Epidemiology of type 1 and type 2 diabetes mellitus in Kazakhstan: data from unified National Electronic Health System 2014–2019
Bibliographic record
Abstract
BACKGROUND: We aimed to explore descriptive epidemiology of T1 and T2 Diabetes Mellitus (DM) and to investigate demographic factors and comorbidities associated with all-cause mortality by aggregating and utilizing large-scale administrative healthcare data from the Unified National Electronic Health System (UNEHS) of Kazakhstan for 2014-2019 years period. METHODS: A total of 475,539 individuals were included in the analyses. The median years of follow-up for Type 1 DM patients accounted for 4.7 years and 4.5 years in Type 2 DM patients. We used Kaplan-Meier and log-rank test to calculate failure function and differences in survival by age, sex, ethnicity, and comorbidities with all-cause mortality for Type 1 and Type 2 DM. Cox proportional hazards regression analysis was used to obtain crude and adjusted hazard ratios. RESULTS: Prevalence of Type 1 and Type 2 DM increased 1.7 times from 2014 to 2019. Mortality of Type 1 and Type 2 DM also increased 4 times and 6 times from 2014 to 2019, respectively. Male sex, older age and Kazakh ethnicity were associated with a higher risk of all-cause death compared to females, younger age and other nationalities than Kazakh in patients with Type 1 and Type 2 DM. Coronary artery disease, diabetic nephropathy, stroke, amputations and neoplasms were associated with a higher risk of all-cause death. CONCLUSION: The prevalence and mortality rate of Type 1 and Type 2 DM increased during the years 2014-2019 in Kazakhstan. Male sex, older age and Kazakh ethnicity were associated with a higher risk of all-cause death compared to females, younger age and other nationalities than Kazakh. Coronary artery disease, diabetic nephropathy, stroke, amputations and neoplasms were associated with a higher risk of all-cause death.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".