A 34-year overview of night work by occupation and industry in France based on census data and a sex-specific job-exposure matrix.

BACKGROUND: Night work has been increasing in the last decades due to new working arrangements for good and services production. Numerous studies have shown that night shift work causes disruptions in circadian rhythms that may affect health. In 2019, night shift work was classified as probably carcinogenic to humans by the International Agency for Research on Cancer, and may contribute to other health disorders. In this context, we assessed the number and proportion of workers exposed to night work today and investigated time trends by occupation and industry in France since 1982 in terms of prevention. METHODS: Using the data on work time schedules collected in the French Labour Force Surveys, sex- and period-specific job-exposure matrices (JEMs) to night work (working between midnight and 5 AM) were developed. After linkage of the JEMs with data of the national censuses of 1982, 1990, 1999, 2007 and 2015, the numbers and proportions of workers usually or occasionally exposed to night work were estimated. RESULTS: The number of night workers (usual and occasional) increased from 3.67 million in 1982 to 4.37 million in 2015 (15.8% vs 16.4%). Night work was more common in men than in women (e.g. 22.4% vs 10.0% in 2015), and usual night work largely increased after 2000 (4.4% in 1999, 7.2% in 2007). In 2015, 1.29 million men worked usually at night, including 882,000 workers in the service sector (63%) and 360,000 in the manufacturing and extracting industries (28%). For the same period, 581,000 women were usual night workers, most of them being employed in the service sector (90%). Among women, a 97% increase of usual night work was observed between 1982 and 2015. CONCLUSIONS: This study shows that night work involves a growing number of workers in France, particularly in women in the service sector. These results raise concern about the public health impact of night work and particularly about the numbers of outcomes attributable to this exposure such as breast or prostate cancers.

Risk of lung cancer among women in relation to lifetime history of tobacco smoking: a population-based case-control study in France (the WELCA study).

BACKGROUND: This study aims to provide new insights on the role of smoking patterns and cigarette dependence in female lung cancer, and to examine differences by histological subtype. METHODS: We conducted a population-based case-control study in the great Paris area among women including 716 incident cases diagnosed between 2014 and 2017 and 757 age-matched controls. Detailed data on smoking history was collected during in-person interviews to assess intensity and duration of tobacco smoking, time since cessation, smoking habits (depth of smoke inhalation, use of filter, type of tobacco, and type of cigarettes) and Fagerström test for cigarette dependence. The comprehensive smoking index (CSI), a score modelling the combined effects of intensity, duration and time since quitting smoking was determined for each subject. Multivariable logistic regression models were fitted to calculate odds ratios (ORs) and their confidence intervals (95%CI) of lung cancer associated with smoking variables. RESULTS: Lung cancer risk increased linearly with intensity and duration of tobacco smoking while it decreased with time since cessation, to reach the risk in never-smokers after 20 years of abstinence. The combined effect of intensity and duration of tobacco smoking was more than multiplicative (p-interaction 0.012). The OR in the highest vs the lowest quartile of CSI was 12.64 (95%CI 8.50; 18.80) (p-trend < 0.001). The risk of small cell or squamous cell carcinomas increased with the CSI more sharply than the risk of adenocarcinomas. Deep smoke inhalation, dark vs blond tobacco, conventional vs light cigarettes, and unfiltered vs filtered cigarettes, as well as having mixed smoking habits, were found to be independent risk factors. Having high cigarette addiction behaviours also increased the risk after adjusting for CSI. CONCLUSION: This study provides additional insights on the effects of tobacco smoking patterns on lung cancer risk among women.

Conditionality of COVID-19 vaccine acceptance in European countries.

The COVID-19 vaccine rollout has offered a powerful preventive measure to help control SARS-CoV-2 transmission. Nevertheless, long-standing public hesitation around vaccines heightened concerns that vaccine coverage would not achieve desired public health impacts, particularly in light of more contagious variants. This cross-sectional survey was conducted online just before the European vaccine rollout in December 2020 among 7000 respondents (aged 18-65) in Belgium, France, Germany, Italy, Spain, Sweden, and Ukraine. The survey included open text boxes for fuller explanation of responses. Overall, 56.9% of respondents would accept a COVID-19 vaccine, 19.0% would not, and 24.1% did not know or preferred not to say. By country, between 44% (France) and 66% (Italy) of respondents would accept a COVID-19 vaccine. Respondents expressed conditionality in open responses, voicing concerns about vaccine safety and mistrust of authorities. We highlight lessons learned about the dynamism of vaccine conditionality and persistence of safety concerns.

Sleep Traits, Night Shift Work and Lung Cancer Risk among Women: Results from a Population-Based Case-Control Study in France (The WELCA Study).

Circadian rhythm disruption due to night shift work and/or sleep disorders is associated with negative health outcomes including cancer. There is only scant evidence of an association with lung cancer, unlike breast and prostate cancer. We explore the role of sleep disorders and night shift work in lung cancer risk among women in a population-based case-control study, including 716 lung cancer cases and 758 controls. Multivariable logistic regression models were used to estimate odds ratios (OR) and 95% confidence intervals (CI) associated with sleep duration per day (<7 h, 7−7.9 h, ≥8 h), a summary index of sleep disorders, chronotype, and night shift work exposure metrics. When compared to women with an average sleep duration of 7−7.9 h per day, the OR was 1.39 (95% CI 1.04−1.86) in long sleepers (≥8 h) and 1.16 (95% CI 0.86−1.56) in short sleepers (<7 h). Overall, lung cancer was not associated with the sleep disorder index, nor with night shift work, regardless of the duration of night work or the frequency of night shifts. However, elevated OR associated with the sleep disorder index were found in the subgroup of current smokers. The U-shaped association of lung cancer with sleep duration was more particularly pronounced among women who worked at night ≥5 years. Our findings suggested that sleep patterns are associated with lung cancer risk in women with a potential modifying effect by night shift work duration or tobacco smoking.

Breast cancer risk in relation to ambient concentrations of nitrogen dioxide and particulate matter: results of a population-based case-control study corrected for potential selection bias (the CECILE study).

BACKGROUND: There is only scant evidence that air pollution increases the risk of breast cancer. OBJECTIVES: We investigated this relationship for three air pollutants: nitrogen dioxide (NO2) and particulate matter with an aerodynamical diameter below 10 µm (PM10) and 2.5 µm (PM2.5). METHODS: We conducted a population-based case-control study on breast cancer in two French départements, including 1,229 women diagnosed with breast cancer in 2005-2007 and 1,316 control women frequency-matched on age. Concentrations of NO2, PM10 and PM2.5 at participants' addresses occupied during the last 10 years were assessed using a chemistry transport model. Odds ratios (OR) and 95% confidence intervals (95% CI) were estimated using multivariable logistic regression models where each woman was assigned a weight depending on her probability of selection into the study. RESULTS: The OR for breast cancer per 10-µg/m3 increase in NO2 was 1.11 (95% CI, 0.98, 1.26), and 1.41 (95% CI 1.07, 1.86) in the highest exposure quintile (Q5), compared to the first. The ORs per 10-µg/m3 NO2 did not markedly differ between pre- (OR 1.09, 95% CI 0.89, 1.35)) and post-menopausal women (OR 1.14, 95% CI 0.97, 1.33)), but the OR was substantially higher for hormone-receptor positive (ER+/PR+) breast tumor subtypes (OR 1.15, 95% CI 1.00, 1.31) than for ER-/PR- tumors (OR 0.95, 95% CI 0.72, 1.26). Breast cancer risk was not associated with either PM10 (OR per 1 µg/m3 1.01, 95% CI, 0.96, 1.06) or PM2.5 (OR per 1 µg/m3 1.02, 95% CI 0.95, 1.08), regardless of the menopausal status or of the breast tumor subtype. DISCUSSION: Our study provides evidence that NO2 exposure, a marker of traffic-related air pollutants, may be associated with an increased risk of breast cancer, particularly ER+/PR+ tumors.

Accuracy of using natural language processing methods for identifying healthcare-associated infections.

OBJECTIVE: There is a growing interest in using natural language processing (NLP) for healthcare-associated infections (HAIs) monitoring. A French project consortium, SYNODOS, developed a NLP solution for detecting medical events in electronic medical records for epidemiological purposes. The objective of this study was to evaluate the performance of the SYNODOS data processing chain for detecting HAIs in clinical documents. MATERIALS AND METHODS: The collection of textual records in these hospitals was carried out between October 2009 and December 2010 in three French University hospitals (Lyon, Rouen and Nice). The following medical specialties were included in the study: digestive surgery, neurosurgery, orthopedic surgery, adult intensive-care units. Reference Standard surveillance was compared with the results of automatic detection using NLP. Sensitivity on 56 HAI cases and specificity on 57 non-HAI cases were calculated. RESULTS: The accuracy rate was 84% (n = 95/113). The overall sensitivity of automatic detection of HAIs was 83.9% (CI 95%: 71.7-92.4) and the specificity was 84.2% (CI 95%: 72.1-92.5). The sensitivity varies from one specialty to the other, from 69.2% (CI 95%: 38.6-90.9) for intensive care to 93.3% (CI 95%: 68.1-99.8) for orthopedic surgery. The manual review of classification errors showed that the most frequent cause was an inaccurate temporal labeling of medical events, which is an important factor for HAI detection. CONCLUSION: This study confirmed the feasibility of using NLP for the HAI detection in hospital facilities. Automatic HAI detection algorithms could offer better surveillance standardization for hospital comparisons.

Use of emergency department electronic medical records for automated epidemiological surveillance of suicide attempts: a French pilot study.

The aim of this study was to determine whether an expert system based on automated processing of electronic health records (EHRs) could provide a more accurate estimate of the annual rate of emergency department (ED) visits for suicide attempts in France, as compared to the current national surveillance system based on manual coding by emergency practitioners. A feasibility study was conducted at Lyon University Hospital, using data for all ED patient visits in 2012. After automatic data extraction and pre-processing, including automatic coding of medical free-text through use of the Unified Medical Language System, seven different machine-learning methods were used to classify the reasons for ED visits into "suicide attempts" versus "other reasons". The performance of these different methods was compared by using the F-measure. In a test sample of 444 patients admitted to the ED in 2012 (98 suicide attempts, 48 cases of suicidal ideation, and 292 controls with no recorded non-fatal suicidal behaviour), the F-measure for automatic detection of suicide attempts ranged from 70.4% to 95.3%. The random forest and naïve Bayes methods performed best. This study demonstrates that machine-learning methods can improve the quality of epidemiological indicators as compared to current national surveillance of suicide attempts.

Annotation methods to develop and evaluate an expert system based on natural language processing in electronic medical records.

The objective of the SYNODOS collaborative project was to develop a generic IT solution, combining a medical terminology server, a semantic analyser and a knowledge base. The goal of the project was to generate meaningful epidemiological data for various medical domains from the textual content of French medical records. In the context of this project, we built a care pathway oriented conceptual model and corresponding annotation method to develop and evaluate an expert system's knowledge base. The annotation method is based on a semi-automatic process, using a software application (MedIndex). This application exchanges with a cross-lingual multi-termino-ontology portal. The annotator selects the most appropriate medical code proposed for the medical concept in question by the multi-termino-ontology portal and temporally labels the medical concept according to the course of the medical event. This choice of conceptual model and annotation method aims to create a generic database of facts for the secondary use of electronic health records data.