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Record W4253819747 · doi:10.21203/rs.3.rs-41864/v1

Updates on malaria epidemiology and profile in Cabo Verde from 2010 to 2019: The goal of Elimination

2020· preprint· en· W4253819747 on OpenAlexfundno aff
Adilson José DePina, Gillian Stresman, Helga Sofia Baptista Barros, António Lima Moreira, Abdoulaye Kane Dia, Ullardina Domingos Furtado, Ousmane Faye, Ibrahima Seck, El Hadji Amadou Niang

Bibliographic record

VenueResearch Square · 2020
Typepreprint
Languageen
FieldMedicine
TopicMalaria Research and Control
Canadian institutionsnot available
FundersMinistério da SaúdeWellcome TrustStyrelsen för Internationellt UtvecklingssamarbeteGlobal Fund to Fight AIDS, Tuberculosis and MalariaMinistère de la Santé et des Services sociauxBill and Melinda Gates Foundation
KeywordsMalariaEpidemiologyGeographyCape verdeHistoryEthnologyBiologyMedicineImmunology

Abstract

fetched live from OpenAlex

Abstract Background Cabo Verde is one of the E-2020 Initiative, a group of 21 countries identified by World Health Organization (WHO) in 2016 as having the potential to eliminate malaria by 2020. Located in west Africa, Cabo Verde is an archipelago consisting of nine inhabited islands. Malaria has been endemic since the settlement of the islands during the during the 16th century and is poised to achieve malaria elimination in January 2021. The aim of this research is to characterise the trends in malaria cases from 2010 to 2019 as the country transitions from endemic transmission to elimination and prevention of reintroduction phases. Methods All confirmed malaria cases reported to the Ministry of Health between 2010 and 2019 were extracted and secondary analysis was conducted. Variables collected as part of the routine reporting for each confirmed case included age, sex, municipality of residence, and if classified as imported, the reported country of travel within the past 30 days, providing the presumed origin of infection. Trends in reported cases were visualised and logistic regression used to identify risk factors associated with imported malaria. Results A total of 819 malaria cases were reported in the country between 2010 and 2019, the majority of which were Plasmodium falciparum. Overall, 554 (67.6%) and 263 (32.1%) of cases were reported as locally acquired and imported, respectively, with the last locally acquired case reported in January 2018. Only two (0.20%) of the cases were classified as introduced, a single case in each of 2018 and 2019. Of the locally acquired cases, 80.5% (446/554) were reported during the outbreak in 2017. The majority of malaria cases were identified in males (766; 73.3%) or those aged 20 years or older (658; 79.7%). The adjusted odds ratio (AOR) of cases being imported was 3.73 (95% CI: 2.47–4.99) in the post epidemic period compared with the pre-epidemic period and reported on Sao Vicente island (AOR = 6.96, 95% CI = 4.40–9.53, p < 0.0001) compared to Boa Vista. Conclusions Cabo Verde has made substantial gains in reducing malaria burden in the country over the past decade and are poised to achieve elimination in 2020. However, the high mobility between the islands and continental Africa where malaria is still highly endemic, means there is a constant risk of malaria reintroduction. Characterisation of imported cases provides useful insight for program and enables better evidence-based decision-making to ensure malaria elimination can be sustained.

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)0.001
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 score0.118
Threshold uncertainty score0.234

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.077
GPT teacher head0.420
Teacher spread0.342 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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