Introduction to Epidemiology
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Transcript of Introduction to Epidemiology
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?