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Unsafe inhaler prescribing, social demographics, and the COVID-19 lockdown

By statistically identifying unfavourable prescribing practices and correlating this with social demographic and geospatial data, we investigate regions at particular risk of unsafe inhaler prescribing leading up to the COVID-19 lockdown.

Project overview

Panic prescribing of inhalers at the onset of the COVID-19 lockdown in March 2020 meant that suppliers started running out of stock for specific brands of inhalers, causing a potential risk for asthma/COPD patients. However, what are unsafe inhaler prescribing practices, and where do these occur under normal circumstances? In this project, we used a data-driven approach to explore inhaler prescription patterns using data from Open Prescribing. First, we used statistical means to identify GP practices that could previously have been at risk of unfavourable prescribing practices, and then look at these in the context of levels social deprivation and panic prescribing seen at the beginning of lockdown.

Data and methods

Open Prescribing is a service that makes monthly prescribing data from every GP practice in England available online. We collected monthly data relating to inhaler prescriptions, then using guidelines from the Asthma UK, the NHSBSA, and Open Prescribing, we calculated 5 measures to quantify unsafe prescribing behaviour. We then used statistical process control (SPC) methodologies, which originate in manufacturing and quantify change in relation to peers, to identify GP practices which are outliers in these measures and could be at risk of unsafe prescribing practices. We then evaluated correlations between these results and social/demographic data, and the change in inhaler prescriptions leading up to the COVID-19 lockdown.

Key findings

The locations showing the level of panic prescribing (change in prescriptions from March 2020 compared to 2019) did not appear to be correlated with deprivation levels (index of multiple deprivation, IMD), or the number of alerts from the SPC algorithms for the 5 prescribing measures. This suggests that prescribing of inhalers was affected universally at the beginning of the COVID-19 lockdown, and was not restricted to areas with specific social demographics, or areas with a history of potentially unsafe prescribing. However, the number of alerts from the SPC algorithm did correlate with the level of deprivation, and followed a similar geospatial pattern, particularly in the South West, the North East, and the East and West Midlands. The correlation implies that in areas with higher levels of social deprivation, more statistically deviant prescribing, and based on our measures, potentially more unsafe prescribing occurs.

Value of the research

These results will be useful for GPs who are interested in reviewing their inhaler prescribing practices in the wake of the COVID-19 lockdown. In 2014, Asthma UK suggested that 127,617 people with asthma in the UK are at a higher risk of death due to unsafe prescribing practices. The deficit in the supply chain due to COVID-19 stockpiling may act as a catalyst for concerned GPs to revise their prescribing practices. This study outlines the geographical and social demographic areas that are at higher risk of unsafe prescribing in 5 domains, which include short-acting beta-agonist (SABA) overprescribing, excessive high dose inhaled corticosteroid (ICS) prescribing, and environmental impact.


  • Stockpiling of inhaler prescriptions appeared to occur ubiquitously with no relation to prior unsafe prescribing or levels of deprivation.
  • There appears to be a relationship between unsafe inhaler prescribing practices identified with SPC algorithms and index of multiple deprivation, particularly in the South West, the North East, and the East and West Midlands.
  • These data can be used as a reference by GPs and GP practices who are interested in national trends in inhaler prescribing practices in the context of the COVID-19 pandemic, and those who may be interested in revising their prescribing behaviour.

Research theme

  • Health informatics
  • Data visualisation and analysis
  • Statistical and mathematical methods
  • Epidemiology


Dr Thomas Richards, LIDA Data Science Intern

Dr Alison Heppenstall, Professor in Geocomputation, University of Leeds, Alan Turing Institute

Dr Roger Beecham, Lecturer in Geographic Data Science, University of Leeds


The Alan Turing Institute


ESRC-Turing ASG Grant No 117487.