Objectives To report updated results on long-term nightshift work and breast cancer risk in Hong Kong women. Method This ongoing case-control study involves three hospitals in Hong Kong. By 31/03/2013, we've consecutively recruited 443 newly diagnosed breast cancer cases and 335 age-matched controls from the hospital that the cases came from, with a response rate of 90%. We expect to collect 1000 cases and 1000 controls by 31/12/2013. We obtained each participant's lifetime occupational history and shift work, exposure to light-at-night and other important risk factors including family cancer history. We performed unconditional logistic regression analyses to calculate odds ratio (OR) after adjusting for potential confounders. Results The age at diagnosis (interview) between cases and controls is comparable (55.1 ± 11.9 vs. 54.2 ± 14.6 years). More cases than controls were non-parity and non-breast feeding, but gave first birth slightly late. A significantly elevated (adjusted OR=1.90, 95% CI: 1.24-2.89) breast cancer risk was observed in never employed women. Among those ever employed, 19.8% of breast cancers had ever worked at nightshift at least once per month for ≥1 year and it was 21.7% for the controls. Further analyses revealed that nightshift work for ≥15 years resulted in an adjusted OR of 1.55 (95% CI: 0.76-3.14) but power is limited. There is no excess breast cancer risk for women with night-shift work for <15 years. Objectives Inhalation beryllium exposures are associated with sensitisation, however dermal exposures are also important. In a previous study, we identified strong correlations between der-mal-air, dermal-surface, and air-surface measurements. The aim of this study was to investigate workplace factors associated with exposures using mixed-effects models and structural equation modelling (SEM). Method Beryllium was measured in personal air, on gloves, and on surfaces at three manufacturing facilities. Predictor variables included substance and activity emission potential (REACH classification), dilution, segregation, PPE, personal behaviour, and work shift. Results The mixed model described 57 and 59% of total variance for air and dermal, respectively. The total variance explained by the SEM model for air and dermal was 0.51 and 0.48% respectively. In both models activity and substance emission potential, surface contamination, dilution, and personal behaviour were significant predictors of air concentrations (p ≤ 0.05); and surface contamination and air concentrations were significant predictors of dermal loading on cotton gloves (p ≤ 0.05). However, work shift and personal behaviour were predic-tive of dermal loading in the SEM (p ≤ 0.03), but not in the mixed model. In addition, the SEM reported a parameter estimate for air concentration as a predictor of dermal loading that was an order of magnitude higher than in the mixed model. Conclusions Although SEM requires relatively large sample sizes, it is useful for modelling multiple, correlated dependent variables. In addition, full-infor...
