@Article{DeMatteis2017, author = "De Matteis, Sara and Jarvis, Deborah and Young, Heather and Young, Alan and Allen, Naomi and Potts, James and Darnton, Andrew and Rushton, Lesley and Cullinan, Paul", title = "Occupational self-coding and automatic recording (OSCAR): a novel web-based tool to collect and code lifetime job histories in large population-based studies", journal = "Scandinavian Journal of Work, Environment & Health", year = "2017", month = "Mar", day = "43", number = "2", pages = "181--186", keywords = "automatic recording; data coding; data collection; exposure assessment method; lifetime job history; occupation; occupational self-coding; OSCAR; population-based study; standard occupational classification; web-based tool", abstract = "'
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OBJECTIVES ': 'The standard approach to the assessment of occupational exposures is through the manual collection and coding of job histories. This method is time-consuming and costly and makes it potentially unfeasible to perform high quality analyses on occupational exposures in large population-based studies. Our aim was to develop a novel, efficient web-based tool to collect and code lifetime job histories in the UK Biobank, a population-based cohort of over 500 000 participants.
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METHODS ': 'We developed OSCAR (occupations self-coding automatic recording) based on the hierarchical structure of the UK Standard Occupational Classification (SOC) 2000, which allows individuals to collect and automatically code their lifetime job histories via a simple decision-tree model. Participants were asked to find each of their jobs by selecting appropriate job categories until they identified their job title, which was linked to a hidden 4-digit SOC code. For each occupation a job title in free text was also collected to estimate Cohen’s kappa (κ) inter-rater agreement between SOC codes assigned by OSCAR and an expert manual coder.
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RESULTS ': 'OSCAR was administered to 324 653 UK Biobank participants with an existing email address between June and September 2015. Complete 4-digit SOC-coded lifetime job histories were collected for 108 784 participants (response rate: 34%). Agreement between the 4-digit SOC codes assigned by OSCAR and the manual coder for a random sample of 400 job titles was moderately good [κ=0.45, 95% confidence interval (95% CI) 0.42–0.49], and improved when broader job categories were considered (κ=0.64, 95% CI 0.61–0.69 at a 1-digit SOC-code level).
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CONCLUSIONS ': 'OSCAR is a novel, efficient, and reasonably reliable web-based tool for collecting and automatically coding lifetime job histories in large population-based studies. Further application in other research projects for external validation purposes is warranted.
", issn = "0355-3140", doi = "10.5271/sjweh.3613", url = "https://www.sjweh.fi/show_abstract.php?abstract_id=3613", url = "https://doi.org/10.5271/sjweh.3613" }