The very rapid worldwide increase in mobile phone use in the last decade has generated considerable interest in the possible health effects of exposure to radio frequency (RF) fields. A multinational case-control study, INTERPHONE, was set-up to investigate whether mobile phone use increases the risk of cancer and, more specifiBaruch Modan is deceased. 123Eur J Epidemiol (2007) 22: 647-664 DOI 10.1007/s10654-007-9152-z cally, whether the RF fields emitted by mobile phones are carcinogenic. The study focused on tumours arising in the tissues most exposed to RF fields from mobile phones: glioma, meningioma, acoustic neurinoma and parotid gland tumours. In addition to a detailed history of mobile phone use, information was collected on a number of known and potential risk factors for these tumours. The study was conducted in 13 countries. Australia, Canada, Denmark, Finland, France, Germany, Israel, Italy, Japan, New Zealand, Norway, Sweden, and the UK using a common core protocol. This paper describes the study design and methods and the main characteristics of the study population. INTERPHONE is the largest case-control study to date investigating risks related to mobile phone use and to other potential risk factors for the tumours of interest and includes 2,765 glioma, 2,425 meningioma, 1,121 acoustic neurinoma, 109 malignant parotid gland tumour cases and 7,658 controls. Particular attention was paid to estimating the amount and direction of potential recall and participation biases and their impact on the study results.

There is public concern that use of mobile phones could increase the risk of brain tumours. If such an effect exists, acoustic neuroma would be of particular concern because of the proximity of the acoustic nerve to the handset. We conducted, to a shared protocol, six population-based case -control studies in four Nordic countries and the UK to assess the risk of acoustic neuroma in relation to mobile phone use. Data were collected by personal interview from 678 cases of acoustic neuroma and 3553 controls. The risk of acoustic neuroma in relation to regular mobile phone use in the pooled data set was not raised (odds ratio (OR) ¼ 0.9, 95% confidence interval (CI): 0.7 -1.1). There was no association of risk with duration of use, lifetime cumulative hours of use or number of calls, for phone use overall or for analogue or digital phones separately. Risk of a tumour on the same side of the head as reported phone use was raised for use for 10 years or longer (OR ¼ 1.8, 95% CI: 1.1 -3.1). The study suggests that there is no substantial risk of acoustic neuroma in the first decade after starting mobile phone use. However, an increase in risk after longer term use or after a longer lag period could not be ruled out.

No abstract

An inverse association between allergic conditions and glioma risk has been reported previously. In this large population-based case-control study, the authors identified cases diagnosed with glioma or meningioma in Denmark, Norway, Finland, Sweden, and southeast England between 2000 and 2004. Detailed information on self-reported physician-diagnosed allergic conditions was collected from 1,527 glioma cases, 1,210 meningioma cases, and 3,309 randomly selected controls. Logistic regression showed an odds ratio of 0.70 (95% confidence interval: 0.61, 0.80) for glioma associated with a diagnosis of any of asthma, hay fever, eczema, or other type of allergy. The risk estimates for glioma were around 0.65 for each allergic condition (asthma, eczema, hay fever, and food allergy), and the 95% confidence intervals were equally consistent, at around 0.55, 0.80. The reduced risks of glioma related to eczema, hay fever, and allergy overall, but not asthma, were confined to current rather than past conditions. Meningioma risk was not associated with allergic conditions, except for eczema (odds ratio = 0.74, 95% confidence interval: 0.60, 0.91). Our results show a reduced risk for glioma associated primarily with current allergic conditions. If this is etiologic, it has implications for the understanding of how allergic conditions might reduce the tumor risk.

Volunteer subjects recalled their recent phone use with moderate systematic error and substantial random error. This large random error can be expected to reduce the power of the Interphone study to detect an increase in risk of brain, acoustic nerve, and parotid gland tumours with increasing mobile phone use, if one exists.

Public concern has been expressed about the possible adverse health effects of mobile telephones, mainly related to intracranial tumors. We conducted a population-based case-control study to investigate the relationship between mobile phone use and risk of glioma among 1,521 glioma patients and 3,301 controls. We found no evidence of increased risk of glioma related to regular mobile phone use (odds ratio, OR 5 0.78, 95% confidence interval, CI: 0.68, 0.91). No significant association was found across categories with duration of use, years since first use, cumulative number of calls or cumulative hours of use. When the linear trend was examined, the OR for cumulative hours of mobile phone use was 1.006 (1.002, 1.010) per 100 hr, but no such relationship was found for the years of use or the number of calls. We found no increased risks when analogue and digital phones were analyzed separately. For more than 10 years of mobile phone use reported on the side of the head where the tumor was located, an increased OR of borderline statistical significance (OR 5 1.39, 95% CI 1.01, 1.92, p trend 0.04) was found, whereas similar use on the opposite side of the head resulted in an OR of 0.98 (95% CI 0.71, 1.37). Although our results overall do not indicate an increased risk of glioma in relation to mobile phone use, the possible risk in the most heavily exposed part of the brain with long-term use needs to be explored further before firm conclusions can be drawn. ' 2007 Wiley-Liss, Inc.Key words: mobile phones; brain tumors; case-control studies Mobile phone use has increased rapidly worldwide since the early 1990s. Mobile phones emit radiofrequency electromagnetic fields that are non-ionizing radiation, i.e. have too low energy to break chemical bonds. Hence, such fields cannot cause DNA damage (mutations), which is required for cancer initiation. 1 However, radiofrequency fields might be involved in cancer development at later stages, including tumor progression or promotion. Despite the fact that no carcinogenic mechanism for radiofrequency radiation has been established, 2 there is public concern about the possible health effects of mobile phone use. This is mainly related to intracranial tumors, as mobile phones are used close to the head and the radiofrequency field is absorbed mostly in the head and neck region. The studies published on the issue have covered a relatively small number of study subjects with long-term exposure, and so far the epidemiological evidence does not suggest any clear increase of intracranial tumors related to mobile phone use, although some positive findings have been reported. [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] We conducted a collaborative population-based case-control study on the association of mobile phone use with intracranial tumors in 5 Northern European countries, using a shared protocol of the INTERPHONE study coordinated by the International Agency for Research on Cancer. 20 We report here the results concerning glioma, based on the combined data from D...

Despite limited evidence, cellular telephones have been claimed to cause cancer, especially in the brain. In this Danish study, the authors examined the possible association between use of cellular telephones and development of acoustic neuroma. Between 2000 and 2002, they ascertained 106 incident cases and matched these persons with 212 randomly sampled, population-based controls on age and sex. The data obtained included information on use of cellular telephones from personal interviews, data from medical records, and the results of radiologic examinations. The authors obtained information on socioeconomic factors from Statistics Denmark. The overall estimated relative risk of acoustic neuroma was 0.90 (95% confidence interval: 0.51, 1.57). Use of a cell phone for 10 years or more did not increase acoustic neuroma risk over that of short-term users. Furthermore, tumors did not occur more frequently on the side of the head on which the telephone was typically used, and the size of the tumor did not correlate with the pattern of cell phone use. The results of this prospective, population-based, nationwide study, which included a large number of long-term users of cellular telephones, do not support an association between cell phone use and risk of acoustic neuroma.

Background: Emergency medical dispatchers fail to identify approximately 25% of cases of out of hospital cardiac arrest, thus lose the opportunity to provide the caller instructions in cardiopulmonary resuscitation. We examined whether a machine learning framework could recognize out-of-hospital cardiac arrest from audio files of calls to the emergency medical dispatch center.Methods: For all incidents responded to by Emergency Medical Dispatch Center Copenhagen in 2014, the associated call was retrieved. A machine learning framework was trained to recognize cardiac arrest from the recorded calls. Sensitivity, specificity, and positive predictive value for recognizing out-of-hospital cardiac arrest were calculated. The performance of the machine learning framework was compared to the actual recognition and time-torecognition of cardiac arrest by medical dispatchers.Results: We examined 108,607 emergency calls, of which 918 (0.8%) were out-of-hospital cardiac arrest calls eligible for analysis. Compared with medical dispatchers, the machine learning framework had a significantly higher sensitivity (72.5% vs. 84.1%, p < 0.001) with lower specificity (98.8% vs. 97.3%, p < 0.001). The machine learning framework had a lower positive predictive value than dispatchers (20.9% vs. 33.0%, p < 0.001). Time-torecognition was significantly shorter for the machine learning framework compared to the dispatchers (median 44 seconds vs. 54 s, p < 0.001).Conclusions: A machine learning framework performed better than emergency medical dispatchers for identifying out-of-hospital cardiac arrest in emergency phone calls. Machine learning may play an important role as a decision support tool for emergency medical dispatchers.