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Record W4403394814 · doi:10.1136/bmj-2024-079937

Progress of nations in the organisation of, and structures for, kidney care delivery between 2019 and 2023: cross sectional survey in 148 countries

2024· article· en· W4403394814 on OpenAlexaff
Ikechi G. Okpechi, Adeera Levin, Somkanya Tungsanga, Silvia Arruebo, Fergus Caskey, Innocent Ijezie Chukwuonye, Sandrine Damster, Jo‐Ann Donner, Udeme E. Ekrikpo, Anukul Ghimire, Vivekanand Jha, Valérie A. Luyckx, Masaomi Nangaku, Syed Saad, Elliot Koranteng Tannor, Marcello Tonelli, Ye Feng, Aminu K. Bello, David W. Johnson

Bibliographic record

VenueBMJ · 2024
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsUniversity of British ColumbiaUniversity of CalgaryUniversity of Alberta
FundersInternational Society of Nephrology
KeywordsMedicineNephrologyWorkforceCross-sectional studyPopulationHealth careEnvironmental healthGlobal healthPublic healthFamily medicineDemographyEconomic growthInternal medicineNursing

Abstract

fetched live from OpenAlex

OBJECTIVE: To assess changes in key measures of kidney care using data reported in 2019 and 2023. DESIGN: Cross sectional survey in 148 countries. SETTING: Surveys from International Society of Nephrology Global Kidney Health Atlas between 2019 and 2023 that included participants from countries in Africa (n=36), Eastern and Central Europe (n=16), Latin America (n=18), the Middle East (n=11), Newly Independent States and Russia (n=10), North America and the Caribbean (n=8), North and East Asia (n=6), Oceania and South East Asia (n=15), South Asia (n=7), and Western Europe (n=21). PARTICIPANTS: Countries that participated in both surveys (2019 and 2023). MAIN OUTCOME MEASURES: Comparison of 2019 and 2023 data for availability of kidney replacement treatment services, access, health financing, workforce, registries, and policies for kidney care. Data for countries that participated in both surveys (2019 and 2023) were included in our analysis. Country data were aggregated by International Society of Nephrology regions and World Bank income levels. Proportionate changes in the status of these measures across both periods were reported. RESULTS: Data for 148 countries that participated in both surveys were available for analysis. The proportions of countries that provided public funding (free at point of delivery) increased from 27% in 2019 to 28% in 2023 for haemodialysis, 23% to 28% for peritoneal dialysis, and 31% to 36% for kidney transplantation services. Centres for these treatments increased from 4.4 per million population (pmp) to 4.8 pmp (P<0.001) for haemodialysis, 1.4 pmp to 1.6 pmp for peritoneal dialysis, and 0.43 pmp to 0.46 pmp for kidney transplantation services. Overall, access to haemodialysis and peritoneal dialysis improved, however, access to kidney transplantation decreased from 30 pmp to 29 pmp. The global median prevalence of nephrologists increased from 9.5 pmp to 12.4 pmp (P<0.001). Changes in the availability of kidney registries and in national policies and strategies for kidney care were variable across regions and country income levels. The reporting of specific barriers to optimal kidney care by countries increased from 55% to 59% for geographical factors, 58% to 68% (P=0.043) for availability of nephrologists, and 46% to 52% for political factors. CONCLUSIONS: Important changes in key areas of kidney care delivery were noted across both periods globally. These changes effected the availability of, and access to, kidney transplantation services. Countries and regions need to enact enabling strategies for preserving access to kidney care services, particularly kidney transplantation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.025
GPT teacher head0.340
Teacher spread0.315 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2024
Admission routes1
Has abstractyes

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