pdf
35. Tillett HE, Smith JW, Clifford RE. Excess morbidity and
mortality associated with inf luenza in England and Wales.
Lancet. 1980;1(8172):793-5.
36. Wielders CC, van Lier EA, van ‘ t Klooster TM, van Gageldonk-
Lafeber AB, van den Wijngaard CC, Haagsma JA , et al.
The burden of 2009 pandemic influenza A(H1N1) in the
Netherlands. Eur J Public Health. 2012;22(1):150-7.
37. Ser fling RE. Methods for current statistical analysis of
excess pneumonia-influenza deaths. Public Health Rep.
1963;78(6):494-506.
38. Barker WH, Mullooly JP. Underestimation of the role of
pneumonia and influenza in causing excess mortalityAm J
Public Health. 1981;71(6):643-5.
39. Kyncl J, Prochazka B, Goddard NL, Havlickova M, Castkova J,
Otavova M, et al. A study of excess mor tality during influenza
epidemics in the Czech Republic, 1982-2000. Eur J Epidemiol.
2005;20(4):365-71.
40. Zucs P, Buchholz U, Haas W, Uphoff H. Influenza associated
excess mor tality in Germany, 1985-2001. Emerg T hemes
Epidemiol. 2005;2:6.
41. Sprenger MJ, Mulder PG, Beyer WE, Van Strik R , Masurel
N. Impact of influenza on mor talit y in relation to age
12 www.eurosurveillance.org
63. Hancock K, Veguilla V, Lu X, Zhong W, Butler EN, Sun H, et al.
Cross-reactive antibody responses to the 2009 pandemic H1N1
influenza virus. N Engl J Med. 2009;361(20):1945-52.
64. Cox CM, Blanton L, Dhara R, Brammer L , Finelli L. 2009
Pandemic Influenza A (H1N1) Deaths among Children—United
States, 2009–2010. Clin Infect Dis. 2011;52 Suppl 1:S69-S74.
65. Australian Paediatric Sur veillance Unit (APSU).
APSU Update. Westmead: APSU; Aug 2010.
Available from: http://www.apsu.org.au/download.
cfm?DownloadFile=C7472E49-EA8F-7A9B-6ACA495A88D7DF11
66. Barker WH, Mullooly JP. Influenza vaccination of
elderly persons. Reduction in pneumonia and influenza
hospitalizations and deaths. JAMA. 1980;244(22):2547-9.
67. Choi K, Thacker SB. From the centers for disease control.
Improved accuracy and specif icity of forecasting deaths
attributed to pneumonia and influenza. J Infect Dis.
1981;144(6):606-8.
68. Aho M, Ly ytikainen O, Nyholm J, Kuitunen T, Ronkko E,
Santanen R, et al. Outbreak of 2009 pandemic influenza
A(H1N1) in a F innish garrison - a serological sur vey. Euro
Surveill. 2010;15(45):pii=19709. Available from: http://ww w.
eurosurveillance.org/ViewAr ticle.aspx?ArticleId=19709
69. Barker WH, Mullooly JP. Impact of epidemic type A
influenza in a defined adult population. Am J Epidemiol.
1980;112(6):798-811.
70. Barker WH, Mullooly JP. “A study of excess mortality during
influenza epidemics in the United States, 1968-1976”. Am J
Epidemiol. 1982;115(3):479-480.
71. Lui KJ, Kendal AP. Impact of influenza epidemics on mortality in
the United States from October 1972 to May 1985. Am J Public
Health. 1987;77(6):712-6.
72. Thompson WW, Weintraub E, Dhankhar P, Cheng PY, Brammer
L, Melt zer MI, et al. Estimates of US influenza-associated
deaths made using four dif ferent methods. Influenza Other
Respi Viruses. 2009;3(1):37-49.
73. Neuzil KM, Maynard C, Griff in MR, Heagerty P. Winter
respiratory viruses and health care use: a population-
based study in the northwest United States. Clin Infect Dis.
2003;37(2):201-7.
74. Stroup DF, Thacker SB, Herndon JL. Application of multiple time
series analysis to the estimation of pneumonia and influenza
mortality by age 1962-1983. Stat Med. 1988;7(10):1045-59.
75. ECDC. Revised estimates of deaths associated with seasonal
influenza in the US Stockholm: ECDC; 2010 [29/11/2010];
Available from: ht tp://ecdc.europa.eu/en/activities/sciadvice/
Lists/ECDC%20Reviews/ECDC_DispForm.aspx?List=512f f74f%
2D77d4%2D4ad8%2Db6d6%2Dbf0f23083f30&ID=952&RootFo
lder=%2Fen%2Factivities%2Fsciadvice%2FLists%2FECDC%20
Reviews
76. Centers for Disease Control and Prevention (CDC). FluView.
Weekly inf luenza surveillance report. Atlanta: CDC. Available
from: http://www.cdc.gov/flu/weekly/
77. Mazick A, Gergonne B, Wuillaume F, Danis K, Vantarakis
A, Uphoff H, et al. Higher all-cause mor tality in children
during autumn 2009 compared with the three previous
years: pooled results from eight European countries. Euro
Surveill.15(5):pii=19480. Available from: http://www.
eurosurveillance.org/ViewAr ticle.aspx?ArticleId=19480
78. Hardelid P, Andrews N, Pebody R. E xcess mortality monitoring
in England and Wales during the influenza A(H1N1) 2009
pandemic. Epidemiol Infect. 2011;139(9):1431- 9.
79. Sachedina N, Donaldson LJ. Paediatric mortality related
to pandemic inf luenza A H1N1 infection in England:
an observational population-based study. Lancet.
2010;376( 9755):1846-52.
80. Gardner JW, Sanborn JS. Years of potential life lost (YPLL) --
what does it measure? Epidemiolog y. 1990;1(4):322- 9.
81. Pandemic flu. A national framework for responding to an
influenza pandemic. London: Department of Health; 2007.
Available from: http://www.dh.gov.uk/prod_consum_dh/
groups/dh_digitalassets/@dh/@en/documents/digitalasset/
dh_080745.pdf
82. Abraham T. The price of poor pandemic communication. BMJ.
2010;340:c2952.
83. European Center for Disease Prevention and Control (ECDC).
Revised pandemic 2009 planning assumptions for Europe.
Stockholm: ECDC; 16 Sep 2009. Available from: http://www.
google.se/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&ve
d=0CDgQFjAB&url=http%3A%2F%2Fecdc.europa.eu%2Fen%2
Factivities%2Fsciadvice%2FLists%2FECDC%2520Reviews%2FE
CDC_DispForm.aspx%3FList%3D512ff 74f-77d4-4ad8-b6d6-bf0
f23083f30%26ID%3D650%26MasterPage%3D1%26PDF%3Dtr
ue&ei=nCiNT72bHMqQ4gSX4ITbDw&usg=AFQjCNFDbPJANxq6
xA_xQm5ruyDYbSpQRw.
84. European Center for Disease Prevention and Control (ECDC).
Now-casting and short-term forecasting during influenza
pandemics. A focused developmental ECDC workshop.
Meeting Report. Stockholm: ECDC; 29–30 November 2007.
Available from: ht tp://www.ecdc.europa.eu/en/publications/
Publications/0711_MER_Forecasting_during _Influenza_
Pandemics.pdf
85. Centers for Disease Control and Prevention (CDC). Updated
CDC Estimates of 2009 H1N1 Influenza Cases, Hospitalizations
and Deaths in the United States, April 2009 – April 10, 2010.
Atlanta: CDC; 14 May 2009. Available f rom: http://www.cdc.
gov/h1n1flu/estimates_2009_h1n1.htm
86. Molbak K. Estimating the mortality and Years of Potental Life
Lost attributable to inf luenza to direct public health action.
2010 European Scientific Conference on Applied Infectious
Disease Epidemiology (ESCAIDE); 11-13 Nov 2010; Lisbon,
Portugal.
87. World Health Organization Regional Office for Europe (WHO/
Europe). Guidance for influenza surveillance in humans.
Copenhagen WHO/Europe; May 2011. Available from: http://
www.euro.who.int/document/e92738.pdf
88. Amato- Gauci A , Zucs P, Snacken R, Ciancio B, Lopez V, Broberg
E, et al. Surveillance trends of the 2009 influenza A(H1N1)
pandemic in Europe. Euro Surveill. 2011;16(26):pii=19903.
Available from: ht tp://www.eurosurveillance.org/ViewArticle.
aspx?ArticleId=19903
89. European Center for Disease Prevention and Control (ECDC).
Number of fatal cases. Stockholm: ECDC; 3 May 2010.
Available from: http://ecdc.europa.eu/en/healthtopics/H1N1/
epidemiological_data/Pages/number_confirmed_fatal_2009_
pandemic_influenza_cases.aspx
90. Health Protection Agency (HPA). Pandemic (H1N1) 2009 in
England: an overview of initial epidemiological findings and
implications for the second wave. London: HPA; 2 Dec 2009.
Available from: ht tp://www.hpa.org.uk/web/HPAwebFile/
HPAweb_C/1258560552857Pandemic (H1N1) 2009 in England:
an overview of initial epidemiological findings and implications
for the second wave
91. van Gageldonk-Lafeber AB, Hooiveld M, Meijer A, Donker
GA, Veldman-Ariesen M-J, van der Hoek W, et al. The relative
clinical impact of 2009 pandemic inf luenza A (H1N1) in the
community compared to seasonal influenza in the Netherlands
was most marked among 5–14 year olds. Influenza Other Respi
Viruses. 2011;5(6):e513-20.
92. Devaux I, Kreidl P, Penttinen P, Salminen M, Zucs P, Ammon
A. Initial surveillance of 2009 influenza A(H1N1) pandemic
in the European Union and European Economic Area, April-
September 2009. Euro Surveill. 2010;15(49):pii=19740.
Available from: ht tp://www.eurosurveillance.org/ViewArticle.
aspx?ArticleId=19740
93. Van Kerkhove MD, Vandemaele KA, Shinde V, Jaramillo-
Gutierrez G, Koukounari A, Donnelly CA, et al. Risk Factors for
Severe Outcomes following 2009 Influenza A (H1N1) Infection:
A Global Pooled Analysis. PLoS Med. 2011;8(7):e1001053.
94. Pebody RG, McLean E, Zhao H, Cleary P, Bracebridge S,
Foster K, et al. Pandemic Influenza A (H1N1) 2009 and
mortality in the United Kingdom: risk factors for death, April
2009 to March 2010. Euro Sur veill. 2010;15(20):pii=19571.
Available from: ht tp://www.eurosurveillance.org/ViewArticle.
aspx?ArticleId=19571
95. Nguyen-Van-Tam J, Openshaw PJM, Hashim A, Gadd EM, Lim
WS, Semple MG, et al. Risk factors for hospitalisation and poor
outcome with pandemic A/H1N1 influenza: United Kingdom first
wave (May-September 2009). Thorax. 2010;65( 7):645-51.
96. Pierce M, Kurinczuk JJ, Spark P, Brocklehurst P, Knight
M, UKOSS. Perinatal outcomes af ter maternal 2009/H1N1
infection: national cohort study. BMJ. 2011;342:d3214.
97. Broberg E, Nicoll A, Amato-Gauci A. Seroprevalence to
A(H1N1)2009 virus – where are we? Clin Vaccine Immunol.
2011;18(8):1205-12.
98. Seroepidemiological studies of pandemic influenza A (H1N1)
2009 virus. Wkly Epidemiol Rec. 2010;85(24):229-35.
99. European Center for Disease Prevention and Control
(ECDC). Pandemic (H1N1) 2009 inf luenza. ECDC Interim risk
assessment. Stockholm: ECDC; 21 Aug 2009. Available
from: http://ecdc.europa.eu/en/healthtopics/H1N1/
Documents/1001_R A_090821.pdf
100. Van Kerkhove MD, Asikainen T, Becker NG, Bjorge S,
Desenclos JC, dos Santos T, et al. Studies needed to address
public health challenges of the 2009 H1N1 influenza pandemic:
insights from modeling. PLoS Med. 2010;7(6):e1000275.
101. Lipsitch M, Riley S, Cauchemez S,Ghani AC, Ferguson
NM. Managing and reducing uncertainty in an emerging
influenza pandemic. N Engl J Med. 2009;361(2):112-5.
102. L aurie KL, Huston P, Riley S, Katz JM, Willison DJ,
Tam JSet al. Inf luenza serological studies to inform public
13www.eurosurveillance.org
health action: best practices to optimise timing, quality and
reporting. Influenza Other Respi Viruses. Forthcoming. DOI:
10.1111/j.1750-2659.2012.0370b.x.
103. World Health Organization Regional Office for Europe
(WHO/Europe), European Center for Disease Prevention and
Control (ECDC). Meeting on influenza surveillance. Ljubljana,
Slovenia, 7-9 June 2011. Summar y report. Copenhagen: WHO/
Europe; 2011. Available from: http://www.euro.who.int/__data/
assets/pdf_file/0007/155509/e96072.pdf
... Despite widespread publicity the resulting EWM in these three countries is slightly higher but not remarkably so [51] (pp. [1][2][3][4][5][6][7][8][9][10][11][12]. However, the national position in the USA conceals wide variation at state level with 76% EWM in New York at May 2020 and 52% EWM in South Dakota occurring at January 2021 [13]. ...
... This anomaly between estimated pandemic deaths and recorded influenza deaths was first noted by Doshi [11]. Put simply, influenza deaths are a subset of EWM and reported 'higher' pandemic deaths may be an artefact of the methods used to estimate influenza deaths [2][3][4][5][6][7][8]61], or due to reporting bias where deaths during the pandemics are not compared to non-pandemic years. ...
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Trends in excess winter mortality (EWM) were investigated from the winter of 1900/01 to 2019/20. During the 1918–1919 Spanish flu epidemic a maximum EWM of 100% was observed in both Denmark and the USA, and 131% in Sweden. During the Spanish flu epidemic in the USA 70% of excess winter deaths were coded to influenza. EWM steadily declined from the Spanish flu peak to a minimum around the 1960s to 1980s. This decline was accompanied by a shift in deaths away from the winter and spring, and the EWM calculation shifted from a maximum around April to June in the early 1900s to around March since the late 1960s. EWM has a good correlation with the number of estimated influenza deaths, but in this context influenza pandemics after the Spanish flu only had an EWM equivalent to that for seasonal influenza. This was confirmed for a large sample of world countries for the three pandemics occurring after 1960. Using data from 1980 onward the effect of influenza vaccination on EWM were examined using a large international dataset. No effect of increasing influenza vaccination could be discerned; however, there are multiple competing forces influencing EWM which will obscure any underlying trend, e.g., increasing age at death, multimorbidity, dementia, polypharmacy, diabetes, and obesity—all of which either interfere with vaccine effectiveness or are risk factors for influenza death. After adjusting the trend in EWM in the USA influenza vaccination can be seen to be masking higher winter deaths among a high morbidity US population. Adjusting for the effect of increasing obesity counteracted some of the observed increase in EWM seen in the USA. Winter deaths are clearly the outcome of a complex system of competing long-term trends. https://www.mdpi.com/1660-4601/19/6/3407
... In the literature, there are two major approaches to the estimation of the intra-annual excess mortality [9,10]. The first one is focused on the variation of mortality across weeks within a year in question and expresses a notion of "seasonality" [11][12][13][14]. ...
... The first one is focused on the variation of mortality across weeks within a year in question and expresses a notion of "seasonality" [11][12][13][14]. The second one (actively used for assessment of the COVID-19-related mortality) investigates the mortality deviations for certain weeks compared to the mortality experience of previous years [10,[15][16][17]. This estimation depends on two components: a general mortality trend during the last few years and seasonal fluctuations. ...
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The COVID-19 pandemic stimulated the interest of scientists, decision makers and the general public in short-term mortality fluctuations caused by epidemics and other natural or man-made disasters. To address this interest and provide a basis for further research, in May 2020, the Short-term Mortality Fluctuations data series was launched as a new section of the Human Mortality Database. At present, this unique data resource provides weekly mortality death counts and rates by age and sex for 38 countries and regions. The main objective of this paper is to detail the web-based application for visualizing and analyzing the excess mortality based on the Short-term Mortality Fluctuation data series. The application yields a visual representation of the database that enhances the understanding of the underlying data. Besides, it enables the users to explore data on weekly mortality and excess mortality across years and countries. The contribution of this paper is twofold. First, to describe a visualization tool that aims to facilitate research on short-term mortality fluctuations. Second, to provide a comprehensive open-source software solution for demographic data to encourage data holders to promote their datasets in a visual framework.
... Specific epidemiological correlates between the 1889-91 and 2020-21 pandemics include the low morbidity among children, the lack of the shift in excess mortality to younger age groups usually seen with pandemic influenza, the magnitude and distribution of peak excess mortality ratios in metropolitan settings, and the rapidity of epidemic propagation within communities (Valleron et al. 2010;Campbell A. and Morgan E. 2020;Nicoll et al. 2012;Nguyen-Van-Tam et al. 2003;Honigsbaum 2010;Smith 1995). While downscaling this synoptic analogy to make short-term forecasts of COVID-19 activity in any given place 130 years later is clearly foolish (short-range forecasts from well-observed local data being very much the preserve of computational modelling), the historical record may provide a richer and more useful understanding of the range of medium-and long-term consequences of a pandemic of this epidemiological pattern on human societies than even the most complex mathematical model. ...
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Although every emerging infectious disease occurs in a unique context, the behaviour of previous pandemics offers an insight into the medium- and long-term outcomes of the current threat. Where an informative historical analogue exists, epidemiologists and policymakers should consider how the insights of the past can inform current forecasts and responses.
... Infectious diseases affect the health of older adults and lead to hospitalization and additional medical costs [2,3]. Respiratory tract infection is a leading cause of mortality among older adults [4]. Recently, outbreaks of novel infectious diseases that affect the respiratory tract have been emerging worldwide and have highlighted the susceptibility of older adults to infections as well as the serious consequences among the elderly. ...
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Objective To evaluate the effectiveness of an information–motivation–behavioral skills (IMB) model-based multi-component intervention on engagement and the quality of preventive behaviors against respiratory infections among community-dwelling older adults. Methods This study was a controlled pretest–posttest study in which 91 community-dwelling older adults aged above 65 years were included. The intervention group (n = 42) received the six-week intervention theoretically based on the IMB model that comprised weekly group education and 5–10 minutes of tele-counseling per week. Results The results showed that, after the intervention, the improvement in the level of knowledge, self-efficacy, self-reported engagement, and the quality of respiratory infection preventive behaviors was significantly greater in the intervention group compared to the control group. There was no significant difference between the two groups for the perceived threat of respiratory infection. Conclusion The IMB model-based intervention improved the engagement and quality of preventive behaviors by increasing the level of knowledge and self-efficacy in community-dwelling older adults. Practice implications The IMB model-based multi-component intervention can be an effective approach to improve preventive behaviors and will contribute to the preparation of communities for outbreaks of respiratory infections.
... One approach to estimating the mortality impact of influenza is by examining the "excess" deaths that occur during influenza epidemics compared to outside of epidemic periods (Farr 1847). Influenza-associated mortality varies from year to year and tends to be higher in pan- demics than in seasonal epidemics (Nicoll et al. 2012). First, the factors were associated with real differences in influenza-associated death. ...
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Influenza virus infections are common in people of all ages. Epidemics occur in the winter months in temperate locations and at varying times of the year in subtropical and tropical locations. Most influenza virus infections cause mild and self-limiting disease, and around one-half of all infections occur with a fever. Only a small minority of infections lead to serious disease requiring hospitalization. During epidemics, the rates of influenza virus infections are typically highest in school-age children. The clinical severity of infections tends to increase at the extremes of age and with the presence of underlying medical conditions, and impact of epidemics is greatest in these groups. Vaccination is the most effective measure to prevent infections, and in recent years influenza vaccines have become the most frequently used vaccines in the world. Nonpharmaceutical public health measures can also be effective in reducing transmission, allowing suppression or mitigation of influenza epidemics and pandemics.
... Further, it is not feasible for all countries to use these methods due to lack of reliable data. Methods to estimate influenza-associated mortality that can be used by more countries is not available (23), though needed. Efforts by CDC, GLaMOR, and IHME help to fill this gap in influenza burden knowledge by calculating country-specific, regional, and global influenza-associated mortality estimates. ...
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Prior to updating global influenza-associated mortality estimates, the World Health Organization convened a consultation in July 2017 to understand differences in methodology and implications on results of three influenza mortality projects from the United States Centers for Disease Control and Prevention (CDC), the Netherlands Institute for Health Service Research (GLaMOR), and the Institute for Health Metrics and Evaluation (IHME). The expert panel reviewed estimates and discussed differences in data sources, analysis, and modeling assumptions. We performed a comparison analysis of the estimates. Influenza-associated respiratory death counts were comparable between CDC and GLaMOR; IHME estimate was considerably lower. The greatest country-specific influenza-associated mortality rate fold differences between CDC/IHME and between GLaMOR/IHME estimates were among countries in South-East Asia and Eastern Mediterranean region. The data envelope used for the calculation was one of the major differences (CDC and GLaMOR: all respiratory deaths; IHME: low respiratory infection deaths). With the assumption that there is only one cause of death for each death, IHME estimates a fraction of the full influenza-associated respiratory mortality that is measured by the other two groups. Wide variability of parameters was observed. Continued coordination between groups could assist with better understanding of methodological differences and new approaches to estimating influenza deaths globally.
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Excess mortality has been used to measure the impact of COVID-19 over time and across countries. But what baseline should be chosen? We propose two novel approaches: an alternative retrospective baseline derived from the lowest weekly death rates achieved in previous years and a within-year baseline based on the average of the 13 lowest weekly death rates within the same year. These baselines express normative levels of the lowest feasible target death rates. The excess death rates calculated from these baselines are not distorted by past mortality peaks and do not treat non-pandemic winter mortality excesses as inevitable. We obtained weekly series for 35 industrialized countries from the Human Mortality Database in 2000–2020. Observed, baseline and excess mortalities were measured by age-standardized death rates. We assessed weekly and annual excess death rates driven by the COVID-19 pandemic in 2020 and those related to seasonal (predominantly) respiratory infections in earlier years. There was a distinct geographic pattern with high excess death rates in Eastern Europe followed by parts of the UK, and countries of Southern and Western Europe. Some Asia-Pacific and Scandinavian countries experienced lower excess mortality. In 2020 and earlier years, the alternative retrospective and the within-year excess mortality figures were higher than estimates based on conventional metrics. While the latter were typically negative or close to zero in “normal” years without extraordinary epidemics, the alternative estimates were substantial. Cumulation of this usual excess over 2–3 years results in human losses comparable to those caused by COVID-19. Challenging the view that non-pandemic seasonal winter mortality is inevitable would focus attention on reducing premature mortality in many countries. As SARS-CoV-2 is unlikely to be the last respiratory pathogen with the potential to cause a pandemic, such measures would also strengthen global resilience in the face of similar threats in the future.
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Public health agencies have promoted a single pathogen view of infection which simplifies the message about vaccination. However, research over the past 20 years shows a far more complex set of interactions between multiple pathogens, the nose/throat microbiota, the gut microbiota, immune function, and ageing. Influenza vaccination is effective at reducing General Practitioner (GP) visits for influenza like illness (ILI), for reducing health care staff sickness absence, and in reducing hospital admission due to influenza. The narrative regarding influenza mortality has been overly simplified and the levels of reported deaths are more correctly due to interactions between a mix of pathogens. There is no such thing as a single pathogen winter. There is mixed evidence that influenza vaccination protects against death from influenza - probably complicated by vaccination increasing life span and hence shifting death to a later point in time. Influenza vaccination does not seem to diminish excess winter mortality (EWM) – with EWM being the complex outcome of the mix of pathogens interacting with metrological variables. In those likely to die in the next year, while influenza vaccination may protect against death from influenza per se, this merely creates space for another pathogen to trigger final demise. The central problem is that there is no 100% accurate method to determine who is in the last year of life. For this reason, all elderly should be vaccinated. The ratio of male to female deaths and admissions appears to be an indicator of which mix of pathogens predominate each winter. Amid all the conflicting trends there is room for the action of a new type or kind of infectious disease. This new disease may be triggered by the novel action of a common pathogen or may be the outcome of the multiple interactions between pathogens throughout each year. While outbreaks of this new disease can occur throughout the year they seem to occur more commonly at the interface between winter and spring. These outbreaks cause deaths and medical admissions to suddenly shift to a higher level, stay at the higher level for most commonly 12 months but shorter and longer periods also occur, deaths then shift back to the usual levels while medical admissions seem to sustain a more lingering effect. The combined interaction of the mix of winter infections plus outbreaks of the new disease generate a complex set of cost and capacity challenges. This complex set of challenges is completely ignored in the funding formulas used to distribute resources between different populations. Issues of population density discussed in Parts 1 and 2 are highly relevant. Steady state thinking and silo mentality is a hindrance when seeking to fully understand issues of financial risk and capacity surges. Part 4 investigates why deaths are serving as a wider proxy for morbidity and how the number of deaths can be used as a tool to determine the optimum size for insurers, HMOs, healthcare commissioners to achieve minimum volatility in costs. A 14-minute interview covering the series is available, https://jjunland.egnyte.com/dl/CPdNnjCVle/?
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Background : Influenza is considered one of the most important global public health issues, and a main contributing factor to significant mortality and morbidity across many countries worldwide, especially among elderlies. This study aimed to evaluate the changes of flu vaccination coverage among elderlies in Hungary over the past years and to analyze the effect of implementing financial incentive related primary care indicators on the vaccination coverage. Methods: 95% Confidence intervals for proportions of people aged 65 or above regarding influenza vaccination status were calculated yearly in order to detect the trends of vaccination coverage in Hungary before and after an 8-year period of the introduction of indicators system. Results: Despite the financial motivational incentives provided to general practitioners to vaccinate their patients against influenza, the vaccination coverage is declining in recent years i.e. before the implementation: 36.01% (95% CI: 35.98 – 36.04); after the implementation: 28.03% (95% CI : 28.01 - 28.06). Conclusions: According to our results, the implementation of indicators system and related financial incentives could not exploit the potential opportunities in the aspect of increasing the flu vaccination coverage among elderlies in Hungary. Therefore, increasing the flu vaccination coverage should be achieved, not only with free -of- charge vaccines and financial incentives, but also with other possible options such as raising the awareness among people and implementing an effective, follow up system.
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In the last six months, a vast amount of clinical and epidemiological data have been generated describing Covid-19 and its propagation in various populations. With the luxury of wealth, low population density, and geographic isolation, Australia and New Zealand have so far avoided the case numbers seen elsewhere, affording our public health agencies valuable time to learn and prepare. The defence of this privileged position has seen the use of reserve public health powers to implement some of the most restrictive policies in the world. Those exercising these powers have an extraordinary responsibility to the community. We argue that our public health agencies and governments must do better in transparently communicating the risks of Covid-19, the justifications for restrictive interventions, and the long-term all-things-considered goals of public health policy. Six months on, it is no longer acceptable that this responsibility be deferred on the basis of emergency. Based on currently available data, it is already possible to identify at least some policies that are likely to be associated with net benefits, some that are non- beneficial, and some that are unfair or, on balance, harmful. It is a false dichotomy to suggest that multiple interventions must be applied at once or none at all - that populations must choose between laissez faire and ‘lockdown’. Public health agencies have a responsibility to consider how to achieve overall public health goals with the least restrictive or burdensome strategies, and to weigh each intervention on its merits.
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European Union (EU) and European Economic Area (EEA) countries reported surveillance data on 2009 pandemic influenza A(H1N1) cases to the European Centre for Disease Prevention and Control (ECDC) through the Early Warning and Response System (EWRS) during the early phase of the 2009 pandemic. We describe the main epidemiological findings and their implications in respect to the second wave of the 2009 influenza pandemic. Two reporting systems were in place (aggregate and case-based) from June to September 2009 to monitor the evolution of the pandemic. The notification rate was assessed through aggregate reports. Individual data were analysed retrospectively to describe the population affected. The reporting peak of the first wave of the 2009 pandemic influenza was reached in the first week of August. Transmission was travel-related in the early stage and community transmission within EU/EEA countries was reported from June 2009. Seventy eight per cent of affected individuals were less than 30 years old. The proportions of cases with complications and underlying conditions were 3% and 7%, respectively. The most frequent underlying medical conditions were chronic lung (37%) and cardio-vascular diseases (15%). Complication and hospitalisation were both associated with underlying conditions regardless of age. The information from the first wave of the pandemic produced a basis to determine risk groups and vaccination strategies before the start of the winter wave. Public health recommendations should be guided by early capture of profiles of affected populations through monitoring of infectious diseases.
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Recent international mandates, and the emergent circulation of pandemic (H1N1) 2009 virus in human populations, call for strengthening influenza surveillance to better target seasonal influenza control programmes and support pandemic preparedness. This document provides technical guidance to establish sentinel site surveillance to assess the virologic and epidemiologic characteristics of respiratory disease leading to outpatient consultation or hospitalization that may be caused by influenza (seasonal or pandemic) or another respiratory virus. A simple, broad case definition for severe acute respiratory infection (SARI) is provided as a standard to enumerate severe influenza infections leading to hospitalization. Case definitions of influenza-like-illness (ILI) and acute respiratory infection (ARI) are proposed for the surveillance of outpatient illness related to mild influenza and other respiratory pathogens. This guidance document also contains practical suggestions for implementing hospital and outpatient-based site surveillance, including: criteria for selecting sentinel sites; forms for epidemiological data collection; procedures for collecting, testing, storing and transporting laboratory specimens; report templates for weekly and annual data summaries; and pandemic preparedness support functions. This document complements, not replaces Human infection with pandemic (H1N1) 2009 virus: updated interim WHO guidance on global surveillance. (aut.ref.)
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How do the number of deaths from pandemic A/H1N1 compare with influenza related mortality in recent years?1 2 The official estimate of influenza mortality is produced by the Health Protection Agency (HPA). It is derived from excess (above “expected” level) all cause death registrations in the winter. The estimates for the past five years in England and Wales are: 1965 (2004-5 winter season), 0 (2005-6), …
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We employed multiple time series analysis to estimate the impact of influenza on mortality in different age groups, using a procedure for updating estimates as current data become available from national mortality data collected from 1962 to 1983. We compared mortality estimates that resulted from a multivariate model for epidemic forecasting with those obtained from univariate models. We found more accurate prediction of deaths from all age groups using the multivariate model. While the univariate models show an adequate fit to the data, the multivariate model often enables earlier detection of epidemics. Additionally, the multivariate approach provides insight into relationships among different age groups at different points in time. For both models, the largest excess mortality due to pneumonia and influenza during influenza epidemics occurred among those 65 years of age and older. Although multiple time series models appear useful in epidemiologic analysis, the complexity of the modelling process may limit routine application.
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Background: Serological studies can detect infection with a novel influenza virus in the absence of symptoms or positive virology, providing useful information on infection that goes beyond the estimates from epidemiological, clinical and virological data. During the 2009 A(H1N1) pandemic, an impressive number of detailed serological studies were performed, yet the majority of serological data were available only after the first wave of infection. This limited the ability to estimate the transmissibility and severity of this novel infection, and the variability in methodology and reporting limited the ability to compare and combine the serological data. Objectives: To identify best practices for conduct and standardisation of serological studies on outbreak and pandemic influenza to inform public policy. Methods/setting: An international meeting was held in February 2011 in Ottawa, Canada, to foster the consensus for greater standardisation of influenza serological studies. Results: Best practices for serological investigations of influenza epidemiology include the following: classification of studies as pre-pandemic, outbreak, pandemic or inter-pandemic with a clearly identified objective; use of international serum standards for laboratory assays; cohort and cross-sectional study designs with common standards for data collection; use of serum banks to improve sampling capacity; and potential for linkage of serological, clinical and epidemiological data. Advance planning for outbreak studies would enable a rapid and coordinated response; inclusion of serological studies in pandemic plans should be considered. Conclusions: Optimising the quality, comparability and combinability of influenza serological studies will provide important data upon emergence of a novel or variant influenza virus to inform public health action.