Effect Of Dietary Fat Intake On Melanoma Risk: A Case-control Study

Relative Risk Calculation

The study design employed in the study

This is a cohort study utilized prospective study. The outcomes were assessed as the study progressed and outcomes measured at the end of the study. 

• Data sources for coronary heart diseases in the study 

–  Swedish national discharge register data source was used to identify the events coronary heart disease from the respondents. 

• Why the authors excluded 1894 participants who rated their general health as “poor”   [2 marks]
• The authors excluded these participants as it would have affected the study because of its confounding effects on the issue of interest in the study which was coronary heart disease emergence. Having respondents with underlying or poor health conditions could lead to coronary heart disease being linked to other causative factors part from the issue of interest which is the respondent’s physical activity. 

• The crude overall CHD incidence in the study 
• The overall crude incidence rate was 59 cases per 10,000 persons over physical activity levels. 

• Comparison of physical activity being undertaken at least twice a week for low income and all other income categories  

• The risks obtained at 95% confidence level reveals that physical activity of twice daily had RR of 0.72 (CI 0.52-1.01) compared to low-income earners having RR of 1.20 (0.95-1.52) and those of all other income earners at RR equals to 1. 

• The interpretation of the relative risk above  

• The relative risks above illustrate that exposure to undertaking physical activity twice a week decreased the exposure of developing coronary heart disease.  
• Low income was further associated with the risk of developing coronary heart disease, thus being a low-income earner had a greater risk of developing coronary heart disease, while all other income earners, in general, had no effect on developing coronary heart disease.  

• The relative risk in this study is better described as risk ratio in that  

• Risk ratio refers to the probability of an outcome in an exposed group to the probable outcome of the exposed group, while rate ratio refers to comparing rates of events taking place at any given time. This study is best explained when risks ratio is assessed on the risks of developing coronary heart disease to factors such as physical activity, income, smoking and BMI assessments for the respondents.  In this case the strength of the association between the risks factors and outcome of developing coronary heart disease. 

• The major confounders in this study which were not included include the following 

• Some of the major confounders which were not assessed in this study include hyperlipidemia, which has been considered as a powerful indicator of coronary heart disease, with the positive association being linked to cholesterol levels. Further hypertension could be a major confounding fact in the development of coronary heart disease. Hypertension has been attributed as a strong and independent risk factor for the development of coronary heart disease causing morbidity and mortality.
• Further diabetes mellitus could a major confounding factor. Both type 1 and 2, have been associated with elevated risks for developing heart disease. Thus it could act as a confounding factor in the development of heart disease. 

• These confounders were not adjusted due to the following reasons  

• The confounding factors were not added into the study due to the longitudinal nature of the study, which followed the group cohorts over 10 years, in this case, effects of variables under study was sought.
• The study was designed in such a way that those respondents who attributed their health state being poor or bad were excluded in the participation. Hence those having poor self-rating of their disease condition were not included in the study, and the study only focused on those who had good health status. 

Tasmanian researchers conducted a case-control study to investigate the effect of dietary fat intake on melanoma (Skin Cancer). They hypothesized that people whose dietary fat intake is low will be more susceptible to skin cancer.  The study compared 500 cases of melanoma with 500 controls. The controls were randomly selected from the state’s electoral roll. The researchers categorized the dietary fat intake into three categories High, Moderate and Low. They found that among skin cancer patients 150 were classified at the low and 80 at the high dietary fat intake whereas among control 130 were at the low and 100 were at the high. 

1. Summarised table for estimation of the level of association of the diseases and fat intake   

The relative risk (RR) of high fat intake versus low fat intake and the RR of medium to high fat intake in relation to the development of melanoma was found to be 

RR of high fat intake versus low fat intake 

= a/(a+b)/ c/(c+d) 

= a-80, b-100, c-150,d-130 

= (80/180)/(150/180) 

= 0.444/0.833 

= 0.533

This result indicates that there is a 50% decrease in the development of the cancer disease among those who are taking high fat intake compared to those who were taking low fat intake. 

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 relative risk indicates that RR medium to high fat intake show that melanoma risk is increased with intake of high fat. Thus cancer risk is increased with the fat exposure. 

These results of the relative risks indicate an inverse association of risk that in that, exposure portrays different results as expected. Hence the actual relative risk of exposure is not obtained from the study calculations. 

1. The attributable risk due to exposure to the low-dietary fat intake on Melanoma  

AR = I_E-I_U 

= P(D/E)-P(D/U)

= a/(a+b)-c/(c+d)

= 150/280-350/720

= 0.53-0.48

AR% = 0.05×100

= 5%

This result indicates that there is a 5% difference in melanoma cancer in exposed and unexposed individual in the study. Thus the attributable risk is lower in these findings.

1. Calculation of the population attributable risk of low-fat intake on melanoma reveal that [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

Thus the excess of the disease in the population attributed to the exposure is 0.4. Thus assuming that there is a causal association, there is low exposure of the disease in the population among low fat intake eaters.

1. The conclusion of the PAR regarding exposure to a low-fat diet to reduce melanoma shows that [4 points

Attributable Risk Calculation

The conclusions reveal that that low intake of intake of fat in the population is attributed to low disease incidence. Hence the population has a low intake of fat have a low occurrence of melanoma cancer.

In a small cohort study investigating the effect of a rare exposure ( E), the following results were found:  

Table 1                                                 Disease

Exposure

• The data above shows that 

There is no association between disease and exposure in the above data; this is observed through calculation of the relative risk which is

RR = a(a+b)/c(c+d)

= (120/480)/(120/380)

= 0.25/0.315

= 0.80

= thus no strength of association

Rate Ratio (Increased risk) Rate Ratio (Decreased risk) Strength of Association

1.0 – 1.2  0.9 – 1.0   None

1.2 – 1.5   0.7 – 0.9   Weak

>1.5   < 0.9   Moderate to Strong

Adapted from Monson (1990)

• A stratified analysis by Age-groups  shows the following:

The relative risks for both older adults and younger adults is calculated as  

RR = a(a+b)/c(c+d)

Among younger adults

RR = (60/240)/(40/200)

= 0.25/0.2

= 1.25

Among older adults

RR = (80/240)/(60/240)

= 0.33/0.25

= 1.32

c- The results   [4 points]

From the above-calculated risks, the values obtained shows that there is a weak strength of association between disease and exposure across the two population groups. Hence the exposure might not be causing the disease; there is the presence of other factors which might have an impact on disease state.

1. a) A classical example of a biasness in Cohort cases 

Subject selection biases. This is related to the exposure and outcome. The subjects are enrolled in the prospective study before developing outcome of interest. This is easier to observe how enrolment is related to exposure. This bias is common in a retrospective study, where individuals have to provide informed consent before participation. Hence those with the most interests in the disease have been exposed.

1. b) Typical biasness in a case-control study 

Selection bias. This occurs when the subjects chosen for the control group do not represent the actual population. A typical example is observed when investigators while undertaking cervical cancer, relating to socioeconomic status. The investigators identified control subjects through a door to door recruitment in the community. The issue arose in that the cases were selected using different method compared to controls hence displaying a typical selection bias of cervical cancer patients based on their socioeconomic status. 

A cohort study was conducted to examine cigarette smoking and the risk of oral cancer.  The investigators selected exposed and unexposed subjects so that they had exactly the same distribution of race.  A typical method of addressing this probable confounding factor is  

An effective method to address this kind of error is through stratification by race. This ensures that all the races in the study have an equal chance of participating in the study. 

A study aims to determine the incidence of type 2 diabetes. A cohort of 200 people age 65 years or older who were initially disease-free participated in the study. One hundred and fifty people were examined at the end of 3 years.  Fifty other participants from the initial cohort could not be examined, including 11 people who had died. 

Participant loss in this study doesn’t imply a source of biases. Studies undertaken have shown that there is no difference on the impact of association between health states and variables being assessed. The effect of participation doesn’t affect the exposure and outcome association being investigated. Various studies have indicated that this effect causes minimal biases in assessing exposure and outcome associations.

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