Trends of national and sub-national burden attributed to kidney dysfunction risk factor in Iran: 1990-2019
Bibliographic record
Abstract
Background: Kidney dysfunction is a risk factor for cardiovascular disease and chronic kidney disease. Herein, we aimed to describe the attributable burden of kidney dysfunction at the national and sub-national levels in Iran. Methods: The Global Burden of Disease (GBD) 2019 data were extracted on the deaths, disability-adjusted life years (DALYs), years of life lost, and years lived with disability attributed to the risk factor of kidney dysfunction by age and sex at the national and provincial levels from 1990-2019. Also, risk exposure was reported by summary exposure value (SEV) with a range of 0 to 100. The estimated values were based on a comparative risk assessment framework. Results: In 2019, the age-standardized death rate and age-standardized DALYs rate attributable to kidney dysfunction were 58.2 (95% uncertainty interval of 48.8-68.1) and 1127.2 (981.1-1282.7) per 100,000 population in Iran, respectively. Also, the Sistan and Baluchistan province (1729.3 [1478.3-2006.4]) and the province of Tehran (681.9 [571.4-809.8]) had the greatest and lowest age-standardized DALYs rates, respectively. Nationally, SEVs increased from 22.8 to 26.2. The age-standardized burden attributable to kidney dysfunction had a positive association with age advancement. The attributable age-standardized deaths and DALYs rates in all socio-demographic index regions decreased from 1990-2019. Also, the highest and lowest attributable age-standardized DALYs rates of kidney dysfunction came from ischemic heart disease and peripheral artery disease in 2019, respectively. Conclusion: Although the attributed age-standardized DALYs and death rates decreased from 1990-2019, risk exposure increased and remains a crucial risk factor in Iran. Therefore, policymakers should consider preparing a preventive program that takes into account different levels of prevention of kidney dysfunction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".