Out-of-pocket expenses and rationing of insulin and diabetes supplies: findings from the 2022 T1International cross-sectional web-based survey
Bibliographic record
Abstract
Introduction: Continue investigating Out-of-Pocket Expenses (OoPEs) and rationing of insulin and diabetes supplies, including impacts of the COVID-19 pandemic, for people with type 1 diabetes (T1D). Methods: A cross-sectional web-based survey was conducted in English and advertised by T1International's global network of patient advocates from May through September 2022. Participants provided monthly OoPEs and rationing frequency for insulin and supplies, impacts of the COVID-19 pandemic, and open-ended comments. Results: In the seven most represented countries, mean monthly OoPEs were highest in the United States, followed by Panama, Canada, and India, and were much lower in the United Kingdom, Germany, and Sweden. OoPEs were highest for participants with partial healthcare coverage, followed by those with no healthcare coverage. The COVID-19 pandemic negatively impacted access and/or affordability of insulin and/or supplies for over half of participants. Globally, 19.5% reported insulin rationing and 36.6% reported rationing glucose testing supplies. Qualitative analysis of open-ended responses identified themes such as 'mental health impacts' and 'limits to life choices.' Discussion: High OoPEs lead to rationing of insulin and supplies for many people with T1D globally. Healthcare systems improvements and price reductions of insulin and supplies are needed to ensure adequate, equitable access for all.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".