Improving predictive asthma algorithms with modelled environment data for Scotland: an observational cohort study protocol

Soyiri, Ireneous N.; Sheikh, Aziz; Reis, Stefan; Kavanagh, Kimberly; Vieno, Massimo; Clemens, Tom; Carnell, Edward J.; Pan, Jiafeng; King, Abby; Beck, Rachel C.; Ward, Hester J.T.; Dibben, Chris; Robertson, Chris; Simpson, Colin R.

Abstract

Abstract
Introduction Asthma has a considerable, but potentially, avoidable burden on many populations globally. Scotland has some of the poorest health outcomes from asthma. Although ambient pollution, weather changes and sociodemographic factors have been associated with asthma attacks, it remains unclear whether modelled environment data and geospatial information can improve population-based asthma predictive algorithms. We aim to create the afferent loop of a national learning health system for asthma in Scotland. We will investigate the associations between ambient pollution, meteorological, geospatial and sociodemographic factors and asthma attacks.

Methods and Analysis We will develop and implement a secured data governance and linkage framework to incorporate primary care health data, modelled environment data, geospatial population and sociodemographic data. Data from 75 recruited primary care practices (n=500 000 patients) in Scotland will be used. Modelled environment data on key air pollutants at a horizontal resolution of 5 km×5 km at hourly time steps will be generated using the EMEP4UK atmospheric chemistry transport modelling system for the datazones of the primary care practices’ populations. Scottish population census and education databases will be incorporated into the linkage framework for analysis. We will then undertake a longitudinal retrospective observational analysis. Asthma outcomes include asthma hospitalisations and oral steroid prescriptions. Using a nested case–control study design, associations between all covariates will be measured using conditional logistic regression to account for the matched design and to identify suitable predictors and potential candidate algorithms for an asthma learning health system in Scotland.

Findings from this study will contribute to the development of predictive algorithms for asthma outcomes and be used to form the basis for our learning health system prototype.

Ethics and dissemination The study received National Health Service Research Ethics Committee approval (16/SS/0130) and also obtained permissions via the Public Benefit and Privacy Panel for Health and Social Care in Scotland to access, collate and use the following data sets: population and housing census for Scotland; Scottish education data via the Scottish Exchange of Data and primary care data from general practice Data Custodians. Analytic code will be made available in the open source GitHub website. The results of this study will be published in international peer reviewed journals.

Citation

Soyiri, I. N., Sheikh, A., Reis, S., Kavanagh, K., Vieno, M., Clemens, T., …Simpson, C. R. (2018). Improving predictive asthma algorithms with modelled environment data for Scotland: an observational cohort study protocol. BMJ open, 8(5), Article e023289. https://doi.org/10.1136/bmjopen-2018-023289

• Journal Article Type: Article
• Acceptance Date: Apr 20, 2018
• Online Publication Date: May 20, 2018
• Publication Date: 2018-05
• Deposit Date: May 15, 2019
• Publicly Available Date: May 17, 2019
• Journal: BMJ Open
• Print ISSN: 2044-6055
• Electronic ISSN: 2044-6055
• Publisher: BMJ Publishing Group
• Peer Reviewed: Peer Reviewed
• Volume: 8
• Issue: 5
• Article Number: e023289
• DOI: https://doi.org/10.1136/bmjopen-2018-023289
• Public URL: https://hull-repository.worktribe.com/output/1740530
• Publisher URL: https://bmjopen.bmj.com/content/8/5/e023289