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

Ireneous N Soyiri1, Aziz Sheikh1, Stefan Reis2,3

  • 1Asthma UK Centre for Applied Research, Usher Institute of Population Health Sciences and Informatics, Centre for Medical Informatics, The University of Edinburgh, Edinburgh, UK.

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Summary

This study integrates environmental and sociodemographic data to improve asthma attack prediction in Scotland. Findings will enhance asthma management through a national learning health system, aiming to reduce the global burden of this respiratory condition.

Keywords:
asthmaenvironmental epidemiologylearning health systempollution effectsprimary care

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Area of Science:

  • Environmental Health
  • Public Health
  • Health Informatics

Background:

  • Asthma poses a significant global health burden, with Scotland experiencing particularly poor outcomes.
  • Existing asthma predictive models do not fully leverage environmental and geospatial data.
  • There is a need to improve population-based asthma predictive algorithms.

Purpose of the Study:

  • To create the afferent loop of a national learning health system for asthma in Scotland.
  • To investigate associations between environmental, meteorological, geospatial, and sociodemographic factors and asthma attacks.
  • To develop and implement a data governance framework for integrating diverse health and environmental datasets.

Main Methods:

  • Utilized a secured data governance and linkage framework incorporating primary care data (500,000 patients from 75 practices), modelled environment data (air pollutants at 5km resolution), geospatial, and sociodemographic data.
  • Employed a longitudinal retrospective observational analysis with a nested case-control study design.
  • Applied conditional logistic regression to identify predictors for asthma outcomes (hospitalisations, oral steroid prescriptions).

Main Results:

  • Associations between environmental, meteorological, geospatial, and sociodemographic factors and asthma outcomes were measured.
  • Identified suitable predictors and candidate algorithms for an asthma learning health system.
  • Established a foundation for developing predictive algorithms to improve asthma outcome management.

Conclusions:

  • Integrating modelled environment and geospatial data can enhance population-based asthma predictive algorithms.
  • The study contributes to developing a national learning health system for asthma in Scotland.
  • Findings will inform strategies to mitigate the burden of asthma through improved prediction and management.