Air pollution and the incidence of ischaemic and haemorrhagic stroke in the South London Stroke Register: a case-cross-over analysis.

BACKGROUND: Few European studies investigating associations between short-term exposure to air pollution and incident stroke have considered stroke subtypes. Using information from the South London Stroke Register for 2005-2012, we investigated associations between daily concentrations of gaseous and particulate air pollutants and incident stroke subtypes in an ethnically diverse area of London, UK. METHODS: Modelled daily pollutant concentrations based on a combination of measurements and dispersion modelling were linked at postcode level to incident stroke events stratified by haemorrhagic and ischaemic subtypes. The data were analysed using a time-stratified case-cross-over approach. Conditional logistic regression models included natural cubic splines for daily mean temperature and daily mean relative humidity, a binary term for public holidays and a sine-cosine annual cycle. Of primary interest were same day mean concentrations of particulate matter <2.5 and <10 µm in diameter (PM2.5, PM10), ozone (O3), nitrogen dioxide (NO2) and NO2+nitrogen oxide (NOX). RESULTS: Our analysis was based on 1758 incident strokes (1311 were ischaemic and 256 were haemorrhagic). We found no evidence of an association between all stroke or ischaemic stroke and same day exposure to PM2.5, PM10, O3, NO2 or NOX. For haemorrhagic stroke, we found a negative association with PM10 suggestive of a 14.6% (95% CI 0.7% to 26.5%) fall in risk per 10 µg/m3 increase in pollutant. CONCLUSIONS: Using data from the South London Stroke Register, we found no evidence of a positive association between outdoor air pollution and incident stroke or its subtypes. These results, though in contrast to recent meta-analyses, are not inconsistent with the mixed findings of other UK studies.

Semi automated transformation to OWL formatted files as an approach to data integration. A feasibility study using environmental, disease register and primary care clinical data.

INTRODUCTION: This article is part of the Focus Theme of METHODS of Information in Medicine on "Managing Interoperability and Complexity in Health Systems". BACKGROUND: Data heterogeneity is one of the critical problems in analysing, reusing, sharing or linking datasets. Metadata, whilst adding semantic description to data, adds an additional layer of complexity in the heterogeneity of metadata descriptors themselves. This can be managed by using a pre-defined model to extract the metadata, but this can reduce the richness of the data extracted. OBJECTIVES: to link the South London Stroke Register (SLSR), the London Air Pollution toolkit (LAP) and the Clinical Practice Research Datalink (CPRD) while transforming data into the Web Ontology Language (OWL) format. METHODS: We used a four-step transformation approach to prepare meta-descriptions, convert data, generate and update meta-classes and generate OWL files. We validated the correctness of the transformed OWL files by issuing queries and assessing results against the original source data. RESULTS: We have transformed SLSR LAP and CPRD into OWL format. The linked SLSR and CPRD OWL file contains 3644 male and 3551 female patients. The linked SLSR and LAP OWL file shows that there are 17 out of 35 outward postcode areas, where no overlapping data can support further analysis between SLSR and LAP. CONCLUSIONS:  Our approach generated a resultant set of transformed OWL formatted files, which are in a query-able format to run individual queries, or can be easily converted into other more suitable formats for further analysis, and the transformation was faithful with no loss or anomalies. Our results have shown that the proposed method provides a promising general approach to address data heterogeneity.