Pediatr Radiol (2014) 44 (Suppl 3):S468–S474 DOI 10.1007/s00247-014-3019-8

IMAGE GENTLY ALARA CT SUMMIT: HOW TO USE NEW CT TECHNOLOGIES FOR CHILDREN

If it is published in the peer-reviewed literature, it must be true? Louis K. Wagner

Received: 18 February 2014 / Revised: 1 April 2014 / Accepted: 25 April 2014 # Springer-Verlag Berlin Heidelberg 2014

Abstract Epidemiological research correlating cancer rates in a population of patients with radiation doses from medical X-rays is fraught with confounding factors that obfuscate the likelihood that any positive relationship is causal. This is a review of four studies involving some of those confounding factors. Comparisons of findings with other studies not encumbered by similar confounding factors can enhance assertions of causation between medical X-rays and cancer rates. Even so, such assertions rest significantly on opinions of researchers regarding the degree of consistency between findings among various studies. The question as to what degree any findings truly represent cause and effect will likely still meet with controversy. The importance of these findings to medicine should therefore not lie in any controversy regarding causation, but in what the findings potentially mean with regard to benefit and risk for patients and the professional practice of medicine. Keywords Epidemiology . Confounding causation . Reverse causation . X-rays . Cancer

Introduction Evolutionary biologist Stephen J. Gould [1] said this in 1986: “Most interesting errors in the history of science reflect just a few common fallacies of reasoning. My primary candidate is … confusion of correlation with … causality. …we often … advocate a causal link from correlation alone.” L. K. Wagner (*) Department of Diagnostic and Interventional Imaging, The University of Texas – Houston Medical School, 6431 Fannin St, MSB 2.130B, Houston, TX 77030, USA e-mail: [email protected]

Confounding and reverse causation In epidemiology, correlation between two factors, such as radiation exposure and cancer prevalence, tests the hypothesis that one might be a cause of the other. However, many correlations can exist circumstantially with no causal link existing between the agent and the outcome. Malaria, for example, gets its name from the association of the disease with “bad air.” The cause of malaria exists in the mosquito and is not the air itself. Sometimes it is not clear which factor is the cause and which is the effect. For example, if people with higher incomes are generally healthier, does good health promote higher income or does higher income promote good health? Epidemiological studies are beleaguered by confounding factors that distract from a valid interpretation of data. In the perfect study that compares disease rates in a population exposed to some agent versus rates in a control population not exposed to the agent, the control population should be an exact clone of the study population and live in identical circumstances except for exposure to the agent. If age distributions, ratio of men to women, proportion of ethnicities, cultural and physical environments of the populations, et cetera, differ between the groups, these factors might obscure or enhance the correlation. For example, if we found that people who wear a top while swimming are more likely to develop breast cancer than those who wear no top, we might conclude that wearing a top causes breast cancer. But since women wear tops and men don’t, the correlation is simply a result of the fact that women are more prone than men to develop breast cancer. Thus there is spurious correlation with no cause and effect. The analysis of data must correct for all differences that could obscure or enhance the correlation. Failure to identify and to account for all confounding issues reduces the strength of and possibly nullifies any conclusion regarding a potential causal relationship between the agent

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and the prevalence of disease. Factors in the two populations that might be hidden from observation but nevertheless skew the results often lead to false conclusions based on correlation alone. In Fig. 1 three diagrams exemplify the difficulties in determining whether a correlation is the result of causation. In particular we examine the association of medical X-ray procedures with disease. Except for a few screening procedures in healthy people, medical X-rays are administered to patients who are ill, injured or have symptoms of illness or injury. Thus, there is a condition that leads to the X-ray procedure. If we wish to determine whether the medical X-ray exposure might increase the risk for some disease that later develops, then we have to find a population of similar people whose lives are essentially the same except for the fact that they did not get X-ray procedure for the condition. Because medical Xray use in health care is so prevalent in modern society, such a comparison is impossible. Therefore, the control population is often individuals who did not have the condition and therefore were not exposed to the X-rays. The control and test populations from the start differ by one important factor — the Xrayed population presents with a condition that the control population does not have. If the cancer rates in the control population are used to estimate the expected cancer rates in the population of patients with the condition, then it must be shown that baseline cancer rates in the population of patients with the condition would in the absence of the X-rays be identical to that of the control population. This is often at the crux of confounding. If in such studies a correlation between the X-ray exposure and the later development of excess cancers is found, the question arises as to whether the X-rays caused the excess

Fig. 1 Confounding in medical X-ray epidemiology. a Causation occurs when the condition plays no part in the later development of the disease, but the X-rays do. b Conditional confounding or reverse causation occurs when the circumstance for which an X-ray is ordered is a factor that selects for patients who are likely to develop the disease but for whom the X-rays do not cause the disease. X-rays become associated with the disease without causing it. In this case the disease caused the X-rays and not vice versa. c Compound effects can occur where both the condition and the X-rays are causal factors in the later development of disease. Both causation and compound effects might amplify causal effects of X-rays if the condition selects for a population of patients more susceptible to the effects of radiation

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cancers (Fig. 1). One reason the X-rays might not have caused the cancer is that the conditions for which the X-rays were ordered might select for patients who are less healthy and more prone to develop cancer (Fig. 1). In other words, maybe the X-rays had no causal effect in inducing the disease, but rather the X-rays are guilty by medical association with an inherently cancer-prone population of patients. Another possibility occurs if an association is found between X-rays and the disease but the condition for which an X-ray is ordered is actually a symptom brought on by the undiagnosed incipient stages of the disease. In this case the correlation of X-rays with later development of the disease is a product of reverse causation. That is, the incipient but undiagnosed disease caused the X-rays, not vice versa. A third explanation (Fig. 1) might exist wherein both the condition for which the X-rays were ordered and the X-rays themselves are increasing the risk for cancer. It might even be possible that the patients with the condition are more sensitive to radiation, in which case an exaggerated correlation should be expected (Fig. 1). And there are other possibilities for the correlations. For example, maybe the control population has less access to advanced technology. This might lead to delayed diagnosis or misdiagnosis of cancers in that population while those with access to advanced technology might have their cancers diagnosed more accurately sooner and be more likely to be X-rayed for other diseases because of their access to technology. The X-rayed group in this case would appear to be at higher risk for cancer when it is not. It’s complicated!

Is there an unhealthy patient effect? Epidemiologists are familiar with a phenomenon called the “healthy worker effect” [2]. One definition of healthy worker effect is “the reduction of mortality or morbidity of occupational cohorts when compared with the general population.” Rates of disease in the general population are often higher than those found in the working population because some individuals in the general population are of insufficient health to assume employment in certain occupations [3]. For analogous reasons, medical radiation studies may plausibly be affected by a similar but antithetical effect wherein a patient population can be expected to experience higher rates of the disease in question than those found in healthier members of the population. This effect might be called the “unhealthy patient effect.” That is, the baseline rates of the disease in question, e.g., cancer, might be greater in a population of patients with an unhealthy status than the baseline rates in members of the population with a healthier status. By selecting a group of patients who had previously undergone CT for a non-carcinogenic health condition, the study might be selecting a group of individuals who are more apt to develop cancer later in life, perhaps from a weaker immune

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system, some genetically inherited factor, or exposures to chemical, biological or other agents in their living environments. Any of these factors might contribute to their initial health problems and they might also make the individual more apt to develop cancer. It might also be possible that this selection process ferrets out individuals who are more sensitive to radiation than healthier individuals. So, a positive finding of cancer in the unhealthy population might have no relation to radiation, might have some relation to radiation, or might be expressing an exaggerated relation to radiation. This unhealthy patient effect might also be behind some findings of a dose–response relationship between the administered CT dose and the rates of cancer. This would be because patients who have increasingly more CT examinations are also increasingly less healthy, meaning they might be naturally more prone to develop cancer or are possibly increasingly more sensitive to radiation. Ascertaining to what extent radiation contributes to a positive finding in an unhealthy population versus that of a healthy population is challenging and at the core of any discussion on cause and effect. The correlation alone is insufficient to determine a causal relationship. Other studies not encumbered by this potential selection bias must be meticulously compared to identify the likelihood for possible radiation effects. And inconsistencies in findings might suggest that selection bias related to an unhealthy patient effect is occurring.

Guidance in scientific research Sometimes multiple plausible explanations for data can exist, with only one explanation being the correct one. When this occurs, scientists often resort to a logic of simplicity. Occam’s razor, named after William of Occam who lived in the 14th century, provides this guidance in reasoning. The gist of Occam’s razor is that among competing hypotheses that explain a collection of data, the hypothesis with the fewest assumptions is more likely to be correct. Simply put, the simplest explanation that is consistent with the facts is probably the correct explanation. Although such logic is not a guarantee for arriving at the correct conclusion, it does demand caution in interpretation of data when either the explanation requires convoluted assumptions or when the explanation relies on assumptions that contradict or are inconsistent with previous data or findings.

Example #1 — A case of reverse causation? In 2011 Eisenberg et al. [4] performed a retrospective investigation of cancer in 82,861 patients following myocardial infarction. Patients were divided into two groups, those who

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had cardiological imaging following myocardial infarction and those who did not. The study found that patients with myocardial infarction (77% or 63,803 patients) who underwent cardiological imaging were more likely to be diagnosed with cancer within 1–5 years after the imaging procedure. Radiation doses from their studies were estimated as effective dose. The data were corrected for age and gender of the patients. The relative risk of cancer in the imaged population was increased at a rate of 3% per 10 mSv effective dose. More than 40% of the cancers were in the abdomen/pelvis. Many letters point out concerns that this study does not reflect causation [5]. We will address only a few of these cited concerns. The fact that effective dose was used instead of organ absorbed dose diminishes the potential strength of this investigation. Effective dose is a surrogate hypothetical wholebody dose that would produce a similar quantitative stochastic risk to that of the absorbed organ doses actually delivered. Correlation of organ absorbed dose to cancer incidence in those specific organs would have made the study more valuable and would be expected to significantly alter or enhance the interpretation of the data. By using effective dose, a cancer that develops in the brain could be attributed to an X-ray exposure confined to the abdomen, an occurrence of which is highly unlikely because of the low level of scattered radiation exposing the brain. Further, by studying only cancers that develop in years 1–5 after exposure an assumption is made that cancers, other than leukemia, are found much earlier after exposure than previous research indicates. Previous research [6] typically finds a minimum latent period of 5–10 years between exposure and diagnosis of cancers other than leukemia. Thus we might invoke Occam’s razor and seek a different explanation that does not make such assumptions. The study reported no corrections for smoking, diet, obesity or level of exercise within each population. The American Cancer Society found that two-thirds of all new cancers in 2012 were related to these factors [7]. Such factors would be expected to exist in the population studied because they are also well known risk factors for myocardial infarction. These are potentially confounding factors that might alter conclusions. Indeed and not surprisingly, cancer overall in this entire study group was higher than rates in the general population [5]. But how could cardiological imaging be associated spuriously to a diagnosis of cancer 1–5 years afterward? The answer might lie in the reasons imaging was ordered in some patients with myocardial infarction and not in others. The fact that the imaging group had more imaging might logically also mean that their follow-up care was more intense than those who had no imaging. Further, many of these patients, because of their histories of smoking, poor diet, obesity and lack of exercise, might be in undiagnosed stages of developing cancer. More intense medical care would lead to an earlier

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diagnosis of cancers that were already present at undiagnosed stages when the population was imaged. The non-imaged group probably did not have the advantage of early diagnosis because of less intense follow-up care. Alternatively, the imaged group might have just been less healthy than the nonimaged group. That is, patients with myocardial infarction who are in poor health are perhaps more likely to have cardiological imaging than are patients with myocardial infarction who are in otherwise better health. It is quite plausible that histories of smoking, diet, obesity and exercise differ between the groups, making the imaged group more likely to already have incipient but undiagnosed cancer at the time of myocardial infarction. These explanations would be consistent with the data, and they would explain the earlier than usual finding of cancer following exposure to X-rays. No unusual assumptions about an altered latent period are required. Occam’s razor therefore points us to a simpler noncausal explanation of the correlation between cardiological imaging and the development of cancer in the first 5 years following the imaging procedure.

Example #2 — A case of hidden confounding? In 2004 Hujoel et al. [8] published a retrospective study correlating dental X-rays in pregnant women with low birth weight. They hypothesized that radiation exposure to the pituitary gland, thyroid gland or hypothalamus might trigger some response that causes low birth weight. The study involved 1,117 women with low-birth-weight infants and 4,468 randomly selected control pregnancies resulting in normalbirth-weight infants. Doses to glands and hypothalamus were roughly 1 mGy. The study identified and corrected for 14 potentially confounding factors, three of which were (1) selfreported smoking habits, (2) adequacy of prenatal care using the Kessner index and (3) ethnicity. These three factors were each found to be correlated with dental X-rays, but only smoking and ethnicity were correlated with low birth weight. The authors found that patients giving birth to low-weight babies were more likely to have had dental radiography than were patients who had normal-weight babies, meaning dental X-rays were correlated with low birth weight. Several factors of this study are unusual. First, the exposure occurs to an organ different from the one wherein the effect is studied. Second, the absorbed doses were very low, on the order of 1 mGy. No data substantively support the concept that such low doses in such a limited population could detect an effect in a directly exposed organ much less an indirectly affected organ, as pointed out by other authors [9, 10]. Further, the correlation between X-rays and low birth weight actually increased after correction for the Kessner index of prenatal care. This finding requires the interpretation that X-rays are more likely to cause low birth weight in babies born to

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mothers with adequate prenatal care than it is in babies born to mothers with inadequate care. So Occam’s razor again suggests that a simpler explanation to the findings would be preferred over one that requires such unusual assumptions. Periodontal disease, for example, has been correlated with low birth weight [11–13]. This fact was only peripherally addressed as a potential confounding factor and was dismissed by the authors as an unlikely factor because the effect occurred for orthodontic as well as endodontic X-rays. There is also the potential for nutrition as a confounding factor because nutrition would be correlated with dental care as well as prenatal health. The authors dismiss that possibility because all the patients were financially secure. However, having access to a healthy diet does not necessarily mean one’s diet is healthy. So, there are probable confounding factors in this study that are not adequately taken into account and which could be influencing this outcome. Without a more thorough investigation of these potential confounding factors, interpreting the association as cause and effect is unwarranted because of the unusual assumptions that must be posited to explain the results.

Example #3 and #4 — Causation mixed with confounding? More recently, two large population studies involving CT scans in children and later diagnoses of cancers have surfaced [14, 15]. The earliest study analyzed cancer rates in children as a function of absorbed dose to red bone marrow and to brain. Thus, contrary to the cardiology study previously discussed, organ doses were estimated in the tissues at risk. Also contrary to the cardiology study, this study purposefully avoided data on leukemia in the first 2 years after CT examinations and avoided data on brain cancers in the first 5 years after the examinations. Thus, the latent period, or lag period, was reasonably taken into consideration. The authors found very linear relationships of increasing cancer incidence (brain and leukemia) versus absorbed dose to the organs of concern. The authors concluded that although the potential for confounding was very high, it was not likely that confounding explained away all of the observed results and that at least some or a large portion of the cancer incidence was likely caused by the exposure to ionizing radiation. The authors further cautioned against interpreting these data in such a manner that medical examinations would be foregone. Rather, the message was tempered in the atmosphere of finding ways to improve the benefit-to-risk of medical X-rays. Let us examine how their data might be confounded by factors not accounted for in the study. Their study population included children undergoing CT examination. Figure 2 plots a hypothetical relationship of excess cancer incidence in the organ of concern against absorbed dose from CT scans of any

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Pediatr Radiol (2014) 44 (Suppl 3):S468–S474 Relative Risk versus Organ Absorbed Dose 14 12 10 8

Relative Risk 6 4 2

D

A C B

0 0

100

200

300

400

500

Absorbed Organ Dose (mGy)

Fig. 2 Hypothetical increase in cancer incidence with increasing exposure to CT. Patient groups A, B, C and D differ significantly by the conditions for which their CTs were ordered. These differences in conditions pose potential confounding factors when trying to ascertain whether the relationship represents cause and effect or just spurious correlation

part of the patient’s body. We have also outlined four dose groups for illustration purposes. The first group, A, is the lower-dose group that includes only CT scans outside the organ of concern — otherwise the dose to the organ of concern would be much higher. That is, only scattered radiation contributes to this lower-dose group. This group differs from the other groups because the symptoms for which the CT was ordered must not have been related to the organ of concern since no CT of that organ was ordered. The second group, B, received a dose on the order of that expected from a single CT of the organ of concern. This group differs from all other groups in that the dose tells us that this group only had one CT of the organ of concern. So, this group is composed of individuals who exhibited some characteristic related to the organ of concern that was sufficient to order one CT, but there was no second CT involving that organ. Why? The likely explanation is that the CT scan was negative or that the findings warranted no follow-up because the patients’ symptoms were brought under control or resolved. The third group, C, had a dose equivalent to that of two CTs, both ordered for the organ of concern. Why were two CTs ordered? The first CT may have been diagnostically inadequate and a second was needed. Or the first CT might have been positive for some condition and a second CT was for follow-up. If there was a condition, it likely resolved sufficiently that a third CT was not warranted. The fourth group, D, had a dose equivalent to that of three CTs. Their condition was probably more serious than the lower-dose groups and warranted more intense follow-up. As the dose increases, conditions of the patient are selectively different. Hypothetically, the medical selection of patients for an increasing number of CTs might unknowingly be selecting groups of patients wherein some in the group are increasingly more likely to develop cancer later in life, perhaps because those individuals are simply more likely to develop disease and could be more prone to develop cancer,

or they might have a condition that initiates or promotes the development of cancer. Another possibility is that patients might be exhibiting very early symptoms of an incipient cancer that is too early to be diagnosed. This would be an example of reverse confounding, evidence of which is found in a later article that appeared on CT in children. If we choose the idea of conditional confounding as an explanation to the data, then we have to posit the existence of unidentified risk factors. Occam’s razor warns us against making too many assumptions. Further, substantial supporting evidence from other epidemiological studies, not related to medical ill-health, suggest that radiation is a probable cause of at least some portion of the cancers [16–18]. So this study supports the likelihood that CT radiation does have some carcinogenic potential. On the other hand some unusual findings suggest that the associations are not entirely consistent with other data. For example, the age dependence of relative risk for brain cancer was opposite that of other studies and the leukemia risk was linear wherein other studies demonstrate a non-linear response. Thus the extent to which the data are related to cause and effect is difficult to assess. The authors estimate a risk of about 1 cancer in 10 years in 5,000 patients for each head CT scan. A later study [15] compared cancer rates in a population of children who underwent CT for non-cancer reasons against those who had no CT for non-cancer reasons. We will refer to these two groups as CT and no-CT groups, understanding that this refers only to non-cancer use of CT. After correction for age, gender and year of birth, the study found that the expected cancer rate in the CT group was about 0.37% while the actual rate was 0.46%, a 25% increase above the expected value. The expected incidence of 0.37% in the CT group is derived from the non-CT group after adjustment for differences in age, gender and date of birth for the two populations. The actual raw incidence in the no-CT population was about 0.56%. The accuracy of the downward correction by one-third from the control rate assumes the rates in the two populations differ only by the factors of age, gender and year of birth. Any unrecognized medical, health, genetic, environmental or other factors that might alter this assumption affect the validity of conclusions regarding cause and effect. The authors found evidence of some reverse confounding in their data and the study might be influenced by other confounding factors that are as yet undiscovered. But as with the previously described study [14], it is not easily posited that all the findings could be related to confounding factors. To assess the extent the findings might be caused by radiation an investigation into hidden confounding factors is warranted. For example, some, but not all, studies find an increased risk for developing brain cancer after trauma to the head [19–21]. The evidence is too weak to posit this as a key factor in these studies, but I use it as an example of

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potential underlying confounding factors that might explain some of the association. Let’s examine two other potential confounding factors that might be exaggerating the results. The first is straightforward; the study as designed might be selecting two very different populations of children wherein the non-CT group is healthier and less likely to develop cancer than the CT group. Evidence for this is that the study reports a significant excess of brain cancer in the CT group for children who had CT of the abdomen or pelvis. This unusual finding suggests that the CT group is at greater risk to develop brain cancer, regardless of radiation exposure. The authors do report evidence for reverse causation in their brain cancer data. Because this suggests that there might be an underlying difference, regardless of radiation, in the cancer rates in the two populations, the ability to separate this effect from the data in order to isolate any causal effect becomes difficult to impossible. The authors suggest that confounding by certain factors is unlikely. Specifically they say this: “Information was not available for potential confounding factors such as alcohol, smoking, sun exposure, or for Down syndrome or other markers of cancer susceptibility. However, because the CT related increase in cancer risk varied very little by socioeconomic status, it is unlikely that our results were substantially biased by confounding factors such as these.” The justification for this statement is not given. A second factor is more subtle. A portion of the control population might not have had easy access to advanced medical technology, such as CT. Not having easy access to CT might result in a delay in a diagnosis of cancer or a misdiagnosis of cancer in the non-CT group. This would alter the baseline rate used to determine the expected cancer rates in the CT group, which clearly did have access to CT. Thus the CT group might have their cancers diagnosed earlier and more accurately because of access to technology. The cancer rates in the two groups might artificially look different, with the CT group appearing to develop cancers earlier than the control group because of their access to more advanced medical technology. This is purely hypothetical but a confounding factor that deserves consideration because it could affect baseline estimates of cancer rates in the study. Correcting for this possibility would be very difficult. Additionally, it is also possible that the CT group is less healthy in general and that this might make them more susceptible to radiation effects. Such a possibility would mean that the elevated incidence would be expected to be greater than that in a healthy population. The truth of the situation is not possible to know without other evidence not encumbered by similar potential confounders. It is also essential to point out that the childhood CT studies only follow patients for a limited time. No breast cancer occurred in this study, probably because the population was too young for breast cancer to develop. But breast buds are

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radiosensitive organs. Presumably, if radiation is causing cancer, then elevated incidence of breast cancer should be expected once the population matures to the ages where breast cancers occur more prevalently. These two CT studies in children support the assertion that CT might be causing cancers in the exposed population. The data are supported by other studies in children that are not directly related to medical conditions [16–18]. These include findings of excess cancer rates in the A-bomb series [16], studies in twin children whose mothers underwent pelvimetry [18] and findings of excess leukemia in children born in areas where background radiation rates are higher [17]. The unanswered question is: “What proportion of the cancers in the childhood CT studies can reasonably be attributed to radiation?” Separating the rate of cancer caused by X-rays from the effects that might be caused by confounding factors is important so that the medical profession can place this risk into perspective with the benefits of imaging. It might appear reasonable to assess the lifetime attributable rate at 1 induced cancer for every 500 to 1,000 CT scans, assuming that not all the observed incidence in the studies can be attributed to CT and assuming more cancers will be attributed to CT when patients live out their full life expectancy. But other factors make the assessment of benefit-to-risk convoluted. For example, a latent period in excess of several decades might pale against the threat imposed in the near future by the patient’s condition. Medical care guided by CT might be necessary to extend the patient’s life expectancy.

Implications for clinical practice Epidemiology studies that investigate the association of medical X-rays with cancer induction do not provide a clear picture of cause and effect and often use non-declarative terms like “association” to assure that there is no claim to causality. Despite the difficulties in separating cause-and-effect from spurious association, ionizing radiation delivered during clinical examinations may represent a low level of risk. As many authors of these studies state, this mandates careful justification of all CT examinations and careful management of the radiation dose delivered by each examination. However in our efforts to manage the number of CT examinations, and the radiation doses they deliver, we must use caution to ensure that a critical diagnosis is not missed. For example, if we assume a 1 in 500 risk of a CT scan inducing a cancer that develops sometime in a patient’s life, then we might assess benefit-to-risk by posing our assessment this way: if 500 patients with an identical condition are presented and all undergo CT, then what are the expected benefits in the entire population of 500 compared to the risk of inducing one extra cancer? The question is not much different from

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the benefit-to-risk posed by vaccinations when some of that population might hypothetically develop the disease as a result of the vaccination. Allowing that fear of vaccination to control our behavior has resulted in greater incidences of certain diseases around the globe [22]. If, in our fervor to reduce use of CT in our 500 patients, our good intentions cause us to omit a scan that inadvertently results in one missed critical diagnosis and fatality, then we will have negated the purpose of our good intentions.

Conclusion Epidemiological studies that investigate associations of medical X-rays with later development of cancer are subject to confounding factors that must be intensely analyzed before any conclusions or assertions can be made regarding causation. When not all potential confounding factors are known, assessing whether an association between X-rays and later development of cancer is causal is not possible from the study alone. Consistencies of findings with other studies that are not limited by similar confounding are necessary before the picture of cause-and-effect can be clearly discerned.

Conflicts of interest Dr. Wagner declares a financial interest as a partner in RM Partnership and has no investigational or off-label uses to disclose.

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Pediatr Radiol (2014) 44 (Suppl 3):S468–S474 5. (2011) Letters to Editor. CMAJ. http://www.cmaj.ca/content/183/4/ 430/reply. Accessed 18 April 2014 6. National Research Council of the National Academies (2006) Biological effects of ionizing radiations (BEIR VII Phase 2). National Academies Press, Washington, DC 7. American Cancer Society (2012) Cancer facts and figures 2012. American Cancer Society, Atlanta 8. Hujoel PP, Bollen AM, Noonan CJ et al (2004) Antepartum dental radiography and infant low birth weight. JAMA 291:1987–1993 9. Boice JD, Mulvihill JJ, Stovall M et al (2004) Dental X-rays and low birth weight (letter). J Radiol Prot 24:321–325 10. Brent RL (2005) Commentary on low birth weight and dental X rays. Health Phys 88:379–381 11. Offenbacher S, Katz V, Fertik G et al (1996) Periodontal infection as a possible risk factor for preterm low birth weight. J Periodontol 67: 1103–1113 12. Jeffcoat MK, Geurs NC, Reddy MS et al (2001) Current evidence regarding periodontal disease as a risk factor in preterm birth. Ann Periodontol 6:183–188 13. Jeffcoat MK, Geurs NC, Reddy MS et al (2001) Periodontal infection and preterm birth: results of a prospective study. J Am Dent Assoc 132:875–880 14. Pearce MS, Salotti JA, Little MP et al (2012) Radiation exposure from CT scans in childhood and subsequent risk of leukaemia and brain tumours: a retrospective cohort study. Lancet 380:499–505 15. Mathews JD, Forsythe AV, Brady Z et al (2013) Cancer risk in 680,000 people exposed to computed tomography scans in childhood or adolescence: data linkage study of 11 million Australians. Br Med J 346:1–18 16. (2013) Radiation Effects Research Foundation. Report series (1993– 2013). http://www.rerf.jp/library/archives_e/rrtoc.html. Accessed 18 April 2014 17. Kendall GM, Little MP, Wakeford R et al (2013) A record-based case–control study of natural background radiation and the incidence of childhood leukaemia and other cancers in Great Britain during 1980–2006. Leukemia 27:3–9 18. MacMahon B (1985) Prenatal X-ray exposure and twins. N Engl J Med 312:576–577 19. Inskip PD, Mellemkjaer L, Gridley G et al (1998) Incidence of intracranial tumors following hospitalization for head injuries (Denmark). Cancer Causes Control 9:109–116 20. Preston-Martin S, Pogoda JM, Schlehofer B et al (1998) An international case–control study of adult glioma and meningioma: the role of head trauma. Int J Epidemiol 27:579–586 21. Nygren C, Adami J, Ye W et al (2001) Primary brain tumors following traumatic brain injury — a population-based cohort study in Sweden. Cancer Causes Control 12:733–737 22. Council on Foreign Relations (2014) Vaccine-preventable outbreaks, 2008–2014. http://www.cfr.org/interactives/GH_Vaccine_Map/#/ intro. Accessed 18 April 2014

If it is published in the peer-reviewed literature, it must be true?

Epidemiological research correlating cancer rates in a population of patients with radiation doses from medical X-rays is fraught with confounding fac...
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