1
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Hunt JH, Mwinnyaa G, Patel EU, Grabowski MK, Kagaayi J, Gray RH, Ssekasanvu J, Wawer MJ, Kigozi G, Chang LW, Kalibbala S, Nakalanzi M, Ndyanabo A, Quinn TC, Serwadda D, Reynolds SJ, Galiwango RM, Laeyendecker O. Longitudinal patterns in indeterminate HIV rapid antibody test results: a population-based, prospective cohort study. Microbiol Spectr 2024; 12:e0325323. [PMID: 38189332 PMCID: PMC10845946 DOI: 10.1128/spectrum.03253-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 11/03/2023] [Indexed: 01/09/2024] Open
Abstract
Rapid HIV tests are critical to HIV surveillance and universal testing and treatment programs. We assessed longitudinal patterns in indeterminate HIV rapid test results in an African population-based cohort. Prospective HIV rapid antibody test results, defined by two parallel rapid tests, among participants aged 15-49 years from three survey rounds of the Rakai Community Cohort Study, Uganda, from 2013 to 2018, were assessed. An indeterminate result was defined as any weak positive result or when one test was negative and the other was positive. A total of 31,405 participants contributed 54,459 person-visits, with 15,713 participants contributing multiple visits and 7,351 participants contributing 3 visits. The prevalence of indeterminate results was 2.7% (1,490/54,469). Of the participants with multiple visits who initially tested indeterminate (n = 591), 40.4% were negative, 18.6% were positive, and 41.0% were indeterminate at the subsequent visit. Of the participants with two consecutive indeterminate results who had a third visit (n = 67), 20.9% were negative, 9.0% were positive, and 70.2% remained indeterminate. Compared to a prior negative result, a prior indeterminate result was strongly associated with a subsequent indeterminate result [adjusted prevalence ratio, 23.0 (95% CI = 20.0-26.5)]. Compared to men, women were more likely to test indeterminate than negative [adjusted odds ratio, 2.3 (95% CI = 2.0-2.6)]. Indeterminate rapid HIV test results are highly correlated within an individual and 0.6% of the population persistently tested indeterminate over the study period. A substantial fraction of people with an indeterminate result subsequently tested HIV positive at the next visit, underscoring the importance of follow-up HIV testing protocols.IMPORTANCERapid HIV tests are a critical tool for expanding HIV testing and treatment to end the HIV epidemic. The interpretation and management of indeterminate rapid HIV test results pose a unique challenge for connecting all people living with HIV to the necessary care and treatment. Indeterminate rapid HIV test results are characterized by any weak positive result or discordant results (when one test is negative and the other is positive). We systematically tested all participants of a Ugandan population-based, longitudinal cohort study regardless of prior test results or HIV status to quantify longitudinal patterns in rapid HIV test results. We found that a substantial fraction (>15%) of participants with indeterminate rapid test results subsequently tested positive upon follow-up testing at the next visit. Our findings demonstrate the importance of follow-up HIV testing protocols for indeterminate rapid HIV test results.
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Affiliation(s)
- Joanne H. Hunt
- Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - George Mwinnyaa
- Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Eshan U. Patel
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - M. Kate Grabowski
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Rakai Health Sciences Program, Kalisizo, Uganda
| | | | - Ronald H. Gray
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | | | - Maria J. Wawer
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | | | - Larry W. Chang
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Rakai Health Sciences Program, Kalisizo, Uganda
| | | | | | | | - Thomas C. Quinn
- Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - David Serwadda
- Rakai Health Sciences Program, Kalisizo, Uganda
- Makerere University, Kampala, Uganda
| | - Steven J. Reynolds
- Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Rakai Health Sciences Program, Kalisizo, Uganda
| | | | - Oliver Laeyendecker
- Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, USA
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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2
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Young PW, Musingila P, Kingwara L, Voetsch AC, Zielinski-Gutierrez E, Bulterys M, Kim AA, Bronson MA, Parekh BS, Dobbs T, Patel H, Reid G, Achia T, Keter A, Mwalili S, Ogollah FM, Ondondo R, Longwe H, Chege D, Bowen N, Umuro M, Ngugi C, Justman J, Cherutich P, De Cock KM. HIV Incidence, Recent HIV Infection, and Associated Factors, Kenya, 2007-2018. AIDS Res Hum Retroviruses 2023; 39:57-67. [PMID: 36401361 PMCID: PMC9942172 DOI: 10.1089/aid.2022.0054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Nationally representative surveys provide an opportunity to assess trends in recent human immunodeficiency virus (HIV) infection based on assays for recent HIV infection. We assessed HIV incidence in Kenya in 2018 and trends in recent HIV infection among adolescents and adults in Kenya using nationally representative household surveys conducted in 2007, 2012, and 2018. To assess trends, we defined a recent HIV infection testing algorithm (RITA) that classified as recently infected (<12 months) those HIV-positive participants that were recent on the HIV-1 limiting antigen (LAg)-avidity assay without evidence of antiretroviral use. We assessed factors associated with recent and long-term (≥12 months) HIV infection versus no infection using a multinomial logit model while accounting for complex survey design. Of 1,523 HIV-positive participants in 2018, 11 were classified as recent. Annual HIV incidence was 0.14% in 2018 [95% confidence interval (CI) 0.057-0.23], representing 35,900 (95% CI 16,300-55,600) new infections per year in Kenya among persons aged 15-64 years. The percentage of HIV infections that were determined to be recent was similar in 2007 and 2012 but fell significantly from 2012 to 2018 [adjusted odds ratio (aOR) = 0.31, p < .001]. Compared to no HIV infection, being aged 25-34 versus 35-64 years (aOR = 4.2, 95% CI 1.4-13), having more lifetime sex partners (aOR = 5.2, 95% CI 1.6-17 for 2-3 partners and aOR = 8.6, 95% CI 2.8-26 for ≥4 partners vs. 0-1 partners), and never having tested for HIV (aOR = 4.1, 95% CI 1.5-11) were independently associated with recent HIV infection. Although HIV remains a public health priority in Kenya, HIV incidence estimates and trends in recent HIV infection support a significant decrease in new HIV infections from 2012 to 2018, a period of rapid expansion in HIV diagnosis, prevention, and treatment.
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Affiliation(s)
- Peter Wesley Young
- Division of Global HIV & TB, U.S. Centers for Disease Control and Prevention, Maputo, Mozambique.,Address correspondence to: Peter Wesley Young, U.S. Embassy Maputo, Avenida Marginal nr 5467, Sommerschield, Distrito Municipal de KaMpfumo, Caixa Postal 783, CEP 0101-11 Maputo, Mozambique
| | - Paul Musingila
- Division of Global HIV & TB, U.S. Centers for Disease Control and Prevention, Nairobi, Kenya
| | - Leonard Kingwara
- National AIDS & STI Control Programme, Ministry of Health, Nairobi, Kenya
| | - Andrew C. Voetsch
- Division of Global HIV & TB, U.S. Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Emily Zielinski-Gutierrez
- Division of Global HIV & TB, U.S. Centers for Disease Control and Prevention, Nairobi, Kenya.,Central America Regional Office, U.S. Centers for Disease Control and Prevention, Guatemala City, Guatemala
| | - Marc Bulterys
- Division of Global HIV & TB, U.S. Centers for Disease Control and Prevention, Nairobi, Kenya
| | - Andrea A. Kim
- Division of Global HIV & TB, U.S. Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Megan A. Bronson
- Division of Global HIV & TB, U.S. Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Bharat S. Parekh
- Division of Global HIV & TB, U.S. Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Trudy Dobbs
- Division of Global HIV & TB, U.S. Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Hetal Patel
- Division of Global HIV & TB, U.S. Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Giles Reid
- Survey Unit, ICAP at Columbia University, New York, New York, USA
| | - Thomas Achia
- Division of Global HIV & TB, U.S. Centers for Disease Control and Prevention, Nairobi, Kenya
| | - Alfred Keter
- National AIDS & STI Control Programme, Ministry of Health, Nairobi, Kenya
| | - Samuel Mwalili
- Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Juja, Kenya
| | | | - Raphael Ondondo
- Division of Global HIV & TB, U.S. Centers for Disease Control and Prevention, Nairobi, Kenya
| | - Herbert Longwe
- Survey Unit, ICAP at Columbia University, New York, New York, USA
| | - Duncan Chege
- Survey Unit, ICAP at Columbia University, New York, New York, USA
| | - Nancy Bowen
- National Public Health Laboratory, Ministry of Health, Nairobi, Kenya
| | - Mamo Umuro
- National Public Health Laboratory, Ministry of Health, Nairobi, Kenya
| | | | - Jessica Justman
- Survey Unit, ICAP at Columbia University, New York, New York, USA
| | | | - Kevin M. De Cock
- Division of Global HIV & TB, U.S. Centers for Disease Control and Prevention, Nairobi, Kenya
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3
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Grant-McAuley W, Laeyendecker O, Monaco D, Chen A, Hudelson SE, Klock E, Brookmeyer R, Morrison D, Piwowar-Manning E, Morrison CS, Hayes R, Ayles H, Bock P, Kosloff B, Shanaube K, Mandla N, van Deventer A, Ruczinski I, Kammers K, Larman HB, Eshleman SH. Evaluation of multi-assay algorithms for cross-sectional HIV incidence estimation in settings with universal antiretroviral treatment. BMC Infect Dis 2022; 22:838. [PMID: 36368950 PMCID: PMC9652879 DOI: 10.1186/s12879-022-07850-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 11/07/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Multi-assay algorithms (MAAs) are used to estimate population-level HIV incidence and identify individuals with recent infection. Many MAAs use low viral load (VL) as a biomarker for long-term infection. This could impact incidence estimates in settings with high rates of early HIV treatment initiation. We evaluated the performance of two MAAs that do not include VL. METHODS Samples were collected from 219 seroconverters (infected < 1 year) and 4376 non-seroconverters (infected > 1 year) in the HPTN 071 (PopART) trial; 28.8% of seroconverter samples and 73.2% of non-seroconverter samples had VLs ≤ 400 copies/mL. Samples were tested with the Limiting Antigen Avidity assay (LAg) and JHU BioRad-Avidity assays. Antibody reactivity to two HIV peptides was measured using the MSD U-PLEX assay. Two MAAs were evaluated that do not include VL: a MAA that includes the LAg-Avidity assay and BioRad-Avidity assay (LAg + BR) and a MAA that includes the LAg-Avidity assay and two peptide biomarkers (LAg + PepPair). Performance of these MAAs was compared to a widely used MAA that includes LAg and VL (LAg + VL). RESULTS The incidence estimate for LAg + VL (1.29%, 95% CI: 0.97-1.62) was close to the observed longitudinal incidence (1.34% 95% CI: 1.17-1.53). The incidence estimates for the other two MAAs were higher (LAg + BR: 2.56%, 95% CI 2.01-3.11; LAg + PepPair: 2.84%, 95% CI: 1.36-4.32). LAg + BR and LAg + PepPair also misclassified more individuals infected > 2 years as recently infected than LAg + VL (1.2% [42/3483 and 1.5% [51/3483], respectively, vs. 0.2% [6/3483]). LAg + BR classified more seroconverters as recently infected than LAg + VL or LAg + PepPair (80 vs. 58 and 50, respectively) and identified ~ 25% of virally suppressed seroconverters as recently infected. CONCLUSIONS The LAg + VL MAA produced a cross-sectional incidence estimate that was closer to the longitudinal estimate than two MAAs that did not include VL. The LAg + BR MAA classified the greatest number of individual seroconverters as recently infected but had a higher false recent rate.
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Affiliation(s)
- Wendy Grant-McAuley
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Oliver Laeyendecker
- Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Baltimore, MD, USA
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Daniel Monaco
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Athena Chen
- Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA
| | - Sarah E Hudelson
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ethan Klock
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ron Brookmeyer
- Department of Biostatistics, UCLA Fielding School of Public Health, Los Angeles, CA, USA
| | - Douglas Morrison
- Department of Public Health Sciences, UC Davis School of Medicine, Davis, CA, USA
| | | | - Charles S Morrison
- Behavioral, Epidemiologic, and Clinical Sciences, Durham, NC, FHI 360, USA
| | - Richard Hayes
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Helen Ayles
- Zambart, University of Zambia School of Public Health, Lusaka, Zambia
- Clinical Research Department, London School of Hygiene and Tropical Medicine, London, UK
| | - Peter Bock
- Desmond Tutu TB Center, Department of Paediatrics and Child Health, Stellenbosch University, Stellenbosch, Western Cape, South Africa
| | - Barry Kosloff
- Zambart, University of Zambia School of Public Health, Lusaka, Zambia
- Clinical Research Department, London School of Hygiene and Tropical Medicine, London, UK
| | - Kwame Shanaube
- Zambart, University of Zambia School of Public Health, Lusaka, Zambia
| | - Nomtha Mandla
- Desmond Tutu TB Center, Department of Paediatrics and Child Health, Stellenbosch University, Stellenbosch, Western Cape, South Africa
| | - Anneen van Deventer
- Desmond Tutu TB Center, Department of Paediatrics and Child Health, Stellenbosch University, Stellenbosch, Western Cape, South Africa
| | - Ingo Ruczinski
- Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA
| | - Kai Kammers
- Division of Biostatistics and Bioinformatics, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - H Benjamin Larman
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Susan H Eshleman
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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4
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Nikolopoulos GK, Tsantes AG. Recent HIV Infection: Diagnosis and Public Health Implications. Diagnostics (Basel) 2022; 12:2657. [PMID: 36359500 PMCID: PMC9689622 DOI: 10.3390/diagnostics12112657] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 10/27/2022] [Accepted: 10/28/2022] [Indexed: 08/15/2024] Open
Abstract
The early period of infection with human immunodeficiency virus (HIV) has been associated with higher infectiousness and, consequently, with more transmission events. Over the last 30 years, assays have been developed that can detect viral and immune biomarkers during the first months of HIV infection. Some of them depend on the functional properties of antibodies including their changing titers or the increasing strength of binding with antigens over time. There have been efforts to estimate HIV incidence using antibody-based assays that detect recent HIV infection along with other laboratory and clinical information. Moreover, some interventions are based on the identification of people who were recently infected by HIV. This review summarizes the evolution of efforts to develop assays for the detection of recent HIV infection and to use these assays for the cross-sectional estimation of HIV incidence or for prevention purposes.
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Affiliation(s)
| | - Andreas G. Tsantes
- Microbiology Department, “Saint Savvas” Oncology Hospital, 11522 Athens, Greece
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5
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Facente SN, Grebe E, Maher AD, Fox D, Scheer S, Mahy M, Dalal S, Lowrance D, Marsh K. Use of HIV Recency Assays for HIV Incidence Estimation and Other Surveillance Use Cases: Systematic Review. JMIR Public Health Surveill 2022; 8:e34410. [PMID: 35275085 PMCID: PMC8956992 DOI: 10.2196/34410] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 01/16/2022] [Accepted: 02/02/2022] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND HIV assays designed to detect recent infection, also known as "recency assays," are often used to estimate HIV incidence in a specific country, region, or subpopulation, alone or as part of recent infection testing algorithms (RITAs). Recently, many countries and organizations have become interested in using recency assays within case surveillance systems and routine HIV testing services to measure other indicators beyond incidence, generally referred to as "non-incidence surveillance use cases." OBJECTIVE This review aims to identify published evidence that can be used to validate methodological approaches to recency-based incidence estimation and non-incidence use cases. The evidence identified through this review will be used in the forthcoming technical guidance by the World Health Organization (WHO) and United Nations Programme on HIV/AIDS (UNAIDS) on the use of HIV recency assays for identification of epidemic trends, whether for HIV incidence estimation or non-incidence indicators of recency. METHODS To identify the best methodological and field implementation practices for the use of recency assays to estimate HIV incidence and trends in recent infections for specific populations or geographic areas, we conducted a systematic review of the literature to (1) understand the use of recency testing for surveillance in programmatic and laboratory settings, (2) review methodologies for implementing recency testing for both incidence estimation and non-incidence use cases, and (3) assess the field performance characteristics of commercially available recency assays. RESULTS Among the 167 documents included in the final review, 91 (54.5%) focused on assay or algorithm performance or methodological descriptions, with high-quality evidence of accurate age- and sex-disaggregated HIV incidence estimation at national or regional levels in general population settings, but not at finer geographic levels for prevention prioritization. The remaining 76 (45.5%) described the field use of incidence assays including field-derived incidence (n=45), non-incidence (n=25), and both incidence and non-incidence use cases (n=6). The field use of incidence assays included integrating RITAs into routine surveillance and assisting with molecular genetic analyses, but evidence was generally weaker or only reported on what was done, without validation data or findings related to effectiveness of using non-incidence indicators calculated through the use of recency assays as a proxy for HIV incidence. CONCLUSIONS HIV recency assays have been widely validated for estimating HIV incidence in age- and sex-specific populations at national and subnational regional levels; however, there is a lack of evidence validating the accuracy and effectiveness of using recency assays to identify epidemic trends in non-incidence surveillance use cases. More research is needed to validate the use of recency assays within HIV testing services, to ensure findings can be accurately interpreted to guide prioritization of public health programming.
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Affiliation(s)
- Shelley N Facente
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, United States.,Facente Consulting, Richmond, CA, United States.,Vitalant Research Institute, San Francisco, CA, United States
| | - Eduard Grebe
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, United States.,Vitalant Research Institute, San Francisco, CA, United States.,South African Centre for Epidemiological Modeling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa
| | - Andrew D Maher
- South African Centre for Epidemiological Modeling and Analysis (SACEMA), Stellenbosch University, Stellenbosch, South Africa.,Institute for Global Health Sciences, University of California San Francisco, San Francisco, CA, United States
| | - Douglas Fox
- Facente Consulting, Richmond, CA, United States
| | | | - Mary Mahy
- Strategic Information Department, The Joint United Nations Programme on HIV/AIDS (UNAIDS), Geneva, Switzerland
| | - Shona Dalal
- Global HIV, Hepatitis and Sexually Transmitted Infections Programmes, World Health Organisation, Geneva, Switzerland
| | - David Lowrance
- Global HIV, Hepatitis and Sexually Transmitted Infections Programmes, World Health Organisation, Geneva, Switzerland
| | - Kimberly Marsh
- Strategic Information Department, The Joint United Nations Programme on HIV/AIDS (UNAIDS), Geneva, Switzerland
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6
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Grant-McAuley W, Klock E, Laeyendecker O, Piwowar-Manning E, Wilson E, Clarke W, Breaud A, Moore A, Ayles H, Kosloff B, Shanaube K, Bock P, Mandla N, van Deventer A, Fidler S, Donnell D, Hayes R, Eshleman SH. Evaluation of multi-assay algorithms for identifying individuals with recent HIV infection: HPTN 071 (PopART). PLoS One 2021; 16:e0258644. [PMID: 34919554 PMCID: PMC8682874 DOI: 10.1371/journal.pone.0258644] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 10/01/2021] [Indexed: 11/18/2022] Open
Abstract
Background
Assays and multi-assay algorithms (MAAs) have been developed for population-level cross-sectional HIV incidence estimation. These algorithms use a combination of serologic and/or non-serologic biomarkers to assess the duration of infection. We evaluated the performance of four MAAs for individual-level recency assessments.
Methods
Samples were obtained from 220 seroconverters (infected <1 year) and 4,396 non-seroconverters (infected >1 year) enrolled in an HIV prevention trial (HPTN 071 [PopART]); 28.6% of the seroconverters and 73.4% of the non-seroconverters had HIV viral loads ≤400 copies/mL. Samples were tested with two laboratory-based assays (LAg-Avidity, JHU BioRad-Avidity) and a point-of-care assay (rapid LAg). The four MAAs included different combinations of these assays and HIV viral load. Seroconverters on antiretroviral treatment (ART) were identified using a qualitative multi-drug assay.
Results
The MAAs identified between 54 and 100 (25% to 46%) of the seroconverters as recently-infected. The false recent rate of the MAAs for infections >2 years duration ranged from 0.2%-1.3%. The MAAs classified different overlapping groups of individuals as recent vs. non-recent. Only 32 (15%) of the 220 seroconverters were classified as recent by all four MAAs. Viral suppression impacted the performance of the two LAg-based assays. LAg-Avidity assay values were also lower for seroconverters who were virally suppressed on ART compared to those with natural viral suppression.
Conclusions
The four MAAs evaluated varied in sensitivity and specificity for identifying persons infected <1 year as recently infected and classified different groups of seroconverters as recently infected. Sensitivity was low for all four MAAs. These performance issues should be considered if these methods are used for individual-level recency assessments.
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Affiliation(s)
- Wendy Grant-McAuley
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Ethan Klock
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Oliver Laeyendecker
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, Maryland, United States of America
| | - Estelle Piwowar-Manning
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Ethan Wilson
- Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - William Clarke
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Autumn Breaud
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Ayana Moore
- FHI360, Durham, North Carolina, United States of America
| | - Helen Ayles
- Zambart, University of Zambia School of Medicine, Lusaka, Zambia
- Clinical Research Department, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Barry Kosloff
- Zambart, University of Zambia School of Medicine, Lusaka, Zambia
- Clinical Research Department, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Kwame Shanaube
- Zambart, University of Zambia School of Medicine, Lusaka, Zambia
| | - Peter Bock
- Desmond Tutu TB Center, Department of Paediatrics and Child Health, Stellenbosch University, Western Cape, South Africa
| | - Nomtha Mandla
- Desmond Tutu TB Center, Department of Paediatrics and Child Health, Stellenbosch University, Western Cape, South Africa
| | - Anneen van Deventer
- Desmond Tutu TB Center, Department of Paediatrics and Child Health, Stellenbosch University, Western Cape, South Africa
| | - Sarah Fidler
- Department of Infectious Disease, Imperial College London, London, United Kingdom
| | - Deborah Donnell
- Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Richard Hayes
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Susan H. Eshleman
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- * E-mail:
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7
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Klock E, Wilson E, Fernandez RE, Piwowar-Manning E, Moore A, Kosloff B, Bwalya J, Bell-Mandla N, James A, Ayles H, Bock P, Donnell D, Fidler S, Hayes R, Eshleman SH, Laeyendecker O. Validation of population-level HIV-1 incidence estimation by cross-sectional incidence assays in the HPTN 071 (PopART) trial. J Int AIDS Soc 2021; 24:e25830. [PMID: 34897992 PMCID: PMC8666582 DOI: 10.1002/jia2.25830] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 09/10/2021] [Indexed: 11/10/2022] Open
Abstract
Introduction Cross‐sectional incidence testing is used to estimate population‐level HIV incidence and measure the impact of prevention interventions. There are limited data evaluating the accuracy of estimates in settings where antiretroviral therapy coverage and levels of viral suppression are high. Understanding cross‐sectional incidence estimates in these settings is important as viral suppression can lead to false recent test results. We compared the accuracy of multi‐assay algorithms (MAA) for incidence estimation to that observed in the community‐randomized HPTN 071 (PopART) trial, where the majority of participants with HIV infection were virally suppressed. Methods HIV incidence was assessed during the second year of the study, and included only individuals who were tested for HIV at visits 1 and 2 years after the start of the study (2016–2017). Incidence estimates from three MAAs were compared to the observed incidence between years 1 and 2 (MAA‐C: LAg‐Avidity <2.8 ODn + BioRad Avidity Index <95% + VL >400 copies/ml; LAg+VL MAA: LAg‐Avidity <1.5 ODn + VL >1000 copies/ml; Rapid+VL MAA: Asanté recent rapid result + VL >1000 copies/ml). The mean duration of recent infection (MDRI) used for the three MAAs was 248, 130 and 180 days, respectively. Results and discussion The study consisted of: 15,845 HIV‐negative individuals; 4406 HIV positive at both visits; and 221 who seroconverted between visits. Viral load (VL) data were available for all HIV‐positive participants at the 2‐year visit. Sixty four (29%) of the seroconverters and 3227 (72%) prevelant positive participants were virally supressed (<400 copies/ml). Observed HIV incidence was 1.34% (95% CI: 1.17–1.53). Estimates of incidence were similar to observed incidence for MAA‐C, 1.26% (95% CI: 1.02–1.51) and the LAg+VL MAA, 1.29 (95% CI: 0.97–1.62). Incidence estimated by the Rapid+VL MAA was significantly lower than observed incidence (0.92%, 95% CI: 0.69–1.15, p<0.01). Conclusions MAA‐C and the LAg+VL MAA provided accurate point estimates of incidence in this cohort with high levels of viral suppression. The Rapid+VL significantly underestimated incidence, suggesting that the MDRI recommended by the manufacturer is too long or the assay is not accurately detecting enough recent infections.
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Affiliation(s)
- Ethan Klock
- Division of Infectious Diseases, Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Ethan Wilson
- Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Reinaldo E Fernandez
- Division of Infectious Diseases, Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Estelle Piwowar-Manning
- Division of Infectious Diseases, Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | | | - Barry Kosloff
- Zambart, Lusaka, Zambia.,Clinical Research Department, London School of Hygiene and Tropical Medicine, London, UK
| | | | - Nomtha Bell-Mandla
- Desmond Tutu Tuberculosis Center, Department of Pediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Anelet James
- Desmond Tutu Tuberculosis Center, Department of Pediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Helen Ayles
- Zambart, Lusaka, Zambia.,Clinical Research Department, London School of Hygiene and Tropical Medicine, London, UK
| | - Peter Bock
- Desmond Tutu Tuberculosis Center, Department of Pediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Deborah Donnell
- Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | | | - Richard Hayes
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Susan H Eshleman
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Oliver Laeyendecker
- Division of Infectious Diseases, Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA.,National Institute of Allergy and Infectious Diseases, National Institutes of Medicine, Bethesda, Maryland, USA
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- Division of Infectious Diseases, Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
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