Epidemic Intelligence: Signals from surveillance systems

1. Epidemic Intelligence:Signals from surveillance systems EpiTrain III – Jurmala, August 2006 Anne Mazick, Statens Serum Institut, Denmark

2. Epidemic intelligence All the activities related to • early identification of potential health threats • their verification, assessment and investigation • in order to recommend public health measures to control them.

4. Components & core functions Early warning Response

5. Indicator vs. Event-based surveillance • Indicator-based surveillance • computation of indicators upon which unusual disease patterns to investigate are detected (number of cases, rates, proportion of strains…) • Event-based surveillance • the detection of public health events based on the capture of ad-hoc unstructured reports issued by formal or informal sources.

6. Scope of this presentation • What surveillance signals are required for EI • Current communicable disease surveillance • Additional more sensitive surveillance for new, unusual or epidemic disease occurence • Basic requirements for signal detection • Use of early warning surveillance systems 3 examples

7. Indicator-based early warning systemsObjectives • to early identify potential health threats - alone or in concert with other sources of EI in order to recommend public health measures to control them • For new, emerging diseases • For unusual or epidemic occurence of known diseases

8. Indicator-based surveillance • Identified risks • Mandatory notifications • Laboratory surveillance • Emerging risks • Syndromic surveillance • Mortality monitoring • Health care activity monitoring • Prescription monitoring • Non-health care based • Poison centers • Behavioural surveillance • Environmental surveillance • Veterinary surveillance • Food safety/Water supply • Drug post-licensing monitoring

9. Current surveillance systems for communicable diseases specificity • Main attributes • Representativity • Completeness • Predictive positive value sensitivity

10. From infection to detectionProportion of infections detected specificity 50 Shigella notifications (5%) 1000 Shigella infections (100%) sensitivity

11. From infection to detection:Timeliness Analyse Interpret Signal time

12. From infection to detection:Timeliness Urge doctors to report timely Frequency of reporting Immediately, daily, weekly Analyse Interpret Signal time

13. From infection to detection:Timeliness Analyse Interpret Signal time

14. Automated analysis, thresholds Signal From infection to detection:Timeliness Signal Automated analysis, thresholds time

15. Potential sources of early signals • Laboratory test volume • Emergency & primary care total patient volume, syndromes • Ambulance dispatches • Over-the-counter medication sales • Health care hotline • School absenteeism Sensitive systems for new, unusual or epidemic diseases time

16. To detect all events as early as possible • More sensitive case definitions • Cave: sensitivity ↑= false alerts ↑ • costs of response • Social and political distress • Combining information from other sources of epidemic intelligence • Frequency of reporting • Automated analysis • Low alert thresholds

17. Current surveillance systems for communicable diseases • Important source for EI, but… • Additional systems needed to fulfil all EI objectives: • Timeliness • Sensitivity • For rapid detection of new, unusual or epidemic diseases

18. Principle of signal detection • To detect excess over the normally expected • Observed – expected = system alert • What are we measuring? Indicators • What is expected? Need historical data • Which statistics to use? Depends on disease • Where to set threshold? Depends on desired sensitivity

19. Early warning indicators • Early warning indicators • Count • Rates • Number of cases/population at risk/time • Proportional morbidity • % of ILI consultations among all consultations • Percentage of specific cases • case fatality ratio • % children under 1 years among measles cases • % of cases with certain strain

20. Statistical methods for early warning • Depends on the epidemiology of the disease under surveillance

21. Thresholds • Choice of threshold affected by • Objectives, epidemiology, interventions • Absolute value • Count: 1 case of AFP • Rate: > 2 meningo. meningitis/100,000/52 weeks • Relative increase • 2 fold increase over 3 weeks • Statistical cut-off • > 90th percentile of historical data • > 1.64 standard deviations from historical mean • Time series analysis

22. Clinical meningitis, Kara Region, Togo 1997

23. 98 97 96 25- 95 20- 15- 10- 5- 0- 37 50 11 24 37 50 11 24 37 50 11 24 37 50 11 24 Week WeeklyNotificationofFoodBorneIllness,NationalEWARN System,France,1994-1998

26. Use of statistics & computer tools • For systematic review of data on a regular basis • to extract significant changes drowned in routine tables of weekly data • They do not on its own detect and confirm outbreaks! • Epidemiological verification, interpretation and assessment ALWAYS required!

27. Tools do not make early warning systems, but early warning systems need appropriate tools

28. System alert interpretation Every system alert Other sources of epidemic intelligence Media reports Rumours Clinician concern Laboratories Food agencies Meteorological data Drug sales/prescription International networks EWRS Validate & analyse Signal Interpret Public health significance? No Alert Alert

29. Danish laboratory surveillance systemof enteric bacterial pathogens • To detect outbreaks and to analyse long-term trends • Administered by Statens Serum Insitute (SSI) • Danish reference laboratory • Receives all salmonella isolates for further typing • Also gets many other strains, including E. coli., for further typing

30. National register of enteric pathogens • At SSI • Includes everybody who test positive for a bacterial GI infection in Dk. • Person, county, agent, date of lab receiving specimen, travel, no clinical information • First-positives only • Mandatory weekly notifications from all 13 clinical laboratories

31. Outbreak algorithm • Computer program, which calculates if the current number of patients exceeds what we saw at the same time of year in the 5 previous years • Time variable: date of lab receiving specimen • Calculation made each week for specimens received in the week before last • Calculation made by county and nationally • Adjustment for season, long-term trends and past outbreaks • Uses poisson regression, principle developed by Farrington and friends

32. Present counts are compared to the counts in 7 weeks in each of the past 5 years week 46 week 48 week 43 week 49 2003 1999 Current week & 35 past weeks 2004

33. Output • Each week the output is assessed by an epidemiologist • Alerts thought to represent real outbreaks are analysed further • Website www.mave-tarm.dk

35. Point source outbreak

36. Point source outbreak

37. Usefulness: Widespread outbreak

38. S. Oranienburg outbreak • Hypothesis generating interviews (7 cases) • All had eaten a particular chokolade from a german retail store • Outbreak in Germany (400 cases) • Case-control study pointed to chokolade • But the particular chokolade was very popular in Germany (not in Denmark) • Same DNA-profil Werber et al. BMC Infectious Diseases 5 7 (2005)

39. What is the most useful? • Systematic weekly analysis • Defines expected levels • Good to detect widespread outbreaks with scattered cases • Good use of advanced lab typing method

40. ”Early” warning signals from mortality surveillance • Excess deaths • due to known disease under surveillance • Increased incidence • Increased virulence • due to disease/threats not under surveillance • Known diseases • New, emerging threats • Environmental threats • Deliberate release

41. Would mortality surveillance been of use in 2003/04to assess the impact of Fujian influenza on children in Denmark? • Absence of signal • Reassurance of public

42. All-cause deaths and influenza like illness (ILI) consultation rate, 1998-2004, Denmark Period of model fitting Forecast

43. Observed and expected all-cause deaths,1998-2004, Denmark, Excess mortality

44. Model testing, season 2003/2004

45. Model testing, season 2003/2004

46. Model testing, season 2003/2004 Signal disease surveillance (flu, meningitis etc) meteorological office -…… Media reports Community concern Rumours Clinician concern

47. Model testing, season 2003/2004 Signal

48. Observed and expected number of death among children (1-15y), Denmark, 1998-2004

49. Model testing, season 2003/2004

50. Evaluation of early warning and response systems • Important: • usefulness has not been established • investigating false alarms is costly • CDC tool for evaluation of surveillance systems for early detection of outbreaks