Epidemiological trends and age-period-cohort effects on subarachnoid hemorrhage burden across the BRICS-plus from 1992 to 2021
Bibliographic record
Abstract
Background: Subarachnoid hemorrhage (SAH) is a major global health concern associated with disproportionately high morbidity and mortality. The BRICS-plus nations (Brazil, Russian Federation, India, China, South Africa, and six other new members), account for a substantial proportion of the global population while being confronted with distinct public health challenges. This study aims to examine epidemiological trends and regional variations in SAH burden across BRICS-plus nations through comprehensive and timely analysis. Methods: Data on the number, all-age rate, age-standardized rate, and relative change in SAH incidence from 1992 to 2021 across eleven BRICS-plus members were sourced from the Global Burden of Disease Study (GBD) 2021. Associations between the incidence rate and the Socio-demographic Index (SDI) were assessed through Pearson correlation analyses. Furthermore, age-period-cohort modeling was utilized to quantify net drift, local drift, age, period, and cohort effects over the past three decades. Results: Except for China, SAH cases were observed to have significantly increased in the other ten BRICS members from 1992 to 2021. All BRICS-plus countries exhibited a declining trend in the age-standardized incidence rate over the study period. Indonesia reported the highest age-standardized incidence rate (10.94 per 100,000 population) in 2021, while China displayed the most significant decrease, at 59.36%. The annual net drift in the SAH incidence rate ranged from -3.36%% for China to -0.50% for the Russian Federation among the eleven countries. A significant negative correlation was observed between the incidence rate of SAH and SDI values. Nations displayed similar age-effect patterns characterized by initial declines followed by subsequent increases with advancing age, along with distinct period and cohort effects that may reflect variations in control measures and temporal burden patterns. Conclusion: Our study demonstrates the overall decline in age standardized incidence rate of SAH, while highlighting the persistent health inequalities among eleven countries potentially attributable to socioeconomic disparities. Furthermore, the findings underscore the imperative for tailored interventions across age, period, and cohort dimensions to mitigate SAH-specific challenges in nations undergoing rapid development.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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".