Impact of depressive symptoms on worklife expectancy:a longitudinal study on Danish employees

Objective Depressive symptoms are associated with sickness absence, work disability and unemployment, but little is known about worklife expectancy (WLE). This study investigates the impact of depressive symptoms on the WLE of a large sample of Danish employees. Methods We used occupational health survey data of 11 967 Danish employees from 2010 and linked them with register data on salary and transfer payments from 2010 to 2015. Depressive symptoms were self-reported using the Major Depression Inventory. We used multistate data and a life table approach with Cox proportional hazard modelling to estimate the WLE of employees, expressed by time in work, unemployment and sickness absence. Separate analyses were conducted for sex and employees with a voluntary early retirement pension scheme. Using age as time axis, we used inverse probability weights to account for differences in educational level, sector, body mass index, smoking habits and loss of employment during sickness absence. Results The WLE of employees reporting depressive symptoms was shorter compared with those not reporting depressive symptoms; that is, the expected time in unemployment and sickness absence was longer, while the expected time in work was shorter. The shorter WLE was most pronounced in women; for example, a 40-year-old woman with depressive symptoms can expect 3.3 years less in work, 0.8 years more in unemployment and 0.7 years more in sickness absence. Employees with a voluntary early retirement pension scheme showed an even lower WLE. Conclusions Our study showed a meaningful impact of depressive symptoms on the WLE of Danish employees using a multistate framework

Exploring the relationship between job characteristics and infection: Application of a COVID-19 job exposure matrix to SARS-CoV-2 infection data in the United Kingdom

OBJECTIVE: This study aimed to assess whether workplace exposures as estimated via a COVID-19 job exposure matrix (JEM) are associated with SARS-CoV-2 in the UK. METHODS: Data on 244 470 participants were available from the Office for National Statistics Coronavirus Infection Survey (CIS) and 16 801 participants from the Virus Watch Cohort, restricted to workers aged 20-64 years. Analysis used logistic regression models with SARS-CoV-2 as the dependent variable for eight individual JEM domains (number of workers, nature of contacts, contact via surfaces, indoor or outdoor location, ability to social distance, use of face covering, job insecurity, and migrant workers) with adjustment for age, sex, ethnicity, index of multiple deprivation (IMD), region, household size, urban versus rural area, and health conditions. Analyses were repeated for three time periods (i) February 2020 (Virus Watch)/April 2020 (CIS) to May 2021), (ii) June 2021 to November 2021, and (iii) December 2021 to January 2022. RESULTS: Overall, higher risk classifications for the first six domains tended to be associated with an increased risk of infection, with little evidence of a relationship for domains relating to proportion of workers with job insecurity or migrant workers. By time there was a clear exposure-response relationship for these domains in the first period only. Results were largely consistent across the two UK cohorts. CONCLUSIONS: An exposure-response relationship exists in the early phase of the COVID-19 pandemic for number of contacts, nature of contacts, contacts via surfaces, indoor or outdoor location, ability to social distance and use of face coverings. These associations appear to have diminished over time

Describing the status of reproductive ageing simply and precisely: A reproductive ageing score based on three questions and validated with hormone levels

Equation 6. Quadratic logistic function approximating the function mu(B)(with age in years). Equation 1. Proportion of women who have regular menstruation for each number of reported menstruations in the last year(with period = number of periods per year, x = number of women answering "Yes" to the question: "Do you have regular periods?", y = number of women answering "No, they have been irregular for a few months" and z = number of women answering "No, my periods have stopped", e.g. x(11) = number of women reporting regular menstruation among those who report 11 menstruations in the last 12 months). Equation 5. Biquadratic exponential function mu(A)depending of the number of periods. Equation 3. Age modification by smoking and oophorectomy. Equation 2. Proportion of women whose menstruations have already stopped, for each reported year of age(with age = age in years, x = number of women answering "Yes" to the question: "Do you have regular periods?", y = number of women answering "No, they have been irregular for a few months", z = number of women answering "No, my periods have stopped", e.g. x(40) = number of women reporting regular menstruations among those who are 40 years old). Equation 7. Final formula to calculate the reproductive ageing score (RAS)(with period being the number of periods per year and age as the age in years, modified according to smoking status and oophorectomy). Objective Most women live to experience menopause and will spend 4-8 years transitioning from fertile age to full menstrual stop. Biologically, reproductive ageing is a continuous process, but by convention, it is defined categorically as pre-, peri- and postmenopause;categories that are sometimes supported by measurements of sex hormones in blood samples. We aimed to develop and validate a new tool, a reproductive ageing score (RAS), that could give a simple and yet precise description of the status of reproductive ageing, without hormone measurements, to be used by health professionals and researchers. Methods Questionnaire data on age, menstrual regularity and menstrual frequency was provided by the large multicentre population-based RHINE cohort. A continuous reproductive ageing score was developed from these variables, using techniques of fuzzy mathematics, to generate a decimal number ranging from 0.00 (nonmenopausal) to 1.00 (postmenopausal). The RAS was then validated with sex hormone measurements (follicle stimulating hormone and 17 beta-estradiol) and interview-data provided by the large population-based ECRHS cohort, using receiver-operating characteristics (ROC). Results The RAS, developed from questionnaire data of the RHINE cohort, defined with high precision and accuracy the menopausal status as confirmed by interview and hormone data in the ECRHS cohort. The area under the ROC curve was 0.91 (95% Confidence interval (CI): 0.90-0.93) to distinguish nonmenopausal women from peri- and postmenopausal women, and 0.85 (95% CI: 0.83-0.88) to distinguish postmenopausal women from nonmenopausal and perimenopausal women. Conclusions: The RAS provides a useful and valid tool for describing the status of reproductive ageing accurately, on a continuous scale from 0.00 to 1.00, based on simple questions and without requiring blood sampling. The score allows for a more precise differentiation than the conventional categorisation in pre-, peri- and postmenopause. This is useful for epidemiological research and clinical trials. Equation 4. The reproductive ageing score as an aggregation function of mu(A)and mu(B)

Spirometric phenotypes from early childhood to young adulthood : a Chronic Airway Disease Early Stratification study

Acknowledgements Cohort-specific acknowledgements are presented in the supplementary material. We also acknowledge collaboration with the EXPANSE consortium (funded by the EU H2020 programme, grant number 874627). We thank Elise Heuvelin, European Respiratory Society, Lausanne, Switzerland, for her assistance on the current project.Peer reviewedPublisher PD

Change in airway inflammatory markers in Danish energy plant workers during a working week

[b]Introduction.[/b] It is well known that exposure to organic dust can cause adverse respiratory effect. The pathogen-associated molecular patterns (PAMPS) in the organic dust, such as endotoxin from Gram-negative bacteria cell wall and fungal components, can trigger the release of cytokine (e.g. Interleukin 1β (IL-1β)) and chemokine (e.g. Interleukin 8 (IL-8)) from the immune cells in the airways. [b]Objective.[/b] To evaluate the potential inflammatory effects of organic dust exposure in energy plants in Denmark. [b]Materials and methods[/b]. Nasal lavage (NAL) and exhaled breath condensate (EBC) were sampled at Monday morning (referred to as before work) and again at Thursday afternoon (referred to as after work). NAL IL-8, EBC pH, IL-1β concentration were measured. Personal exposure to endotoxin and dust was calculated from time spent on different tasks and measured average work area exposures. [b]Results.[/b] Before work, workers from biofuel plants had a higher IL-1β and IL-8 concentration compared to conventional fuel plants (control group). Specifically, the IL-1β level of moderately and most exposed group, and IL-8 level of the least exposed group were higher compared to the control group. The changes of IL-1β, pH and IL-8 during a work week were not significant. Workers with rhinitis had a lower percentage change of IL-8 compared to healthy workers. [b]Conclusions[/b]. An increased level of EBC IL-1β in biofuel energy plant workers before work indicated a chronic or sub-chronic inflammation. The percentage change of IL-8 was lower in workers with rhinitis compared to healthy workers

Predictors of smoking cessation : A longitudinal study in a large cohort of smokers

Background: There are few studies on predictors of smoking cessation in general populations. We studied the smoking cessation rate in relation to several potential predictors, with special focus on respiratory and cardiovascular disease. Methods: Smokers (n = 4636) from seven centres in Northern Europe, born between 1945 and 1973, who answered a questionnaire in 1999-2001 (the RHINE study) were followed up with a new questionnaire in 2010-2012. Altogether 2564 answered the questionnaire and provided complete data on smoking. Cox regression analyses were performed to calculate hazard ratios (HRs). Results: A total of 999 subjects (39%) stopped smoking during the study period. The smoking cessation rate was 44.9/1000 person-years. Smoking cessation was more common with increasing age, higher education and fewer years of smoking. Asthma, wheeze, hay fever, chronic bronchitis, diabetes and hypertension did not significantly predict smoking cessation, but smokers hospitalized for ischaemic heart disease during the study period were more prone to stopping smoking (HR 3.75 [2.62-5.37]). Conclusions: Successful smoking cessation is common in middle-aged smokers, and is associated with few smoking years and higher education. A diagnosis of respiratory disease does not appear to motivate people to quit smoking, nor do known cardiovascular risk factors; however, an acute episode of ischaemic heart disease encouraged smoking cessation in our study population

Exposures during the prepuberty period and future offspring's health: evidence from human cohort studies†

Emerging evidence suggests that exposures in prepuberty, particularly in fathers-to-be, may impact the phenotype of future offspring. Analyses of the RHINESSA cohort find that offspring of father's exposed to tobacco smoking or overweight that started in prepuberty demonstrate poorer respiratory health in terms of more asthma and lower lung function. A role of prepuberty onset smoking for offspring fat mass is suggested in the RHINESSA and ALSPAC cohorts, and historic studies suggest that ancestral nutrition during prepuberty plays a role for grand-offspring's health and morbidity. Support for causal relationships between ancestral exposures and (grand-)offspring's health in humans has been enhanced by advancements in statistical analyses that optimize the gain while accounting for the many complexities and deficiencies in human multigeneration data. The biological mechanisms underlying such observations have been explored in experimental models. A role of sperm small RNA in the transmission of paternal exposures to offspring phenotypes has been established, and chemical exposures and overweight have been shown to influence epigenetic programming in germ cells. For example, exposure of adolescent male mice to smoking led to differences in offspring weight and alterations in small RNAs in the spermatozoa of the exposed fathers. It is plausible that male prepuberty may be a time window of particular susceptibility, given the extensive epigenetic reprogramming taking place in the spermatocyte precursors at this age. In conclusion, epidemiological studies in humans, mechanistic research, and biological plausibility, all support the notion that exposures in the prepuberty of males may influence the phenotype of future offspring.</p