Dépôt numérique

Inter-rater Agreement Between Exposure Assessment Using Automatic Algorithms and Using Experts

Florath, Ines, Glass, Deborah C., Rhazia, Mounia S., Parent, Marie-Élise ORCID: https://orcid.org/0000-0002-4196-3773 et Fritschi, Lin (2019). Inter-rater Agreement Between Exposure Assessment Using Automatic Algorithms and Using Experts Annals of Work Exposures and Health , vol. 63 , nº 1. p. 45-53. DOI: 10.1093/annweh/wxy084.

Ce document n'est pas hébergé sur EspaceINRS.


Objectives: To estimate the inter-rater agreement between exposure assessment to asthmagens in current jobs by algorithms based on task-based questionnaires (OccIDEAS) and by experts.

Methods: Participants in a cross-sectional national survey of exposure to asthmagens (AWES-Asthma) were randomly split into two subcohorts of equal size. Subcohort 1 was used to determine the most common asthmagen groups and occupational groups. From subcohort 2, a random sample of 200 participants was drawn and current occupational exposure (yes/no) was assessed in these by OccIDEAS and by two experts independently and then as a consensus. Inter-rater agreement was estimated using Cohen's Kappa coefficient. The null hypothesis was set at 0.4, because both the experts and the automatic algorithm assessed the exposure using the same task-based questionnaires and therefore an agreement better than by chance would be expected.

Results: The Kappa coefficients for the agreement between the experts and the algorithm-based assessments ranged from 0.37 to 1, while the agreement between the two experts ranged from 0.29 to 0.94, depending on the agent being assessed. After discussion by both experts the Kappa coefficients for the consensus decision and OccIDEAS were significantly larger than 0.4 for 7 of the 10 asthmagen groups, while overall the inter-rater agreement was greater than by chance (P < 0.0001).

Conclusions: The web-based application OccIDEAS is an appropriate tool for automated assessment of current exposure to asthmagens (yes/no), and requires less time-consuming work by highly-qualified research personnel than the traditional expert-based method. Further, it can learn and reuse expert determinations in future studies.

Type de document: Article
Mots-clés libres: -
Centre: Centre INRS-Institut Armand Frappier
Date de dépôt: 07 août 2019 15:55
Dernière modification: 15 févr. 2022 20:55
URI: https://espace.inrs.ca/id/eprint/8109

Actions (Identification requise)

Modifier la notice Modifier la notice