P092 NICE biologics pathways for inflammatory arthritis exhibit regional variability due to modification by CCG’s: results from a national survey of pathways in England
Bibliographic record
Abstract
Abstract Background/Aims NICE publishes guidance underpinned by act of Parliament and legally enforceable, on the use of biological therapies in the treatment of rheumatoid arthritis (RA), psoriatic arthritis (PsA) and ankylosing spondylitis (AS) which should allow harmonisation of access independent of region. However, sufficient guidance is not provided on the use of sequential biologics nor is a numerical cap placed on the number of biologics a patient can attempt if they have had an inadequate response. We have previously reported that in a limited sample, Clinical Commissioning Groups (CCGs) interpret NICE guidance variably and restrict access to NICE approved treatments depending on geography, the so-called “postcode lottery”. We determined the variability of biologics pathways in all CCG’s in England to examine whether a potentially unfair postcode lottery exists for sequential biologics use. Methods All 135 England CCGs covering over 55 million people, were sent Freedom of Information requests, for their biologic pathways for RA, PsA and AS. Where CCGs did not have this information, the relevant acute trusts were contacted, with responses recorded under that CCG. For every CCG the local biologics pathways were examined for detail on the number and type of biologics commissioned before an Individual Funding Request was needed. “No Cap” was recorded if CCG’s responded with no restriction on the number of biologics. Results Responses were obtained from 124/135 CCG’s for RA, 122/135 for PsA and AS, all covering an estimated population in excess of 45 million people. For RA, 55% CCG’s had no cap on the number of commissioned RA biologics. 45% had a variable cap from 3 to 6 commissioned biologics. For PsA, the figures were 54% with no cap and 46% with variable capping between 2-5 biologics allowed, for AS the figures were 51% and 49% respectively. In total this represented 41 different local pathways for RA, 29 different pathways for PsA and in AS where fewer biologics choices exist, 25 different pathways depending on CCG and location. Conclusion There is wide regional variation in the interpretation of NICE guidance by CCG’s resulting in many different local pathways depending on geography. Approximately 50% of pathways restricted biologics prescribing by mandating the type and sequence of biologics used, potentially compromising patient care and delaying treatment by requiring an IFR for a NICE approved biologic. Moreover, pathways varied as to which biologics could be used at any point of management by region as well. As exemplars of good practice, approximately 50% of CCG’s had no cap, allowing clinical freedom to prescribe the most appropriate biologic. The results of this national study demonstrate the variability of biologics pathways in many areas of England ensuring a postcode lottery still exists in many regions. Disclosure J. Mistry: None. D. Hill: None. A. Bosworth: None. A. Kaul: None.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".