Epidemic Intelligence: Signals from surveillance systems EpiTrain III – Jurmala, August 2006 Anne...

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


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


AnalyseInterpret

Signal

Exposed

Clinical specim

en

Sym

ptoms

Pos

. specimen

Infected

Seek m

edical attention

Report

time


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

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


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



Signal

Media reportsCommunity concernRumoursClinician concern

disease surveillance(flu, meningitis etc)meteorological office-……


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


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



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/

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