Case-Control Studies
EPI 200A
October 29 and November 3, 2009
Case-Control study; the history
A disease looking for a cause
Vincent Memorial Hospital: 8 women of 15-22 years of age
with vaginal cancer between 1966-1969

A very rare disease, especially in young women
 No common exposure to tampons or drugs; none used
oral contraceptives (OCs)
 1 case to 4 controls without the disease matched on
age, born in the same hospital
 Similar data on X-rays, maternal smoking, pregnancy
complications, childhood diseases, etc.
2
Case-Control study; the history
A disease looking for a cause

7 of 8 case mothers had used diethyl-stilbesterol
(DES); none of the 32 mothers for controls had used
DES.

Herbst et al. N Engl J Med 1971; 284: 878-81.
3
Exp Cases Non-cases
+
a
b
c
d
a/c > b/d
if a cause
a/c and b/d
are exposure odds
4
The idea of a case-control study
dates back to Hippocrates …..
5
Hippocrates in first epidemics
”By paying attention
to what was common to every case, and particular to each case,
to the patient; the prescriber and the prescription,
to the epidemic constitution generally, and its local mood,
to the habits of life and occupation of each patient,
to his speech, conduct, silences, thought, sleep, wakefulness,
and dreams – their content and incidence,
to his pickings and scratchings, tears, stools, urine, spit and vomit,
to earlier and later forms of illness during the same prevalence,
to critical or fatal determinations,
to sweat, chill, rigor, hiccup, sneezing, breathing, belching,
to passage of wind, silently or with noise;
to bleedings, and
to piles.”
6
The philosophy of the case-control study was taken from JSM
as stated in MacMahon, Pugh and Ipsen: Epidemiologic
Methods. London: Churchill, 1960.
John Stuart Mill’s logic of causation
1. Method of difference
2. Method of agreement
Only qualitative estimates
7
Broders 1920: JAMA; 74: 656-64.
Cancer of the lip; 537 cases and 500 controls; similar
smoking habits (80%), 78% of cases smoked the pipe;
38% among controls.
Schreck and Lenowitz 1947: Cancer Research; 7: 180187.
Cancer of the penis; circumcision as a protective factor.
 1950s smoking and lung cancer
8
Doll and Hill on Smoking & Carcinoma of the Lung.
BMJ September, 1950 / UK, Mortality Rates
Year
1901 - 20
1936 - 9

Rates 100,000
Males
1.1
10.6
Females
0.7
2.5
This increase was also seen in USA, Canada, Australia,
Denmark, Switzerland.
9
Doll and Hill on Smoking & Carcinoma of the Lung.
BMJ September, 1950



Better diagnostic tools?
Hypotheses: Air pollution (cars, industries) or tobacco
smoking
Reports from Germany 1939, 3 out of 86 lung cancer
patients were non-smokers. Similar reports from the U.S. in
1950 (Schrek, Wynder, Graham)
10
Methods

20 London Hospitals were asked to notify all patients with
cancer of the lung, stomach, colon, and rectum.
Interviewers were also asked to select non-cancer patients
of the same sex, age and from the same hospital.

Hospital diagnosis on discharge accepted
 2370



cancer cases identified
> 75 years (150)
Wrong diagnosis (80)
No interview (too late (189))
(too ill (116))
(dead (67))
(too def, etc. (37))
11
Methods, cont.


No patients refused
Study population









Carcinoma of lung:
Carcinoma of stomach:
Carcinoma of colon/rectum:
Other malignant diseases
Controls (other patients)
Other cases
Excluded
All
709
206
431
81
709
335
4
2475
Other cases – interviewed as cancer cases but the diagnosis
was not confirmed or redundant non-cancer controls – without a
match
12
Assessment of smoking

Smoking habit change as a function of e.g. price (duty raised in
1947) and disease

Were asked:

A smoker = at least 1 cigarette per day at least 1 year
1. smoked any period of their life
2. age at which they started or stopped
3. current intensity
4. changes in smoking habits
5. type of tobacco smoking
6. inhaled or not
13
Assessment of smoking, cont.

Two interviews done 6 months apart
First Interviewer Second Interviewer Cigarette
Cigarette
0
1
5
15
25
0
1
5
15
25
50+
8
All
8
1
4
1
6
1
13
4
18
50+
3
9
1
1
1
3
0
0
13
5
0
All
9
5
17
14
4
1
50
14
Assessment of smoking, cont.
Sex
Disease
Non Smokers
Males
Lung cancer
Controls
Lung cancer
Controls
Females

P
2(0.3%)
27(4.2%)
647
622
<0.001
19(31.7%)
32(53.3%)
41
28
<0.02
Then they showed


Smokers
Lung cancer patients smoked more, had smoked for a longer time period,
smoking a pipe carried less risk
Inhaling:
Assessing 688 living cancer patients, 61.6% said they inhaled.
650 other patients, 67.2% said they inhaled.
15
Assessment of smoking, cont.

Interpretation
 Selection bias
=
=

Information bias
more lung cancer patients from rural
areas restriction to greater London
– same results
control patients – did they have a disease
that prevented them from smoking or was
prevented by smoking?
- different patient control groups – same results
interviewed before they were diagnosed
blinding of interviewers – did not work
compared smoking data for patients
suspected for lung cancer but who did
not have the disease
16
Assessment of smoking, cont.

It is not reasonable, in our view, to attribute the results to any
special selection of cases or to bias in recording… there is a
real association between carcinoma of the lung and smoking.

This is not necessarily to say that smoking causes lung cancer.
The association would occur if carcinoma of the lung caused
people to smoke or if both attributes were end-effects of a
common cause.

Only carcinogenic substance found in tobacco smoke is
arsenic. Because carcinogenic testing at this time was based
upon a skin-rat-test.
17

Disease ORs and exposure ORs are similar
Closed Cohort
Exp
D
D
N
+
-
a
c
b
d
N+
N-
ND
ND
a/N+
b/N+
c/Nd/N-
=
a/b
c/d
a/ND
Exposure odds =
c/ND
ratio
b/ND
d/ND
=
a/c
b/d
Disease odds =
ratio
a/b = axd = a/c
c/d
cxb
b/d
18
So
the exposure odds ratio OR ; =
a/c
b/d
a/b
is equal to the disease odds ratio
c/d
and RR is closed to the disease OR when the disease is rare RR =
CI+
CI-
~
CI+ / (1- CI+ )
CI- / (1- CI -)
1-CI close to 1, if the disease is rare.
19
Advantages of the Case-Control Method

Well suited to the study of rare diseases or diseases with
long latency periods
Allows study of multiple potential causes of a disease

Relatively quick to mount and conduct

Relatively inexpensive

Requires comparatively few subjects

Existing records can occasionally be used

Often no risk to subjects

20
Disadvantages of the Case-Control Method
 Relies often on recall or records for information on
past exposures
 Validation of information is difficult or sometimes
impossible
 Control of extraneous variables may be incomplete
 Selection of an appropriate control group may be
difficult
 Vulnerable to selection bias
 Rates of disease in exposed and unexposed
individuals cannot be determined (not always true)
21
Advantages of the cohort method





In principle, provides a complete description of experience
subsequent to exposure, including rates of progression,
staging of disease, and natural history
Allows study of multiple potential effects of a given
exposure, thereby obtaining information on potential
benefits as well as risks
Allows for the calculation of rates of disease in exposed
and unexposed individuals
Permits flexibility in choosing variables to be
systematically recorded
Allows for thorough quality control in measurement of
study variables (not time in historical cohorts)
22
Disadvantages of the cohort method






Large numbers of subjects are required to study
rare diseases
Potentially long duration for follow-up
Current practice, usage, or exposure to study
factors may change, making findings irrelevant
Relatively expensive to conduct
Maintaining follow-up is difficult
Control of extraneous variables may be incomplete
23
A
disease “looking” for a cause
 Case-control study
 A cause “looking” for a disease
 Follow-up study
24
Modern case-control methods

The terminology is still confusing. You will find terms
such as retrospective studies, TROHOC studies, casereferent studies, case-base studies, case-cohort studies,
case-non-case studies and case-control studies.

If we forget John Stuart Mill and start with a cohort and
the estimates of effect measures this study provides, we
have:
25
A cohort study of CS2 exposure and AMI
E
+
-
D+
400
200
D9,600
9,800
N
10,000
10,000
T
9,800
9,900
RR = 400/10,000) / (200/10,000) = 2.0
IRR = (400/9,800) / (200/9,900) = 2.02
OR = (400/9,600) / (200/9,800) = 2.04
26
If we for some reason would reconstruct OR by using a more
economic sampling approach, we would do a case-non-case
study:
E
+
N
D+
400
200
600
Controls (D-)
9,600/19,400 x 600 = 296.9
9,800/19,400 x 600 = 303.1
600
400/200
OR =
= 2.04
296.9/303.1
27
A cohort study of CS2 exposure and AMI
E
+
-
D+
400
200
D9,600
9,800
N
10,000
10,000
T
9,800
9,900
RR = 400/10,000) / (200/10,000) = 2.0
IRR = (400/9,800) / (200/9,900) = 2.02
OR = (400/9,600) / (200/9,800) = 2.04
28
If we wanted to estimate RR, we would select
a different sampling strategy:
The case-cohort study
E
+
N
D+
400
200
600
Controls (D-)
10,000/20,000 x 600 = 300
10,000/20,000 x 600 = 300
600
OR = (400/200) / 300/300) = 2.0
This is a study for a fixed cohort with no loss to follow up.
29
A cohort study of CS2 exposure and AMI
E
+
-
D+
400
200
D9,600
9,800
N
10,000
10,000
T
9,800
9,900
RR = 400/10,000) / (200/10,000) = 2.0
IRR = (400/9,800) / (200/9,900) = 2.02
OR = (400/9,600) / (200/9,800) = 2.04
30
In a cohort with loss to follow-up or in a dynamic population,
one would aim at estimating the IRR. As it is seen in the
cohort example, we need to sample controls to estimate the
distribution of exposed and unexposed observation time.
E
+
N
D+
400
200
600
Controls (D-)
9,800/19,700 x 600 = 298.5
9,900/19,700 x 600 = 301.5
600
OR = 2.02
31
To obtain this estimate, we sample from the population at risk at the time of the onset
of the disease (incidence density sampling). In a small population like this:
1
2
3
4
5
6
7
8
D
D
D
time
t
3 is our case at time t, and the population at risk is number 1, 4, 5, 6 and 7.
All selected controls that get the disease during recruitment should also
become cases and controls may be selected more than once.
32
Summary:
Assume an underlying follow-up study like
Exp D+
+
a
c
Db
d
N
N+
N-
T
t+
t-
RR = (a/N+) / (c/N-) or (a/c) / (N+/N-)
IRR = (a/t+) / (c/t-) or (a/c) / (t+/t-)
OR = (a/b) / (c/d) or (a/c) / (b/d)
The right-hand figures are what we want
controls to estimate.
33
Food poisoning: diarrhea and fever within 48 hours
following a picnic
Food/drinks
N
Disease
All
480
24
Shrimp salad
122
8
Olives
326
20
Fried chicken
430
10
Barbecued chicken
183
18
Beans
256
12
Potato salad
375
17
Bread
178
7
Beer
466
23
How would you
get data?
How would you
analyze data?
How would you do
a case-control
study?
34
Cohort
RRB-chicken =
18/183
6/297
= 4.869
Case cohort approach
Exp
+
-
Cases
Controls
18
6
9.15
14.85
24
24
Sampling fraction, r, 48/480 = 0.10
4.869 =
4.869 =
4.869 =
18/(480x0.10-0.10N-)
6/0/10N18x0.10N6x(48-0.10N-)
1.8N288-0.6N-
1402.27 – 2.921N- = 1.8NN- = 297
35
Summary:
Assume an underlying follow-up study like
Exp D+
+
a
c
Db
d
N
N+
N-
T
t+
t-
RR = (a/N+) / (c/N-) or (a/c) / (N+/N-)
IRR = (a/t+) / (c/t-) or (a/c) / (t+/t-)
OR = (a/b) / (c/d) or (a/c) / (b/d)
The right-hand figures are what we want
controls to estimate.
36

In a case control study we get estimates of
relative effect measure. We usually cannot
estimate absolute measures of association, why
not?

In some situations we can
37
We sample a fraction r then
r+N+/r-N- = N+/N- if r+ = rr+t+/r-t- = t+/t-
if
r+ = r-
r+b/r-d = b/d
if r+ = r-
Since we in a study with a known source population, N, get
data on RR and have data on a and c, we get:
a/(rN-rN-)
RR =
c/rN-
That equation can be solved for
N- given r is known and
absolute risks can be estimated
38
Or in the book (ME3) terminology:
Follow-up
Exp
+
-
D
A+
A-
A+
I+ =
T+
D
B+
B-
T
T+
T-
AI- =
T-
We sample a rate r of controls per unit time
B+/T+ = B-/T- = r
or B+/r = T+ and B-/r = T-
39
In the case-control study, we have the following pseudo rate:
A+/B+ and A-/BTo get incidence rates I+ = A+/T+
We:
I+ = A+/B+ x r
or
I+ = A+/B+ x B+/T+ = A+/T+
If r is not known we still get:
Pseudo rate+
=
Pseudo rate-
A+ /((B+ /T+)T+)
A+ /B+
=
A-/((B-/T-)T-)
A-/B-
A+ /(r xT+)
= A-/(r xT-) =
A+/T+
A-/T-
=
requires incidence density sampling
IRR
40

Case-control studies are not conceptually retrospective. They do
not compare cases with non-cases, but exposed with not
exposed. They apply a specific sampling strategy to provide the
relative effect measures in the underlying cohort.

They provide estimates with far less observations than in the
cohort study. Given the necessary exposure data and sampling
data are available, they are equivalent in quality to the cohort
approach. In fact they represent just a different approach to
obtain the cohort result.

Case-control studies are the studies of choice if you can
reconstruct exposure data back in time (for the exposure of
interest as well as for confounders). They represent often the
design of choice in genetic studies
41

If you want to study if bacterial vaginoses causes
preterm birth, how would you sample cases and
controls?
42

If you want to study if antibiotics prevent preterm births,
what is the source population (study base)?

If you want to study if use of bicycle helmets prevents
head injuries, what is the source population (study
base)?
43

The described type of case-control study is a
study with a primarily defined study base.
 Cases
come from a well-defined cohort and we
may sample controls from this cohort.

Or
 Cases
come from a well-defined population. We
have complete ascertainment and we may
sample controls from this population at given
points in time.
Be careful if these conditions are not met.
 Sometimes cases are prevalent cases.

44
Since prevalence is a function of incidence and
duration (D)
P/I-P= I x D
 Determinants of prevalence reflect aetiologic as
well as prognostic factors.

45
Example: Exercise and AMI
Exp
+
-
Exp
+
N
D+ S+
S-
30
30
10 1,000
20 1,000
20
10
DS+
20
10
30
N
Cohort
15
15
30
RR = (30/1,000) / (30/1,000)
= 1.0
OR = (20/10) / (15/15) = 2.0
46


The same rules as for risks will apply for estimating
effect measures based on prevalence data.
A case-non-case study will estimate
P /(1  P )
OR 
P /(1  P )
Control sampling from the entire population (including
prevalent cases) will estimate:
P
OR 
P
47

Controls are ideally randomly sampled from
the same population that gave rise to the
cases.

Controls will then estimate the exposure
distribution in the source population but this
estimate will be subject to random sampling
variation.
48

It will often be difficult to make random sampling
and:
If the selected sampling strategy produces
exposure estimates that are interchangeable
with the exposure distribution in the study base,
results will be unbiased. If not, effect estimates
will be biased.
49

If all cases cannot be ascertained (no registry, not all
come to the health care system), a case-control study
should be designed to take this lack of ascertainment
into consideration. This type of case-control study is
usually ”weak”. Our source population definition will be:
 All
potential cases define the source population.
 The
conditions that actually led to case identification
should lead to identification of all member of the source
population. (those who would enter the case group if they
have the conditions that were seen for cases – may
depend upon disease characteristics, insurance
conditions, financial means etc)
50

How to design a case-control study on male risk
factors of infertility. Only half of those with an
infertility problem seek medical help?
51
Selection of population controls
The method of choice is to use a register that includes the entire
population that gave rise to the cases, without such register it is
more difficult to make sure all have same chance of being
selected
RDD - random digit dialling
who has a telephone
who is home
how many are home
who has more numbers
+
+
how many do not respond to unsolicited calls
+
+
Neighbourhood controls
make sure they were residents at case diagnosis risk of
overmatching
Friend controls
52
Population controls - sampling from
a list of list of residents
Complicated if time must be taken into consideration. Best
would be “density sampling” or could be sampled at one point in
time.
One option:
1. Select a date at random from the case ascertainment
period
2. Select a person at random from the list
3. If resident at the selected date (1) - then OK as a control
4. Repeat 1-3 until the desired number of controls is
reached
5. Exp. Data is collected according to date at onset of the
disease or the random date (1)
53
Sampling of time - not persons
Sampling within an existing cohort e.g. diet and cancer
a. make list of time units (e.g. 1 month) for all
participants
b. sample from these units
54
Use of patient controls rather than population controls.
This idea stems from Mill’s “method of scientific
inference”, not from sampling from the underlying
cohort.
55
If case ascertainment depends on a factor (e.g. access to
medical care) sampling of controls must have similar
dependency (e.g. hosp. Controls)
Advantages of hospital controls
easy to sample
better response rate
symmetry in data collection
56
The “control disease” must neither be caused nor
prevented by the exposure
If cases are referred to the case ascertainment hospital hospital controls must have the same referral pattern
Use a single disease if an ideal ‘control’ disease exist, but
it may also be acceptable to:
Exclude all diseases with a known or suspected
association with exposure
- and make use of the remaining diseases as controls
One control group, or more than one
57
Two stage sampling
Exposure
Levels
Cases
Controls
0
1
2
c
a1
a2
d
b1
b2
First stage case-control sampling could be based upon inexpensive (perhaps already
existing) data.
A second stage sample could take analytical costs into consideration. Could be:
1. All cases and a random sample of controls
2. Oversampling of more informative cases and/or cohorts. For example,
those with the highest exposure levels. Such a sampling strategy must be taken
into consideration when doing the analysis.
58
Matching
Definition:
Cases and controls are selected to be similar with
respect to certain variables - usually controls are
selected to be similar to cases. Maching could be 1:1,
1:2, …, 1:5.
59
1
2
3
4
5
6
7
8
D
D
t
Time
At time t, 1, 3, 4 and 6 are candidates. Which ones
fit the matching criteria?
60
If matching is done for four age groups, two sex groups
and four socioeconomic groups, there are 4 x 2 x 4 = 32
classifications - it may be difficult to find a match.
61
Matching is usually done on confounders, but matching in a
case-control study does not in itself eliminate confounding
why?
E
D
2
1
C
Confounding requires:
1. The confounder is a cause (1)
2. The confounder (c) is associated with E (2).
62





Matching on (1) does not eliminate a causal association causation is a fact of life independently of our
manipulations.
We compare exposed and not exposed. We should not try
to compare cases with non-cases. We try to identify notexposed according to our counterfactual ideal. We have no
similar guidelines for cases and controls. We may use
restrictions –but then they should be used for cases as well
as controls. It is a mistake to think controls should be as
healthy as possible.
Matching may produce a well-balanced data set for
analyses.
Matching usually requires matched analysis.
The matched sets are kept in the analyses and should be
identifiable.
63

Matching may even lead to confounding (create an
association between E and C) in situations where this was
not present in the study base. All of this is very different
from using matching in follow-up studies.

The effect of the matching variables on the outcome cannot
be studied.

Matching is not always done on confounders; could be done
on time (incidence density sampling) or on a sampling
criteria (like data or birth).

Is birth weight correlated with cancer of the testis? Select
controls among boys born in the same hospital before and
after the birth of the cases. What is wrong with that?
64
E
D
Example:
C
Evaluation of a screening programme for cervical cancer
matching on the ”GP factor”.
Setting:
A doctors screen 80%
B doctors 20%
65
A
10,000
8,000 sc+ 40 D(0.5%)
2,000 sc- 20 D(1.0%)
B
10,000
2,000 sc+ 10 D(0.5%)
8,000 sc- 80 D(1.0%)
RR = 0.5
66
Case-cohort study
E
D
Cohort
+
-
50
100
66
84
OR = RR = 0.64
67
Stratified analysis or analysis of matched sets
will solve the problems
GP
E
D
Cohort
OR
A
+
-
40
20
48
12
0.50
B
+
-
10
80
18
72
0.50
68
In order to have true confounding, GPs must be a risk
factor of cervical cancer
E
D
C
69
Setting:
A
10,000
8,000 sc+
2,000 sc-
40 D(0.5%)
20 D(1.0%)
B
10,000
2,000 sc+ 20 D(1.0%)
8,000 sc- 160 D(2.0%)
RR = 0.5, but now confounding in the study base
70
The cohort:
Matched
case-cohort study:
E
D
All
RR
+
-
60
180
10,000
10,000
0.33
E
D
Cohort
OR
+
-
60
180
84
156
0.62
71
Again, stratification will solve the problems
GP
E
D
Cohort
OR
A
+
-
40
20
48
12
0.50
B
+
-
20
160
36
144
0.50
72

Cross-sectional study – a survey

An observational study in which all variables are
measured at a single point in time
73

Are used to estimate prevalences of diseases
and frequencies of exposures.

Diseases of short duration will not be well
presented since prevalence is a function of
incidence and duration
74
A study of peripheral vascular disease (PVD) in Scotland
and smoking
PVD
No PVD
All


Smoking
Ever
Never
23
8
1704
1291
1727
1299
All
31
2995
3026
Measures of association?
Interpretation?
75

Because exposure and disease are assessed at
the same time, cross-sectional studies may not
be able to establish that exposure preceded
onset of the disease process.
76
77
78
79
80
81
Case-crossover design
Cases and controls should come from the same studybase. Fulfilled if cases are also the controls.
For most exposures, we move from being exposed to
unexposed. If we have no carry-over effect and the
cause-effect relationship is short, we may compare IR in
the two time segments.
IRexp / IRnon-exp
82

As always a case-control study samples the
underlying population experience. Each case
represents the follow-up of one person.

If cases are their own controls, we adjust for
subject characteristics, sometimes for
confounding by indication.
83
If the time period before onset of the case status equals the
reference time period, 4 outcomes are possible
CaseType
period
1 exp
2 exp
3 not-exp
4 not-exp
Reference
period
exp
not-exp
exp
not-exp
Type 1 and 4 provide no indication of causal relevance.
Type 2 indicates causal association. Type 3 indicates the opposite.
 type2
OR 
 type3
84

The design rules out time-stable personal habits
as confounders but not time-dependent factors.

Selection bias if type 2 and type 3 cases decide
on participation based upon their case status.

Information bias is a potential problem if
exposure status is based upon recall.
85

The case-crossover design is biased if the
exposure varies over the time period under
study.

The case-time study tries to incorporate
adjustment for this change over time by including
data on exposure used over time for controls.

This will not automatically adjust for confounding
by indication. Data on disease severity are
needed.
86
Case-crossover study
N Engl J Med 1997;336:453-58
Aim:
Use of cellular telephones - a risk factor for motor
vehicle accidents?
Methods:
Case-crossover = case ascertainment North York
Collision Reporting Centre, Toronto. July 1, 1994
- August 31, 1995, 10-18 hours, Monday-Friday.
Note! Centre does not include accidents with injuries, only
substantial property damage.
Criteria:
Excl. drivers who had no cellular phone or no
billing records.
87
Case-crossover study
N Engl J Med 1997;336:453-58
Timing of the accident
Subject statement
Police records
Call to emergency
Two out of 3 = exact
Timing of exposure:
10 minutes prior to accident
Reference exposure time:
Workday before the accident
Same weekday
The week before the accident
Adjustment for driving
88
Case-crossover study
N Engl J Med 1997;336:453-58
5890 drivers - 1064 had a phone - 742 participated 699 had a billing record
Time of accident:
exact
inexact
231
468
170 had used the phone 10 minutes prior to the accident
37
the weekday before
crude OR 6.5 (4.5, 9.9)
adj
OR 4.3 (3.0, 6.5)
89
Table 2. Relative risk of a motor vehicle collision in 10minute periods, according to selected characteristics
Characteristics
All subjects
Age (yr)
< 25
25-39
40-54
≥ 55
No. with telephone
use in 10 min before
collision
Relative Risk
(95% CI)
170
4.3 (3.0-6.5)
21
95
44
10
6.5 (2.2 -  )
4.4 (2.8 - 8.8)
3.6 (2.1 - 8.7)
3.3 (1.5 -  )
Sex
Male
Female
123
47
4.1 (2.8 - 6.4)
4.8 (2.6 - 14.0)
High-school
graduation
Yes
No
153
17
4.0 (2.9 - 6.2)
9.8 (3.0 -  )
Type of job
Prof
Other
34
136
3.6 (2.0 - 10.0)
4.5 (3.1 - 7.4)
90
Characteristics
No. with
telephone use in
10 min before
collision
Relative Risk (95% CI)
0-9
Driving
10-19
experience
20-29
(yr)
≥ 30
40
67
36
27
6.2 (2.8 - 25.0)
4.3 (2.6 - 10.0)
3.0 (1.7 - 7.0)
4.4 (2.1 - 17.0)
Cellular
telephone
experience
(yr)
0 or 1
2 or 3
4 or 5
≥ 6
51
39
36
44
7.8 (3.8 - 32.0)
4.0 (2.2 - 12.0)
2.8 (1.7 - 6.7)
4.1 (2.3 - 12.0)
Type of
cell phone
Hand-held
Hands free
129
41
3.9 (2.7 - 6.1)
5.9 (2.9 - 24.0)
91
Fig. 1. Relative Risk of a collision for different control periods
Relative risk
of a collision
10
8
6
4
2
0
Day before
Workday
Weekday
Max-use Matching day
day
Comparison Day
92
Fig. 2 Time of cellular-telephone call in relation to the relative risk of
a collision
10
8
6
•
4
2
0
•
•
•
93
Fig. 3 Consistency of relative risks obtained from different
collision times


100.0
10.0
•
•
•
•
•
•
•
•
•
•
1.0
0.1
Time of Day
Day of Week
94