Occupational self-coding and automatic recording (OSCAR): a novel web-based tool to collect and code lifetime job histories in large population-based studies

• Main Authors: Sara De Matteis (Author), Deborah Jarvis (Author), Heather Young (Author), Alan Young (Author), Naomi Allen (Author), James Potts (Author), Andrew Darnton (Author), Lesley Rushton (Author), Paul Cullinan (Author)
• Format: Book
• Published: Nordic Association of Occupational Safety and Health (NOROSH), 2017-03-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 0355-3140; 1795-990X; 10.5271/sjweh.3613
• Keywords: population-based study; occupational self-coding; oscar; automatic recording; web-based tool; lifetime job history; exposure assessment method; standard occupational classification; data coding; occupation; data collection; Public aspects of medicine; RA1-1270
• Source: Scandinavian Journal of Work, Environment & Health, Vol 43, Iss 2, Pp 181-186 (2017)

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. 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. 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). 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.