Exposure Determinants in the French Database COLCHIC (1987–2019): Statistical Modeling across 77 Chemicals
Notice bibliographique
Résumé
OBJECTIVES: The COLCHIC database contains workplace exposure results of chemical samples collected by the French prevention network since 1987. We aimed to investigate potential associations between exposure levels and ancillary variables in COLCHIC across a broad range of chemical agents in order to provide insight into how to best interpret and exploit the information in this national database. METHODS: We selected personal and area measurements in COLCHIC and collected outside respiratory personal protective equipment (PPE), restricted to chemical agents that had at least 1000 samples available. We used Tobit models to estimate associations between exposure concentrations and sample year, sampling duration, PPE, workforce size, collective protective equipment, origin of request, and reason for request for each chemical agent for the period 1987-2019. Models for period 2002-2019 also included type of process (open/closed) and exposure frequency. We used separate models for each combination of agent, period, and personal or area samples. We then applied Bayesian meta-analytical methods to assess average effects and effect heterogeneity of exposure factors across agents. RESULTS: COLCHIC contained 720 282 exposure results (62% personal and 38% area samples) to 77 agents, including 346 766 results for the more recent period 2002-2019 (67% personal and 33% area samples). Sample year and duration, PPE, and process type had the strongest and most consistent associations with exposure levels across agents. Personal and area exposure levels decreased yearly (6% for the entire period and 9% since 2002), and 30-min samples were approximately twice as high as 240-min samples. Workers wearing PPE were exposed to levels 1.7 times higher on average than those without PPE for both area and personal samples. Personal exposure levels associated with enclosed or semi-enclosed processes were approximately 20-30% lower compared with open processes. The associations for the other exposure variables were weaker and more inconsistent between agents. Between-agent heterogeneity of estimated effects, based on 80% prediction intervals, was lowest for sampling duration, time trends, and the presence of PPE. CONCLUSIONS: Sampling duration, time trends, and the presence of PPE are important factors to take into account when analyzing COLCHIC and had similar associations with exposure levels across agents. Other variables generally showed weaker associations or variable effects. These results will be used to adjust exposure estimates for the French working population from measurements stored in COLCHIC.
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Comment cette classification a été obtenuedéplier
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.
Scores Codex et Gemma par catégorie
| 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.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».