The cohort of young Danish farmers – A longitudinal study of the health effects of farming exposure

Working in agriculture poses a serious risk for development of respiratory diseases, especially when working in animal housing. Animal workers are exposed to a mixture of organic and inorganic dust together with fumes and gases, including allergens and microbial-associated molecular patterns with a potentially major impact on respiratory health and the immune system. Exposure to microbial agents in animal housing is associated with an increased prevalence of respiratory symptoms, including bronchial hyperresponsiveness, accelerated lung function decline, and neutrophil-mediated inflammation. These clinical findings are often seen without IgE-mediated sensitization. In fact it has been found in recent studies that the prevalence of atopic sensitization and atopic asthma is low among farmers compared with other populations. The SUS study was designed to identify the type and occurrence of respiratory symptoms and disease, and to investigate risk factors for respiratory disorders and changes in lung function among young farming students. The cohort of young Danish farmers was established in 1992/1994 and followed up in 2007/2008 with a participation rate of 51.7%. The cohort consists of 1734 male farming students, 230 female farming students, and 407 army recruits as controls

Archaea and Bacteria Exposure in Danish Livestock Farmers

Objectives Methanogenic archaea have been found to make up part of the bioaerosols in pig, cattle, and poultry farms. So far no attempts have been made to determine how season, farm type, and farm characteristics may affect workers’ exposure to archaea. Methods Personal filter samples from 327 farmers working on 89 Danish farms were analysed for the number of 16S rRNA gene copies from archaea and bacteria and for their dust and endotoxin content. The farms were visited during summer and winter. Information on farm type and stable characteristics were collected using self-reported activity diaries and walk-through surveys. Differences in archaea and bacteria levels with farm type and stable characteristics and correlations with dust and endotoxin levels were examined. Results Personal archaea exposure was documented in all farm types including, for the first time, during mink farming. At 7.3*104 gene copies m−3 the archaea levels were around two orders of magnitude lower than bacteria levels at 5.7*106 gene copies m−3. At 1.7*105 gene copies m−3 among pig farmers and 1.9*104 gene copies m−3 among cattle farmers the archaea levels differed with farm type (P < 0.0005). The archaea and bacteria levels correlated weakly with a Pearson correlation coefficient of 0.17. Neither archaea nor bacteria levels differed by season. In pig farms the archaea levels differed by type of ventilation and by wetness of the floor. Conclusions Archaea levels were not neglible and appeared to vary greatly between farm types. In pig farms they varied with some farm characteristics. Archaea levels appeared to depend on factors that differed from those of bacteria

A Quantitative General Population Job Exposure Matrix for Occupational Noise Exposure

Occupational noise exposure is a known risk factor for hearing loss and also adverse cardiovascular effects have been suggested. A job exposure matrix (JEM) would enable studies of noise and health on a large scale. The objective of this study was to create a quantitative JEM for occupational noise exposure assessment of the general working population. Between 2001-2003 and 2009-2010, we recruited workers from companies within the 10 industries with the highest reporting of noise-induced hearing loss according to the Danish Working Environment Authority and in addition workers of financial services and children day care to optimize the range in exposure levels. We obtained 1343 personal occupational noise dosimeter measurements among 1140 workers representing 100 different jobs according to the Danish version of the International Standard Classification of Occupations 1988 (DISCO 88). Four experts used 35 of these jobs as benchmarks and rated noise levels for the remaining 337 jobs within DISCO 88. To estimate noise levels for all 372 jobs, we included expert ratings together with sex, age, occupational class, and calendar year as fixed effects, while job and worker were included as random effects in a linear mixed regression model. The fixed effects explained 40% of the total variance: 72% of the between-jobs variance, -6% of the between-workers variance and 4% of the within-worker variance. Modelled noise levels showed a monotonic increase with increasing expert score and a 20 dB difference between the highest and lowest exposed jobs. Based on the JEM estimates, metal wheel-grinders were among the highest and finance and sales professionals among the lowest exposed. This JEM of occupational noise exposure can be used to prioritize preventive efforts of occupational noise exposure and to provide quantitative estimates of contemporary exposure levels in epidemiological studies of health effects potentially associated with noise exposure

Dust exposure and the impact on hospital readmission of farming and wood industry workers for asthma and chronic obstructive pulmonary disease (COPD)

Objectives It is still not well established how occupational air pollutants affect the prognosis of asthma or chronic obstructive pulmonary disease (COPD). This study uses nationwide Danish registers and quantitative dust industry exposure matrices (IEM) for the farming and wood industries to estimate whether previous year dust exposure level impacts hospital readmissions for workers diagnosed with asthma or COPD. Methods We identified all individuals with a first diagnosis of either asthma (769 individuals) or COPD (342 individuals) between 1997 and 2007 and followed them until the next hospital admission for asthma or COPD, emigration, death or 31 December 2007. We included only individuals who worked in either the wood or farming industries at least one year during follow-up. We used logistic regression analysis to investigate associations between dust exposure level in the previous year and hospital readmission, adjusting for sex, age, time since first diagnosis, socioeconomic status, and labor force participation. Results Asthma readmissions for individuals with low and high dust exposure were increased [adjusted rate ratio (RR adj) 2.52, 95% confidence interval (CI) 1.45-4.40] and RR adj2.64 (95% CI 1.52-4.60), respectively. For COPD readmission, the risk estimates were RR adj1.36 (95% CI 0.57-3.23) for low and RR adj1.20 (95% CI 0.49-2.95) for high exposure level in the previous year. For asthma readmission, stratified analyses by type of dust exposure during follow-up showed increased risks for both wood dust [RR adj2.67 (95% CI 1.35-5.26) high exposure level] and farming dust [RR adj3.59 (95% CI 1.11-11.59) high exposure level]. No clear associations were seen for COPD readmissions. Conclusions This study indicates that exposure to wood or farm dust in the previous year increases the risk of hospital readmission for individuals with asthma but not for those with COPD

Development of a quantitative North and Central European job exposure matrix for wood dust

Wood dust is an established carcinogen also linked to several non malignant respiratory disorders. A major limitation in research on wood dust and its health effects is the lack of (historical) quantitative estimates of occupational exposure for use in general population-based case-control or cohort studies. The present study aimed to develop a multinational quantitative Job Exposure Matrix (JEM) for wood dust exposure using exposure data from several Northern and Central European countries. For this, an occupational exposure database containing 12653 personal wood dust measurements collected between 1978 and 2007 in Denmark, Finland, France, The Netherlands, Norway, and the United Kingdom (UK) was established. Measurement data were adjusted for differences in inhalable dust sampling efficiency resulting from the use of different dust samplers and analysed using linear mixed effect regression with job codes (ISCO-88) and country treated as random effects. Fixed effects were the year of measurement, the expert assessment of exposure intensity (no, low, and high exposure) for every ISCO-88 job code from an existing wood dust JEM and sampling duration. The results of the models suggest that wood dust exposure has declined annually by approximately 8%. Substantial differences in exposure levels between countries were observed with the highest levels in the United Kingdom and the lowest in Denmark and Norway, albeit with similar job rankings across countries. The jobs with the highest predicted exposure are floor layers and tile setters, wood-products machine operators, and building construction labourers with geometric mean levels for the year 1997 between 1.7 and 1.9 mg/m3. The predicted exposure estimates by the model are compared with the results of wood dust measurement data reported in the literature. The model predicted estimates for full-shift exposures were used to develop a time-dependent quantitative JEM for exposure to wood dust that can be used to estimate exposure for participants of general population studies in Northern European countries on the health effects from occupational exposure to wood dust.Development of a quantitative North and Central European job exposure matrix for wood dustpublishedVersio

Evaluation of two-year recall of self-reported pesticide exposure among Ugandan smallholder farmers

OBJECTIVES: To evaluate smallholder farmers' recall of pesticide use and exposure determinants over a two-year period in a low-income country context. METHODS: The Pesticide Use in Tropical Settings (PESTROP) study in Uganda consists of 302 smallholder farmers who were interviewed in 2017. In the same season in 2019, these farmers were re-questioned concerning pesticide use (e.g., use of active ingredients) and exposure information (e.g., crops, personal protective equipment [PPE], hygienic behaviours) they had previously provided. The extent of recall bias was assessed by comparing responses at follow-up in 2019 with practices and behaviours reported from the baseline interview in 2017. RESULTS: An 84% (n = 255) follow-up response rate was attained. We found instances of better recall (e.g., overall agreement >70% and Area Under the Curve (AUC) values > 0.7) for the use of some active ingredients, commonly used PPE items, and washing clothes after application, whereas only 13.3% could correctly recall their three major crops. We observed a trend where more individuals reported the use of active ingredients, while fewer reported the use of PPE items, two years later. In general, we found better agreement in the recall of years working with pesticides compared to hours per day or days per week in the field, with no apparent systematic over or under reporting by demographic characteristics. CONCLUSIONS: While some of these findings provide consistency with those from high-income countries, more research is needed on recall in poorly educated agriculture communities in low- and middle-income settings to confirm these results

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

Supporting the working life exposome: Annotating occupational exposure for enhanced literature search

Anindividual’s likelihood of developing non-communicable diseases is often influenced by the types, intensities and duration of exposures at work. Job exposure matrices provide exposure estimates associated with different occupations. However, due to their time-consuming expert curation process, job exposure matrices currently cover only a subset of possible workplace exposures and may not be regularly updated. Scientific literature articles describing exposure studies provide important supporting evidence for developing and updating job exposure matrices, since they report on exposures in a variety of occupational scenarios. However, the constant growth of scientific literature is increasing the challenges of efficiently identifying relevant articles and important content within them. Natural language processing methods emulate the human process of reading and understanding texts, but in a fraction of the time. Such methods can increase the efficiency of both finding relevant documents and pinpointing specific information within them, which could streamline the process of developing and updating job exposure matrices. Named entity recognition is a fundamental natural language processing method for language understanding, which automatically identifies mentions of domain-specific concepts (named entities) in documents, e.g., exposures, occupations and job tasks. State-of-the-art machine learning models typically use evidence from an annotated corpus, i.e., a set of documents in which named entities are manually marked up (annotated) by experts, to learn how to detect named entities automatically in new documents. We have developed a novel annotated corpus of scientific articles to support machine learning based named entity recognition relevant to occupational substance exposures. Through incremental refinements to the annotation process, wedemonstrate that expert annotators can attain high levels of agreement, and that the corpus canbeusedtotrain high-performance named entity recognition models. The corpus thus constitutes an important foundation for the wider development of natural language processing tools to support the study of occupational exposures.Supporting the working life exposome: Annotating occupational exposure for enhanced literature searchpublishedVersio

Self-reported and urinary biomarker-based measures of exposure to glyphosate and mancozeb and sleep problems among smallholder farmers in Uganda

OBJECTIVE: We aim to showcase the impact of applying eight different self-reported and urinary biomarker-based exposure measures for glyphosate and mancozeb on the association with sleep problems in a study among 253 smallholder farmers in Uganda. METHODS: The questionnaire-based exposure measures included: (1) the number of application days of any pesticide in the last 7 days (never, 1-2; >2 days) and six glyphosate and mancozeb-specific measures: (2) application status over the last 12 months (yes/no), (3) recent application status (never, last 7 days and last 12 months), (4) the number of application days last 12 months, (5) average exposure-intensity scores (EIS) and (6) EIS-weighted number of application days in last 12 months. Based on 384 repeated urinary biomarker concentrations of ethylene thiourea (ETU) and glyphosate from 84 farmers, we also estimated (7) average biomarker concentrations for all 253 farmers. Also in the 84 farmers the measured pre-work and post-work biomarker concentrations were used (8). Multivariable logistic regression models were used to assess the association between the exposure measures and selected Medical Outcomes Study Sleep Scale (MOS-SS) indices (6-item, sleep inadequacy and snoring). RESULTS: We observed positive associations between (1) any pesticide application in the last 7 days with all three MOS-SS indices. Glyphosate application in the last 7 days (3) and mancozeb application in the last 12 months (3) were associated with the 6-item sleep problem index. The estimated average urinary glyphosate concentrations showed an exposure-response association with the 6-item sleep problem index and sleep inadequacy in the same direction as based on self-reported glyphosate application in the last 7 days. In the analysis with the subset of 84 farmers, both measured and modelled post-work urinary glyphosate concentration showed an association with snoring. CONCLUSIONS: Self-reported, estimated average biomarker concentrations and measured urinary biomarker exposure measures of glyphosate and mancozeb showed similar exposure-response associations with sleep outcomes