Evidence map›Paper›PMID 36962526›Full record

ArticlePLOS global public health2022

Performance of HIV rapid testing algorithm in Nigeria: Findings from a household-based Nigeria HIV/AIDS Indicator and Impact Survey (NAIIS).

Hetal K Patel, Sunday Ikpe, Megan Bronson, Sehin Birhanu, Alash'le Abimiku, Ibrahim Jahun, Mervi Detorio, Kathryn Lupoli, Daniel Yavo, Orji O Bassey and 16 more

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Article in PLOS global public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
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

8 citing papers in PubMed.

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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

26 authors.

Hetal K PatelCenters for Disease Control and Prevention, Atlanta, Georgia, United States of America.ORCID https://orcid.org/0000-0002-5468-2591
Sunday IkpeUniversity of Maryland School of Medicine Institute of Human Virology, Baltimore, Maryland, United States of America.ORCID https://orcid.org/0000-0001-9036-4526
Megan BronsonCenters for Disease Control and Prevention, Atlanta, Georgia, United States of America.ORCID https://orcid.org/0000-0002-8538-8141
Sehin BirhanuCenters for Disease Control and Prevention, Atlanta, Georgia, United States of America.ORCID https://orcid.org/0000-0003-1006-4772
Alash'le AbimikuUniversity of Maryland School of Medicine Institute of Human Virology, Baltimore, Maryland, United States of America.ORCID https://orcid.org/0000-0003-2213-0204
Ibrahim JahunCenters for Disease Control and Prevention, Atlanta, Georgia, United States of America.ORCID https://orcid.org/0000-0003-4190-1289
Mervi DetorioCenters for Disease Control and Prevention, Atlanta, Georgia, United States of America.
Kathryn LupoliCenters for Disease Control and Prevention, Atlanta, Georgia, United States of America.ORCID https://orcid.org/0000-0001-6766-1522
Daniel YavoCenters for Disease Control and Prevention, Atlanta, Georgia, United States of America.
Orji O BasseyCenters for Disease Control and Prevention, Atlanta, Georgia, United States of America.ORCID https://orcid.org/0000-0002-5770-1989
Tapdiyel D JelpeCenters for Disease Control and Prevention, Atlanta, Georgia, United States of America.ORCID https://orcid.org/0000-0002-4756-6509
Brian KagurusiUniversity of Maryland School of Medicine Institute of Human Virology, Baltimore, Maryland, United States of America.ORCID https://orcid.org/0000-0001-8615-6732
Nnaemeka C IriemenamCenters for Disease Control and Prevention, Atlanta, Georgia, United States of America.ORCID https://orcid.org/0000-0003-2187-8894
Divya PatelCenters for Disease Control and Prevention, Atlanta, Georgia, United States of America.ORCID https://orcid.org/0000-0002-7153-7505
McPaul I OkoyeCenters for Disease Control and Prevention, Atlanta, Georgia, United States of America.ORCID https://orcid.org/0000-0001-8745-8441
Ibrahim T DalhatuCenters for Disease Control and Prevention, Atlanta, Georgia, United States of America.ORCID https://orcid.org/0000-0002-3287-5173
Stephen OhakanuUniversity of Maryland School of Medicine Institute of Human Virology, Baltimore, Maryland, United States of America.ORCID https://orcid.org/0000-0002-6487-0166
Andrew C VoetschCenters for Disease Control and Prevention, Atlanta, Georgia, United States of America.
Sani AliyuNigeria AIDS Control Agency, Abuja, Nigeria.ORCID https://orcid.org/0000-0002-5571-2940
Gregory AsheforNigeria AIDS Control Agency, Abuja, Nigeria.
Aliyu GamboNigeria AIDS Control Agency, Abuja, Nigeria.
Gabriel O IkwulonoFederal Ministry of Health, Abuja, Nigeria.
Charles NzeluFederal Ministry of Health, Abuja, Nigeria.
Isaac F AdewoleFederal Ministry of Health, Abuja, Nigeria.ORCID https://orcid.org/0000-0002-5341-2514
Mahesh SwaminathanCenters for Disease Control and Prevention, Atlanta, Georgia, United States of America.ORCID https://orcid.org/0000-0001-5827-3245
Bharat ParekhCenters for Disease Control and Prevention, Atlanta, Georgia, United States of America.ORCID https://orcid.org/0000-0002-1088-4263

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe Nigeria AIDS Indicator and Impact Survey (NAIIS), a cross-sectional household survey, was conducted in 2018 with primary objectives to estimate HIV prevalence, HIV-1 incidence, and status of UNAIDS 90-90-90 cascade. We conducted retrospective analysis of the performance of HIV rapid tests and the national HIV testing algorithm used in Nigeria.

methodsThe national algorithm included Determine HIV-1/2 as test 1 (T1), Unigold HIV-1/2 as test 2 (T2), and StatPak HIV-1/2 as the tie-breaker test (T3). Individuals reactive with T1 and either T2 or T3 were considered HIV-positive. HIV-positive specimens from the algorithm were further confirmed for the survey using supplemental test Geenius HIV-1/2. If Geenius did not confirm HIV-positive status, HIV-1 Western blot was performed. We calculated the concordance between tests and positive predictive value (PPV) of the algorithm on unweighted data.

resultsOf 204,930 participants (ages ≥18 months) 5,103 (2.5%) were reactive on T1. Serial testing of T1 reactive specimens with T2 or if needed by tiebreaker T3 identified 2958 (1.44%) persons as HIV-positive. Supplemental testing confirmed 2,800 (95%) as HIV-positive (HIV-1 = 2,767 [98.8%]; HIV-2 = 5 [0.2%]; dual infections = 22 [0.8%]). Concordance between T1 and T2 was 56.6% while PPV of the national algorithm was 94.5%.

conclusionsOur results show high discordant rates and poor PPV of the national algorithm with a false-positive rate of about 5.5% in the NAIIS survey. Considering our findings have major implications for HIV diagnosis in routine HIV testing services, additional evaluation of testing algorithm is warranted in Nigeria.

Identifiers

PMID36962526
PMCPMC10021238

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