developing algorithms fordeveloping algorithms for early

SIDARTHaSIDARTHaSIDARTHaSIDARTHa

European Emergency DataEuropean Emergency DataEuropean Emergency DataEuropean Emergency Data----based Syndromic Surveillance Systembased Syndromic Surveillance Systembased Syndromic Surveillance Systembased Syndromic Surveillance System

Grant Agreement No. 2007208Grant Agreement No. 2007208Grant Agreement No. 2007208Grant Agreement No. 2007208

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

Draft report (January 2010)Draft report (January 2010)Draft report (January 2010)Draft report (January 2010)

Developing Algorithms for Early Public Health Threat Detection in Europe ii

© SIDARTHa 2010 DRAFT January 2010

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)

© SIDARTHa 20© SIDARTHa 20© SIDARTHa 20© SIDARTHa 2010101010

SIDARTHa Scientific/Technical Coordination Office, c/o GEOMED Research Forschungsgesellschaft mbH, Hauptstr. 68, D-53604 Bad Honnef, Tel. +49 2224 7799896, Fax. +49 2224 7799897, [email protected], www.sidartha.eu

Developing Algorithms for Early Public Health Threat Detection in Europe iii


ContentsContentsContentsContents

CCCCONTENTSONTENTSONTENTSONTENTS .................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................... IIIIIIIIIIII

FFFFIGURESIGURESIGURESIGURES ................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................ IIIIVVVV

AAAABBREVIATIONSBBREVIATIONSBBREVIATIONSBBREVIATIONS ........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................ VVVV

AAAACKNOWLEDGEMENTSCKNOWLEDGEMENTSCKNOWLEDGEMENTSCKNOWLEDGEMENTS ........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................ VVVVIIIIIIII

1111 INTRODUCTION: THE SIINTRODUCTION: THE SIINTRODUCTION: THE SIINTRODUCTION: THE SIDARTHA PROJECTDARTHA PROJECTDARTHA PROJECTDARTHA PROJECT.................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................... 1111

2222 BACKGROUND, OBJECTIVBACKGROUND, OBJECTIVBACKGROUND, OBJECTIVBACKGROUND, OBJECTIVES & METHODOLOGYES & METHODOLOGYES & METHODOLOGYES & METHODOLOGY .................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................... 3333

2.12.12.12.1 BBBBACKGROUNDACKGROUNDACKGROUNDACKGROUND:::: AAAALGORITHMS FLGORITHMS FLGORITHMS FLGORITHMS FOR OR OR OR EEEEARLY ARLY ARLY ARLY HHHHEALTH EALTH EALTH EALTH TTTTHREAT HREAT HREAT HREAT DDDDETECTIONETECTIONETECTIONETECTION ................................................................................................................................................................................................................................................................................................................................................................................ 3333

2.22.22.22.2 OOOOBJECTIVESBJECTIVESBJECTIVESBJECTIVES .................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................... 4444

2.32.32.32.3 MMMMETHODOLOGYETHODOLOGYETHODOLOGYETHODOLOGY:::: IIIINTRODUCTIONNTRODUCTIONNTRODUCTIONNTRODUCTION ................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................ 5555

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

2.62.62.62.6 SSSSYNDROME YNDROME YNDROME YNDROME GGGGENERATIONENERATIONENERATIONENERATION .................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................... 6666

2222.7.7.7.7 DDDDESCRIPTIVE ESCRIPTIVE ESCRIPTIVE ESCRIPTIVE AAAANALYSISNALYSISNALYSISNALYSIS ............................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................ 6666

2.82.82.82.8 DDDDETECTION ETECTION ETECTION ETECTION AAAALGORITHMSLGORITHMSLGORITHMSLGORITHMS .................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................... 6666

2.92.92.92.9 SSSSOFTWAREOFTWAREOFTWAREOFTWARE ............................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................ 7777

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

3.13.13.13.1 PPPPREPARATIONREPARATIONREPARATIONREPARATION:::: IIIIMPLEMENTATION SITES MPLEMENTATION SITES MPLEMENTATION SITES MPLEMENTATION SITES &&&& DATA SOURCESDATA SOURCESDATA SOURCESDATA SOURCES ................................................................................................................................................................................................................................................................................................................................................................................................ 8888

3.23.23.23.2 SIDARTHSIDARTHSIDARTHSIDARTHA A A A SSSSTANDARD TANDARD TANDARD TANDARD DDDDATA ATA ATA ATA SSSSETETETET .................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................... 10101010

3.33.33.33.3 SSSSYNDROME YNDROME YNDROME YNDROME GGGGENERATIONENERATIONENERATIONENERATION ................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................ 11111111

3.43.43.43.4 DDDDESCRIPTIVE ANALYSISESCRIPTIVE ANALYSISESCRIPTIVE ANALYSISESCRIPTIVE ANALYSIS ........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................ 12121212

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

RRRREFERENCESEFERENCESEFERENCESEFERENCES ................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................ 17171717

Developing Algorithms for Early Public Health Threat Detection in Europe iv


FiguresFiguresFiguresFigures

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


AbbreviationsAbbreviationsAbbreviationsAbbreviations

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

ARIMAARIMAARIMAARIMA Autoregressive integrated moving averageAutoregressive integrated moving averageAutoregressive integrated moving averageAutoregressive integrated moving average

ATATATAT AustriaAustriaAustriaAustria

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

CICICICI Confidence IntervalConfidence IntervalConfidence IntervalConfidence Interval

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

EDEDEDED Emergency DepartmEmergency DepartmEmergency DepartmEmergency Departmentententent

EEDEEDEEDEED European Emergency DataEuropean Emergency DataEuropean Emergency DataEuropean Emergency Data

EMDEMDEMDEMD Emergency Medical DispatchEmergency Medical DispatchEmergency Medical DispatchEmergency Medical Dispatch

EMSEMSEMSEMS Emergency Medical Service Emergency Medical Service Emergency Medical Service Emergency Medical Service

EPEPEPEP Emergency PhysicianEmergency PhysicianEmergency PhysicianEmergency Physician

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

GLMMGLMMGLMMGLMM Generalized Linerar Mixed ModelGeneralized Linerar Mixed ModelGeneralized Linerar Mixed ModelGeneralized Linerar Mixed Model

GPSGPSGPSGPS Global Positioning SystemGlobal Positioning SystemGlobal Positioning SystemGlobal Positioning System

HPRHPRHPRHPR Highest Priority ResponseHighest Priority ResponseHighest Priority ResponseHighest Priority Response

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

ILIILIILIILI InfluenzaInfluenzaInfluenzaInfluenza----LikeLikeLikeLike----IllnessIllnessIllnessIllness

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)

nHPRnHPRnHPRnHPR NonNonNonNon----Highest Priority ResponseHighest Priority ResponseHighest Priority ResponseHighest Priority Response

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

SPO2SPO2SPO2SPO2 Pulse oximetryPulse oximetryPulse oximetryPulse oximetry

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


AcknowledgementsAcknowledgementsAcknowledgementsAcknowledgements

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

SepteSepteSepteSeptemmmmber 2009)ber 2009)ber 2009)ber 2009)

� Netherlands: Maastricht University, Department of

International Health, Prof. Helmut Brand,

Dr. Genc Burazeri, Nicole Rosenkötter, Janneke Kraan

ScientificScientificScientificScientific----Technical partners (Associated Partners/Technical Technical partners (Associated Partners/Technical Technical partners (Associated Partners/Technical Technical partners (Associated Partners/Technical

Unit) Unit) Unit) Unit)

� GEOMED Research Forschungsgesellschaft mbH

(Germany): Dr. Thomas Krafft, Alexandra Ziemann,

Tim Tenelsen, Martina Schorbahn, Nico Reinke,

Dr. Axel Kortevoß (2 – GEOMED – DE)

� University of Bielefeld, Department of Public Health

Medicine (Germany): Prof. Alexander Krämer,

Dr. Paulo Pinheiro (15 – UniBi – DE)

External Scientific Advisory BoardExternal Scientific Advisory BoardExternal Scientific Advisory BoardExternal Scientific Advisory Board

� Prof. Helmut Brand, Maastricht University (Netherlands,

Chair)

� Dr. Andrea Ammon, European Centre for Disease

Prevention and Control (Sweden)

� Dr. Enrico Davoli, World Health Organization Regional

Office for Europe, Division of Country Health Systems

(Spain)

� Dr. Per Kulling, European Commission, Health Threat Unit

(Luxembourg)

� Prof. Francisco Javier Llorca Diaz, University of Cantabria

(Spain)

� Jerry Overton, MPA, Road Safety International (USA)

� Dr. Santiago Rodriguez, Health Service Cantabria (Spain)

� Prof. Mark Rosenberg, Queen’s University, Department of

Geography and Department of Community Health and

Epidemiology (Canada)

Developing Algorithms for Early Public Health Threat Detection in Europe 1


1111 Introduction: The SIDARTHa ProjectIntroduction: The SIDARTHa ProjectIntroduction: The SIDARTHa ProjectIntroduction: The SIDARTHa Project

Syndromic surveillance can detect public health threats earlier

than traditional surveillance and reporting systems. Pre-

hospital emergency medical services (EMS) and emergency

medical dispatch centres (EMD), and in-hospital emergency

departments (ED) across Europe routinely collect electronic

data that provides the opportunity to be used for near real

time syndromic surveillance of communicable and non-

communicable health threats such as heat-related diseases or

Influenza-Like-Illness (ILI). The European Commission co-

funded project SIDARTHa (Grant Agreement No. 2007208) for

the first time systematically explores the use of emergency

data to provide a basis for syndromic surveillance in Europe.

The project started in June 2008 and will run until December

2010. It is an initiative of emergency medical professionals

organised in the European Emergency Data (EED) – Research

Network1.

ObjectivesObjectivesObjectivesObjectives

The objective of the European project SIDARTHa is to

conceptualise, develop, implement/test and evaluate the

European Emergency Data-based System for Information on,

Detection and Analysis of Risks and Threats to Health

(SIDARTHa).

Methodology Methodology Methodology Methodology

During the conceptualisation phase, information on

international state-of-the-art in the early detection of health

threats and on the current practice of health surveillance and

alert systems in Europe are brought together with the

possibilities of emergency data for detection of health threats

and specific public health authority and emergency

professional desires for SIDARTHa’s system features. On this

basis the Geographic Information-System (GIS)-based

surveillance system SIDARTHa will be tested and evaluated

during the implementation phase in four regions2

(cf. Figure 1).

The project group constitutes a high-level expert panel of

emergency professionals, public health experts and health

1 www.eed-network.eu 2 SIDARTHa Implementation sites: District of Kufstein, Austria; Capital Region,

Denmark, County of Goeppingen, Germany, Autonomous Region Cantabria,

Spain

authority representatives under guidance of an

interdisciplinary steering committee. A sequence of focused

methods such as group discussions, Strengths - Weaknesses -

Opportunities - Threats analysis of existing procedures, half-

standardised surveys to seek input from potential futures

users, statistical analyses and modelling, and geo-processing

methods will be applied.

Expected Results & ProductsExpected Results & ProductsExpected Results & ProductsExpected Results & Products

The SIDARTHa project provides a methodology and software

application for syndromic surveillance at the regional level3 in

Europe based on routinely collected emergency data. The

SIDARTHa syndromic surveillance system automatically

analyses the actual demand for emergency services and

detects temporal and spatial aberrations from the expected

demand. The system will automatically alert decision makers in

the emergency medical institution and the regional public

health authority. Via the established reporting ways the

regional public health authority can inform national or

supranational authorities on an event (cf. Figure 2).

It is expected that SIDARTHa improves the timeliness and

cost-effectiveness of European and national health

surveillance by providing a basis for systematic syndromic

surveillance that supplements the existing surveillance

structures.

The main outputs of the project will be a syndromic

surveillance application (software) publicly available free-of-

charge and guidelines for future users on how to use the

application and how to transform emergency data into

syndromes and into the common SIDARTHa data set that the

application can analyse, including recommendations on

technical infrastructure, reporting procedures and

interpretation of the results. Furthermore, the guidelines will

cover the utilisation of the interactive user display and risk

communication platform.

3 In the SIDARTHa project the term regional is used referring to the smallest

administrative level at which a health authority responsible for surveillance

and reporting is established in a European country depending on the national

definition and rules. This level can be a community, city, county, district or

state. The implementation of the SIDARTHa syndromic surveillance system can

be based on data collected for the same administrative level or also for a part

of this area or based on the catchment areas of one or more participating

emergency institutions.



Evaluation

Implemen-

tationInformation

Possibilities

Needs

PHASE I - Conceptualisation PHASE II - Implementation

Project Coordination

Dissemination of Project Results

Project Evaluation

Figure Figure Figure Figure 1111: SIDARTHa Project Methodology: SIDARTHa Project Methodology: SIDARTHa Project Methodology: SIDARTHa Project Methodology

M = Month of the project time

ROUTINE DATA

ROUTINE DATA

ROUTINE DATA

REPORT/ALERT

REPORT/ALERT

REPORT/ALERT

REPORT/ALERT

REPORT/ALERT

REPORT/ALERT

REPORT/ALERT

Routine data from (i) emergency

medical dispatch centres, (ii)

ambulance patient documentations

and (iii) emergency department

information systemsis analysed for spatial and

temporal abberations at the

regional level.

SIDARTHa alerts emergency professionals and regional public

health authorities if a threshold is exceeded;

Via national authorities the European Commission, ECDC and

WHO can be informed about regional and cross-border alerts;

SIDARTHa can be used for risk communication about the event;

SIDARTHa only complements

but does not replace any existing system.

Figure Figure Figure Figure 2222: SIDARTHa Approach: SIDARTHa Approach: SIDARTHa Approach: SIDARTHa Approach

ECDC = European Centre for Disease Prevention and Control, WHO = World Health Organization



2222 Background, Background, Background, Background, ObjectivesObjectivesObjectivesObjectives & Methodology& Methodology& Methodology& Methodology

2.12.12.12.1 Background: Background: Background: Background:

Algorithms for Early Health Algorithms for Early Health Algorithms for Early Health Algorithms for Early Health

Threat DetectionThreat DetectionThreat DetectionThreat Detection

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.



Spatial Scan StatisticSpatial Scan StatisticSpatial Scan StatisticSpatial Scan Statisticssss (SaTScan)(SaTScan)(SaTScan)(SaTScan)

SaTScan is a free software that analyses spatial, temporal,

and space-time data using the spatial, temporal, or space-

time scan statistics. It uses either a Poisson-based model, a

Bernoulli model, a space-time permutation model, an ordinal

model, an exponential model, or a normal model. The software

is used in the syndromic surveillance systems of NYCDOH and

in the United States Department of Defense's Electronic

Surveillance System for the Early Notification of Community-

based Epidemics (ESSENCE) syndromic surveillance system.

SaTScan imposes a circular window on the map and lets the

circle centroid move across the region under study. For any

given position in the centroid, the radius of the window is

changed continuously to take any value between zero and the

upper limit. SaTScan does not require specification of the

location or size of a cluster. It uses a circular window to

determine potential cluster boundaries which may not

represent the population at risk (Kleinmann 2005 (8)).

2.2.2.2.2222 ObjectivesObjectivesObjectivesObjectives

The main objective of this task of Work Package 6 (Task 8) is

to assess the utility of different detection algorithms for the

SIDARTHa syndromic surveillance system by applying different

algorithms on historical emergency data from the four

implementation sites, simulating events and comparing the

detection results with actual public health department reports.

Another objective of the historical data analysis was to test

the coding manual developed as part of WP 5 with real

emergency data to suggest adjustments to the Coding

Manual.



2.2.2.2.3333 MethodologyMethodologyMethodologyMethodology: Introduction: Introduction: Introduction: Introduction

During the first Technical Workshop the implementation site

representatives4 and the technical unit came together with

additional experts for statistical modelling and software

programming to discuss the different established approaches

towards detection methods and a methodology for testing the

utility of detection algorithms for the system. The algorithms

will be adjusted and assessed during simulated events based

on factitious or actual event data provided by regional health

authorities. A methodology for historical data analysis

foreseeing the following steps that are applied to every data

set of the different emergency institutions in every

implementation site was developed:

Preparation of the dataPreparation of the dataPreparation of the dataPreparation of the data: : : :

� Quantity structure

� Data fields/contents

� Data completeness & quality

SIDARTHa SIDARTHa SIDARTHa SIDARTHa Standard Standard Standard Standard Data SetData SetData SetData Set::::

� Possibility to generate the SIDARTHa Standard Data Set

SyndromeSyndromeSyndromeSyndromes:s:s:s:

� Possibility to generate syndromes

DetectionDetectionDetectionDetection algorithms:algorithms:algorithms:algorithms:

� Descriptive analysis of regular patterns in time and

space

� Familiarisation with algorithms and identification of

software for testing algorithms and for automatic

programming of algorithms for SIDARTHa syndromic

surveillance system;

� Test of algorithms for different spatial and temporal

levels, different syndromes and different detection

algorithms, including simulations;

4 SIDARTHa Implementation sites: District of Kufstein, Austria; Capital Region,

Denmark, County of Goeppingen, Germany, Autonomous Region Cantabria,

Spain

� Calculation of initial baselines and thresholds per

algorithm, per implementation site/data set for actual

test runs during test/evaluation phase.

Next to the task leader the implementation sites will be

important partners not only in providing historical data but

also in testing algorithms with their own modelling and

statistical analysis expertise. The Dutch country consortium

leader, Maastricht University, accomplished the historical data

analysis and the tests/simulations.

During the Technical Workshop I it was agreed that the tests

start with the temporal algorithms C1, C2, C3, Holt-Winter-

Smoothing and CUSUM, time series modelling using ARIMA

and then will go on to spatial scan statistics. Software used for

historical data analysis and test of the algorithms will be SPSS,

Microsoft Excel, and SaTScan.

Data from the four implementation sites from previous years

(referred to as historical data) are sent to Maastricht

University.

The historical data analysis can be seen as test phase before

the real implementation of the surveillance system. Deeper

insights in the implementation site specific data are gained.

Variables which are available are identified, and a selection of

variables useful for syndrome generation can be made.

It is explored if regular differences in the occurrence of

emergency cases, like seasonal or daily patterns exist.

Furthermore, knowledge is gained about the overall

occurrence of events. Do cases occur frequently or do they

occur seldom? This information is necessary and prerequisite

for the correct application of detection methods.

In this first draft report historical EMD and ED data from

Austria (State of Tyrol) as well as Emergency Physician (EP)

data from Germany (County of Goeppingen) are analysed. The

historical data analysis is performed first on the overall

amount of cases starting with C1, C2, C3. In the following

months the analysis will be extended to analyse syndrome-

specific events and apply the other algorithms. Data from the

other implementation sites and other emergency institutions

will be included.

The tests and simulations are currently ongoing and will be an

integral part of the implementation phase until mid 2010.

For better readability, all tables and figures are included in a

separate appendix to this report.



2.2.2.2.4444 Preparation of the Data SetsPreparation of the Data SetsPreparation of the Data SetsPreparation of the Data Sets

A general preparation and cleansing of the raw data must take

place in the first step. Useful data variables and data set parts

are selected for syndromic surveillance from the raw data and

the quality and quantity of data are described to get an

overview on the availability of data useful for syndromic

surveillance (e.g., missing data, implausible data entries). In

this step adjustments to the data might be necessary (e.g.,

recoding of text into numeric data fields) to allow an analysis.

2.2.2.2.5555 SIDARTHa Standard Data SetSIDARTHa Standard Data SetSIDARTHa Standard Data SetSIDARTHa Standard Data Set

Seven data fields must be generated from individual

emergency data sets in order to allow the automated

SIDARTHa system to analyse the data:

1. Anonymous case identifying number (necessary)

2. Date/time (necessary)

3. Geographic reference (spatial surveillance)

4. Syndrome (syndromic surveillance)

5. Age (modifier)

6. Gender (modifier)

7. Severity (modifier)

Syndromes in their complexity are analysed in a separate

step. For the other data fields the quantity and quality of all

potential relevant data fields in the original emergency data

set is evaluated which allows for the selection of the most

suitable data variables for each implementation site or

emergency data set.

2.2.2.2.6666 Syndrome GenerationSyndrome GenerationSyndrome GenerationSyndrome Generation

The SIDARTHa syndromes Influenza-Like-Illness,

Gastrointestinal Syndrome, Respiratory Syndrome, Intoxication

Syndrome, and Environment-related Illness can be defined by

using specific variables of the original emergency data sets.

The quantity and quality of all potential relevant data fields in

the original emergency data set is evaluated which allows for

the selection of the most suitable data variables for each

implementation site or emergency data set. The total amount

of cases can be used to identify aberrations in the number of

cases for a defined time period and area, i.e., the Unspecific

Syndrome.

2222....7777 Descriptive AnalysisDescriptive AnalysisDescriptive AnalysisDescriptive Analysis

For the description of the data sets the frequencies of

relevant variables and the mean are calculated. The focus lies

on describing daily or seasonal patterns like cases per year,

month, week and day of the week. Furthermore, the

occurrence according to gender and age of the emergency

cases is examined. This information is a prerequisite and

should be taken into account when applying detection

algorithms for surveillance activities.

2.2.2.2.8888 Detection AlgorithmsDetection AlgorithmsDetection AlgorithmsDetection Algorithms

The SIDARTHa consortium decided to apply the detection

algorithms on a daily basis, which means that each day the

observed and expected frequencies are compared and

assessed.

C1, C2, C3 C1, C2, C3 C1, C2, C3 C1, C2, C3

As a first algorithm the C1, C2, C3 detection algorithm which is

implemented in the early aberration reporting system (EARS)

of the Centers for Disease Control and Prevention (CDC) is

applied.

The C1, C2, C3 detection methods standardise 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.

)(

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The threshold of the C1 algorithm is fixed at the point when

the C1 statistic exceeds a value of three. This is

correspondent to a value being higher than three sample

standard deviations above the sample mean (Fricker et al.

2008 (9)).



The C2 algorithm includes also seven days for calculation but

inserts a 2-day lag to avoid influences of an upswing of a

probable outbreak. Therefore, the observations nine to three

days before the day of interest are included.

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For the C2 algorithm the same threshold as for the C1

algorithm applies. C2 statistics >3 exceed the expected

values (Fricker et al. 2008 (9)).

The C3 method uses the C2 statistic from the current day and

two days prior to the current observation.

[ ]∑−

=

−=2

23 1)(,0max)(t

ti

iCtC

Values > 2 should be judged as alerting signals (Fricker et al.

2008 (9)).

In medical emergency data different patterns of case

occurrence are known. There are more cases during

weekdays than on weekends and also public holidays

influence the amount of emergency cases. Since in C1, C2

and C3 the baseline is build by the amount of cases which

occurred in the same season (7 to 9 days before the current

observation) seasonal variations are taken indirectly into

account (Hutwagner et al. 2005 (10)). Tokars and colleagues

(2009 (11)) modified the algorithms to be able to take also

the day-of-week into account which might increases sensitivity.

Therefore the detection methodology will be used in two

different ways:

1. Taking the last seven to nine days into account as it is

described by Fricker et al. 2008 (9) (referred to as

unstratified baselineunstratified baselineunstratified baselineunstratified baseline)

2. Stratifying baseline data in weekdays and weekend days.

Depending on the day of the current observation,

weekdays or weekend days are used to calculate the

sample mean and standard deviation. This stratification is

known as W2 algorithm (1, 3) (referred to as stratified stratified stratified stratified

baselinebaselinebaselinebaseline).

In the following sections the two ways of applying C1, C2, C3

are used for the overall, daily amount of cases/events. The

predefined thresholds are at a value of three for C1 and C2

and a value of two for the C3 algorithm.

2.2.2.2.9999 SoftwareSoftwareSoftwareSoftware

Data are stored in Microsoft Office Access databases (Version

2003). For descriptive analyses data are exported in SPSS

(Version 15.01). In SPSS frequency tables are generated, the

distribution of missing values is analysed and cleansing of the

data sets has been performed as well as generation of

variables like day of the week, number of the week, etc.

For developing the graphs frequency tables of SPSS are

exported to Microsoft Office Excel (Version 2003).

Furthermore the C1, C2, C3 algorithm has been applied in

Microsoft Office Excel (Version 2003).



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

3333.1.1.1.1 Preparation: Preparation: Preparation: Preparation: ImplementationImplementationImplementationImplementation

sites sites sites sites &&&& data sourcesdata sourcesdata sourcesdata sources

AustriaAustriaAustriaAustria: State of Tyrol: State of Tyrol: State of Tyrol: State of Tyrol

ED: District of Kufstein ED: District of Kufstein ED: District of Kufstein ED: District of Kufstein –––– ED(AT)ED(AT)ED(AT)ED(AT)

The ED(AT) data set contains admissions from the General

Districht Hospital Kufstein. The hospital is settled in the

Kufstein district of the State of Tyrol in Austria. The district

has a geographical area of 969.9 km2. 99,394 inhabitants are

living in the district on 31st December 20095 which results into

a population density of 102 inhabitants per km2.

In this data set, all inpatients that accessed the ED in 2008

(1/1/2008 - 31/12/2008) are included (n = 30,669).

The origin counties/regions of patients are mainly the Tyrol

region and other regions in Austria (93%). 1,571 patients are

from Germany and 558 from other countries.

As a geographic reference, the place of residence, the zip

code and the country code are given in the data set. Patients

can be described by age and gender. Information on

syndromes or diagnosis of the patients is not part of the data

set.

The data set contains no missing values or coding errors

(Table 1). The variables which are currently used are marked

grey in Table 1. For the historical data analysis it was

necessary to add or recode some variables.

Added variables are

� The day of the week (Monday, Tuesday, …Sunday);

� The week number of the year (from 1 to 52).

5 Amt der Tiroler Landesregierung, Raumordnung Statistik. Demografische

Daten Tirol. Innsbruck 2009

(http://www.tirol.gv.at/fileadmin/www.tirol.gv.at/themen/zahlen-und-

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

Kufstein Kufstein Kufstein Kufstein –––– EMD(AT)EMD(AT)EMD(AT)EMD(AT)

The EMD Tyrol currently is responsible for the area of the City

of Innsbruck (118,035 inhabitants), the County of Innsbruck

(164,027 inhabitants) and the District of Kufstein (99,394

inhabitants).

In this data set, all the events of the EMD from 1/1/2003 until

31/12/2008 (six years) are documented (n = 937,604).

The origin countries/regions of reported patients were mainly

the Tyrol region and other regions in Austria (99.75%).

2,112 patients were from Germany and 247 patients were

from other countries. As a geographic reference of the event,

the country, region, zip code, city, street, house-number and

GPS coordinates are given.

No further personal information like gender or age is

represented in the data set. Diagnostic information is given by

AMPDS codes and sub-codes. These variables are used for

syndrome generation.

Since the EMD(AT) data set contains all activities dispatched

by the EMD it was necessary to select the events to be used in

SIDARTHa.

These events are emergency medical events that require

immediate attention and that occur in an unplanned manner.

Planned activities like transportation activities (transporting

patients to regular dialysis appointments, etc.) or stand-by

activities during public events are not selected for syndromic



surveillance. These are therefore excluded when applying the

detection algorithms in historical data analysis.

It turned out that the EMD(AT) data contained 46.5% events

that did not meet the inclusion criteria. After exclusion of

these cases the data set consisted of 500,977 cases. The

documentation of case selection can be found in Table 2.

A description of the available variables is given in Table 3;

rows marked in grey indicate that the variables have been

used for analysis. The dataset contains missing values and

coding errors (Table 3). For historical data analysis it was

necessary to add or recode some variables.

Added variables are


� The week number of the year (from 1 to 52):

� The month of the year (January, February, … December).

Furthermore, a filter variable has been defined to extract the

relevant events. The selection criteria are described in Table

2. The basis for the filter generation was a recoded variable

giving the event type (übergeord_Einsatzstichwort) and the

variable giving the complete event/AMPDS code

(Einsatzcode).

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 County of Goeppingen belongs to the federal State of

Baden-Wuerttemberg in Germany. The county has a

geographical area of 642.4 km2. 255,807 inhabitants were

living in the county on 31st December 20096 which results into

a population density of 398.2 inhabitants per km2.

In this data set, all EP responses from 1/7/2005 until

31/12/2008 (two and a half years) (n = 14,869).

The origin countries/regions of patients are the district of

Göppingen and other regions in Germany. As a geographic

reference of the emergency event, the community code, the

zip code, and place of the event (e.g. home, work place) are

given.

6 Statistisches Landesamt Baden-Wuerttemberg. Statistische Berichte Baden-

Wuerttemberg. Bevoelkerung und Erwerbstaetigkeit. Stuttgart 2009

(http://www.statistik.baden-

wuerttemberg.de/Veroeffentl/Statistische_Berichte/3126_08001.pdf,

accessed January 2010)

Information on age and gender is represented in the data set.

Furthermore, information on diagnosis and severity are

available. The medical information is given as ICD-10 codes

and codes (KRANK 1-8) which are part of a standardised

system used by the physicians in Göppingen to describe the

disability or illness (MIND 2). Additionally, information on

breathing status, oxygen saturation, severity (Glasgow Coma

Scale), pain and body temperature is available. This

information can be used to generate syndromes.

A description of selected variables and their availability is

shown in Table 4. The dataset contains missing values and

coding errors (Table 4). Unfortunately, the field which gives

information on the body temperature contained no valid data.

The only values available do not correspond to a realistic

body temperature (values were -1 or -01). For historical data

analysis it was necessary to add or recode some variables.

Added variables are


� The week number of the year (from 1 to 52).

Due to coding errors it was necessary to recode the variables

� Age: This variable was present (PATALTER) but not

usable in SPSS. Therefore, the age has been newly

calculated on the basis of date of the event (DATUM) and

date of birth of the patient (GEBDAT);

� Gender: Due to some double coding for the same variable

values the existing gender variable (GESCHL) has been

cleansed and the new variable gender has been

generated;

� ATM1, KRANK1, KRANK2, KRANK3, KRANK4, KRANK5,

KRANK6, KRANK7, KRANK8: All of these variables included

two different codes for the same variable value. The

variables have been cleansed and new variables have

been generated.



3333....2222 SIDARTHa Standard Data SetSIDARTHa Standard Data SetSIDARTHa Standard Data SetSIDARTHa Standard Data Set



Data quality in the ED(AT) data set is good; there were no

missing values or coding errors. Five items of the SIDARTHa

Standard Data Set can be generated by information available

in the data set. Out of these items other information relevant

for a surveillance system can be generated (e.g. day of the

week). There is no information available which can be used for

syndrome generation or assessment of severity of the cases.

VariablesVariablesVariablesVariables ED(AT)ED(AT)ED(AT)ED(AT) VariablesVariablesVariablesVariables

Identifier �

Date/time � Date and time

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


For five out of seven SIDARTHa Standard Data Set items

information is available in the data set. Out of these items

other information relevant for a surveillance system can be

generated (e.g. day of the week). There is no information

available on age and gender of the events. The geographic

information is very specific including besides address

information also GPS coordinates. Sufficient information for

syndrome generation is represented in the data set (0.01%

missing values regarding AMPDS codes (variable:

Einsatzcode). Specific information on the severity (NACA

score) is only available for approximately 10% of the events.

So, this information cannot be used in a surveillance system

but the general differentiation into HPR and nHPR events

provides information on severity.

VariablesVariablesVariablesVariables EMD(AT)EMD(AT)EMD(AT)EMD(AT) VariableVariableVariableVariablessss

Identifier �


Geographic reference

�

County, region, zip code, city, street, house number, GPS coordinates

Age �

Gender �

Severity � NACA Score

Syndrome 1-n � AMPDS codes and subcodes



In the EP(DE) data set information on all items of the

SIDARTHa standard data set is available. After data cleansing

the number of coding errors could be decreased remarkably.

Out of these items other information relevant for a

surveillance system can be generated (e.g. day of the week).

Sufficient information for syndrome generation and on severity

is available. Except the body temperature variables useful for

syndrome generation are available. However, the quality of

the variables varies. The first diagnosis coded by ICD-10 is

collected in 70% of the cases. The Goeppingen specific

diagnostic categories (KRANK 1-8) are only collected for one

third of the events, so the amount of cases with missing

values is 68.1%. The amount of missing values for the

following variables give an overview on the availability of

symptomatic information:

� 19% for first respiratory status (AF1);

� 21% for first oxygen saturation status (SAOZ1);

� 12% for first breathing status (ATM1);

� 13% for pain (SCHMERZ1);

� 100% for body core temperature (KTM1).

This leads to the decision that syndromes will be generated

mainly on the basis of the main ICD-10 diagnoses.



VariablesVariablesVariablesVariables EPEPEPEP(DE)(DE)(DE)(DE) VariablesVariablesVariablesVariables

Identifier �


Geographic reference

� community code, zip code, place (e.g., home, work place)

Age �

Gender �

Severity � NACA Score, Glasgow Coma Scale

Syndrome 1-n �

ICD-10 codes, Goeppingen specific codes (KRANK 1-8) and information on different symptoms

3333....3333 Syndrome GenerationSyndrome GenerationSyndrome GenerationSyndrome Generation


In the EMD(AT) data set syndromes can be generated from

the variable Einsatzcode containing AMPDS codes. This

variable describes the event and the chief complaint of the

patient. The exact use of AMPDS codes for syndrome

description can be found in the SIDARTHa Coding Manual

(Garcia-Castrillo Riesgo et al. 2009 (12)).

For the Unspecific Syndrome the total amount of all events

selected for SIDARTHA are used.

In the ED(AT) data set syndrome generation is not possible

since information on symptoms or diagnoses are not

available.


In the EP(DE) data set syndromes can be generated based on

ICD-10 codes. Additional information can be derived out of

eight specific diagnostic variables (KRANK 1-8) as well as

symptomatic variables (first respiratory status (AF1), first

oxygen saturation status (SAOZ1), first breathing status

(ATM1), pain and body core temperature (KTM1)).

ICD-10 codes are subdivided into first to third diagnosis. The

main diagnosis (ICD1) will be used for syndrome generation.

The exact use of ICD-10 codes for syndrome generation can

be found in the SIDARTHa Coding Manual (Garcia-Castrillo

Riesgo et al. 2009 (12)). The application of the variables

KRANK 1-8 and the symptomatic variables for syndrome

generation is described in the following paragraph and is

visualized in Table 5.

The symptomatic variables (first respiratory status (AF1), first

oxygen saturation status (SAOZ1), first breathing status

(ATM1), pain and body core temperature (KTM1)) provide

relevant information for generation of the SIDARTHa

syndromes:

Influenza-Like-

Illness

� Breathing: dyspnoematic, cyanotic, spastic or

rattling

� Respiratory rate: > 20 breaths per minute

� Pulse oximetry oxygen saturation: Sp02 <

95%

� Body core temperature: >38.5° C

Respiratory

Syndrome

� Breathing: dyspnoematic, cyanotic, spastic or

rattling

� Pulse oximetry oxygen saturation: Sp02 <

95%

Gastrointestinal

Syndrome VAS pain score >3



Regarding the variables KRANK1-8 the following selection was

chosen for syndrome generation:

Influenza-Like-

Illness

Airway disorder (KRANK3): Pneumonia/Bronchitis

or other respiratory disease

Respiratory

Syndrome

Airway disorder (KRANK3): Asthma or COPD

exacerbations or aspiration or

Pneumonia/Bronchitis or hyperventilation

or/Tetany or Croup/Epiglottitis or other respiratory

disease

Gastrointestinal

Syndrome

� Abdominal disorders (KRANK4): acute

abdomen or gastrointestinal bleeding or colic

or other disease abdomen

� Metabolic disease (KRANK6): Dehydrated

Intoxication

Syndrome

� CNS disorders (KRANK1): seizure or other

CNS disorders

� Psychiatric disorders (KRANK5): alcohol

intoxication or drug intoxication or intoxication

medical drugs

� Other diseases (KRANK8): other intoxication

For the generation of the Unspecific Syndrome the total

amount of EP(DE) cases is used.

3333....4444 Descriptive analysis Descriptive analysis Descriptive analysis Descriptive analysis



The ED(AT) data set contains information on hospital

admissions through the ED in the District of Kufstein in 2008.

In this period, 30,669 hospital admissions were documented.

On a monthly basis, approximately 2,400 to 2,800 hospital

admissions were counted, with 2,556 admissions per month

on average. A seasonal variation can be identified: more

hospital admissions were documented from January until April

and in July (Figure 1). Since this region is a touristic area with

a lot of skiing activities in the winter periods and hiking

activities during summer, these seasonal increases might be

caused by this circumstance. However, there is no increase in

admissions in August which is also a typical month for summer

holidays.

On a weekly basis, a stable amount of admissions was

documented (approximately 500 to 650), with an average of

579 admissions. As an exception, there were fewer

admissions in the last week of the year (Figure 2).

On a daily basis the higher amount of hospital admissions

occurred on working days (Figure 3.) with approximately 70

to 110 admissions ( x = 97). During the weekend the

amount of admissions decreased to 52 cases on average

(Figure 3).

Females were more often admitted to the hospital than males

(Figure 4). The mean age of admitted patients in 2008 was

52.3 years (Figure 5). Besides the age group of 25-64 years,

patients aged 65 or older were admitted most frequently

(Figure 6). Remarkable was the decrease in the admission of

elderly in week 32 and 52. At the same time, the admission of

children and adolescents increased (Figure 5, Figure 6).

SummarySummarySummarySummary

Daily amount of admissions

� Weekdays: x = 97

� Weekend days: x =52

Seasonal pattern

Higher amount of cases from January to April, and in July





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.



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).


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

� Weekend days: Weekend days: Weekend days: Weekend days: x =186=186=186=186

Seasonal pattern

Higher amount of cases in December and January,

March, and in July


� No differences in the daily amount of cases has been

observed

� Number of cases per day on average 11 to 12

Seasonal pattern

Higher amount of cases in June and July



3333....5555 Results of the detection Results of the detection Results of the detection Results of the detection

alalalalgorithm C1, C2, C3gorithm C1, C2, C3gorithm C1, C2, C3gorithm C1, C2, C3



Unstratified baselineUnstratified baselineUnstratified baselineUnstratified baseline

Data are available for 2008. No aberration from the expected

values could be identified by C1, C2, C3 (Figure 16).

Stratified baselineStratified baselineStratified baselineStratified baseline

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).



UnstraUnstraUnstraUnstratified baselinetified baselinetified baselinetified baseline

Data are available for 2003 to 2008. For signal detection only

the relevant events (n=500,977) were included. On average

C1 signals 1 time per year, C2 signals 1.8 and C3 4.5 times

per year. For most of the years signals occurred mainly in the

beginning of the year, during summer, or in the end of

December.

Table 6 shows the date and the day of the week of the signals

which could be detected from 2003 to 2008. The results will

be summarized later on together with the results of the

stratified analysis.

In 2003 one C2 signal and three C3 signals have been

detected. These signals occurred in the beginning of August

(Table 6, Figure 17).

In 2004 one C1 and one C2 signal have been observed as

well as three C3 signals. All signals occurred in the beginning

of February (Table 6, Figure 18).

In 2005 one C2 signal and three consecutive C3 signals

occurred. They occurred in the end of June (Table 6, Figure

19).

In 2006 two C1 and C2 signals and six C3 signals have been

identified. There are two major time periods: First, at the end

of July, second, in the middle and at the end of December


In 2007, two C1 signals occurred in mid-May and at the end

of December. There were also two C2 and four C3 signals in

mid-May. At the end of December three C2 and five C3 signals

have occurred (Table 6, Figure 21).

In 2008 one C1 signal occurred in the beginning of March.

One C1 and one C2 signal as well as three C3 signals occurred

in the end of December (Table 6, Figure 22).

C2 and C3 signaled often on the same day which was mostly

followed by C3 signals on two consecutive days.


After stratifying weekdays and weekend days for baseline

determination the overall number of C1, C2, C3 signals

increased approximately five-fold. The unstratified analysis

resulted into 44 signals, when using a stratified baseline 206

signals were detected. On average C1 signals six times per

year, C2 signals eight and C3 signals 20 times per year.

Figure 23 shows the occurrence of signals in a calendar

format. Signals of the unstratified analysis are also indicated.

Aberrations from baseline occurred nearly every year in the

beginning of January and end of December, mostly after

Christmas. Only in December 2005 and January 2006 no

signals have occurred.

Furthermore, signals occurred always in one or two spring

and summer months. Signals during autumn occurred in

2003, 2006, 2007, and 2008.



Unstratified baselineUnstratified baselineUnstratified baselineUnstratified baseline

Data are available for July 2005 to 2008. Table 7 shows the

signals which could be detected in this period. Several signals

occurred over the years 2005 to 2008.

In 2005 in total, fifteen signals occurred, four C1 signals,

three C2 signals and eight C3 signals. These signals occurred

in mid July, at the end of September and at the beginning of

October. Thus, most of the year’s signals are in summer and

autumn of 2005 (Table 7, Figure 24).



Summary 2005Summary 2005Summary 2005Summary 2005

Signals occurred in summer as well as in autumn 2005.

In 2006 in total, 47 signals occurred. Twelve C1 signals, nine

C2 signals and twenty-six C3 signals have been detected


These signals broadly occurred in the end of January, in the

beginning of March, in mid April, in the beginning of August, in

mid August, in the end of September, in the end of November

and in the beginning of December. Thus, most of the year’s

signals occurred over the whole year, except for the early

summer. As can be seen in Table 7, signals are occurring on

mainly every day of the week, but a higher rate of signals

occurred on weekdays.


Signals occurred over the whole year. There are a little bit

more signals on weekdays.

In 2007 in total, 45 signals occurred. Nine C1 signals, ten C2

signals and twenty-six C3 signals have been detected (Table

7, Figure 26).

These signals broadly occurred at the beginning of January, at

the end of March, at the end of April, in mid June, at the end

of June, at the end of September, in mid October and at the

end of November. Thus, most of the year’s signals occurred

over the whole year, no clear pattern can be seen. Signals are

occurring on every day of the week, in this, also no clear

pattern can be found.


Signals occurred over the whole year. No clear signal pattern

can be seen on months or weekdays.

In 2008 in total 36 signals occurred. Seven C1 signals, eight

C2 signals and twenty-one C3 signals have been detected.

These signals broadly occurred at the beginning of January, at

the end of March, at the end of May, at the end of August, at

the end of September and in mid/at the end of November.

Thus, most of the year’s signals occurred over the whole year.

The signals seem to occur mainly in the end of the months,

but no clear pattern could be seen in the week- or weekend-

days (Table 7, Figure 27).


Signals occurred over the whole year. Mainly the signals

occurred in the end of the months.

In every year it has been observed that C1, C2, C3 (or C2, C3)

signal mostly together which is followed by a C3 signal on the

two consecutive days (Table 7).


After stratifying weekdays and weekend days for baseline

determination the overall number of C1, C2, C3 signals stayed

nearly the same. 95 signals occurred before stratification and

105 signals occurred after. On average C1 signals 4.5 times

per year, C2 signals nine and C3 signals 25.5 times per year.

All signals also from the analysis with unstratified baseline can

be found in a calendar format in Figure 28.

In 2005 in total 22 signals occurred. Four C1 signals, four C2

signals and twelve C3 signals have been detected. These

signals occurred over the whole year, from July to November,

except for December. Thus, no clear pattern in months can be

seen in the signals in 2005.

In 2006 in total 49 signals occurred. Nine C1 signals, ten C2

signals and 30 C3 signals have been detected. These signals

occurred broadly in all the months of the whole period. Thus,

no clear pattern in months can be seen in the signals in 2006.

In 2007 in total 36 signals occurred. Eight C1 signals, seven

C2 signals and 21 C3 signals have been detected. These

signals occurred over the first months of the year, from the

beginning of October the amount of signals declines. Thus,

most of the signals occur before October 2007.

In 2008 in total 44 signals occurred. Eight C1 signals, ten C2

signals and 26 C3 signals have been detected. These signals

occurred over the whole year but the amount of signals seems

to be less in spring.

As it has been described for the unstratified analysis in most

cases C1, C2, C3 (or C2, C3) signal together and C3 signals

on the two consecutive days.



4444 Summary Summary Summary Summary &&&& Next StepsNext StepsNext StepsNext Steps

In the last section data from two test sites have been

described. These data sets have been generated from three

different emergency medical care services, namely EMD

events, EP responses and ED admissions.

It turned out that most events per day occurred in the

EMD(AT) data set (weekdays: 245, weekend: 186), followed

by the ED(AT) data set (weekdays: 97, weekend: 52) and the

smallest amount have been seen in the EP(DE) data set (11

to 12 events).

On these data sets an easy-to-use detection algorithm (C1,

C2, C3) has been applied. We analysed the overall occurrence

of cases or events and tested for aberration from the

expected numbers based on the frequencies of the previous

days.

When applying C1, C2, C3 on the total amount of events per

day the lowest amount of signals occurred in the ED(AT) data

set. There were no signals when using an unstratified baseline

and two signals in the stratified analysis.

The highest amount of signals occurred in the EP(DE) data

set, the data set with the lowest amount of cases per day. The

amount of signals in this data set stayed stable irrespective if

the baseline was calculated after stratification in weekdays

and weekend days or without stratification. Furthermore,

signals identified by stratified analysis in the EP(DE) data set

happened quite often on different days compared to the

unstratified analysis. Whereas in the EMD(AT) data, signals of

the unstratified analysis were mostly confirmed by the

stratified one. However, in the Tyrol dispatch data set the

number of signals increased five-fold when calculating a

stratified baseline.

In the EP(DE) data set signals occurred all over the year

without a clear pattern. In the EMD(AT) data set a clear

pattern of signals could be observed. Signals occurred every

year – despite 2005 – at the end of December. Furthermore

in almost every year a clear cluster of signals occurred in

summer, in 2003 and 2008 several signals in autumn have

occurred. Signals in spring were more scattered in the

investigated years.

After description of data sets and the first application of an

detection algorithm investigation of background information is

necessary in order to be able to interpret the signals identified

in the data sets.

Furthermore, it is intended to apply other detection algorithms

and to analyse aberrations by syndrome.



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