Quantifying the spatial inequality and temporal trends in maternal smoking rates in Glasgow

Lee, D. and Lawson, A. (2016) Quantifying the spatial inequality and temporal trends in maternal smoking rates in Glasgow. Annals of Applied Statistics, 10(3), pp. 1427-1446. (doi: 10.1214/16-AOAS941) (PMID:28580047) (PMCID:PMC5449583)

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Maternal smoking is well known to adversely affect birth outcomes, and there is considerable spatial variation in the rates of maternal smoking in the city of Glasgow, Scotland. This spatial variation is a partial driver of health inequalities between rich and poor communities, and it is of interest to determine the extent to which these inequalities have changed over time. Therefore in this paper we develop a Bayesian hierarchical model for estimating the spatiotemporal pattern in smoking incidence across Glasgow between 2000 and 2013, which can identify the changing geographical extent of clusters of areas exhibiting elevated maternal smoking incidences that partially drive health inequalities. Additionally, we provide freely available software via the R package CARBayesST to allow others to implement the model we have developed. The study period includes the introduction of a ban on smoking in public places in 2006, and the results show an average decline of around 11% in maternal smoking rates over the study period.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Lee, Professor Duncan
Authors: Lee, D., and Lawson, A.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Annals of Applied Statistics
Publisher:Institute of Mathematical Statistics
ISSN (Online):1941-7330
Copyright Holders:Copyright © 2016 Institute of Mathematical Statistics
First Published:First published in Annals of Applied Statistics 10(3): 1427-1446
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
647701A flexible class of Bayesian spatio-temporal models for cluster detection, trend estimation and forecasting of disease risk.Duncan LeeMedical Research Council (MRC)MR/L022184/1M&S - STATISTICS