Real Time and Syndromic Surveillance

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1 NATO Centre of Excellence for Military Medicine Centre d Epidémiologie et de Santé Publique des Armées Real Time and Syndromic Surveillance [preferably «epidemiological surveillance for early warning»] Hervé Chaudet Sciences Economiques & Sociales de la Santé & Traitement de l'information Médicale UMR 912 Inserm/IRD/Aix-Marseille Université Centre d épidémiologie et de Santé Publique des Armées, (CESPA), Service de Santé des Armées NATO Deployment Health Surveillance Course (DHSC) - Level 1 COE-MED-M June 2014

2 The beginnings Post 11/09/2001 Prague Summit (November 2002) - NATO adaptation to the threat of terrorism: In the «Prague package»: 5 defence initiatives against WMD, including an interoperable disease surveillance system for early warning «In order to provide Commander s with decision quality information, NATO member nations will have interoperable medical surveillance systems, with common data fields, to identify illness and suspected infectious disease cases in real time based on actual or suspected CBRN threats of operational concern» 2

3 Course outline Why early warning surveillance? How doing early warning surveillance? How RTSS works Focus on some important methodological points Conclusion 3

4 Why early warning surveillance? 4

5 Deployments and biological threats Usual Re-emerging Emerging Accidental Intentional 5

6 Epidemiological surveillance «ongoing, systematic collection, analysis, and interpretation of health data [...], closely integrated with the timely dissemination of these data to those who need to know» Thacker,

7 Epidemiological surveillance «ongoing, systematic collection, analysis, and interpretation of health data [...], closely integrated with the timely dissemination of these data to those who need to know» Thacker, 1994 Practicability 7

8 Epidemiological surveillance «ongoing, systematic collection, analysis, and interpretation of health data [...], closely integrated with the timely dissemination of these data to those who need to know» Thacker, 1994 Practicability Strategy Periodic (weekly) and systematic reporting. Population-exhaustive, But disease-selective («notifiable diseases»). 8

9 Epidemiological surveillance «ongoing, systematic collection, analysis, and interpretation of health data [...], closely integrated with the timely dissemination of these data to those who need to know» Thacker, 1994 Practicability Strategy Periodic (weekly) and systematic reporting. Population-exhaustive, But disease-selective («notifiable diseases»). «Disease» definition 9

10 Epidemiological surveillance «ongoing, systematic collection, analysis, and interpretation of health data [...], closely integrated with the timely dissemination of these data to those who need to know» Thacker, 1994 Practicability Strategy Periodic (weekly) and systematic reporting. Population-exhaustive, But disease-selective («notifiable diseases»). «Disease» definition Diagnoses Clinical pictures or syndromes Environmental exposures 10

11 Choosing a notifiable diseases Significance criteria The disease is a important burden or threat for the population or the individual. The disease is a specific and significant threat for the population or the individual. The disease has a prevention program (and it must be evaluated). Practicability criteria There is a simple and specific clinical [and/or a biological] definition of a case ( diagnosis). A medical or public health response exists. [Not a too frequent disease] 11

12 Examples of systems EPINATO (NATO) Message Epidémiologique Hebdomadaire (FRA) Armed Forces Reportable Medical Events (USA) 12

13 Concept of epidemiological surveillance for early warning Concept: Detect as soon as possible: The start of an expected natural or aggressive health threat. An unknown, unexpected or emerging public health threat. 13

14 Main principles of surveillance for early warning Objectives: For reassurance or prompt response only. Allows the planning of epidemiological stabilising counter-measures during a «window of opportunity» for hindering the epidemiological event. Gives cues and time for a parallel aetiological public health investigation. Strategy: Collection and analysis as soon as possible of early, simple and general health-related data, especially before a confirmed diagnosis. 14

15 Key points Reportable disease strategy = population exhaustive + disease selective Reportable disease = diagnoses, syndromes, exposures Surveillance for early warning: detect an outbreak as soon as possible For reassurance or prompt response only (stabilising counter-measures) Need early data 15

16 How doing early warning surveillance? 16

17 Unsuitability of the traditional surveillance Lengthy delays Weekly periodicity of transmission and analysis. Time required for biological confirmation in case of inclusion criteria. Time lap of 1 to 2 weeks (even more) between the event and its detection. Scotomatous system Looking only at a list of notifiable diseases. 17

18 Solutions Timeliness Real time Earliness Real-time syndromic surveillance (RTSS) Syndromic surveillance Pre-diagnostic / syndromes Broadness 18

19 Syndromic surveillance Real-time (or near real-time) collection, analysis, interpretation and dissemination of health-related data to enable the early identification of the impact (or absence of impact) of potential human or veterinary public-health threats which require effective public health action. Based not on the laboratory confirmed diagnosis of a disease but on nonspecific health indicators including clinical signs, symptoms as well as proxy measures (e.g. absenteeism, drug sales, animal production collapse) that constitute a provisional diagnosis (or "syndrome"). The data are usually collected for purposes other than surveillance and, where possible, are automatically generated so as not to impose an additional burden on data providers. Triple S-AGE, Lancet Nov 26;378(9806):

20 The added value of syndromic surveillance Detect the start of an expected event (e.g. a seasonal event). Based on non-specific and wide ranging health indicators and can thus help detect a broader range of events (expected or unexpected). Demonstrate the lack of public-health impact of a known threat (i.e. provide reassurance). Often based on symptoms reported to health services within the previous 24 hours so can indicate a rise in symptoms in advance of laboratory confirmation. May detect symptoms of known or emerging diseases for which there are no existing disease specific system. Can identify individuals for microbiological sampling. Can be used for short term surveillance during mass gatherings. 20

21 A spectrum of health-related data sources 21

22 A spectrum of health-related data sources Traditional 22

23 A spectrum of health-related data sources Syndromic Traditional 23

24 A spectrum of health-related data sources Environmental Syndromic Traditional 24

25 A spectrum of health-related data sources Candidate for a Role1 surveillance Environmental Syndromic Traditional 25

26 A new surveillance taxonomy Reportable diseases Nosocomial infections Specific surveillance Accidents Epidemiological surveillance Specific data collecting Syndromic surveillance Data reuse Deaths Births... Syndromes (pre-diagnostic) Care activity (ambulances dispatch, overthe-counter sales...) Health related data (absenteism) Social activity (Twitter, Google, forum...) 26

27 Key points Early warning surveillance (EWS) require data: Timeliness Earliness Broadness Current best solution = syndromic surveillance + real time Syndromic surveillance surveillance of syndromes Syndromic surveillance: use of pre-diagnostic and broadly health-related data not specifically collected for epidemiological surveillance purpose Best role 1 EWS: data collected during first contact with healthcare system 27

28 How RTSS works 28

29 Main lines of surveillance for early warning Concept: Detect as soon as possible: The start of an expected natural or aggressive health threat Objectives: Allows the planning of epidemiological stabilising counter-measures during a «window of opportunity» for hindering the epidemiological event. Strategy: Using syndromic surveillance data (unspecific and before a confirmed diagnosis). 29

30 Resulting surveillance dataflow [Outbreak] [Health data] Action 30

31 Resulting surveillance dataflow [Outbreak] [Health data] Situation control Action 31

32 Resulting surveillance dataflow [Health data] [Outbreak] Man-machine collaboration Situation control Action 32

33 Resulting surveillance dataflow [Outbreak] [Health data] Collect Transmit Agregate Analyse Alarm Situation analysis (sense making) Alert Action 33

34 Practical example (ASTER) Combatant Contamination Incubation First signs Consultation Medical team Registration Transmission SYSTEM Window of opportunity EHR Server Signs & symptoms No1fiable diseases EPINATO Counter-measures Requests Analysis Alarms Epidemiologists Situation diagnosis Alert 34

35 Concepts of operations Earliness: man as a detector, as soon as a combatant becomes a patient, before a confirmed diagnosis. Electronic information transmission. Automatic alarm triggering. 35

36 Concepts of operations Earliness: man as a detector, as soon as a combatant becomes a patient, before a confirmed diagnosis. Electronic information transmission. Automatic alarm triggering. More sensitive than specific. 36

37 Concepts of operations Earliness: man as a detector, as soon as a combatant becomes a patient, before a confirmed diagnosis. Electronic information transmission. Automatic alarm triggering. More sensitive than specific. Parallel evaluation of several potential health threats. 37

38 Concepts of operations Earliness: man as a detector, as soon as a combatant becomes a patient, before a confirmed diagnosis. Electronic information transmission. Automatic alarm triggering. More sensitive than specific. Parallel evaluation of several potential health threats. Alarm to alert transformation handled by an epidemiologist team. For reacting within the window of opportunity. Backing a epidemiological command & control. 38

39 Key points Surveillance for action: stabilising counter-measures only Man-machine collaboration Alarm Alert Alarm = detection of epidemiological signal (statistical) anomaly = ringings Alert = detection of a possible outbreak by the man-machine system Time pressure More sensitive than specific 39

40 Focus on some important methodological points 40

41 Return to the surveillance dataflow [Health data] [Outbreak] Special focus Collect Transmit Agregate Analyse Alarm Situation diagnosis (sense making) Alert Action 41

42 Which variable must be collected? Collect Purpose: Detecting diseases related to biological threats (e.g botulism, anthrax, arboviroses...), outbreaks of military interest (e.g. dengue, pertussis, malaria...), chemical threats (environmental or aggressive). Medical information Signs and symptoms Scores Provisional diagnosis Presented during the first contact with the health support Time: apparition date, consultation date, notification date, transmission date Geolocation Population 42

43 Examples of signs, symptoms and scores Collect Fever and general alterations Temperature ( C) Fever, chills Tired, weak Aches and pains Adenopathy Sweating Tension, anxiety Cardio-vascular symptoms Systolic blood pressure (mmhg) Heart rate (pulses/min) Cardiac arrest (transitory) Cutaneous mottling Chest pain Racing heart 43

44 How should the data be aggregated? Agregate Signs, symptoms of provisional diagnoses are aggregated for building «syndromic indicators» or «syndromes» (clinical picture associated to the suspicion of the presence of a disease). Examples Epidemiological scenario Seasonal/epidemic Influenza Respiratory Gastro-intestinal syndrome Extreme temperatures Heat Syndromic indicators useful for monitoring scenario Fever Influenza like-illness Acute respiratory infection (upper and lower) Fever, bronchilolitis, bronchitis, cough, dyspnea, pneumonia, sinusitis, sore throat, etc Diarrhoea, nausea, vomiting, abdominal pain, dehydratation Dehydration, heat stroke, hyponatremia Malaise Injury (falls) Mortality 44

45 Is something happening? Analyse Alarm Epidemic threshold: significative number of excess cases in comparison with what is expected on the basis of previous experience (from Fox et al., 1970). Signal aberration: Change in the occurring of an epidemiological event that become statistically different from historical data (from D. Stroup). Alarm Signal aberration Threshold? Outbreak 45

46 Example of a visualisation tool: control chart Analyse Alarm NOW! Out of control area 10 Running window Signal Expected value -2 In control area weeks 0 Upper control limit (Lower control limit) 46

47 Situation diagnosis Situation diagnosis (sense making) Alert Signal analysis Alarm Perception Population of interest 47

48 Situation diagnosis Situation diagnosis (sense making) Alert What is actually going on? Where? What (from symptoms to possible diseases)? Clinical knowledge base Patient records Geographic information system Signal analysis Alarm Who? Comprehension Perception Population of interest 48

49 Situation diagnosis Situation diagnosis (sense making) Alert Situation refinement What environment for the situation? Medical intelligence Traditional surveillance Clinical knowledge base Patient records Geographic information system Signal analysis Alarm Comprehension Perception Population of interest 49

50 Situation diagnosis Situation diagnosis (sense making) Alert What will most likely happen if...? What evaluation context for the situation? Medical intelligence Doctrines Health resources Weather Projection Medical intelligence Traditional surveillance Clinical knowledge base Patient records Geographic information system Signal analysis Alarm Comprehension Perception Population of interest 50

51 Situation diagnosis Situation diagnosis (sense making) Alert Decisions Counter-measures Medical intelligence Doctrines Health resources Weather Alert Projection Medical intelligence Traditional surveillance Clinical knowledge base Patient records Geographic information system Signal analysis Alarm Comprehension Perception Population of interest 51

52 Key points Collecting of geolocated signs, symptoms and scores fetched from a patient electronic record Signs agregated in syndromes (epidemiological signal) Signal aberration (over a threshold) outbreak Main epidemiological work = sense making about an alarm = situation diagnosis Situation diagnosis = man-machine collaboration 52

53 Conclusion 53

54 Key points of key points Surveillance for early warning: detect an outbreak as soon as possible Current best solution = syndromic surveillance + real time Syndromic surveillance: use of pre-diagnostic and broadly health-related data not specifically collected for epidemiological surveillance purpose Alarm Alert Alarm = detection of epidemiological signal (statistical) anomaly = ringings Alert = detection of a possible outbreak by the man-machine system More sensitive than specific Situation diagnosis = man-machine collaboration 54