Evaluation of Automatically Assigned Job-Specific Interview Modules.

OBJECTIVE: In community-based epidemiological studies, job- and industry-specific 'modules' are often used to systematically obtain details about the subject's work tasks. The module assignment is often made by the interviewer, who may have insufficient occupational hygiene knowledge to assign the correct module. We evaluated, in the context of a case-control study of lymphoid neoplasms in Asia ('AsiaLymph'), the performance of an algorithm that provided automatic, real-time module assignment during a computer-assisted personal interview. METHODS: AsiaLymph's occupational component began with a lifetime occupational history questionnaire with free-text responses and three solvent exposure screening questions. To assign each job to one of 23 study-specific modules, an algorithm automatically searched the free-text responses to the questions 'job title' and 'product made or services provided by employer' using a list of module-specific keywords, comprising over 5800 keywords in English, Traditional and Simplified Chinese. Hierarchical decision rules were used when the keyword match triggered multiple modules. If no keyword match was identified, a generic solvent module was assigned if the subject responded 'yes' to any of the three solvent screening questions. If these question responses were all 'no', a work location module was assigned, which redirected the subject to the farming, teaching, health professional, solvent, or industry solvent modules or ended the questions for that job, depending on the location response. We conducted a reliability assessment that compared the algorithm-assigned modules to consensus module assignments made by two industrial hygienists for a subset of 1251 (of 11409) jobs selected using a stratified random selection procedure using module-specific strata. Discordant assignments between the algorithm and consensus assignments (483 jobs) were qualitatively reviewed by the hygienists to evaluate the potential information lost from missed questions with using the algorithm-assigned module (none, low, medium, high). RESULTS: The most frequently assigned modules were the work location (33%), solvent (20%), farming and food industry (19%), and dry cleaning and textile industry (6.4%) modules. In the reliability subset, the algorithm assignment had an exact match to the expert consensus-assigned module for 722 (57.7%) of the 1251 jobs. Overall, adjusted for the proportion of jobs in each stratum, we estimated that 86% of the algorithm-assigned modules would result in no information loss, 2% would have low information loss, and 12% would have medium to high information loss. Medium to high information loss occurred for <10% of the jobs assigned the generic solvent module and for 21, 32, and 31% of the jobs assigned the work location module with location responses of 'someplace else', 'factory', and 'don't know', respectively. Other work location responses had ≤8% with medium to high information loss because of redirections to other modules. Medium to high information loss occurred more frequently when a job description matched with multiple keywords pointing to different modules (29-69%, depending on the triggered assignment rule). CONCLUSIONS: These evaluations demonstrated that automatically assigned modules can reliably reproduce an expert's module assignment without the direct involvement of an industrial hygienist or interviewer. The feasibility of adapting this framework to other studies will be language- and exposure-specific.

Triaging jobs in a community-based case-control study to increase efficiency of the expert occupational assessment method.

OBJECTIVES: Expert assessment is useful to assess occupational exposures in cases where measured exposure data are not available. However, the process may be inefficient in a community-based study with low prevalence of exposure. This study aimed to determine if formally triaging the jobs as to likelihood of exposure before the experts review those jobs could improve study efficiency. METHODS: One thousand nine hundred and sixty-one jobs from a case-control study were triaged by study staff (non-occupational health professionals) into four groups depending on the likelihood of exposure to solvents. For jobs in one group, we had additional information available in the form of job-specific modules and automatic exposure assignments for solvents based on rules pre-programmed into the job-specific module. After the automatic assignment, two experts reviewed the jobs to assign exposure to solvents in order to evaluate the process. The prevalence of exposure and the agreement between the two raters and between the raters' and the automatic assignments were compared for the four triage groups. RESULTS: The majority of jobs (76%) were triaged as unexposed by study staff and very few of these jobs were assigned as exposed by the raters (1%). For jobs with automatic assignment (18% of total), the raters tended to agree with the automatic assignment if that assignment was unexposed or probably exposed. There was less agreement for jobs in which the automatic assignment was possible exposure. For jobs triaged as ones with potential exposure based only on job title but with no further information available, the level of disagreement between the raters tended to be higher. CONCLUSIONS: Formal triaging of jobs can improve the efficiency of the expert assessment process. Of the 75% of jobs initially triaged as unexposed, virtually no exposures were found, and omitting manual review of this group would save considerable time.

OccIDEAS: web-based assessment of occupational agent exposure.

Most epidemiological studies of disease aetiology do not consider potential risk factors at work. This may be because work is a complex exposure: people usually have a series of different jobs over their working lifetime; within each job there may be a range of different tasks; and there may be a variety of exposures in each job. Because of this complexity, many epidemiologists do not have the expertise or training to assess occupational exposures accurately. Our web-based application, OccIDEAS, manages the process of occupational agent assessment in epidemiological studies. The epidemiologist chooses the agents of interest for the study and OccIDEAS provides an online set of questionnaires that are tailored to those agents. The participant is asked specific questions about their job and evidence-based algorithms provide an assessment of exposure to each agent. OccIDEAS puts the world's best occupational epidemiological expertise within reach of any researcher.

OccIDEAS: retrospective occupational exposure assessment in community-based studies made easier.

Assessing occupational exposure in retrospective community-based case-control studies is difficult as measured exposure data are very seldom available. The expert assessment method is considered the most accurate way to attribute exposure but it is a time consuming and expensive process and may be seen as subjective, nonreproducible, and nontransparent. In this paper, we describe these problems and outline our solutions as operationalized in a web-based software application (OccIDEAS). The novel aspects of OccIDEAS are combining all steps in the assessment into one software package; enmeshing the process of assessment into the development of questionnaires; selecting the exposure(s) of interest; specifying rules for exposure assignment; allowing manual or automatic assessments; ensuring that circumstances in which exposure is possible for an individual are highlighted for review; providing reports to ensure consistency of assessment. Development of this application has the potential to make high-quality occupational assessment more efficient and accessible for epidemiological studies.