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O42-1 Occupational self coding and automatic recording (OSCAR): an innovative validated web-based tool to collect lifetime job histories in large population-based studies
  1. Sara De Matteis1,
  2. Deborah Jarvis1,
  3. Heather Young2,
  4. Alan Young2,
  5. Andy Darnton3,
  6. Lesley Rushton1,
  7. Paul Cullinan1
  1. 1Imperial College London, London, UK
  2. 2Oxford University, Oxford, UK
  3. 3Health and Safety Laboratory, Derbyshire, Buxton, Uk

Abstract

Background The standard approach for assessing lifetime individual occupational exposure is the manual collection and coding of self-reported job-histories. However this process is time-consuming and unfeasible in large population-based studies such as the UK Biobank cohort. Our aim was to develop a new valid and efficient web-based tool to collect, and automatically code, individual lifetime job-histories in the UK Biobank cohort to investigate work-related COPD.

Methods UK Biobank is a population-based cohort of 502,682 subjects, aged 40–69 years, recruited in 2006–2010. We developed an online job-questionnaire, termed OSCAR (Occupations Self Coding Automatic Recording tool), based on the hierarchical structure of the UK Standard Occupational Classification (SOC) 2000 to allow participants to self-collect and automatically code their lifetime job-histories via a decision tree model. Each participant was asked to identify each job through lists of job categories ending in a job title linked to a hidden 4-digit SOC-code. Also for each job a job title in free text was collected to allow OSCAR validation. Cohen’s kappa coefficient (k) was used to estimate inter-rater agreement between SOC codes assigned by OSCAR and by an expert manual coder (gold standard).

Results OSCAR was submitted in June-August 2015 to the 324,653 Biobank participants with an available email. Lifetime 4-digit SOC-coded job-histories for 108,784 responders were obtained (participation rate 34%). A random sample of 400 job titles in free text was used to perform OSCAR validation. The agreement between the 4-digit SOC codes assigned by OSCAR and the manual coder was moderately good (k = 0.46; 0.42–0.49). The agreement improved when considering job categories (0.70 at a 1-digit SOC-code level).

Conclusions Our web-based job questionnaire OSCAR is a new efficient and valid tool for collecting and automatically coding lifetime job histories in large population-based studies and is adaptable for use in other health research projects.

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