Access to biologicals in Crohn’s disease in ten European countries
Bibliographic record
Abstract
AIM: To analyze access (availability, affordability and acceptability) to biologicals for Crohn's disease (CD) in ten European countries and to explore the associations between these dimensions, the uptake of biologicals and economic development. METHODS: A questionnaire-based survey combined with desk research was carried out in May 2016. Gastroenterologists from the Czech Republic, France, Germany, Hungary, Latvia, Poland, Romania, Slovakia, Spain and Sweden were invited to participate and provide data on the availability of biologicals/biosimilars, reimbursement criteria, clinical practice and prices, and use of biologicals. An availability score was developed to evaluate the restrictiveness of eligibility and administrative criteria applied in the countries. Affordability was defined as the annual cost of treatment as a share of gross domestic product (GDP) per capita. Correlations with the uptake of biologicals, dimensions of access and GDP per capita were calculated. RESULTS: At the time of the survey, infliximab and adalimumab were reimbursed in all ten countries, and vedolizumab was reimbursed in five countries (France, Germany, Latvia, Slovakia, Sweden). Reimbursement criteria were the least strict in Sweden and Germany, and the strictest in Hungary, Poland and Slovakia. Between countries, the annual cost of different biological treatments differed 1.6-3.3-fold. Treatments were the most affordable in Sweden (13%-37% of the GDP per capita) and the least affordable in the Central and Eastern European countries, especially in Hungary (87%-124%) and Romania (141%-277%). Biosimilars made treatments more affordable by driving down the annual costs. The number of patients with CD on biologicals per 100000 population was strongly correlated with GDP per capita (0.91), although substantial differences were found in the uptake among countries with similar economic development. Correlation between the number of patients with CD on biologicals per 100000 population and the availability and affordability was also strong (-0.75, -0.69 respectively). CONCLUSION: Substantial inequalities in access to biologicals were largely associated with GDP. To explain differences in access among countries with similar development needs further research on acceptance.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".