Bayesian identification and estimation of radon-related increased hazard rates of cancer death in the updated French cohort of uranium miners (1946-2014).

OBJECTIVE: A recent update of the French cohort of uranium miners added seven years of follow-up data. We use these new data to look for new possible radon-related increased risks and refine the estimation of the potential association between cumulative radon exposure and four cancer sites: lung cancer, kidney cancer, brain and central nervous system (CNS) cancer and leukemia (excluding chronic lymphocytic leukemia, which is not radiation-induced). METHODS: Several parametric survival models are proposed, fitted and compared under the Bayesian paradigm, to perform new and original exposure-risk analyses. In line with recent UNSCEAR recommendations, we consider time-related effect modifiers and exposure rate as potential effect modifying factors. We use Bayesian model selection criteria to identify radon-related increased hazard rates. RESULTS: Under the assumption of a linear exposure-risk relationship, we found a substantial evidence for a strictly positive effect of cumulative radon exposure on the hazard rate of death by lung cancer among French uranium miners. Given the current available data under the assumptions of a linear or log-linear exposure-risk relationship, it is not possible to conclude in favour of the absence or the existence of a strictly positive effect of chronic exposure to radon on the hazard rate of death by kidney cancer. Regarding death by brain and CNS cancer, there is a substantial evidence for the absence of radon-related effect. Finally, under the assumption of a log-linear exposure-risk relationship, a small positive radon-related effect appears when looking at the risk of death by leukemia (excluding CLL). CONCLUSION: This study investigates the existence of radon-related increased risk of death by lung cancer, kidney cancer, brain and CNS cancer and leukemia under a Bayesian framework and assumptions of linear and log-linear exposure-risk relationships. If there is no doubt in the interpretation of the results for lung cancer and brain and CNS cancer, the conclusion is less clear-cut in the case of kidney cancer and leukemia (excluding CLL). A future update of the French cohort, increasing the follow-up time for miners, may help to reach a clearer conclusion for these two cancer sites.

A cautionary comment on the generation of Berkson error in epidemiological studies.

Exposure measurement error can be seen as one of the most important sources of uncertainty in studies in epidemiology. When the aim is to assess the effects of measurement error on statistical inference or to compare the performance of several methods for measurement error correction, it is indispensable to be able to generate different types of measurement error. This paper compares two approaches for the generation of Berkson error, which have recently been applied in radiation epidemiology, in their ability to generate exposure data that satisfy the properties of the Berkson model. In particular, it is shown that the use of one of the methods produces results that are not in accordance with two important properties of Berkson error.

Citrulline directly modulates muscle protein synthesis via the PI3K/MAPK/4E-BP1 pathway in a malnourished state: evidence from in vivo, ex vivo, and in vitro studies.

Citrulline (CIT) is an endogenous amino acid produced by the intestine. Recent literature has consistently shown CIT to be an activator of muscle protein synthesis (MPS). However, the underlying mechanism is still unknown. Our working hypothesis was that CIT might regulate muscle homeostasis directly through the mTORC1/PI3K/MAPK pathways. Because CIT undergoes both interorgan and intraorgan trafficking and metabolism, we combined three approaches: in vivo, ex vivo, and in vitro. Using a model of malnourished aged rats, CIT supplementation activated the phosphorylation of S6K1 and 4E-BP1 in muscle. Interestingly, the increase in S6K1 phosphorylation was positively correlated (P < 0.05) with plasma CIT concentration. In a model of isolated incubated skeletal muscle from malnourished rats, CIT enhanced MPS (from 30 to 80% CIT vs. Ctrl, P < 0.05), and the CIT effect was abolished in the presence of wortmannin, rapamycin, and PD-98059. In vitro, on myotubes in culture, CIT led to a 2.5-fold increase in S6K1 phosphorylation and a 1.5-fold increase in 4E-BP1 phosphorylation. Both rapamycin and PD-98059 inhibited the CIT effect on S6K1, whereas only LY-294002 inhibited the CIT effect on both S6K1 and 4E-BP1. These findings show that CIT is a signaling agent for muscle homeostasis, suggesting a new role of the intestine in muscle mass control.

Medical practice patterns and socio-economic factors may explain geographical variation of end-stage renal disease incidence.

BACKGROUND: Incidence rates of renal replacement therapy (RRT) for end-stage renal disease (ESRD) vary geographically not only between but within countries. This study uses data from the French REIN registry to quantify the extent to which socio-economic environment, health care supply and medical practice patterns such as early dialysis initiation or greater propensity to accept frail or elderly patients for dialysis, may explain spatial patterns of ESRD incidence in 85 French districts. METHODS: The association between age- and sex-adjusted incidence rates of RRT in 2008-09 and 17 indicators was explored at the district level with geographically appropriate methods, before and after controlling for the effects of diabetes and the other significant indicators. Rate ratios (RR) and credible intervals (CI) were estimated for a 1-SD increase of each covariate. RESULTS: Crude RRT incidence by district ranged from 85.8 to 225.5 per million inhabitants. The age- and sex-adjusted RRT incidence increased with the proportion of people unemployed (RR: 1.05, 95% CI 1.01-1.09), the population density (RR: 1.07, 95% CI 1.02-1.12) and the prevalence of diabetes (RR: 1.08, 95% CI 1.03-1.12). It also increased with the proportions of incident ESRD patients >85 years (RR: 1.02, 95% CI 0.99-1.06), of deaths within the first 3 months of RRT (RR: 1.03, 95% CI 1.0-1.06) and of nephrologists in private practice (RR: 1.05, 95% CI 1.01-1.08) and with the median estimated glomerular filtration rate (eGFR) at dialysis initiation (RR: 1.06, 95% CI 1.02-1.09). CONCLUSION: This study confirms that socio-economic factors and diabetes explain substantial between-area variations in RRT incidence and highlights the variability of practice patterns, especially decisions about RRT and their potential impact on incidence.

The timing of dialysis initiation affects the incidence of renal replacement therapy.

BACKGROUND: Variations in the timing of dialysis initiation may explain some geographical variations in renal replacement therapy (RRT) incidence, but this effect has never been quantified. METHODS: Using data from the French Renal Epidemiology and Information Network registry, we quantified the association between RRT incidence in 2006-07 and median estimated glomerular filtration rate (eGFR) values before starting dialysis at the administrative district level with geographically appropriate methods. RESULTS: Crude RRT incidence varied from 80.4 to 238.6 pmi between administrative districts, and median eGFR at dialysis initiation from 5.9 to 11.8 ml/min/1.73 m(2). Age- and sex-adjusted RRT incidence, associated with a 1.2-ml/min/1.73m(2) increase in median eGFR, rose 8% (4-13%) before and 9% (5-13%) after controlling for the effect of nine potential socioeconomic and medical risk factors. CONCLUSION: The impact of increased eGFR at initiation should be taken into account in guidelines recommending earlier dialysis start.

Bayesian Profile Regression to Deal With Multiple Highly Correlated Exposures and a Censored Survival Outcome. First Application in Ionizing Radiation Epidemiology.

As multifactorial and chronic diseases, cancers are among these pathologies for which the exposome concept is essential to gain more insight into the associated etiology and, ultimately, lead to better primary prevention strategies for public health. Indeed, cancers result from the combined influence of many genetic, environmental and behavioral stressors that may occur simultaneously and interact. It is thus important to properly account for multifactorial exposure patterns when estimating specific cancer risks at individual or population level. Nevertheless, the risk factors, especially environmental, are still too often considered in isolation in epidemiological studies. Moreover, major statistical difficulties occur when exposures to several factors are highly correlated due, for instance, to common sources shared by several pollutants. Suitable statistical methods must then be used to deal with these multicollinearity issues. In this work, we focused on the specific problem of estimating a disease risk from highly correlated environmental exposure covariates and a censored survival outcome. We extended Bayesian profile regression mixture (PRM) models to this context by assuming an instantaneous excess hazard ratio disease sub-model. The proposed hierarchical model incorporates an underlying truncated Dirichlet process mixture as an attribution sub-model. A specific adaptive Metropolis-Within-Gibbs algorithm-including label switching moves-was implemented to infer the model. This allows simultaneously clustering individuals with similar risks and similar exposure characteristics and estimating the associated risk for each group. Our Bayesian PRM model was applied to the estimation of the risk of death by lung cancer in a cohort of French uranium miners who were chronically and occupationally exposed to multiple and correlated sources of ionizing radiation. Several groups of uranium miners with high risk and low risk of death by lung cancer were identified and characterized by specific exposure profiles. Interestingly, our case study illustrates a limit of MCMC algorithms to fit full Bayesian PRM models even if the updating schemes for the cluster labels incorporate label-switching moves. Then, although this paper shows that Bayesian PRM models are promising tools for exposome research, it also opens new avenues for methodological research in this class of probabilistic models.

Shared and unshared exposure measurement error in occupational cohort studies and their effects on statistical inference in proportional hazards models.

Exposure measurement error represents one of the most important sources of uncertainty in epidemiology. When exposure uncertainty is not or only poorly accounted for, it can lead to biased risk estimates and a distortion of the shape of the exposure-response relationship. In occupational cohort studies, the time-dependent nature of exposure and changes in the method of exposure assessment may create complex error structures. When a method of group-level exposure assessment is used, individual worker practices and the imprecision of the instrument used to measure the average exposure for a group of workers may give rise to errors that are shared between workers, within workers or both. In contrast to unshared measurement error, the effects of shared errors remain largely unknown. Moreover, exposure uncertainty and magnitude of exposure are typically highest for the earliest years of exposure. We conduct a simulation study based on exposure data of the French cohort of uranium miners to compare the effects of shared and unshared exposure uncertainty on risk estimation and on the shape of the exposure-response curve in proportional hazards models. Our results indicate that uncertainty components shared within workers cause more bias in risk estimation and a more severe attenuation of the exposure-response relationship than unshared exposure uncertainty or exposure uncertainty shared between individuals. These findings underline the importance of careful characterisation and modeling of exposure uncertainty in observational studies.

Accounting for Berkson and Classical Measurement Error in Radon Exposure Using a Bayesian Structural Approach in the Analysis of Lung Cancer Mortality in the French Cohort of Uranium Miners.

Many occupational cohort studies on underground miners have demonstrated that radon exposure is associated with an increased risk of lung cancer mortality. However, despite the deleterious consequences of exposure measurement error on statistical inference, these analyses traditionally do not account for exposure uncertainty. This might be due to the challenging nature of measurement error resulting from imperfect surrogate measures of radon exposure. Indeed, we are typically faced with exposure uncertainty in a time-varying exposure variable where both the type and the magnitude of error may depend on period of exposure. To address the challenge of accounting for multiplicative and heteroscedastic measurement error that may be of Berkson or classical nature, depending on the year of exposure, we opted for a Bayesian structural approach, which is arguably the most flexible method to account for uncertainty in exposure assessment. We assessed the association between occupational radon exposure and lung cancer mortality in the French cohort of uranium miners and found the impact of uncorrelated multiplicative measurement error to be of marginal importance. However, our findings indicate that the retrospective nature of exposure assessment that occurred in the earliest years of mining of this cohort as well as many other cohorts of underground miners might lead to an attenuation of the exposure-risk relationship. More research is needed to address further uncertainties in the calculation of lung dose, since this step will likely introduce important sources of shared uncertainty.

New insights into handling missing values in environmental epidemiological studies.

Missing data are unavoidable in environmental epidemiologic surveys. The aim of this study was to compare methods for handling large amounts of missing values: omission of missing values, single and multiple imputations (through linear regression or partial least squares regression), and a fully Bayesian approach. These methods were applied to the PARIS birth cohort, where indoor domestic pollutant measurements were performed in a random sample of babies' dwellings. A simulation study was conducted to assess performances of different approaches with a high proportion of missing values (from 50% to 95%). Different simulation scenarios were carried out, controlling the true value of the association (odds ratio of 1.0, 1.2, and 1.4), and varying the health outcome prevalence. When a large amount of data is missing, omitting these missing data reduced statistical power and inflated standard errors, which affected the significance of the association. Single imputation underestimated the variability, and considerably increased risk of type I error. All approaches were conservative, except the Bayesian joint model. In the case of a common health outcome, the fully Bayesian approach is the most efficient approach (low root mean square error, reasonable type I error, and high statistical power). Nevertheless for a less prevalent event, the type I error is increased and the statistical power is reduced. The estimated posterior distribution of the OR is useful to refine the conclusion. Among the methods handling missing values, no approach is absolutely the best but when usual approaches (e.g. single imputation) are not sufficient, joint modelling approach of missing process and health association is more efficient when large amounts of data are missing.

Global determinants of mortality in under 5s: 10 year worldwide longitudinal study.

OBJECTIVE: To assess at country level the association of mortality in under 5s with a large set of determinants. DESIGN: Longitudinal study. SETTING: 193 United Nations member countries, 2000-09. METHODS: Yearly data between 2000 and 2009 based on 12 world development indicators were used in a multivariable general additive mixed model allowing for non-linear relations and lag effects. MAIN OUTCOME MEASURE: National rate of deaths in under 5s per 1000 live births RESULTS: The model retained the variables: gross domestic product per capita; percentage of the population having access to improved water sources, having access to improved sanitation facilities, and living in urban areas; adolescent fertility rate; public health expenditure per capita; prevalence of HIV; perceived level of corruption and of violence; and mean number of years in school for women of reproductive age. Most of these variables exhibited non-linear behaviours and lag effects. CONCLUSIONS: By providing a unified framework for mortality in under 5s, encompassing both high and low income countries this study showed non-linear behaviours and lag effects of known or suspected determinants of mortality in this age group. Although some of the determinants presented a linear action on log mortality indicating that whatever the context, acting on them would be a pertinent strategy to effectively reduce mortality, others had a threshold based relation potentially mediated by lag effects. These findings could help designing efficient strategies to achieve maximum progress towards millennium development goal 4, which aims to reduce mortality in under 5s by two thirds between 1990 and 2015.