Holly Tibble

Project: Medication non-adherence in asthma: data-driven approaches to understanding patient behaviour

Predicting asthma attacks enables health professionals to provide timely health education, such as asthma attack action plans and inhaler technique assessments, as well as to prompt further monitoring and pharmacological interventions. Previous attempts to construct data-driven risk prediction models of asthma attacks have lacked clinical utility: either producing inaccurate predictions or requiring patient data which are not cost-effective to collect on a large scale (such as electronic monitoring device data). Electronic Health Record (EHR) use throughout the UK enables researchers to harness comprehensive and panoramic patient data, but their cleaning and pre-processing requires sophisticated empirical experimentation and data analytics approaches. 

One factor contributing to excessive asthma morbidity and mortality is medication non-adherence: individuals not undergoing their treatment as prescribed. Estimating adherence from prescription records requires precise extraction of information from free-text data including dose directions. Furthermore, there are many approaches to measuring medication adherence from these processed EHRs, and appraising the best use-case for various methods is a core component of my research.  

About me

My research interests are prediction modelling, data linkage, and machine learning. 

Holly is now an Early Career Researcher within the Centre. 

Holly's Early Career Research profile

Publications

• Tibble H, Sheikh A, Tsanas A. (2022) Estimating Medication Adherence from Electronic Health Records Using Rolling Averages of Single Refill-based Estimates. 2022 IEEE International Engineering in Medicine and Biology Conference  (EMBC) (In Press).
• Tibble H, Sheikh A, Tsanas A. (2021) Estimation of Asthma Severity from Electronic Prescription Records using British Thoracic Society and Scottish Intercollegiate Guidelines Network Treatment Steps. 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM): Fifth Edition of Workshop Processes and Algorithms for Healthcare and Life Quality Improvement (CBPBL).  (In Press).

• Nwaru BI, Shah SA, Tibble H, Pillinger R, McLean S, Ryan D, Critchley H, Hawrylowicz CM, Simpson CR, Soyiri IN, Appiagyei F, Price D, Sheikh A. (2021) Hormone replacement therapy and risk of severe asthma exacerbation in peri-menopausal and post-menopausal women: 17-year national cohort study.  Journal of Allergy and Clinical Immunology in Practice. (DOI: 10.1016/j.jaip.2021.02.052)

• Sheikh A, Anderson M, Albala S, Casadei B, Dean Franklin B, Richards M, Taylor D, Tibble H, Mossialios E. (2021) Health information technology and digital innovation for national learning health and care systems.  Lancet Digital Health.  (DOI: 10.1016/S0140-6736(21)00232-4)
• Nwaru BI, Tibble H, Shah SA, Pillinger R, McLean S, Ryan D, Critchley HOD, Price D, Hawrylowicz CM, Simpson CR, Soyiri IN, Appiagyei F, Sheikh A. (2021) Hormonal contraception and the risk of severe asthma exacerbation: 17-year population-based cohort study. Thorax.  76(2). p.109-115. DOI: 10.1136/thoraxjnl-2020-215540 
• Stage Baxter M, Tibble H, Bush A, Sheikh A, Schwarze J. (2021) Efficacy of mobile health interventions to improve nasal corticosteroid adherence in allergic rhinitis: a systematic review. Clinical and Translational Allergy. 11(9). e12075.

• Tibble H, Flook M, Sheikh A, Tsanas A, Horne R, Vrijens B, De Geest S, Stagg HR. (2020) Measuring and reporting treatment adherence: what can we learn by comparing two respiratory conditions? British Journal of Clinical Pharmacology. 87(3). p.825-836.

• Tibble H, Chan AHY, Mitchell EA, Horne E, Doudesis D, Horne R, Mizani MA, Sheikh A, Tsanas A. (2020) A Data-Driven Typology of Asthma Medication Adherence using Cluster Analysis.  Scientific Reports. 10(1). 14999. DOI: 10.1038/s41598-020-72060-0 
• Tibble H, Lay-Flurrie J, Sheikh A, Horne R, Mizani MA, Tsanas A, The Salford Lung Study Team. (2020) Linkage of Primary Care Prescribing Records and Pharmacy Dispensing Records in the Salford Lung Study: Application in Asthma.  BMC Medical Research Methodology. 20(1). 303. DOI: 10.1186/s12874-020-01184-8
• Tibble H, Horne E, Horne R, Mizani AM, Simpson CR, Sheikh A, Tsanas A. (2019) Predicting asthma attacks in primary care: protocol for developing a machine learning-based prediction model. BMJ Open. 9(7), e028375. DOI: 10.1136/bmjopen-2018-02837

Acknowledgements

Funded by Health Data Research UK. This PhD is affiliated with the Asthma UK Centre for Applied Research.