Regional hotspots for chronic kidney disease: A multinational study from the ISN-GKHA
Bibliographic record
Abstract
Chronic kidney disease (CKD) disproportionately affects certain populations as demonstrated by well-established subnational geographic hotspots of CKD in Central America and South Asia. Using data from the third iteration of the International Society of Nephrology Global Kidney Health Atlas (ISN-GKHA), we aimed to systematically identify sub-national geographic or population clusters with high prevalence of CKD. The ISN-GKHA survey was conducted from July to September 2022, and included questions regarding whether a regional CKD hotspot existed in the respondents' country and possible contributors. A CKD hotspot was defined as a population cluster with a high risk of kidney failure requiring dialysis or transplant, or people dying from kidney failure. Overall, 46 out of 162 responding countries reported subnational hotspots for CKD within their country. Hotspots were reported across all regions, except for the Middle East. Latin America had the highest percentage (12 of 21, 57%) of countries reporting a regional CKD hotspot followed by the regions of North and East Asia, and Western Europe. Adults aged 18 to 44 years and rural populations were most commonly identified as the primary groups affected. Clinical factors were most commonly identified as contributors to CKD (hypertension in 74% and diabetes in 72%), followed by cultural (e.g., diet and herbal medications in 67%), and environmental (e.g., polluted water in 43%) factors. Latin American countries more commonly reported climate, cultural, and environmental factors as contributors compared to other regions. Across the world, there are a number of subnational regions where in-country experts identify a disproportionately higher burden of CKD, commonly occurring among younger age groups with clinical, cultural, and environmental contributors specific to these geographic regions. In-depth studies, starting with systematic epidemiology efforts, are needed to investigate the aetiopathogenesis of these CKD hotspots around the world so that tailored interventions can be offered.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".