Global, regional, and national burden of diabetes and kidney diseases, 1990–2021: a trend and health inequality analyses based on the Global Burden of Disease Study 2021
Bibliographic record
Abstract
BACKGROUND: Diabetes and kidney diseases pose a major global public health challenge, impacting both health and socioeconomic development. Comprehensive analyses combining long-term trend decomposition (1990-2021) and inequality measurements are lacking. METHODS: Using data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021, we conducted comprehensive analyses to examine the disease burden through two complementary approaches (1): decomposition analysis to quantify the relative contributions of population growth, aging effects, and epidemiologic changes; and (2) inequality assessment using both the slope index of inequality and concentration index to evaluate socioeconomic disparities in disease burden across countries. RESULTS: According to GBD 2021, the global figures for diabetes and kidney diseases in 2021 included 1,081,017,594 prevalent cases, 44,905,586 incident cases, 123,704,574 DALYs, and 3,195,034 deaths. The age-standardized rates (ASR) of estimated annual percentage change (EAPC) and average annual percentage change (AAPC) for both prevalence and incidence were positive across all countries and territories, denoting an upward trend. Population (36.92%), aging (28.64%), and epidemiologic change (i.e., changes in age-specific disease risk independent of demographic shifts, driven by diagnostics, risk factors, or treatments; 34.44%) were key drivers over 1990-2021. Significant absolute and relative inequalities in the burden of diabetes and kidney diseases, measured by sociodemographic index (SDI), were observed and showed a substantial increase over time. CONCLUSION: Understanding these patterns-particularly the rising burden in high-SDI nations and widening cross-country inequalities-is crucial for tailoring interventions for diabetes and kidney diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".