Regional Differences in Diagnosis Journey and Healthcare Utilization: Results from the International Map of Axial Spondyloarthritis (IMAS)
Bibliographic record
Abstract
INTRODUCTION: To assess differences in the diagnosis journey and access to care in a large sample of patients with axial spondyloarthritis (axSpA) from around the world, included in the International Map of Axial Spondyloarthritis (IMAS). METHODS: IMAS was a cross-sectional online survey (2017-2022) of 5557 unselected patients with axSpA from 27 countries. Across five worldwide geographic regions, the patient journey until diagnosis and healthcare utilization in the last 12 months prior to survey were evaluated. Univariable and multivariable linear regression was used to analyze factors associated with higher healthcare utilization. RESULTS: Of 5557 participants in IMAS, the diagnosis took an average of 7.4 years, requiring more than two visits to HCPs (77.7% general practitioner and 51.3% rheumatologist), and more than two diagnostic tests [67.5% performed human leukocyte antigen B27 (HLA-B27), 64.2% x-ray, and 59.1% magnetic resonance imaging (MRI) scans]. North America and Europe were the regions with the highest number of healthcare professional (HCP) visits for diagnosis, while the lowest number of visits was in the Asian region. In the previous 12 months, 94.9% (n = 5272) used at least one healthcare resource, with an average of 29 uses per year. The regions with the highest healthcare utilization were Latin America, Europe, and North America. In the multiple linear regression, factors associated with higher number of healthcare utilization were younger age (b = - 0.311), female gender (b = 7.736), higher disease activity (b = 1.461), poorer mental health (b = 0.624), greater functional limitation (b = 0.300), greater spinal stiffness (b = 1.527), and longer diagnostic delay (b = 0.104). CONCLUSION: The diagnosis of axSpA usually takes more than two visits to HCPs and at least 7 years. After diagnosis, axSpA is associated with frequent healthcare resource use. Younger age, female gender, higher disease activity, poorer mental health, greater functional limitation, greater spinal stiffness, and longer diagnostic delay are associated with higher healthcare utilization. Europe and North America use more HCP visits and diagnostic tests before and after diagnosis than the other regions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".