Published in Skeptic Vol 24, No. 4.
It has been 20 years since Dr. Angela Raffle published an article in prestigious medical journal The Lancet with the provocative title “How long will screening myths survive?” [1]. Although screening myths have been discussed extensively in peer-reviewed articles since Raffle’s publication, I think the public still needs balanced information. This article will review important concepts and myths of screening.
Screening is systematic search for disease, through medical tests, in people without symptoms of the disease being screened for. Common population screening programs include screenings for breast, cervical, colorectal and prostate cancers.
One of the myths discussed in Raffle’s article was regarding the comment “A recent analysis…has shown a reduction in the risk of cervical cancer by 95% for at least 8 years.” Another example is the statement made by Rudy Giuliani, former New York City Mayor who had been diagnosed with prostate cancer. When he was running for president in 2008, Giuliani tried to make a political statement that the American health system was much better than the “socialized” medicine of England, when he claimed he had an 82% of chance of surviving prostate cancer in the United States, compared to only 44% in England [2].
Probably without knowing it, Giuliani compared patients that were diagnosed in different ways, making his comparison invalid. Screening in the United States was much more common than in England. It is not possible to compare screened patients with non-screened patients due to the healthy screened-effect—people who get screened tend to be healthier, physically fit, non-smokers, and to have less social problems than those who do not get screened [3].
That is why randomization is used in clinical trials—to assure the groups are equal with the only difference being the intervention under analysis. In a screening context, however, those numbers could have come from randomized clinical trials and it still would have been misleading.
The numbers Giuliani referred to are the 5-year survival rate for prostate cancer in the United States and England in 2000. Five-year survival rate is the proportion of patients with a specific cancer who are still alive five years after the diagnosis. It is probably the most common statistic used to measure cancer prognosis [4]. The problem happens when the survival rates include screened patients.
The idea of screening is to advance in time the moment of diagnosis to allow early detection—a cancer that would have been diagnosed due to symptoms in advanced stages is now detected years before in the asymptomatic phase. Imagine that a group of patients without screening is diagnosed due to symptoms at age 63 years, but die from the cancer at 65. Now, consider that screening detects the tumor at age 59 and the patients still die from the cancer at 65. Thus, without screening the 5-year survival rate was 0%, but with screening it is 100%, even though screening did not make any of them to live longer—both group of patients died at the same time. This is called the lead time bias [2, 3] and it is illustrated in Figure 1.
Another problem with screening is the length-time bias. This happens because screening is done periodically, and cancer, contrary to what most people think, is a heterogeneous disease with different progressions rates [2,3]. Really aggressive and more lethal cancers tend not to be detected by screening because they grow fast and cause symptoms between screening rounds. Similarly, screening tends to detect slow-progressive cancers. As a result, a group of patients whose cancers were detected by screening will live longer than those diagnosed clinically simple because screening selected a group of patients with a better prognosis. In fact, some of those would not have progressed or would have regressed spontaneously. That means that screening detects abnormalities that meet the pathological criteria of cancer, but would not have caused symptoms or death in the patient’s lifetime. This is called overdiagnosis [5]. The problem of overdiagnosis is that at the time of diagnosis it is not possible to know which cases will progress and which will not, so almost everyone is treated, leading to overtreatment.
Overdiagnosis also inflates the survival statistic [2, 3]. Figure 2 shows a hypothetical example. Imagine that without screening, of 1000 people with a specific cancer, after five years 900 are dead and 100 are alive. Now imagine that screening correctly identifies those 1000 patients, but also identifies 4000 patients whose cancer would not have progressed—they were overdiagnosed. Screening increased 5 year survival rate from 10% to 82%, but the number of deaths was the same in both scenarios.
Had Rudy Giuliani compared the prostate cancer mortality rates in both countries, he would have seen that they were roughly the same in 2000: 26 and 27 per 100,000 in US and England, respectively [2]. Contrary to his claim of a superior healthcare system in the US, higher survival rates and unchanged mortality actually show the opposite: Americans are more likely to be overdiagnosed and overtreated for prostate cancer, but for no reason because there is no benefit from those extra diagnosed and treated patients.
An inflated survival rate without screening saving lives is not just a speculation. In a randomized trial of chest X-ray screening for lung cancer in smokers, the 5-year survival rate was 35% for the screened group and 19% for the control group, but the mortality was slightly higher in the screened group [6]. Moreover, another study [7] found no correlation between differences in 5-year survival rates and mortality rates for 20 different types of cancers in the US. Between 1950 to 1995 in the US, the most drastic change in 5-year survival rate was for prostate cancer (43% to 93%) [7], a period where the incidence of prostate cancer increased substantially especially after screening started.
The screening for neuroblastoma is another illuminating case [3]. Neuroblastoma is a cancer that occurs in children and usually has a better prognosis when it appears before age one, and a worse prognosis after that. In 1985, screening children for neuroblastoma started nationwide in Japan. Until 1988, screening detected 337 cases and they had a 97% survival rate, much higher than the 5-year survival rate of 50-55% of unscreened children. Even though screening increased considerably the incidence of neuroblastoma, the number of children diagnosed after age one did not change. More critically, the mortality of neuroblastoma in Japan was similar of other countries that had not introduced screening. Due to considerable overdiagnosis and lack of clear benefits, the neuroblastoma screening in children ended in Japan in 2004.
Despite biased survival statics being widely used for the promotion of screening, medical doctors are not informed about it. In a 2012 survey [8], 76% of American physicians wrongly thought that better 5-year survival rates are evidence of screening benefits. Furthermore, 47% wrongly considered that more cancers detected in screened than non-screened populations represent evidence that screening saves lives. Only mortality can be used as evidence for screening efficacy. But it is also not that simple.
The main outcome used to measure screening efficacy is the death rate caused by the screened cancer—cancer specific mortality. There are two problems with cancer-specific mortality. First, it is more common to be overdiagnosed and overtreated than to avoid a death caused by the screened-cancer. For example, in the ERSPC study, which reported a reduction in prostate cancer mortality, 27 men had to be treated to avoid one prostate cancer death [9]. As another example, screening for breast cancer leads to more mastectomies [10]. Moreover, treatment can increase mortality for other causes. For instance, radiotherapy for breast cancer increases mortality for lung cancer and heart disease [10]. Thus, to look only for cancer specific mortality could miss deaths caused by treatment. Second, it has been documented that misclassification of the cause death is another source of bias in favor of screening [10]. A good example of how biased cancer specific mortality might be is the Swedish trials of mammography screening: for every 1,000 women screened every other year for 12 years, while one breast cancer death was avoided, the total number of deaths increased by six [11].
Thus, the only unbiased outcome is overall mortality. Only a reduction in overall mortality actually shows what we want to know: whether screening save lives. The best evidence of a screening reducing overall mortality was for lung cancer using low-dose CT in smokers, but a subsequently systematic review did not find that effect [12]. Since we are dealing with healthy people, a large sample is required to detect a difference in overall mortality. As H. G. Welch wrote in his 2015 book Less Medicine, More Health [13], “we should dump “screening saves lives language.” We should publicly acknowledge that we cannot be sure whether early detection lengthens, shortens, or has no effect on how long people live. And we should be clear that if it takes so many people to find out for sure, then the benefit must be, at best, small.”
The only way for people who get screened to understand its harms is, as Raffle ended her article, to “avoid contributing myths and fallacies to the debate.”
References
1. Raffle, A. 1999. “How long will screening myths survive?” Lancet, 354:431-43
2. Gigerenzer, G., Gaissmaier, W., Kurz-Milcke, E., et al. 2007. “Helping Doctors and Patients Make Sense of Health Statistics.” Psychological Science in The Public Interest. 8(2):53-96.
3. Raffle, A.E., Gray, J.A.M. 2007. Screening: Evidence and Practice. Oxford: Oxford University Press.
4. Wegwarth, O., Gaissmaier, W., Gigerenzer, G. 2010. “Deceiving Numbers.” Medical Decision Making 3: 386-394.
5. Brodersen, J.,Schwartz, L.M., Heneghan, C., et al. 2018. ‘Overdiagnosis: what it is and what it isn’t.” BMJ Evid Based Med. 23:1-3.
6. Woloshin, S., Schwartz, L.M., Welch, H.G. 2008. Know Your Chances: Understanding Health Statistics. Berkeley (CA): University of California Press.
7. Welch, H.G., Schwart, L.M., Woloshin, S. 2000. “Are increasing 5-year survival rates evidence of success against cancer?” JAMA 283: 2975-2978.
8. Wegwarth, O., L.M. Schwartz, S. Woloshin, et al. 2012. “Do physicians understand cancer screening statistics? A national survey of primary care physicians in the United States.” Annals of Internal Medicine 156:340-349.
9. Fenton, J.J., Weyrich, M.S., Durbin, S., et al. 2018. “Prostate-specific antigen–based screening for prostate cancer: A systematic evidence review for the U.S. Preventive Services Task Force.” Agency for Healthcare Research and Quality, Evidence Synthesis No. 154. AHRQ Publication No. 17-05229-EF-1.
10. Gøtzsche PC, Jørgensen KJ. 2013. “Screening for breast cancer with mammography.” Cochrane Systematic Review.
Due to overdiagnosis, radiotherapy is used more in screened than non-screened groups. A radiotherapy meta-analysis reported 78% and 27% excess mortality from lung cancer and heart disease, respectively. Bias in cause of death is another issue.
First, determining the cause of death when patients have multiple diagnoses is a source of error. And, in many mammography screening trials, cause of death was not assessed on blind review, increasing the chance of bias. Furthermore, as radiotherapy reduces the chance of breast cancer local recurrence, it makes it more likely that deaths in screened women with breast cancer will be misclassified as from other causes.
11. Gøtzsche P.C., Olsen O. 2000. “Is screening for breast cancer with mammography justifiable?” Lancet. 355:129-34.
12. Prasad, V., Lenzer, J., Newman, D.H. 2016. “Why cancer screening has never been shown to ‘save lives’—and what we can do about it.” BMJ, 352.
13. Welch, H.G. 2015. Less Medicine, More Health: 7 Assumptions That Drive Too Much Medical Care. Beacon Press.
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