Evidence map›Paper›PMID 31204867›Full record

ArticleAIDS research and human retroviruses2019

Field Validation of Limiting-Antigen Avidity Enzyme Immunoassay to Estimate HIV-1 Incidence in Cross-Sectional Survey in Swaziland.

Yen T Duong, Trudy Dobbs, Yvonne Mavengere, Julius Manjengwa, Erin Rottinghaus, Suzue Saito, Naomi Bock, Neena Philip, Jessica Justman, George Bicego and 2 more

Open access · greenAbstract read
In one paragraph

Article in AIDS research and human retroviruses, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
3.0field-weighted citation impact, top 8% of its field
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

21 citing papers in PubMed, 1 synthesis or guideline pooled it, 27 citations in OpenAlex.

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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

12 authors at 2 institutions in 1 country.

Yen T DuongDivision of Global HIV & TB, Centers for Disease Control and Prevention, Atlanta, Georgia.
Trudy DobbsDivision of Global HIV & TB, Centers for Disease Control and Prevention, Atlanta, Georgia.
Yvonne MavengereICAP at Columbia University, Mailman School of Public Health, New York, New York.
Julius ManjengwaICAP at Columbia University, Mailman School of Public Health, New York, New York.
Erin RottinghausDivision of Global HIV & TB, Centers for Disease Control and Prevention, Atlanta, Georgia.
Suzue SaitoICAP at Columbia University, Mailman School of Public Health, New York, New York.
Naomi BockDivision of Global HIV & TB, Centers for Disease Control and Prevention, Atlanta, Georgia.
Neena PhilipICAP at Columbia University, Mailman School of Public Health, New York, New York.
Jessica JustmanICAP at Columbia University, Mailman School of Public Health, New York, New York.
George BicegoDivision of Global HIV & TB, Centers for Disease Control and Prevention, Atlanta, Georgia.
John N NkengasongDivision of Global HIV & TB, Centers for Disease Control and Prevention, Atlanta, Georgia.
Bharat S ParekhDivision of Global HIV & TB, Centers for Disease Control and Prevention, Atlanta, Georgia.
Columbia University · USICAP Global Health

Funding

Intramural CDC HHS CC999999NCHHSTP CDC HHS U2G PS002005PEPFAR
6 · The paper itself

Abstract

Reliable and accurate laboratory assays to detect recent HIV-1 infection have potential as simple and practical methods of estimating HIV-1 incidence in cross-sectional surveys. This study describes validation of the limiting-antigen (LAg) avidity enzyme immunoassay (EIA) in a cross-sectional national survey, conducted in Swaziland, comparing it to prospective follow-up incidence. As part of the Swaziland HIV-1 Incidence Measurement Survey (SHIMS), 18,172 individuals underwent counseling and HIV rapid testing in a household-based, population survey conducted from December 2010 to June 2011. Plasma samples from HIV-positive persons were classified as recent infections using an incidence testing algorithm with LAg-Avidity EIA (normalized optical density ≤1.5) followed by viral load (VL ≥1,000 copies/mL). All HIV-seronegative samples were tested for acute HIV-1 infection by nucleic acid amplification test (NAAT) pooling. HIV-seronegative individuals who consented to follow-up were retested ∼6 months later to detect observed HIV-1 seroconversion. HIV-1 incidence estimates based on LAg+VL and NAAT were calculated using assay-specific parameters and were compared with prospective incidence estimate. A total of 5,803 (31.9%) of 18,172 survey participants tested HIV seropositive; of these 5,683 (97.9%) were further tested with LAg+VL algorithm. The weighted annualized incidence from the longitudinal cohort study was 2.4% (95% confidence interval 2.0-2.7). Based on cross-sectional testing of HIV positives with LAg+VL algorithm, overall weighted annualized HIV-1 incidence was 2.5% (2.0-3.0), whereas NAAT-based incidence was of 2.6%. In addition, LAg-based incidence in men (1.8%; 1.2-2.5) and women (3.2%; 2.4-3.9) were similar to estimates based on observed incidence (men = 1.7%, women = 3.1%). Changes in HIV-1 incidence with age in men and women further validate plausibility of the algorithm. These results demonstrate that the LAg EIA, in a serial algorithm with VL, is a cost-effective tool to estimate HIV-1 incidence in cross-sectional surveys.

Indexed as

Acute DiseaseAdolescentAdultAlgorithmsAntibody AffinityAnti-HIV AgentsCost-Benefit AnalysisCross-Sectional StudiesEswatiniFemaleFollow-Up StudiesGeography, MedicalHealth SurveysHIV-1HIV AntibodiesHIV AntigensAnti-HIV AgentsHIV AntibodiesHIV AntigensRNA, ViralHIVincidenceLAgSHIMSSwazilandviral load

Identifiers

PMID31204867
PMCPMC11301577
OpenAlexW2949915899

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.