Download Infectious disease epidemiology Frequency and

Infectious disease epidemiology
Frequency and measures of association
University of Copenhagen
Anders Koch, senior researcher, Ph.D. MPH
Statens Serum Institut
&
Registrar, infectious Diseases
Rigshospitalet University Hospital
Agenda
• Measures of frequency
• Measures of association
• Particular measures in infectious disease
epidemiology
• Introduction to infectious disease way of thought
• Basic concepts and simple pitfalls
Chain of transmission
Severity of infection
Time course of infections
Course of infection
Exposure
No effect
Dead
Clinical infection
Immune
Subclinical
Susceptible
Bearer
Bearer
Frequencies
• How often does colon cancer occur in Denmark?
• How often does influenza occur in Denmark?
• How many HIV cases are there in Denmark?
• How many cases of chronic obstructive lung
diseases are there in Denmark?
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Types of frequency measures
• Incidence
• Prevalence
• Incidence number of new cases per time unit
• Prevalence number of cases at a specific point of time
• What are the differences?
• Incidence expression of risk
• Prevalence expression of burden of disease
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Measures of frequency
• Incidence (incidence rate)
Number of new cases of disease in a specific period
Sum of time at risk for the population
• Prevalence (prevalence rate)
Number of sick persons at given time
Number of persons in the population
– Point prevalence – prevalence at given time (Christmas eve)
– Periodic prevalence – prevalence in a period (Christmas holiday)
What measure to use?
The measure depends on the question!
Relevance of HIV prevalence DK 2009?
(5,300 persons, 0.2%)
Relevance of HIV incidence DK 2009?
(236 new cases)
HIV rates?
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‘Natural’ cause of an epidemic?
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Calculations - HIV in Greenland
•
•
•
•
Prevalence?
Incidence?
Cumulative incidence?
Interpretations?
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Incidence and prevalence – a
complicated example
• Respiratory tract infections in children in Greenland
• Prevalence and incidence of episodes?
3
15
3
15
3
11
Time at risk
Time of observation
Day 100
Day 1
• Prevalence
– Time with disease relative to
time of observation
– (15+15+11)/100 = 41%
– Measure of?
• Incidence
– Episodes per unit of time at risk
– 3/(100-41-9) = 3 per 50 days
– Measure of?
Khanty-Mansiyskiy autonomus okrug - UGRA
Number of cases
Age-sex structure of patients with new cases of
tuberculosis (KMAO 2011)
Age
Population at risk
• Essential that the populationen at risk is defined!
• Influenza
– All who haven’t had the type in focus or are unvaccinatd
• Cervical cancer
– Women 25-69 years
• Breast cancer
– All
• Salmonella outbreak in restaurant
– Everybody who tasted the food
• Hospital infections
– Salmonella in central kitchen
– Defect pan washer in ward
Background population
• England 1983: ’Windscale – the Nuclear Factory’ (Sellafield)
• Statistically significant increase in cases of childhood
leukemia in the settlement Seascale
• Should the plant be shut down?
The Texas sharpshooter
Epidemics
20
Definition
• When is something epidemic?
• When something is too much! (~occurrence of a
disease or condition more frequent than
expected)
• When is something too much?
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When is too much too much?
• 500 cases in Copenhagen of pneumonia in toddlers
january 2013 against 50 in June 2012. Epidemic?
– Every winter 500 cases - RS-virus
• 2 cases of anthrax pneumonia in adults in the USA
– 18 cases in this century – in 2001 terrorism
• In Russia?
– 10 cases per year
• Kaposis sarcoma in San Francisco
–
–
–
–
1977 and 1978: 2
1979 and 1980: 4
1981:
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1982:
85
Endemic – Epidemic - Pandemic
R>1
R=1
Time
R<1
• Endemic
– Transmission at constant rate – main part of infections in population
• Epidemic
– Number of cases higher than expected
• Pandemic
– Epidemic spanning more than one continent
What pattern is this?
And this?
Respiratory tract infections in children
in Greenland
Incidence
URI
LRI
Aug.
Dec.
1996
Apr.
Aug.
1997
Dec.
Apr.
1998
Aug.
What is this?
And this?
2 epidemic patterns
Point source
Person to person / propagated
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Influenza in Denmark
Infectious disease modelling
R>1
R=1
R<1
Time
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Infection dynamics – Basic reproductive
number
• Basic reproductive number (R0)
– Average number of persons in a totally
susceptible population, that is directly
infected by an infectious case through
the full period of infectiousness
• What is R0 in this case?
(1+2+0+1+3+2+1+1+2+1+2)/10 = 1.5
R0 < 1
R0 = 1
R0 > 1
Disease dies out
Endemic
Epidemic
Calculation of R0 (R nought)
probability of transmission per contact
R0 = p • c • d
contacts per unit time
duration of infectiousness
Real life –
Effective reproductive number
• Effective reproductive number R = R0 * X
– R0 in real populations, where some are immune
– X = proportion of susceptible in the populationen
• When the fraction of immune is above a certain level, an epidemic
will die out (R<1) and herd immunity is acquired
R0 = p • c • d
R = R0 – (p • R0)
(p = proportion of immune)
• Aim for vaccine strategies
Herd immunity
• The level of immune persons in a population at which the
effective reproductive number <1 ~ the disease dies out
• R0 = 2
• R0 = 4
• R0 = 15
R < 1 when immune >50%
R < 1 when immune >75%
R < 1 when immune >94%
• R0 for measles = 15
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Why use herd imunity?
• To determine the level of immune (or vaccinated) in
population so that a given epidemic dies out
• Fraction of measles vaccinated children in Denmark
2012?
• 89% (no herd immunity)
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Infectious disease modelling –
SIR model
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Measures of association
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An outbreak of gastroenteritis
Gastroenteritis
(5 people)
No gastroenteritis
(10 people)
Quiche
II
IIIII III
Cheesecake
IIII
I
Swiss roll
III
IIII
Chokolate cake
I
II
Cheese dip
IIII
IIIII II
• Did any of these food stuffs cause gastroenteritis?
Hint:
• Calculate risk of disease for each food item
• Calculate relative risk for each food item
• Interpret these results
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(Absolute) risk and relative risk
• Risk =
Number of indivduals who ate this food who are ill
Total number of individuals who ate this food
• Relative risk =
Risk in individuals exposed to a factor
Risk in individuals not exposed to that factor
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Gastroenteritis outbreak
Risk for illness if
eating food item
Risk of illness if
not eating food
Relative risk for illness
Quiche
0.2 (2/10)
0.6 (3/5)
0.33 (0.2/0.6)
Cheesecake
0.8 (4/5)
0.1 (1/10)
8.0 (0.8/0.1)
Siss roll
0.43 (3/7)
0.25 (2/8)
1.72 (0.43/0.25)
Chokolate cake
0.33 (1/3)
0.33 (4/12)
1.0 (0.33/0.33)
Cheese dip
0.36 (4/11)
0.25 (1/4)
1.44 (0.36/0.25)
• What is the cause of the outbreak?
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Risk
• Absolute – risk of dying if you smoke
• Relative – risk of dying if you smoke compared with those who don’t smoke
• ’Attributable risk’ – the extra risk attributable to the factor
Example: Smoking and death over 7 years
Participants
Dead
Death rate pr. 1000
Smokers
25,769
133
5.16
Non-smokers
5,439
3
0.55
Absolute risk for death for smokers?
5.16 pr. 1000
Relative risk for smokers/non-smokers?
5.16/0.55
9.38
Attributable risk ?
5.16-0.55
4.61 pr. 1000
Odds ratio in practice. Salmonella in
Wales 1989
31 cases office
workers
6 cases canteen staff
In total 37 cases
Hereof 3 attended
doctor, other identified
through interviews or
faecal tests
58 controls
1400 employees
Salmonella outbreak in Wales 1989
Gastroenteritis
No gastroenteritis
Eaten
Not eaten
Eaten
Not eaten
Lunch 22/1
6
31
9
48
Lunch 23/1
18
19
14
43
Salad
12
24
5
52
Sandwiches
16
21
14
44
Chicken
4
33
4
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Odds ratio
• Odds =
Number of persons exposed
Number of persons not exposed
• Odds for having eaten in the canteen January 22 for cases
= 6/31 = 0.193
• Odds for having eaten in the canteen January 22 for controls
= 9/48 = 0.188
• Odds ratio = Odds for cases/odds for controls
• Odds ratio for having eaten in the canteen at January 22
= 0.193/0.188 = 1.03
Risk factors in Wales outbreak
OR
95% CI
Lunch Jan. 22
1.03
0.33 – 3.18
Lunch Jan. 23
2.93
1.21 – 7.09
Salad
5.20
1.65 – 16.4
Sandwiches
2.39
0.99 – 5.8
Chicken
1.64
0.38 – 7.01
OR <> RR
• 1.400 in building, 37 cases
• How many got sick out of those having eaten in the
canteen?
• Number of eating and number of sick unknown
• Sample of a population
• Therefore the rate (=risk) cannot be calculated in casecontrol study
But
• If disease is rare, then OR ~ RR
With other words:
Case-control studies measure
• The extent of exposure among the sick compared with the
extent of exposure among the healthy
• Odds ratio expresses this
• There are no units of Odds ratios
• Odds ratio is not the same as risk, as the risk in the
population is unknown (a sample of the population is drawn),
But
• If the disease is rare (relatively), then OR ~ RR!
Other infectious disease rates
• Attack rate
• Case-fatality rate
• Mortality
Sick
Exposed
Dead
Clinically sick
Died by disease per year
Total population
• CF rate may be high but mortality low if disease
is rare!
Can an association be confounded by
another factor?
• A factor significantly associated with exposure as
well as outcome but not a part of causal chain
Exposure
Outcome
Confounder
Music and risk of hip fractures
Risk of hip fracture
among fans of
Vikingarna 1/20,000
Risk of hip fracture
among fans of
Britney S. 1/200,000
Causal association?
Is taste of music associated with risk of
hip fractures?
Exposure
Outcome
Confounders
Age
Sex
Gait disturbances
Nutrition
Training/physical activity
Carpets in house
Less subcutaneous fat in elderly
Hormone status
Dancing around the room
More?
Confounder control
• Compare only persons within same level of confounding
• Compare similar groups where only the variable in focus
is different
• Design
– Randomisation
– Restriction
– Matching
• Analysis
– Stratified analysis
– Multivariate analysis
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Interpretation of tables: H. Pylori in
Greenlanders
H. pylori: multivariate model
Conclusions I
• Frequency measures include incidence and prevalence
• Incidence denotes new cases per time unit and reflects risk of
disease
• Prevalence denotes number of cases at a point of time and
reflects burden of disease
• Infections occur in endemic, epidemic and pandemic patterns
• An epidemic denotes a higher number of cases than
expected
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Conclusions II
• Herd immunity denotes the level of non-susceptibility to an
infection that protects the population from an epidemic
• Relative risk expresses the risk of a disease if exposed to a risk
factor compared to if not exposed
• Odds ratio expresses the odds for having been exposed to a risk
factor for sick versus healthy and have no units
• An alternative explanation of an observed association may be
confounding
• Not all that appear to be associated is associated!
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Thank you for your attention
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