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Epidemic Intelligence: Signals from surveillance systems. EpiTrain III – Jurmala, August 2006 Anne Mazick, Statens Serum Institut, Denmark. Epidemic intelligence. All the activities related to early identification of potential health threats

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Epidemic intelligence signals from surveillance systems l.jpg

Epidemic Intelligence:Signals from surveillance systems

EpiTrain III – Jurmala, August 2006

Anne Mazick, Statens Serum Institut, Denmark


Epidemic intelligence l.jpg

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.


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Components & core functions

Early warning

Response


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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.


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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


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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


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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


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Current surveillance systems for communicable diseases

specificity

  • Main attributes

    • Representativity

    • Completeness

    • Predictive positive value

sensitivity


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From infection to detectionProportion of infections detected

specificity

50 Shigella notifications (5%)

1000 Shigella infections (100%)

sensitivity


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From infection to detection:Timeliness

Analyse

Interpret

Signal

time


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From infection to detection:Timeliness

Urge doctors to report timely

Frequency of reporting

Immediately, daily, weekly

Analyse

Interpret

Signal

time


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From infection to detection:Timeliness

Analyse

Interpret

Signal

time


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Automated analysis,

thresholds

Signal

From infection to detection:Timeliness

Signal

Automated analysis,

thresholds

time


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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


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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


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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


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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


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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


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Statistical methods for early warning

  • Depends on the epidemiology of the disease under surveillance


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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


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Clinical meningitis, Kara Region, Togo 1997


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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


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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!


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


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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


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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


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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


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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


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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


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Output

  • Each week the output is assessed by an epidemiologist

  • Alerts thought to represent real outbreaks are analysed further

  • Website www.mave-tarm.dk


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Point source outbreak


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Point source outbreak


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Usefulness: Widespread outbreak


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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)


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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


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”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


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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


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All-cause deaths and influenza like illness (ILI) consultation rate, 1998-2004, Denmark

Period of model fitting

Forecast


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Observed and expected all-cause deaths,1998-2004, Denmark,

Excess mortality


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Model testing, season 2003/2004


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Model testing, season 2003/2004


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Model testing, season 2003/2004

Signal

disease surveillance

(flu, meningitis etc)

meteorological office

-……

Media reports

Community concern

Rumours

Clinician concern


Model testing season 2003 200447 l.jpg

Model testing, season 2003/2004

Signal


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


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Model testing, season 2003/2004


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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


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Early warning system in Serbia

  • ALERT implemented 2002

    To strenghten early detection of outbreaks of epidemic prone and emerging infectious diseases

    • 11 syndromes to detect priority communicable diseases

    • All primary health facilities report weekly aggregated data

    • Complements routine surveillance of individual confirmed cases


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Evaluation of ALERT 2003

  • ALERT detected outbreaks more timely than the routine systems but ALERT did not detect all outbreaks

    • Missed clusters of brucellosis and tularaemia

  • ALERT procedures & response not regulated by law

  • Investigation and verification process that follows system alerts and signals not fully understood

  • Recommendations

    • Add data source (eg emergency wards) to increase sensitivity

    • Better integration with routine system

    • Change in surveillance perspective requires TRAINING!

Valenciano et al, Euro surv 2004; 9(5);1-2


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Useful links

  • CDC. Framework for evaluating public health surveillance systems for early detection of outbreaks. http://www.cdc.gov/mmwr/preview/mmwrhtml/rr5305a1.htm

  • Annotated Bibliography for Syndromic Surveillance http://www.cdc.gov/EPO/dphsi/syndromic/index.htm

  • The RODS Open Source Project, Open Source Outbreak and Disease Surveillance Software http://openrods.sourceforge.net/


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