Rising Incidence of Acute Hospital Admissions due to Gout
Bibliographic record
Abstract
OBJECTIVE: To describe trends in acute hospital admissions due to gout in England, with rheumatoid arthritis (RA) as a comparator, alongside prescribing trends for common gout medications. METHODS: An ecological study was performed using UK National Health Service (NHS) Digital Hospital Episode Statistics data to calculate the incidence of unplanned admissions with primary diagnoses of gout or RA in adults in England between April 2006 and March 2017. NHS Digital Community Prescription data for allopurinol, febuxostat, and colchicine were considered over a similar period. RESULTS: The incidence of unplanned gout admissions increased by 58.4% over the study period, from 7.9 admissions per 100,000 population in 2006/07 to 12.5 admissions per 100,000 population in 2016/17 (p < 0.0001). Gout admissions increased as a proportion of all hospital admissions, and accounted for 349,768 bed-days cumulatively. Unplanned RA admissions halved over the study period, from 8.6 admissions per 100,000 population in 2006/07 to 4.3 admissions per 100,000 population in 2016/17 (p < 0.0001). Community prescriptions dispensed for allopurinol and colchicine have increased by 71.4% and 165.6%, respectively, since 2006 (p < 0.0001). Febuxostat prescriptions have increased 20-fold since 2010 (p < 0.0001), when prescription data became available. CONCLUSION: Acute gout admissions in England increased between 2006 and 2017, accompanied by increasing prescription of gout therapies. Acute admissions due to RA halved over the same time period. These data call for aggressive target-driven therapy for this highly treatable disease.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".