1. Cohort, case-control & cross- sectional studies
Kostas Danis
EPIET Introductory course
Menorca, Spain
16/9-12/10/2012
Source: Alain Moren, EPIET Introductory courses

2. Epidemiological studies
Two types
Observation
Experiment

3. Unethical to perform experiments on people
Exposed
Disease
occurrence
Exposure
assigned
Not exposed
Unethical to perform experiments on people
if exposure is harmful

4. If exposure not harmful
Treatment
Preventive measure (vaccination)
Randomised
Controlled
Trial
Blinded
Doses
Time period
Risk - effect
No bias
If RCT not possible
Left with observation of experiments
designed by Nature
Cohort studies
Cross-sectional studies
Case control studies

5. Cohort studies marching towards outcomes

6. What is a cohort? One of 10 divisions of a Roman legion
Group of individuals
sharing same experience
followed up for specified period of time
Examples
EPIET cohort 2012
birth cohort
cohort of guests at barbecue
occupational cohort of chemical plant workers
influenza vaccinated in

7. follow-up period

8. Calculate measure of frequency
Cumulative incidence
incidence proportion
attack rate (outbreak)
Incidence rate
end of follow-up

9. Cohort studies Purpose Cohort membership
Study if an exposure is associated with outcome(s)
Estimate risk of outcome in exposed and unexposed cohorts
Compare risk of outcome in two cohorts
Cohort membership
Being at risk of outcome(s) studied
Being alive and
Being free of outcome at start of follow-up

10. Cohort studies
unexposed
exposed

11. Incidence among unexposed
Cohort studies
exposed
Incidence among
exposed
Incidence among unexposed
unexposed

12. Presentation of cohort data: 2x2 table
ill
not ill Total
ate ham
a b a+b
did not eat ham
c d c+d
Risk in exposed= a/a+b
Risk in unexposed= c/c+d

13. Number of NEW cases of disease Total person - time of observation
Incidence rate
Number of NEW cases of disease
Total person - time of observation
It is more precise because it includes the time at risk in the denominator. The incidence rate is the number of new cases divided by the total number of person time observed

14. Number of NEW cases of disease Total person - time of observation
Incidence rate
Number of NEW cases of disease
Total person - time of observation
Rate
Denominator:
- is a measure of time
- the sum of each individual’s time at risk and free from disease
It is improtant to know, when using the term rate - a measurement of time is included in the denominator and
That only persons are included in the denominator until they either develop disease or they are not at risk for developing disease any more (i.e diseased, moved out, immune, lost, …)
So the denominator is the sum of each individuals time at risk and free from disease
Lets say what it means on a graph

15. Person-time x x A B C D E 6.0 10.0 8.5 5.0 Total years at risk 35.5
Time at risk A B C D E
6.0
10.0
8.5
5.0 x x
Assume a population of 5 persons
Person A is followed from 1990 until 1996 … because he may die or be lost to follow up… he will contribute 6 years to our observation
Person B is born in 1992 and develops disease in He or she will contribute 6 years to our observation
Person C contributes to the entire observation period of 10 years
Person D moves to the study area in 1991 and leaves the area in mid He will contribute for 8,5 yrs
Person E develops disease after 5 yrs
So our denominator will be the sum of the time of each individualat risk when they are free of disease which in our example are 35.5 years
Total years at risk
-- time followed
x disease onset

16. Incidence rate (IR) (Incidence density)
Time at risk
IR = 2 / person years
= cases / person year
= 5.6 cases / 100 person years
= 56 cases / 1000 person years A B C D E
6.0
10.0
8.5
5.0 x x
This total person time observed will become our denominator and the number of persons who diseased are in the numerator
Therefore the incidence rate or incidence density will be 2/35,5 person years which equals cases per person year
Which is the same as 5.6 cases per 100 person years
Or expressed as 56 cases per 1000 person years
Note that the incidence density is not limited and may reach infinite values
Total years at risk
-- time followed
x disease onset

17. Presentation of cohort data: Person-years at risk
Tobacco smoking and lung cancer,
England & Wales, 1951
Person-years Cases
Smoke ,
Do not smoke ,
Source: Doll & Hill

18. Presentation of data: Various exposure levels

19. Prospective cohort study
Disease
occurrence
Exposure
Study starts
time
time
Exposure
Study starts
Disease
occurrence

20. Retrospective cohort study
Disease
occurrence
Study starts
Exposure
time

21. Recipe: Cohort study Identify group of Measure incidence of disease
exposed subjects
unexposed subjects
Measure incidence of disease
Compare incidence between exposed and unexposed group

22. Incidence among unexposed
Cohort studies
exposed
Incidence among
exposed
Incidence among unexposed
unexposed

23. Effect measures in cohort studies
Absolute measures
Risk difference (RD) Ie - Iue
Relative measures
Relative risk (RR)
Rate ratio
Risk ratio Ie
Iue
Ie = incidence in exposed
Iue= incidence in unexposed

24. Cohort study Non cases Risk % Total Cases Exposed 100 50 50 50 %%
Not exposed
100 %
Risk ratio % / 10% = 5

25. ill
not ill Incidence
ate ham %
did not eat ham %
Risk difference % % = 10%
Relative risk % / 40% =

26. Interpretation of Risk Ratios
RR>1
RR=1
RR<1
Risk factor
No association
Protective factor 26

27. Does HIV infection increase risk of developing TB among drug users?

28. Vaccine efficacy (VE) VE = 1 - RR = 1 - 0.28 = 72%
So, a vaccine efficacy of 72% means that 72% of the cases which would have occurred among the vaccinated would they have been unvaccinated.
have been prevented.
VE = 1 - RR =
= 72%

29. Various exposure levels
Population Cases Incidence
at risk a1
High N1 I1 a2
Medium N2 I2 a3
Low N3 I3 c
Unexposed
Nne
Iue

30. Various exposure levels
Population Cases Incidence RR
at risk a1
High N1 I1
RR1 a2
Medium N2 I2
RR2 a3
Low N3 I3
RR3 c
Unexposed
Nne
Iue
Reference

31. Cohort study: Tobacco smoking and lung cancer, England & Wales, 1951
Source: Doll & Hill

32. Disadvantages of cohort studies
Large sample size
Latency period
Cost
Time-consuming
Loss to follow-up
Exposure can change
Multiple exposure = difficult
Ethical considerations

33. Strengths of cohort studies
Can directly measure
incidence in exposed and unexposed groups
true relative risk
Well suited for rare exposure
Temporal relationship exposure-disease is clear
Less subject to selection biases
outcome not known (prospective)

34. Cohort studies Can examine multiple effects for a single exposure
Population Outcome 1 Outcome 2 Outcome 3 exposed Ne Ie1 Ie2 Ie3
unexposed Nne Iue1 Iue2 Iue3
RR1 RR2 RR3

35. A cohort study allows to calculate indicators which have a clear, precise meaning.
The results are immediately understandable.

37. Cross-sectional (prevalence) studies

38. Cross-sectional studies
Observation of a cross-section of a population at a single point in time
Recruitment of study participants
Population
Population sample
Observation for the presence of:
One or more outcomes
One or more exposures

39. Sampling Sampling Population Target Population Sample
When we want to take a sample we first need to define our target population. The target population is the population about which you want information, that you wish to make conclusions about from the results of the study. The study or sampling population is the population from which the sample (sampling frame) is drawn (the population from which you select your sample). It may be a more limited, an accessible population. For example, suppose you want to estimate the prevalence of flu-like symptoms in a country and you conduct telephone interviews; Your target population will be the total population in the country and the study population will be all people with telephones. Or if you want to estimate the vaccination coverage among 6 year old children in Spain and you take a sample of school children; your target population is all 6 year olds in Spain, whereas your sampling population is all first Grammar class school children.
Target Population

40. Uses of cross-sectional surveys in public health
Estimate prevalence of disease or their risk factors
Estimate burden
Measure health status in a defined population
Plan health care services delivery
Set priorities for disease control
Generate hypotheses
Examine evolving trends
Before / after surveys
Iterative cross-sectional surveys

41. Potential objectives of a cross sectional study
Descriptive
Estimate prevalence
Analytic
Compare the prevalence of a disease in various subgroups, exposed and unexposed
Compare the prevalence of an exposure in various subgroups, affected and unaffected

42. Presentation of the data of an analytical cross sectional study in a 2 x 2 table
Ill Non ill Total
Exposed a b a+b
Non exposed c d c+d
Simultaneous measurement of outcomes and exposures

43. Cross-sectional study
Non
cases Prevalence %
Total
Cases
Exposed
1,000 %
Not exposed
1,000 %
Prevalence ratio (PR) % / 10% = 5

44. Measuring association in analytical cross-sectional surveys
Prevalence among exposed / prevalence among unexposed
Prevalence ratio
Formula equivalent to risk ratio
Concept different
No incidence
Only prevalence
depends on both occurrence of new cases & duration of disease

45. Prevalence of West Nile virus (WNV) infection by place of residence, Central Macedonia,Greece, 2010
Infected
Total
Prevalence
ratio
Rural 38
491
7.7%
5.9
Urban 3
232
1.3%
Ref

46. Prevalence of HIV infection by socioeconomic status, African country X, 1999
Infected
Total
Prevalence
ratio
High class 15
235
6.4%
2.6
Low class 11
450
2.4%
Ref
Women of higher socioeconomic status are more likely to survive longer with HIV

47. Prevalence of hepatitis C (HCV) infection by quantity of therapeutic injections, Hazabad, Pakistan, 1993
No.of injections
Infected
Total
Prevalence
ratio
>10 9 41
22% 22
0-10 4 52 8% 8 1 82 1%
Ref
Another potential explanation for the association
between injections and HCV infection is that HCV
infected individuals became symptomatic from their
infection and so sought more health care, and so
received more injections.

48. Advantages of cross-sectional surveys
Fairly quick
Easy to perform
Less expensive
Adapted to chronic diseases

49. Limitations of cross-sectional surveys
Limited capacity to document causality
(exposure and outcome measured at the same time
-difficult to establish time sequence of events)
Not useful to study disease etiology
Not suitable for the study of rare / short diseases
Not adapted to severe / acute diseases
Not adapted to incidence measurement.

50. Limitations of causal inference in analytical cross sectional studies
Prevalent cases
Exposure and outcome examined at the same time

52. Principle of case control studies

53. Our objective is to compare
the incidence rate in the exposed population
to the rate that would have been observed
in the same population, at the same time
if it had not been exposed

54. Source population
Exposed
Unexposed

55. Source population
Cases
Exposed
Unexposed

56. Source population
Cases
Exposed
Sample
Unexposed
Controls

57. Cases Controls Source population Exposed Sample Unexposed Controls:
Sample of the denominator
Representative with
regard to exposure
Controls

58. Intuitively if the frequency of exposure is
higher among cases than controls
then the incidence rate will probably be
higher among exposed than non-exposed

59. Case control study
Exposure ?
Disease
Controls
Retrospective nature

60. Distribution of cases and controls according to exposure
in a case control study
Cases Controls
Exposed a b
Not exposed c d
Total a + c b + d
% exposed

61. Distribution of cases and controls according to exposure
in a case control study
Cases Controls
Exposed a b
Not exposed c d
Total a + c b + d
% exposed a/(a+c) b/(b+d)

62. Distribution of myocardial infarction by oral contraceptive use
in cases and controls
Oral Myocardial
contraceptives Infarction Controls
Yes No
Total
% exposed 69.3% %

63. Distribution of myocardial infarction by amount of physical activity in cases and controls
Physical Myocardial
activity Infarction Controls
>= 2500 Kcal
< 2500 Kcal
Total
% exposed 51.9% %

64. Volvo factory, Sweden, 3000 employees,
Cohort study
200 cases of gastroenteritis
Water Cases Controls
Consumption
YES ?
NO ?
Total

65. Two types of case control studies
Exploratory New disease New risk factors Several exposures "Fishing expedition"
Analytical Define a single hypothesis Dose response

66. Cohort studies Case control studies
Rate/risk
Rate/risk difference
Rate Ratio/Risk ratio (strength of association)
Case control studies
No calculation of rates/risks
Proportion of exposure
Any way of estimating measures of association?

67. Odds Probability that an event will happen
Probability that an event will not happen
Probability that cases/controls will be exposed
Probability that cases/controls will not be exposed
The odds are often used in horse races and are a ratio
The odds are the probability that an event will happen divided the probability that an event will not happen

68. a/c b/d Case control study Cases Controls Odds ratio a b Exposed
OR= (a/c) / (b/d)
= ad / bc
Not exposed
c d
a+c
Total
b+d
% exposed
a/(a+c)
b/(b+d)
d/(b+d)
% unexposed
c/(a+c)
Odds of
exposure
a/c
b/d

69. Case control study Cases Controls Odds ratio 50 20 4 a b Exposed
a b
Exposed
Not exposed
c d
Total
100
100
OR= (a/c) / (b/d)
= ad / bc
= (50x80) / (20x50)
= 4
Odds of
exposure
50/50
20/80

70. Case control study design
Cases
Controls
Odds ratio E a b
a b a x d =
c d b x c c d E

71. Cases Controls Odds ratio Ate chicken 181 251 2.1 Did not eat chicken
Frequency of chicken consumption in campylobacter cases and controls, Republic of Ireland and Northern Ireland, 2003
Cases
Controls
Odds ratio
Ate chicken
181
251
2.1
Did not eat chicken 15 44
Ref

72. Cases Controls Odds ratio Contact with dog Yes 29 93 0.40 No 158 201
Frequency of contact with a dog in campylobacter cases and controls, Republic of Ireland and Northern Ireland, 2003
Cases
Controls
Odds ratio
Contact with dog
Yes 29 93
0.40 No
158
201
Ref

73. Distribution of myocardial infarction by recent oral contraceptive use
in cases and controls
Oral Myocardial
contraceptives Infarction Controls OR
Yes
No Ref.
Total
Odds /307= /680=
of exposure

74. Distribution of myocardial infarction by amount of physical activity
in cases and controls
Physical Myocardial
activity Infarction Controls OR
>= 2500 Kcal
< 2500 Kcal Ref.
Total
odds of /176= /136=
exposure

75. Distribution of cases of endometrial cancer
by oestrogen use in cases and controls
Oestrogen use
Cases
Controls
Odds ratio
High a1 b1
a1d/b1c
Low a2 b2
a2d/b2c
None c d
Reference

76. Relation of hepatocellular adenoma
to duration of oral contraceptive use
in 79 cases and 220 controls
Months of
OC use
Cases
Controls
Odds ratio
0-12 7
121
Ref.
13-36 11 49
3.9
37-60 20 23
15.0
61-84 21
18.1
>= 85
49.7
Total 79
220
Source: Rooks et al. 1979

77. Advantages of case control studies
Rare diseases
Several exposures
Long latency
Rapidity
Low cost
Small sample size
Available data
No ethical problem

78. Limitations of case-control studies
Cannot compute directly risk
Not suitable for rare exposure
Temporal relationship exposure-disease difficult to establish
Biases +++
control selection
recall biases when collecting data
Loss of precision due to sampling

79. The cohort study is the gold-standard of analytical epidemiology
CASE-CONTROL STUDIES HAVE THEIR PLACE IN EPIDEMIOLOGY, but if cohort study possible, do not settle for second best

80. Thank you!

81. Back-up slides

82. } } a c E a P1 I1 = a / P1 c P0 E I0 = c /P0 I1 = -------- P1/10 E a
Population
denominator
Cases E a P1 }
I1 = a / P1
a/P1
I1/ I0 =
c/P0 c P0 E
I0 = c /P0
Population
sample
Cases a
I1 =
P1/10 E } a
P1 /10
a/P1
I1/ I0 =
c/P0 c
I0 =
P0/10 c
P0 /10 E

83. } Source population E a P1 I1 = a / P1 c P0 E I0 = c /P0 E a b c d E
Cases
Pop. E a P1
I1 = a / P1 }
a/P1
I1/ I0 =
c/P0 c P0 E
I0 = c /P0
= sample
Cases
Controls E a b
P1 b
= ----
P0 d c d E

84. } Source population E a P1 I1 = a / P1 c P0 E I0 = c /P0 E a b c d E
Cases
Pop. E a P1
I1 = a / P1 } c P0 E
I0 = c /P0
a/P a . P0 a . d
I1 / I0 = = = =
c/P0 c . P1 c . b
a / c
------
b / d
= sample
Cases
Controls E
Since d/b = P0 / P1 a b
P1 b
= ----
P0 d c d E