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Record 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 on OpenAlexaff
Daniel Youkee, Marina Soley‐Bori, Gibrilla F. Deen, Prabhat Jha, Anteneh Assalif, Charles Wolfe, Catherine Sackley, Zainab F Conteh, Julia Fox‐Rushby, Iain Marshall

Bibliographic record

VenueInternational Journal of Stroke · 2025
Typearticle
Languageen
FieldMathematics
TopicCensus and Population Estimation
Canadian institutionsCentre for Global Health ResearchUniversity of Toronto
FundersMedical Research CouncilNational Institute for Health and Care ResearchGovernment of the United Kingdom
KeywordsSierra leoneMedicineVerbal autopsyStroke (engine)PopulationDemographyEpidemiologyCause of deathPediatricsGerontologyDiseaseEnvironmental healthSocioeconomicsInternal medicine

Abstract

fetched live from 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.

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)0.015
metaresearch head score (Gemma)0.036
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 score0.046
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.036
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0070.005
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.000
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 head0.027
GPT teacher head0.347
Teacher spread0.320 · 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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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