Oral contraceptive use and ovarian cancer answers

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COH 315 assignment

Part 1. Oral Contraceptive Use and Ovarian Cancer

In 1980, ovarian cancer ranked as the fourth leading cause of cancer mortality among women in the United States. An estimated 18,000 new cases and more than 11,000 attributable deaths occurred among American women that year.

Several studies had noted an increased risk of ovarian cancer among women of low parity, suggesting that pregnancy exerts a protective effect. By preventing pregnancy, oral contraceptives (OCs) might be expected to increase the risk of ovarian cancer. On the other hand, by simulating pregnancy through suppression of pituitary gonadotropin release and inhibition of ovulation, OCs might be expected to protect against the subsequent development of ovarian cancer. Because by 1980 OCs had been used by more than 40 million women in the United States, the public health impact of an association in either direction could be substantial.

To study the relationship between oral contraceptive use and ovarian cancer (as well as breast and endometrial cancer), CDC initiated a case‐control study – the Cancer and Steroid Hormone (CASH) Study in 1980. Case‐patients were enrolled through eight regional cancer registries participating in the Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute.

Question 1. (2 Point)

In case-control study, biases has always been a tremendous concern in this study type. It can be confounding, selection bias, and information bias. In confounding, when true association of risk factor to the disease under investigation is determined but are intrigued to derive a causal inference instead, when the matter of fact the relationship is not causal. In selection biases individuals are normally chosen to which the exposure status showed positive association to the disease being studied. In information bias, it involves recall biases in which individuals may not have recollections of things that happened in the past. Example, when women cannot recall the duration or time period of taking OCs.

What types of bias are of particular concern in this case‐control study? What steps might you take to minimize these potential biases?

The study design included several features to minimize selection and information bias. Ascertainment bias of disease status ) a type of selection bias ) was minimized by attempting to enroll as cases all women ages 20‐54 years with newly diagnosed, histologically confirmed, primary ovarian cancer who resided in one of the eight geographic areas covered by the cancer registries. Controls were women ages 20‐54 years selected randomly using telephone numbers from the same geographic areas. Because 93% of U.S. households had telephones, virtually all women residing in the same areas as the cases were eligible to be controls. (Interestingly, all the women enrolled with ovarian cancer had telephones.)

To minimize interviewer bias, CDC investigators conducted group sessions to train interviewers in the administration of the pretested standard questionnaire. The same interviewers and questionnaires were used for both cases and controls. Neither cases nor controls were told of the specific a priori hypotheses to be tested by the study. Recall bias of oral contraceptive exposure was minimized by showing participants a book with photographs of all OC preparations ever marketed in the United States and by using a calendar to relate contraceptive and reproductive histories to other life events.

The primary purpose of the CASH study was to measure and test the association between OC use and three types of reproductive cancer ) breast cancer, endometrial cancer, and ovarian cancer. Enrollment of subjects into the study began in December 1980. During the first 10 months of the study, 179 women with ovarian cancer were enrolled, as well as larger numbers of women with endometrial or breast cancer. During the same period, 1,872 controls were enrolled to equal the number of subjects with breast cancer. The same control group was used for the ovarian cancer analysis; however, the investigators excluded 226 women with no ovaries at the time of interview and four controls whose OC use was unknown, leaving 1,642 women to serve as controls. The distribution of exposure to OCs among cases and controls is shown in Table 1.

Table One. Ever‐use of oral contraceptives among ovarian cancer cases and controls, Cancer and Steroid Hormone Study, 1980‐1981

	Cases	Controls	totals
Ever used OC	93	959	1,052
Never used OC's	86	683	769
	179	1,642	1,821

Question 2 (1 Point) From these data, can you calculate the risk of ovarian cancer among oral contraceptive users? Why or why not?

Yes, it is possible to calculate. In this case we will need the odds ratio (OR) for our estimates as our measure of association in relation to ovarian cancer.

Question 3 (1 point) Describe the rationale behind using the odds ratio as an estimate of the risk ratio. When is the odds ratio not an appropriate estimate of the risk ratio?

Since this is a case-control study, we cannot directly calculate risk ratio, it is because we do not have the data for the denominator. The data on b and d cells represents the control group. The odds ratio is not a good approximation of risk ratio when the disease under study is not rare.

Question 4 (1 Point) What is confounding? Under what circumstances would age be a confounder in this study?

Confounding is misinterpretation of association between the exposure and the disease. Because of the correlation of the exposure, the disease , and some other factors (factor X), for investigators to determine that factor X is confounder it needs to be analyzed and derive a conclusion that factor X indeed a known risk factor of the disease that would associates factor A and factor B. Example, to convey that age is the confounder in this case, age must be associated to the exposure, and with the outcome which in this case the ovarian cancer.

In the analysis of use of oral contraceptives and ovarian cancer, age was related both to OC use and to case‐control status. (OC users were younger than never‐users; case‐patients were younger than controls.) Therefore, the investigators decided to stratify the data by age and calculate stratum‐specific and, if appropriate, summary statistics of the stratified data.

Question 5 (1 point) What is stratification? Why stratify data? How do you decide on which variables to stratify?

Stratification is when investigator bring all population under study and group them together according to the study design. Example, to stratify by age an investigator would group cases age 20-39, and all controls age 20-39, then calculate odds ratio (OR) for this group and so as the rest of the age groups.

If ever we determine that age will be a problem, we can stratify this by data. When we do stratification, it will provide us the comparison of association exposure and reviewing the results from different other groups.

Question 6 (1 point) What is effect modification? How do you look for it?

Effect modification (EM) happens when the odds ratio result among different groups are unusually different.

Effect modification is a variable of different rate (positive and negative) that modifies the observed outcome of a risk factor on the disease condition. If it is positive, we can see different odds ratio on various age groups.

Question 7 (3 points) Using the data in Table 2, calculate the odds ratio for each age stratum.

OR = ad/bc

Ages 20-39 (46x51) / (285x51) = 2,346/14,535 = .16

Ages 40-49 (30x301) / (463x30) = 9,030/13,890 = .65

Ages 50-54 (17x331) / (211x44) = 5,627/9,284 = .61

Table 2. Ever‐use of oral contraceptives and risk of ovarian cancer, stratified by age, Cancer and Steroid Hormone Study, 1980‐1981

Ages 20‐39 years
	Cases	Controls	Totals
Ever Used OC	46	285	331
Never Used OCs	51	51	63
	336	336	394

Ages 40‐49 years
	Cases	Controls	Totals
Ever Used OC	30	463	493
Never Used OCs	30	301	331
	60	764	824

Ages 50‐54 years
	Cases	Controls	Totals
Ever Used OC	17	211	228
Never Used OCs	44	331	375
	61	542	603

Question 8 (1 points) Do you think age is an effect modifier of the oral contraceptive and ovarian cancer association?

Age 20-39 group appears to have an outcome of odds ratio of (.16) and the other age groups that has (.65) for ages 40-49 and (.61) for ages 50-54 which is outside (0.77) range. Therefore, I conclude that age is not an effect modifier of OC and ovarian cancer.

Question 9 (1 point) Do you think age is a confounding variable in this analysis of the association between OC use and ovarian cancer?

To conclude if age is a confounder variable in this case, we will need to examine and identify the difference in odds ratio. The crude odds ratio (0.77) is obviously outside the range of our stratum specific odds ratio (OR)( .16, .65, and .69). Therefore, in this case age is confounder variable.

Question 10 (1 point) What are the other ways of eliminating confounding in a study?

randomization

restriction

matching

modeling

stratification

In the introduction to this case study, pregnancy was described as apparently protective against ovarian cancer. The investigators were interested in seeing whether the association between OC use and ovarian cancer differed for women of different parity.

Table 3 shows parity‐specific data.

Parity	Use of OCs	# of Cases	# of Controls	Age‐Adjusted ORs
0	Ever Used OC	20	67	0.3
	Never Used OCs	25	80

1 to 2	Ever Used OC	42	369	0.8
	Never Used OCs	26	199

3 or more	Ever Used OC	30	520	0.7
	Never Used OCs	35	400

Question 11 (1 point) Is there any evidence for effect modification in the data presented in Table 3?

I would say yes, because the odds ratio shows different results in each age group.

In their published report, the investigators wrote the following about the possible effect modification by parity: "Parity appeared to be an effect modifier of the association between oral contraceptive use and the risk of ovarian cancer...[Table 3]. Among nulliparous women, the age‐standardized odds ratio was 0.3 (95% confidence interval: 0.1‐0.8). Among parlous women, however, the odds ratios were closer to, but still less than, 1.0....It is possible, therefore, that oral contraceptives are most protective for women not already protected by pregnancy." Although this case study deals with the data collected over the first 10 months (phase 1) of the study, an additional 19 months of data (phase 2) were collected and analyzed subsequently. The following table summarizes the apparent role of parity as an effect modifier in the two phases of the study.

Table 4. Age-adjusted odds ratios (aOR) and 95% confidence intervals for the association of oral contraceptive use and ovarian cancer, by parity and phase of study, CASH Study, 1980-1982

		Phase 1		Phase 2		Totals
Parity		(months 1-10)		(months 11-29)		(months 1-29)
	aOR		(95% CI)	aOR	(95% CI)	aOR	(95% CI)
0	0.3		(0.1-0.8)	0.7	(0.5-1.2)	0.7	(0.4-1.0)
1-2	0.8		(0.4-1.5)	0.5	(0.3-0.7)	0.5	(0.4-0.8)
>3	0.7		(0.4-1.2)	0.5	(0.4-0.8)	0.6	(0.4-0.8)
Total	0.6		(0.4-0.9)	0.5	(0.4-0.7)	(0.4-0.7)	(0.5-0.7)

On the basis of the full study results, it appeared that the effect of oral contraceptives on ovarian cancer was not substantially different for nulliparous women and for parous women. Although there were no published studies of oral contraceptives and ovarian cancer when this study was launched, there were several by the time this study was published. Almost all showed an apparently protective effect of oral contraceptives on ovarian cancer.

Question 12 (1 point) What are the public health and/or policy implications of the apparently protective effect of oral contraceptives on ovarian cancer?

It would be appropriate to go over the benefits of OC and at the same time the risk of using OC which includes the side effects which varies from every individual. Taking OC can cause blood clots in some people and before taking any OC an individual has to make sure that there will be no harsh side effects on them that could trigger possible health problems.

Part 2 – Types of Bias

Confounding is a bias that results when the risk factor being studied is so mixed up with other possible risk factors that its single effect is very difficult to distinguish. For example, it might be thought that smoking is a risk factor for heart disease, because people who are exposed to smoking have a higher occurrence of heart disease. However, the case is not quite so clear as it might appear. It turns out that people who smoke also drink alcohol—so is it the smoking, the alcohol or both that are responsible for heart disease? Unless these tangled effects are untangled with advanced mathematical methods (which, remember, we are not getting into), the association between smoking and heart disease, as measured using the relative risk formula we have, is probably too high or too low—that is, it is biased.

Selection bias is a distortion in the estimate of association between risk factor and disease that results from how the subjects are selected for the study. Selection bias could occur because the sampling frame is sufficiently different from the target population or because the sampling procedure cannot be expected to deliver a sample that is a mirror image of the sampling frame.

Information bias is a distortion in the estimate of association between risk factor and disease that is due to systematic measurement error or misclassification of subjects on one or more variables, either risk factor or disease status. It is important to realize that these errors are part of being human and they are not occurring because the physicians or researchers are not being sufficiently careful. It is not so much the random mismeasure or misdiagnosis of an individual that is problematic (although random errors in diagnosis will tend to bias the association toward a relative risk of 1.0, because the true association is diluted with noise). It is the method of measurement or classification that is the greater problem, because it systematically exerts an effect on each of the individual measurements in the sample.

An investigator would like to assess the association of melanoma (skin cancer) and exposure to infrared skin tanning services by using a hospital‐based case–control study. Hospitalized individuals with melanoma will be compared with hospitalized patients without melanoma (controls). This hospital, located in a low‐income area of the city, is famous nationwide for its expertise in melanoma. Individuals with melanoma (cases) from all over the country go to that hospital to get the highest quality care that can be provided. However, this hospital is not as well known for any other medical conditions as it is for melanoma. Therefore, cases would come from all over the country, and controls will be mostly local low‐income individuals. The investigator predicts that an overestimation of the association between melanoma and skin tan services may occur.

Question 13 (1 point) Do you agree or disagree with the investigator? Explain your answer in a few sentences.

Yes, I absolutely agree with the investigator. Individuals will more likely to explore infrared skin tanning services in areas that are known to have high end tanning services compare to low income area that might have low quality of tanning services. Given this scenario, individuals who has high income probably will have higher exposure to infrared skin tanning services (cases) than those of low income individuals (controls).

Question 14 (1 point) Please explain in a few words what type of bias may be present?

Selection bias may be present, because sample sizes on cases and controls are obviously different.

A study to assess the association of diabetes and smoking compared a group of hospitalized individuals with diabetes (cases) with a group of volunteer individuals without diabetes (controls) who were full‐ time employees of the same hospital where the cases were identified. The results from this study reported, for the first time in the literature, a strong association between diabetes and smoking.

Question 15 (1 point) What type of bias may be present? Why do you suspect the presence of the bias you have identified?

In this case selection bias may be present. Given that individuals who self- volunteer as the controls in this study, they're most likely to be healthy people who are hardly exposed to smoking. Example, a diabetic patient is hospitalized, this does not mean that smoking has direct relation to diabetes. However, while hospitalized the care provider may of include him in the study it's because he/she has diabetes.

Question 16 (1 point) The magnitude of this association is likely to be either over‐or underestimated. Which do you think is the case, and what makes you think so?

In this case the magnitude of association is likely to be overestimated. There can be a lot of smokers in the case group than in control group ( individuals who self volunteer are more likely to be in perfect health and are not smokers that could result in unrealistic representation of the overall population.

A case–control study was conducted to assess the association of passive smoking and asthma. Newly diagnosed asthmatic individuals (cases) were compared with a random sample of individuals without asthma (controls) in regard to exposure to smoke from smokers at home or in the workplace for the previous 10 years

Question 17 (1 point) Which type and mode of bias could be introduced into this study?

In this case recall bias is present. some individuals may have some trouble to exactly remember if they were exposed to second hand smoke ten years ago. Additionally, while second hand smoking is one significant factor for the case group, but it is not that much for the control group. For the case group, secondhand smoking has a definite effect on their health either it is mild irritations that escalates into having trouble breathing for which it triggers asthma attacks.

Part 3 – Review for Final

Smithville is a community of 100,000 persons (45,000 females, 55,000 males). During 1985, there were 1,000 deaths from all causes. All cases of tuberculosis have been found, and they total 300: 200 males and 100 females. Fifty of the cases have been diagnosed in the past year. During 1985, there were 60 deaths from tuberculosis, 50 of them in males. Use the above data for questions 1 through 5.

Question 18 (1 point) Calculate the rude mortality rate in Smithville. Show all work

Mortality rate = total # of deaths from all causes in a year X1,000

# of persons in the population at midyear

1,000/100,000 = .01 X 1,000 = 10

Question 19 (1 point) The proportionate mortality due to tuberculosis is:

proportionate mortality rate = # of deaths from tuberculosis X 100

total # of deaths

60/1,000 = .06 X 100 = 6

Question 20 (1 point) The case fatality rate for tuberculosis in 1985 is:

case fatality rate (percent) = # of individuals dying during a specified period of time x 100

# of individuals with the specified diseases

60/300 = .2 X 100 = 20%

Question 21 (1 point) The cause‐specific mortality rate for tuberculosis is:

cause- specific mortality rate = # of deaths from tuberculosis in one year X 1,000

# of person in population at midyear

60/100,000 = .06 x 1,000 = 60

Question 22 (1 point) The sex‐specific mortality rate for tuberculosis in males is:

sex-specific mortality rate = # of deaths from all causes in one year in males X 1,000

# of individuals in the population that are males

1,000/55,000 = .0181 x 1,000 = 18.18 (males)

1,000/45,000 = .0222 X 1,000 = 22.22 (females)

Question 23 (1 point) Arkansas recorded 200 cases of Tuberculosis with 77 deaths in 1997. What would be the case fatality rate for Tuberculosis in Arkansas?

care fatality rate (percent)

case-fatality rate = # of individuals dying during a specified period of time X 100

# of individuals with specified disease

77/200 = .385 X 100 = 38.5%

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