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

De Matteis S

First

;Jarvis D;Young H;Young A;Allen N;Potts J;Darnton A;Rushton L;Cullinan P

2017-01-01

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Abstract

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 (K) 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 [kappa=0.45, 95% confidence interval (95% CI) 0.42-0.49], and improved when broader job categories were considered (kappa=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.