Trends in Systemic Glucocorticoid Utilization in the United Kingdom from 1990 to 2019: A Population-Based, Serial Cross-Sectional Analysis
Bibliographic record
Abstract
Andrew N Menzies-Gow,1,2 Trung N Tran,3 Brooklyn Stanley,4 Victoria Ann Carter,4 Josef S Smolen,5 Arnaud Bourdin,6 J Mark Fitzgerald7 ,† Tim Raine,8 Jatin Chapaneri,2 Benjamin Emmanuel,3 David J Jackson,9,10 David B Price4,11 1Royal Brompton and Harefield Hospitals, Guys & St Thomas’ NHS Foundation Trust, London, UK; 2AstraZeneca, Cambridge, UK; 3AstraZeneca, Gaithersburg, MD, USA; 4Observational and Pragmatic Research Institute, Singapore; 5Medical University of Vienna, Vienna, Austria; 6Université de Montpellier, CHU Montpellier, PhyMedExp, INSERM, CNRS, Montpellier, France; 7The University of British Columbia, Vancouver, British Columbia, Canada; 8Cambridge University Hospitals NHS Foundation Trust, Addenbrooke’s Hospital, Cambridge, UK; 9Guy’s Severe Asthma Centre, Guy’s & St Thomas’ NHS Trust, London, UK; 10School of Immunology & Microbial Sciences, King’s College London, London, UK; 11Centre of Academic Primary Care, Division of Applied Health Sciences, University of Aberdeen, Aberdeen, UK†J. Mark Fitzgerald passed away on January 18, 2022Correspondence: David B Price, Centre of Academic Primary Care, Division of Applied Health Sciences, University of Aberdeen, Polwarth Building-Foresterhill, Aberdeen, AB25 2ZD, UK, Tel +65 3105 1489, Email dprice@opri.sgPurpose: Associations between systemic glucocorticoid (SGC) exposure and risk for adverse outcomes have spurred a move toward steroid-sparing treatment strategies. Real-world changes in SGC exposure over time, after the introduction of steroid-sparing treatment strategies, reveal areas of successful risk mitigation as well as unmet needs.Patients and Methods: A population-based ecological study was performed from the Optimum Patient Care Research Database to describe SGC prescribing trends of steroid-sparing treatment strategies in primary care practices before and after licensure of biologics in the United Kingdom from 1990 to 2019. Each analysis year included patients aged ≥ 5 years who were registered for ≥ 1 year with a participating primary care practice. The primary analysis was SGC exposure, defined as total cumulative SGC dose per patient per year, for asthma, severe asthma, chronic obstructive pulmonary disease (COPD), nasal polyps, Crohn’s disease, rheumatoid arthritis, ulcerative colitis, and systemic lupus erythematosus. Secondary outcomes were percentages of patients prescribed SGCs and number of SGC prescriptions per patient per year.Results: The number of patients who met study inclusion criteria ranged from 219,862 (1990) to 1,261,550 (2019). At the population level, patients with asthma or COPD accounted for 67.7% to 73.2% of patients per year with an SGC prescription. Over three decades, decreases in SGC total yearly dose ≥ 1000 mg have been achieved in multiple conditions. Patients with COPD prescribed SGCs increased from 5.8% (1990) to 34.8% (2017). SGC prescribing trends for severe asthma, Crohn’s disease, and ulcerative colitis show decreased prescribing trends after the introduction of biologics.Conclusion: Decreases in total yearly SGC doses have been shown in multiple conditions; however, for conditions such as severe asthma and COPD, an unmet need remains for increased awareness of SGC burden and the adoption or development of SGC-sparing alternatives to reduce overuse.Keywords: glucocorticoids, practice patterns, drug prescriptions, biological products, drug utilization
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".