A study of variations in the reported haemophilia A prevalence around the world
Bibliographic record
Abstract
The objectives of this paper were to study the reported haemophilia A prevalence (per 100 000 males) on a country-by-country basis and address the following: Does the reported prevalence of haemophilia A vary by national economies? We collected prevalence data for 106 countries from the World Federation of Hemophilia (WFH) annual global surveys and the literature. We found that the reported haemophilia A prevalence varied considerably among countries, even among the wealthiest of countries. The prevalence (per 100 000 males) for high income countries was 12.8 +/- 6.0 (mean +/- SD) whereas it was 6.6 +/- 4.8 for the rest of the world. Within a country, there was a strong trend of increasing prevalence over time--the prevalence for Canada ranged from 10.2 in 1989 to 14.2 in 2008 (R = 0.94 and P < 0.001) and for the United Kingdom it ranged from 9.3 in 1974 to 21.6 in 2006 (R = 0.94 and P < 0.001). Prevalence data reported from the WFH compared well with prevalence data from the literature. Patient registries generally provided the highest quality of prevalence data. The lack of accurate country-specific prevalence data has constrained planning efforts for the treatment and care of people with haemophilia A. With improved information, healthcare agencies can assess budgetary needs to develop better diagnostic and treatment facilities for affected patients and families and work to ensure adequate supplies of factor VIII concentrates for treatment. In addition, this information can help manufacturers plan the production of concentrates and prevent future shortages.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".