Tag Archives: risk assessment

Risk of pandemic identified in 2012, Germany

“The German government, led by Angela Merkel, had been warned more than 7 years ago of the very real potential for a pandemic and that the country’s health care system was not prepared to deal with such a crisis.

Yet, instead of preparing for the probable scenario, the government poured tens of billions of euros into fighting the imaginary “climate crisis”. Now, as Germany reels from the current COVID-19 pandemic, critical voices are asking why the government never bothered to take action to implement preventive measures.” click here

Computer modeling of coronavirus-related deaths does not reflect the physical reality, United Kingdom

“There has been much media coverage about the danger to life posed by the COVID-19 coronavirus pandemic. While it is clearly a serious threat, one should consider whether the best evidence supports the current degree of panic and hence government policy. Much of the concern resulted from a non-peer reviewed study published by the COVID-19 Response Team from Imperial College (Ferguson et al 2020[1]). In this article, I examine whether data from the Diamond Princess cruise ship – arguably the most useful data set available – support the fatality rate assumptions underlying the Imperial study. I find that it does not do so. The likely fatality rates for age groups from 60 upwards, which account for the vast bulk of projected deaths, appear to be much lower than those in the Ferguson et al. study.” click here

Small epidemiological associations do not imply a significant risk

“When effects are this small, it is extremely possible that the effects are not real, but are artifacts of the statistical methods used in the original analysis.  If these findings had had Relative Risks or Risk Ratios of 4.0 or 7.9 or any value that might indicate a strong association, then I would be more convinced.  But with so many of the metrics not even passing the most basic test of significance, I am concerned that the findings represent only what John P.A. Ioannidis has termed “simply accurate measures of the prevailing bias.” “  click here

Even Nobel Laureates can make a mistake needing correction

Edward J. Calabrese. Muller’s Nobel Prize data: Getting the dose wrong and its significanceEnvironmental Research 176 (2019) 108528

This paper evaluates the significant historical paper of Muller and Mott-Smith (1930), which successfully disputed the proposal of Olson and Lewis (1928) that background ionizing radiation is the driving mechanism of evolution. While the present analysis supports the general conclusion that background radiation is not a quantifiable factor affecting evolution, the paper reveals methodological errors and questionable conclusions in the Muller and Mott-Smith (1930) paper, which may have impacted the acceptance of the linear non-threshold (LNT) model. Most importantly, this paper reveals that in Muller’s (1927) Nobel Prize research he used a treatment exposure (total dose) that was 95 million-fold greater than the average background exposure, a value far greater than the 200,000 fold reported by Muller and Mott-Smith (1930). Such a large exposure rate dis- crepancy may be historically important as it may have led to the over-reliance on Muller’s research in support of the derivation and use of the LNT single-hit model.

When politics override the best available science when deciding a regulatory policy…

“… overpowering influence of low dose biostatistical modeling perspectives that swayed the quantitatively overwhelmed chemical toxicologists. This resulted in the LNT policy going forward, becoming broadly institutionalized across many governmental agencies and in multiple countries. The rest is history.”

Edward J. Calabresea, Robert J. Golden. Why toxicologists resisted and radiation geneticists supported EPA’S Tadoption of LNT for cancer risk assessment Chemico-Biological Interactions Volume 310, 1 September 2019, 108736

The linear non-threshold (LNT) dose response model for cancer risk assessment has been a controversial concept since its initial proposal during the 1930s. It was long advocated by the radiation genetics community in the 1950s, some two decades prior to being generally adopted within the chemical toxicology community. This paper explores possible reasons for such major differences in the acceptance of LNT for cancer risk assessment by these two key groups of scientists.

LNT model adopted for regulatory convenience

E.J. Calabrese. EPA adopts LNT: New historical perspectives. Chemic-Biological Interactions 308 (2019) 110-112

This paper provides an historical assessment of how the linear non-threshold (LNT) model became adopted as policy by the United States Environmental Protection Agency (US EPA) in 1975 [1] and how prior United States National Academy of Sciences (US NAS) radiation advisory panels may have affected this EPA decision. The paper highlights a generally unrecognized set of recommendations of the 1960 Biological Effect of Atomic Radiation [2] Genetics and Medical/Pathology Panels that did not support LNT for cancer risk assessment due to their judgements of its scientific limitations and unacceptable uncertainties. These convergent, independent and high profile recommendations were not promoted by the sponsors (i.e., Rockefeller Foundation and the NAS), and were ignored by the media, Congress and the scientific community in contrast to the vast attention directed to the linearity recommendation for germ cell mutation by the BEAR Genetics Panel in 1956 [3,4]. The subsequent Biological Effects of Ionizing Radiation (BEIR) I Committee (1972) [5] report ignored these BEAR Panel (1960) [2] recommendations, only commenting on the BEAR 1956 linearity supporting recommendation [3,4]. These actions are documented and assessed for how they influenced why and how EPA adopted linearity for cancer risk assessment based on the BEIR I report.

Linear-no-threshold default assumption no longer valid

“The linear no-threshold paradigm, which asserts there are no safe exposure levels, is the product of flawed and corrupted science.” click here