Interventional probability of causation (IPoC) with epidemiological and partial mechanistic evidence: benzene vs. formaldehyde and acute myeloid leukemia (AML).

INTRODUCTION: Causal epidemiology for regulatory risk analysis seeks to evaluate how removing or reducing exposures would change disease occurrence rates. We define interventional probability of causation (IPoC) as the change in probability of a disease (or other harm) occurring over a lifetime or other specified time interval that would be caused by a specified change in exposure, as predicted by a fully specified causal model. We define the closely related concept of causal assigned share (CAS) as the predicted fraction of disease risk that would be removed or prevented by a specified reduction in exposure, holding other variables fixed. Traditional approaches used to evaluate the preventable risk implications of epidemiological associations, including population attributable fraction (PAF) and the Bradford Hill considerations, cannot reveal whether removing a risk factor would reduce disease incidence. We argue that modern formal causal models coupled with causal artificial intelligence (CAI) and realistically partial and imperfect knowledge of underlying disease mechanisms, show great promise for determining and quantifying IPoC and CAS for exposures and diseases of practical interest. METHODS: We briefly review key CAI concepts and terms and then apply them to define IPoC and CAS. We present steps to quantify IPoC using a fully specified causal Bayesian network (BN) model. Useful bounds for quantitative IPoC and CAS calculations are derived for a two-stage clonal expansion (TSCE) model for carcinogenesis and illustrated by applying them to benzene and formaldehyde based on available epidemiological and partial mechanistic evidence. RESULTS: Causal BN models for benzene and risk of acute myeloid leukemia (AML) incorporating mechanistic, toxicological and epidemiological findings show that prolonged high-intensity exposure to benzene can increase risk of AML (IPoC of up to 7e-5, CAS of up to 54%). By contrast, no causal pathway leading from formaldehyde exposure to increased risk of AML was identified, consistent with much previous mechanistic, toxicological and epidemiological evidence; therefore, the IPoC and CAS for formaldehyde-induced AML are likely to be zero. CONCLUSION: We conclude that the IPoC approach can differentiate between likely and unlikely causal factors and can provide useful upper bounds for IPoC and CAS for some exposures and diseases of practical importance. For causal factors, IPoC can help to estimate the quantitative impacts on health risks of reducing exposures, even in situations where mechanistic evidence is realistically incomplete and individual-level exposure-response parameters are uncertain. This illustrates the strength that can be gained for causal inference by using causal models to generate testable hypotheses and then obtaining toxicological data to test the hypotheses implied by the models-and, where necessary, refine the models. This virtuous cycle provides additional insight into causal determinations that may not be available from weight-of-evidence considerations alone.

The relationship of the clinicopathological characteristics and treatment results of post-Chornobyl papillary thyroid microcarcinomas with the latency period and radiation exposure.

Introduction: A worldwide increase in the incidence of thyroid cancer during the last decades is largely due to papillary thyroid microcarcinomas (MPTCs), which are mostly low-risk tumors. In view of recent clinical recommendations to reduce the extent of surgery for low-risk thyroid cancer, and persisting uncertainty about the impact of radiation history, we set out to address whether clinicopathological characteristics and prognosis of post-Chornobyl MPTCs were changing with regard to: i) the latency period, ii) probability of causation (POC) of a tumor due to radiation, and iii) tumor size. Methods: Patients (n = 465) aged up to 50 years at diagnosis who lived in April, 1986 in six northern, most radiocontaminated regions of Ukraine were studied. Results: Latency period was statistically significantly associated with the reduction of POC level, tumor size and the frequency of fully encapsulated MPTCs. In contrast, the frequency of oncocytic changes and the BRAFV600E mutation increased. Invasive properties and clinical follow-up results did not depend on latency except for a lower frequency of complete remission after postsurgical radioiodine therapy. The POC level was associated with more frequent extrathyroidal extension, and lymphatic/vascular invasion, less frequent oncocytic changes and BRAFV600E , and did not associate with any clinical indicator. Tumor size was negatively associated with the latency period and BRAFV600E , and had a statistically significant effect on invasive properties of MPTCs: both the integrative invasiveness score and its components such as lymphatic/vascular invasion, extrathyroidal extension and lymph node metastases increased. The frequency of total thyroidectomy, neck lymph node dissection and radioiodine therapy also increased with the larger tumor size. The duration of the latency period, POC level or tumor size did not associate with the chance of disease recurrence. Discussion: In summary, we did not observe overall worsening of the clinicopathological features or treatment results of radiogenic MPTCs that could be associated with the latency period or POC level, suggesting that radiation history did not strongly affect those in the analyzed MPTC patients. However, the increase in the invasive properties with tumor size indicates the need for individual risk stratification for each MPTC patient, regardless of radiation history, for treatment decision-making.

A preliminary study on the probability of causation of male breast cancer for occupational radiation workers / 中华放射医学与防护杂志

Objective:To explore the probability of causation method ology of male breast cancer and to provide theoretical basis for the diagnosis of occupational radiogenic male breast cancer in China.Methods:Using the male excess relative risk model (EAR) fitted from the Japanese atomic bomb survivors and the female excess absolute risk model (ERR) provided by the Biological Effect of Ionizing Radiation Committee VII (BEIRVII), the breast dose and the probability of causation of the previous case of male breast cancer were calculated.Results:The average probability of causation ( PC) calculated by male ERR model was 94.6%, and the upper limit of 95% PC was 98.3%. Using female EAR model and female breast cancer incidence, the average PC was 70.3%, and the upper limit of 95% PC was 153.3%. when male breast cancer incidence was used, the average PC was 99.2%.By both methods, the male breast cancer patient could be determined to be caused by occupational radiation exposure. Conclusions:The upper limit of 95% PC calculated by female EAR model and female breast cancer incidence was higher than that by male ERR model.The uncertainty of probability of causation for female EAR model still need further research. Occupational radiogenic male breast was proposed to listed in occupational radiogenic neoplasms, which will make the list more perfect and scientific and reasonable to meet potential claims.

[Analysis of the diagnosis of occupational radiogenic neoplasms in three cases of leukemia].

Objective: To analyze the diagnosis of 3 cases of leukemia applying for the diagnosis of occupational radiogenic neoplasms. Methods: Retrospective analysis the occupational history, the disease history and the probability of causation (PC value) information of 3 radiological workers. Results: Two cases' PC value of 95% confidence limit of were >50%, and they were diagnosed as radiogenic neoplasms. One case was <50% and diagnosed as nonoccupational radiogenic neoplasms. Conclusion: The probability of causation analysis has important guiding significance for the diagnosis of occupational radiogenic neoplasms. Radiological workers should improve their awareness of self-protection and reduce the occurrence of occupational diseases.

Thinking about Causation: A Thought Experiment with Dominos.

We argue that population attributable fractions, probabilities of causation, burdens of disease, and similar association-based measures often do not provide valid estimates or surrogates for the fraction or number of disease cases that would be prevented by eliminating or reducing an exposure because their calculations do not include crucial mechanistic information. We use a thought experiment with a cascade of dominos to illustrate the need for mechanistic information when answering questions about how changing exposures changes risk. We suggest that modern methods of causal artificial intelligence (CAI) can fill this gap: they can complement and extend traditional epidemiological attribution calculations to provide information useful for risk management decisions.

Toward practical causal epidemiology.

Population attributable fraction (PAF), probability of causation, burden of disease, and related quantities derived from relative risk ratios are widely used in applied epidemiology and health risk analysis to quantify the extent to which reducing or eliminating exposures would reduce disease risks. This causal interpretation conflates association with causation. It has sometimes led to demonstrably mistaken predictions and ineffective risk management recommendations. Causal artificial intelligence (CAI) methods developed at the intersection of many scientific disciplines over the past century instead use quantitative high-level descriptions of networks of causal mechanisms (typically represented by conditional probability tables or structural equations) to predict the effects caused by interventions. We summarize these developments and discuss how CAI methods can be applied to realistically imperfect data and knowledge - e.g., with unobserved (latent) variables, missing data, measurement errors, interindividual heterogeneity in exposure-response functions, and model uncertainty. We recommend that CAI methods can help to improve the conceptual foundations and practical value of epidemiological calculations by replacing association-based attributions of risk to exposures or other risk factors with causal predictions of the changes in health effects caused by interventions.

Generalizing experimental results by leveraging knowledge of mechanisms.

We show how experimental results can be generalized across diverse populations by leveraging knowledge of local mechanisms that produce the outcome of interest, only some of which may differ in the target domain. We use structural causal models and a refined version of selection diagrams to represent such knowledge, and to decide whether it entails the invariance of probabilities of causation across populations, which then enables generalization. We further provide: (i) bounds for the target effect when some of these conditions are violated; (ii) new identification results for probabilities of causation and the transported causal effect when trials from multiple source domains are available; as well as (iii) a Bayesian approach for estimating the transported causal effect from finite samples. We illustrate these methods both with simulated data and with a real example that transports the effects of Vitamin A supplementation on childhood mortality across different regions.

On Causal Inferences for Personalized Medicine: How Hidden Causal Assumptions Led to Erroneous Causal Claims About the D-Value.

Personalized medicine asks if a new treatment will help a particular patient, rather than if it improves the average response in a population. Without a causal model to distinguish these questions, interpretational mistakes arise. These mistakes are seen in an article by Demidenko [2016] that recommends the "D-value," which is the probability that a randomly chosen person from the new-treatment group has a higher value for the outcome than a randomly chosen person from the control-treatment group. The abstract states "The D-value has a clear interpretation as the proportion of patients who get worse after the treatment" with similar assertions appearing later. We show these statements are incorrect because they require assumptions about the potential outcomes which are neither testable in randomized experiments nor plausible in general. The D-value will not equal the proportion of patients who get worse after treatment if (as expected) those outcomes are correlated. Independence of potential outcomes is unrealistic and eliminates any personalized treatment effects; with dependence, the D-value can even imply treatment is better than control even though most patients are harmed by the treatment. Thus, D-values are misleading for personalized medicine. To prevent misunderstandings, we advise incorporating causal models into basic statistics education.

Probability of causation for occupational cancer after exposure to ionizing radiation.

BACKGROUND: Probability of causation (PC) is a reasonable way to estimate causal relationships in radiation-related cancer. This study reviewed the international trend, usage, and critiques of the PC method. Because it has been used in Korea, it is important to check the present status and estimation of PC in radiation-related cancers in Korea. METHODS: Research articles and official reports regarding PC of radiation-related cancer and published from the 1980s onwards were reviewed, including studies used for the revision of the Korean PC program. PC has been calculated for compensation-related cases in Korea since 2005. RESULTS: The United States National Institutes of Health first estimated the PC in 1985. Among the 106 occupational diseases listed in the International Labor Organization Recommendation 194 (International Labor Office (ILO), ILO List of Occupational Diseases, 2010), PC is available only for occupational cancer after ionizing radiation exposure. The United States and United Kingdom use PC as specific criteria for decisions on the compensability of workers' radiation-related health effects. In Korea, PC was developed firstly as Korean Radiation Risk and Assigned Share (KORRAS) in 1999. In 2015, the Occupational Safety and Health Research Institute and Radiation Health Research Institute jointly developed a more revised PC program, Occupational Safety and Health-PC (OSH-PC). Between 2005 and 2015, PC was applied in 16 claims of workers' compensation for radiation-related cancers. In most of the cases, compensation was given when the PC was more than 50%. However, in one case, lower than 50% PC was accepted considering the possibility of underestimation of the cumulative exposure dose. CONCLUSIONS: PC is one of the most advanced tools for estimating the causation of occupational cancer. PC has been adjusted for baseline cancer incidence in Korean workers, and for uncertainties using a statistical method. Because the fundamental reason for under- or over-estimation is probably inaccurate dose reconstruction, a proper guideline is necessary.

Probability of causation for occupational cancer after exposure to ionizing radiation

BACKGROUND: Probability of causation (PC) is a reasonable way to estimate causal relationships in radiation-related cancer. This study reviewed the international trend, usage, and critiques of the PC method. Because it has been used in Korea, it is important to check the present status and estimation of PC in radiation-related cancers in Korea. METHODS: Research articles and official reports regarding PC of radiation-related cancer and published from the 1980s onwards were reviewed, including studies used for the revision of the Korean PC program. PC has been calculated for compensation-related cases in Korea since 2005. RESULTS: The United States National Institutes of Health first estimated the PC in 1985. Among the 106 occupational diseases listed in the International Labor Organization Recommendation 194 (International Labor Office (ILO), ILO List of Occupational Diseases, 2010), PC is available only for occupational cancer after ionizing radiation exposure. The United States and United Kingdom use PC as specific criteria for decisions on the compensability of workers’ radiation-related health effects. In Korea, PC was developed firstly as Korean Radiation Risk and Assigned Share (KORRAS) in 1999. In 2015, the Occupational Safety and Health Research Institute and Radiation Health Research Institute jointly developed a more revised PC program, Occupational Safety and Health-PC (OSH-PC). Between 2005 and 2015, PC was applied in 16 claims of workers’ compensation for radiation-related cancers. In most of the cases, compensation was given when the PC was more than 50%. However, in one case, lower than 50% PC was accepted considering the possibility of underestimation of the cumulative exposure dose. CONCLUSIONS: PC is one of the most advanced tools for estimating the causation of occupational cancer. PC has been adjusted for baseline cancer incidence in Korean workers, and for uncertainties using a statistical method. Because the fundamental reason for under- or over-estimation is probably inaccurate dose reconstruction, a proper guideline is necessary.

[Oncological disease: dimensions of the phenomenon and role ionizing radiation]. / La malattia oncologica: dimensioni del fenomeno e ruolo delle radiazioni ionizzanti.

OBJECTIVES: The continuous scientific advances against neoplastic diseases affecting all areas of oncology biomedical research. Age is an extremely important factor in cancer development, since the incidence of cancer increases significantly with age. Because of aging of the Italian population, although the incidence is kept constant, the number of cancer diagnosis is inevitably going to increase over time only to increasing age. METHODS: Survival after the diagnosis of cancer is one of the main indicators that allow to evaluate the effectiveness of the health system against the cancer disease. The 5-year survival after diagnosis is a widely used indicator. If we consider the relative survival data after 5 years of diagnosis, for cancer cases diagnosed in subsequent three-year periods, from 1990-1992 to 2005-2007, it shows that the 5-year survival has increased significantly over time for both men and women. Many so-called patients "long-term survivors "are of working age and should return to work. This aims to ensure both the mental and social well-being of the worker, both industrial production. For the oncogenic risk assessment by ionizing radiation, the ICRP Publication 60 has referred to the mortality and cancer data collected from 1950 to 1985 by the RERF, Japan-US bi-national institution with headquarters in Hiroshima that leads the research program called Life Span study (LSS), that is the study of the long-term effects on survivors of the bomb A. For the thyroid, instead, reference is made to the data from medical irradiations, as well as for liver and bone, using in this case adapted data relating to exposure to alpha rays (thorium and radio). The interpretation model is the traditional one: the linear dose-effect assumptions without a threshold even at small doses (LNT theory) when epidemiological data are not more informative for statistical uncertainty, although we resort to radiobiological studies. RESULTS: In transferring the risk among different populations ICRP in Publication 103 accommodates the idea that for each type of cancer is more suitable, from time to time, the additive or multiplicative model or a combination of the two. CONCLUSIONS: To study the oncogenic role of occupational exposure to ionizing radiation in the onset of neoplastic disease, the probability of cause (PC), is a "reasonable way to address the problem of evaluation of the likelihood that previous exposure to ionizing radiation (IR) is responsible for an oncogenic event "(Committee on Radiation Protection and Measurements - NCRP - Statement N. 7 of 30/09/92).

[Probability of Causation (PC): historical motivations and legal transposition]. / PC: motivazioni storiche e recepimento giuridico.

OBJECTIVES: The Probability of Causation (PC) was introduced to compensate objectively and more possible legally the U.S. diseased subjects involved in the nuclear armament activities. METHODS: The methodology is related to the attributable risk concept, but it is widely different from it, since it doesn't evaluate the "attributablity" from a collective point of view, but from a "personalistic" point, that is from the particular exposure condition, from the specific physical parameters and from the biological individual features of the single exposed subject. So the PC become an evaluation of the harm probability "tailored" for "that" specific exposed person, on the basis of the epidemiological indications coming from an exposed group with very similar characteristics of the under investigation individual. This is clearly possible owing to the large and exhaustive amount of epidemiological studies in the specific field of radiation exposure. The process to reach the PC adoption took a long time, was plodding and politically thwarted and various reexaminations and bills during time were necessary to extended the laws to the different exposure categories. RESULTS: Now in the U.S. three departments (Health, Energy and Labour) are involved in the evaluation processes; they gather the personal, dosimetric and clinical data and with a computer program (usable on line also) based on the updated knowledge, evaluate the eligibility for compensation on the basis of the "more likely than not" criterion. CONCLUSIONS: The method meets the interest and the favor at international level and organizations in prominent positions in the pacific use of nuclear energy and in the radiation protection fields, like: NCRP, IAEA, WHO, ILO,... fight for it use. Now many institutional organism and the more enlightened justice courts utilize the PC to settle cases (increasing in frequency) in work and health activities, for which more often compensation claims are dealing with.

[AIRM recommendations for the application of probability of causation method.] / Indirizzi AIRM per l'utilizzo della Probabilità di Causa.

OBJECTIVES: L'applicazione del metodo della Probability of Causation (PC) è certamente ampio poiché è oggi lo strumento riconosciuto per la individuazione del nesso di causa non solo nelle richieste di indennizzo in ambito assicurativo (per il quale è stata utilizzato inizialmente) ma anche per dirimere contenziosi giuridici in ambito civilistico e penalistico. METHODS: Thanks to the Italian Association of Medical Radiation Protection (AIRM), PC method has been recently proposed as an aid for the radiation protection occupational physician in medical assessments involving both the mandatory actions that, in case of suspicion of occupational disease, the physician needs to perform (report / complaint / reporting) and the expression of the fitness evaluation in case of return to work after cancer and clinical recovery. RESULTS: For all these uses PC value, calculated through the method, should be used in a flexible manner, and thus lead to different decisions, "modulated" on the basis of purposes and listed contexts; and this not only within the legal framework, but also in the strictly professional one. CONCLUSIONS: According to different purposes, different PC values are proposed as a reference for the decisions to be taken.

Who really gets strep sore throat? Confounding and effect modification of a time-varying exposure on recurrent events.

Unmeasured confounding is the fundamental obstacle to drawing causal conclusions about the impact of an intervention from observational data. Typically, covariates are measured to eliminate or ameliorate confounding, but they may be insufficient or unavailable. In the special setting where a transient intervention or exposure varies over time within each individual and confounding is time constant, a different tack is possible. The key idea is to condition on either the overall outcome or the proportion of time in the intervention. These measures can eliminate the unmeasured confounding either by conditioning or by use of a proxy covariate. We evaluate existing methods and develop new models from which causal conclusions can be drawn from such observational data even if no baseline covariates are measured. Our motivation for this work was to determine the causal effect of Streptococcus bacteria in the throat on pharyngitis (sore throat) in Indian schoolchildren. Using our models, we show that existing methods can be badly biased and that sick children who are rarely colonized have a high probability that the Streptococcus bacteria are causing their disease. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

Epidemiological evidence in law: a comment on Supreme Court Decision 2011Da22092, South Korea.

This paper offers a commentary on three aspects of the Supreme Court's recent decision (2011Da22092). First, contrary to the Court's finding, this paper argues that epidemiological evidence can be used to estimate the probability that a given risk factor caused a disease in an individual plaintiff. Second, the distinction between specific and non-specific diseases, upon which the Court relies, is shown to be without scientific basis. Third, this commentary points out that the Court's finding concerning defect of expression effectively enables tobacco companies to profit from the efforts of epidemiologists and others involved in public health to raise awareness of the dangers of smoking.

Epidemiological evidence in law: a comment on Supreme Court Decision 2011Da22092, South Korea / 한국역학회지

This paper offers a commentary on three aspects of the Supreme Court's recent decision (2011Da22092). First, contrary to the Court's finding, this paper argues that epidemiological evidence can be used to estimate the probability that a given risk factor caused a disease in an individual plaintiff. Second, the distinction between specific and non-specific diseases, upon which the Court relies, is shown to be without scientific basis. Third, this commentary points out that the Court's finding concerning defect of expression effectively enables tobacco companies to profit from the efforts of epidemiologists and others involved in public health to raise awareness of the dangers of smoking.

A case of chronic myeloid leukemia in a diagnostic radiographer.

BACKGROUND: Occupational radiation exposure causes certain types of cancer, specifically hematopoietic diseases like leukemia. In Korea, radiation exposure is monitored and recorded by law, and guidelines for compensation of radiation-related diseases were implemented in 2001. However, thus far, no occupation-related disease was approved for compensation under these guidelines. Here, we report the first case of radiation-related disease approved by the compensation committee of the Korea Workers' Compensation and Welfare Service, based on the probability of causation. CASE PRESENTATION: A 45-year-old man complained of chronic fatigue and myalgia for several days. He was diagnosed with chronic myeloid leukemia. The patient was a diagnostic radiographer at a diagnostic radiation department and was exposed to ionizing radiation for 21 years before chronic myeloid leukemia was diagnosed. His job involved taking simple radiographs, computed tomography scans, and measuring bone marrow density. CONCLUSION: To our knowledge, this is the first approved case report using quantitative assessment of radiation. More approved cases are expected based on objective radiation exposure data and the probability of causation. We need to find a resolution to the ongoing demands for appropriate compensation and improvements to the environment at radiation workplaces.

A case of chronic myeloid leukemia in a diagnostic radiographer

BACKGROUND: Occupational radiation exposure causes certain types of cancer, specifically hematopoietic diseases like leukemia. In Korea, radiation exposure is monitored and recorded by law, and guidelines for compensation of radiation-related diseases were implemented in 2001. However, thus far, no occupation-related disease was approved for compensation under these guidelines. Here, we report the first case of radiation-related disease approved by the compensation committee of the Korea Workers' Compensation and Welfare Service, based on the probability of causation. CASE PRESENTATION: A 45-year-old man complained of chronic fatigue and myalgia for several days. He was diagnosed with chronic myeloid leukemia. The patient was a diagnostic radiographer at a diagnostic radiation department and was exposed to ionizing radiation for 21 years before chronic myeloid leukemia was diagnosed. His job involved taking simple radiographs, computed tomography scans, and measuring bone marrow density. CONCLUSION: To our knowledge, this is the first approved case report using quantitative assessment of radiation. More approved cases are expected based on objective radiation exposure data and the probability of causation. We need to find a resolution to the ongoing demands for appropriate compensation and improvements to the environment at radiation workplaces.

Changes in values of probability of causation for lung cancer due to radiation in Chinese uranium miners / 中华放射医学与防护杂志

Objective To evaluate the difference of PC values based on GBZ 97-2002 and on GBZ 97-2009 for lung cancer cases in Chinese uranium miners.Methods Using 19 lung cancer data ascertained in the past epidemiological study,PC values were calculated according to GBZ 97-2002 and GBZ 97-2009.Results In the 19 lung cancer cases,those cases that could not be judged as radiogenic cancers based on GBZ 97-2002,but may be judged as radiogenic cancers with GBZ 97-2009.The probability was enlarged by 1.1 times at least.The major reason was that the used value was the upper limit of 95％ in GBZ 2009 but the median in 2002.Conclusions Compared to GBZ 97-2002,the criteria value of PC in GBZ 97-2009 drops obviously.

Research advances in probability of causation calculation of radiogenic neoplasms / 军事医学科学院院刊

Probability of causation (PC) was used to facilitate the adjudication of compensation claims for cancers diagnosed following exposure to ionizing radiation. In this article, the excess cancer risk assessment models used for PC calculation are reviewed. Cancer risk transfer models between different populations, dependence of cancer risk on dose and dose rate, modification by epidemiological risk factors and application of PC are also discussed in brief.