Background Hydraulic fracturing, a method used in Northeastern British Columbia (Canada) to extract natural gas, can release contaminants with potential deleterious health effects on fetal development. To date, the association between hydraulic fracturing activity and birth outcomes has not been evaluated in this region. Objective To evaluate the association between the hydraulic fracturing well density/proximity and birth outcomes (birthweight, head circumference, preterm birth and small for gestational age (SGA)). Methods We used birth records from the Fort St John hospital between December 30, 2006 and December 29, 2016 (n = 6333 births). To estimate gestational exposure, we used inverse distance weighting (IDW) to calculate the density/proximity of hydraulic fracturing wells to pregnant women's postal code centroid. For each birth, we calculated three IDWs using 2.5, 5, and 10 km buffer zones around women's postal code centroid. We used linear and logistic regressions to evaluate associations between quartiles of postal code well density/proximity and birth outcomes, controlling for relevant covariates. Results No associations were found between postal code well density/proximity and head circumference or SGA. A negative association was found between postal code well density/proximity and birthweight for infants born to women in the 2nd quartile of the 10 km buffer (β [95% confidence interval (CI)]: −47.28 g [−84.30; −10.25]), and in the 2nd (β [95% CI]: −40.87 g [−78.01; −3.73]) and 3rd (β [95% CI]: −42.01 g [−79.15; −4.87]) quartiles of the 5 km buffer. Increased odds of preterm birth were observed among women in the 2nd quartile of the 2.5 km buffer (odds ratio (OR) [95% CI]: 1.60 [1.30; 2.43]). Conclusions This is the first epidemiological study in Northeastern British Columbia evaluating associations between hydraulic fracturing and health outcomes. Our results show inconsistent patterns of association between hydraulic fracturing, preterm birth and reduced birthweight, and effect estimates did not match expected dose-response relationships.

Objectives A vast data mining project called ‘TRACking and moniToring Occupational Risks in agriculture’ (TRACTOR) was initiated in 2017 to investigate work-related health events among the entire French agricultural workforce. The goal of this work is to present the TRACTOR project, the challenges faced during its implementation, to discuss its strengths and limitations and to address its potential impact for health surveillance. Methods Three routinely collected administrative health databases from the National Health Insurance Fund for Agricultural Workers and Farmers (MSA) were made available for the TRACTOR project. Data management was required to properly clean and prepare the data before linking together all available databases. Results After removing few missing and aberrant data (4.6% values), all available databases were fully linked together. The TRACTOR project is an exhaustive database of agricultural workforce (active and retired) from 2002 to 2016, with around 10.5 million individuals including seasonal workers and farm managers. From 2012 to 2016, a total of 6 906 290 individuals were recorded. Half of these individuals were active and 46% had at least one health event (e.g. declared chronic disease, reimbursed drug prescription) during this 5-year period. Conclusions The assembled MSA databases available in the TRACTOR project are regularly updated and represent a promising and unprecedent dataset for data mining analysis dedicated to the early identification of current and emerging work-related illnesses and hypothesis generation. As a result, this project could help building a prospective integrated health surveillance system for the benefit of agricultural workers.

Objectives The occupational environment represents an important source of exposures to multiplehazards for workers’ health. Although it is recognized that mixtures of agents may have differenteffects on health compared to their individual effects, studies generally focus on the assessment ofindividual exposures. Our objective was to identify occupational co-exposures occurring in the United States using the multi-industry occupational exposure databank of the Occupational Safety and Health Administration (OSHA). Methods Using OSHA’s Integrated Management Information System (IMIS), measurement data from workplace inspections occurring from 1979 to 2015 were examined. We defined a workplace situation (WS) by grouping measurements that occurred within a company, within the same occupation (i.e. job title) within 1 year. All agents present in each WS were listed and the resulting databank was analyzed with the Spectrosome approach, a methodology inspired by network science, to determine global patterns of co-exposures. The presence of an agent in a WS was defined either as detected, or measured above 20% of a relevant occupational exposure limit (OEL). Results Among the 334 648 detected exposure measurements of 105 distinct agents collected from 14 513 US companies, we identified 125 551 WSs, with 31% involving co-exposure. Fifty-eight agents were detected with others in >50% of WSs, 29 with a proportion >80%. Two clusters were highlighted, one for solvents and one for metals. Toluene, xylene, acetone, hexone, 2-butanone, and N-butyl acetate formed the basis of the solvent cluster. The main agents of the metal cluster were zinc, iron, lead, copper, manganese, nickel, cadmium, and chromium. 68 556 WS were included in the analyses based on levels of exposure above 20% of their OEL, with 12.4% of co-exposure. In this analysis, while the metal cluster remained, only the combinations of toluene with xylene or 2-butanone were frequently observed among solvents. An online web application allows the examination of industry specific patterns. Conclusions We identified frequent co-exposure situations in the IMIS databank. Using the spectrome approach, we revealed global combination patterns and the agents most often implicated. Future work should endeavor to explore the toxicological effects of prevalent combinations of exposures on workers’ health to prioritize research and prevention efforts.

Given the increased prevalence of cancer, respiratory diseases, and reproductive disorders, for which multifactorial origins are strongly suspected, the impact of the environment on the population represents a substantial public health challenge. Surveillance systems have become an essential public health decision-making tool. Networks have been constructed to facilitate the development of analyses of the multifactorial aspects of the relationships between occupational contexts and health. The aim of this study is to develop and present an approach for the optimal exploitation of observational databases to describe and improve the understanding of the (occupational) environment–health relationships, taking into account key multifactorial aspects. We have developed a spectral analysis (SA) approach that takes into account both the multi-exposure and dynamic natures of occupational health problems (OHPs) and related associations. The main results of this paper are to present the construction method of the “spectrum” and “spectrosome” of OHPs (range and structured list of occupational exposures) and describe the information contained therein with an illustrative example. The approach is illustrated using the case of non-Hodgkin lymphoma (NHL) from the French National Occupational Diseases Surveillance and Prevention Network database as a working example of an occupational disease. We found that the NHL spectrum includes 40 sets of occupational exposures characterized by important multi-exposures, especially solvent combinations or pesticide combinations, but also specific exposures such as polycyclic aromatic hydrocarbons, formaldehyde and ionizing radiation. These findings may be useful for surveillance and the assessment of occupational exposure related to health risks.

Background: Children are exposed to p,p'-dichlorodiphenyltrichloroethane (p,p'-DDT) and p,p'dichlorodiphenyldichloroethylene (p,p'-DDE) through placental and lactational transfer. Some studies have suggested that early-life exposure to these compounds could lead to increased body mass index (BMI) during childhood. Our aim was to assess whether children's exposure during the first 2 years of life is associated with BMI z-score in Japanese children at 42 months of age. Methods: We used data from a birth cohort (n = 290) of the Tohoku Study of Child Development. p,p'-DDT and p, p'-DDE levels were measured in breast milk samples collected 1 month after birth, and levels in children were estimated using a toxicokinetic model for three exposure periods (0-6 months, 6-12 months, 12-24 months). Associations between exposure estimates and BMI z-score at 42 months of age were assessed using multivariate linear regression models. Results: We found no significant association between levels of p,p'-DDT measured in breast milk or estimated in children and BMI z-score. However, we observed associations between estimated p,p'-DDE levels in girls during all postnatal exposure periods and BMI z-score; for each log increase in the estimated p,p'-DDE levels, BMI z-score increased by 0.23 (C.I. 95%: 0.01, 0.45) for the 0-6 months exposure period, 0.26 (C.I. 95%: 0.06, 0.47) for the 6-12 months exposure period, and 0.24 (C.I. 95%: 0.05, 0.43) for the 12-24 months exposure period. Conclusion: In this study of Japanese children, estimated postnatal p,p'-DDE levels were associated with increased BMI z-score at 42 months of age, mostly in girls. These results are in line with previous studies supporting that earlylife exposure to p,p'-DDE may be associated with higher BMI during childhood.

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This difference justifies the usefulness of taking into account the multiplicity of causes leading to a health event, which is a clear asset of the Spectrosome method.

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