epidemic intelligence: signals from surveillance systems epitrain iii – jurmala, august 2006 anne...
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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
their verification, assessment and investigation
in order to recommend public health measures to control them.
Early warning
Response
Components & core functions
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
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 systems3 examples
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
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
Exposed
Clinical specimen
Symptoms
Pos. specimen
Infected
Seek medical attention
Report Main attributes
– Representativity– Completeness– Predictive positive
value
sensitivity
specificity
From infection to detectionProportion of infections detected
Exposed
Clinical specimen
Symptoms
Pos. specimen
Infected
Seek medical attention
Report
1000 Shigella infections (100%)
50 Shigella notifications (5%)
sensitivity
specificity
Exposed
Clinical specim
en
Sym
ptoms
Pos
. specimen
Infected
Seek m
edical attention
Report
time
From infection to detection:Timeliness
AnalyseInterpret
Signal
Exposed
Clinical specim
en
Sym
ptoms
Pos
. specimen
Infected
Seek m
edical attention
Report
time
From infection to detection:Timeliness
AnalyseInterpret
Signal
Urge doctors to report timely
Frequency of reportingImmediately, daily, weekly
Exposed
Clinical specim
en
Sym
ptoms
Pos
. specimen
Infected
Seek m
edical attention
Report
time
From infection to detection:Timeliness
AnalyseInterpret
Signal
Exposed
Clinical specim
en
Sym
ptoms
Pos
. specimen
Infected
Seek m
edical attention
Report
time
From infection to detection:Timeliness
Automated analysis,thresholds
Signal
Automated analysis,thresholds
Signal
Exposed
Clinical specim
en
Sym
ptoms
Pos
. specimen
Infected
Seek m
edical attention
Report
time
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
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 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
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
– 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 Depends on the epidemiology of the
disease under surveillance
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
Clinical meningitis, Kara Region, Togo 1997
0
5
10
15
20
Week 8 Week 9 Week 10 Week 11 Week 12 Week 13
A.R
. x 1
00.0
00 1997
3 non-epidemic years
Threshold
Weekly Notification of Food Borne Illness,National EWARN System, France,1994-1998
95 96 97 98
37 50 11 24 37 50 11 24 37 50 11 24 37 50 11 24
0-
5-
10-
15-
20-
25-
Week
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!
Tools do not make early warning systems,
but early warning systems need appropriate tools
System alert interpretation
Every system alert
AlertNo Alert
Validate & analyseMedia reportsRumoursClinician concern
LaboratoriesFood agenciesMeteorological dataDrug sales/prescription
International networksEWRS
InterpretPublic health significance?
Other sources of epidemic intelligence
Signal
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
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 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
2004week 48
week 49week 43
2003
1999
week 46
Output
Each week the output is assessed by an epidemiologist
Alerts thought to represent real outbreaks are analysed further
Website www.mave-tarm.dk
Point source outbreak
Point source outbreak
Usefulness: Widespread 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?
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
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
Would mortality surveillance been of use in 2003/04
to 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
0
200
400
600
800
1000
1200
1400
1600
1800
1998
_25
1998
_35
1998
_45
1999
_3
1999
_13
1999
_23
1999
_33
1999
_43
2000
_1
2000
_11
2000
_21
2000
_31
2000
_41
2000
_51
2001
_9
2001
_19
2001
_29
2001
_39
2001
_49
2002
_7
2002
_17
2002
_27
2002
_37
2002
_47
2003
_5
2003
_15
2003
_25
2003
_35
2003
_45
2004
_3
2004
_13
Week
Nu
mb
er o
f d
eath
s
0
5
10
15
20
25
30
ILI
con
sult
atio
n r
ate
Data ILI rate
Period of model fitting Forecast
Observed and expected all-cause deaths,1998-2004, Denmark,
0
200
400
600
800
1000
1200
1400
1600
1800
1998
_25
1998
_35
1998
_45
1999
_3
1999
_13
1999
_23
1999
_33
1999
_43
2000
_1
2000
_11
2000
_21
2000
_31
2000
_41
2000
_51
2001
_9
2001
_19
2001
_29
2001
_39
2001
_49
2002
_7
2002
_17
2002
_27
2002
_37
2002
_47
2003
_5
2003
_15
2003
_25
2003
_35
2003
_45
2004
_3
2004
_13
Week
Nu
mb
er o
f d
eath
s
0
5
10
15
20
25
30
ILI
con
sult
atio
n r
ate
Model 90%CI Data 90%CI ILI rate
Excess mortality
Model testing, season 2003/2004
0
200
400
600
800
1000
1200
1400
1600
2002_19 2002_29 2002_39 2002_49 2003_7 2003_17 2003_27 2003_37 2003_47 2004_5 2004_15
Week
Nr
of
de
ath
s
0
5
10
15
20
25
30
ILI
co
ns
ult
atio
n r
ate
Model 90%CI Data ILI rate
Model testing, season 2003/2004
0
200
400
600
800
1000
1200
1400
1600
2002_19 2002_29 2002_39 2002_49 2003_7 2003_17 2003_27 2003_37 2003_47 2004_5 2004_15
Week
Nr
of
de
ath
s
0
5
10
15
20
25
30
ILI
co
ns
ult
atio
n r
ate
Test season 03/04 Model 90%CI Data ILI rate Series7
0
200
400
600
800
1000
1200
1400
1600
2002_19 2002_29 2002_39 2002_49 2003_7 2003_17 2003_27 2003_37 2003_47 2004_5 2004_15
Week
Nr
of
de
ath
s
0
5
10
15
20
25
30
ILI
co
ns
ult
atio
n r
ate
Test season 03/04 Model 90%CI Data ILI rate Series7
Model testing, season 2003/2004
Signal
Media reportsCommunity concernRumoursClinician concern
disease surveillance(flu, meningitis etc)meteorological office-……
Model testing, season 2003/2004
0
200
400
600
800
1000
1200
1400
1600
2002_19 2002_29 2002_39 2002_49 2003_7 2003_17 2003_27 2003_37 2003_47 2004_5 2004_15
Week
Nr
of
de
ath
s
0
5
10
15
20
25
30
ILI
co
ns
ult
atio
n r
ate
Test season 03/04 Model 90%CI Data ILI rate Series7
Signal
Observed and expected number of death among children (1-15y), Denmark, 1998-2004
0
1
2
3
4
5
6
7
1998
_17
1998
_29
1998
_41
1999
_1
1999
_13
1999
_25
1999
_37
1999
_49
2000
_9
2000
_21
2000
_33
2000
_45
2001
_5
2001
_17
2001
_29
2001
_41
2002
_1
2002
_13
2002
_25
2002
_37
2002
_49
2003
_9
2003
_21
2003
_33
2003
_45
2004
_5
2004
_17
Data (4w MAVG) Forecast Upper 95% CI
0
1
2
3
4
5
6
7
1998
_17
1998
_29
1998
_41
1999
_1
1999
_13
1999
_25
1999
_37
1999
_49
2000
_9
2000
_21
2000
_33
2000
_45
2001
_5
2001
_17
2001
_29
2001
_41
2002
_1
2002
_13
2002
_25
2002
_37
2002
_49
2003
_9
2003
_21
2003
_33
2003
_45
2004
_5
2004
_17
Data (4w MAVG) Forecast Upper 95% CI Test season
0
200
400
600
800
1000
1200
1400
1600
2002_19 2002_29 2002_39 2002_49 2003_7 2003_17 2003_27 2003_37 2003_47 2004_5 2004_15
Week
Nr
of
de
ath
s
0
5
10
15
20
25
30
ILI
co
ns
ult
atio
n r
ate
Test season 03/04 Model 90%CI Data ILI rate Series7
Model testing, season 2003/2004
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
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 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
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/