ArticleJACC. Asia2025
Stroke Mortality in Kazakhstan: Comparison of National Health Records to Global Burden of Disease Study.
Article in JACC. Asia, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Beyond the golden hour: prehospital delay and in-hospital stroke mortality in an elderly cohort reaching a national emergency-medicine center in Kazakhstan.Frontiers in neurology · 2026Article
- Epidemiology of Status Epilepticus in Kazakhstan: A 10-Year Population-Based Study.Journal of clinical medicine · 2025Article
- Understanding the role of hypertension in stroke outcomes using Bayesian analysis.Scientific reports · 2025Article
- Discrepancies in Stroke Mortality Estimates: Kazakhstan's National Health Records and the Global Burden of Disease.JACC. Asia · 2025Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundStroke is a major public health concern requiring valid estimates for planning and evaluating health interventions. The GBD (Global Burden of Disease) studies have become a major source of information; however, data sources have historically been a limitation.
objectivesWe sought to compare stroke mortality estimates in Kazakhstan with those reported by the GBD study.
methodsMortality data were extracted from the Unified Electronic Healthcare System of Kazakhstan (UNEHS). We used the autoregressive integrated moving average (ARIMA), Bayesian structural time-series (BSTS), and Extreme Gradient Boosting (XGBoost) to model data from the UNEHS and forecast its trends until 2030. The accuracy metrics were mean absolute error, root mean square error, and mean absolute percentage error. We calculated the standardized difference in mortality estimates between the databases for the observed and forecasted estimates.
resultsThe BSTS, ARIMA, and XGBoost models revealed slight variations in accuracy metrics, which depended on forecasting horizons and mostly favored XGBoost. During 2014-2030, the absolute difference in death counts was 207,108 between the GBD and UNEHS. The GBD estimates were twice as many across both the observed and predicted periods, with a moderate standardized difference (0.73) when considering their average. This study showed a systematic difference between GBD and national data.
conclusionsWe found that UNEHS estimates were not comparable despite our efforts to replicate the GBD methods. Further studies are needed to explore the discrepancies between the national or regional data and GBD. Current limitations related to primary data and reproducibility require caution when interpreting GBD findings.
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