What is the economic burden of delayed axial spondyloarthritis diagnosis in the UK?
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to develop an economic model to determine the annual cost of delayed axial SpA diagnosis in the UK, adopting both National Health Service (NHS) and societal perspectives. METHODS: We developed a Markov economic model to estimate the costs of delayed axial SpA diagnosis in the UK. Model parameters were sourced from a 2016 National Axial Spondyloarthritis Society patient survey, anonymized patient-level data, the published literature, and expert opinion. A literature-defined, mixed cohort (64% male) of people assumed to have axial SpA, whose age at symptom onset was 26 years, were targeted. To assess the robustness of the results, base case and probabilistic sensitivity analyses were performed. RESULTS: In a simulated cohort of 1000 patients, with a mean time to diagnosis of 8.5 years, we estimate the cumulative costs of delayed diagnosis per person living with axial SpA to be £193 512 (95% CI: 108 770-306 789). The costs were led by productivity losses (65.1%) and out-of-pocket expenses (31.3%). The total annual cost resulting from delayed axial SpA diagnosis in the UK has been estimated at £3.1 billion and £12.5 billion, based on a prevalence of 0.3% (Assessment of SpondyloArthritis international Society classification criteria) and 1.2% (European Spondyloarthropathy Study Group classification criteria), respectively. CONCLUSION: Delayed axial SpA diagnosis carries high costs for society due to productivity losses. Early diagnosis and treatment could offer significant benefits to the patient and potentially reduce productivity losses; however, future research is needed to evaluate the long-term health economic impact.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".