Evidence map›Paper›PMID 37693871›Full record

ArticleThe Lancet regional health. Western Pacific2023

Analytical approaches for antimalarial antibody responses to confirm historical and recent malaria transmission: an example from the Philippines.

Maria Lourdes M Macalinao, Kimberly M Fornace, Ralph A Reyes, Tom Hall, Alison Paolo N Bareng, John H Adams, Christèle Huon, Chetan E Chitnis, Jennifer S Luchavez, Kevin K A Tetteh and 4 more

Abstract read
In one paragraph

Article in The Lancet regional health. Western Pacific, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

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  12. Serology reveals comparable patterns in the transmission intensities ofFrontiers in cellular and infection microbiology · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Maria Lourdes M MacalinaoDepartment of Parasitology and National Reference Laboratory for Malaria and Other Parasites, Research Institute for Tropical Medicine, Department of Health, Muntinlupa City, Philippines.
Kimberly M FornaceFaculty of Infectious and Tropical Diseases, Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Ralph A ReyesDepartment of Parasitology and National Reference Laboratory for Malaria and Other Parasites, Research Institute for Tropical Medicine, Department of Health, Muntinlupa City, Philippines.
Tom HallFaculty of Infectious and Tropical Diseases, Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Alison Paolo N BarengDepartment of Parasitology and National Reference Laboratory for Malaria and Other Parasites, Research Institute for Tropical Medicine, Department of Health, Muntinlupa City, Philippines.
John H AdamsUniversity of South Florida, Tampa, FL 33612, USA.
Christèle HuonMalaria Parasite Biology and Vaccines Unit, Department of Parasites and Insect Vectors, Institut Pasteur, Paris, France.
Chetan E ChitnisMalaria Parasite Biology and Vaccines Unit, Department of Parasites and Insect Vectors, Institut Pasteur, Paris, France.
Jennifer S LuchavezDepartment of Parasitology and National Reference Laboratory for Malaria and Other Parasites, Research Institute for Tropical Medicine, Department of Health, Muntinlupa City, Philippines.
Kevin K A TettehFaculty of Infectious and Tropical Diseases, Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Katsuyuki YuiSchool of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan.
Julius Clemence R HafallaFaculty of Infectious and Tropical Diseases, Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Fe Esperanza J EspinoDepartment of Parasitology and National Reference Laboratory for Malaria and Other Parasites, Research Institute for Tropical Medicine, Department of Health, Muntinlupa City, Philippines.
Chris J DrakeleyFaculty of Infectious and Tropical Diseases, Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom.

Funding

Medical Research Council MR/N019199/1Wellcome Trust
6 · The paper itself

Abstract

Background: Assessing the status of malaria transmission in endemic areas becomes increasingly challenging as countries approach elimination. Serology can provide robust estimates of malaria transmission intensities, and multiplex serological assays allow for simultaneous assessment of markers of recent and historical malaria exposure. Methods: Here, we evaluated different statistical and machine learning methods for analyzing multiplex malaria-specific antibody response data to classify recent and historical exposure to Findings: Measurements of antibody responses and seroprevalence were consistent with the 3 sites' known endemicity status. Among the models tested, a machine learning (ML) approach (Random Forest model) using 4 serological markers (PfGLURP R2, Etramp5.Ag1, GEXP18, and PfMSP1 Interpretation: Our study emphasizes the utility of serological markers in predicting recent and historical exposure in a sub-national elimination setting, and also highlights the potential use of machine learning models using multiplex antibody responses to improve assessment of the malaria transmission status of countries aiming for elimination. This work also provides baseline antibody data for monitoring risk in malaria-endemic areas in the Philippines. Funding: Newton Fund, Philippine Council for Health Research and Development, UK Medical Research Council.

Indexed as

Analytical approachesMachine learningMalariaMultiplex serologySerosurveillance

Identifiers

PMID37693871
PMCPMC10485684

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.