Investigating trends in asthma and COPD through multiple data sources: A small area study. (November 2016)

• Record Type: Journal Article
• Authors: Boulieri, Areti; Hansell, Anna; Blangiardo, Marta
• Is Part Of: Spatial and spatio-temporal epidemiology. Volume 19(2016)
• Page Start: 28
• Page End: 36
• Publication Date: 2016-11
• Subjects: Space-time analysis -- Detection -- Asthma and COPD; Epidemiology -- Statistical methods -- Periodicals; Epidemiology -- Periodicals; 614.4072
• DOI: 10.1016/j.sste.2016.05.004
• Languages: English
• ISSNs: 1877-5845

Abstract

Highlights: Bayesian modelling of asthma and COPD. Use of multiple data sources to assess disease prevalence, morbidity and mortality. Spatial and temporal patterns across England over the period August 2010 to March 2011. Detection of areas with unusual temporal patterns. Abstract: This paper investigates trends in asthma and COPD by using multiple data sources to help understanding the relationships between disease prevalence, morbidity and mortality. GP drug prescriptions, hospital admissions, and deaths are analysed at clinical commissioning group (CCG) level in England from August 2010 to March 2011. A Bayesian hierarchical model is used for the analysis, which takes into account the complex space and time dependencies of asthma and COPD, while it is also able to detect unusual areas. Main findings show important discrepancies across the different data sources, reflecting the different groups of patients that are represented. In addition, the detection mechanism that is provided by the model, together with inference on the spatial, and temporal variation, provide a better picture of the respiratory health problem.