The Letter to the Editor you see below is a draft version I wrote  to respond to the publication of a study on brain cancer trends and mobile phone use. I had, and still have, some concerns about the analyses in that paper. This study was published in an Economics Journal: Contemporary Economic Policy, which seems an odd place to publish such a paper. I contacted the Editor-in-Chief, who unfortunately was not willing to publish the letter. I am not familiar with how these things are normally dealt with in economics, but if I understand it correctly, because I could not prove that the analyses are indeed wrong (which I suppose, in the absence of the data, is correct). Having gone through the trouble of drafting my Letter, I thought it would be a waste to let it just collect dust. So please find it below. Feel free to comment on it, but keep in mind this was a first draft only.....
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The recent publication by Mialon and Nesson (1) described an interesting methodological approach to investigating whether brain cancer may be associated with the use of mobile phone in which national-level ecological time series data from 25 countries was analysed using a difference-in- difference (DiD) method to create a natural experiment. Given the topic which, although it describes the time period in which 2G-4G mobile phones were used, will be relevant for ongoing discussions on the possible health effects from 5G, and will therefore possibly be scrutinized more closely by the public health and environmental epidemiological research communities than by the field of economic policy. It is surprising that the authors describe an overall average effect in 88 countries that suggests that mobile phone subscription rates are associated with increased brain cancer deaths 15-19 later based on data that spans 1990 to 2015, given that comparable analyses of timeseries from individual countries, or groups of countries (some of which included in (1)) all concluded there was no evidence of mobile phone use being an important putative factor for brain cancer (for example in Northern Europe (2), the United Kingdom (3),  the USA (4), and in a set of 11 countries (5), but a detailed overview and discussion of studies up to 2011 can be found in (6). As well as more recently in for example in New Zealand (7) and Australia (8,9)). Surprisingly, these references are not included in the current study. It is possible that the use of a DiD statistical approach could have uncovered associations not previously observed using other statistical methods, but it seems more likely that there is some methodological artefact in the combination of timeseries data from many different countries to obtain an average effect rather than a true effect of mobile phone use that could explain the findings. For example, given the relatively limited length of the timeseries of up to 26 data points (years), the large number of independent variables in the model, although admirable in an attempt to account for possible confounding factors, suggests the models may have been overspecified; a notion strengthened by explained variances (adjR2) in the range of 0.98-0.99. There are various possible alternative explanations worth considering. It is for example known that there are marked differences between the quality of cancer registry information in different countries (10,11) which broadly relate to development and GDP and because these will improve over time, especially in the developing world, these could partly explain observed increases in reported incidences. Moreover, this will correlate with the start and penetration rate of mobile phones in society and with trends in improvement of diagnostic techniques, especially at older age (7). Specifically for glioblastoma multiforme, a rare and aggressive brain cancer subtype hypothesized to be a specific target for radiofrequency radiation and for which the reported incidence has been increasing rapidly, also mobile phones has been shown to be an unlikely explanation (12). It has also been postulated that the time period covered by these analyses is not sufficiently long to detect any effects (13), which may explain the absence of effects related to mobile phone observed in timeseries studies to date if there was one, but would contradict the observed effects in the current study. The above discussion of the findings of the study by Mialon and Nesson remain speculative in nature only. Given the relevance of this study to the ongoing discussion on whether radiofrequency radiation may be carcinogenic to humans and the fact that the study was based on publicly available data, it would commendable if the authors would be willing to make their dataset (and possibly analytic script) available to the scientific community (for example by uploading it to an online repository) for further exploration. Regardless, given that their findings contradict most of the available literature and that there are clear limitations to the current study (as there are to the other studies based on timeseries data), from an epidemiological perspective, perhaps an epistemological more modest conclusions would have been more appropriate. REFERENCES  1. Mialon HM, Nesson ET. THE ASSOCIATION BETWEEN MOBILE PHONES AND THE RISK OF BRAIN CANCER  MORTALITY: A 25‐YEAR CROSS‐COUNTRY ANALYSIS. Contemp Econ Policy [Internet]. 2019 Dec 4;coep.12456. Available from: 2. Deltour I, Johansen C, Auvinen A, Feychting M, Klaeboe L, Schüz J. Time trends in brain tumor incidence rates in Denmark,  Finland, Norway, and Sweden, 1974-2003. J Natl Cancer Inst. 2009; 3. De Vocht F, Burstyn I, Cherrie JW. Time trends (1998-2007) in brain cancer incidence rates in relation to mobile phone use in  England. Bioelectromagnetics. 2011;32(5).   4. Inskip PD, Hoover RN, Devesa SS. Brain cancer incidence trends in relation to cellular telephone use in the United States.  Neuro Oncol. 2010; 5. Saika K, Katanoda K. Comparison of time trends in brain and central nervous system cancer mortality (1990-2006) between  countries based on the WHO mortality database. Japanese Journal of Clinical Oncology. 2011.   6. IARC. Non-Ionizing radiation, Part II: Radiofrequency electromagnetic fields (IARC monographs on the evaluation of  carcinogenic risk to humans; v. 102) [Internet]. Lyon, France; 2013. Available from:  Series/Iarc-Monographs-On-The-Identification-Of-Carcinogenic-Hazards-To-Humans/Non-ionizing-Radiation-Part-2-Radiofrequency-  Electromagnetic-Fields-2013  7. J-H Kim S, Ioannides SJ, Elwood JM. Trends in incidence of primary brain cancer in New Zealand, 1995 to 2010. Aust N Z J  Public Health. 2015; 8. Chapman S, Azizi L, Luo Q, Sitas F. Has the incidence of brain cancer risen in Australia since the introduction of mobile  phones 29 years ago? Cancer Epidemiol. 2016;   9. Karipidis K, Elwood M, Benke G, Sanagou M, Tjong L, Croft RJ. Mobile phone use and incidence of brain tumour histological types, grading or anatomical location: A populationbased ecological study. BMJ Open. 2018; 10. Parkin DM, Bray F. Evaluation of data quality in the cancer registry: Principles and methods Part II. Completeness. Eur J  Cancer. 2009; 11. Bray F, Parkin DM. Evaluation of data quality in the cancer registry: Principles and methods. Part I: Comparability, validity  and timeliness. Eur J Cancer. 2009; 12. de Vocht F. Analyses of temporal and spatial patterns of glioblastoma multiforme and other brain cancer subtypes in relation to  mobile phones using synthetic counterfactuals. Environ Res. 2019;168:329–35.   13. Kundi M. Comments on de Vocht et al. “Time trends (1998-2007) in brain cancer incidence rates in relation to mobile phone  use in England.” Bioelectromagnetics. 2011.
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