Yap, Christina Beverland, Iain J Heal, Mathew R Cohen, Geoffrey R Robertson, Chris Henderson, Deborah E J Ferguson, Neil S Hart, Carole L Morris, George Agius, Raymond M eng 0020015/Department of Health/United Kingdom G0800808/Medical Research Council/United Kingdom Research Support, Non-U.S. Gov't England 2012/10/30 06:00 Occup Environ Med. 2012 Dec;69(12):916-24. doi: 10.1136/oemed-2011-100600. Epub 2012 Oct 26. ; International audience ; OBJECTIVE: This study investigated the association between long-term exposure to black smoke (BS) air pollution and mortality in two related Scottish cohorts with 25 years of follow-up. METHODS: Risk factors were collected during 1970-1976 for 15331 and 6680 participants in the Renfrew/Paisley and Collaborative cohorts respectively. Exposure to BS during 1970-1979 was estimated by inverse-distance weighted averages of observed concentrations at monitoring sites and by two alternative spatial modelling approaches which included local air quality predictors (LAQP). RESULTS: Consistent BS-mortality associations (per 10 mug m(-3) increment in 10-year average BS) were observed in the Renfrew/Paisley cohort using LAQP-based exposure models (all-cause mortality HR 1.10 (95% CI 1.04 to 1.17); cardiovascular HR 1.11 (1.01 to 1.22); ischaemic heart disease HR 1.13 (1.02 to 1.25); respiratory HR 1.26 (1.02 to 1.28)). The associations were largely unaffected by additional adjustment for area-level deprivation category. A less consistent and generally implausible pattern of cause-specific BS-mortality associations was found for inverse-distance averaging of BS concentrations at nearby monitoring sites. BS-mortality associations in the Collaborative cohort were weaker and not statistically significant. CONCLUSIONS: The association between mortality and long-term exposure to BS observed in the Renfrew/Paisley cohort is consistent with hypotheses of how air pollution may affect human health. The dissimilarity in pollution-mortality associations for different exposure models highlights the critical importance of reliable estimation of exposures on intraurban spatial scales to avoid potential misclassification bias.