COVID-19 Pandemic Highlights Global Inequities in Chronic Hemodialysis Care: A DOPPS/ISN Survey
Bibliographic record
Abstract
Background: Patients receiving chronic hemodialysis (HD) are highly vulnerable in all settings. It is unknown whether the COVID-19 pandemic has disproportionately affected the care of chronic HD patients in low (LIC) and low-middle income (LMIC) settings. This survey aimed to identify global challenges and inequities in HD care delivery during the COVID-19 pandemic. Methods: The Dialysis Outcomes and Practice Patterns Study (DOPPS) and the International Society of Nephrology (ISN) conducted a global online survey of HD units (HDU). Sample HDUs included DOPPS sites in China, a random sample stratified by region and HDU population, and an open invitation via ISN's membership list. The survey assessed availability of COVID-19 diagnostics and personal protective equipment, the impact of COVID-19 on HD delivery and patient outcomes from COVID-19. Responses were stratified by country income according to World Bank classification. Results: Responses were received from 412 HDUs across 78 countries (Table 1). Conclusions: Striking global inequities were identified in access to COVID-19 diagnostics, infection prevention, and access to routine HD care during the pandemic. Higher apparent mortality in patients on chronic HD in LICs and LMICs is likely multifactorial, reflecting poorer access to the diagnosis and care of COVID-19, as well as greater disruptions to HD delivery. Urgent action is required to address these inequities, which disproportionately affect low-income settings, exacerbate pre-existing vulnerabilities and lead to worse outcomes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".