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Enregistrement W4412958182 · doi:10.1177/17474930251367517

Estimating annual deaths from stroke in adults under 70 years of age in Freetown Sierra Leone: A comparative analysis of a hospital-based stroke register and a population-based verbal autopsy study

2025· article· en· W4412958182 sur OpenAlex

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Notice bibliographique

RevueInternational Journal of Stroke · 2025
Typearticle
Langueen
DomaineMathematics
ThématiqueCensus and Population Estimation
Établissements canadiensCentre for Global Health ResearchUniversity of Toronto
Organismes subventionnairesMedical Research CouncilNational Institute for Health and Care ResearchGovernment of the United Kingdom
Mots-clésSierra leoneMedicineVerbal autopsyStroke (engine)PopulationDemographyEpidemiologyCause of deathPediatricsGerontologyDiseaseEnvironmental healthSocioeconomicsInternal medicine

Résumé

récupéré en direct d'OpenAlex

Background: In Sub-Saharan Africa (SSA), most stroke epidemiological data comes from hospital-based registers, which are prone to selection bias, and data may be unrepresentative of stroke burden at the population level. The degree of incompleteness and bias in hospital-based registers has been assessed in high-income countries but not in an SSA country. Aims: The study describes and compares estimates of annual deaths from stroke under 70 years of age, from a hospital-based stroke register and a population-based verbal autopsy (VA) study. We describe the sociodemographic and clinical differences between patients captured and those missed by a hospital-based register and estimate the completeness of a hospital-based register in Sierra Leone. Methods: We compared people under 70 years of age who died from stroke in the Stroke in Sierra Leone (SISLE) prospective longitudinal hospital-based register to the Healthy Sierra Leone (HEAL-SL) population-based VA study which sampled 2.5% of households in the Western Area. We included participants from SISLE and HEAL-SL who died within the same dates (1st May 2019 until 30th September 2021) and geographical area. We conducted data linkage using probabilistic matching and manual clerical review by two authors. To assess selection bias, we used univariable analysis to identify variables associated with capture by the hospital register. To estimate annual deaths from stroke, two-source capture-recapture analysis was conducted using the Lincoln-Petersen-Chapman estimator. Estimates of completeness were adjusted for undermatching and for the positive predictive value of VA for stroke diagnosis. Deaths rates from stroke were calculated as deaths per 100,000 individuals, with population estimates sourced from the 2021 Mid-term Population and Housing Census. Results: A total of 345 participants were identified in the SISLE dataset, 46 in the VA dataset, and 4 in both datasets. Excluding individuals captured in both datasets, individuals identified by VA had a mean age of 58 years compared to 55 years in SISLE ( p = 0.07), 59.5% were male compared to 50.7% in SISLE ( p = 0.28), and 52.3% had no formal education compared to 39.0% ( p = 0.09) in SISLE. Individuals identified by VA were more likely to be employed 36.7% vs 59.5% ( p = 0.002), were less likely to have sought formal healthcare 48.5% vs 100% ( p < 0.001), more likely to have died suddenly 14.3% vs 4.1% ( p < 0.001), and less likely to have died in hospital 19.0% vs 67.5%. Estimates of annual deaths from stroke using capture-recapture methods ranged from 41 to 106/100,000. The completeness of SISLE register for fatal stroke ranged from 10.6% (95% CI: 9.6%–11.7%) to 27.2% (95% CI: 24.8%–30.0%). Discussion: In this setting, a hospital-based stroke register underestimated deaths from stroke in adults younger than 70 years to a much greater degree than estimates from high-income country settings. For people who died from SISLE, employed people, people who did not seek formal healthcare, and people who died within 24 hours were less likely to be included in the hospital-based stroke register. Investment in routine death registration systems and population-based stroke surveillance is essential to provide accurate estimates of population-level stroke burden in our setting.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,055
Score d'incertitude au seuil0,708

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,027
Tête enseignante GPT0,347
Écart entre enseignants0,320 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle