ArticleBMJ open2018
Improving predictive asthma algorithms with modelled environment data for Scotland: an observational cohort study protocol.
Article in BMJ open, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 16 citations in OpenAlex.
- Development and validation of a machine learning risk prediction model for asthma attacks in adults in primary care.NPJ primary care respiratory medicine · 2025Article
- Prevalence and predictors of annual asthma reviews in Scottish primary care data: an observational study.BJGP open · 2025Article
- Cause of death coding in asthma.BMC medical research methodology · 2024Article
- 21st century (clinical) decision support in nursing and allied healthcare. Developing a learning health system: a reasoned design of a theoretical framework.BMC medical informatics and decision making · 2023Review
- Estimating medication adherence from Electronic Health Records: comparing methods for mining and processing asthma treatment prescriptions.BMC medical research methodology · 2023Article
- Derivation of asthma severity from electronic prescription records using British thoracic society treatment steps.BMC pulmonary medicine · 2022Article
- Predicting asthma attacks in primary care: protocol for developing a machine learning-based prediction model.BMJ open · 2019Article
Corrections and comments
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Authors and funding
14 authors at 7 institutions in 2 countries.
Funding
Abstract
introductionAsthma 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.
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