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Transcript of 1 Outbreak Investigation WHO
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SEARO CSR Training on Outbreak Investigation
Outbreak Investigation
Best Practice/MethodsPractical Reference Points
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Learning Objectives
At the end of the session, the participants
will be able to:
List down the objectives of outbreak
Describe steps in outbreak investigation
Explain the importance of conducting timely
outbreak investigation
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Detecting an outbreak
DetectionRoutine surveillance
Clinical/laboratory
Rumor verification
General public
Media
Is this an
Outbreak?
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Is it an outbreak?
More than expected cases Illustrates the importance of surveillance and timely
analysis
Clustered in time, place or person
Pattern recognition
Concern from a HCW, school or media
Rumour verification
Encourage participation in the system
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Why investigate outbreaks?
Stop the outbreak (new cases)
Increase our knowledge
Prevent new episodes Evaluate the surveillance system
Establish a surveillance system
L
earn field epidemiology by doing
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Specific objectives of an investigation
Identify:
Causal agent
Mode of transmission
Source
Carrier
Population at risk
Exposure causing disease (risk factors)
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Real time vs. retrospective
investigation
Outbreaks in existence for several
days, weeks, months.
Based on the memory of the people
Data already collected
To be or not to be used
It is never to late,
but it can also be more difficult
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Preparation
Collect preliminary information Available data
Consult experts (microbiologist, veterinarian,entomologist etc)
Check search engines e.g., PUBmed
Search from both formal and informal surveillancesystem (event based and indicator based)
Prepare a short memo
Inform the concerned
Get authorization, travel itinerary
Investigation committee
Multidisciplinary Assign person in charge
Define tasks
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Example: Community epidemic due to S.
Typhimurium, Jura, May-June 1997
Context
Alert: PH medical officer
80 cases of salmonellosis in 5 weeks
Salmonella Typhimurium
Clustered in the South department of Jura
No connection (a priori) among cases
Pressure of media, of politicians
L
ocal Department of Public Health, VeterinaryServices, Centre National Reference, National
Institute of Public Health
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Steps in Outbreak Investigation
Descriptive steps
1) Determine existence of an outbreak
2) Confirm the diagnosis:
Which diseases are we talking about?
3) Define a case; find and count cases
4) Orient data as to:
Time (When?) Place (Where?)
Person (Who?)
the sequence is not important !
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Analyse5) Generate hypotheses
6) Test the hypotheses
7) Compare each hypothesis with facts
8) Plan a more systematic study
Synthesis and action
9) Write a report, communicate findings10) Control measure and prevention
Steps in Outbreak Investigationthe sequence is not important !
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1. Determine existence of an outbreak
Outbreak n observed cases > n expected cases
Expected cases? Surveillance data
Clinicians, hospital registers Hospital investigation, lab, doctors, schools..
Be careful of artefacts! Seasonal variation: (diarrhoea)
Notification variation: (new surveillance system inplace) Diagnostic variation: (new technique) Diagnostic mistake: (false epidemic")
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Number ofLegionella cases per week, France
January 1996 August 1997
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2. Confirm the diagnosis
Laboratory serology
isolation, serotype, lysotype, etc.
toxic agent
Meet the doctors
See the patients
Visit the laboratories
It is not necessary to confirm all the cases
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3. Define a case; find and count cases
A case definition is a standard set of criteria for
deciding whether an individual should be
classified as having the health condition of
interest Includes:
clinical criteria
restrictions by time, place and person (epidemiological
link)
laboratory findings (generally)
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Suspected
cases clinical case
definition enough for
immediate action
Confirmed
cases Collect relevant
samples laboratory
few cases (10-20)
Do not wait for laboratory results tostart treatment and control activities!
3.1. Define a case
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Example: Case DefinitionOutbreak of S. Typhimurium, Jura, May-June 1997
Confirmed Probable
Clinical Diarrhoea (> 2 liquidstools/day)
or Fever > 38C ( + one
day)
Diarrhoea (> 2 liquid
stools/day)
or Fever > 38C ( + one
day)
AND
Place,
Person
resident in Jura or
neighbourhood
resident in Jura or
neighbourhood
Time Since 12 May 1997 Since 12 May 1997
Biological Identification of S.Typhimurium
None, but contact with
confirmed case
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3.2. Find and count the cases
Information sources All possible sources (NGOs, local leaders, etc) Hospitals, health centres, laboratories, doctors,
nurses schools, camps, settlements Radio, door to door
snow ball Laboratory
How many? No strictly all
Collected information
Demographics clinical and biological
eventual exposure
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4. Orient data as to:
Time
Place
Persons
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4.1 Data description: TIME
epidemic curve
Case distribution over time (according tothe date - hour, week - of onset of signs)
Onset, peak, importance, time, end of
epidemic Abnormal cases
Allow to make hypothesis: incubation period, pathogen responsible
source, mode of transmission time of exposure
Epidemic evolution
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Example: Epidemic curve of Cases due to S.Typhimurium
by week of onset of symptoms of isolated bacteria,
Jura, May- June 1997.N o m b r e d e c a s
3 0
1 c a s
2 5
2 0
1 5
1 0
5
1 4 1 5 1 6 1 7 1 8 1 9 2 0 2 1 2 2 2 3 2 4 2 5 2 6 2 7 2 8
A v r i l M a i J u in J u i l le t
S e m a i n e s d is o l e m e n t o u d e d b u t d e s s y m p t m e s
One Case
April May June July
Number of Cases
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4.2 Data description: PLACE
Residence
Place of exposure
work, food places, journeys, tour Maps (mud maps, points, attack rate)
Identify areas at risk,Identify population at risk
Identify priority areas for control activities
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4.3 Data description: PERSON
Distribution of cases by age, sex, profession,etc (Numerator) ex: 50 women, 100 men
Distribution of variables in the population from
where cases are coming (Denominator) ex: 1500 women, 1000 men
Compute attack rate ex: women 50/1500 , men 100/1000
=> Identification of sub-group(s) at risk
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Example: Data Description - Person
Infection by S.Typhimurium
Attack Rate by age group, Jura, May-June 1997
Age group
(years)
Number of
Cases
65 9Total 98
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Example: Data Description - Person
Infection by S.Typhimurium,
Attack Rate by age group, Jura, may-June 1997
Age Group
(years)
Number of Cases Population Attack Rate/
100,000
65 9 41,948 22Total 98 246,858 40
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5. Generate hypotheses
Starting from: Descriptive information (TPP) Knowledge of the disease
Exploratory study on some cases
Explaining: Causal agent
Source
Way of transmission
Carrier
DIFFICULT !!!!
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Example: Generating Hypothesis
Infection of S.Typhimurium
Descriptive data: Agent: S. Typhimurium lysotype 12 atypical
Time, epidemic curve: persistent commonsource
Place: cases clustered in the south of Jura
Persons: Attack rate higher among children
All ages affected
Muslim among the cases
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Example: Formulating Hypothesis
Infection S.Typhimurium
May June 1997
Descriptive information Hypothesis
S. Typhimurium Meat (cow), salami, poultry,
milk products, etc
South of Jura Regional products, local
distribution
Children more affected Consumed products (also) by
some children
Muslim among cases Pork less probable
Good weather condition Barbecue, poultry
Documentation Epidemic of roasted poultry
described
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5. Generate HypothesisExploratory Survey
Formulate hypothesis
Interview some cases:
Open questionnaire and complete
Common exposure?
Example Jura:
Big questionnaire, inclusion of regionalproducts (cheese)
17 cases interviewed
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Example: Results of Exploratory Survey
Exposure of cases to specific food
Jura, May - June 1997
Food No. of cases
who ate
No. of
respondents
% of cases
exposed
Chipolatas 6 15 40
Cooked chicken 5 17 29
Raw chicken 7 16 44
Minced beef 7 17 41
Pork 9 17 53
Veal 8 17 47
Cheese (comte) 13 17 77Cheese A
(Fromage a)14 16 88
Rochfort (Bleu de
Gex)6 10 60
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6. Test the hypothesis
Objectives
Specific exposure: the carrier and the source Factors facilitating or protective
host, agent, environment
Survey to identify aetiology:
Cohort uses attack rates best in a small, well-defined population
Case-control odds ratio quantifies the relationship b/w exposure and
disease
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6. Test the hypothesis
Case-control
Compare Proportion of exposed among cases
Proportion of exposed among the non cases (controls)
Compute Odds Ratio (OR) and Confidence Interval(CI) at 95%
Select controls Not sick
Susceptible (e.g., not immunised)
Coming from the same population of cases
The same chances of being exposed
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Exposure of Cases and Controls to specific food
Jura, May - June 1997
Food No (%) of
exposed cases
Odds Ratio CI 95%
(N = 42) (N = 42)
Pats 11 (26) 17 (40) 0.5 0.2 1.3
Sausage 24 (57) 28 (67) 0.7 0.3 1.6
Beef 32 (78) 33 (79) 1 0.3 3.5
Pork 23 (59) 29 (76) 0.5 0.2 1.5
Veal 22 (54) 19 (46) 1.4 0.6 3.4
Chicken 30 (71) 34 (81) 0.6 0.2 1.7
Munster(cheese)
4 (10) 1 (2) 4.0 0.5 35.8
Bleu de Gex(blue cheese)
12 (35) 10 (24) 3.0 0.6 14.9
Comt (cheese) 36 (86) 37 (88) 0.8 0.3 2.7
Fromage A(cheese)
33 (83) 23 (55) 6.5 1.4 28.8
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7. Compare the hypothesis with facts
Compare the results clinical observation
biological examinations
epidemiological studies
statistical tests
The hypothesis should be:
plausible
biologically acceptable explain causal agent, source,
mode transmission, time of exposure
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Example:
Comparison of hypotheses with observed facts,
Jura, May-June 1997
Cheese A
Raw milk (plausible)
Consumed by children (meets personsaffected by the outbreak)
Regional product(meets place affected)
Collect cheese among the cases (datamicrobiological) S. Typhimurium identified in 3 cheeses A
Other cheeses negatives
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8. Plan a more systematic study
At the same time, and oriented by theepidemiological survey
Environmental survey
Microbiological survey
Plan more systematic studies (if needed)
More cases, more controls
Dose-effect, facilitating factors..
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Example: Complementary studies
Epidemic of S. Typhimurium
Jura, May-June, 1997
Microbiological survey: Food collection among cases
Sample collection among cases suppliers
Comparison of human specimens and food
products
Survey on the distribution network of cheese A
Survey among the producers: Veterinary Labour medicine
Environmental
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Survey distribution network
CREMERIE
Wholesaler
Cheese
dairy
Example: Complementary studiesEpidemic of S. Typhimurium
Jura, May-June, 1997
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9. Write a report; communicate findings
To be written on site
Promotes synthesis (of the objectives)
Documents the event(for evaluation/ legalpurposes)
Allows communication of results
Provides recommendations
Pedagogical tool (training material)
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10. Control measures and prevention
Dont wait for the end of the investigation : General measures at beginning Specific measures according to the results
Kinds of measures to control : The source (e.g.: chlorination of water)
The transmission (e.g.: hygiene measures) The carrier (e.g.: recall a lot of suspected cheese)
Reduce the susceptibility of host (e.g.: vaccination)
Communicate risk if outbreak is affecting the public
Example Jura:
Personal Hygiene Adequate cooking of meat
Recall of the incriminated product (Fromage A)
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" The art of epidemiological reasoning is tomake some reasonable conclusions
starting from imperfect data"
George W. Comstock (1915-2007)Physician and Professor Emeritus
Johns Hopkins Bloomberg School ofPublic Health
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But better information leads to better results
This means having: A good description of Time, Place, Person
(TPP)
Good data collection and preservation ofsamples
A well coordinated multidisciplinary team
Immediate
detection
Immediate
response
Reduced morbidity
and mortality
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0
10
20
30
40
50
6070
80
90
1 4 7 10 13 16 19 22 25 28 31 34 37 40
DAY
CASES
LabConfirmation
Outbreak Detection and Response
Response
Opportunity
for control
Detection/Reporting
First
Case
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0
10
20
30
40
50
60
70
80
90
1 4 7 10 13 16 19 22 25 28 31 34 37 40
DAY
CASES
Outbreak Detection and Response
First
CaseDetection/
ReportingLabConfirmationResponse Opportunity
for control
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Ethical Aspect
Securing consent (participants)
Informing local authorities,
communities
Ethical treatment of animals
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Best practices
Establish clear and concise policies andprocedures
Careful documentation and proper
recording of events/results Effective communication skills
Evaluate and review responses
Expect the unexpected Key people away, new, emerging pathogen,
demystifying rumour, etc.
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References
De Valk, Henriette, French Institute for
Public Health Surveillance (Institut de veille
sanitaire, InVS)
European Programme on Intervention
Epidemiology Training
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