You have just finished a health education in-service to the community on the hazards of smoking.  A representative of the tobacco industry is present at your in-service and makes the following comment regarding your presentation: "You gave a nice presentation.  However, I disagree with you that smoking can cause lung cancer. There is still not enough evidence to indicate that smoking can cause cancer." Your task is as follows: 1. Respond to his statement and indicate why there is a cause-effect relationship between smoking and lung cancer using the five criteria for causality .  2. What is your interpretation of the evidence on how smoking affects lung cancer?  READ: FIVE CRITERIA FOR CAUSALITY Assignment Expectations, in order to earn full credit: Please write your paper in your own words. That is the only way I can evaluate your level Analytic epidemiology is defined as the study of the determinants of disease or reasons for relatively high or low frequency in specific groups.  Analytic epidemiology answers questions regarding why the rate is high or low in a particular group.  Observations of differences lead to formation of hypotheses. Analytic Studies There are basically two types of studies: experimental and observational.  In an experimental study, the exposure has not occurred yet.  The investigator controls the exposure in the study groups and studies the impact.  For example, he may immunize one group with an experimental vaccine that has been developed for a disease and compare the frequency with which the disease develops to the control group (which had no modification).  In an observational study, the exposure has already occurred.  The exposures and outcomes are observed and analyzed, not created experimentally.  Observational studies are often more practical and continue to provide the major contribution to our understanding of diseases.  There are two main types of observational studies: cohort (prospective) and case-control (retrospective) studies. In a cohort study, a group of people who share a common experience within a defined time period (cohort) are categorized based upon their exposure status.  For example, individuals at a work place where an asbestos exposure occurred would be considered a cohort.  Another example would be individuals attending a wedding where a foodborne illness occurred.  Cohort studies have well-defined populations.   Often, cohort studies involve following a cohort over time in order to determine the rate at which a disease develops in relation to the exposure.   In a cohort study, relative risk is used to determine whether an association exists between an exposure and a disease.  Relative risk is defined as ratio of the incidence rate among exposed individuals to the incidence rate among unexposed individuals.  To calculate the relative risk, you would use the following formula: (a/a+b) / (c/c+d)  where: a = the number of individuals with a disease who were exposed.  b = the number of individ.

1. You have just finished a health education in-service to the
community on the hazards of smoking. A representative of the
tobacco industry is present at your in-service and makes the
following comment regarding your presentation: "You gave a
nice presentation. However, I disagree with you that smoking
can cause lung cancer. There is still not enough evidence to
indicate that smoking can cause cancer."
Your task is as follows:
1. Respond to his statement and indicate why there is a cause-
effect relationship between smoking and lung cancer using the
five criteria for causality
.
2. What is your interpretation of the evidence on how smoking
affects lung cancer?
READ: FIVE CRITERIA FOR CAUSALITY
Assignment Expectations, in order to earn full credit:
Please write your paper in your own words. That is the only way
I can evaluate your level
Analytic epidemiology is defined as the study of the
determinants of disease or reasons for relatively high or low
frequency in specific groups. Analytic epidemiology answers
questions regarding why the rate is high or low in a particular
group. Observations of differences lead to formation of
hypotheses.
Analytic Studies
There are basically two types of studies: experimental and
observational. In an experimental study, the exposure has not
occurred yet. The investigator controls the exposure in the
study groups and studies the impact. For example, he may
immunize one group with an experimental vaccine that has been
developed for a disease and compare the frequency with which
the disease develops to the control group (which had no
modification). In an observational study, the exposure has
already occurred. The exposures and outcomes are observed

2. and analyzed, not created experimentally. Observational studies
are often more practical and continue to provide the major
contribution to our understanding of diseases. There are two
main types of observational studies: cohort (prospective) and
case-control (retrospective) studies.
In a cohort study, a group of people who share a common
experience within a defined time period (cohort) are categorized
based upon their exposure status. For example, individuals at a
work place where an asbestos exposure occurred would be
considered a cohort. Another example would be individuals
attending a wedding where a foodborne illness occurred.
Cohort studies have well-defined populations. Often, cohort
studies involve following a cohort over time in order to
determine the rate at which a disease develops in relation to the
exposure.
In a cohort study, relative risk is used to determine whether an
association exists between an exposure and a disease. Relative
risk is defined as ratio of the incidence rate among exposed
individuals to the incidence rate among unexposed individuals.
To calculate the relative risk, you would use the following
formula:
(a/a+b) / (c/c+d)
where:
a = the number of individuals with a disease who were exposed.
b = the number of individuals without a disease who were
exposed.
c = the number of individuals with a disease who were NOT
exposed.
d = the number of individuals without a disease who were NOT
exposed.
In a case-control study, the sample is based upon illness status,
rather than exposure status. The researcher identifies a group of
people who meet the case definition and a group of people who
do not have the illness (controls). The objective is to determine
if the two groups differ in the rate of exposure to a specific
factor or factors.

3. In contrast to a cohort study, the total number of people
exposed in a case-control study is unknown. Therefore, relative
risk cannot be used. Instead, an odds ratio or risk ratio is used.
An odds ratio measures the odds that an exposed individual will
develop a disease in comparison to an unexposed individual.
Please click the button below to learn how to calculate an odds
ratio.
To calculate an odds ratio, you would use the following
formula:
ad/bc
where:
a = the number of individuals with a disease who were exposed.
b = the number of individuals without a disease who were
exposed.
c = the number of individuals with a disease who were NOT
exposed.
d = the number of individuals without a disease who were NOT
exposed.
Below is an example...
If a researcher selects 50 Lyme disease cases and 100 controls
for a case-control study, and the results indicated that 45 cases
and 10 controls recently hiked in a national forest, the odds
ratio would be inserted into the 2x2 table below:
Lyme Disease
No Disease
TOTAL
Exposure to Hiking
45
10
55
No Hiking
5
90
95
TOTAL

4. 50
100
150
The odds ratio would be calculated as follows:
Odds ratio = (45 x 90) / (10 x 5) = 81
Interpretation of Odds Ratios and Relative Risk
A relative risk or odds ratio that is approximately equal to 1.0
indicates that there is no association between the exposure and
the outcome. If the relative risk or odds ratio is significantly
greater than 1.0, then the outcome and exposure are positively
associated. If the relative risk or odds ratio is significantly less
than 1.0, then the outcome and exposure are negatively
associated and the exposure is referred to as being protective.
For example, exercise may be negatively associated with lung
cancer because individuals who smoke are less likely to
exercise.
To determine if a value is statistically significant, confidence
intervals are often calculated using computer software
programs. A 95% confidence interval is defined as a range of
values that has a 95% probability of containing the value being
estimated (e.g. odds ratio or relative risk). For example, if the
95% confidence interval for the odds ratio of 81 in the above
example is 23.5-302.9, then it tells you that there is a 95%
probability that the odds ratio will be between 23.5 and 302.9.
Confidence intervals that are above 1.0 and DO NOT include
1.0 are statistically significant and may indicate that a food
item is contaminated. For example, a confidence interval of 1.1
- 7.9 is significant because 1.1 (the left number of the
confidence interval) is above 1.0. The number 1.0 is NOT
between 1.1 and 7.9.
Confidence intervals that include 1.0 are NOT significant and
indicate that the food item is probably NOT contaminated.
Using pepsi as an example, the attack rate table indicates that
the 95% confidence interval equals: .8 - 10.5. Because 1.0 is
between .8 and 10.5, it includes one and therefore is probably
NOT contaminated.

5. To identify the contaminated food item you need to identify the
food items that have significant confidence intervals and pick
the food with the highest relative risk
To view an example of how to calculate a relative risk, click
here
Often these values are put into the following 2x2 table:
Disease
No Disease
Exposed
a
b
Unexposed
c
d
The attack rate is a form of incidence in which the numerator is
the number of new cases of a health problem during an
outbreak, and the denominator is the population at the
beginning of the period. Food-specific attack rates are
frequently used in foodborne outbreak investigations to compare
those who ate a specific food with those who did not eat the
food. A high attack rate among persons who ate a specified
food suggests that a food is associated with the illness. A low
attack rate among persons who ate the food suggests that the
food is not associated with the illness. The risk difference is
the difference in attack rates (i.e. the percent ill among those
who ate a specified food minus the percent ill among those who
did not eat the food). Usually, the risk difference is large for
the contaminated food and small for other foods. For example,
the risk difference for cheese in the table below is 74 - 56 = 18.
Food
Those who ate specified food
Those who did not eat food
Ill
Well
Total

6. Attack rate
Ill
Well
Total
Attack rate
Cheese
17
6
23
74%
9
7
16
56%
A statistically significant association between an exposure and a
disease does not necessarily mean that there is a cause-effect
relationship between the exposure and illness. The association
could reflect biases in the design, conduct, or analysis of the
study. The association may also occur because the exposure
and the disease are related to some common underlying
condition. Please click
here
to view the criteria that are widely used to evaluate whether an
association is causal.
http://www.epidemiolog.net/evolving/AnalyticStudyDesigns.pdf
Required Readings
See, A. (2000). Use of Human Epidemiology Studies in Proving
Causation. Defense Counsel Journal, 67 (4). Retrieved on
February 21, 2013 at
http://ruby.fgcu.edu/Courses/Twimberley/EpiRiskAsst/Causatio
n.pdf
The University of Pittsburgh. (2005, March 3). Supercourse:
Web of Causation; Exposure and Disease Outcomes. February
21, 2013 at:

7. http://www.pitt.edu/~super1/lecture/lec19071/index.htm
The missed lessons of Sir Austin Bradford Hill Carl V
Phillips1,2,3 and Karen J Goodman1 Epidemiol Perspect Innov.
2004; 1: 3. Retrieved February 21, 2013 from
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC524370/
CDC (2004) How to Investigate an Outbreak. Retrieved
February 21, 2013 from
http://www.cdc.gov/EXCITE/classroom/outbreak/objectives.htm