Impact of the COVID-19 pandemic on transplantation by income level and cumulative COVID-19 incidence: a multinational survey study
Bibliographic record
Abstract
OBJECTIVES: The COVID-19 pandemic significantly affected the provisions of health services to necessary but deprioritised fields, such as transplantation. Many programmes had to ramp-down their activity, which may significantly affect transplant volumes. We aimed to pragmatically analyse measures of transplant activity and compare them by a country's income level and cumulative COVID-19 incidence (CCI). DESIGN, SETTING AND PARTICIPANTS: From June to September 2020, we surveyed transplant physicians identified as key informants in their programmes. Of the 1267 eligible physicians, 40.5% from 71 countries participated. OUTCOME: Four pragmatic measures of transplant activity. RESULTS: Overall, 46.5% of the programmes from high-income countries anticipate being able to maintain >75% of their transplant volume compared with 31.6% of the programmes from upper-middle-income countries, and with 21.7% from low/lower-middle-income countries (p<0.001). This could be because more programmes in high-income countries reported being able to perform transplantation/s (86.8%%-58.5%-67.9%, p<0.001), maintain prepandemic deceased donor offers (31.0%%-14.2%-26.4%, p<0.01) and avoid a ramp down phase (30.9%%-19.7%-8.3%, p<0.001), respectively. In a multivariable analysis that adjusted for CCI, programmes in upper-middle-income countries (adjusted OR, aOR=0.47, 95% CI 0.27 to 0.81) and low/lower-middle-income countries (aOR 0.33, 95% CI 0.16 to 0.67) had lower odds of being able to maintain >75% of their transplant volume, compared with programmes in high-income countries. Again, this could be attributed to lower-income being associated with 3.3-3.9 higher odds of performing no transplantation/s, 66%-68% lower odds of maintaining prepandemic donor offers and 37%-76% lower odds of avoiding ramp-down of transplantation. Overall, CCI was not associated with these measures. CONCLUSIONS: The impact of the pandemic on transplantation was more in lower-income countries, independent of the COVID-19 burden. Given the lag of 1-2 years in objective data being reported by global registries, our findings may inform practice and policy. Transplant programmes in lower-income countries may need more effort to rebuild disrupted services and recuperate from the pandemic even if their COVID-19 burden was low.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".