Evidence map›Paper›PMID 36336700›Full record

ArticleMalaria journal2022

Novel application of one-step pooled molecular testing and maximum likelihood approaches to estimate the prevalence of malaria parasitaemia among rapid diagnostic test negative samples in western Kenya.

Monica P Shah, Winnie Chebore, Robert H Lyles, Kephas Otieno, Zhiyong Zhou, Mateusz Plucinski, Lance A Waller, Wycliffe Odongo, Kim A Lindblade, Simon Kariuki and 4 more

Abstract read
In one paragraph

Article in Malaria journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

14 authors.

Monica P ShahDivision of Parasitic Diseases and Malaria, Center for Global Health, Centers for Disease Control and Prevention, Atlanta, GA, USA. MShah2@cdc.gov.
Winnie CheboreKenya Medical Research Institute, Centre for Global Health Research, Kisumu, Kenya.
Robert H LylesDepartment of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
Kephas OtienoKenya Medical Research Institute, Centre for Global Health Research, Kisumu, Kenya.
Zhiyong ZhouDivision of Parasitic Diseases and Malaria, Center for Global Health, Centers for Disease Control and Prevention, Atlanta, GA, USA.
Mateusz PlucinskiDivision of Parasitic Diseases and Malaria, Center for Global Health, Centers for Disease Control and Prevention, Atlanta, GA, USA.
Lance A WallerDepartment of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
Wycliffe OdongoKenya Medical Research Institute, Centre for Global Health Research, Kisumu, Kenya.
Kim A LindbladeDivision of Parasitic Diseases and Malaria, Center for Global Health, Centers for Disease Control and Prevention, Atlanta, GA, USA.
Simon KariukiKenya Medical Research Institute, Centre for Global Health Research, Kisumu, Kenya.
Aaron M SamuelsDivision of Parasitic Diseases and Malaria, Center for Global Health, Centers for Disease Control and Prevention, Atlanta, GA, USA.
Meghna DesaiDivision of Parasitic Diseases and Malaria, Center for Global Health, Centers for Disease Control and Prevention, Atlanta, GA, USA.
Rebecca M MitchellDepartment of Computer Science, Emory University, Atlanta, GA, USA.
Ya Ping ShiDivision of Parasitic Diseases and Malaria, Center for Global Health, Centers for Disease Control and Prevention, Atlanta, GA, USA. yps0@cdc.gov.

Funding

Virology and Molecular Biomarkers CoreP30AI050409 · NIAID · EMORY UNIVERSITY · PI Ann M Chahroudi, Colleen F Kelley · 2002 to 2026
$74.0M
6 · The paper itself

Abstract

backgroundDetection of malaria parasitaemia in samples that are negative by rapid diagnostic tests (RDTs) requires resource-intensive molecular tools. While pooled testing using a two-step strategy provides a cost-saving alternative to the gold standard of individual sample testing, statistical adjustments are needed to improve accuracy of prevalence estimates for a single step pooled testing strategy.

methodsA random sample of 4670 malaria RDT negative dried blood spot samples were selected from a mass testing and treatment trial in Asembo, Gem, and Karemo, western Kenya. Samples were tested for malaria individually and in pools of five, 934 pools, by one-step quantitative polymerase chain reaction (qPCR). Maximum likelihood approaches were used to estimate subpatent parasitaemia (RDT-negative, qPCR-positive) prevalence by pooling, assuming poolwise sensitivity and specificity was either 100% (strategy A) or imperfect (strategy B). To improve and illustrate the practicality of this estimation approach, a validation study was constructed from pools allocated at random into main (734 pools) and validation (200 pools) subsets. Prevalence was estimated using strategies A and B and an inverse-variance weighted estimator and estimates were weighted to account for differential sampling rates by area.

resultsThe prevalence of subpatent parasitaemia was 14.5% (95% CI 13.6-15.3%) by individual qPCR, 9.5% (95% CI (8.5-10.5%) by strategy A, and 13.9% (95% CI 12.6-15.2%) by strategy B. In the validation study, the prevalence by individual qPCR was 13.5% (95% CI 12.4-14.7%) in the main subset, 8.9% (95% CI 7.9-9.9%) by strategy A, 11.4% (95% CI 9.9-12.9%) by strategy B, and 12.8% (95% CI 11.2-14.3%) using inverse-variance weighted estimator from poolwise validation. Pooling, including a 20% validation subset, reduced costs by 52% compared to individual testing.

conclusionsCompared to individual testing, a one-step pooled testing strategy with an internal validation subset can provide accurate prevalence estimates of PCR-positivity among RDT-negatives at a lower cost.

Indexed as

MalariaMalaria, FalciparumClinical Trials as TopicDiagnostic Tests, RoutineHumansKenyaLikelihood FunctionsMolecular Diagnostic TechniquesParasitemiaPrevalenceSensitivity and SpecificityGroup testingPooled testingSubpatent malaria parasitemia

Identifiers

PMID36336700
PMCPMC9638440

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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.