Impact of Variability in Job Coding on Reliability in Exposure Estimates Obtained via a Job-Exposure Matrix
Notice bibliographique
Résumé
OBJECTIVES: The use of a job-exposure matrix (JEM) to assess exposure to potential health hazards in occupational epidemiological studies requires coding each participant's job history to a standard occupation and/or industry classification system recognized by the JEM. The objectives of this study were to assess the impact of inter-coder variability in job coding on reliability in exposure estimates derived from linking the job codes to the Canadian job-exposure matrix (CANJEM) and to identify influent parameters. METHOD: Two trained coders independently coded 1000 jobs sampled from a population-based case-control study to the ISCO-1968 occupation classification at the five-digit resolution level, of which 859 could be linked to CANJEM using both assigned codes. Each of the two sets of codes was separately linked to CANJEM and thereby generated, for each of the 258 occupational agents available in CANJEM, two exposure estimates: exposure status (yes/no) and intensity of exposure (low, medium, and high) for exposed jobs only. Then, inter-rater reliability (IRR) was computed (i) after stratifying agents in 4 classes depending, for each, on the proportion of occupation codes in CANJEM defined as 'exposed' and (ii) for two additional scenarios restricted to jobs coded differently: the first one using experts' codes, the other one using codes randomly selected. IRR was computed using Cohen's kappa, PABAK and Gwet's AC1 index for exposure status, and weighted kappa and Gwet's AC2 for exposure intensity. RESULTS: Across all agents and based on all jobs, median (Q1, Q3; Nagents) values were 0.68 (0.59, 0.75; 220) for kappa, 0.99 (0.95, 1.00; 258) for PABAK, and 0.99 (0.97, 1.00; 258) for AC1. For the additional scenarios, median kappa was 0.28 (0.00, 0.45; 209) and -0.01 (-0.02, 00; 233) restricted to jobs coded differently using experts' and random codes, respectively. A similar decreasing pattern was observed for PABAK and AC1 albeit with higher absolute values. Median kappa remained stable across exposure prevalence classes but was more variable for low prevalent agents. PABAK and AC1 decreased with increasing prevalence. Considering exposure intensity and all exposed jobs, median values were 0.79 (0.68, 0.91; 96) for weighted kappa, and 0.95 (0.89, 0.99; 102) for AC2. For the additional scenarios, median kappa was, respectively, 0.28 (-0.04, 0.42) and -0.05 (-0.18, 0.09) restricted to jobs coded differently using experts' and random codes, with a similar though attenuated pattern for AC2. CONCLUSION: Despite reassuring overall reliability results, our study clearly demonstrated the loss of information associated with jobs coded differently. Especially, in cases of low exposure prevalence, efforts should be made to reliably code potentially exposed jobs.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
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| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
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