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Record W4412355013 · doi:10.1101/2025.07.10.25331329

Stroke Burden Attributable to Risk Factors in the Americas, 1990–2021: A Temporal Trends Analysis from the Global Burden of Disease Study 2021

2025· preprint· en· W4412355013 on OpenAlexaff
Felipe Fregni, Bassel Almarie, Ramón Martínez, Verónica V. Olavarría, Carlos Abanto, Matías Alet, Tony Fabián Álvarez, Pablo Amaya, Sebastián F. Ameriso, Antonio Araúz, Miguel A. Barboza, Hernán Bayona, Antonio Bernabé‐Ortiz, Juan Manuel Calleja-Castillo, Vanessa Cano-Nigenda, Leonardo Augusto Carbonera, Rodrigo M. Carrillo‐Larco, Ángel Basilio Corredor-Quintero, Ana Cláudia de Souza, Jose Danilo Bengzon Diestro, Rodrigo Guerrero, Claudio Jimenez, Fernando Laņas, Sheila Cristina Ouriques Martins, Paula Muñoz Venturelli, Víctor Navia, Nelson Novarro, Bruce Ovbiagele, Octávio Marques Pontes‐Neto, Pedro Ordúñez, Virginia Pujol Lereis, Alejandro A. Rabinstein, Julieta Rosales, Andrés Rosende, Gisele Sampaio Silva, Gustavo Saposnik, Souvik Sen, Luciano A. Sposato, Fernando D. Testai, Victor Urrutia, Craig S. Anderson, Pablo M. Lavados

Bibliographic record

VenuemedRxiv · 2025
Typepreprint
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsWestern UniversityUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsRegional sciencePolitical scienceGeography

Abstract

fetched live from OpenAlex

Abstract Background Stroke remains a major public health concern with marked disparities across populations, particularly in the Americas. Despite documented declines in overall burden, limited data exist on how modifiable risk factors contribute to contemporary stroke trends across the continent. Methods Using data from the Global Burden of Disease 2021 study, we analyzed age- standardized mortality rates and age-standardized disability-adjusted life-years (DALYs) rates from stroke attributable to 23 modifiable risk factors across 39 countries and territories in the Americas. Estimates were stratified by age, sex, stroke subtype, and Socio-Demographic Index (SDI). Temporal trends from 1990 to 2021 were assessed using the average annual percent change (AAPC), which was estimated applying Joinpoint regression analysis. Results In 2021, 78% of stroke deaths and 77% of stroke DALYs were attributable to modifiable risk factors in the Americas. High systolic blood pressure was the leading risk factor (20.87 deaths [95% uncertainty interval: 15.17–25.8] and 453.30 DALYs [333.16–556.41] per 100,000), contributing over half of stroke burden. From 1990–2021, the largest reductions occurred in household air pollution (deaths AAPC −5.09% [−5.17–−5.02]; DALYs AAPC −4.92% [−4.98–−4.86]) and secondhand smoke (deaths AAPC −3.45% [−3.49–−3.41]; DALYs AAPC −3.37% [−3.41–−3.33]). In contrast, several risk factors showed minimal progress: high body-mass index (deaths AAPC −0.54% [−0.59–−0.49]; DALYs AAPC −0.42% [−0.46–−0.37]) and high fasting glucose (deaths AAPC −1.10% [−1.14–−1.06]; DALYs AAPC −0.99% [−1.05–−0.93]). High temperature demonstrated increases, with AAPCs exceeding +3.5% in select countries. High systolic blood pressure declined moderately (deaths AAPC −1.89% [−1.93–−1.85]; DALYs AAPC −1.84% [−1.89–−1.80]), with stroke burden remaining disproportionately higher in low-SDI countries. Conclusion Although encouraging declines in stroke burden attributable to risk factors have been observed over three decades, substantial variation persists by geography and SDI. High systolic blood pressure remains the leading modifiable risk factor. Minimal progress in obesity and metabolic factors highlights the need for equity-focused strategies to address persistent and emerging stroke risks across the Americas.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.006
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.022
GPT teacher head0.310
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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