SEARO – CSR Training on Outbreak Investigation

Outbreak Investigation

Best Practice/MethodsPractical Reference Points

Outbreak Investigation – Best Practices/ Methods SEARO – CSR Training on Outbreak Investigation

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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

Outbreak Investigation – Best Practices/ Methods SEARO – CSR Training on Outbreak Investigation

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Detecting an outbreak

DetectionRoutine surveillanceClinical/laboratoryRumor verificationGeneral publicMedia

Is this anOutbreak?

Outbreak Investigation – Best Practices/ Methods SEARO – CSR Training on Outbreak Investigation

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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• Learn 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

Outbreak Investigation – Best Practices/ Methods SEARO – CSR Training on Outbreak Investigation

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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 surveillance

system (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• Local Department of Public Health, Veterinary

Services, Centre National Reference, National Institute of Public Health

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Steps in Outbreak Investigation

• Descriptive steps1) Determine existence of an outbreak2) Confirm the diagnosis:

– Which diseases are we talking about?

3) Define a case; find and count cases4) Orient data as to:

– Time (When?)– Place (Where?) – Person (Who?)

the sequence is not important !

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• Analyse5) Generate hypotheses6) Test the hypotheses7) Compare each hypothesis with facts 8) Plan a more systematic study

• Synthesis and action9) Write a report, communicate findings10) Control measure and prevention

Steps in Outbreak Investigation the 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

in place)– Diagnostic variation: (new technique)– Diagnostic mistake: (“false epidemic")

Outbreak Investigation – Best Practices/ Methods SEARO – CSR Training on Outbreak Investigation

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Number of Legionella 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 Definition Outbreak of S. Typhimurium, Jura, May-June 1997

Confirmed Probable

Clinical Diarrhoea (> 2 liquid stools/day)or Fever > 38°C ( + one day)

Diarrhoea (> 2 liquid stools/day)or Fever > 38°C ( + 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 to the 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.Nombre de cas

30

1 cas

25

20

15

10

5

14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 Avril Mai Juin Juillet

Semaines d’isolement ou de début des symptômes

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 - PersonInfection by S.Typhimurium

Attack Rate by age group, Jura, May-June 1997

Age group (years)

Number of Cases

<1 2

1 – 5 36

6 – 14 22

15 – 64 29

> 65 9

Total 98

Outbreak Investigation – Best Practices/ Methods SEARO – CSR Training on Outbreak Investigation

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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

<1 2 522 383

1 – 5 36 16,014 225

6 – 14 22 30,385 72

15 – 64 29 157,989 18

> 65 9 41,948 22

Total 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 !!!!

Outbreak Investigation – Best Practices/ Methods SEARO – CSR Training on Outbreak Investigation

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Example: Generating HypothesisInfection of S.Typhimurium

• Descriptive data:– Agent: S. Typhimurium lysotype 12 atypical – Time, epidemic curve: persistent common

source– Place: cases clustered in the south of Jura– Persons:

• Attack rate higher among children• All ages affected• Muslim among the cases

Outbreak Investigation – Best Practices/ Methods SEARO – CSR Training on Outbreak Investigation

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Example: Formulating HypothesisInfection 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

Outbreak Investigation – Best Practices/ Methods SEARO – CSR Training on Outbreak Investigation

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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 regional

products (cheese)• 17 cases interviewed

Outbreak Investigation – Best Practices/ Methods SEARO – CSR Training on Outbreak Investigation

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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 40Cooked chicken 5 17 29Raw chicken 7 16 44Minced beef 7 17 41Pork 9 17 53Veal 8 17 47Cheese (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 hypothesisCase-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 foodJura, May - June 1997

Food No (%) of exposed cases

Odds Ratio CI 95%

(N = 42) (N = 42)

Patés 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

Comté (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

Outbreak Investigation – Best Practices/ Methods SEARO – CSR Training on Outbreak Investigation

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Example: Comparison of hypotheses with observed facts,

Jura, May-June 1997

Cheese A• Raw milk (plausible)• Consumed by children (meets persons

affected by the outbreak)• Regional product (meets place affected)

• Collect cheese among the cases (data microbiological)– S. Typhimurium identified in 3 cheeses A– Other cheeses negatives

Outbreak Investigation – Best Practices/ Methods SEARO – CSR Training on Outbreak Investigation

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8. Plan a more systematic study

• At the same time, and oriented by the epidemiological 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 studiesEpidemic 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/ legal purposes)

• Allows communication of results• Provides recommendations• Pedagogical tool (training material)

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10. Control measures and prevention

• Don’t 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)

Outbreak Investigation – Best Practices/ Methods SEARO – CSR Training on Outbreak Investigation

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" The art of epidemiological reasoning is to make some reasonable conclusions

starting from imperfect data"

George W. Comstock (1915-2007)Physician and Professor Emeritus

Johns Hopkins Bloomberg School of Public 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 of

samples – A well coordinated multidisciplinary team

Immediate detection

Immediate response

Reduced morbidity and mortality

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0

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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

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1 4 7 10 13 16 19 22 25 28 31 34 37 40

DAY

CASES

Outbreak Detection and Response

First CaseDetection/

ReportingLabConfirmation Response

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 and procedures

• 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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