Burden of Parkinson’s disease in Central Asia from 1990 to 2021: findings from the Global Burden of Disease study
Bibliographic record
Abstract
BACKGROUND: Central Asia is known to face various ecological challenges that constitutes major risk factors for Parkinson's disease (PD). This study examines the burden of PD in Central Asia, a region where data on neurological disorders is notably sparse. METHODS: Building on the latest Global Burden of Disease Study (GBD 2021), this study investigates the Years of Life Lost (YLLs), Years Lived with Disability (YLDs), and Disability-Adjusted Life Years (DALYs) associated with PD in Central Asia and its countries from 1990 to 2021. The authors calculated average annual percent change (AAPC) to analyze trends, and compared individual country estimates to global figures. Additionally, incorporating data from the World Bank, both Bayesian hierarchical and non-hierarchical frequentist regression models were employed to assess their impact on DALYs. RESULTS: The DALYs varied across the study period, primarily driven by YLLs. While YLLs showed a uniform trend, YLDs were mostly incremental. Kazakhstan had the highest estimates across all metrics and was the only country aligned with global patterns. Age- and sex-specific estimates revealed substantial variations, with notably high figures found in male subjects from Tajikistan. The YLLs, YLDs, and DALYs for Kazakhstan, Uzbekistan, and Turkmenistan saw a significant increase in AAPCs. In contrast, Kyrgyzstan and Tajikistan saw declines, likely attributable to civic conflict and inter-country differences in population structure. Further comparison of DALY trends revealed significant deviations for all countries from the global pattern. CONCLUSION: This study showed an overall increase in PD burden from 1990 to 2021. These findings underscore the need for targeted strategies to reduce PD burden, with a particular focus on Kazakhstan. Integrating historical information is crucial for discussing the plausible mechanisms in studies sourced from the GBD.
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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 | 0.002 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".