Multidimensional analysis of the effect of occupational exposure to organic solvents on lung cancer risk: the ICARE study

Unlocking the secrets: Volatile Organic Compounds (VOCs) and their devastating effects on lung cancer

Lung cancer (LCs) is still a serious health problem globally, with many incidences attributed to environmental triggers such as Volatile Organic Compounds (VOCs). VOCs are a broad class of compounds that can be released via various sources, including industrial operations, automobile emissions, and indoor air pollution. VOC exposure has been linked to an elevated risk of lung cancer via multiple routes. These chemicals can be chemically converted into hazardous intermediate molecules, resulting in DNA damage and genetic alterations. VOCs can also cause oxidative stress, inflammation, and a breakdown in the cellular protective antioxidant framework, all of which contribute to the growth of lung cancer. Moreover, VOCs have been reported to alter critical biological reactions such as cell growth, apoptosis, and angiogenesis, leading to tumor development and metastasis. Epidemiological investigations have found a link between certain VOCs and a higher probability of LCs. Benzene, formaldehyde, and polycyclic aromatic hydrocarbons (PAHs) are some of the most well-researched VOCs, with comprehensive data confirming their cancer-causing potential. Nevertheless, the possible health concerns linked with many more VOCs and their combined use remain unknown, necessitating further research. Identifying the toxicological consequences of VOCs in LCs is critical for establishing focused preventative tactics and therapeutic strategies. Better legislation and monitoring mechanisms can limit VOC contamination in occupational and environmental contexts, possibly reducing the prevalence of LCs. Developing VOC exposure indicators and analyzing their associations with genetic susceptibility characteristics may also aid in early identification and targeted therapies.

Benzene Exposure and Lung Cancer Risk: A Systematic Review and Meta-Analysis of Human Studies

Lung cancer is a leading cause of death with nearly 1.8 million deaths estimated worldwide in 2020. Although benzene is classified as a human carcinogen (Group 1) on the basis of its association with acute myeloid/non-lymphocytic leukaemia, there is still limited evidence that it may influence lung cancer risk. This study examined the potential link between benzene exposure and risk of lung cancer using a systematic review of epidemiological studies and meta-analysis. We searched through PubMed, Web of Science and Scopus databases up to 10 February 2023 to identify all articles on the association between benzene exposure and lung cancer (incidence or prevalence) and/or mortality. We extracted the risk estimates of the highest and the lowest reported categories of benzene exposure and conducted a meta-analysis using a random-effects model. Heterogeneity and publication bias were analysed using an I2 test and funnel plots asymmetry, respectively. Twenty-one studies were included in the final analysis, with a total of 10,750 lung cancer cases and 2899 lung cancer deaths. Overall, risk estimates of lung cancer prevalence and mortality in association with benzene exposure were 1.20 (n = 14; 95% CI 1.05-1.37) and 1.15 (n = 13; 95% CI 1.02-1.30), respectively. In all cases, heterogeneity was quite large, while no significant publication bias was observed. When only studies that adjusted for smoking habit were selected, the risk for lung cancer increased by up to 34% (n = 9; 95% CI 1.10-1.64). Our data, which show a strong association between benzene exposure and lung cancer risk, may have important public health implications. However, further studies are needed to identify the lung cancer risk associated with benzene exposure considering different smoking conditions.

Dirichlet process mixture models for regression discontinuity designs

The regression discontinuity design is a quasi-experimental design that estimates the causal effect of a treatment when its assignment is defined by a threshold for a continuous variable. The regression discontinuity design assumes that subjects with measurements within a bandwidth around the threshold belong to a common population, so that the threshold can be seen as a randomising device assigning treatment to those falling just above the threshold and withholding it from those who fall below. Bandwidth selection represents a compelling decision for the regression discontinuity design analysis as results may be highly sensitive to its choice. A few methods to select the optimal bandwidth, mainly from the econometric literature, have been proposed. However, their use in practice is limited. We propose a methodology that, tackling the problem from an applied point of view, considers units' exchangeability, that is, their similarity with respect to measured covariates, as the main criteria to select subjects for the analysis, irrespectively of their distance from the threshold. We cluster the sample using a Dirichlet process mixture model to identify balanced and homogeneous clusters. Our proposal exploits the posterior similarity matrix, which contains the pairwise probabilities that two observations are allocated to the same cluster in the Markov chain Monte Carlo sample. Thus we include in the regression discontinuity design analysis only those clusters for which we have stronger evidence of exchangeability. We illustrate the validity of our methodology with both a simulated experiment and a motivating example on the effect of statins on cholesterol levels.

Lung cancer and occupational exposure: hospital-based case-control study

OBJECTIVE

To analyze the relationship between occupation and lung cancer in patients at a national referral hospital for cancer care in southern Brazil.

METHOD

Hospital-based case-control study conducted between February and October 2019. Occupational histories were coded according to international classifications and translated into occupations associated with lung cancer (List A and B). The odds ratios were adjusted for smoking, with a 95% confidence interval, calculated by conditional logistic regression.

RESULTS

99 cases and 227 controls were included. Among men, the occupation of painters (list A) was associated with lung cancer (OR = 14.3; 95% CI: 1.8-116.5), there were no occupations in list B associated with lung cancer. In women, no increased risks were found.

CONCLUSIONS

It has been shown that exposure to occupational carcinogens among repair and construction painters increases the risk of lung cancer.

Câncer de pulmão e exposição ocupacional: estudo caso-controle de base hospitalar

RESUMO Objetivo Analisar a relação entre ocupação e câncer de pulmão em pacientes de um hospital de referência nacional para atendimento oncológico no Sul do Brasil. Método Estudo caso-controle de base hospitalar realizado entre fevereiro e outubro de 2019. Os históricos ocupacionais foram codificados de acordo com classificações internacionais e traduzidos em ocupações associadas ao câncer de pulmão (Lista A e B). As odds ratios foram ajustadas para tabagismo, com intervalo de confiança de 95%, calculadas por regressão logística condicional. Resultados Foram incluídos 99 casos e 227 controles. Entre os homens a ocupação de pintores (lista A) estava associada ao câncer de pulmão (OR= 14,3; IC 95%: 1,8-116,5), não houve ocupações da lista B associadas ao câncer de pulmão. Nas mulheres não foram encontrados riscos aumentados. Conclusões Evidenciou-se que a exposição aos carcinógenos ocupacionais entre pintores de reparo e da construção civil aumentam o risco de câncer de pulmão.

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.

[Primary lung cancer and occupational exposure in a North African population]

Les cancers broncho-pulmonaires (CBP) sont parmi les cancers les plus fréquents, de pronostic redoutable, l´origine professionnelle est fréquente, mais souvent sous-estimés. L'objectif était d´évaluer la proportion des Cancers Broncho-pulmonaires (CBP) présumés d´origine professionnelle et de rechercher la relation entre la nature de l´exposition et le type histologique du CBP. Cette étude épidémiologique rétrospective, a été réalisée au service de pneumologie de l´Etablissement Public Hospitalier (EPH) de Rouïba. Entre janvier 2014 et juin 2019, nous avons colligé 357 cas atteints de CBP avec preuve histologique. Le recueil des histoires médicales et professionnelles fut effectué. Les matrices emploi-exposition ont été utilisées pour le repérage des différentes expositions professionnelles. La population d´étude comprenait 357 patients dont la moyenne d´âge était de 63,9±11,1 ans et un sex-ratio de 7,4 hommes pour une femme. Il y avait 76,5% des sujets qui fumaient ou avaient fumé en moyenne 42 P/A. Le type histologique était dans 88,8% un carcinome bronchique non à petite cellule. L'ensemble des professions étudiées seraient responsables de 50,7% des cancers bronchiques primitifs, dont 26,5% pour les professions de chauffeurs poids lourds et conducteurs d´engins. L´imputabilité des cancers broncho-pulmonaires (CBP) à l´origine professionnelle est loin d´être négligeable mais souvent méconnue; du fait du caractère multifactoriel et du temps de latence entre l´exposition et l´apparition de la maladie, avec un impact sur le type histologique du cancer broncho-pulmonaire.

Clustering method for censored and collinear survival data

In this paper we propose a Dirichlet process mixture model for censored survival data with covariates. This model is suitable in two scenarios. First, this method can be used to identify clusters determined by both the censored survival data and the predictors. Second, this method is suitable for highly correlated predictors, in cases when the usual survival models cannot be implemented because they would be unstable due to multicollinearity. The Dirichlet process mixture model links a response vector to covariate data through cluster membership and in this paper this model is extended for mixtures of Weibull distributions, which can be used to model survival times and also allow for censoring. We propose two variants of this model, one with a shape parameter common to all clusters (referred to as a global parameter) for the Weibull distributions and one with a cluster-specific shape parameter. The first satisfies the proportional hazard assumption, while the latter is very flexible, as it has the advantage of allowing estimation of the survival curve whether or not the proportional hazards assumption is satisfied. We present a simulation study and, to demonstrate the applicability of the method in practice, a real application to sleep surveys in older women from The Australian Longitudinal Study on Women’s Health. The method developed in the paper is available in the R package PReMiuM.

Modeling tails for collinear data with outliers in the English Longitudinal Study of Ageing: Quantile profile regression

Research has shown that high blood glucose levels are important predictors of incident diabetes. However, they are also strongly associated with other cardiometabolic risk factors such as high blood pressure, adiposity, and cholesterol, which are also highly correlated with one another. The aim of this analysis was to ascertain how these highly correlated cardiometabolic risk factors might be associated with high levels of blood glucose in older adults aged 50 or older from wave 2 of the English Longitudinal Study of Ageing (ELSA). Due to the high collinearity of predictor variables and our interest in extreme values of blood glucose we proposed a new method, called quantile profile regression, to answer this question. Profile regression, a Bayesian nonparametric model for clustering responses and covariates simultaneously, is a powerful tool to model the relationship between a response variable and covariates, but the standard approach of using a mixture of Gaussian distributions for the response model will not identify the underlying clusters correctly, particularly with outliers in the data or heavy tail distribution of the response. Therefore, we propose quantile profile regression to model the response variable with an asymmetric Laplace distribution, allowing us to model more accurately clusters that are asymmetric and predict more accurately for extreme values of the response variable and/or outliers. Our new method performs more accurately in simulations when compared to Normal profile regression approach as well as robustly when outliers are present in the data. We conclude with an analysis of the ELSA.

Occupational and work-related disease underestimated and linked to temporary disability through Primary Health Care Services

AIM

To estimate the occupational disease linked to temporary incapacity (not work-related illness).

BACKGROUND

The under-reporting of occupational disease is a recognized problem in Europe.

METHODS

Cross-sectional study conducted in Spanish adult population using periods of temporary incapacity recorded in primary care during 2015. Rates of occupational disease were estimated using the García & Gadea study.

RESULTS

A total of 130,771 episodes of temporary incapacity were studied from 91,448 people, and 56,092 were women (61.3%). The overall prevalence rate was 2,096 and was higher in women (2,504) than in men (1,665) (p < 0.001). It was estimated that 6,580 (7.2%) could be suffering from an occupational disease. Musculoskeletal complaints were the commonest, affecting 75,165 people (82.2%). For the binary logistic regression, the dependent variable was the existence or lack of incapacity due to a musculoskeletal pathology. Age acts as a protective factor (adjusted OR = 0.978). The periods off work due to musculoskeletal complaints are lower in the first quarter of the year [OR > 1; p < 0.001].

CONCLUSIONS

7.2% of the population was estimated to be off work due to an occupational disease. This estimate reflects the well-known under-reporting of occupational diseases which are dealt with by the Public Health System instead of specialized clinics.

IMPLICATIONS FOR NURSING MANAGEMENT

The interdisciplinary role of the occupational health nurse makes it an adequate professional to detect the cases of occupational diseases in primary care settings. Occupational Health Nursing has been recognized by the managers as the most suitable professionals to carry out the screening of occupational diseases in Primary Care.

Multi-pollutant Modeling Through Examination of Susceptible Subpopulations Using Profile Regression

PURPOSE OF REVIEW

The inter-correlated nature of exposure-based risk factors in environmental health studies makes it a challenge to determine their combined effect on health outcomes. As such, there has been much research of late regarding the development and utilization of methods in the field of multi-pollutant modeling. However, much of this work has focused on issues related to variable selection in a regression context, with the goal of identifying which exposures are the "bad actors" most responsible for affecting the health outcome of interest. However, the question addressed by these approaches does not necessarily represent the only or most important questions of interest in a multi-pollutant modeling context, where researchers may be interested in health effects from co-exposure patterns and in identifying subpopulations associated with patterns defined by different levels of constituent exposures.

RECENT FINDINGS

One approach to analyzing multi-pollutant data is to use a method known as Bayesian profile regression, which aids in identifying susceptible subpopulations associated with exposure mixtures defined by different levels of each exposure. Identification of exposure-level patterns that correspond to a location may provide a starting point for policy-based exposure reduction. Also, in a spatial context, identification of locations with the most health-relevant exposure-mixture profiles might provide further policy relevant information. In this brief report, we review and describe an approach that can be used to identify exposures in subpopulations or locations known as Bayesian profile regression. An example is provided in which we examine associations between air pollutants, an indicator of healthy food retailer availability, and indicators of poverty in Los Angeles County. A general tread suggesting that vulnerable individuals are more highly exposed and have limited access to healthy food retailers is observed, though the associations are complex and non-linear.

Associations between occupational exposure to benzene, toluene and xylene and risk of lung cancer in Montréal

BACKGROUND

Benzene, toluene and xylene (BTX) are aromatic hydrocarbons with inconclusive evidence of lung carcinogenicity. The aim of this research was to assess the associations between occupational exposures to BTX agents and lung cancer.

METHODS

In a population-based case-control study of lung cancer, occupational histories were obtained and exposures were assessed by experts. Unconditional multivariate logistic regression was used to estimate ORs and 95% CIs, among men, between various metrics of occupational exposure to BTX and lung cancer, while adjusting for established and possible risk factors.

RESULTS

Considerable overlap was found between occupational exposure to BTX, where the majority of exposed participants were exposed to all three chemicals. Lung cancer was associated with exposure to benzene (OR=1.35; 95% CI 0.99 to 1.84), toluene (OR=1.31; 95% CI 0.99 to 1.74) and xylene (OR=1.44; 95% CI 1.03 to 2.01). While these results were adjusted for smoking and other recognised and possible lung cancer risk factors, they were not mutually adjusted among the three BTX agents.

CONCLUSIONS

Our study provides suggestive evidence that occupational exposure to one or more of the BTX agents may be associated with lung cancer.

Occupational exposure to textile dust and lung cancer risk: Results from the ICARE Study

BACKGROUND

To investigate the association of lung cancer with occupational exposure to textile dust and specifically to cotton dust in the population-based case-control study ICARE.

METHODS

Lifelong occupational history of 2926 cases and 3555 controls was collected using standardized questionnaires, with specific questions for textile dust exposure. Odds ratios (ORs) and 95% confidence intervals (CI) were estimated using unconditional logistic regression models controlling for confounding factors including smoking and asbestos exposure.

RESULTS

An inverse association between textile dust exposure and lung cancer was found among workers exposed ≥5% of their work time (OR = 0.80, 95%CI = 0.58-1.09), more pronounced for distant exposures (40+ years; up to a 56% reduced risk, statistically significant). The OR of lung cancer was significantly decreased among workers exposed to cotton fibers (OR = 0.70, 95%CI = 0.48-0.97).

CONCLUSIONS

Our results provide some evidence of a decreased risk of lung cancer associated with exposure to textile dust, particularly cotton.

Respiratory effects of trichloroethylene

Trichloroethylene (TCE) is a chlorinated solvent that has been used widely around the world in the twentieth century for metal degreasing and dry cleaning. Although TCE displays general toxicity and is classified as a human carcinogen, the association between TCE exposure and respiratory disorders are conflicting. In this review we aimed to systematically evaluate the current evidence for the respiratory effects of TCE exposure and the implications for the practicing clinician. There is limited evidence of an increased risk of lung cancer associated with TCE exposure based on animal and human data. However, the effect of other chlorinated solvents and mixed solvent exposure should be further investigated. Limited data are available to support an association between TCE exposure and respiratory tract disorders such as asthma, chronic bronchitis, or rhinitis. The most consistent data is the association of TCE with autoimmune and vascular diseases such as systemic sclerosis and pulmonary veno-occlusive disease. Although recent data are reassuring regarding the absence of an increased lung cancer risk with TCE exposure, clinicians should be aware of other potential respiratory effects of TCE. In particular, occupational exposure to TCE has been linked to less common conditions such as systemic sclerosis and pulmonary veno-occlusive disease.