Association between long-term exposure to air pollution and specific causes of mortality in Scotland

Yap, Christina and Beverland, Iain and Heal, M.R. and Cohen, G.R. and Robertson, Chris and Henderson, Deborah Elizabeth Jayne and Ferguson, Neil and Hart, C.L. and Morris, George and Agius, R.M. (2012) Association between long-term exposure to air pollution and specific causes of mortality in Scotland. Occupational and Environmental Medicine, 69 (12). pp. 916-924. ISSN 1351-0711 (https://doi.org/10.1136/oemed-2011-100600)

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Abstract

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. 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). Consistent BS–mortality associations (per 10 μg 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. 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.

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