SIDARTHa SIDARTHa SIDARTHa SIDARTHa European Emergency Data European Emergency Data European Emergency Data European Emergency Data-based Syndromic Surveillance System based Syndromic Surveillance System based Syndromic Surveillance System based Syndromic Surveillance System Grant Agreement No. 2007208 Grant Agreement No. 2007208 Grant Agreement No. 2007208 Grant Agreement No. 2007208 Developing Algorithms for Developing Algorithms for Developing Algorithms for Developing Algorithms for Early Public Early Public Early Public Early Public Health Threat Detection Health Threat Detection Health Threat Detection Health Threat Detection in in in in Europe Europe Europe Europe Re Re Re Results from the SIDARTHa project sults from the SIDARTHa project sults from the SIDARTHa project sults from the SIDARTHa project Draft report (January 2010) Draft report (January 2010) Draft report (January 2010) Draft report (January 2010)
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Developing Algorithms forDeveloping Algorithms forDeveloping Algorithms forDeveloping Algorithms for Early Public Early Public Early Public Early Public
Health Threat DetectionHealth Threat DetectionHealth Threat DetectionHealth Threat Detection in in in in EuropeEuropeEuropeEurope ReReReResults from the SIDARTHa projectsults from the SIDARTHa projectsults from the SIDARTHa projectsults from the SIDARTHa project
SIDARTHa - European Emergency Data-based Syndromic Surveillance System
The project ‘European Emergency Data-based System for Information on, Analysis and Detection of Risks and Threats to Health – SIDARTHa’ is co-funded by the European Commission under the Programme of Community Action in the Field of Public Health 2003-2008 (Grant Agreement-No.: 2007208).
SIDARTHa Steering Committee
Luis Garcia-Castrillo Riesgo (Project Leader), Thomas Krafft (Scientific-Technical Coordinator), Matthias Fischer, Alexander Krämer, Freddy Lippert, Gernot Vergeiner SIDARTHa Project Group
Dispatch Centre Tyrol (Austria), contact person: Gernot Vergeiner; Federal Government, Department of Public Health (Belgium), contact person: Agnes Meulemans; Emergency Medical Service Prague (Czech Republic), contact person: Milana Pokorna; Capital Region (Denmark), contact person: Freddy Lippert; University Hospital Kuopio (Finland), contact person: Jouni Kurola; Emergency Medical Service Province Hauts de Seine (France), contact person: Michel Baer; Hospitals of County of Goeppingen (Germany), contact person: Matthias Fischer; GEOMED Research Forschungsgesellschaft mbH (Germany), contact person: Thomas Krafft; University of Bielefeld, Department of Public Health Medicine (Germany), contact person: Alexander Krämer; National Emergency Medical Service (Hungary), contact person: Gabor Göbl; San Martino University Hospital Genoa (Italy), contact person: Francesco Bermano; Haukeland University Hospital Bergen (Norway), contact person: Guttorm Brattebo; University of Cantabria (Spain), contact person: Luis Garcia-Castrillo Riesgo; University Hospital Antalya (Turkey), contact person: Hakan Yaman Advisory Board
Helmut Brand (The Netherlands, Chair), Andrea Ammon (ECDC), Enrico Davoli (WHO-Euro), Per Kulling (EU Health Threat - Unit), Javier Llorca (Spain), Jerry Overton (USA), Santiago Rodriguez (Spain), Mark Rosenberg (Canada) Coordination Office
Alexandra Ziemann (Science Officer), Weyma Notel (Project Assistant), Juan-José San Miguel Roncero (Financial Officer)
Developing Algorithms for Early Public Health Threat DetectionDeveloping Algorithms for Early Public Health Threat DetectionDeveloping Algorithms for Early Public Health Threat DetectionDeveloping Algorithms for Early Public Health Threat Detection in Europein Europein Europein Europe
Results from the SIDARTHa project.
Draft report (January 2010)
This report describes the methodology of developing detection algorithms for the European syndromic surveillance system for early public health threat detection and risk communication SIDARTHa and forms deliverable D6 as defined in the Grant Agreement. Compiled by
Nicole Rosenkötter, Janneke Kraan, Alexandra Ziemann, Martina Schorbahn, Genc Burazeri, Helmut Brand
Editors
Nicole Rosenkötter, Janneke Kraan, Alexandra Ziemann, Martina Schorbahn, Genc Burazeri, Helmut Brand, Thomas Krafft, Tim Tenelsen, Luis Garcia-Castrillo Riesgo, Matthias Fischer, Alexander Krämer, Freddy Lippert, Gernot Vergeiner for the SIDARTHa project group
Please cite as:
Rosenkötter N, Kraan J, Ziemann A, Schorbahn M, Burazeri G, Brand H, Krafft T, Tenelsen T, Garcia-Castrillo Riesgo L, Fischer M, Krämer A, Lippert F,
Vergeiner G, for the SIDARTHa project group (ed.) (2010): Developing Algorithms for Early Public Health Threat Detection in Europe – Results from
the SIDARTHa project. Draft Report (January 2010). Bad Honnef.
Cover Figure
Cluster detection based on emergency data (own creation)
1111 INTRODUCTION: THE SIINTRODUCTION: THE SIINTRODUCTION: THE SIINTRODUCTION: THE SIDARTHA PROJECTDARTHA PROJECTDARTHA PROJECTDARTHA PROJECT.................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................... 1111
2.42.42.42.4 PPPPREPARATION OF THE REPARATION OF THE REPARATION OF THE REPARATION OF THE DDDDATA ATA ATA ATA SSSSETSETSETSETS .................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................... 6666
2.52.52.52.5 SIDARTHSIDARTHSIDARTHSIDARTHA A A A SSSSTANDARD TANDARD TANDARD TANDARD DDDDATA ATA ATA ATA SSSSETETETET .................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................... 6666
3333 APPLYING DETECTION AAPPLYING DETECTION AAPPLYING DETECTION AAPPLYING DETECTION ALGORITHMS IN THE SIDLGORITHMS IN THE SIDLGORITHMS IN THE SIDLGORITHMS IN THE SIDARTHA IMPLEMENTATIONARTHA IMPLEMENTATIONARTHA IMPLEMENTATIONARTHA IMPLEMENTATION SITES: FIRST RESULTSSITES: FIRST RESULTSSITES: FIRST RESULTSSITES: FIRST RESULTS ............................................................................................................................................................................................................ 8888
3.23.23.23.2 SIDARTHSIDARTHSIDARTHSIDARTHA A A A SSSSTANDARD TANDARD TANDARD TANDARD DDDDATA ATA ATA ATA SSSSETETETET .................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................... 10101010
3.53.53.53.5 RRRRESULTS OF THE DETECTESULTS OF THE DETECTESULTS OF THE DETECTESULTS OF THE DETECTIIIION ALGORITHM ON ALGORITHM ON ALGORITHM ON ALGORITHM C1,C1,C1,C1, C2,C2,C2,C2, C3C3C3C3 ............................................................................................................................................................................................................................................................................................................................................................................................................ 14141414
4444 SUMMARY & NEXT STEPSSUMMARY & NEXT STEPSSUMMARY & NEXT STEPSSUMMARY & NEXT STEPS ........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................ 16161616
FFFFIGURE IGURE IGURE IGURE 1:1:1:1: SIDARTHSIDARTHSIDARTHSIDARTHA A A A PPPPROJECT ROJECT ROJECT ROJECT MMMMETHODOLOGYETHODOLOGYETHODOLOGYETHODOLOGY .................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................... 2222
FFFFIGURE IGURE IGURE IGURE 2:2:2:2: SIDARTHSIDARTHSIDARTHSIDARTHA A A A AAAAPPROACHPPROACHPPROACHPPROACH ............................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................ 2222
Developing Algorithms for Early Public Health Threat Detection in Europe v
14 14 14 14 ---- AKFAM AKFAM AKFAM AKFAM –––– TRTRTRTR SIDARTHa associated partner abbreviation foSIDARTHa associated partner abbreviation foSIDARTHa associated partner abbreviation foSIDARTHa associated partner abbreviation for the University Hospital Antalya, Turkeyr the University Hospital Antalya, Turkeyr the University Hospital Antalya, Turkeyr the University Hospital Antalya, Turkey
AMPDSAMPDSAMPDSAMPDS Advanced Medical Priority Dispatch SystemAdvanced Medical Priority Dispatch SystemAdvanced Medical Priority Dispatch SystemAdvanced Medical Priority Dispatch System
C 1, C2, C3C 1, C2, C3C 1, C2, C3C 1, C2, C3 Detection AlgorithmDetection AlgorithmDetection AlgorithmDetection Algorithm used in the Early Abused in the Early Abused in the Early Abused in the Early Abererererrrrration Reporting Systemation Reporting Systemation Reporting Systemation Reporting System
CDCCDCCDCCDC Centers for DiseCenters for DiseCenters for DiseCenters for Disease Control and Preventionase Control and Preventionase Control and Preventionase Control and Prevention
CUSUMCUSUMCUSUMCUSUM Cumulative SumCumulative SumCumulative SumCumulative Sum
D6D6D6D6 Deliverable No. Deliverable No. Deliverable No. Deliverable No. 6666 of the SIDARTHa projectof the SIDARTHa projectof the SIDARTHa projectof the SIDARTHa project
DEDEDEDE GermanyGermanyGermanyGermany
EARSEARSEARSEARS Early Aberration Reporting SystemEarly Aberration Reporting SystemEarly Aberration Reporting SystemEarly Aberration Reporting System
ECDCECDCECDCECDC European Centre for Disease Prevention and ControlEuropean Centre for Disease Prevention and ControlEuropean Centre for Disease Prevention and ControlEuropean Centre for Disease Prevention and Control
ESSENCEESSENCEESSENCEESSENCE Surveillance System for the Early Notification of CommunitySurveillance System for the Early Notification of CommunitySurveillance System for the Early Notification of CommunitySurveillance System for the Early Notification of Community----based Epidemicsbased Epidemicsbased Epidemicsbased Epidemics
EUEUEUEU European UnionEuropean UnionEuropean UnionEuropean Union
8 8 8 8 –––– FOD Health DG 1 FOD Health DG 1 FOD Health DG 1 FOD Health DG 1 –––– BE BE BE BE SIDARTHa associated partner abbreviation for the federal public service health, food chain safety and environment, SIDARTHa associated partner abbreviation for the federal public service health, food chain safety and environment, SIDARTHa associated partner abbreviation for the federal public service health, food chain safety and environment, SIDARTHa associated partner abbreviation for the federal public service health, food chain safety and environment,
BelgiumBelgiumBelgiumBelgium
2 2 2 2 ---- GEOMED GEOMED GEOMED GEOMED –––– DE DE DE DE SIDARTHa associated partner abbreviation for the GEOMED Research Forschungsgesellschaft mbH, GermanySIDARTHa associated partner abbreviation for the GEOMED Research Forschungsgesellschaft mbH, GermanySIDARTHa associated partner abbreviation for the GEOMED Research Forschungsgesellschaft mbH, GermanySIDARTHa associated partner abbreviation for the GEOMED Research Forschungsgesellschaft mbH, Germany
GISGISGISGIS GeograpGeograpGeograpGeographic Information Systemhic Information Systemhic Information Systemhic Information System
GLMGLMGLMGLM Generalized Linear ModelGeneralized Linear ModelGeneralized Linear ModelGeneralized Linear Model
16 16 16 16 –––– HSanMartino HSanMartino HSanMartino HSanMartino –––– ITITITIT SIDARTHa associated partner abbreviation for the San Martino University HospSIDARTHa associated partner abbreviation for the San Martino University HospSIDARTHa associated partner abbreviation for the San Martino University HospSIDARTHa associated partner abbreviation for the San Martino University Hospital Genoa, Italyital Genoa, Italyital Genoa, Italyital Genoa, Italy
17 17 17 17 –––– HUS HUS HUS HUS –––– NONONONO SIDARTHa associated partner abbreviation for the Haukeland University Hospital Bergen, NorwaySIDARTHa associated partner abbreviation for the Haukeland University Hospital Bergen, NorwaySIDARTHa associated partner abbreviation for the Haukeland University Hospital Bergen, NorwaySIDARTHa associated partner abbreviation for the Haukeland University Hospital Bergen, Norway
ICDICDICDICD International Classification of DiseasesInternational Classification of DiseasesInternational Classification of DiseasesInternational Classification of Diseases
4 4 4 4 –––– ILL GmbH ILL GmbH ILL GmbH ILL GmbH –––– AU AU AU AU SIDARTHa associated partner abbreSIDARTHa associated partner abbreSIDARTHa associated partner abbreSIDARTHa associated partner abbreviation for the Dispatch Centre Tyrol, Austriaviation for the Dispatch Centre Tyrol, Austriaviation for the Dispatch Centre Tyrol, Austriaviation for the Dispatch Centre Tyrol, Austria
7 7 7 7 –––– KAE KAE KAE KAE –––– DE DE DE DE SIDARTHa associated partner abbreviation for the Klinik am Eichert (Clinics of the County of Goeppingen), GermanySIDARTHa associated partner abbreviation for the Klinik am Eichert (Clinics of the County of Goeppingen), GermanySIDARTHa associated partner abbreviation for the Klinik am Eichert (Clinics of the County of Goeppingen), GermanySIDARTHa associated partner abbreviation for the Klinik am Eichert (Clinics of the County of Goeppingen), Germany
9 9 9 9 –––– KUH KUH KUH KUH –––– FIFIFIFI SIDARTHa associated partner abbreviation for the University HospitSIDARTHa associated partner abbreviation for the University HospitSIDARTHa associated partner abbreviation for the University HospitSIDARTHa associated partner abbreviation for the University Hospital Kuopio, Finlandal Kuopio, Finlandal Kuopio, Finlandal Kuopio, Finland
MMMM Month of the SIDARTHa projectMonth of the SIDARTHa projectMonth of the SIDARTHa projectMonth of the SIDARTHa project
MINDMINDMINDMIND Minimaler Notarztdatensatz (minimum emergency physician data set)Minimaler Notarztdatensatz (minimum emergency physician data set)Minimaler Notarztdatensatz (minimum emergency physician data set)Minimaler Notarztdatensatz (minimum emergency physician data set)
NYCDONYCDONYCDONYCDOHHHH New York City Department of HealthNew York City Department of HealthNew York City Department of HealthNew York City Department of Health
5 5 5 5 –––– OMSZ OMSZ OMSZ OMSZ –––– HU HU HU HU SIDARTHa associated partner abbreSIDARTHa associated partner abbreSIDARTHa associated partner abbreSIDARTHa associated partner abbreviation for the viation for the viation for the viation for the National Emergency Medical Service HungaryNational Emergency Medical Service HungaryNational Emergency Medical Service HungaryNational Emergency Medical Service Hungary
3 3 3 3 –––– RegH RegH RegH RegH –––– DK DK DK DK SIDARTHa associated partner abbreviation for the Capital Region DenmarkSIDARTHa associated partner abbreviation for the Capital Region DenmarkSIDARTHa associated partner abbreviation for the Capital Region DenmarkSIDARTHa associated partner abbreviation for the Capital Region Denmark
Developing Algorithms for Early Public Health Threat Detection in Europe vi
RLSRLSRLSRLS Recursive Least SquareRecursive Least SquareRecursive Least SquareRecursive Least Square
RODSRODSRODSRODS RealRealRealReal----time time time time OutbreakOutbreakOutbreakOutbreak and Disease Surveillance and Disease Surveillance and Disease Surveillance and Disease Surveillance
6 6 6 6 –––– SAMU SAMU SAMU SAMU –––– FR FR FR FR SIDARTHa assSIDARTHa assSIDARTHa assSIDARTHa associated partner abbreviation for the System of Emergency Medical Assistance Garches, Franceociated partner abbreviation for the System of Emergency Medical Assistance Garches, Franceociated partner abbreviation for the System of Emergency Medical Assistance Garches, Franceociated partner abbreviation for the System of Emergency Medical Assistance Garches, France
SIDARTHaSIDARTHaSIDARTHaSIDARTHa European Emergency DataEuropean Emergency DataEuropean Emergency DataEuropean Emergency Data----based System for Information on, Detection and Analysis of Risks and Threats to Healthbased System for Information on, Detection and Analysis of Risks and Threats to Healthbased System for Information on, Detection and Analysis of Risks and Threats to Healthbased System for Information on, Detection and Analysis of Risks and Threats to Health
SMARTSMARTSMARTSMART Small Area Regression and TestingSmall Area Regression and TestingSmall Area Regression and TestingSmall Area Regression and Testing
15 15 15 15 –––– UNIBI UNIBI UNIBI UNIBI –––– DE DE DE DE SIDARTHa associated partner abbreviation for the University of Bielefeld, GermanySIDARTHa associated partner abbreviation for the University of Bielefeld, GermanySIDARTHa associated partner abbreviation for the University of Bielefeld, GermanySIDARTHa associated partner abbreviation for the University of Bielefeld, Germany
1 1 1 1 –––– UNICAN UNICAN UNICAN UNICAN –––– ES ES ES ES SIDARTHa associated partner abbreviation for the University of Cantabria, SpainSIDARTHa associated partner abbreviation for the University of Cantabria, SpainSIDARTHa associated partner abbreviation for the University of Cantabria, SpainSIDARTHa associated partner abbreviation for the University of Cantabria, Spain
USAUSAUSAUSA United States of AmericaUnited States of AmericaUnited States of AmericaUnited States of America
WPWPWPWP Work Package of the SIDARTHa projectWork Package of the SIDARTHa projectWork Package of the SIDARTHa projectWork Package of the SIDARTHa project
WHOWHOWHOWHO World Health OrganizationWorld Health OrganizationWorld Health OrganizationWorld Health Organization
13 13 13 13 –––– ZZSHMP ZZSHMP ZZSHMP ZZSHMP –––– USZS USZS USZS USZS –––– CZ CZ CZ CZ SIDARTHa associated partner abbreviation for the Emergency Medical Service Prague, Czech RepublicSIDARTHa associated partner abbreviation for the Emergency Medical Service Prague, Czech RepublicSIDARTHa associated partner abbreviation for the Emergency Medical Service Prague, Czech RepublicSIDARTHa associated partner abbreviation for the Emergency Medical Service Prague, Czech Republic
Developing Algorithms for Early Public Health Threat Detection in Europe vii
This report presents the results of the sixth Work Package
(WP) of the European Commission co-funded project
“SIDARTHa – European Emergency Data-based Syndromic
Surveillance System” and forms deliverable D6 as defined in
the Grant Agreement (No. 2007208). The results presented
in this report were compiled by the leader of the Dutch
Country Consortium and the Scientific/Technical Coordination
Office. The authors would like to thank all project group
members for their contributions:
Country Consortia (leading organisations/Associated Country Consortia (leading organisations/Associated Country Consortia (leading organisations/Associated Country Consortia (leading organisations/Associated
ParParParParttttners)ners)ners)ners)
� AustriAustriAustriAustria:a:a:a: Dispatch Centre Tyrol, Ing. Gernot Vergeiner,
Andreas Maurer (4 – ILL GmbH – AU)
� Belgium:Belgium:Belgium:Belgium: Federal Government, Department of Public
Health, Dr. Agnes Meulemans, Dr. Jean Bernard Gillet
(8 – FOD Health DG 1 – BE)
� Czech Republic:Czech Republic:Czech Republic:Czech Republic: Emergency Medical Service Prague,
Dr. Milana Pokorná, Dr. Petr Zajíĉek
(13 – ZZSHMP-USZS – CZ)
� Denmark:Denmark:Denmark:Denmark: Capital Region, Prof. Freddy Lippert
(3 – RegH – DK)
� Finland:Finland:Finland:Finland: University Hospital Kuopio, Dr. Jouni Kurola,
Dr. Tapio Kettunen (9 – KUH – FI)
� France:France:France:France: Emergency Medical Service Province Hauts de
Seine, Dr. Michel Baer, Dr. Anna Ozguler
(6 – SAMU – FR)
� Germany:Germany:Germany:Germany: Hospitals of the County of Goeppingen, Prof.
Matthias Fischer, Dr. Martin Messelken (7 – KAE – DE)
� Hungary:Hungary:Hungary:Hungary: National Emergency Medical Service, Dr. Gábor
Gőbl (5 – OMSZ – HU)
� Italy:Italy:Italy:Italy: San Martino University Hospital Genoa,
Prof. Francesco Bermano, Dr. Lorenzo Borgo
(16 – HSanMartino – IT)
� Norway:Norway:Norway:Norway: Haukeland University Hospital Bergen,
Dr. Guttorm Brattebø, Lars Myrmel (17 – HUS – NO)
� Spain:Spain:Spain:Spain: University of Cantabria, Prof. Luis Garcia-Castrillo
Riesgo, Weyma Notel, Juan José San Miguel Roncero, Prof.
Francisco Javier Llorca Diaz (1 – UNICAN – ES)
� Turkey:Turkey:Turkey:Turkey: University Hospital Antalya, Dr. Hakan Yaman,
Sercan Bulut (14 - AKFAM – TR)
The country consortia consist of emergency medical care
institutions and local/regional public health authorities.
New Country Consortium (leading organisation, from New Country Consortium (leading organisation, from New Country Consortium (leading organisation, from New Country Consortium (leading organisation, from
Early detection of public health threats relies on two major
components: timely and reliable data and the sensitivity,
specificity, and timeliness of detection algorithms. Detection
algorithms should be assessed considering costs of false
alerts versus the delay for a confirmed true alert. There are
three major groups of detection algorithms: control charts,
temporal modelling approaches and spatial-temporal
algorithms (Siegrist and Pavlin 2004 (1), Mandl et al. 2004
(2)).
Cumulative Sum (CUSUM)Cumulative Sum (CUSUM)Cumulative Sum (CUSUM)Cumulative Sum (CUSUM)
CUSUM is a short-term surveillance algorithm to indicate
recent data changes by comparing moving averages. Major
syndromic surveillance systems applying CUSUM are CDC’s
BioSense and EARS, the syndromic surveillance system of the
New York City Department of Health (NYCDOH) and FirstWatch.
Variations from the average of more than two standard
deviations issue an alert. Because of high variations in the
data individual CUSUM values are calculated for each data
source – syndrome combination at a regional level. CUSUM
can be applied to data from population and hospital-based
reporting systems. It controls for fixed confounders, and
hence allows for more robust evaluation of potential
associations than methods based on spontaneous adverse
event reporting. CUSUM detects aberrations very quickly and
is able to detect small shifts from the mean (Hutwanger et al.
2003 (3)).
C1, C2, C3 C1, C2, C3 C1, C2, C3 C1, C2, C3
The C1, C2, C3 detection algorithm which is applied in the CDC
syndromic surveillance system EARS standardises each
observation by using a moving sample average and sample
standard deviation Fricker et al. 2008 (9). The C1 algorithm
uses seven previous days to calculate the sample average
and sample standard deviation. For the C2 algorithm the same
threshold as for the C1 algorithm applies. The C3 method
uses the C2 statistic from the current day and two days prior
to the current observation. C1 is better for detection of point-
source distribution while C2 is more sensitive than C1 in
signalling a continued outbreak. With emphasis on timely
detection of outbreaks within the first few days of onset C2 is
suggested. It appears to be also least effected by serial
correlation. C3 is not more sensitive than C2 once the false
alert rate is held constant (Jackson et al. 2007 (4),
Hutwagner et al. 2003 (3), Zhu et. al. 2005 (5)).
Linear ModelsLinear ModelsLinear ModelsLinear Models
The RecursivRecursivRecursivRecursive Least Square (RLS)e Least Square (RLS)e Least Square (RLS)e Least Square (RLS) algorithm is an
autoregressive linear model applied for example in the Real-
time Outbreak and Disease Surveillance (RODS) syndromic
surveillance system. It predicts the current amount of
syndrome cases within a region based on historical data and
adjusts its model coefficients based on prediction errors. An
alert is triggered above the 95% CI of the estimated number
of cases (Najmi and Magruder 2005 (6)).
The Generalized Linear Model (GLM)Generalized Linear Model (GLM)Generalized Linear Model (GLM)Generalized Linear Model (GLM) uses a three-year
baseline and Poisson errors adjusting for day of week,
holiday, monthly and linear time trends. The model was found
to be more sensitive than C1, C2, C3. A weakness is the
detection of only large, rapidly increasing case numbers
(Jackson et al. 2007 (4)). The Generalised Linear MixedThe Generalised Linear MixedThe Generalised Linear MixedThe Generalised Linear Mixed
Model (GLMM) Model (GLMM) Model (GLMM) Model (GLMM) estimates the probability that a subject under
surveillance is a case, per spatial and temporal unit. Such a
model can be used especially for varying population sizes. The
model does not detect clusters extended across two
neighbouring spatial units (Kleinmann et al. 2004 (7)). The
Small Area Regression and TestingSmall Area Regression and TestingSmall Area Regression and TestingSmall Area Regression and Testing (SMART) model is an
adoption of the GLMM taking into account multiple
comparisons controlling also for day of week, holiday and day
after holiday, and seasonal trends. Predictions are based on a
Poisson distribution of the events (Kleinmann et al. 2005
(8)). SMART is used in the CDC’s BioSense syndromic
surveillance system.
Developing Algorithms for Early Public Health Threat Detection in Europe 4
3333 Applying Applying Applying Applying Detection Detection Detection Detection Algorithms in Algorithms in Algorithms in Algorithms in the SIDARTHa the SIDARTHa the SIDARTHa the SIDARTHa
Implementation SImplementation SImplementation SImplementation Sitesitesitesites: First Results: First Results: First Results: First Results
fakten/statistik/downloads/BEV2008.pdf, accessed January 2010)
Recoded variables are
� Gender was recoded from text into a numeric, nominal
(dichotomous) variable;
� The zip code and the zip category (region) of the patient
were added and recoded from the free text
(alphanumerical) variable Residence.
EMD: City of Innsbruck, County of Innsbruck, District of EMD: City of Innsbruck, County of Innsbruck, District of EMD: City of Innsbruck, County of Innsbruck, District of EMD: City of Innsbruck, County of Innsbruck, District of
Geographic reference � Place of residence, ZIP-code
Age �
Gender �
Severity �
Syndrome 1-n �
EMEMEMEMD: City of Innsbruck, County of Innsbruck, District of D: City of Innsbruck, County of Innsbruck, District of D: City of Innsbruck, County of Innsbruck, District of D: City of Innsbruck, County of Innsbruck, District of
EMD: City of Innsbruck, County of Innsbruck, District of EMD: City of Innsbruck, County of Innsbruck, District of EMD: City of Innsbruck, County of Innsbruck, District of EMD: City of Innsbruck, County of Innsbruck, District of
The EMD(AT) data set contains information on dispatch
events in the Kufstein district during 2003 to 2008. In this
period 937,604 dispatch events were documented. The data
set of selected events for SIDARTHa contains 500,977 cases.
On average 156,267 events occurred per year. The amount
of dispatch events increased steadily during 2003 to 2008. A
clear linear trend can been observed (R2=0.95, p<0.05).
The number of events in 2008 (n=175,316) is 18% higher
than in 2003 (n=143,861) (Figure 7A). As can be seen in
Figure 7B, on average 83,496 events occurred per year that
were selected for SIDARTHa. The increasing trend of this
amount of relevant events per year is comparable to the
increasing trend of the total amount of events (R2=0.94,
p<0.05). The number of relevant events in 2008 (n =
95,483) is 22% higher than in 2003 (n = 74,338). The
increase of dispatch events over time is a known trend all over
Europe.
On a monthly basis, depending on the analysed year, on
average 12,000 to 14,500 events for the dispatch centre
occurred. Within the selected data set on average 6,000 to
9,000 events occurred. A seasonal variation can be identified:
higher amounts of events in December and January, in March,
and in July in each year. For 2008 a peek in October is visible
in addition (Figure 8A and 8B).
On a daily basis the higher amount of events for the dispatch
centre occurred during weekdays with an average of 496
events. During the weekend the average amount of events
decreased to 256 (Figure 9A). The same trend can be
observed in the selected data set with an average of 245
cases per day. During weekends, the average amounts of
events decrease to 186 cases per day.
GermanyGermanyGermanyGermany: County of: County of: County of: County of GöppingenGöppingenGöppingenGöppingen
EP: County of Göppingen EP(DE)EP: County of Göppingen EP(DE)EP: County of Göppingen EP(DE)EP: County of Göppingen EP(DE)
The EP(DE) data set documented 14,869 cases during three
and a half years (01/07/2005 to 31/12/2008).
The yearly amount of cases can be seen in Figure 10.
Approximately 4,500 cases occurred in 2006 and 2007. In
2005 data were only available for the second half of the year.
The proportional amount of cases in 2005 and the total
amount in 2008 were less than in 2006 and 2007. The
reduced amount of cases in 2005 and 2008 becomes also
visible on a monthly and weekly basis (see Figure 11, 12).
On average 354 cases occurred per month. In 2006, 2007,
and 2008 a seasonal variation can be seen during summer.
Increases during autumn and/or winter are particularly visible
in 2007 (Figure 11). The average amount of cases per week
in Goeppingen was 80 (Figure 12) There were no daily
differences in the amount of cases in the EP(DE) data. On a
daily average, irrespective of a working day or during
weekends, 11 to 12 cases occurred. In the investigated
period at least two cases and at maximum 30 cases occurred
per day.
The proportion of male and female cases was mostly equally
distributed. In 2007 a slightly higher proportion of male
patients were treated (Figure 13). The mean age of the
patients was 53.8 years. During summer in 2006 to 2008 the
average age seems to decrease (Figure 14). There is no clear
pattern visible when analysing the data by age category for
2006 to 2008 (Figure 15).
SummarySummarySummarySummary
Yearly increase of events
22% more events in 2008 compared to 200322% more events in 2008 compared to 200322% more events in 2008 compared to 200322% more events in 2008 compared to 2003
Daily amount of events
� Weekdays: Weekdays: Weekdays: Weekdays: x =245=245=245=245
When stratifying baseline in weekdays and weekend days two
signals have been generated by C1. The first occurred on 3rd
March 2008 (Monday); the second one occurred on 1st June
2008 (Sunday).
EMD: City of Innsbruck, County of Innsbruck, District of EMD: City of Innsbruck, County of Innsbruck, District of EMD: City of Innsbruck, County of Innsbruck, District of EMD: City of Innsbruck, County of Innsbruck, District of
2.2.2.2. Mandl KD, Reis B, Cassa C. Measuring outbreakMandl KD, Reis B, Cassa C. Measuring outbreakMandl KD, Reis B, Cassa C. Measuring outbreakMandl KD, Reis B, Cassa C. Measuring outbreak----detection performance by using controlled feature set simulations. MMWR Morb detection performance by using controlled feature set simulations. MMWR Morb detection performance by using controlled feature set simulations. MMWR Morb detection performance by using controlled feature set simulations. MMWR Morb
3.3.3.3. Hutwagner L, Thompson W, Seeman GM, Treadwell T. The bioterrorism preparedness and response Early Aberration Reporting Hutwagner L, Thompson W, Seeman GM, Treadwell T. The bioterrorism preparedness and response Early Aberration Reporting Hutwagner L, Thompson W, Seeman GM, Treadwell T. The bioterrorism preparedness and response Early Aberration Reporting Hutwagner L, Thompson W, Seeman GM, Treadwell T. The bioterrorism preparedness and response Early Aberration Reporting
4.4.4.4. Jackson ML, Baer A, Painter I, Duchin J. A simulation study comparinJackson ML, Baer A, Painter I, Duchin J. A simulation study comparinJackson ML, Baer A, Painter I, Duchin J. A simulation study comparinJackson ML, Baer A, Painter I, Duchin J. A simulation study comparing aberration detection algorithms for syndromic surveillance. g aberration detection algorithms for syndromic surveillance. g aberration detection algorithms for syndromic surveillance. g aberration detection algorithms for syndromic surveillance.
BMC Med Inform Decis Mak. 2007;7:6.BMC Med Inform Decis Mak. 2007;7:6.BMC Med Inform Decis Mak. 2007;7:6.BMC Med Inform Decis Mak. 2007;7:6.
5.5.5.5. Zhu Y, Wang W, Atrubin D, Wu Y. Initial evaluation of the early aberration reporting systemZhu Y, Wang W, Atrubin D, Wu Y. Initial evaluation of the early aberration reporting systemZhu Y, Wang W, Atrubin D, Wu Y. Initial evaluation of the early aberration reporting systemZhu Y, Wang W, Atrubin D, Wu Y. Initial evaluation of the early aberration reporting system--------Florida. MMWR Morb Mortal Wkly Rep. Florida. MMWR Morb Mortal Wkly Rep. Florida. MMWR Morb Mortal Wkly Rep. Florida. MMWR Morb Mortal Wkly Rep.
2005 Aug 26;54 Suppl:1232005 Aug 26;54 Suppl:1232005 Aug 26;54 Suppl:1232005 Aug 26;54 Suppl:123----30.30.30.30.
6.6.6.6. Najmi AH, Magruder SF. An adaptive prediction and detection algorithm for multistream syndromic surveillance. BMC Med Inform Najmi AH, Magruder SF. An adaptive prediction and detection algorithm for multistream syndromic surveillance. BMC Med Inform Najmi AH, Magruder SF. An adaptive prediction and detection algorithm for multistream syndromic surveillance. BMC Med Inform Najmi AH, Magruder SF. An adaptive prediction and detection algorithm for multistream syndromic surveillance. BMC Med Inform
7.7.7.7. Kleinmann KKleinmann KKleinmann KKleinmann KPPPP, Lazarus R, Platt R. , Lazarus R, Platt R. , Lazarus R, Platt R. , Lazarus R, Platt R. A Generalized Linear Mixed Models Approach for Detecting Incident Clusters A Generalized Linear Mixed Models Approach for Detecting Incident Clusters A Generalized Linear Mixed Models Approach for Detecting Incident Clusters A Generalized Linear Mixed Models Approach for Detecting Incident Clusters of Disease in Small of Disease in Small of Disease in Small of Disease in Small
Areas, with an Application tAreas, with an Application tAreas, with an Application tAreas, with an Application to Biological Terrorism. Am Jo Biological Terrorism. Am Jo Biological Terrorism. Am Jo Biological Terrorism. Am J Epidemiol. 2004;Epidemiol. 2004;Epidemiol. 2004;Epidemiol. 2004;159159159159((((3333))))::::217217217217----224224224224....
8.8.8.8. Kleinmann KP, Abrams A, Mandl K, Platt R. Kleinmann KP, Abrams A, Mandl K, Platt R. Kleinmann KP, Abrams A, Mandl K, Platt R. Kleinmann KP, Abrams A, Mandl K, Platt R. Simulation and Other Evaluation Approaches Simulation for Assessing Statistical Simulation and Other Evaluation Approaches Simulation for Assessing Statistical Simulation and Other Evaluation Approaches Simulation for Assessing Statistical Simulation and Other Evaluation Approaches Simulation for Assessing Statistical
Methods of Biologic TerroMethods of Biologic TerroMethods of Biologic TerroMethods of Biologic Terrorism Surveillance. MMWRrism Surveillance. MMWRrism Surveillance. MMWRrism Surveillance. MMWR Morb Mortal Wkly Rep.Morb Mortal Wkly Rep.Morb Mortal Wkly Rep.Morb Mortal Wkly Rep. 2005 Aug 26;54(Suppl):2005 Aug 26;54(Suppl):2005 Aug 26;54(Suppl):2005 Aug 26;54(Suppl):101101101101----108108108108....
9.9.9.9. Fricker RD, Jr., Hegler BL, Dunfee DA. Fricker RD, Jr., Hegler BL, Dunfee DA. Fricker RD, Jr., Hegler BL, Dunfee DA. Fricker RD, Jr., Hegler BL, Dunfee DA. Comparing syndromic surveillance detection methods: EARS' versus a CUSUMComparing syndromic surveillance detection methods: EARS' versus a CUSUMComparing syndromic surveillance detection methods: EARS' versus a CUSUMComparing syndromic surveillance detection methods: EARS' versus a CUSUM----based based based based
methodology. Stat Med. 2008 Jul 30;27(17):3407methodology. Stat Med. 2008 Jul 30;27(17):3407methodology. Stat Med. 2008 Jul 30;27(17):3407methodology. Stat Med. 2008 Jul 30;27(17):3407----29.29.29.29.
10.10.10.10. Hutwagner Hutwagner Hutwagner Hutwagner LC, Thompson WW, Seeman GM, Treadwell T. A simulation model for assessing aberration detection methods used in LC, Thompson WW, Seeman GM, Treadwell T. A simulation model for assessing aberration detection methods used in LC, Thompson WW, Seeman GM, Treadwell T. A simulation model for assessing aberration detection methods used in LC, Thompson WW, Seeman GM, Treadwell T. A simulation model for assessing aberration detection methods used in
public health surveillance for systems with limited baselines. Stat Med. 2005 Feb 28;24(4):543public health surveillance for systems with limited baselines. Stat Med. 2005 Feb 28;24(4):543public health surveillance for systems with limited baselines. Stat Med. 2005 Feb 28;24(4):543public health surveillance for systems with limited baselines. Stat Med. 2005 Feb 28;24(4):543----50.50.50.50.
11.11.11.11. Tokars JI, Burkom H, Xing J, English R, Bloom Tokars JI, Burkom H, Xing J, English R, Bloom Tokars JI, Burkom H, Xing J, English R, Bloom Tokars JI, Burkom H, Xing J, English R, Bloom S, Cox K, et al. Enhancing timeS, Cox K, et al. Enhancing timeS, Cox K, et al. Enhancing timeS, Cox K, et al. Enhancing time----series detection algorithms for automated series detection algorithms for automated series detection algorithms for automated series detection algorithms for automated
12.12.12.12. GarciaGarciaGarciaGarcia----Castrillo Riesgo L, Krafft T, Ziemann A, Fischer M, Lippert F, Vergeiner G, Krämer A, Brand H for Castrillo Riesgo L, Krafft T, Ziemann A, Fischer M, Lippert F, Vergeiner G, Krämer A, Brand H for Castrillo Riesgo L, Krafft T, Ziemann A, Fischer M, Lippert F, Vergeiner G, Krämer A, Brand H for Castrillo Riesgo L, Krafft T, Ziemann A, Fischer M, Lippert F, Vergeiner G, Krämer A, Brand H for tttthe SIDARTHa project group he SIDARTHa project group he SIDARTHa project group he SIDARTHa project group
(ed(ed(ed(edssss.). .). .). .). The SIDARTHa Coding Manual The SIDARTHa Coding Manual The SIDARTHa Coding Manual The SIDARTHa Coding Manual –––– How to generate syndromes based on routinely collected emergency care data for the How to generate syndromes based on routinely collected emergency care data for the How to generate syndromes based on routinely collected emergency care data for the How to generate syndromes based on routinely collected emergency care data for the
European syndromic surveillance system SIDARTHa. European syndromic surveillance system SIDARTHa. European syndromic surveillance system SIDARTHa. European syndromic surveillance system SIDARTHa. 2009, 2009, 2009, 2009, Bad Honnef.Bad Honnef.Bad Honnef.Bad Honnef.