Sensitivity and specificity for malaria classification of febrile persons by rapid diagnostic test, microscopy, parasite DNA, histidine-rich protein 2, and IgG: Dakar, Senegal 2015. (August 2022)

• Record Type: Journal Article
• Authors: Badiane, Aida; Thwing, Julie; Williamson, John; Rogier, Eric; Diallo, Mamadou Alpha; Ndiaye, Daouda
• Is Part Of: International journal of infectious diseases. Volume 121(2022)
• Page Start: 92
• Page End: 97
• Publication Date: 2022-08
• Subjects: Malaria -- Diagnostics -- PCR -- Antigen detection -- IgG detection -- Latent class analysis; Communicable diseases -- Periodicals; Communicable Diseases -- Periodicals; Communicable diseases; Periodicals; Electronic journals; 616.9
• DOI: 10.1016/j.ijid.2022.04.060
• Languages: English
• ISSNs: 1201-9712

Abstract

Abstract: Objectives: Different methods for detecting Plasmodium parasite infection or exposure are available, but a systematic comparison of all these methodologies to predict malaria infection is lacking. Understanding the characteristics of respective tests is helpful in choosing the most appropriate tests for epidemiological or research purposes. Methods: We performed microscopy, rapid diagnostic tests (RDTs), and polymerase chain reaction (PCR) for 496 patients presenting with febrile illness in Dakar, Senegal, in 2015. Blood samples had laboratory multiplex assays performed for Immunoglobin G serology and detection of histidine-rich protein 2 (HRP2) antigen. Sensitivity (Se) and specificity (Sp) for different tests were calculated using PCR as the gold standard for detecting active infection. Modeling through latent class analysis compared each test to a modeled gold standard for Se/Sp estimates. Results: Against PCR, Se/Sp were 95.2%/93.7% for RDT, 90.4%/100.0% for microscopy, and 97.9%/48.1% for laboratory HRP2 detection. Compared with the modeled gold standard, Se of microscopy was 93.5% and Se of RDT, PCR, and laboratory HRP2 detection were all greater than 99%. Se/Sp of Immunoglobin G serology were substantially lower for detecting active infection. Conclusions: Compared with single tests, a combinatorial latent class analysis approach of multiple biomarkers for detecting malaria infection from patient samples provides greater sensitivity and specificity for epidemiological estimates and research objectives.