Inferring the 1985-2014 impact of mobile phone use on selected brain cancer subtypes using Bayesian structural time series and synthetic controls

AbstractBackgroundMobile phone use has been increasing rapidly in the past decades and, in parallel, so has the annual incidence of certain types of brain cancers. However, it remains unclear whether this correlation is coincidental or whether use of mobile phones may cause the development, promotion or progression of specific cancers. The 1985–2014 incidence of selected brain cancer subtypes in England were analyzed and compared to counterfactual ‘synthetic control’ timeseries.MethodsAnnual 1985–2014 incidence of malignant glioma, glioblastoma multiforme, and malignant neoplasms of the temporal and parietal lobes in England were modelled based on population-level covariates using Bayesian structural time series models assuming 5,10 and 15year minimal latency periods. Post-latency counterfactual ‘synthetic England’ timeseries were nowcast based on covariate trends. The impact of mobile phone use was inferred from differences between measured and modelled time series.ResultsThere is no evidence of an increase in malignant glioma, glioblastoma multiforme, or malignant neoplasms of the parietal lobe not predicted in the ‘synthetic England’ time series. Malignant neoplasms of the temporal lobe however, have increased faster than expected. A latency period of 10years reflected the earliest latency period when this was measurable and related to mobile phone penetration rates, and indicated an additional increase of 35% (95% Credible Interval 9%:59%) during 2005–2014; corresponding to an additional 188 (95%CI 48–324) cases annually.ConclusionsA causal factor, of which mobile phone use (and possibly other wireless equipment) is in agreement with the hypothesized temporal association, is related to an increased risk of developing malignant neoplasms in the temporal lobe

Systematic Review of the Exposure Assessment and Epidemiology of High-Frequency Voltage Transients

Conclusions of epidemiological studies describing adverse health effects as a result of exposure to electromagnetic fields are not unanimous and often contradictory. It has been proposed that an explanation could be that high frequency voltage transients (dirty electricity [DE]) which are superimposed on 50/60Hz fields, but are generally not measured, is the real causal agent. DE has been linked to many different health and wellbeing effects, and on the basis of this an industry selling measurement and filtering equipment is growing. We reviewed the available peer-reviewed evidence for DE as a causal agent for adverse human health effects.A literature search was performed in the Cochrane Library, PubMed, Web of Science, Google Scholar and additional publications were obtained from reference lists and from the grey literature. This search resulted in 25 publications; 16 included primary epidemiological and/or exposure data. All studies were reviewed by both authors independently, and including a re-review of studies included in a review of data available up to July 31 2009 by one of the authors. DE has been measured differently in different studies and comparison data are not available. There is no evidence for 50 Graham/Stetzer (G/S) units as a safety threshold being anything more than arbitrary. The epidemiological evidence on human health effects of DE is primarily based on, often re-used, case descriptions. Quantitative evidence relies on self-reporting in non-blinded interventions, ecological associations, and one cross-sectional cohort study of cancer risk which does not point to DE as the causal agent. The available evidence for DE as an exposure affecting human health at present does not stand up to scientific scrutiny

Перспективы развития рынка еврокапитала в современных условиях

Clinical and research staff who work around magnetic resonance imaging (MRI) scanners are exposed to the static magnetic stray fields of these scanners. Although the past decade has seen strong developments in the assessment of occupational exposure to electromagnetic fields from MRI scanners, there is insufficient insight into the exposure variability that characterizes routine MRI work practice. However, this is an essential component of risk assessment and epidemiological studies. This paper describes the results of a measurement survey of shift-based personal exposure to static magnetic fields (SMF) (B) and motion-induced time-varying magnetic fields (dB/dt) among workers at 15 MRI facilities in the Netherlands. With the use of portable magnetic field dosimeters, >400 full-shift and partial shift exposure measurements were collected among various jobs involved in clinical and research MRI. Various full-shift exposure metrics for B and motion-induced dB/dt exposure were calculated from the measurements, including instantaneous peak exposure and time-weighted average (TWA) exposures. We found strong correlations between levels of static (B) and time-varying (dB/dt) exposure (r = 0.88–0.92) and between different metrics (i.e. peak exposure, TWA exposure) to express full-shift exposure (r = 0.69–0.78). On average, participants were exposed to MRI-related SMFs during only 3.7% of their work shift. Average and peak B and dB/dt exposure levels during the work inside the MRI scanner room were highest among technical staff, research staff, and radiographers. Average and peak B exposure levels were lowest among cleaners, while dB/dt levels were lowest among anaesthesiology staff. Although modest exposure variability between workplaces and occupations was observed, variation between individuals of the same occupation was substantial, especially among research staff. This relatively large variability between workers with the same job suggests that exposure classification based solely on job title may not be an optimal grouping strategy for epidemiological purposes