Facilitators and Barriers to Accessing Antifibrinolytic and Iron Therapies for Patients Who Menstruate - a Global Healthcare Provider Survey
Bibliographic record
Abstract
Background: Heavy menstrual bleeding (HMB) negatively impacts quality of life directly and indirectly due to symptoms associated with resultant iron deficiency (ID) and iron deficiency anemia (IDA). Antifibrinolytics, to decrease blood loss, and iron supplementation, to replace deficits, should be cornerstones of HMB management. Despite an abundance of evidence supporting their effectiveness and safety, clear, consistent guidance on treating HMB and secondary ID/IDA is lacking which renders access to care suboptimal and inequitable. Objective: To identify barriers and facilitators to health care provider (HCP) access of oral antifibrinolytic agents and iron supplementation for patients with HMB globally. Methods: We conducted a global survey on HCPs' experience in accessing antifibrinolytics, oral and intravenous (IV) iron. Participants were identified via snowball sampling using professional connections and/or social media. Descriptive analysis was used. Given the voluntary nature of our survey, the total number of responses varied with each question. This study was approved by the institutional research ethics board. Results: A total of 113 HCPs responded, and practiced in North America (74%), Europe (17%), Central and South America (2%), Middle East (2%), Africa (1%), and Australia (1%). Specialties included obstetrics and gynecology (41%), hematology (21%), family medicine (16%), internal medicine (11%), pediatrics (9%), and maternal fetal medicine (1%). See Figures 1 and 2 for antifibrinolytic and iron barriers by region, respectively. Antifibrinolytic Top Global Barriers: survey respondents reported product monograph contraindications (50/103, 49%), prescription requirement (46/103, 45%), lack of insurance coverage (34/103, 33%), patient safety concerns and drug cost (both 33/103, 32%). Antifibrinolytic Top Global Facilitators: HCPs reported access to websites for knowledge translation (35/85, 41%), readily available education materials (27/85, 32%), and drug access navigators (16/85, 19%). Oral Iron Top Barriers: HCPs reported patient concern regarding adverse effects of oral iron (64/101, 63%), inappropriately low ferritin reference ranges (38/101, 38%), drug cost (36/101, 36%), lack of private insurance coverage (27/101, 27%), and lack of public insurance coverage (26/101, 26%). Oral Iron Top Facilitators: HCPs reported readily available education materials (43/82, 52%), websites for knowledge translation (33/82, 40%), and drug access navigators (19/82, 23%). IV Iron Top Barriers: HCPs reported lack of infusion center capacity (58/101, 57%), drug cost (51/101, 51%), administrative burden (50/101, 50%), lack of public insurance coverage (40/101, 40%), and lack of private insurance coverage (37/101, 37%). IV Iron Top Facilitators: HCPs reported publicly funded infusion clinics (55/99, 56%), private infusion clinics (29/99, 29%), readily available education materials (25/99, 25%), websites for knowledge translation (23/99, 23%), and drug access navigators (21/99, 21%). Conclusions: Despite geographical variability in funding of health services and medications, there was a surprising consistency in the top reported barriers and facilitators to antifibrinolytic and iron access among international survey respondents. A key limitation is the predominant representation from North American and European countries. Further research in other regions is needed to allow us to gauge and address global barriers and facilitators more comprehensively.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".