Cohort Study on Physical Activity and CHD, Case-Control Study on Dietary Fat and Skin Cancer, Bias in Cohort and Case-Control Studies

This article analyzes a cohort study on physical activity and CHD, a case-control study on dietary fat and skin cancer, and discusses biases in cohort and case-control studies. The cohort study found that physical activity reduces the incidence of CHD, while low-income earners have an increased risk. The case-control study found that high-fat diets increase the risk of skin cancer. The article also discusses biases in cohort and case-control studies, such as data collection and selection bias. The subject is health research. References are included.

University
Task
ASSIGNMENT 2
Date

Based on the research article
Question 1
(a) The type of study portrayed by the above article is (1 mark)
- This is a cohort study which entails following the certain group of persons of
interest for a certain period of time to assess exposure outcomes on a longitudinal
study.
(b) Data used to obtain the first study participants entailed [2 marks]
- The study utilized data from the Swedish Annual Level of Living Survey, which
details a national representative of the non-institutionalized population. This
sample obtained included 7090 women and men between ages 35-74 surveyed
between 1988 and 1989.
(c) The authors excluded a total of 1984 participants from the study because [2 marks]
- They rated their general health as poor hence this was for a reason to remove
underlying conditions which might affect the assessment of coronary heart
disease. As these factors could have a confounding effect on the focus of the
study.
(d) The crude overall incidence for CHD was [2 marks]
- The crude incidence rate was 59 cases per 10,000 persons over the various levels
of physical activity.
(e) Comparison of relative risks and physical activity levels [4 marks]
- The physical activity engagement of twice a week had RR of 0.72 at CI between
0.52-1.01 compared to low-income earners having RR of 1.20 at CI interval of
0.95-1.52 compared to those of other incomes at RR at 1.
(f) The relative risks discussed above can be best explained through [3 marks]
- Physical activity engagement of twice a week led to a reduced incidence rate of
coronary disease exposure.
- The low-income earners were linked to increased risks of exposure towards
getting coronary heart disease while other income category earners had no effect
on the emergence of the disease.
(g) Relative risks in this study are better described as [2 marks]
- The relative risks in this task can be described as risk ratio, as it outlines the
probability of developing coronary heart disease on exposure to physical activity
compared to those not engaging in intense physical activity and at the same time
level of income as a risk factor of developing coronary heart disease.
(h) The major confounding factors which were not considered in this study which could
have a significant factor on the study results include; [4 marks]

- The major confounders which were not considered in this study include disease
and social factors such as hypertension, smoking, obesity, and
hypercholesterolemia.
- These factors have the opportunity to progress the development of coronary heart
disease as they are major underlying factors and risks factors for CHD
development. However the researchers omitted the participations who had poorly
rated health state, the above factors could be within the recruited participants.
- These risk factors thus signify profound confounding factors which were not
litigated in the study. Thus physical activity could not be extensively be assessed
with regard to the development of physical; activity among the participants.
(i) Why do you think the author could not adjust for these confounders? Hint: think
about the way this study was designed [4 marks]
- The authors of this study could not confound the factors mentioned above due to
the study framework in that it excluded the participants who rated their self-
health as poor. This could mean that people who were obese, overweight,
smoking and even having other factors such as diabetes were excluded from
participating in the study.
- Further, as the study was prospective in nature, the participants could have
developed these conditions as the study was progressing. As mentioned and
outline during the study, these assessments were not undertaken hence could
mean that the respondents could have been having underlying health issues which
coupes up to be coronary heart disease being a final factor and cause of death.
Question 2:
A case-control study on the intake of dietary fat and the development of skin cancer
Comparison groups in the study involved
Cancer cases =500
Case-control subjects= 500
Dietary intake cases
Low consumption intake = 150
High consumption intake = 80
Control
Low consumption intake = 130
High consumption intake cases 100
a) Data summary table for disease state and fat intake [4 points]
Having disease Without the disease Total
High fat intake 80 100 180
Moderate fat
intake
270 270 540
Low fat intake 150 130 280
Total 500 500 1000

b) Comparison of relative risks of high intake of fat compared to low intake of fat and
the Relative risks of medium to high fat intake in relation to the development of
melanoma cancer [4 points].
= a/(a+b)/ c/(c+d)
= a-80, b-100, c-150,d-130
= (80/180)/(150/280)
= 0.444/0.535
= 0.833
This results obtained indicate that there are lowered risks of developing skin cancer
upon intake of a high-fat diet, thus concluding that that skin cancer can be reduced
through the intake of high-fat diet.
RR of medium to high fat intake
= a/(a+b)/ c/(c+d)
= a-270, b-270, c-80,d-100
= (270/540)/(80/180)
= 0.5/0.44
= 1.1336
This result indicates the intake of medium o high fat consumption of food leading to
increased incidence of the risk of melanoma, thus the rate of melanoma is increased
with high intake of fat in the body.
c) The attributable risk related to exposure of low fat intake on skin cancer development
is ; [4 points]
Cases Controls Totals
Exposed 150 130 280
Unexposed 350 370 720
Total 500 500 1000
AR = IE-IU
= P(D/E)-P(D/U)
= a/(a+b)-c/(c+d)
= 150/280-350/720
= 0.53-0.48
AR% = 0.05x100
= 5%
The result obtained indicates that there is a 5% attributable risk difference in the
development of exposed and unexposed group of individuals in the study.

d) Population attributable risk of low fat intake on melanoma can be calculated by [4
points]
PAR = (IT-IU)/IT
= Pe(RR-1)/Pe(RR-1)+1)
= 5(1.1336-1)/ 5(1.1336-1)+1
= 0.668/1.668
= 0.40
- The disease rate attribution in this study is found to be 0.4 which reflect a causal
relationship, indicating that there is low exposure and development of the disease
among low fat diet consumers
e) The conclusions of PAR above the display that ; [4 points}
- The population attributable risks show that low intake of dietary fat is linked to
the low occurrence of disease, which means that they have low disease rate. Thus
4% of the population experience incidence of melanoma cancer.
Question 3:
In a small cohort study investigating the effect of a rare exposure ( E), the following results
were found:
Table 1 Disease
Exposure
A- Association of exposure and the disease [2 points]
- Association between the exposure and the disease can be best calculated using
relative risks assessment.
RR = a(a+b)/c(c+d)
= (120/480)/(120/380)
= 0.25/0.315
= 0.80
The results above show no association between the exposure and the occurrence
of the disease.
B- A stratified analysis by Age-groups shows the following:
Younger adults Older adults
Disease Disease
Exposure
Yes No
Exposure Yes No
Yes 60 180 Yes 80 160
No 40 160 No 60 180
Yes No
Yes 120 360
No 120 360

The relative risks of the exposure and disease development between younger adults and older
adults shows that ; [ 4 points]
RR = a(a+b)/c(c+d)
Younger adults
Relative Risk = (60/240)/(40/200)
= 0.25/0.2
= 1.25
Older adults
Relative Risk = (80/240)/(60/240)
= 0.33/0.25
= 1.32
c- The interpretation of the above results shows that; [4 points]
- There is a weak strength of association between exposure and disease among the
two groups under study. This can imply that the causation of the disease might
not be the exposure under study thus other factors could have a play in the
development of the disease.
Question 4:
a) An example of a typical bias in a cohort study is ; [4 marks]
- Data collection bias
This bias occurs when the researchers beliefs and included tend to influent the way
information is being processed. In qualitative research, measurement bias can occur when the
tools being utilized have not been assessed in terms of their validity and reliability. Through
avenues such as shared decision-making model, (Sica, 2006).
A case example in retrospective studies is when filling forms on eating habits, where the data
tools rely on recall, which the participants may not recall past foods consumed.
b) A classical example of bias in a case-control study is ; [4 marks]
Selection bias
This aspect relates to a process of participant's recruitment and inclusion criteria in the study.
Effective research approaches begin with recruiting study participants who meet the aims of
the study, (Francis et al., 2010). A typical example is when participants are invited to an
online survey, which excludes the users of the internet.
Q 5 An effective way of enabling the confounding factor is to undertake a (2 points)
Stratification
- This is a process where the overall population varies in the population, which
could lead to an advantage when sampling the subpopulation. Thus distributing
the homogeneity of the population into subgroups and offering equal treatment of
process is an effective way of handling the cofounder displayed in the case study.

Question 6:
Effect of loss to participants in the above case study shows that [4 points]
- In this there is no effect on the study findings as studies have shown that the
impact caused by participant loss can be minimal, thus can't affect much in the
case study provided. Further, the participant loss is natural and data analysis can
be adjusted to suit the remainder of the participants to validate the results,
(Krieger, 2012).

References
Francis, J. J., Johnston, M., Robertson, C., Glidewell, L., Entwistle, V., Eccles, M. P., &
Grimshaw, J. M. (2010). What is an adequate sample size? Operationalising data
saturation for theory-based interview studies. Psychology and Health, 25(10), 1229-
1245.
Krieger, N. (2012). Who and what is a “population”? Historical debates, current
controversies, and implications for understanding “population health” and rectifying
health inequities. The Milbank Quarterly, 90(4), 634-681.
Sica, G. T. (2006). Bias in research studies. Radiology, 238(3), 780-789.