Evaluation of the Suitability of an Existing Job-Exposure Matrix for the Assessment of Exposure of UK Biobank Participants to Dust, Fumes, and Diesel Exhaust Particulates

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

An individual'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, we demonstrate that expert annotators can attain high levels of agreement, and that the corpus can be used to train 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.

Physical work exposure matrix for use in the UK Biobank

BACKGROUND

UK Biobank (UKB) is a large prospective cohort capturing numerous health outcomes, but limited occupational information (job title, self-reported manual work and occupational walking/standing).

AIMS

To create and evaluate validity of a linkage between UKB and a job exposure matrix for physical work exposures based on the US Occupational Information Network (O*NET) database.

METHODS

Job titles and UK Standard Occupational Classification (SOC) codes were collected during UKB baseline assessment visits. Using existing crosswalks, UK SOC codes were mapped to US SOC codes allowing linkage to O*NET variables capturing numerous dimensions of physical work. Job titles with the highest O*NET scores were assessed to evaluate face validity. Spearman's correlation coefficients were calculated to compare O*NET scores to self-reported UKB measures.

RESULTS

Among 324 114 participants reporting job titles, 323 936 were linked to O*NET. Expected relationships between scores and self-reported measures were observed. For static strength (0-7 scale), the median O*NET score was 1.0 (e.g. audiologists), with a highest score of 4.88 for stone masons and a positive correlation with self-reported heavy manual work (Spearman's coefficient = 0.50). For time spent standing (1-5 scale), the median O*NET score was 2.72 with a highest score of 5 for cooks and a positive correlation with self-reported occupational walking/standing (Spearman's coefficient = 0.56).

CONCLUSIONS

While most jobs were not physically demanding, a wide range of physical work values were assigned to a diverse set of jobs. This novel linkage of a job exposure matrix to UKB provides a potentially valuable tool for understanding relationships between occupational exposures and disease.

Is Environmental and Occupational Particulate Air Pollution Exposure Related to Type-2 Diabetes and Dementia? A Cross-Sectional Analysis of the UK Biobank

Human exposure to particulate air pollution (e.g., PM_2.5) can lead to adverse health effects, with compelling evidence that it can increase morbidity and mortality from respiratory and cardiovascular disease. More recently, there has also been evidence that long-term environmental exposure to particulate air pollution is associated with type-2 diabetes mellitus (T2DM) and dementia. There are many occupations that may expose workers to airborne particles and that some exposures in the workplace are very similar to environmental particulate pollution. We conducted a cross-sectional analysis of the UK Biobank cohort to verify the association between environmental particulate air pollution (PM_2.5) exposure and T2DM and dementia, and to investigate if occupational exposure to particulates that are similar to those found in environmental air pollution could increase the odds of developing these diseases. The UK Biobank dataset comprises of over 500,000 participants from all over the UK. Environmental exposure variables were used from the UK Biobank. To estimate occupational exposure both the UK Biobank’s data and information from a job exposure matrix, specifically developed for UK Biobank (Airborne Chemical Exposure–Job Exposure Matrix (ACE JEM)), were used. The outcome measures were participants with T2DM and dementia. In appropriately adjusted models, environmental exposure to PM_2.5 was associated with an odds ratio (OR) of 1.02 (95% CI 1.00 to 1.03) per unit exposure for developing T2DM, while PM_2.5 was associated with an odds ratio of 1.06 (95% CI 0.96 to 1.16) per unit exposure for developing dementia. These environmental results align with existing findings in the published literature. Five occupational exposures (dust, fumes, diesel, mineral, and biological dust in the most recent job estimated with the ACE JEM) were investigated and the risks for most exposures for T2DM and for all the exposures for dementia were not significantly increased in the adjusted models. This was confirmed in a subgroup of participants where a full occupational history was available allowed an estimate of workplace exposures. However, when not adjusting for gender, some of the associations become significant, which suggests that there might be a bias between the occupational assessments for men and women. The results of the present study do not provide clear evidence of an association between occupational exposure to particulate matter and T2DM or dementia.