reporting the opposite side of use. Even if a patient does not intentionally distort the answer, recent surgery may cause memory deficiencies leading to recall bias. Hepworth et al. (2006) argued that the reduced risk on the contralateral side indicates such recall bias. However, this risk reduction was due to an artifact of the method applied. Estimate of relative risk for contralateral mobile phone use was based on nonregular and ipsilateral phone users as reference. It follows that when- ever the relative risk of ipsilateral phone use is > 1, the relative risk of contralateral use must be < 1 [the expected value of the OR in this case is (πo+πi)/(πo+ψπi), where πo is the pro- portion in the population of nonusers, πI is the proportion in the population of ipsilat- eral users, and ψ is the OR for ipsilateral use]. All meta-analytical ORs for ipsilateral mobile phone use are > 1, and those for glioma and acoustic neuroma are statistically significant. If there was no misclassification bias in controls and perfect sensitivity, then a small recall bias in the direction of a preference for reporting mobile phone use at the side of the tumor of about 3% would reduce these enhanced ORs for long-term (≥ 10 years) use to 1. However, considering overall results for ipsilateral use, recall bias must reduce specificity in cases by 30–40% to remove the observed enhanced risk. The specificity that reduces an observed increased OR to 1 is given by [1+(ψ*–1)π]–1, where ψ* is the observed OR and π is the exposure prevalence in the population, given that sensitivity in both cases and controls and specificity in controls are 1 (Rothman et al. 2008). For example, taking the study of Hardell et al. (2005a) with an overall OR for ipsilateral mobile and cordless phone use of approximately 3.0 for acoustic neuroma and a prevalence of about 23% of ipsilateral mobile or cordless phone use, the specificity must be as low as 68% to remove the observed effect. That is, 32% of those not exposed at all or not on the side of the tumor must have falsely stated they have been exposed. In other words, more than half of mobile phone users among cases and none among controls must have given the wrong side of the head for their predominant use to remove the observed increased risk. It should also be noted that the case-only approach of Takebayashi et al. (2008) cannot solve the problem of recall bias. As can be seen in Table 1, several Interphone groups (Christensen et al. 2005; Klaeboe et al. 2007; Lahkola et al. 2007, 2008; Lönn et al. 2005) reported ORs that were sig- nificantly < 1, implying a protective effect of mobile phone use for brain tumors. Although there is a remote possibility that mobile phone use may enhance apoptosis or activate DNA repair, such processes will hardly affect tumor development at an advanced stage; thus, this is not a valid explanation for these reduced risk estimates but rather it suggests systematic bias. Selection bias, as delineated above, could explain some of these results. However, there are likely additional biases that contributed to the overall effect. Let us consider this aspect from the perspective of the ceteris paribus con- dition (i.e., that cases and controls have essen- tially the same features in all relevant aspects except exposure) and in particular from the condition of population equivalence (i.e., the condition that both cases and controls stem from populations that are equivalent for all attributes that are relevant for the dis- ease under study). Mobile phone use is not randomly distributed within the population, but usage patterns will be associated with cer- tain attributes such as occupation, sex, socio- economic status (SES), and age. Some of these attributes can be accounted for by matching or during analysis, but there could be an associa- tion with the disease that cannot be removed by these procedures. Exploration of prior symptoms in brain tumor cases often reveals indications of the disease process many years in the past (e.g., epileptic seizures, personality changes, a variety of cognitive and perception problems). Some of these symptoms could reduce the probability that a person chooses to use a mobile phone—or a telephone in gen- eral. Such habit changes would have the great- est impact on measures of cumulative duration and intensity of use. If aspects of the disease influence mobile phone use, the ceteris paribus condition is violated from the very beginning. It is evident that this also implies a violation of the condition of temporal relation, because a reversal of cause and effect may occur. These difficulties are related to the generally short duration of mobile phone use. A solution could be inclusion of case history information and formation of distinct subgroups differing in duration of symptoms related to the devel- oping disease. Obviously, influence of symp- toms on mobile phone use will predominantly reduce risk estimates, because the odds for mobile phone use in cases will be lowered. Because of the mentioned biases that could operate in case–control studies, one may be inclined to put some weight on the only cohort study (Johansen et al. 2001; Schüz et al. 2006b) presently available. However, this investigation is severely flawed and cannot contribute to risk assessment and was therefore not included in the meta-analysis (see also Hardell et al. 2008). Confounding. Environmental equivalence seems to be sufficient in all investigations and confounding seems an unlikely cause of bias because there are only a few known risk fac- tors for brain tumors that would induce bias if disregarded in the analyses. Therapeutic and, to a lesser degree, diagnostic X rays to the head region increase the risk for several types of brain tumors. In some studies this was consid- ered a possible confounder but without effect Environmental Health Perspectives • volume 117 | number 3 | March 2009 Mobile phones and cancer on the risk estimates (e.g., Hardell et al. 2001), indicating that there is no correlation between such irradiation and mobile phone use. Other possible confounders include neurofibro- matosis and tuberous sclerosis, family history of brain tumors, and medical treatment with growth factors, all of which are very rare con- ditions without reason to assume a relationship with mobile phone use. Age and sex are the most important confounders that have been considered in all studies either by matching or during analysis. SES has also been indicated as a possible confounder and was included in most analyses. However, including SES in the analysis will not remove selection bias that seems to be related to SES in some Interphone investigations (e.g., Hepworth et al. 2006). Assessment. Overall, arguments in favor of or against an association between mobile phone use and brain tumors are not equally strong. There is evidence for selection bias, exposure misclassification from excluding cord- less phone use, reversal of cause and effect from neglecting early symptoms of the disease, and short exposure duration. All these factors lead to reduction of the observed risk estimates. The only strong argument against an asso- ciation is the possible impact of recall bias. There may be underreporting especially of early mobile phone use because of memory deficits after surgery, but most concerns have been raised for a potential distortion in reporting side of the head the phone is held during calls. Handedness correlates not very highly with side of the head the phone is used [concordances of only about 60% have been determined (Hepworth et al. 2006)], and there are no other methods at hand to validate these data. There are, however, some arguments that speak against a decisive influence of recall bias: Most participants of the Interphone and Hardell studies were enrolled during 1997–2003 at a time when mobile phone use was not widely discussed as a potential risk factor for brain tumors. As reported by Hardell et al. (2002a), among 232 brain tumor cases who expressed their views about potential causes of their dis- ease, only two named mobile phones. Lönn (2004) asked 70 brain tumor cases and con- trols whether they considered mobile phones as a risk factor for brain tumors and found no difference between both groups. Even if patients consider mobile phones a factor con- tributing to their disease, what would they gain if they gave the wrong side of use? Most people choose to use mobile phones, but do they want to blame themselves for their disease? In several studies, mobile phone use was only one of the different risk factors investigated (e.g., Lönn et al. 2006). Bias from patients’ attribution would likely extend also to these other factors, and spuriously increased risks should have been observed in some of them. Although report- ing bias of this type may explain an increased 321