Using routine emergency care data for public health surveillance in Europe
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1 Using routine emergency care data for public health surveillance in Europe Conference Making an Impact: what's new in emergency pre-hospital care research, Cardiff/UK 27 February 2012 Thomas Krafft Alexandra Ziemann Department of International Health, Maastricht University, Netherlands & European Emergency Data Research Network CAPHRI School for Public Health and Primary Care
2 Contents Introduction to syndromic surveillance Syndromic surveillance activities in the UK Syndromic surveillance based on routine prehospital emergency care data SIDARTHa Faculty of Health, Medicine and Life Sciences 2
3 Faculty of Health, Medicine and Life Sciences 3
4 Faculty of Health, Medicine and Life Sciences 4
5 Faculty of Health, Medicine and Life Sciences 5
6 A Case for Syndromic Surveillance Detecting (re)emerging diseases Situational awareness during disasters & mass gatherings Timely and reliable health reporting Human and animal health data Surveillance of communicable & noncommunicable health threats Health care management and planning Surveillance of natural and man-made events Faculty of Health, Medicine and Life Sciences 6
7 Syndromic Surveillance Health surveillance using information generated prior to laboratory confirmed diagnoses to buy time Based on existing data sources, e.g., calls to NHS Direct, GP visits, pharmacy sales Emergency data one main source for syndromic surveillance t* * t = time between detection by syndromic (prediagnostic) surveillance and detection by traditional (diagnosis-based) surveillance Henning, K (2004): Overview of Syndromic Surveillance. What is Syndromic Surveillance? MMWR Supplement September 24, 2004 / 53(Suppl);5-11 Faculty of Health, Medicine and Life Sciences 7
8 Contents Introduction to syndromic surveillance Syndromic surveillance activities in the UK Syndromic surveillance based on routine prehospital emergency care data SIDARTHa Faculty of Health, Medicine and Life Sciences 8
9 Syndromic Surveillance in the UK One of the longest established syndromic surveillance system in Europe (since 1999) Data sources: NHS Direct/NHS 24, emergency departments, GP visits, unscheduled care, pilot: school absenteeism Faculty of Health, Medicine and Life Sciences 9
10 Syndromic Surveillance in the UK Used for various events with (potential) public health impact Usefulness especially during unexpected events for which no other information is rapidly available (situational awareness) Faculty of Health, Medicine and Life Sciences 10
11 Contents Introduction to syndromic surveillance Syndromic surveillance activities in the UK Syndromic surveillance based on routine prehospital emergency care data SIDARTHa Faculty of Health, Medicine and Life Sciences 11
12 Who is using pre-hospital data for syndromic surveillance? USA: First Watch, New York City Denmark: Bioalarm Austria (Tyrol): SIDARTHa India: SEED Australia, Canada, Italy, Switzerland: single studies Faculty of Health, Medicine and Life Sciences 12
13 for what? Influenza-like illness: Australia, Austria, Denmark, USA Heat waves: Australia, Canada, Italy, Switzerland Gastroenteritis: Austria Mass gathering, situational awareness: Austria, France, USA Fever/other infectious diseases: India Faculty of Health, Medicine and Life Sciences 13
14 Example: Denmark, Influenza-like illness Faculty of Health, Medicine and Life Sciences 14
15 Example: Switzerland, Heat wave Faculty of Health, Medicine and Life Sciences 15
16 Example: India, Dengue outbreak Faculty of Health, Medicine and Life Sciences 16
17 Example: USA, Wildfires Faculty of Health, Medicine and Life Sciences 17
18 Contents Introduction to syndromic surveillance Syndromic surveillance activities in the UK Syndromic surveillance based on routine prehospital emergency care data SIDARTHa Faculty of Health, Medicine and Life Sciences 18
19 Our research activities at the interface of emergency care and public health EED : European Emergency Data Project III: European Emergency Data-based Health Monitoring Indicators (co-funded by European Commission) : Emergency care indicators for the EU Injury Data Base (co-funded by European Commission) : SIDARTHa - European Emergency Data-based Syndromic Surveillance System (co-funded by European Commission) : SEED I SIDARTHa@INDIA (co-funded by Indian Council Medical Research & German Ministry Education/Research) : SEED II SIDARTHa@TIROL (co-funded by Leitstelle Tirol Gesellschaft mbh) : SAM@EMR - SIDARTHa@Euregio Maas-Rhine : Triple S-AGE Syndromic Surveillance Systems in Europe (co-funded by European Commission) Faculty of Health, Medicine and Life Sciences 19
20 SIDARTHa: European emergency data-based syndromic surveillance at regional level Partners: Regional consortia of emergency care services and public health authorities from 12 European countries Co-funded by European Commission (DG Sanco), Project elements: Assessment of emergency data utility for syndromic surveillance in Europe Conceptualisation of a European-wide regional syndromic surveillance approach and system Implementation, test and evaluation of the syndromic surveillance system SIDARTHa in four regions in Europe Faculty of Health, Medicine and Life Sciences 20
21 SIDARTHa Approach Faculty of Health, Medicine and Life Sciences 21
22 SIDARTHa Partners Faculty of Health, Medicine and Life Sciences 22
23 SIDARTHa - Basis Faculty of Health, Medicine and Life Sciences 23
24 SIDARTHa - Basis Influenza-like-illness Gastrointestinal Syndrome Environmental Syndrome Respiratory Syndrome Unspecific Syndrome (volume) Faculty of Health, Medicine and Life Sciences 24
25 SIDARTHa - Basis SIDARTHa Data Set Faculty of Health, Medicine and Life Sciences 25
26 SIDARTHa System design Data Transfer Syndromic Alert/Report Communication/ Collaboration Existing Management Pathways Database Emergency Care Institution Daily Database Query Syndromic Surveillance Data Set Field 1: Date Field 2: Syndrome (Field 3: Location) (Field 4: Age) (Field 5: Gender) (Field 6: Severity) Local/Regional Health Authority Emergency Care Institution Syndromic Alert/Report Table C1, C2, C3 CUSUM normal Chart CUSUM Poisson Intervention/ Reporting Resource Management Map Space-Time Scan Statistic Faculty of Health, Medicine and Life Sciences 26
27 SIDARTHa Two active automated systems Faculty of Health, Medicine and Life Sciences 27
28 SIDARTHa Case study results Region: Tyrol (Austria) Event: Norovirus Outbreak (in tourists) Data Source: Ambulance Service (staffed with emergency physicians) Strength: Sensitivity (space-time algorithm) Weakness: Sensitivity: rare disease in emergency care - only severe or large clusters of gastrointestinal cases can be detected Faculty of Health, Medicine and Life Sciences 28
29 SIDARTHa Case study results Region: Cantabria (Spain) Event: Seasonal influenza Data Source: Emergency Department Strength: Timeliness, Specificity (specific case definition, treatment seeking behavoiur), Sensitivity (CUSUM Algorithm) Weakness: Transferability (case definition not as specific in other emergerncy departments/countries or other data sources) Faculty of Health, Medicine and Life Sciences 29
30 SIDARTHa Case study results Region: Tyrol (Austria) Event: Volcanic Ash Plume 2010 (Respiratory syndrome, cardiac syndrome, traffic accidents) Data Source: Emergency Medical Dispatch Data Strength: Flexibility (new syndromes), Timeliness (reporting) Weakness: Specificity: cases cannot be related to ash cloud Faculty of Health, Medicine and Life Sciences 30
31 Future Plans Implementation sites are further developing SIDARTHa (e.g., from signal to alert, communication strategies) Dutch dispatch centres using Advanced Medical Priority Dispatch Systems are interested to install SIDARTHa Chinese Center for Disease Control and Prevention is interested in SIDARTHa and we are exploring potential areas of joint collaboration Faculty of Health, Medicine and Life Sciences 31
32 Pre-hospital emergency data in public health Routine data emergency care Operational data Demographic data Geographic data Treatment data Patient condition data Analysis Demand Technical performance Clinical performance Patient condition Application area in public health Health reporting Early warning Surveillance Benchmarking Prevention Faculty of Health, Medicine and Life Sciences 32
33 Correspondence Thomas Krafft Alexandra Ziemann Department of International Health School of Public Health and Primary Care Maastricht University European Emergency Data Research Network Faculty of Health, Medicine and Life Sciences 33
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