Ionizing radiations epidemiology does not support the LNT model

Ricci, Paolo F.; Tharmalingam, Sujeenthar

doi:10.1016/j.cbi.2018.11.014

Cited by 19 publications

(5 citation statements)

References 43 publications

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“…Furthermore, due to the high baseline cancer risk compared to the risk increased from low-dose radiation exposure, the population size required for detecting low-dose radiation risk with statistical significance exponentially increases as the target dose decreases (Ozasa, 2016 ; Ozasa et al, 2019 ; UNSCEAR, 2008 ; Valentin, 2006 ). To address some of the challenges, studies that use a wider range of data, such as the Million Person Study (Boice et al, 2022 ), are being conducted (Calabrese, 2015 ; Ricci & Tharmalingam, 2019 ; Tubiana et al, 2009 ; Valentin, 2008 ; Weber & Zanzonico, 2017 ). The utilization of population-level exposure variables and health outcomes data adopted in this study can serve as a valuable resource for future research.…”

Section: Discussionmentioning

confidence: 99%

Evaluating county-level lung cancer incidence from environmental radiation exposure, PM2.5, and other exposures with regression and machine learning models

Lee,

Hanson,

Logan

et al. 2024

Environ Geochem Health

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Characterizing the interplay between exposures shaping the human exposome is vital for uncovering the etiology of complex diseases. For example, cancer risk is modified by a range of multifactorial external environmental exposures. Environmental, socioeconomic, and lifestyle factors all shape lung cancer risk. However, epidemiological studies of radon aimed at identifying populations at high risk for lung cancer often fail to consider multiple exposures simultaneously. For example, moderating factors, such as PM2.5, may affect the transport of radon progeny to lung tissue. This ecological analysis leveraged a population-level dataset from the National Cancer Institute’s Surveillance, Epidemiology, and End-Results data (2013–17) to simultaneously investigate the effect of multiple sources of low-dose radiation (gross $$\gamma$$ γ activity and indoor radon) and PM2.5 on lung cancer incidence rates in the USA. County-level factors (environmental, sociodemographic, lifestyle) were controlled for, and Poisson regression and random forest models were used to assess the association between radon exposure and lung and bronchus cancer incidence rates. Tree-based machine learning (ML) method perform better than traditional regression: Poisson regression: 6.29/7.13 (mean absolute percentage error, MAPE), 12.70/12.77 (root mean square error, RMSE); Poisson random forest regression: 1.22/1.16 (MAPE), 8.01/8.15 (RMSE). The effect of PM2.5 increased with the concentration of environmental radon, thereby confirming findings from previous studies that investigated the possible synergistic effect of radon and PM2.5 on health outcomes. In summary, the results demonstrated (1) a need to consider multiple environmental exposures when assessing radon exposure’s association with lung cancer risk, thereby highlighting (1) the importance of an exposomics framework and (2) that employing ML models may capture the complex interplay between environmental exposures and health, as in the case of indoor radon exposure and lung cancer incidence. Graphical abstract

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Section: Discussionmentioning

confidence: 99%

Evaluating county-level lung cancer incidence from environmental radiation exposure, PM2.5, and other exposures with regression and machine learning models

Lee,

Hanson,

Logan

et al. 2024

Environ Geochem Health

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show abstract

“…Furthermore, due to the high baseline cancer risk compared to the risk increased from low-dose radiation exposure, the population size required for detecting low-dose radiation risk with statistical signi cance exponentially increases as the target dose decreases (Ozasa, 2016;Ozasa et al, 2019;Radiation, 2008;Valentin, 2006). To address some of the challenges, studies that use a wider range of data, such as the Million Person Study (Boice et al, 2022), are being conducted (Calabrese, 2015;Ricci & Tharmalingam, 2019;Tubiana et al, 2009;Valentin, 2008;Weber & Zanzonico, 2017). The utilization of population-level exposure variables and health outcomes data adopted in this study can serve as a valuable resource for future research.…”

Section: Discussionmentioning

confidence: 99%

Evaluating County-level Lung Cancer Incidence From Environmental Radiation Exposure, Pm 2.5 , and Other Exposures With Regression and Machine Learning Models

Lee

Hanson

Logan

et al. 2023

Preprint

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Characterizing the interplay between exposures shaping the human exposome is vital for disease etiology. For example, cancer incidence is attributable to the independent and interactive multifactorial external exposures that shape health. Lung cancer is a perfect example of increased risk linked to environmental, socioeconomic, and lifestyle factors. However, radon epidemiological studies often fail to consider multiple exposures simultaneously. For example, moderating factors, such as PM2.5, may affect the transport of radon progeny to lung tissue. This ecological analysis leveraged a population-level dataset from the National Cancer Institute’s Surveillance, Epidemiology, and End-Results data (2013-17) to simultaneously investigate the effect of multiple sources of low-dose radiation (gross activity and indoor radon) and PM2.5 on lung cancer rates in the United States. The county-level factors (environmental, sociodemographic, lifestyle) were controlled, and Poisson regression and random forest were used to assess associations with lung and bronchus cancer rates. Tree-based ML method improved over traditional regression: Poisson regression: 7.58/7.39 (mean absolute percentage error, MAPE); Poisson random forest regression: 1.21/1.16 (MAPE). Effect of PM2.5 increased with the concentration of environmental radon, thereby confirming findings from previous studies that investigated the possible synergistic effect of radon and PM2.5 on health outcomes. In summary, the results demonstrated (1) a need to include multiple environmental exposures when assessing radon exposure’s association with lung cancer risk, thereby highlighting exposomics framework and (2) that employing ML models may capture the complex interplay between environmental exposures and health, as in the case of environmental radiation exposure and lung cancer incidence.

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“…Ricci and Tharmalingam 99 further describe the historical and scientific foundational errors of the LNT risk model assumptions since its inception dating back to 1946. Ricci and Tharmalingam state: “We show that linear interpolations are incorrect because both the biological and epidemiological evidence for thresholds or other non-linearities, are more than substantial.…”

Section: Health Risks Of Very Low Level Radiation Diagnostic X-raysmentioning

confidence: 99%

Radiography and Clinical Decision-Making in Chiropractic

Lopes¹,

Coleman²,

Cremata³

2021

Dose-Response

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The concern over x-ray exposure risks can overshadow the potential benefit of radiography, especially in cases where manual therapy is employed. Spinal malalignment cannot be accurately visualized without imaging. Manual therapy and the load tolerances of injured spinal tissues raise different criteria for the use of x-rays for spinal disorders than in medical practice. Current regulatory bodies rely on radiography risk assessments based on Linear-No-Threshold (LNT) risk models. There is a need to consider radiography guidelines for chiropractic which are different from those for medical practice. Radiography practice guidelines are summaries dominated by frequentist interpretations in the analysis of data from studies. In contrast, clinicians often employ a pseudo-Bayesian form of reasoning during the clinical decision-making process. The overrepresentation of frequentist perspectives in evidence-based practice guidelines alter decision-making away from practical assessment of a patient’s needs, toward an overly cautious standard applied to patients without regard to their risk/benefit likelihoods relating to radiography. Guidelines for radiography in chiropractic to fully assess the condition of the spine and spinal alignment prior to manual therapy, especially with high velocity, low amplitude spinal manipulation (HVLA-SM), should necessarily differ from those used in medical practice.

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Ionizing radiations epidemiology does not support the LNT model

Cited by 19 publications

References 43 publications

Evaluating county-level lung cancer incidence from environmental radiation exposure, PM2.5, and other exposures with regression and machine learning models

Evaluating county-level lung cancer incidence from environmental radiation exposure, PM2.5, and other exposures with regression and machine learning models

Evaluating County-level Lung Cancer Incidence From Environmental Radiation Exposure, Pm 2.5 , and Other Exposures With Regression and Machine Learning Models

Radiography and Clinical Decision-Making in Chiropractic

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