Jill Marshall MPH 510 Applied Epidemiology Case Study Cigarette Smoking and Lung Cancer June 8, 2013

Transcription

1 A causal relationship between cigarette smoking and lung cancer was first suspected in the 1920s on the basis of clinical observations. To test this apparent association, numerous epidemiologic studies were undertaken between 1930 and Two studies were conducted by Richard Doll and Austin Bradford Hill in Great Britain. The first was a case-control study begun in 1947 comparing the smoking habits of lung cancer patients with the smoking habits of other patients. The second was a cohort study begun in 1951 recording causes of death among British physicians in relation to smoking habits. This case study deals first with the case-control study, then with the cohort study. Data for the case-control study were obtained from hospitalized patients in London and vicinity over a 4-year period (April February 1952). Initially, 20 hospitals, and later more, were asked to notify the investigators of all patients admitted with a new diagnosis of lung cancer. These patients were then interviewed concerning smoking habits, as were controls selected from patients with other disorders (primarily non-malignant) who were hospitalized in the same hospitals at the same time. Data for the cohort study were obtained from the population of all physicians listed in the British Medical Register who resided in England and Wales as of October Information about present and past smoking habits was obtained by questionnaire. Information about lung cancer came from death certificates and other mortality data recorded during ensuing years. Question 1: What makes the first study a case-control study? The first study is a case-control study.it meets the definition of a case-control study as defined by Friis and Sellers: an analytic study of persons diagnosed with the disease of interest and a suitable control group of patients without the disease (Friis and Sellers, 2014). In this case, this was study of persons diagnosed with lung cancer (with the disease of interest) and patients of other disorders who were hospitalized at the same time (a suitable control group of patients without the disease). References: Learning, LLC. Page 303. Question 2: What makes the second study a cohort study? The second study is a cohort study. It meets the definition of a cohort study as defined by Friis and Selers: a population group or subset therof (distinguished by a common characteristic), that is followed over a period of time (Friis and Sellers, page 325, 2014). In this case, this was a study of the past smoking habits of persons who resided in England and Whales (population) as of October 1951 and died during the ensuing years (period of time). References: Page 1 of 12

2 Learning, LLC. Page 325. The remainder of Part I deals with the case-control study. Question 3: Why might hospitals have been chosen as the setting for this study? According to Friis and Sellers, a preferred approach is to conduct case-control studies in which cases and controls are population based. They explain, when the selection of population-based study groups are not feasible, cases may need to be derived from one or more major hospitals. They also explain that although hospital-based studies are inherently subject to greater potential for errors than population based studies, their use is certainly justified when little information has be reported about a particular exposure-disease association or when a population based case registry is not available (Friis and Sellers, page 308, 2014). In this case, there was little information and no population based case registry available, so the use of the hospital was justified. References: Learning, LLC. Page 308. Question 4: What other sources of cases and controls might have been used? According to Friis and Sellers, the ideal situation is to identify and enroll all incident cases in a defined population in a specified time period is using a disease registry or a complete listing of all available cases from a source such as the Vital Statistics Bureau. They also explain that the best way to ensure that the distribution of exposure among the controls represents the exposure levels in the population is to select population-based controls. A suggested method to identify such controls is to obtain a list that contains names and addresses of most residents in the same geographic area as the cases (Friis and Sellers, 2014) References: Learning, LLC. Page Question 5: What are the advantages of selecting controls from the same hospitals as cases? The advantages to selecting controls from the same hospitals and cases include that the cases and controls are from the same geographic area. Additionally, Friis and Sellers explains that other advantages to selecting controls from the same hospital are that the study personnel who are already in the hospital to interview cases may achieve time efficiency by also interviewing controls. The time saving, plus the fact that hospital controls may be more likely than population controls to participate, ultimately equates to cost savings (Friis and Sellers, pages , 2014). Page 2 of 12

3 References: Learning, LLC. Page Question 6: How representative of all persons with lung cancer are hospitalized patients with lung cancer? At the time of the study using hospitalized patients with lung cancer was probably fairly representative of all persons with lung cancer, as outpatient treatments and diagnostic methods were not available. Currently, there is more advanced knowledge of cancer including knowledge about the differing stages of the disease that may not require hospitalization. Advancements in outpatient treatment coupled with early identification may exclude those diagnosed with the disease from being represented by the cases making the sample not very representative of all persons with lung cancer. Question 7: How representative of the general population without lung cancer are hospitalized patients without lung cancer? Not very, as persons who are hospitalized generally have poorer health conditions than those of the general population. They may also engage in more risk-taking behavior or high-risk activities such as smoking, drinking, drug use, or working in situations where exposure to other carcinogens may increase their propensity to develop lung cancer. Generally, people who engage in healthier activities stay out of hospitals. Question 8: How may these representativeness issues affect interpretation of the study's results? Friis and Sellers explain that one of the major limitations of case-control studies is indeterminate representativeness of the cases and controls. If the cases and controls are not representative of the population, the results of the study will be biased. This would be a case of selection bias. Selection bias occurs when the relation between exposure and disease is different from those participating and those who would be theoretically eligible to participate but do not (Friis and Sellers, 2014). References Learning, LLC. Pages 316, 442. Over 1,700 patients with lung cancer, all under age 75, were eligible for the case-control study. About 15% of these persons were not interviewed because of death, discharge, severity of illness, or inability to speak English. An additional group of patients were interviewed but later excluded when initial lung cancer diagnosis proved mistaken. The final study group included 1,465 cases (1,357 males and 108 females). The following table shows the relationship between cigarette smoking and lung cancer among male cases and controls. Page 3 of 12

4 Table 1. Smoking status before onset of the present illness, lung cancer cases and matched controls with other diseases, Great Britain, Cases Controls Cigarette smoker Non-smoker 7 61 Total Question 9: From this table, calculate the proportion of cases and controls who smoked. Cases Controls Cigarette smoker Non-smoker 7 61 Total /1357 = Proportion Smoked = 99.5% 95.5% Proportion smoked, cases: 99.5% Proportion smoked, controls: 95.5% Question 10: What do you infer from these proportions? Smoking is a behavior that occurs in more than 95% of both the cases and controls. Question 11a: Calculate the odds of smoking among the cases. Cases Controls Cigarette smoker Non-smoker 7 61 Total /1357 = Proportion Smoked = 99.5% 95.5% smokers/non-smokers Odds 192.9/1 21.2/1 The odds of smoking among the cases is 192.9/1 Page 4 of 12

5 Question 11b: Calculate the odds of smoking among the controls. Cases Controls Cigarette smoker Non-smoker 7 61 Total /1357 = Proportion Smoked = 99.5% 95.5% smokers/non-smokers Odds 192.9/1 21.2/1 The odds of smoking among the controls is 21.2/1 Question 12: Calculate the ratio of these odds. How does this compare with the cross-product ratio? Cases Controls Cigarette smoker Non-smoker 7 61 Total /1357 = Proportion Smoked = 99.5% 95.5% smokers/non-smokers Odds 192.9/1 21.2/1 [(smokers/nonsmokers)cases]/(smokers/nonsmokers)(controls) 9.1 Odds Ratio (OR) 9.1 The Odds Ratio is 9.1 Question 13: What do you infer from the odds ratio about the relationship between smoking and lung cancer? The Odds Ratio (OR) is 9.1 and according to Friis and Sellers the OR literally measures the odds for exposure to a given disease (Friis and Sellers, page 310, 2014). In this case the OR is saying that persons who smoke are 9X more likely to develop lung cancer than those who do not smoke References Page 5 of 12

6 Learning, LLC. Page 310. Table 2 shows the frequency distribution of male cases and controls by average number of cigarettes smoked per day. Table 2. Most recent amount of cigarettes smoked daily before onset of the present illness, lung cancer cases and matched controls with other diseases, Great Britain, Daily Number of Cigarettes Number of Cases Number of Controls Odds Ratio referent All smokers Question 14: Compute the odds ratio by category of daily cigarette consumption, comparing each smoking category to nonsmokers. Daily Number of Cigarettes Number of Cases Number of Controls Odds Ratio smokers (cases) * non-smokers (controls) A smokers (controls) * nonsmokers (cases) B referent All smokers A/B The Odds Ratio for 1-14 cigarettes is 7.0; cigarettes is 9.5; 25+ cigarettes is 16.3; and all smokers is 9.1. Question 15: Interpret these results. These results could be interpreted to mean that the odds of developing lung cancer increases with the number of cigarettes a person uses daily. Although the study demonstrates a clear association between smoking and lung cancer, cause-andeffect is not the only explanation. Page 6 of 12

7 Question 16: What are the other possible explanations for the apparent association? There could be simply a correlation but not causation; chance; bias including selection, information, or confounding, or investigator error. The next section of this case study deals with the cohort study. Data for the cohort study were obtained from the population of all physicians listed in the British Medical Register who resided in England and Wales as of October Questionnaires were mailed in October 1951, to 59,600 physicians. The questionnaire asked the physicians to classify themselves into one of three categories: 1) current smoker, 2) ex-smoker, or 3) nonsmoker. Smokers and ex-smokers were asked the amount they smoked, their method of smoking, the age they started to smoke, and, if they had stopped smoking, how long it had been since they last smoked. Nonsmokers were defined as persons who had never consistently smoked as much as one cigarette a day for as long as one year. Usable responses to the questionnaire were received from 40,637 (68%) physicians, of whom 34,445 were males and 6,192 were females. Question 17: How might the response rate of 68% affect the study's results? This response rate could suggest selection bias and skew the results of the study. The next section of this case study is limited to the analysis of male physician respondents, 35 years of age or older. The occurrence of lung cancer in physicians responding to the questionnaire was documented over a 10-year period (November 1951 through October 1961) from death certificates filed with the Registrar General of the United Kingdom and from lists of physician deaths provided by the British Medical Association. All certificates indicating that the decedent was a physician were abstracted. For each death attributed to lung cancer, medical records were reviewed to confirm the diagnosis. Diagnoses of lung cancer were based on the best evidence available; about 70% were from biopsy, autopsy, or sputum cytology (combined with bronchoscopy or X-ray evidence); 29% were from cytology, bronchoscopy, or X-ray alone; and only 1% were from just case history, physical examination, or death certificate. Of 4,597 deaths in the cohort over the 10-year period, 157 were reported to have been caused by lung cancer; in 4 of the 157 cases this diagnosis could not be documented, leaving 153 confirmed deaths from lung cancer. The following table shows numbers of lung cancer deaths by daily number of cigarettes smoked at the time of the 1951 questionnaire (for male physicians who were nonsmokers and current smokers only). Person-years of observation ("person-years at risk") are given for each smoking category. The number of cigarettes smoked was available for 136 of the persons who died from lung cancer. Table 3. Number and rate (per 1,000 person-years) of lung cancer deaths by number of cigarettes smoked per day, Doll and Hill physician cohort study, Great Britain, Page 7 of 12

8 Daily Number of Cigarettes Deaths Person-years at Risk Mortality Rate per 1,000 personyears Rate Ratio Rate difference per 1000 personyears referent referent All smokers Total Question 18: Compute lung cancer mortality rates, rate ratios, and rate differences for each smoking category. What do each of these measures mean? Daily Number of Cigarettes Deaths Person-years at Risk Mortality Rate per 1,000 personyears Rate Ratio Rate difference per 1000 personyears referent referent All smokers Total These results could be interpreted to mean that mortality rates increase with the number of cigarettes a person uses daily. Question 19: What proportion of lung cancer deaths among all smokers can be attributed to smoking? What is this proportion called? The proportion of lung cancer deaths among all smokers that can be attributed to smoking is 94.6%. This proportion is called Absolute Risk. Page 8 of 12

9 Absolute Risk = [(1.30/0.07)/1.30]*100 = 94.6 Daily Number of Cigarettes Deaths Person-years at Risk Mortality Rate per 1,000 person-years Rate Ratio Rate difference per 1000 personyears referent referent All smokers Total Question 20: If no one had smoked, how many deaths from lung cancer would have been averted? Based on the proportion in the previous question, 95 percent of deaths from lung cancer can be attributed to smoking. Using this information, approximately 126 deaths of the 133 could have been averted if no one had smoked. 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. Table 4. Mortality rates (per 1,000 person-years), rate ratios, and excess deaths from lung cancer and cardiovascular disease by smoking status, Doll and Hill physician cohort study, Great Britain, Mortality rate per 1,000 person-years Smokers Non-smokers All Rate Ratio Excess deaths per 1000 personyears Attributable risk percent among smokers Lung Cancer % Cardiovascular Disease % Question 21: Which cause of death has a stronger association with smoking? Why? Deaths resulting from lung cancer have a stronger association with smoking. The rate ratio demonstrates that there is a higher association between smoking and lung cancer at 18.5 than there is between smoking and cardiovascular disease at 1.3. In calculating the attributable risk percent, the excess lung cancer deaths attributable to smoking is expressed as a percentage of all lung cancer mortality among all smokers. The attributable risk Page 9 of 12

10 percent of 95% for smoking may be interpreted as the proportion of lung cancer deaths among smokers that could have been prevented if they had not smoked. A similar measure, the population attributable risk ercent expresses the excess lung cancer deaths attributable to smoking as a percentage of all lung cancer mortality among the entire population. From a prevention perspective, the population attributable risk percent for a given exposure can be interpreted as the proportion of cases in the entire population that would be prevented if the exposure had not occurred. The population attributable risk percent is often used in assessing the cost-effectiveness and costbenefit of community-based intervention programs. One formula for the population attributable risk percent is: PAR% = (Incidence in entire population * Incidence in unexposed) / Incidence in entire population. Question 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? Mortality rate per 1,000 person-years Smokers Non-smokers All Rate Ratio Excess deaths per 1000 personyears Attributable risk percent among smokers Lung Cancer % Cardiovascular Disease % PAR = (All *Non-smokers)/All*100 PAR for Lung Cancer is 92.5% PAR for Cardiovascular disease is 17.4% The compare in that death as a result of lung cancer and cardiovascular disease can be attributed to smoking. They differ in that a much higher percentage of deaths from lung cancer (92.5 percent) can be attributed to smoking than they can be for cardiovascular disease (at 17.4 percent). Question 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: 0.87 deaths per 1,000 person-years (PAR*All) Cardiovascular Disease: 1.54 deaths per person-years (PAR*All) The following table shows the relationship between smoking and lung cancer mortality in terms of the effects of stopping smoking. Table 5. Number and rate (per 1,000 person-years) of lung cancer deaths for current smokers and exsmokers by years since quitting, Doll and Hill physician cohort study, Great Britain, Page 10 of 12

11 Cigarette Smoking Status Lung Cancer Deaths Rate per 1000 personyears Rate Ratio Current smokers For ex-smokers, years since quitting < 5 years non-smokers Question 24: What do these data imply for the practice of public health and preventive medicine? The data implies that smokers are the highest risk group for lung cancer and while quitting reduces the risk, those who never smoke are in the lowest risk group. Public health initiatives and preventive medicine efforts to prevent people from smoking would have the most benefit. Additionally, educating those who smoke to quit smoking will help to reduce deaths from lung cancer. As noted at the beginning of this case study, Doll and Hill began their case-control study in They began their cohort study in The odds ratios and rate ratios from the two studies by numbers of cigarettes smoked are given in the table below. Table 6. Comparison of measures of association from Doll and Hill s case-control study and Doll and Hill s physician cohort study, by number of cigarettes smoked daily, Great Britain. Daily number of Cigarettes Smoked Rate Ratio from Cohort Study Odds Ratio from case-control study (ref) 1.0 (ref) All Smokers Question 25: Compare the results of the two studies. Comment on the similarities and differences in the computed measures of association. Page 11 of 12

12 Both studies demonstrate that lung cancer deaths are associated with smoking. Both studies also demonstrate that the risk of a lung cancer death increases with the daily number of cigarettes smoked. The difference is in comparing the rate ratio to the odds ratio. The odds ratios from the case-control are not as strong (high of a number) as the rate ratios from the cohort study. Question 26: What are the advantages and disadvantages of case-control vs. cohort studies? Answer 26 Case-control Cohort Sample size smaller/ disadvantage larger/advantage Costs inexpensive/advantage expensive/disadvantage Study time quick/advantage lengthy/disadvantage Rare disease good for this/advantage not good for this/disadvantage Rare exposure not good for this/disadvantage good for this/advantage Multiple exposures good for this/advantage not good for this/disadvantage Multiple outcomes not good for this/disadvantage good for this/advantage Progression, spectrum of illness not good for this/disadvantage good for this/advantage Disease rates not good for this/disadvantage good for this/advantage Recall bias potential for bias/disadvantage less potential for bias/advantage Loss to follow-up less potential for loss/advantage potential for loss/disadvantage Selection bias potential for bias/disadvantage less potential for bias/advantage Question 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? I would have conducted a case-control study first especially if there were an accessible disease registry available. A case-control study is cost effective and less time consuming than a cohort study. I might do a cohort study if there is evidence to support the findings from the case-control study. Question 28: Which of the following criteria for causality are met by the evidence presented from these two studies? Answer 28 Strong association Consistency among studies Exposure precedes disease Dose-response effect Biologic plausibility Yes X X X X No X Page 12 of 12