7 research outputs found
Asthma risk and eosinophilic airway inflammation in allergen-exposed workers
Occupational asthma (OA) accounts for up to 15% of all adult-onset asthma. Though OA is preventable it is under-recognised in the UK. Fractional exhaled nitric oxide (FENO) is a non-invasive measure of eosinophilic airway inflammation used in asthma management, but its use in occupational settings is debated. In order to understand asthma risk and the role of airway inflammation in allergen-exposed workers, this thesis examined two hypotheses: firstly, that risk of respiratory symptoms and asthma increases with allergen exposure; and secondly, that airway inflammation relates to asthma risk in exposed populations.
A systematic review of asthma risk in woodworkers explores existing evidence for associations between allergen exposure and asthma. Three cross-sectional studies, including 773 workers, examine allergen exposure and asthma, airway inflammation, and lung function in the wood, foundry, and laboratory animal industries.
Symptoms were prevalent but did not clearly relate to allergen exposure. In woodworkers, atopy was the strongest modifier of asthma risk. Airway inflammation was common in foundry workers and related to increasing isocyanate exposure. Laboratory animal workers with more than three years’ exposure had more airway inflammation and poorer spirometry. There was little overlap between airway inflammation and airflow obstruction in symptomatic workers. Airway inflammation significantly increased the risk of work-related symptoms and asthma among symptomatic workers. However, no clear relationship between allergen exposure and airway inflammation was found.
In OA, FENO could be useful in a number of ways: in health surveillance; in asthma diagnosis; and as a measure of allergen exposure. Longitudinal studies are needed to explore how airway inflammation relates to future asthma risk, and to understand how workplace allergen exposures modify airway inflammation in workers at risk of OA
Asylum seeker and refugee healthcare:a survey of healthcare professionals’ knowledge and experience in the United Kingdom
P096 Statistical modelling and development of a quantitative job exposure matrix for wood dust in the wood manufacturing industry
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