Introduction
The linking of national surveillance systems in a pan European project
has been supported by the European Union (EU). The European Influenza
Surveillance Scheme (EISS) is such a project (1,2,3). The surveillance
of influenza among the members of EISS is based on an integrated clinical
and virological surveillance model. Sentinel primary care physicians report
clinical cases and take swabs from patients for laboratory testing (3,4).
The morbidity indicators are used for the estimation of the influenza
activity (influenza-attributable illnesses in the population) while the
virological data are used to link the excess morbidity to the circulation
of the agent and to get specific information on the circulating types,
subtypes and strains (5,6). The weekly assessment of influenza activity
is usually compared to peak values of the clinical indicator observed
in the past. For estimations of the total seasonal impact, the excess
morbidity and the duration (area under curve) is considered (7,8).
For surveillance purposes, the countries have the following in common:
- Morbidity is recorded in primary care facilities
- Morbidity and virological data are collected from the same sample of
the population
- The systems cover large areas (more than 50% and usually the whole)
of the country
- The population under surveillance is at least 0.5%, and usually 1% or
more
- The systems have been functioning and stable for over three years
- The observation period is 30 weeks or more, beginning with the 40th
week of the year.
Despite this basic congruence, considerable differences in the output
data remain (3,5,9). This can be explained by differences in the healthcare
systems and other influences on consultation behaviour, for example,
reimbursement issues for medication and consultations, and organisational
needs (such as certificates needed for absenteeism).
Additionally, different emphasis of the main effort and presuppositions
taken into account for the set up of each network led to the use of
different case definitions. For the networks included in the study,
the case definitions can be summarised in three groups:
- Influenza-like illnesses (ILI) with case definition*
- ILI without a case definition (illnesses that are considered to be
influenza)
- acute respiratory tract infections (ARI) with case definition*
*The case definitions used are not uniform.
Different denominators are in use. A population based denominator is
ideal, but cannot always be directly calculated, for instance, in healthcare
systems where patients have a free choice of general practitioner (GP)
to be consulted. Therefore, the number of reporting practices or the
total number of consultations are used as denominators instead (10).
The estimation of population based rates has been encouraged for networks
where population based reporting is not possible. This was one harmonisation
measure used by EISS after the index study had been started (3,5). Nevertheless,
the numerical observations of the various national networks differ considerably,
and the data and resulting graphs can be interpreted only with an in
depth knowledge of each specific network (5,6).This study explores the
usefulness of an index based on simple principles for harmonising the
scaling of national data and assisting the interpretation and estimation
of influenza activity.
Material and methods
For this pilot study data from eight countries which were able to provide
the necessary historical data were chosen. The following morbidity indicators
are recorded in these countries: ILI (without case definition) per population (England) ILI (with case definition) per population (Netherlands and Portugal) ILI (with case definition) per consultations (Belgium and Switzerland) ARI (with case definition) per population (Czech Republic) ARI (with case definition) per consultations (Germany and France).
The concept of the index was to assess the weekly influenza-attributable
excess morbidity for each country, which is a reliable and commonly
used indicator for influenza activity. This was put in relation to the
excess morbidity expected for the peak weeks of 'usual' seasons in the
respective networks.
Calculation of the background activity for each country (6,11,12).
As a simple and practical method, the mean value of recent years (minimum
seven years) for each specific week of the period was calculated, excluding
all weeks where influenza activity was considered to be more than sporadic
(influenza-attributable excess morbidity is detectable) by those responsible
for interpretation of the data (13).
In cases where data from under two weeks could be used for the calculation
of a specific week - for example, due to frequent influenza activity
during this week in recent years - adjacent (previous and following)
week values were included for the estimation. A sliding mean value over
three weeks was practical for smoothing periods where influenza circulated
frequently. In periods with sufficient weekly values, smoothing was
avoided because the typical weekly pattern (imprinted by public holidays,
Christmas, etc.) might be softened.
Calculating the excess morbidity typical for the peak weeks of
a 'usual season'.
This 'reference' value represents the excess during the peak period
of a usual activity and was calculated from at least two epidemics considered
as usual by the national scientists responsible. The mean of the influenza
attributable excess (recorded value minus background) during the three
peak weeks of all selected seasons represents the increase of the indicator
that can be expected when the level of influenza activity is no higher
than usual (13).
The seasons and weeks selected to assess this reference value for each
country are listed in table 1.

Calculation of the index per week:
The index for each week is calculated as the excess morbidity (observed
value for the week minus 'background' expected for the week) in relation
to the reference value representing the increase indicating usual influenza
activity. If the actual measured value is equal to, or lower than, the
'background', there is no more than background morbidity (no influenza
attributable excess), and the index is set at zero. Consider, for example,
the value of 50 ILI/100 000 persons in country X for week 45. Assuming
the background for week 45 has been determined for country X to be 40
ILI/100 000 persons. Thus, the excess is 10 ILI/100 000 persons. If
the reference, ie the average excess of the weeks with highest activity
during usual influenza seasons, was 100 ILI/100 000 persons, then the
index had a value of 10/100=10%.
Results
In Figure 1 (graphs 1 to 8) the morbidity indicators for the 2001/02
season are shown as recorded by the different systems. The number of
positive influenza assays is also given, to indicate the period with
increased circulation of the viruses. In most of the graphs, the beginning
and end of the influenza epidemic can be recognised. In some network
this is only possible with the additional consideration of the laboratory
data. An assessment of the magnitude of influenza-attributable excess
morbidity (as indicator for the influenza activity) and its development
during the season is impossible without additional information.

In Figure 2 (graphs 9 to 16) the index for the season 2001/02 is shown
for the respective countries along with the number of positive Influenza
assays. The flow of the curves is generally unchanged, as only the estimated
background morbidity is eliminated. This is relevant for ARI recording
systems in particular, where the background is usually large (6). The
uniform scaling leads to a stretching or compression of the curves forcing
the indicator level typical for a usual influenza peak activity to an
index value around 100. The beginning, peak, and end of the influenza
epidemics can now be recognised more easily. The indicator aids a definite
impression of the magnitude of the excess morbidity.

The categories currently used by EISS to describe the level of influenza
activity derive from assessments for the entire season and discriminate
(5): Low: no influenza activity or influenza activity is at baseline level Medium: level of influenza activity usually seen when influenza virus
is circulating in the country, based on historical data High: higher than usual influenza activity compared to historical
data
Very high: influenza activity is particularly severe compared to historical
data
This does not provide categories for the range 'no activity' to 'medium',
which is the range for most of the season and the phase of increasing
and decreasing activity. The index shown allows further discriminations
particularly in this range and additional categories are suggested: Low activity: the excess morbidity is insignificant or low
(0-40%). Moderate activity: the excess morbidity is significant but still clearly
below a usual peak level (41-80%). Usual (or medium) activity: level of influenza activity usually seen
in the peak period of epidemics considered as usual based on historical
data (81-120%).
Discussion
Harmonisation is a key issue in international collaboration with regard
to surveillance systems. Minimum requirements regarding representativity
and common surveillance principles are important aspects of harmonisation.
Morbidity data based on at least 0.5 to 1% of the population under surveillance
are generally considered to be sufficient for influenza that causes
symptomatic illness in approximately 5% to 10% of the population during
average seasons (15-18). The networks collaborating in EISS fulfil these
requirements.
The study investigates whether an index projecting the indicators used
in the different networks to a uniform and relative scaling is in principal
useful for harmonisation. For this harmonisation measure the individual
turn of the scale for each system that indicates 'usual' peak activity
has been estimated for each system. Usual activity can be understood
as representative for influenza seasons disregarding the 'non influenza
seasons' and seasons with unusually high activity (13).
The methods used are simple, practical and easy to apply. The use of
these seemingly crude methods, together with the arbitrary momentum
from the assessment of national experts, appears tolerable considering
the reliability of the recorded data which are affected by unclear selection
steps during registration due to consultation behaviour, interpretation
of the criteria given by individual physicians, etc. A fine tuning of
the method has not yet been performed, and the differences, and advantages
and disadvantages of alternatives to estimate the background, reference
value, etc. have not been assessed. (for example, for the estimation
of the reference the average of a number of peak weeks from all seasons
would be a practical alternative if an appropriate number of seasons
is available.)
The index shown assists the interpretation of the national data without
the need to present historical data or detailed knowledge of each national
network and healthcare system. The assessment of the influenza activity
is supported and the better harmonisation may allow a finer discrimination
of the intensity levels for each week. Additional categories for influenza
activity to allow for discrimination are suggested, particularly in
the range when the usual activity has not been exceeded. This is the
case in many seasons and during the phase of increasing and decreasing
activity. This could enable establishment of a more widely accepted
terminology.
Compared to the activity levels currently used by EISS the "usual
activity" indicated by the index is somewhat lower than the 'medium'
activity due to the exclusion of seasons with high activity for the
calculation of the reference value. For the consideration of the seasonal
peak phase it should be considered that the reference value of the index
takes three peak values into account, and hence the mean of the three
peak values of the current season is decisive for influenza activity.
Despite the uniform scaling of the different national data, direct comparability
of the index is limited. Basic differences between networks are not
compensated or affected by the indexing eg the different sensitivity
to certain syndromes, due to the consultation behaviour in the country
and the case definition used. If, for instance, a wider definition such
as acute respiratory infection is used (and people frequently consult
with mild symptoms) this system is probably much more sensitive to influenza
A/H1N1. This subtype is currently mainly infecting the younger age groups
and causes mild symptoms in the majority of the cases. For a system
using a strict definition with high fever, cough, joint pain etc. in
a healthcare system where people consult only when they are severely
ill, a relatively lower sensitivity against Influenza A/H1N1 illness
can be expected (9). Disadvantages of the indicators used at present
due for example to aggregation levels - with regards to regions and
their geographic dimension or age groups - are not compensated by the
index. For instance interferences of other co-circulating agents (eg
respiratory syncytial virus (RSV)) with the morbidity indicator persist,
because the index only projects the indicator to a uniform and relative
scaling. The key problem is the unknown link - or function - between
the true incidence in the population and the indicators registered for
surveillance, which probably differs for the various networks. The index
gives weight to the impression based on the indicators currently used
without compensating differences in that function. This leads to a declining
reliability of the index particularly when the influenza activity is
unusually high. This may be further explored taking into account historical
experiences of severe epidemics. Thus the index may be helpful in making
those basic differences more apparent and so encourage their further
investigation.
The link between the true incidence of the disease in the population
and the registered indicator is mainly dependent on the selection steps
for the registration of the cases. Differences in case definitions and
denominators used are perhaps the most obvious factors, and their harmonisation
is an important option. It should, however, be considered that a shift
of these criteria might interrupt the continuity of the national observation
and hence comparability to historical data. An adjustment phase of unpredictable
duration might follow requiring additionally costly quality control
measures. On the other hand many differences are a consequence of the
healthcare systems, consultation behaviour, cultural beliefs, etc. Those
influences will remain, and a satisfactory comparability may not be
achieved. This is intimated by countries using very similar indicators,
such as England and the Netherlands, or Portugal with a still very limited
comparability of the recorded values. For example, the average of the
three peak weeks of all epidemics from 1992/93 to 1996/97 in England
was 163 ILI per 100 000 while in the Netherlands it is 291 ILI per 100
000 (10). A generally higher influenza activity in the Netherlands is
not plausible. Applying the index calculation to these rates, the result
for England would be 137% of the reference value and 138% for the Netherlands.
Considerable regional differences in some countries exist despite the
use of uniform numerators and denominators and a uniform healthcare
system (8,14). Such measures should be carefully explored in pilot studies
and are not considered to be short term options. Indexing the data may
be a useful short term alternative and may allow a gradual harmonisation.
The graphs for the seasons 2000/01 and 1998/99 can be ordered as Excel
files by emailing helmut.uphoff@kilian.de
or helmut.uphoff@uphoffs.de
Acknowledgements
We thank our colleagues in the EISS group ; Michele Aymard, Helena de
Andrade, Aad Bartelds, Pilar Peres Brena, Jan Cloetta, Isabel Marinho
Falcao, Martina Havlickova, Salvador de Mateo, Rolf Heckler, Marie-Louiese
Heijnen, Jan de Jong, Bruno Lina, Jean Claude Manuguerra, Hans Matter,
Anne Mosnier, Brunhilde Schweiger, Rene Snacken, Bela Tumova, Martine
Valette, Tomas Vegas, Koos van der Velden, John Watson, Sylvie van der
Werf, Werner Wunderli, Fernande Yane, and Maria Zambon for the support
and for supplying data. We additionally thank Udo Buchholz and John Paget
for their comments and support. |