Lindsay Ann Abrigo
Week 5 Assignment – Smoking and Lung Cancer Case Study
April 11, 2013
1. What makes the first study a case-control study?
The first study is considered a case-control study because a case-control study is used when the
population under investigation is large and not well defined. It is used generally when there is a
communitywide outbreak where participants are generally selected based on the presence or
absence of the disease outcome and investigators will then work backwards from the effect to
find the cause. The patients who were hospitalized for the disorders other than lung cancer
represented the controls, which were the comparison group necessary for analysis in a casecontrol study.
2. What makes the second study a cohort study?
The case-subjects represent a small, well-defined population, or cohort, and they are categorized
accordingly based on their exposure to a specific risk factor, which in this case would be
smoking cigarettes. In this study, the rate of death from lung cancer for smokers was compared
with the rate for nonsmokers. This is considered a cohort study, specifically a prospective cohort
study, because individuals were followed overtime to determine the occurrence of disease.
3. Why might hospitals have been chosen as the setting for this study?
Hospitals may have been chosen as the setting for this study because there is a higher likelihood
to find lung cancer patients in hospitals receiving care and treatment; there is greater potential to
match controls with case-subjects; it is more convenient and provides more controls; there is a
higher probability for accurate diagnosis of lung cancer; hospital records will be readily available
to investigators; and patients may be more willing to participate.
4. What other sources of cases and controls might have been used?
Investigators could have used other sources as a means of choosing cases and controls. For
selecting cases, they could have used cancer registries, death certificates, insurance files, doctors’
offices, and medical records. For selecting controls, they could have resorted to neighbors,
friends, and acquaintances of the case-subjects; patients who may see the same doctors as the
case-study patients but have other illnesses; and/or a random sampling from the population that
is not ill.
5. What are the advantages of selecting controls from the same hospitals as cases?
The advantages of selecting controls from the same hospitals as cases mean that the controls
from the same hospital are likely to come from the same population as the case-subjects. This
would ultimately control for variables such as socioeconomic status, access to health care, and
environmental conditions. Other advantages include increased convenience for all involved,
increased participation by those patients who are being treated and cared for in the hospital, and
similar medical record maintenance methods.
6. How representative of all persons with lung cancer are hospitalized patients with lung
cancer?
The hospitalized patients with lung cancer are very representative of all persons with lung cancer
because they are likely patients who have a past history of frequent smoking and were also likely
to be hospitalized because of the adverse health effects.
7. How representative of the general population without lung cancer are hospitalized
patients without lung cancer?
The hospitalized patients without lung cancer are not very representative of the general
population who do not have lung cancer because hospitalized patients are more than likely to
smoke than the general population.
8. How may these representativeness issues affect interpretation of the study’s results?
If case-subjects and controls do not closely represent the intended population, the results of the
study will be biased, meaning that the relationship between the exposure and the health outcome
will be over- or underestimated. The control group provides the prevalence of exposure for
people without the health problem under study. Because the control population in this study
tends to smoke more than the population it represents, the exposure prevalence among controls
will be higher than expected. The net effect of this inconsistency is to underestimate the true risk
of lung cancer associated with smoking.
9. From this table, calculate the proportion of cases and controls who smoked.
Proportion of case-subjects who smoked: 1,350/1,356 = 0.99557522 = 99.5%
Proportion of controls who smoked: 1,296/1,357 = 0.9550479 = 95.5%
10. What do you infer from these proportions?
According to these proportions, I can infer that the prevalence of smoking is really similar in
both groups (99.5% vs. 95.5%) and also extremely high in both groups (>95%)
11. A) Calculate the odds of smoking among the cases.
Odds of smoking for case-subjects: 1,350/7 = 192.857143 = 192.9/1
B) Calculate the odds of smoking among the controls.
Odds of smoking for controls: 1,296/61 = 21.2459016 = 21.2/1
12. Calculate the ratio of these odds. How does this compare with the cross-product ratio?
Odds Ratio: (1,350/7)/(1,296/61) = 192.857143/21.2459016 = 192.9/21.2 = 9.0990566 = 9.1
13. What do you infer from the odds ratio about the relationship between smoking and lung
cancer?
From the odds ratio about the relationship between smoking and lung cancer, one can infer that
the odds of being a smoker is 9.1 times higher for case-subjects (those who smoke cigarettes)
than for controls (nonsmokers). One can also infer that the risk of lung cancer is 9 times higher
for cigarette smokers than for nonsmokers.
Table 2 shows the frequency distribution of male cases and controls by average number of
cigarettes smoked per day.
14. Compute the odds ratio by category of daily cigarette consumption, comparing each
smoking category to nonsmokers.
1 – 14 cigarettes: (565/7)/(706/61) = (565 x 61)/(706 x 7) = 34,465/4,942 = 6.97389721 = 7.0
15 – 24 cigarettes: (445 x 61)/(408 x 7) = 27,145/2,856 = 9.50455182 = 9.5
25+ cigarettes: (340 x 61)/(182 x 7) = 20,740/1,274 = 16.2794349 = 16.3
All smokers: (1,350 x 61)/(1,296 x 7) = 82,350/9,072 = 9.07738095 = 9.1
15. Interpret these results.
The results show that the odds of acquiring lung cancer increases as the daily number of
cigarettes smoked also increases.
16. What are the other possible explanations for the apparent association?
Other possible explanations for the apparent association between smoking and lung cancer may
include information or selection bias, confounding, an error on the part of the investigator, or
simply chance, although the data indicates that chance is not likely.
17. How might the response rate of 68% affect the study’s results?
A response rate of 68% can affect the study’s results by introducing selection bias into the study
and can potentially lead to conclusions that are systematically different from the truth. In this
particular case, the bias would cause an underestimation of the risk of lung cancer that is
associated with smoking.
18. Compute lung cancer mortality rates, rate ratios, and rate differences for each smoking
category. What do each of these measures mean?
Mortality rate per 1,000: Lung cancer mortality rates increase as the amount of cigarettes
smoked per day also increases.
1 – 14: 22/38,600 = 0.00056995 x 1,000 = 0.56994819 = 0.57
15 – 24: 54/38,600 = 0.00138817 x 1,000 = 1.38817481 = 1.39
25+: 57/25,100 = 0.00227092 x 1,000 = 2.27091633 = 2.27
All smokers: 133/102,600 = 0.0012963 x 1,000 = 1.2962963 = 1.30
Total: 136/145,400 = 0.00093535 x 1,000 = 0.93535076 = 0.94
Rate ratio: When comparing the categories 1 – 14 and 0 cigarettes a day, the rate ratio is 8.1. As
a result, people who smoke 1 – 14 cigarettes a day are 8.1 times more likely than nonsmokers to
develop lung cancer. In comparing the categories 15 – 24 and 0 cigarettes a day, the rate ratio is
19.8, thus people who smoke 15 – 24 cigarettes a day are 19.8 times more likely than
nonsmokers to develop lung cancer. People who smoke 25+ cigarettes a day are 32.4 times more
likely than nonsmokers to develop lung cancer.
1 – 14: 0.57/0.07 = 8.14285714 = 8.1
15 – 24: 1.39/0.07 = 19.8571429 = 19.8
25+: 2.27/0.07 = 32.4285714 = 32.4
All smokers: 1.30/0.07 = 18.5714286 = 18.6
Rate difference: The excess death attributed to smoking increases as the quantity of cigarettes
smoked per day also increased. It increased from 0.50 to 2.20, and in sum causes 1.23 excess
deaths per 1,000 people per year.
1 – 14: 0.57-0.07 = 0.50
15 – 24: 1.39-0.07 = 1.32
25+: 2.27-0.07 = 2.20
All smokers: 1.30-0.07 = 1.23
19. What proportion of lung cancer deaths among all smokers can be attributed to smoking?
What is the proportion called?
AR% = (1.30 ! 0.07) / 1.30 = 0.946 x 100% - 94.6%
Approximately 95% of the deaths due to lung cancer in the exposure group (all smokers) may be
attributable to smoking. This proportion is known as the attributable risk percent and can also be
interpreted as the proportion of lung cancer deaths that could have been prevented among
smokers if they had not smoked.
20. If no one had smoked, how many deaths from lung cancer would have been averted?
In sum, 95% of the lung cancer deaths among smokers are attributable to smoking. If no one had
smoked, 95% of the 133 deaths, which would be approximately 126, due to lung cancer would
have been averted.
The cohort study also provided mortality rates for cardiovascular disease among smokers and
nonsmokers. The following table presents lung cancer mortality data and comparable
cardiovascular disease mortality data.
21. Which cause of death has a stronger association with smoking? Why?
The rate ratio is the primary means of measuring association. As such, it indicates a stronger
association between smoking and lung cancer (18.5) than between smoking and cardiovascular
mortality (1.3). Smokers are more likely to die from lung cancer than from cardiovascular
disease, as suggested by the data.
22. Calculate the population attributable risk percent for lung cancer mortality and for
cardiovascular disease mortality. How do they compare? How do they differ from the
attributable risk percent?
PAR% (lung cancer) = (0.94 ! 0.07) ÷ 0.94 = 0.925 x 100% = 92.5%
PAR% (cardiovascular disease) = (8.87 ! 7.32) ÷ 8.87 = 0.174 x 100% = 17.4%
Therefore, 92.5% of all deaths due to lung cancer and 17.4% of all deaths due to cardiovascular
disease in the study population are attributable to smoking. From a prevention perspective, the
PAR% for a given exposure can be interpreted as the proportion of lung cancer cases in the
entire study population that would have been prevented if exposure had not occurred.
23. How many lung cancer deaths per 1,000 persons per year are attributable to smoking
among the entire population? How many cardiovascular disease deaths?
Lung cancer: 0.925 x (0.94) = 0.87 deaths per 1,000 person years
Cardiovascular disease: 0.174 x (8.87) = 1.54 deaths per 1,000 person years
The number of smoking-related deaths per 1,000 people per year is greater for cardiovascular
disease than for lung cancer, even though the relative risk (rate ratio) is considerably lower. Thus,
if no one smoked, more deaths would be prevented from cardiovascular disease than from lung
cancer.
The following table shows the relationship between smoking and lung cancer mortality in terms
of the effects of stopping smoking.
24. What does these data imply for the practice of public health and preventive medicine?
The data in the table above shows that smokers have the highest risk of death from lung cancer,
and nonsmokers have the lowest. Although the risk for people who have stopped smoking
decreases over time after quitting, the risk is still almost three times that of nonsmokers. These
data implies, that from a public health and preventive medicine perspective, smoking cessation
efforts are worthwhile because of the reduction of risk, but preventative efforts are most valuable
and most effective because of the lower risk associated with not smoking at all.
25. Compare the results of the two studies. Comment on the similarities and differences in
the computed measures of association.
Results from the studies are very consistent, exhibit a strong association between cigarette
smoking and lung cancer, and include evidence of a dose-response relationship.
Compared with the rate ratios from the cohort study, the odds ratios in the case-control study
consistently underestimate the strength of association. This discrepancy is most likely due to the
fact that the controls in this study were also very likely to be smokers.
26. What are the advantages and disadvantages of case-control vs. control studies?
Sample Size
Costs
Study time
Case-Control
Small
Less
Short
Cohort
Large
More
Long
Rare diseases
Rare exposure
Multiple exposures
Multiple outcomes
Advantage
Disadvantage
Advantage
Disadvantage
Disadvantage
Advantage
Disadvantage
Advantage
Progression, spectrum of illness
Disease rates
Disadvantage
Cannot measure
Advantage
Advantage
Recall bias
Loss to follow-up
Selection bias
Potential problem
Advantage
Potential problem
Less of a problem
Potential problem
Less of a problem
27. Which type of study (cohort or case-control) would you have done first? Why? Why do a
second study? Why do the other type of study?
A case-control study is quicker, easier, and less costly. If the case-control study results reveal a
significant relationship between exposure and disease, then it is appropriate to do a second study
to confirm the findings. Although a cohort study is more difficult and expensive to mount, as
well as slower to yield results, it can confirm the results of previous studies, allow for the
calculation of disease rates, and enable investigators to assess the natural progression from
exposure to disease.
28. Which of the following criteria for causality are met by the evidence presented from these
two studies?
Strong association
Consistency among studies
Exposure precedes disease
Dose-response effect
Biologic plausibility
YES
X
X
X
X
NO
X