Epidemiology of type 1 and type 2 diabetes mellitus in Kazakhstan: data from unified National Electronic Health System 2014–2019
Why this work is in the frame
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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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it