8287355 Assessing the effectiveness of automatically assigning occupational exposure modules in a multi-center hospital-based case-control study in Asia

<h3>Objective</h3> Exposure-oriented questionnaire modules provide crucial task information for occupational exposure assessment. We evaluated the effectiveness of an algorithm for automatically assigning modules during interviews in a hospital-based case-control study in Asia. <h3>Methods</h3> We used a rule-based expert system based on a keyword search of occupational history responses and screening questions pertaining to paints/stains, solvents/glues/degreasers, and engineered woods to assign jobs to one of 23 modules. If no module was identified, we assigned a Work Location module that redirected some participants to more detailed modules. Post interview, each job was coded to standardized occupation and industry classifications. For each job and industry code we determined the ‘ideal’ module. We reviewed each ‘ideal’ vs. ‘assigned’ module combination and characterized potential information loss: none, task loss, and industry loss. We evaluated the screening questions’ positive and negative predictive value (PPV, NPV) compared to task responses for jobs assigned the Solvent module. For those redirected from the Work Location to the Solvent module, we calculated the task prevalence. <h3>Results</h3> The algorithm assignment for 26,608 jobs was based on keywords for 55.4% and screening questions for 8.6%; the remainder received the work location module. Potential information loss was identified for 8.8% of jobs (7.2% task loss; 1.6% industry loss). For the 5,847 jobs completing the Solvent module, the overall PPV and NPV of the screening questions was 82.4% and 70.0%, respectively, with higher NPVs for engineered woods and paints/stains than for solvents/glues/degreasers. The Work Location re-directed 663 jobs to the Solvent module; the most frequently-reported activities for these jobs were cleaning hands with solvents (14%), paint bystander (8.0%), cleaning/degreasing bystander (5.1%) and glues/adhesives bystander (5.0%). <h3>Conclusions</h3> Overall, our automated approach resulted in excellent capture of tasks of interest. Jobs identified with potential information loss will be prioritized for additional review during exposure assessment efforts.