Introduction to Epidemiology

Philippe DuboisFrom WHO Lyon: Marta Valenciano & Denis Coulombier

April 8 – 12, 2013

Phom Penh, Cambodia

Epidemiology is …

•For a student– “the worst taught course in Medical school”

•For a clinician– “The science of making the obvious”

•For a average person– “ The study of skin diseases”

•And for you ?

Epidemiology

Epi = upon

Demos = population

Logos = study of

Definition

« The study of the distribution and determinants

of health related states or events in specified human populations

and its application to the control of health problems. »

Last, 1988

Key words:

• Distribution Time, place, person

• Determinants Cause, risk factors

• Event / Health status

• Population Public health

• Application Information for action

Clinic vs. EpidemiologyC

linician•P

erson•M

edical history, physical examination•D

ifferential diagnosis •D

iagnostic test •T

reatment

Epidemiologist

•Population

•Surveillance descriptive epidemiology

•Comparison

•Analytical epidemioogy

•Intervention (prevention/control)

Epidemiological axioms

•Diseases (or other health events) don’t occur at random

•Diseases (or other health events) have causal and preventive factors which can be identified

Epidemiology helps to

•Determine the magnitude and trends

•Identify the aetiology or cause of disease

•Determine the mode of transmission

•Identify risk factors or susceptibility

•Determine the role of the environment

•Evaluate the impact of the control measures

Main activities of the epidemiologist•D

escribe an event in terms of :– Time When?– Place Where?– Person Who?

•Analyse the association between the event (disease, death) and its determinants (risk factors)

•Make recommendations: preventive actions, control measures

Basic Epidemiology Methods

•Observation

•Counts cases (events)

•Determines rates, proportions

(incidence, prevalence)

•Compare rates

•Develop and test hypothesis

•Implement actions (control, prevention)

Epidemiology Classification•E

xperimental

•Observational– Descriptive– Analytic

• Cross-sectional• Cohort• Case-control

Descriptive epidemiology

•First step in all data analysis

•Allows to organise and summarise data in terms or time, place and person

•Basis for building an inductive thought

Descriptive epidemiology

Why describing ?

•To get familiar with:– the data– the problem, its characteristics and magnitude

•To determine groups at risk

•To gather information for generating hypothesis on aetiology, transmission ….

•To communicate the results

Descriptive Epidemiology

•When have they been affected

•Where have they been affected

•Who has been affected

Time

Place

Person

Time

•Health events present variations over time

•Graphical representation of data in a X, Y system:– Magnitude of the problem– Trends and potential evolution– Type of transmission – Other related events

Daily Notification of Cholera Cases, Paris, France, March-September 1832

01/04/1832 01/05/1832 01/06/1832 01/07/1832 01/08/1832 01/09/1832

Day

0

200

400

600

800

1000

Source: Bulletin sanitaire journalier, Le Moniteur Universel.

Number of cholera cases by week, Guatemala, 1998

0

100

200

300

400

500

600

700

S1 S7 S13

S19

S25

S31

S37

S43

S49

Weeks

Num

ber o

f cas

es

Hurricane Mitch

Source: Ministerio de Salud, MSF

Time Outbreaks/Epidemics

Epidemic curve

Cyclic, endemic or chronic phenomena

TrendsSeasonal

Secular

•Graphic: Histogram

•Time interval :– Hours, days, weeks…– Depends on incubation period

•Helps in generating hypothesis about– Agent, – Source– Transmission route

Epidemic curve

Date of onset

Number of cases

One case

12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 270

9

5

3

7

Gastroenteritis cases among residents of a long stay centre by date of onset of illness, Pennsylvania, 1986

Cas

es

Watery diarrhea 15 Bloody diarrhea Diarrhea with mucous 13 11 9 7 5 3 1

7/20

7/21

7/22

7/23

7/24

7/25

7/26

7/27

7/28

7/29

7/30

7/31

8/1

8/2

8/3

8/4

8/5

8/6

8/7

8/8

8/9

8/10

8/11

8/12

8/13

8/14

8/15

Day of onset

Cas

es

Diarrhoea cases by date of onset, Tubna, Jordan July-August 2001

Elements in an epidemic curve

10

5

4 5 6 7 8 9 10 11 12 13 14 15 16 17

1 case

Number of cases

Time (hours, days, weeks, months)

Distribution of cases (N = ) by day/month of onset/diagnostic, Region X, month/year.

Epicurves and Modes of Transmission

10

5

4 5 6 7 8 9 1011121314151617

N Continuous source

10

5

4 5 6 7 8 9 1011121314151617

Point common sourceN

10

5

4 5 6 7 8 9 1011121314151617

Person to personN

Influenza cases among residents by date of onsetMinessota, April 24 - May 21, 1979

0

5

10

15

20

25

24-avr

26-avr

28-avr

30-avr

02-mai

04-mai

06-mai

08-mai

10-mai

12-mai

14-mai

16-mai

18-mai

20-mai

Onset

Cas

es

Source: CDC

Person to Person Transmission

Ebola deaths,Bandudu province, Zaire. March-April 1995

02468

1012141618

04-mars

07-mars

10-mars

13-mars

16-mars

19-mars

22-mars

25-mars

28-mars

31-mars

03-avr

06-avr

09-avr

12-avr

15-avr

18-avr

21-avr

24-avr

Date of death

Num

ber o

f dea

ths

Source: CDC

Person to Person Transmission

Hepatitis A by date of onsetOgemaw County, Michigan, April-Mai 1968

2 8 14 20 26 2 8 14 20 26 1 70

2

4

6

8

10

12

14

1 case

Days

Num

ber o

f cas

es

15 days

50 days

30 days

Exposure

Trends over time•S

easonal trends– Linear graphic– Weeks, months

•Secular trends– Periods including several years (>10 y)– Allows :

• to predict evolution • To study the effect of control measures or other related events

0

200

400

600

800

1000

1200

1400

1600

1800

1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1 4 7 10 1

Months

Num

ber o

f cas

es

92 93 9594 97 20009998

Source: SILAIS Leon, Nicaragua

Dengue cases by month Leon, Nicaragua, 1992-2000

Source: CDC

Weekly reported diarrhoea cases in kura district, Irbid Governorate, Jordan 1994-2001

157

0

50

100

150

200

250

week

1994 1995 1196 1997

Week 31,

Place

•Gives information on :– Geographic distribution– Cluster of cases– Dissemination route

•Use of maps – Representation of cases and possible sources of exposure

Place

•Geographical locations– Place of residence, work place, services in a hospital…

•Geographical units – District, municipalities, neighbourhoods, streets, rooms…

•Categories– Urban-rural, autochthonous-imported…

•Place of exposure vs. place of notification

Cholera outbreak, London 1854 WORK

HOUSE

CA

RN

AB

Y S

TRE

ET

MA

RS

HA

LL STR

EE

T

RE

GE

NT S

TRE

ET

GR

EA

T PU

LTEN

EY

STR

EE

T

BRE

WE

RY

BER

WIC

K S

TRE

ET

BROAD STREET

SILVER STREET

X

X

XX

X

PO

LAN

D S

TRE

ET

GOLDEN

SQUARE

N

S

EW

PUMP B

PUMP

PUMP C

PUMP A

PUMP

Ebola, attack rate by villageZaire, 1976

Yaimba 0.1

Bunduki 0.4Eboy 1.4

Bokoy 0.2 Bigi 1.0

Yambala 0.7

Yasoku 0.2

Paipaie 0.2

Bakata 0.3

Lolo 0.7

Bongolo 1.6Badjoki 1.8

Bosambi 0.9

Koloko 0.8

Yaenengu 0.2Bongulu 0.1

Mogbakele 0.2

Lotaka 0.3

Bodala 0.4

Yandongi 0.9Yaeto Liku 1.3

Bovange 0.7Baisa 1.0

Yamisole 2.7 Yamolembia II 2.1

Yamolembia I 2.6

4.6 Yambuku

10 km

Yapiki Moke 0.7

Mdojambole 0.8

Mombwasa 0.2

Yamisoli 0.1

Person•D

etermine who is at risk•D

ifferent categories– Demographic: age,sex, ethnicity…– Socio-economic: education, occupation, access to services…– Individuals: blood group, vaccination status, smokers…

•Presentation in table, graphs

•Importance +++

•Reflects: – Susceptibility– Differences in exposure– Latency, incubation period

Person: the age

Attack rate by age group. S. Typhimurium, outbreak in Jura, France, May-June 1997

Age group (year)

Case 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

Source: Institut de Veille Sanitaire, Paris

•Men and women different in– Susceptibility– Physiologic response– Exposures

• Habits• Occupations

Person: sex

Person: others

•Ethnic groups – Common social and biologic characteristics– Associated with socio-economic factors

•Socio-economic– Difficult to establish– Occupation, education, income– Can reflect differences in exposure

or access to services

Syphilis cases. US 1981-90

0

10000

20000

30000

40000

50000

60000

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990Years

Cas

es

Syphilis cases by sex. US 1981-90

0

5000

10000

15000

20000

25000

30000

35000

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990

Years

Cas

es

Men

Women

Syphilis cases by sex and racial group. US 1981-90

0

5000

10000

15000

20000

25000

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990

Years

Cas

es

Black men

Black women

White menWhite women

George W. Comstock

The art of epidemiological thinking

is to draw conclusions

from imperfect data

Questions? Comments? Discussions?