Letter to the Editor – Brain Tumours: Rise in Glioblastoma Multiforme Incidence

Authors’ Comment on “Brain Tumours: Rise in Glioblastoma Multiforme Incidence in England 1995–2015 Suggests an Adverse Environmental or Lifestyle Factor”, Alasdair Philips, Denis L. Henshaw, Graham Lamburn, and Michael J. O’Carroll

Journal of Environmental and Public Health

Letter to the Editor (3 pages), Article ID 2170208, Volume 2018 (2018)

Published 25 June 2018

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Journal of Environmental and Public Health

Volume 2018, Article ID 7910754, 10 pages

https://doi.org/10.1155/2018/7910754

Research Article

Brain Tumours: Rise in Glioblastoma Multiforme Incidence in England 1995–2015 Suggests an Adverse Environmental or Lifestyle Factor

Alasdair Philips

,1,2 Denis L. Henshaw,1,3 Graham Lamburn,2 and Michael J. O’Carroll4

1Children with Cancer UK, 51 Great Ormond Street, London, WC1N 3JQ, UK

2Powerwatch, Cambridgeshire, UK

3Professor Emeritus, University of Bristol, UK

4Professor Emeritus, Vice–Chancellor’s Office, University of Sunderland, UK

Correspondence should be addressed to Alasdair Philips; alasdair.philips@childrenwithcancer.org.uk

Received 19 December 2017; Revised 14 March 2018; Accepted 21 March 2018; Published 24 June 2018

Academic Editor: Evelyn O. Talbott

Copyright © 2018 Alasdair Philips et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Objective. To investigate detailed trends in malignant brain tumour incidence over a recent time period. Methods. UK Office of National Statistics (ONS) data covering 81,135 ICD10 C71 brain tumours diagnosed in England (1995–2015) were used to calculate incidence rates (ASR) per 100k person–years, age–standardised to the European Standard Population (ESP–2013). Results. We report a sustained and highly statistically significant ASR rise in glioblastoma multiforme (GBM) across all ages. The ASR for GBM more than doubled from 2.4 to 5.0, with annual case numbers rising from 983 to 2531. Overall, this rise is mostly hidden in the overall data by a reduced incidence of lower-grade tumours. Conclusions. The rise is of importance for clinical resources and brain tumour aetiology. The rise cannot be fully accounted for by promotion of lower–grade tumours, random chance or improvement in diagnostic techniques as it affects specific areas of the brain and only one type of brain tumour. Despite the large variation in case numbers by age, the percentage rise is similar across the age groups, which suggests widespread environmental or lifestyle factors may be responsible. This article reports incidence data trends and does not provide additional evidence for the role of any particular risk factor.

1. Introduction

The causes of brain tumours in adults remain largely unknown [1]. In 2011, the World Health Organisation (WHO) prioritised the monitoring of detailed brain tumour incidence trends through population–based cancer registries [2]. This article reports recent changes in malignant brain tumour incidence in England that include age, sex, morphology and tumour location.

2. Materials and Methods

2.1. Data

The International Classification of Diseases for Oncology (ICD–O) is a dual classification, with coding systems for both topography and morphology [3]. The relevant topology codes are listed in Table 1, along with the number of tumours diagnosed in 1995 and 2015.

Table 1: ONS WHO ICD10 brain tumour data for England.

There are 102 different ICD–O–3.1 morphology codes used in the data set, though many have few cases. The morphology code describes the cell type and its biological activity / tumour behaviour.

WHO last updated their classifications in 2016, but their changes have minimal impact on our analysis of the data [4, 5]. Malignant brain neoplasms without histology are recorded as ICD–10 D43 (D43.0 & D43.2 supratentorial).

We used anonymised individual–level national cancer registration case data from the UK Office of National Statistics (ONS) for all 81,135 ICD10–C71 category primary malignant brain tumours diagnosed in England for the years from 1995 to 2015, plus 8,008 ICD10–D43 supratentorial malignant tumours without histology/morphology data from 1998–2015. The initial data is supplied by the National Cancer Registration Service (NCRS). The ONS then apply further validation checks and the UK Department of Health use the ONS data to inform policy making. The ONS state their cancer data are generally within 2% of the correct values [6]. Until about 2005, some cases in the oldest age–groups will not have been recorded in the cancer registries. Since 2005 this error is likely to be small.

Glioblastoma Multiforme (GBM), the most common and most malignant primary tumour of the brain, is associated with one of the worst five–year survival rates among all human cancers, with an average survival from diagnosis of only about 1 year. This ensures that few cases will be unrecorded in the ONS database and we show that their number of GBM tumours is similar to NHS hospital inpatient numbers. The data include the year of diagnosis, age at diagnosis, sex of patient, primary site and morphology code. National population estimates of age and gender by calendar year were also obtained from ONS data [7] and age–specific incidence rates per 100,000 person–years and for a wide variety of tumour types were calculated in 5-year age group bins for males and females separately.

Some published incidence analyses have used different criteria as to which glioma and astrocytoma should be considered malignant. WHO considers Grades I to IV as biologically malignant even if they have not been graded histologically malignant. We have taken the WHO/IARC morphology behaviour codes /3, /6 and /9 as being histologically malignant which means that Grade I and II tumours are classed as low–grade malignancies.

We are not aware of any specific bias in the ONS data. There is a slight data–lag in cancer registry data, which are regularly checked and updated if necessary, but are generally stable after 3 to 5 years. Our ONS data extract is dated July 2017.

Brodbelt et al. (2015) [8] reported an analysis of treatment and survival for 10,743 GBM cases in England over the period 2007–2011, which had an overall median survival of only 6.1 months, rising to 14.9 months with maximal treatment. Brodbelt et.al.’s GBM case total from English hospital data is only 0.5% higher that our ONS GBM total of 10,687 cases for the same time period; this suggests that a very complete UK cancer diagnosis and registration system is now in place. In contrast, Ostrom et al. (2015) [9] reporting on USA SEER brain tumour data provide a scatter–plot that shows a median complete registration and histological confirmation level of only about 65%, with the best examples returning less than 75% full completion in 2012.

2.2. Confounding

We had a large number of categories and sub–categories in the data. It was necessary to combine some of these to increase the resolving power. We ran analyses separately for each site (C71.0 to C71.9), for each main type of tumour, and for tumour grade (I to IV). It was immediately obvious that the most significant change was in the incidence of GBM in frontal and temporal lobes. The obvious potential confounders would be the C71.8 (overlapping) and C71.9 (unspecified) categories due to better imaging techniques and we discuss this later.

2.3. Standardisation

Incidence rates rise dramatically with age and standardisation is necessary as population age profiles are changing with time. We calculated age–standardised incidence rates (ASR) per 100k person–years to the current recommended European Standard Population (ESP–2013), as it best represents the reality of the case burden on society [10]. Adjusting European cancer incidence to the World Standard Population is not helpful as the age-spectra are so different.

Table 2 lists the morphology codes with the highest case numbers, totalling 80354 tumours. Included in our analyses are an additional 781 cases in 78 other categories,

each with fewer than 100 cases over the 21 years. A full listing of all the cases in the data set is provided in the Supplementary File [S1].

Table 2: ICD-O-3 morphology codes with more than 100 cases between 1995-2015 inclusive. (A full listing of all the morphology codes and cases is present in the Supplementary file).

We needed to group data to improve resolution and reduce random data noise. We examined infant and child neoplasms separately, but did not find any statistically significant time–trends. Three age-groups seemed reasonable. We chose a child, teenage and young-adult group (0-29), a main middle-age group (30-54) and an older age group (over 55 years of age). These reasonably split the population into three roughly equal (20, 18 and 16 million) groups of people. The case totals in the three groups were about 9.5k, 19.5k and 52k respectively. We tested moving the cut-point boundaries by 5 years in both directions and it made little difference to the overall results.

2.4. Analysis

The cases were analysed by morphology, topology, sex, age, age–specific and age–standardised incidence. The Annual Average Percentage Change (AAPC) and corresponding 95% CI and p–values were calculated using Stata SE12.1 (StataCorp). A linear model on the log of the age–standardised rates, which tests for a constant rate of change (), best fitted the data. See Supplementary File sections S2 and S3.

2.5. Background

In a major 2013 review article, Hiroko Ohgaki and Paul Kleihues [11] wrote “Glioblastoma is the most frequent and malignant brain tumor. The vast majority of glioblastomas (~90%) develop rapidly de novo in elderly patients, without clinical or histologic evidence of a less malignant precursor lesion (primary glioblastomas). Secondary glioblastomas progress from low-grade diffuse astrocytoma or anaplastic astrocytoma. They manifest in younger patients, have a lesser degree of necrosis, are preferentially located in the frontal lobe, and carry a significantly better prognosis.”

Overall primary malignant brain tumour ASRs are only rising slowly and are often considered fairly static. Figure 1 shows the age–standardised trends from 1971 to 2015. From the 1970s to about 2000 there was a fairly steady rise in recorded overall incidence, however since then the rise has slowed, though clinicians have been reporting a rise in high-grade, aggressive tumours.

Figure 1: Age–standardised overall trends from 1971 to 2015 using data in ONS MB1 series, including a smaller number of supratentorial neoplasms without histology or morphology data coded D43.0 & D43.2. The data table for this figure is in the SI file as [S4].

Overall adult survival for all malignant brain tumours after diagnosis during 2006–2010 was about 35% for one year and 15% for five years, falling to about 3% for aggressive grades–III and IV tumours. ONS data show age-standardised death rates from malignant brain tumours (C71) have increased by 7% between 2001 and 2015, showing that improvements in treatment alone are inadequate and that there is a need to find ways of preventing brain cancer [12].

3. Results

Comparing new case numbers in 2015 with 1995 shows an extra 1548 aggressive GBM tumour cases annually. Figure 2 and Table 3 show that up to about 2004 the

overall rise in GBM incidence (Annual Average Percentage Change (AAPC) 5.2%, 95% CI 3.7–6.6, p < 0·00003) could be mostly compensated for by the fall in incidence of all lower grade astrocytoma and “glioma, malignant, NOS, ICD10–93803”. This leaves a fairly steady rise in the GBM ASR from 2004 to 2015 (AAPC 2.2%, 95% CI 1.4–3.0, p < 0.0001).

Table 3: ICD10-C71 and (D43.0 + D43.2) cases and age-standardised (ESP-2013) incidence rates.

Figure 2: Age–standardised incidence rates for all C71 glioma cases diagnosed between 1995 and 2015 analysed by type and year (Data in Table 3). Grouping details: (1) = 94403–94433 (2) = 93843, 94003–94303 (3) = 93803 (4) = 93813, 93823, 93903–93943, 94503–94733.

Ohgaki and Kleihues [11] reported that most secondary GBMs are found in younger middle-age people and most primary GBMs are in over 60s. We tested our (30–54) and (>54) age group data, splitting the total GBM into de novo and promoted tumours. We estimated the maximum possible number of promoted tumours using the change in the grades II and III diffuse and anaplastic astrocytomas. The results are shown in Figures 3(a) and 3(b). These are discussed later.

Figure 3: Age–standardised rates for two age groups. The possible split between de novo and secondary promoted GBMs is based on incidence change of Grades II and III diffuse and anaplastic astrocytoma.

We found a large decrease of ASR over time for Grade–II diffuse astrocytoma, a slight rise in ASR for WHO Grade–III anaplastic astrocytoma (94013; 2832 cases). There was little change in rates of anaplastic oligodendroglioma (94513; 1339 cases), anaplastic ependymoma (93923; 313 cases) Grade–II oligodendroglioma (94503; 2671cases), embryonal, or ependymal tumours.

Figure 4 shows the relative increase in age-specific GBM incidence between the averaged periods (1995–1999) and (2011–2015) for 5–year age–groups. This 1.5-fold change is remarkably similar across the age–groups, suggesting a universal factor.

Figure 4: Relative change in GBM age–specific incidence rates (ASpR) averaged over two five-year periods 1995-1999 and 2011-2015 in 5-year age bands and gender.

Figure 5 shows ASR GBM rates for frontal lobe, temporal lobe, unspecified & overlapping (C71.8 & C71.9) and ‘all other brain regions’. Most of the rise is in the frontal and temporal lobes, and most of the cases are in people over 55 years of age, with a highly statistically significant overall AAPC of 7.6% (see Table 4). There was an extra rise in frontal and temporal GBM incidence between 2006 and 2008, which coincided with a slight reduction in the GBM ASR in overlapping and unspecified regions and may be due to improved imaging.

Table 4: Age standardised incidence rates to ESP-2013 (/100k people).

Figure 5: Frontal and temporal lobe GBM age–standardised incidence rates by tumour site and year (data table in the SI as [S6]).

4. Discussion

Using sufficiently high–quality data, we present a clearer picture of the changing pattern in incidence of brain tumour types than any previously published. We report a sustained and highly statistically significant ASR rise in GBM across all ages and throughout the 21 years (1995–2015), which is of importance both for clinical resources and brain tumour aetiology.

Dobes et al. (2011) [13] reported a significant increase in malignant tumour incidence from 2000 to 2008 in the ≥65–year age group. In a second article they noted an increasing incidence of GBM (APC, 3.0; 95% CI, 0.5–5.6) in patients in the same age group, especially in temporal and frontal lobes [14]. De Vocht et al. (2011) [15] reported a rise in temporal lobe tumour incidence in ONS data, but dismissed its significance. In a 2016 paper he claimed no increase in GBM incidence, but later published a major correction to the paper that shows an increase [16].

Zada et al. (2012) [17] using USA SEER data for 1992–2006 reported a rising trend in frontal and temporal lobe tumours, the majority of which were GBM, with a decreased incidence of tumours across all other anatomical sub–sites. Ho et al. (2014) [18] reported a 2.2–fold increase in glioblastoma incidence in the Netherlands over the period 1989–2010 (APC 3.1, p<0.001).

There were no material classification changes over the analysis period that might explain our findings [19], though multidisciplinary team working was strengthened (2005 onwards) and better imaging has resulted in improved diagnosis along with a more complete registration of brain tumours in the elderly. We analysed our data in 5-year age group categories to look for evidence of improved diagnosis; the data do suggest diagnosis and registration have improved in people aged over 70. However, at earlier ages the incidence rate of ‘all’ glioma (and all C71) registrations have remained almost constant, whereas the rates for lower–grade tumours fell until about 2006 and have since remained fairly static as the rate for GBM has risen steadily.

Most GBM cases seem to originate without any known genetic predisposition. GBMs from promoted lower–grade gliomas usually have different molecular genetic markers from de novo GBMs [20]. The 2016 revision of the WHO classification of CNS tumours [3, 4] highlights the need for recording molecular genetic markers and divides glioblastomas into two main groups. The IDH–wildtype mostly corresponds to clinically defined primary or de novo glioblastoma and accounts for about 90% of cases. The remaining 10% are IDH–mutant cases, which usually arise in younger patients and mostly correspond to secondary or promoted lower–grade diffuse glioma [11, 21]. Figures 3(a) and 3(b) support the conclusion of Ohgaki and Kleihues [11] that promoted (secondary) tumours mainly occur in younger people and that de novo GBMs dominate in the over-54 age group. It is important that this pattern is monitored using modern genetic techniques.

GBM tumours are almost always fatal and are not likely to have been undiagnosed in the time-frame of our data. It is possible that some elderly cases were not fully classified, but then they should have been recorded as ICD10–D43. However, as D43 rates have remained very constant over this time period (see Figure 1), this is unlikely to have been a significant confounder.

4.1. Possible Causal Factors

We cite examples of some possible causal factors that have been discussed in the literature that could contribute changes in GBM incidence. In an important 2014 “state of science” review of glioma epidemiology, Ostrom et al. [22] list and discuss a number of potential factors that have been associated with glioma incidence, some of which we list below.

Ionising radiation, especially from X-rays used in CT scans, has the most supportive evidence as a causal factor. Due to the easy availability of CT imaging and relative

lack and higher cost of MRI imaging in UK NHS hospitals, CT scans are often used, especially for initial investigations. Their use over the period 1995-2013 is shown in the Supplementary File S6. Given the time-frame of the trend that we have identified, we suggest that CT imaging X-ray exposures should be further investigated for both the promotion and initiation of the rising incidence of GBM tumours that we have identified.

Preston et al. (2007) [23] concluded that radiation–associated cancer persists throughout life regardless of age at exposure and that glioma incidence shows a statistically significant dose response. Our oldest age group also experienced atmospheric atomic bomb testing fallout and some association with ingested and inhaled radionuclides should not be dismissed as a possible factor. England was in one of the highest exposed regions for atmospheric testing fallout as determined by the United Nations Scientific Committee on the Effects of Atomic Radiation, UNSCEAR 2000 Report [24]. Further information is given in Supplementary File S7. If only some of the population were susceptible and received a significant dose, any resulting extra cancers would show up in the ONS data.

The European Study of Cohorts for Air Pollution Effects by Andersen et al. (2017) [25] found suggestive evidence of an association between traffic-related air pollution and malignant brain tumours.

There is increasing evidence literature that many cancers including glioma have a metabolic driver due to mitochondrial dysfunction resulting in downstream genetic changes in the nucleus [26–28].

The International Agency for Research on Cancer (IARC) judged both power–frequency ELF (2002) [29] and radio–frequency RF (2011) [30] electromagnetic fields as Group 2B ‘possible human carcinogens’. Villeneuve et al. (2002) [31] concluded that occupational (ELF) magnetic field exposure increases the risk of GBM with an OR = 5.36 (95% CI: 1.2 – 24.8). Hardell and Carlberg (2015) [32] have reported an increase in high–grade glioma associated with mobile phone use. The multi-country Interphone study [33] collected data from 2000 to 2003 and included few people over 55 years of age and would have been unable to resolve any association involving older–aged people. Volkow et al. (2011) [34] found that, in healthy participants and compared with no exposure, a 50-minute cell phone exposure produced a statistically significant increase in brain glucose metabolism in the orbitofrontal cortex and temporal pole regions closest to the handset.

5. Conclusions

(1)We show a linear, large and highly statistically significant increase in primary GBM tumours over 21 years from 1995–2015, especially in frontal and temporal lobes of the brain. This has aetiological and resource implications.(2)Although most of the cases are in the group over 54 years of age, the age–standardised AAPC rise is strongly statistically significant in all our three main analysis age groups.(3)The rise in age–standardised incidence cannot be fully accounted for by improved diagnosis, as it affects specific areas of the brain and just one type of brain tumour that is generally fatal. We suggest that widespread environmental or lifestyle factors may be responsible, although these results do not provide additional evidence for the role of any particular risk factor.(4)Our results highlight an urgent need for funding more research into the initiation and promotion of GBM tumours. This should include the use of CT imaging for diagnosis and also modern lifestyle factors that may affect tumour metabolism.

Data Availability

The data were obtained from the UK Office for National Statistics (ONS), who are the legal owners of the data. Some data are publicly available in the ONS annual MB1 data series, which are freely downloadable from the ONS website, but this article uses the latest updated data, plus ICD–O–3 morphology codes, extracted under personal researcher contract from the ONS database in July 2017. ONS Data Guardian approval was required for the supply, control and use of the data. A nominal charge is made by the ONS for such data extraction. We are not permitted to supply the raw ONS extracted data to anyone else. Other researchers can obtain the latest data directly from the ONS in a similar manner. The authors provide some extra tables and figures in the Supplementary File downloadable from the journal website.

Conflicts of Interest

Alasdair Philips: Independent Engineer and Scientist. (a) Trustee of Children with Cancer UK (unpaid); (b) On a voluntary unpaid basis, has run Powerwatch for 25 years (a small UK NGO providing free information on possible health associations with EMF/RF exposure); (c) Technical Director and shareholder of EMFields Solutions Ltd., who design and sell EMF/RF measuring instruments and protective shielding items; (d) Shareholder of Sensory Perspective Ltd.; (e) Occasional voluntary advisor to the Radiation Research Trust (Registered Charity). Denis L. Henshaw: (a) Scientific Director of Children with Cancer UK (honorarium basis); (b) Shareholder of Track Analysis Systems Ltd., a company offering radon measurement services; (c) Voluntary scientific advisor for Electrosensitivity UK (Registered Charity). Michael J. O’Carroll: (a) Chairman of Rural England against Overhead Line Transmission group; (b) Occasional advisor to the Radiation Research Trust. Graham Lamburn: (a) Acts as voluntary unpaid ‘Technical Manager’ for Powerwatch.

Authors’ Contributions

Alasdair Philips and Graham Lamburn conceived the study and first–drafted most of the manuscript with significant input from Denis L. Henshaw and Michael J. O’Carroll. Graham Lamburn organised the data obtained from the UK ONS and wrote the database analysis scripts. All authors had full access to the results of all analyses and have provided strategic input over several years of following the ONS brain tumour data. All authors have approved the final manuscript. Alasdair Philips is the guarantor for the ONS data.

Funding

This research received no funding from any external agency or body. The ONS data extracts were paid for personally by Alasdair Philips. Administration costs were paid for personally by the authors.

Acknowledgments

We are very grateful to Professor Geoffrey Pilkington and Professor Annie Sasco for their invaluable comments on early drafts of this paper. We thank the ONS for providing the data and Michael Carlberg, MSc for advice regarding statistical analysis.

Supplementary Materials

S1. Table of data morphology coding and the case numbers used in the study. S2. GBM case numbers and age-specific incidence rate data used in the study. S3. Sample STATA data and DO script. S4. Data table for Figure 1. S5. Data table for

Figure 5. S6. CT and MRI use in the UK NHS. S7. Some notes on atomic bomb testing and other nuclear fallout in England. (Supplementary Materials)

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Letter to the Editor

 

Downloaded from bmjopen.bmj.com

ABSTRACT Objectives: We performed a re-analysis of the data from Navarro et al (2003) in which health symptoms related to microwave exposure from mobile phone base stations (BSs) were explored, including data obtained in a retrospective inquiry about fear of exposure from BSs. Design: Cross-sectional study.

Setting: La Ñora (Murcia), Spain. Participants: Participants with known illness in 2003 were subsequently disregarded: 88 participants instead of 101 (in 2003) were analysed. Since weather circumstances can influence exposure, we restricted data to measurements made under similar weather conditions.

Outcomes and methods: A statistical method indifferent to the assumption of normality was employed: namely, binary logistic regression for modelling a binary response (eg, suffering fatigue (1) or not (0)), and so exposure was introduced as a predictor variable. This analysis was carried out on a regular basis and bootstrapping (95% percentile method) was used to provide more accurate CIs.

Results: The symptoms most related to exposure were lack of appetite (OR=1.58, 95% CI 1.23 to 2.03); lack of concentration (OR=1.54, 95% CI 1.25 to 1.89); irritability (OR=1.51, 95% CI 1.23 to 1.85); and trouble sleeping (OR=1.49, 95% CI 1.20 to 1.84). Changes in –2 log likelihood showed similar results. Concerns about the BSs were strongly related with trouble sleeping (OR =3.12, 95% CI 1.10 to 8.86).

The exposure variable remained statistically significant in the multivariate analysis. The bootstrapped values were similar to asymptotic CIs. Conclusions: This study confirms our preliminary results. We observed that the incidence of most of the symptoms was related to exposure levels— independently of the demographic variables and some possible risk factors. Concerns about adverse effects from exposure, despite being strongly related with sleep disturbances, do not influence the direct association between exposure and sleep.

The health risk due to exposure to radiofrequency electromagnetic fields (RF EMFs) continues to be discussed today.

The study that led to this debate was initiated after verification that the US embassy in Moscow was being subjected to such radiation from 1953 to May 1975.

Recently, a review of that episode reopened the debate about the potential harmfulness of RF EMFs.

The increasing number of base stations (BSs) on masts and buildings has increased public awareness. This issue has prompted scientific research to establish to what extent low-intensity EMFs may affect the health of humans and other organisms.

Furthermore, the term electromagnetic hypersensitivity has been recently introduced in discussions attributing symptoms to exposure to EMFs.

A review of this topic  in 2010 found that 8 of the 10 studies evaluated through PubMed had reported increased prevalence of adverse neurobehavioral symptoms or cancer in populations living at distances <500 m from BSs.

None of the studies reported exposure above accepted international guidelines, suggesting that current guidelines may be inadequate in protecting health.

Thus, the need emerges to revaluate our pioneering work in this field in order to add new procedures and data.

Few articles have addressed the possible association between microwave sickness and microwave exposure from Global System for Mobile Communications (GSM) BSs since the publication of our first study.

Chronologically, Santini et al and Gadzicka et al reported differences in the distance dependent prevalence of symptoms such as headache, impaired concentration and Strengths and limitations of this study ▪ We used a robust statistical analysis with a highly homogeneous sample in a homogeneous environment. ▪ A participation bias cannot be ruled out.

The late query about concerns (as a possible confounder) may render the results less valid. ▪ We observed that the incidence of most of the symptoms was related to exposure levels. Gómez-Perretta C, Navarro EA, Segura J, et al. BMJ Open 2013;3:e003836. doi:10.1136/bmjopen-2013-003836 1 Open Access Research Downloaded from bmjopen.bmj.com on December 31, 2013 – Published by group.bmj.com irritability.

A later Austrian study showed a positive association between the measured electrical field (GSM 900/ 1800) in bedrooms and headaches, cold hands and feet and difficulties in concentration.

An Egyptian study showed a prevalence of neurological symptoms, such as headache, memory changes, dizziness, tremors, depressive symptoms and sleep disturbances among participants directly exposed to GSM signals from BSs. The symptoms reported by all the above cited authors belong to those attributed to the microwave syndrome.

However, one article using personal monitored data from GSM-UMTS frequency bands found no statistical association in adults. More recently, the same authors observed no association in children, contradictory results in children and adolescents, and concluded that the few observed significant associations were not causal but rather occurred by chance.

Blettner et al reported in phase 1 of their study more health problems closer to BSs, but in phase 2 they concluded that measured EMF emissions were not related to adverse health effects.

Other researchers focused their work on the possible existence of participants with sensitivity to GSM or UMTS signals according to psychological, cognitive or autonomic assessment. These researchers used short term exposure (only 30–50 min) under laboratory conditions and revealed a large disparity between participants.

Recently, a study measuring several biological stress markers found that RF EMF emitted by mobile phone BSs from 5.2 to 2126.8 μW/m2 increased cortisol and salivary α-amylase, while IgA concentration was not significantly modified. The Selbitz study in 2010 described a significant dose–response relationship in symptoms related with sleep, mood, joints, infections, skin condition, as well as neurological, cardiovascular, visual and auditory systems and the gastrointestinal tract.

The existence of short-term physiological effects of EMF on sleep quality was not evident in the work of Danker-Hopfe et al; however, it was stated that the presence of BSs per se (not the EMF) may have a negative impact on sleep quality.

A Polish study in 2012 did not show a correlation between electrical field strength and frequency of subjective symptoms; however, it showed a correlation between subjective symptoms and the distance to BSs.

A study carried out in Egypt revealed that exposure to EMF emitted either from mobile phones or BSs had significant effects on the pituitary–adrenal axis. More recently, work developed in Iran indicated that symptoms such as nausea, headache, dizziness, irritability, discomfort, nervousness, depression, sleep disturbance, memory loss and lowering of libido were statistically significant in people living near BSs (<300 m distances) compared with those living far from the BSs (>300 m).

In our cross-sectional analysis, of  symptoms showed statistically significant higher scores in the group with the maximum exposure level. The symptoms are included in the microwave syndrome.

It also reported statistically significant correlation coefficients between the measured electrical field and  of symptoms.

A review recently established several conditions for epidemiological studies to be eligible for introduction in general analysis: eligible studies must quantify exposure using objective measures (such as distance to the nearest BS, spot or personal exposure measurements in a specific frequency range); possible confounders must be considered and the selection of the study population must be clearly free of bias in terms of exposure and outcomes.

Accordingly, in this reanalysis of our previous study, possible confounders were included in addition to the specific RF EMF measurements made in 2001 (covering the specific range between 900 and 1800 MHz).

Therefore, we coanalysed the effects of other variables such as sociodemographic data and the use of electronic devices. Concern about being damaged by radiation from antennas was also analysed. The new statistical approach tested the possible influences of other variables, such as demographic data and the use of electronic devices.

Moreover, since some concerns have been raised about possible health consequences caused by the emitted microwaves, we analysed whether these symptoms might be related to fear of exposure.

As some participants refused to allow measurements in their homes, we analysed whether symptom status or subjective distance to the BS could be a bias of participation in the study.

Interestingly, this period was free of other sources of RF such as WIFI or UMTS or the massive use of mobile phones, enabling a specific study of GSM technology.

Finally, the suitability of the size of the sample was analysed.

METHODS Study design We chose a small urban area with mixed rural characteristics: low levels of environmental pollution (more agricultural than industrial); no major differences in socioeconomic characteristics throughout the region (excluding large cities); similar ethnicity (white Caucasian) and language (Spanish) and with mobile phone communication operative for at least 2 years.

La Ñora was chosen because it had the features of a small city, and was located near the capital (Murcia) in a rural environment without any particular health or environmental problems. Consequently, La Ñora was representative of small urban areas in eastern Spain with fewer than 20 000 inhabitants—such rural areas accounting for 19.8% of the population and 35.9% of the territory in Spain.

Two BS masts, each about 30 m height, were sited at different positions to provide GSM-900-1800 coverage.

The GSM 900 BS was positioned not before 1997 while the GSM 1800 BS was built in December 1999.

Data regarding the main demographic characteristics of the sample and their use of electronic devices was collected through a Spanish-language questionnaire.

All  Gómez-Perretta C, Navarro EA, Segura J, et al. BMJ Open 2013;3:e003836. doi:10.1136/bmjopen-2013-003836 Open Access Downloaded from bmjopen.bmj.com on December 31, 2013 – Published by group.bmj.com of the participants were of the same ethnic origin, shared similar family income levels and general standard of living, and were born in La Ñora or nearby.

All the residents in the study were living in the village before the erection of both BSs. All of the residents were at home for more than 8 h a day for at least 6 days a week and normally slept at home. The core of the questionnaire was a symptom checklist for estimating the frequency of  health-related symptoms attributed to microwave sickness. These symptoms were fatigue, irritability, headaches, nausea, loss of appetite, sleep disorders, depressive tendency, dizziness, concentration difficulties, memory loss, skin lesions, visual and hearing deficiencies, walking difficulties and cardiovascular problems.

The frequency was quantified as never suffer = 0, sometimes = 1, often = 2 and very often =3.

 

The percentage of residents who reported electrical transformers less than 10 m from their home was 21.6%, while 42% reported high-voltage power lines less than 100 m from home. Finally, 40% of residents reported a TV transmitter within a radius of around 4 km.

The questionnaire included a statement that its purpose was health research and that the data gathered would be confidential. Some 215 questionnaires were randomly distributed through 17 streets representing practically the entire village. The houses were selected using a street map of the village. In total, 150 questionnaires were collected with the remainder being uncollected because nobody was at home (31) or there was a refusal by the householder to complete the questionnaire (34).

During 2001, 101 RF EMF measurements in bedrooms were made. The other (49) residents who refused admittance for taking the measurements (16) were not at home for the scheduled measurement appointment (10) or had serious health problems (23). However, some changes are now being introduced in this reanalysis. Thirteen of the participants included in the original study have now been eliminated: 2 participants were eliminated (one regarding alcohol abuse and another regarding pregnancy) to increase the requirement on health criteria and 11 participants were eliminated to increase the homogeneity of the RF EMFs measurements because there was a change (it was raining) in the usual dry weather conditions when the respective broadband measurements were registered. The reanalysis of the dataset, which is the main focus of this paper, was finally performed with 88 participants (45 women and 43 men) instead of the 101 analysed in 2001. Concerns about microwave exposure Sixty-six of the 88 participants were reached by telephone in February 2012 and asked two questions: A. Were you worried about the masts (BSs) when they were erected? B. Did you believe their radiation (BSs) could damage your health? In all cases, those who were worried about the masts were concerned about health consequences. Twentyseven participants (40.9%) responded ‘no’ and 39 (59.1%) responded ‘yes’. Responses were analysed relative to age (analysis of variance (ANOVA) test), sex (λ statistic) and subjective distance to BS (Somers’ D statistic).

RESULTS
Demographic data and the percentage of users of personal computers and mobile phones were analysed. The
mean age was 42 and 17 years (SD±17. 61, interval 15–81). Women totalled 51.1% (mean age=45.08 years,
SD=17.98; interval=15–81) and 48.9% were men (mean age = 39.12 years, SD=16.88; interval=15–75). A total of
13.6% participants regularly used computers and 23.9%used mobile phones.
No differences related with age and use of mobile phones or computers were found between the sexes.
The univariate logistic regression indicated that age was inversely associated with irritability (OR=0.97, 95%
CI 0.95 to 0.99) and that the oldest had the greatest difficulties hearing (OR=1.03, 95% CI 1.01 to 1.06) and
walking (OR=1.04, 95% CI 1.01 to 1.07). However,gender clearly did not influence the outcome of any
dependent variable. Use of mobile phones was linked with lack of appetite and vertigo, while worry about the
radiation from BSs was associated with trouble sleeping(table 1). However, concern about radiation from BSs
was unrelated to age (ANOVA test), sex (λ statistic) or subjective distance to BS (Somers’ D statistic).
Most of the symptoms were related with GSM exposure especially fatigue, irritability, lack of appetite,
trouble sleeping, depression and lack of concentration.

Change in– log likelihood showed similar results(table 2). Figure 1 shows the distribution of EMF measurements
throughout the sample.ROC curves for each of the logistic regression models (GSM exposure vs each symptom) oscillated between0 .65 and 0.87 (table 3). Headaches (0.84), nausea (0.86), appetite (0.87) and vascular problems (0.85) showed the highest values, while memory (0.67), skin (0.67) and visual disturbances (0.65) showed the lowest
values.

The Hosmer and Lemeshow test indicated that most analyses showed no significant p values. The exceptions
were fatigue (0.003), depression (0.003) and vertigo (0.03). In the majority of the cases, the models
predicted better specificity than sensitivity. Only in the case of headaches and sleep disorder, did sensitivity
prevail over specificity (table 3—classification table). In the extreme case, skin and vascular problems showed
null or minimum sensitivity and 100% specificity.
Nagelkerke pseudo R2 showed acceptable coefficients with the exception of the symptoms related with vertigo
and skin problems (table 3).Threshold cut-off values of GSM for sleep, attention, irritability and memory are also shown (table 3). The remaining cut-off values were not considered since sensitivity or specificity was reported at below 0.50%.

2013_subjective_symptoms_related_to_gsm_radiation_from_mobile_phone_base_stations1

CONCLUSIONS
This new study partially confirms our preliminary results about microwave sickness resulting from exposure to emissions from GSM mobile phone BSs. Fatigue, irritability, lack of appetite, sleep troubles, depression and lack of concentration were especially related with GSM exposure.
These results were independent of the main sociodemographic variables, other EMF exposures and anxiety
about being irradiated. Nevertheless, we confirm that apprehension about modern technology could predict
some symptoms, especially those related with sleep problems.
Our results agree with those who claimed that by distorting perceptions of risk, disproportionate precaution might
paradoxically lead to illness that would not otherwise occur.

However, health changes related with GSM exposure seem to occur in a manner unrelated with those fears.
Finally, exposure was very low during the period and also very low in comparison with Spanish recommendations
and international guidelines. file:///E:/MEDICAL/2013_subjective_symptoms_related_to_gsm_radiation_from_mobile_phone_base_stations1.pdf

1 Introduction
Radiofrequency (RF) refers to the electromagnetic waves ranging between 10 MHz and 300 GHz. RF have been widely used as a signal carrier in telecommunications. Recent advances in mobile phone technology have resulted in the exponential use of mobile phone communication around the world. The increasing exposure of humans to RF fields has raised wide concerns for potential adverse effects of RF fields on human health (http://www.fcc.gov/oet/rfsafety, http://www.fda.gov/cdrh/phones/index.html, http://www.who.int/emf, http://www.iegmp.org.uk/, http://www.verum-foundation.de/).
While it is clear that high energy-electromagnetic waves, such as X-rays have strong biological effects through ionizing damage, it is uncertain whether the low energy, non-ionizing RF fields could have effects on biological systems. Several epidemiological studies suggest a link between long-term RF exposures and pathological consequences such as cancer [1–7]. Molecular studies also suggest the possible influence of RF fields on various aspects of biological activities [8–13]. Although these studies have provided many clues to the issue of RF biological effects, the results are inconclusive and even controversial.
In this study, we used genome-wide gene expression as the indicator to address the issue of biological effects of RF. We used a 2.45 GHz waveguide system to expose human HL-60 cells. We used the serial analysis of gene expression (SAGE) technique to analyze the RF effect on gene expression at the genome level [14]. Although gene expression has been used as an indicator in previous RF studies, those studies focused only on a handful number of genes pre-selected with defined functions. We aim to provide genome-wide coverage of the expressed genes regardless their functional categories in the RF treated cells to address if RF has biological effects [15,16]. We consider it particularly important to use this approach for the subject that there is limited biological information available. Our study shows that under the conditions used in our experimental system, the 2.45 GHz RF fields caused the expression changes of a number of genes.
2 Materials and methods
2.1 Cell culture
Human HL-60 cell line was purchased from ATCC. Cells were cultured in the RPMI 1640 medium + 10% fetal bovine serum (FBS) in an incubator at 37 °C with 5% CO2. Cells used for experiments were at the exponential growth phase. Prior to RF exposure, cells were spanned down and re-suspended in 10 ml of fresh medium at the density of 106/ml. The cells were then transferred to a 25 ml culture flask for RF exposure.
2.2 RF exposure system
The RF exposure system used for experiments was described in detail (Gerber et al. manuscript in preparation). Briefly, the RF source was a pulsed magnetron (Cober Muegge). It was pulsed at duration of 155 μs and a duty cycle of 7.5%, producing a peak power of 3 W into the waveguide. The measured average power was 225 mW, of which 100 mW was absorbed by the 10 ml cell suspension to provide the average SAR value of 10 W/kg. Using the measured 2.61 S/m conductivity of the medium at 2.45 GHz with the 133 W/kg SAR during the pulse, the calculated electric field is 320 V/m. A control waveguide, identical to the experimental waveguide was used for a sham exposure. Restricted by the cost of SAGE experiment, only the 2-h sham exposed cells were used as the control for the 2 and 6 h RF exposed cells. A flask containing a 10 ml HL-60 cell suspension at 106/ml was placed inside a WR340 brass waveguide having inside dimensions of 86.36 × 43.18 mm. The cells were allowed to settle down to the bottom of the flask to form a monolayer before exposure. The bottom of the flask is ground flat and coated with mineral oil to obtain good thermal conduction between the cell monolayer and brass waveguide. The bottom of the waveguide has an exterior plastic water channel glued to it such that the turbulent flowing water is in direct contact with the brass surface. A 5% air–CO2 mixture was introduced into the waveguide through a hole in its top surface. The brass surface was maintained at 37 °C through the use of a temperature-controlled water circulator. Two temperature probes (Luxtron) were inserted into the bottom surface of the flask to monitor the temperature. The temperature was maintained at 37.2 ± 0.2 °C during the exposure period.
2.3 SAGE process
The SAGE process followed the standard procedures [14,17]. Briefly, it includes the following steps: mRNA isolation from the cells, cDNA synthesis, NlaIII digestion of cDNAs, 3′cDNA collection, tag releasing from 3′ cDNA, ditags formation, ditag concatemerization, cloning, and DNA sequencing. SAGE tag sequences were extracted from the raw sequences using SAGE300 software. The SAGE data is deposited in NCBI with accession number GSE3025 (www.ncbi.nlm.nih.gov/projects/geo).
2.4 SAGE data analysis
To determine the gene origin of SAGE tags, the experimental SAGE tags were matched to the SAGEmap database (www.ncbi.nlm.nih/SAGEmap). A SAGE tag is assigned to a gene if it has a match in the reference database; and a SAGE tags is defined as a novel tag if it has no match in the SAGEmap database. To identify a specific gene for the SAGE tags shared by multiple genes in SAGEmap database, these tags were matched to a tissue-specific SAGE annotation database under the cell type “HL-60” (www.basic.northwestern.edu/SAGE/). By using the microarray expression data from the specific tissue type to annotate the SAGE tags collected from the same tissue type, this database provides high accuracy of gene prediction for SAGE tags shared by multiple genes (Ge et al., manuscript in preparation). To identify the differences in SAGE tags between the control and exposed cells, the method of Audic and Claverie ([18]; http://telethon.bio.unipd.it/bioinfo/IDEG6_form/), a statistical method designed for SAGE analysis, was used for the comparison under P < 0.05 as the cut-off. Greater than 4-fold differences between samples was set as the second cut-off threshold to provide high confidence for the identification of alternatively expressed genes between different samples. To visualize the changes of gene expression, the “Cluster” and “Treeview” programs were used to generate the average linkage hierarchical clustering using Pearson’s correlation coefficient as a distance metrics [19]. The Gene Ontology “biological process” terms were used to identify the functional categories of RF-response genes at P < 0.05 ([20]; http://www.geneontology.org

Full Article here 2.45 GHz radiofrequency fields alter gene expression in cultured human cells

file:///E:/MEDICAL/2.45ghz_rf_fields_alter_gene_expression_2005.pdf

Abstract

With recent advances in millimeter-wave technology, including the availability of high-power sources, in this band, it has become necessary to understand the biological implications of this energy for human beings. This paper gives the millimeter-wave absorption efficiency for the human body with and without clothing. Ninety to ninety-five percent of the incident energy may be absorbed in the skin with dry clothing, with or without an intervening air gap, acting as an impedance transformer. On account of the submillimeter depths of penetration in the skin, superficial SAR’s as high as 65-357 W/Kg have been calculated for power density of incident radiation corresponding to the ANSI guideline of 5 mW/cm/sup 2/. Because most of the millimeter-wave absorption is in the region of the cutaneous thermal receptors (0.1-1.0 mm), the sensations of absorbed energy are likely to be similar to those of IR. For the latter, threshold of heat perception is near 0.67 mW/cm/sup 2/, with power densities on the order of 8.7 mW/cm/sup 2/ likely to cause sensations of ”very warm to hot” with a latency of 1.0 +- 0.6 s. Calculations are made for thresholds of hearing of pulsed millimeter waves. Pulsed energy densities of 143/579 ..mu..J/cm/sup 2/ are obtained for the frequency band 30-300 GHz. These are 8-28 times larger than the threshold for microwaves below 3 GHz. The paper also points to the need for evaluation of ocular effects of millimeter-wave irradiation because of high SAR’s in the cornea.

Authors:
Gandhi, O.P.Riazi, A.
Publication Date:
Research Org.:
Department of Electrical Engineering, University of Utah, Salt Lake City, UT 84112
OSTI Identifier:
5631509
Resource Type:
Journal Article
Resource Relation:
Journal Name: IEEE Trans. Microwave Theory Tech.; (United States); Journal Volume: 34:2
Country of Publication:
United States
Language:
English
Subject:
63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTANT EFFECTS ON LIVING ORGS. AND BIOL. MAT.HUMAN POPULATIONSHEALTH HAZARDSMICROWAVE RADIATIONSKIN ABSORPTIONCALCULATION METHODSCLOTHINGCORNEAELECTROMAGNETIC RADIATIONENERGY DENSITYGHZ RANGE 01-100INFRARED RADIATIONPOWER DENSITYTHRESHOLD DOSEABSORPTIONBODYBODY AREASDOSESEYESFACEFREQUENCY RANGEGHZ RANGEHAZARDSHEADORGANSPOPULATIONSRADIATION DOSESRADIATIONSSENSE ORGANSUPTAKE560400* – Other Environmental Pollutant Effects

Citation Formats

Gandhi, O.P., and Riazi, A.. Absorption of millimeter waves by human beings and its biological implications. United States: N. p., 1986. Web. doi:10.1109/TMTT.1986.1133316.

Arousing from their very deep slumber, MSM is finally covering the environmental impacts of wireless and what 5g will mean to wildlife.

“Technology is quite literally destroying nature, with a new report further confirming that electromagnetic radiation from power lines and cell towers can disorientate birds and insects and destroy plant health. The paper warns that as nations switch to 5G this threat could increase.”

“This is not a new finding, as studies dating back for years have come to the same conclusion. In fact, one study from 2010 even suggested that this electromagnetic radiation may be playing a role in the decline of certain animal and insect populations. The radio waves can disrupt the magnetic ‘compass’ that many migrating birds and insects use. The creatures may become disorientated, AFP reported.”

http://www.newsweek.com/migratory-birds-bee-navigation-5g-technology-electromagnetic-radiation-934830

 

Aftermath of the peer-review of the NTP study: Do not hold your breath…

…updated on April 3rd, 2018…

It happened again – a big surprise. Scientists decided that NTP study shows effects in rats.

When group of experts is assembled and the experts’ careers do not depend, in either direct or indirect way, on the telecoms, then the real scientific evaluation takes place. Not just a rubber-stamping of an opinion that is convenient for the telecoms, as it is done by the ICNIRP, WHO EMF Project and ICES. Because in ICNIRP and WHO EMF Project and ICES careers of the scientists are industry-dependent, even when the scientists are not directly employed by the industry. However, at ICES, the majority of the scientists is either employed by the industry or are consultants for the industry.

This real scientific debate happened just recently with the peer-review of the NTP study completed at the US NIEHS. The same happened in 2011 at IARC with classification of carcinogenic potential of the cell phone radiation.

In both cases (NIEHS 2018 & IARC 2011) the industry-independent scientists were able, in both cases, through thorough scientific debate, convince the industry-dependent scientists that science is more important and come to conclusions based on scientific evidence.No matter whether telecoms liked it or not (most likely the did not).

Result of the NIEHS 2018 evaluation of the NTP study were elegantly compiled by Joel Moskowitz (see updated table and explanatory material here).

However, please do not hold your breath. Scientists come to certain conclusions and… bureaucrats do nothing, because industry lobby is too powerful. Where science and big money collide the winner is easily predictable…

As I recently tweeted… US FDA will do nothing in response to the NTP study:

The same happened after IARC classified cell phone radiation as a possible human carcinogen = nothing happened… no action whatsoever to tighten safety limits…

https://betweenrockandhardplace.wordpress.com/2018/03/30/aftermath-of-the-peer-review-of-the-ntp-study-do-not-hold-your-breath/

Many declarations of interests and claims to be free from ties to industry in the past have later been proven to be false.

One such recent example is prof. Anders Ahlbom who never reported as a conflict of interest that his brother was a long term lobbyist for the major Swedish Telecom operator Telia in Brussels. while Anders Ahlbom was an “independent expert” for years to EU, ICNIRP, WHO and dominated all Swedish expert opinions on the issue. He also omitted his involvement in the lobbying firm of his brother. http://wwwc.aftonbladet.se/nyheter/9905/21/eu.html
Other example from other issues is professor Ragnar Rylander who secretly had a non-declared contract with Philip Morris for 30 year – until it was discovered.

We who work seriously with this issue cannot see any other logic than that your career as expert must be dependent of industry and that your “positions” all along have been to the benefit of industry when doing a “who-benefits” analysis of your statements that ignores or downplays the massive amount of data and research results that clearly and repeatedly show negative health effects..Also when analyzing the industry positions we can clearly see that they appreciate and make use of your “positions”.

You never declare that your “position” is in fact a minority position::over 200 other scientists do not agree, Neither do we who are checking the facts.

That your career is industry dependent is particularly striking in view of the following facts:
1. your clear lack of own research merits from the issue of health effects from EMF
2. your position as influential expert on ICNIRP, EU, WHO, Sweden, Holland and more….(why in view of 1?)
3. your constant denial of any health risks although evidence of harm is abundant (why? see for instance http://www.stralskyddsstiftelsen.se/forskning/)
4. your close involvement in the WHO EMF project while it was not clearly and openly declared as funded by GSM Association and MMF – or did you declare that in your declarations of interests?
5 your many years of involvement in IEEE/ICES – an industry organization.

And still although you make HIGHLY dubious statements that are clearly false (see example below) you are not prepared to take personal responsibility for the statements.

I asked you 1 year ago:
“Are you prepared to take full responsibility for the statement in the report of the Health Council of the Netherlands, in view of the fact that 75% of Swedish teenagers (girls) use their smartphones for over 3 hours a day” ? Are you thereby prepared to take full responsibility that there are no observed (or appeared) health and cancer risks for a normal use of a smart phone today among Swedes i.e. 3 hours a day or even lower (1 hour a day) for a period of up to 15 year s of mobile phone use”?

You had claimed:

“Altogether it (research results”) provides no or at most little indications for a risk for up to approximately 15 years of mobile phone use.”

This is clearly false – to claim “no indications for a risk up to 15 years” or even that there is “little”. Four meta-analysis published 2017 concludes that altogether studies on mobile phone use show increased risks for brain tumors.
Many brain tumors are deadly.

Most teenagers use the mobile phone for hours today. Much more than what has been shown in repeated studies to increase the risk of brain tumors (i e from 20-30 min/day)

The responsibility of denial of brain tumour and cancer risks from all the evidence available today for a clear increased risk should be huge.

By  Mona Nilsson 

 

The following is a study that has concluded that Non-ionizing EMF radiation could provide the tipping point of multiple harmful agents.

There appears to be sufficient data among diverse research groups that adverse health effects from non-ionizing EMF radiation combinations exist in at least selected ‘windows’ of parameter space. Overall, the number and extent of these ‘windows’ need to be identified, to ascertain their overlaps with the operational  Modified Health Effects of Non-ionizing Electromagnetic Radiation Combined… non-ionizing EMF radiation parameter space. This overlap would provide some indication or estimate of potential real-world health effects.

Resolving Differences
The first step in this process would identify major areas of disagreement, where strong adverse effects have been shown or predicted by the proponents, and typically no adverse effects have been shown or predicted by the opponents. The above studies, focused on the conditions that produced these adverse effects, would be re-done with multiple performers participating, representing diverse viewpoints.
The study criteria would match objectives, methodology, and operational environment as closely as possible. Any differences in results could be examined on a uniform basis.
The second step would involve expanding the parameter values to understand the boundaries of the ‘window’ in parameter space in which adverse health impacts can occur. The third (and most difficult) step is the inclusion of other potential co-promoters to reflect more closely the following real-world conditions. People are not exposed only to non-ionizing EMF radiation in isolation, or non-ionizing EMF radiation combined with one potential co-promoter. People are exposed to many potentially harmful agents, either harmful in their own right, harmful only when combined with non-ionizing EMF radiation, harmful only when combined with non-ionizing EMF radiation and one or more other agents, and so on. For example, there could be three agents which, by themselves, would exhibit no harmful effects, and in any combination of two might exhibit no harmful effects, but in combination of three would exhibit harmful effects.

Unfortunately, to identify these potential harmful combinations experimentally would require astronomical levels of effort. In a recent eBook (Kostoff 2015), the first author identified ~800 pervasive foundational causes of disease; i.e., 800 tangible contributing factors to myriad diseases, and these results were viewed as an extremely conservative estimate.

The number of potential combinations of these pervasive contributing factors to disease would be determined by the binomial coefficient. For example, the number of combinations of three potentially toxic stimuli from the list of 800 is 800!/(3!*797!), or approximately 85 million, and the number of combinations of two is approximately 320,000. Thus (in the latter case), we would need to perform 320,000 experiments to identify potentially harmful effects of any two toxic stimuli (from the ~800 identified in (Kostoff 2015)) combined with non-ionizing EMF radiation.

Given the problem of ‘windows’ in parameter space described above, each experiment would be fairly complex, involving examination over large ranges of parameters such as non-ionizing EMF radiation frequency, intensity, duration, etc. Realistically, we would need to prioritize the pool of potential toxic stimuli, and then examine their effects in very small combinations with non-ionizing EMF radiation.

http://stip.gatech.edu/wp-content/uploads/2017/03/371048_1_En_4_Chapter_OnlinePDF.pdf

By Markab Algedi

Every week, people who pay attention notice new people being injured by vaccines. A post might go viral on social media of a parent asking for help after their child becomes injured shortly after a shot. Other people, like former MMA fighter Nick Catone, had their children taken by what they understand to be vaccine injury and become devoted to the cause for life.

We created something we call the People’s VAERS Report to periodically report on the victims of vaccine injury and similar relevant stories in a medium that is captivating to people who have no regular interest in this. This is edition 1, for February 9, 2018.

Every week, there are new reports of people being injured from vaccines. The individual stories become a lot to make sense of on a regular basis: stories of paralysis or death shortly after a shot, or a person getting the illness a vaccine was meant to prevent immediately after.

The People’s VAERS report is a periodical video we make to get people up to date with the victims of vaccine injury, and related stories on a regular basis: to compile it all into one easily digestible report, for any person interested in learning about this.

This is edition one, for February 9th, 2018.

Headlines are being made about people dying from the flu. Several of these people, one might wonder if it isn’t most of them, were actually given the flu shot shortly before they got the illness.

The flu shot basically gives you the flu. You might have had this common experience, where you get the worst cold of your life a week or two after a flu shot. Maybe a relative or family member has had this experience.

This year’s flu shot was officially admitted to only be 10 percent effective: yet it seems to be particularly dangerous.

It seems to be giving people a flu-like sickness, worsened by the other ingredients of the flu shot, for instance mercury and formaldehyde.

The standard Sanofi flu shot contains polysorbate-80, which dangerously allowschemicals to cross the blood brain barrier.

People who research vaccine injury are largely aware that most vaccines are a concoction of toxic chemicals, from mercury, to aluminum, to formaldehyde, so when you add polysorbate-80, and have the blood brain barrier forced open, you have a recipe for serious brain damage or even infection.

Sorbitol is another vaccine additive that opens the blood brain barrier.

As a consequence, Encephalitis, or swelling of the brain is a common occurrenceshortly after people receive vaccines such as the Pertussis shot, which is included in the combination shot DTAP (also known as the TDAP).

They keep changing the name of it because it contains as much mercury as 3 or 4 vaccines and has hurt a lot of people.

Provided you have all the required, and never officially admitted info necessary to understand the topic, here are some things that happened this week. Sources can be found in the description of this video.

1. The director of the Center for Disease Control, a monolith institution that promotes harmful vaccines at every turn, has resigned amid a scandal.

Brenda Fitzgerald is reportedly making shady deals to protect big tobacco, but the CDC is much more guilty for spending billions of dollars annually on vaccines that come directly from corporations it is in bed with: like Merck and Sanofi.

After all, a previous CDC director from the Bush and Obama administrations, Julie Gerberding, left the CDC to become the president of one of the world’s largest vaccine producers, Merck.

Then she became Merck’s executive vice president for global strategic communications. That’s a scary revolving door.

2. This is happening in many places right now: in New York, there reportedly has been a 21 percent jump in people hospitalized for the flu, and a 50 percent increase in confirmed influenza cases. 2,200 new hospitalizations and over 11,000 confirmed influenza cases: just what is being reported.

People know the flu shot is probably causing this, yet New York politicians are pressuring people to get vaccinated.

3. A teacher in Weatherford, Texas died after getting the flu recently.

She used the horrible product Tamiflu, and her condition worsened until she sadly lost her life. Tamiflu is linked to this type of thing, to say the least.

4. Sanofi, the multinational pharma giant who makes most flu shots and several other vaccines, is refusing a refund to the entire nation of people they gave a dangerous, completely inverted dengue vaccine.

The Philippines was plagued with a Sanofi dengue vaccine that actually causes the disease, instead of preventing it.

It’s like the flu shot, but a lot more obvious. Sanofi is saying “no refunds.”

5. Do you know how many people have accidentally received a Gardasil HPV shot?

One toddler, Chace Topperwein of New Zealand,  got it by mistake and died of leukemia shortly after. Leukemia, the kind of cancer HPV shot victim Hayley Willar had.

It happened again, and because CPS doesn’t believe the vaccine caused the injury, the mother of a 4-month-old baby mistakenly given the HPV vaccine intended for pre-teens is fighting to get her child back from the state.

In Texas, Anita Vasquez continues to fight the state for custody of her daughter Aniya, who was afflicted with many symptoms after the accidental injection, including lethargy, weight loss, decreased appetite, dehydration, and others.

CPS believes these symptoms are coming from nowhere and the mother is crazy for thinking it was the shot.

6. A study performed by researchers from 3 universities proved that vaccinated, farmed fish show more symptoms of disease when vaccinated, and the vaccines don’t even work.

That’s right: fish farmers, who raise the fish we eat, vaccinate fish and the vaccines not only fail to work, but they make fish sicker. And they wonder why mercury is found in fish.

7. In Brentwood, California, a 16-year-old student passed away from pneumonia complications. She is the fourth student to pass away in unrelated incidents this year at the school.

This was the first People’s VAERS report. If this was informative, stay tuned for the next one.

Vaccine injury is an ongoing crisis.

This article may be freely republished with attribution to the author, and a working link back to this article at Edge Canopy.

{Also check out worldmercuryproject dot org for more horror stories research.}

SCIENCE One of the first arguments people make when they are introduced to the idea that vaccines could be causing harm is, “That isn’t so, check the science.” THE GREATER GOOD Team fully agrees with this premise.

This compilation of research contains over 180 papers published in peer reviewed medical and scientific journals that explore and discuss injuries linked to vaccines.

We have compiled this catalogue of science to help parents, lawmakers, medical practitioners and scientists understand several important points about the vaccine issue:

• there is abundant science published in mainstream medical and scientific journals suggesting cause for concern about the safety of vaccines;

• the vaccine debate is not a debate between parents and doctors but rather amongst scientists with opposing views;

• vaccines may be linked to a host of chronic illnesses and conditions such as asthma, allergies, learning disabilities, behavioral problems, autism, unexplained infant death and autoimmune diseases such as diabetes, rheumatoid arthritis, lupus, MS, and others;

• there are connections between the gut, immune, and neurological issues often seen in vaccine injuries. This compilation does not contain all the science on vaccines but rather the science that suggests cause for concern.

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