Prediction of disease-free survival for precision medicine using cooperative learning on multi-omic data.

In precision medicine, both predicting the disease susceptibility of an individual and forecasting its disease-free survival are areas of key research. Besides the classical epidemiological predictor variables, data from multiple (omic) platforms are increasingly available. To integrate this wealth of information, we propose new methodology to combine both cooperative learning, a recent approach to leverage the predictive power of several datasets, and polygenic hazard score models. Polygenic hazard score models provide a practitioner with a more differentiated view of the predicted disease-free survival than the one given by merely a point estimate, for instance computed with a polygenic risk score. Our aim is to leverage the advantages of cooperative learning for the computation of polygenic hazard score models via Cox's proportional hazard model, thereby improving the prediction of the disease-free survival. In our experimental study, we apply our methodology to forecast the disease-free survival for Alzheimer's disease (AD) using three layers of data. One layer contains epidemiological variables such as sex, APOE (apolipoprotein E, a genetic risk factor for AD) status and 10 leading principal components. Another layer contains selected genomic loci, and the last layer contains methylation data for selected CpG sites. We demonstrate that the survival curves computed via cooperative learning yield an AUC of around $0.7$, above the state-of-the-art performance of its competitors. Importantly, the proposed methodology returns (1) a linear score that can be easily interpreted (in contrast to machine learning approaches), and (2) a weighting of the predictive power of the involved data layers, allowing for an assessment of the importance of each omic (or other) platform. Similarly to polygenic hazard score models, our methodology also allows one to compute individual survival curves for each patient.

Polygenic hazard score models for the prediction of Alzheimer's free survival using the lasso for Cox's proportional hazards model.

The prediction of the susceptibility of an individual to a certain disease is an important and timely research area. An established technique is to estimate the risk of an individual with the help of an integrated risk model, that is, a polygenic risk score with added epidemiological covariates. However, integrated risk models do not capture any time dependence, and may provide a point estimate of the relative risk with respect to a reference population. The aim of this work is twofold. First, we explore and advocate the idea of predicting the time-dependent hazard and survival (defined as disease-free time) of an individual for the onset of a disease. This provides a practitioner with a much more differentiated view of absolute survival as a function of time. Second, to compute the time-dependent risk of an individual, we use published methodology to fit a Cox's proportional hazard model to data from a genetic SNP study of time to Alzheimer's disease (AD) onset, using the lasso to incorporate further epidemiological variables such as sex, APOE (apolipoprotein E, a genetic risk factor for AD) status, 10 leading principal components, and selected genomic loci. We apply the lasso for Cox's proportional hazards to a data set of 6792 AD patients (composed of 4102 cases and 2690 controls) and 87 covariates. We demonstrate that fitting a lasso model for Cox's proportional hazards allows one to obtain more accurate survival curves than with state-of-the-art (likelihood-based) methods. Moreover, the methodology allows one to obtain personalized survival curves for a patient, thus giving a much more differentiated view of the expected progression of a disease than the view offered by integrated risk models. The runtime to compute personalized survival curves is under a minute for the entire data set of AD patients, thus enabling it to handle datasets with 60,000-100,000 subjects in less than 1 h.

Modeling metastatic progression from cross-sectional cancer genomics data.

MOTIVATION: Metastasis formation is a hallmark of cancer lethality. Yet, metastases are generally unobservable during their early stages of dissemination and spread to distant organs. Genomic datasets of matched primary tumors and metastases may offer insights into the underpinnings and the dynamics of metastasis formation. RESULTS: We present metMHN, a cancer progression model designed to deduce the joint progression of primary tumors and metastases using cross-sectional cancer genomics data. The model elucidates the statistical dependencies among genomic events, the formation of metastasis, and the clinical emergence of both primary tumors and their metastatic counterparts. metMHN enables the chronological reconstruction of mutational sequences and facilitates estimation of the timing of metastatic seeding. In a study of nearly 5000 lung adenocarcinomas, metMHN pinpointed TP53 and EGFR as mediators of metastasis formation. Furthermore, the study revealed that post-seeding adaptation is predominantly influenced by frequent copy number alterations. AVAILABILITY AND IMPLEMENTATION: All datasets and code are available on GitHub at https://github.com/cbg-ethz/metMHN.

A Statistical Testing Strategy Accounting for Random and Nonrandom (Skewed) X-Chromosome Inactivation Identifies Lung Cancer Susceptibility Loci among Smokers.

INTRODUCTION: Lung cancer is the most common cancer worldwide in mortality and the second in incidence. Epidemiological studies found a higher lung cancer risk for smoking women in comparison to men, but these sex differences, irrespective of smoking habits, remain controversial. One of the hypotheses concerns the genetic contribution of the sex chromosomes. However, while genome-wide association studies identified many lung cancer susceptibility loci, these analyses have excluded X-linked loci. METHODS: To account for nongenetic factors, we first presented an association test based on an additive-multiplicative hazard model accounting for random/nonrandom X-inactivation process. A simulation study was performed to investigate the properties of the proposed test as compared with the Wald test from a Cox model with random X-inactivation process and the partial likelihood ratio test proposed by Xu et al. accounting for nonrandom X-inactivation process. Then, we performed an X chromosome-wide association study on 9,261 individuals from the population-based cohort CARTaGENE to identify susceptibility loci for lung cancer among current and past smokers. We adjusted for the PLCOm2012 lung cancer risk score used in screening programs. RESULTS: Simulation results show the good behavior of the proposed test in terms of power and type I error probability as compared to the Xu et al. and the Wald test. Using the proposed test statistic and adjusting for the PLCOm2012 score, the X chromosome-wide statistical analysis identified two SNPs in low-linkage disequilibrium located in the IL1RAPL1 (IL-1 R accessory protein-like) gene: rs12558491 (p = 2.75×10-9) and rs12835699 (p = 1.26×10-6). For both SNPs, the minor allele was associated with lower lung cancer risk. Adjusting for multiple testing, no signal was detected using the Wald or the Xu et al. likelihood ratio tests. CONCLUSION: By taking into account smoking behavior and the X-inactivation process, the investigation of the X chromosome has shed a new light on the association between X-linked loci and lung cancer. We identified two loci associated with lung cancer located in the IL1RAPL1 gene. This finding would have been overlooked by examining only results from other test statistics.

A transformer model for cause-specific hazard prediction.

BACKGROUD: Modelling discrete-time cause-specific hazards in the presence of competing events and non-proportional hazards is a challenging task in many domains. Survival analysis in longitudinal cohorts often requires such models; notably when the data is gathered at discrete points in time and the predicted events display complex dynamics. Current models often rely on strong assumptions of proportional hazards, that is rarely verified in practice; or do not handle sequential data in a meaningful way. This study proposes a Transformer architecture for the prediction of cause-specific hazards in discrete-time competing risks. Contrary to Multilayer perceptrons that were already used for this task (DeepHit), the Transformer architecture is especially suited for handling complex relationships in sequential data, having displayed state-of-the-art performance in numerous tasks with few underlying assumptions on the task at hand. RESULTS: Using synthetic datasets of 2000-50,000 patients, we showed that our Transformer model surpassed the CoxPH, PyDTS, and DeepHit models for the prediction of cause-specific hazard, especially when the proportional assumption did not hold. The error along simulated time outlined the ability of our model to anticipate the evolution of cause-specific hazards at later time steps where few events are observed. It was also superior to current models for prediction of dementia and other psychiatric conditions in the English longitudinal study of ageing cohort using the integrated brier score and the time-dependent concordance index. We also displayed the explainability of our model's prediction using the integrated gradients method. CONCLUSIONS: Our model provided state-of-the-art prediction of cause-specific hazards, without adopting prior parametric assumptions on the hazard rates. It outperformed other models in non-proportional hazards settings for both the synthetic dataset and the longitudinal cohort study. We also observed that basic models such as CoxPH were more suited to extremely simple settings than deep learning models. Our model is therefore especially suited for survival analysis on longitudinal cohorts with complex dynamics of the covariate-to-outcome relationship, which are common in clinical practice. The integrated gradients provided the importance scores of input variables, which indicated variables guiding the model in its prediction. This model is ready to be utilized for time-to-event prediction in longitudinal cohorts.

Impact of organized and opportunistic screening on excess mortality and on social inequalities in breast cancer survival.

In most developed countries, both organized screening (OrgS) and opportunistic screening (OppS) coexist. The literature has extensively covered the impact of organized screening on women's survival after breast cancer. However, the impact of opportunistic screening has been less frequently described due to the challenge of identifying the target population. The aim of this study was to describe the net survival and excess mortality hazard (EMH) in each screening group (OrgS, OppS, or No screening) and to determine whether there is an identical social gradient in each groups. Three data sources (cancer registry, screening coordination centers, and National Health Data System [NHDS]) were used to identify the three screening groups. The European Deprivation Index (EDI) defined the level of deprivation. We modeled excess breast cancer mortality hazard and net survival using penalized flexible models. We observed a higher EMH for "No screening" women compared with the other two groups, regardless of level of deprivation and age at diagnosis. A social gradient appeared for each group at different follow-up times and particularly between 2 and 3 years of follow-up for "OrgS" and "OppS" women. Net survival was higher for "OrgS" women than "OppS" women, especially for the oldest women, and regardless of the deprivation level. This study provides new evidence of the impact of OrgS on net survival and excess mortality hazard after breast cancer, compared with opportunistic screening or no screening, and tends to show that OrgS attenuates the social gradient effect.

A simple Cox approach to estimating risk ratios without sharing individual-level data in multi-site studies.

Epidemiologic studies frequently use risk ratios to quantify associations between exposures and binary outcomes. When the data are physically stored at multiple data partners, it can be challenging to perform individual-level analysis if data cannot be pooled centrally due to privacy constraints. Existing methods either require multiple file transfers between each data partner and an analysis center (e.g., distributed regression) or only provide approximate estimation of the risk ratio (e.g., meta-analysis). Here we develop a practical method that requires a single transfer of eight summary-level quantities from each data partner. Our approach leverages an existing risk-set method and software originally developed for Cox regression. Sharing only summary-level information, the proposed method provides risk ratio estimates and confidence intervals identical to those that would be provided - if individual-level data were pooled - by the modified Poisson regression. We justify the method theoretically, confirm its performance using simulated data, and implement it in a distributed analysis of COVID-19 data from the U.S. Food and Drug Administration's Sentinel System.

Direct and indirect impact of the COVID-19 pandemic on the survival of kidney transplant recipients: A national observational study in France.

During the pandemic period, health care systems were substantially reorganized for managing COVID-19 cases. Corresponding consequences on persons with chronic diseases remain insufficiently documented. This observational cohort study investigated the direct and indirect impact of the pandemic period on the survival of kidney transplant recipients (KTR). Using the French National Health Data System, incident persons with end-stage kidney disease between 2015 and 2020, and who received a kidney transplant during this period were included and followed up from their transplantation date to December 31, 2021. The survival of KTR during the prepandemic and pandemic periods was investigated using Cox models with time-dependent covariates. There were 10 637 KTR included in the study, with 324 and 430 deaths observed during the prepandemic and pandemic periods, respectively. The adjusted risk of death during the pandemic period was similar to that observed during the prepandemic period (hazard ratio [HR] [95% confidence interval]: 0.92 [0.77-1.11]), COVID-19-related hospitalization was associated with an increased risk of death (HR: 10.62 [8.46-13.33]), and a third vaccine dose was associated with a lower risk of death (HR: 0.42 [0.30-0.57]). The pandemic period was not associated with an indirect higher risk of death in KTR with no COVID-19-related hospitalization.

Time-varying associations of patient and tumor characteristics with cancer survival: an analysis of SEER data across 14 cancer sites, 2004-2017.

PURPOSE: Surveillance, Epidemiology, and End Results (SEER) cancer registries provides information about survival duration and cause of death for cancer patients. Baseline demographic and tumor characteristics such as age, sex, race, year of diagnosis, and tumor stage can inform the expected survival time of patients, but their associations with survival may not be constant over the post-diagnosis period. METHODS: Using SEER data, we examined if there were time-varying associations of patient and tumor characteristics on survival, and we assessed how these relationships differed across 14 cancer sites. Standard Cox proportional hazards models were extended to allow for time-varying associations and incorporated into a competing-risks framework, separately modeling cancer-specific and other-cause deaths. For each cancer site and for each of the five factors, we estimated the relative hazard ratio and absolute hazard over time in the presence of competing risks. RESULTS: Our comprehensive consideration of patient and tumor characteristics when estimating time-varying hazards showed that the associations of age, tumor stage at diagnosis, and race/ethnicity with risk of death (cancer-specific and other-cause) change over time for many cancers; characteristics of sex and year of diagnosis exhibit some time-varying patterns as well. Stage at diagnosis had the largest associations with survival. CONCLUSION: These findings suggest that proportional hazards assumptions are often violated when examining patient characteristics on cancer survival post-diagnosis. We discuss several interesting results where the relative hazards are time-varying and suggest possible interpretations. Based on the time-varying associations of several important covariates on survival after cancer diagnosis using a pan-cancer approach, the likelihood of the proportional hazards assumption being met or corresponding interpretation should be considered in survival analyses, as flawed inference may have implications for cancer care and policy.

Gestational diabetes mellitus and risk of long-term all-cause and cardiac mortality: a prospective cohort study.

BACKGROUND: To investigate the association between gestational diabetes mellitus (GDM) without subsequent overt diabetes and long-term all-cause and cardiac mortality. METHODS: This prospective cohort study included 10,327 women (weighted population: 132,332,187) with a pregnancy history from the National Health and Nutrition Examination Survey (2007 to 2018). Participants were divided into three groups (GDM alone, overt diabetes, and no diabetes). Mortality data was linked from the National Death Index up to December 31, 2019. Multivariable Cox regression analysis was performed to examine the association between GDM alone and overt diabetes with all-cause mortality and cardiac mortality. Data analysis was performed from October 2022 to April 2023. RESULTS: Among the participants, 510 (weighted 5.3%) had GDM alone and 1862 (weighted 14.1%) had overt diabetes. Over a median follow-up period of 6.7 years (69,063 person-years), there were 758 deaths. The GDM group did not show an increased risk of all-cause mortality (hazard ratio [HR] 0.67; 95% CI, 0.25-1.84), while the overt diabetes group had a significantly higher risk (HR 1.95; 95% CI, 1.62-2.35). Similarly, the GDM group did not exhibit an elevated risk of cardiac mortality (HR 1.48; 95% CI, 0.50-4.39), whereas the overt diabetes group had a significantly higher risk (HR 2.37; 95% CI, 1.69-3.32). Furthermore, sensitivity analysis focusing on women aged 50 or above showed that the HR of GDM history for all-cause mortality was 1.14 (95% CI, 0.33-3.95) and the HR for cardiac mortality was 1.74 (95% CI, 0.49-6.20). CONCLUSIONS: GDM alone was not associated with an increased risk of all-cause and cardiac mortality, while overt diabetes was significantly associated with both types of mortality.

Ecosystem services at risk from disturbance in Europe's forests.

Global change impacts on disturbances can strongly compromise the capacity of forests to provide ecosystem services to society. In addition, many ecosystem services in Europe are simultaneously provided by forests, emphasizing the importance of multifunctionality in forest ecosystem assessments. To address disturbances in forest ecosystem policies and management, spatially explicit risk analyses that consider multiple disturbances and ecosystem services are needed. However, we do not yet know which ecosystem services are most at risk from disturbances in Europe, where the respective risk hotspots are, nor which of the main disturbance agents are most detrimental to the provisioning of multiple ecosystem services from Europe's forests. Here, we quantify the risk of losing important ecosystem services (timber supply, carbon storage, soil erosion control and outdoor recreation) to forest disturbances (windthrows, bark beetle outbreaks and wildfires) in Europe on a continental scale. We find that up to 12% of Europe's ecosystem service supply is at risk from current disturbances. Soil erosion control is the ecosystem service at the highest risk, and windthrow is the disturbance agent posing the highest risk. Disturbances challenge forest multifunctionality by threatening multiple ecosystem services simultaneously on 19.8 Mha (9.7%) of Europe's forests. Our results highlight priority areas for risk management aiming to safeguard the sustainable provisioning of forest ecosystem services.

Workplace Violence Against Primary Care Clinicians: A Narrative Review.

Workplace violence (WPV) is a commonly reported occupational hazard in healthcare and its prevalence is increasing. WPV occurs in all types of practice settings, but little is known about WPV in primary care settings in the United States (US). Because primary care practice settings differ from the inpatient settings, further examination of WPV in primary care is warranted. Our objective was to summarize the available literature highlight important gaps. We conducted a search using Pubmed and OVID for US studies of WPV in US-based adult primary care practices. Studies including only pediatric populations were excluded. Due to the lack of available literature conducted in US primary care settings, we expanded our search to include international studies. We identified 70 studies of which 5 were US based. Due to the lack of significant numbers of US-based studies, we opted to conduct a narrative review of all available studies. The evidence shows that WPV is a common occurrence in primary care settings in many countries and that the majority of primary care clinicians have experienced at least some form of non-physical violence in their careers. Most of the studies conducted were cross-sectional in design and reported on both non-physical and physical forms of WPV. There was not a consistent trend between genders in experiencing the major forms of WPV, but women were consistently more likely to be subjected to sexual harassment. Potential root causes for WPV could generally be categorized as patient-level, clinician-level, clinical encounter specific, and operational root causes. While most WPV was found to be non-physical, it still had significant emotional and job-related impacts on clinicians. These troubling results highlight the need for further studies to be conducted in the US.

Reply to "Matters arising: cost-effectiveness of first-line immunotherapy combinations with or without chemotherapy for advanced non-small cell lung cancer: a modelling approach".

In this article, we read with great attention the correspondence by Bullement et al., regarding our published study on cost-effectiveness of first-line immunotherapy combinations with or without chemotherapy for advanced non-small cell lung cancer. We referred to a few the most important comments from Bullement et al. in our opinion, including proportional hazard (PH) assumption, accelerated failure time (AFT) model, and health utility, and made some explanations.

Time-dependent efficacy analysis of first-line immunotherapies for advanced non-small cell lung cancer.

BACKGROUND: Many randomized controlled trials (RCTs) and network meta-analyses have demonstrated that the progression-free survival (PFS) and overall survival (OS) of advanced non-small cell lung cancer (NSCLC) patients can be improved through combination immunotherapy or monotherapies. However, time-dependent analysis of the treatment effect is currently lacking. Thus, we aimed to evaluate the efficacy of first-line immunotherapy, and establish a hazard ratio function to reflect the time-varying progression or mortality risk of patients with NSCLC. METHODS: Seventeen clinical trials were selected based on search strategy. Baseline characteristics, including the age, sex, smoking status, geographical region, and Eastern Cooperative Oncology Group (ECOG) performance status of patients, were balanced, resulting in ten immunotherapies from nine appropriate clinical trials to conduct treatment effect comparison. RESULTS: We found that nivolumab plus ipilimumab (nivo + ipi) improved the PFS and OS over time. The hazard ratio of nivo + ipi, relative to that of pembrolizumab, decreased from 1.11 to 0.36 for PFS, and from 0.93 to 0.49 for OS over a 10-year period. In terms of the response to immunotherapy in patients with different PD-L1 expression levels, patients with PD-L1 > = 50% experienced lower rates of progression and a reduced mortality risk over time. The hazard ratio of patients with PD-L1 > = 50% relative to all of the patients decreased from 0.73 to 0.69 for PFS, and from 0.78 to 0.67 for OS. CONCLUSIONS: Based on the fact that time-dependent progression and mortality risk existed during the treatment duration, physicians should select a suitable treatment regimen for patients based on the hazard ratio.

Effect of Chronic Kidney Disease on All-Cause Mortality After Hip Fracture Surgery: A Retrospective Cohort Study.

In this retrospective cohort study, we investigated: (1) The impact of comorbid chronic kidney disease (CKD) on postoperative mortality in patients with a hip fracture; (2) mortality variations by dialysis type, potentially indicating CKD stage; (3) the efficacy of different hip fracture surgical methods in reducing mortality for patients with CKD. This study included 25,760 patients from the Korean National Health Insurance Service-Senior cohort (2002-2019) who underwent hip fracture surgery. Participants were categorized as CKD and Non-CKD. Mortality rate was determined using a generalized linear model with a Poisson distribution. The effect size was presented as a hazard ratio (HR) through a Cox proportional-hazard model. During follow-up, we ascertained that 978 patients (3.8%) had CKD preoperatively. Compared to the Non-CKD group, the mortality risk (HR) in the CKD group was 2.17 times higher (95% confidence interval [CI], 1.99-2.37). In sensitivity analysis, the mortality risk of in patients who received peritoneal dialysis and hemodialysis was 6.21 (95% CI, 3.90-9.87) and 3.62 times (95% CI, 3.11-4.20) higher than that of patients who received conservative care. Mortality risk varied by surgical method: hip hemiarthroplasty (HR, 2.11; 95% CI, 1.86-2.40), open reduction and internal fixation (HR, 2.21; 95% CI, 1.94-2.51), total hip replacement (HR, 2.27; 95% CI, 1.60-3.24), and closed reduction and percutaneous fixation (HR, 3.08; 95% CI, 1.88-5.06). Older patients with CKD undergoing hip fracture surgery had elevated mortality risk, necessitating comprehensive pre- and postoperative assessments and management.

The Association of Stair Climbing Behaviors With Hazard of All-Cause Mortality in Adults With or At Risk of Knee Osteoarthritis.

OBJECTIVE: To investigate the association of stair climbing difficulty and stair climbing frequency with the risk of all-cause mortality over 13 years in adults with or at high risk for knee OA. METHODS: We used data from the Osteoarthritis Initiative (OAI), a prospective cohort study of community-dwelling adults with or at high risk for symptomatic knee OA. The exposures were stair climbing difficulty and frequency, assessed at baseline using self-report questionnaires. The outcome was all-cause mortality, assessed from baseline through 13 years of follow-up. Kaplan-Meier survival curves and Cox proportional hazards regression were used to investigate the association between stair climbing exposures and all-cause mortality. RESULTS: Three hundred seven (6.81%) and 310 (6.84%) participants in the difficulty and frequency samples, respectively, died during 13 years of follow-up. Those who were limited in any capacity in terms of their stair climbing ability had 54% to 84% greater hazard of all-cause mortality, and those who climbed at least 7 flights of stairs per week had 38% lower hazard of all-cause mortality. CONCLUSION: Adults with or at high risk for knee OA who report difficulty with climbing stairs or who infrequently use stairs are at greater hazard of all-cause mortality. Stair climbing difficulty and frequency are simple to collect and changes may occur early in OA progression, allowing for early intervention. Brief questions about stair climbing behaviors can serve as a functional vital sign within the clinician's toolbox.

Genetic and early life factors influence on time-to-multiple sclerosis diagnosis: A UK Biobank study.

BACKGROUND: Previous investigations into multiple sclerosis (MS) risk factors predominantly relied on retrospective studies, which do not consider different follow-up times and assume a constant risk effect throughout lifetime. OBJECTIVE: We aimed to evaluate the impact of genetic and early life factors on MS diagnosis by employing a time-to-event analysis in a prospective cohort. METHODS: We used the UK Biobank data, considering the observation period from birth up to 31 December 2022. We considered genetic risk, using a multiple sclerosis polygenic risk score (MS-PRS), and various early life factors. Tobacco smoking and infectious mononucleosis diagnosis were also considered as time-varying variables along the follow-up. Using a Cox proportional hazards model, we examined the associations between these factors and MS diagnosis instantaneous risk. RESULTS: We analyzed 345,027 participants, of which 1669 had an MS diagnosis. Our analysis revealed age-dependent effects for sex (females vs males) and higher MS-PRS, with greater hazard ratios observed in young adults. CONCLUSION: The age-dependent effects suggest that retrospective studies could have underestimated sex and genetic variants' risk roles during younger ages. Therefore, we emphasize the importance of a time-to-event approach using longitudinal data to better characterize age-dependent risk effects.

A prioritization strategy for functional alternatives to bisphenol A in food contact materials.

The use of bisphenol A (BPA), a substance of very high concern, is proposed to be banned in food contact materials (FCMs) in the European Union. To prevent regrettable substitution of BPA by alternatives with similar or unknown hazardous properties, it is of importance to gain the relevant toxicological information on potential BPA alternative substances and monitor them adequately. We created an inventory of over 300 substances mentioned as potential BPA alternatives in regulatory reports and scientific literature. This study presents a prioritization strategy to identify substances that may be used as an alternative to BPA in FCMs. We prioritized 20 potential BPA alternatives of which 10 are less familiar. We subsequently reviewed the available information on the 10 prioritized less familiar substances regarding hazard profiles and migration potential obtained from scientific literature and in silico screening tools to identify a possible risk of the substances. Major data gaps regarding the hazard profiles of the prioritized substances exist, although the scarce available data give some indications on the possible hazard for some of the substances (like bisphenol TMC, 4,4-dihydroxybenzophenone, and tetrachlorobisphenol A). In addition, very little is known about the actual use and exposure to these substances. More toxicological research and monitoring of these substances in FCMs are, therefore, required to avoid regrettable substitution of BPA in FCM.

A framework for integrating evidence to assess hazards and risk.

To accurately characterize human health hazards, human, animal, and mechanistic data must be integrated and the relevance to the research question of all three lines of evidence must be considered. Mechanistic data are often critical to the full integration of animal and human data and to characterizing relevance and uncertainty. This novel evidence integration framework (EIF) provides a method for synthesizing data from comprehensive, systematic, quality-based assessments of the epidemiological and toxicological literature, including in vivo and in vitro mechanistic studies. It organizes data according to both the observed human health effects and the mechanism of action of the chemical, providing a method to support evidence synthesis. The disease-based component uses the evidence of human health outcomes studied in the best quality epidemiological literature to organize the toxicological data according to authors' stated purpose, with the pathophysiology of the disease determining the potential relevance of the toxicological data. The mechanism-based component organizes the data based on the proposed mechanisms of effect and data supporting events leading to each endpoint, with the epidemiological data potentially providing corroborating information. The EIF includes a method to cross-classify and describe the concordance of the data, and to characterize its uncertainty. At times, the two methods of organizing the data may lead to different conclusions. This facilitates identification of knowledge gaps and shows the impact of uncertainties on the strength of causal inference.

A screening-level human health risk assessment of dietary intake of pesticide residues in produce as compared to consumer guide recommendations.

Consumers are confronted with conflicting information regarding the safety of specific foods. For example, the Environmental Working Group (EWG) publishes an annual consumer guide in which they rank the pesticide contamination of 46 popular fruits and vegetables, which includes designating the 12 with the greatest pesticide contamination as the "Dirty Dozen," to help consumers reduce exposures to toxic pesticides. However, consumer guides like EWG's only incorporate some hazard assessment principles and do not reflect a dietary risk assessment. Therefore, the purpose of this study is to apply risk assessment techniques to EWG's Dirty Dozen list using a uniform screening-level approach to estimate pesticide exposures for U.S. consumers and to characterize the associated chronic human health risks. The most commonly detected pesticide and its representative residue concentrations were identified for each produce type on the 2022 Dirty Dozen list using the USDA Pesticide Data Program database. Estimates of mean dietary consumption in the U.S. were used to calculate dietary exposure to each pesticide-produce combination for adults and children. Pesticide-specific U.S. EPA dietary health-based guidance values (HBGVs) were then used as benchmarks to evaluate the chronic human health risk of consuming each produce type. Overall, the estimated daily exposure for each pesticide-produce combination was below the corresponding HBGV for all exposure scenarios. The current analysis demonstrates that excessive produce-specific pesticide exposure is unexpected as the amount of produce that would need to be consumed on a chronic basis, even among children, far exceeds typical dietary intake. Future research is necessary to assess acute dietary exposure scenarios and to consider cumulative risk.