277 Threshold value estimation for respirable quartz dust exposure and silicosis incidence in the German porcelain worker cohort. (11th October 2013) - Morfeld, P; Mundt,; Taeger,; Guldner,; Steinig,; Miller,
Abstract : Objectives: To estimate a threshold value tau for the respirable quartz dust concentration and silicosis incidence (1/1, ILO 1980/2000) in the German porcelain worker cohort (Birk et al 2009, 2010, Mundt et al 2011). Methods: 17, 144 porcelain workers (128, 688 person-yrs) were followed for silicosis incidence (40 cases). Respirable quartz dust exposure was determined by combining detailed individual employment histories with a job exposure matrix based on 8, 000 historical industrial hygiene measurements. Cox regression with age as the time variable was used to evaluate silicosis morbidity by log (cumulative quartz dust exposure + 0.01 mg/m 3 -yrs) time-dependently, controlling for sex and smoking status. Curvature of the relationship was explored by restricted cubic splines (RCS, ≤ 7 knots) and fractional polynomials (FP, degrees ≤ 5). Estimation of tau was performed by subtracting a series of candidate values from the annual concentration data (setting the result to zero if negative) and recalculation of the time-dependent cumulative exposures. The partial likelihood profile was used to derive point and 95%-confidence interval (CI) estimates. Non-nested models were compared by information criteria (AIC). Lagging exposures by 10 years, using different offsets in the log-function (0, 0.1, 1 mg/m 3 -yrs) and varying sets of covariates were explored. Results: We estimated tau = 0.25 mg/m 3 (0.95-CI: 0.15 mg/m 3, 0.30 mg/m 3 ). Applying this estimated concentration threshold led to lower degree optimal FPs and returned pronounced better fits (∆ AIC > 5) in log-linear Cox models, 5-knots RCS Cox models and 2-degree FP Cox models. The overall exposure-response could be appropriately described by a Cox model on log (unlagged cumulative exposure + 0.01 mg/m 3 -yrs) after applying tau = 0.25 mg/m 3 . Conclusions: A threshold Cox model fitted the data significantly better than a non-threshold model and summarised the cohort information without a loss in extracted information and much more simply than the curvilinear procedures (RCS, FP).