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
Background Detection 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 adj...
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| Vydané v: | Malaria journal Ročník 21; číslo 1; s. 319 - 11 |
|---|---|
| Hlavní autori: | , , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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London
BioMed Central
06.11.2022
BioMed Central Ltd Springer Nature B.V BMC |
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| ISSN: | 1475-2875, 1475-2875 |
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| Abstract | Background
Detection 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.
Methods
A 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.
Results
The 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.
Conclusions
Compared 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. |
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| AbstractList | Detection 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.
A 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.
The 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.
Compared 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. Background Detection 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. Methods A 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. Results The 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. Conclusions Compared 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. Abstract Background Detection 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. Methods A 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. Results The 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. Conclusions Compared 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. Detection 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.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.A 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.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.The 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.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.Compared 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.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. Background Detection 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. Methods A 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. Results The 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. Conclusions Compared 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. Keywords: Pooled testing, Group testing, Subpatent malaria parasitemia Detection 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. A 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. The 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. Compared 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. Background Detection 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. Methods A 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. Results The 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. Conclusions Compared 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. |
| ArticleNumber | 319 |
| Audience | Academic |
| Author | Shi, Ya Ping Chebore, Winnie Kariuki, Simon Zhou, Zhiyong Otieno, Kephas Waller, Lance A. Plucinski, Mateusz Shah, Monica P. Mitchell, Rebecca M. Samuels, Aaron M. Lyles, Robert H. Lindblade, Kim A. Odongo, Wycliffe Desai, Meghna |
| Author_xml | – sequence: 1 givenname: Monica P. surname: Shah fullname: Shah, Monica P. email: MShah2@cdc.gov organization: Division of Parasitic Diseases and Malaria, Center for Global Health, Centers for Disease Control and Prevention, Department of Epidemiology, Rollins School of Public Health, Emory University – sequence: 2 givenname: Winnie surname: Chebore fullname: Chebore, Winnie organization: Kenya Medical Research Institute, Centre for Global Health Research – sequence: 3 givenname: Robert H. surname: Lyles fullname: Lyles, Robert H. organization: Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University – sequence: 4 givenname: Kephas surname: Otieno fullname: Otieno, Kephas organization: Kenya Medical Research Institute, Centre for Global Health Research – sequence: 5 givenname: Zhiyong surname: Zhou fullname: Zhou, Zhiyong organization: Division of Parasitic Diseases and Malaria, Center for Global Health, Centers for Disease Control and Prevention, Department of Epidemiology, Rollins School of Public Health, Emory University – sequence: 6 givenname: Mateusz surname: Plucinski fullname: Plucinski, Mateusz organization: Division of Parasitic Diseases and Malaria, Center for Global Health, Centers for Disease Control and Prevention, Department of Epidemiology, Rollins School of Public Health, Emory University – sequence: 7 givenname: Lance A. surname: Waller fullname: Waller, Lance A. organization: Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University – sequence: 8 givenname: Wycliffe surname: Odongo fullname: Odongo, Wycliffe organization: Kenya Medical Research Institute, Centre for Global Health Research – sequence: 9 givenname: Kim A. surname: Lindblade fullname: Lindblade, Kim A. organization: Division of Parasitic Diseases and Malaria, Center for Global Health, Centers for Disease Control and Prevention, Department of Epidemiology, Rollins School of Public Health, Emory University – sequence: 10 givenname: Simon surname: Kariuki fullname: Kariuki, Simon organization: Kenya Medical Research Institute, Centre for Global Health Research – sequence: 11 givenname: Aaron M. surname: Samuels fullname: Samuels, Aaron M. organization: Division of Parasitic Diseases and Malaria, Center for Global Health, Centers for Disease Control and Prevention, Department of Epidemiology, Rollins School of Public Health, Emory University – sequence: 12 givenname: Meghna surname: Desai fullname: Desai, Meghna organization: Division of Parasitic Diseases and Malaria, Center for Global Health, Centers for Disease Control and Prevention, Department of Epidemiology, Rollins School of Public Health, Emory University – sequence: 13 givenname: Rebecca M. surname: Mitchell fullname: Mitchell, Rebecca M. organization: Department of Computer Science, Emory University – sequence: 14 givenname: Ya Ping surname: Shi fullname: Shi, Ya Ping email: yps0@cdc.gov organization: Division of Parasitic Diseases and Malaria, Center for Global Health, Centers for Disease Control and Prevention, Department of Epidemiology, Rollins School of Public Health, Emory University |
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Detection of malaria parasitaemia in samples that are negative by rapid diagnostic tests (RDTs) requires resource-intensive molecular tools. While... Detection of malaria parasitaemia in samples that are negative by rapid diagnostic tests (RDTs) requires resource-intensive molecular tools. While pooled... Background Detection of malaria parasitaemia in samples that are negative by rapid diagnostic tests (RDTs) requires resource-intensive molecular tools. While... Abstract Background Detection of malaria parasitaemia in samples that are negative by rapid diagnostic tests (RDTs) requires resource-intensive molecular... |
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| SubjectTerms | Biomedical and Life Sciences Biomedicine Blood & organ donations Clinical Trials as Topic Cost control Diagnostic tests Diagnostic Tests, Routine Entomology Estimates Group testing Human diseases Humans Infections Infectious Diseases Kenya - epidemiology Laboratories Likelihood Functions Malaria Malaria - diagnosis Malaria - epidemiology Malaria, Falciparum - epidemiology Microbiology Microscopy Molecular Diagnostic Techniques Nucleotide sequence Parasitemia - diagnosis Parasitemia - epidemiology Parasites Parasitology Polymerase chain reaction Pooled testing Prevalence Public Health Random sampling Sensitivity and Specificity Specificity Subpatent malaria parasitemia Testing Trends Tropical Medicine Validation studies Vector-borne diseases |
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| Title | 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 |
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