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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

Epidemic Intelligence:Signals from surveillance systems

EpiTrain III – Jurmala, August 2006

Anne Mazick, Statens Serum Institut, Denmark

epidemic intelligence
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.
components core functions
Components & core functions

Early warning

Response

indicator vs event based surveillance
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.
scope of this presentation
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

indicator based early warning systems objectives
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
indicator based surveillance
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
current surveillance systems for communicable diseases
Current surveillance systems for communicable diseases

specificity

  • Main attributes
    • Representativity
    • Completeness
    • Predictive positive value

sensitivity

from infection to detection proportion of infections detected
From infection to detectionProportion of infections detected

specificity

50 Shigella notifications (5%)

1000 Shigella infections (100%)

sensitivity

from infection to detection timeliness
From infection to detection:Timeliness

Analyse

Interpret

Signal

time

from infection to detection timeliness12
From infection to detection:Timeliness

Urge doctors to report timely

Frequency of reporting

Immediately, daily, weekly

Analyse

Interpret

Signal

time

from infection to detection timeliness13
From infection to detection:Timeliness

Analyse

Interpret

Signal

time

from infection to detection timeliness14

Automated analysis,

thresholds

Signal

From infection to detection:Timeliness

Signal

Automated analysis,

thresholds

time

potential sources of early signals
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

to detect all events as early as possible
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
current surveillance systems for communicable diseases17
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
principle of signal detection
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
early warning indicators
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
statistical methods for early warning
Statistical methods for early warning
  • Depends on the epidemiology of the disease under surveillance
thresholds
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
weekly notification of food borne illness national ew arn system france 1994 1998

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
use of statistics computer tools
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!
system alert interpretation
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

danish laboratory surveillance system of enteric bacterial pathogens
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
national register of enteric pathogens
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
outbreak algorithm
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
current week 35 past weeks
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

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

what is the most useful
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
early warning signals from mortality surveillance
”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
slide41
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
all cause deaths and influenza like illness ili consultation rate 1998 2004 denmark
All-cause deaths and influenza like illness (ILI) consultation rate, 1998-2004, Denmark

Period of model fitting

Forecast

model testing season 2003 200446
Model testing, season 2003/2004

Signal

disease surveillance

(flu, meningitis etc)

meteorological office

-……

Media reports

Community concern

Rumours

Clinician concern

evaluation of early warning and response systems
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
early warning system in serbia
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
evaluation of alert 2003
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

useful links
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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