Research & Evidence

Evidence brief: Pre-analytical errors during HIV viral load (HIV-VL) and early infant diagnosis (EID) testing in Cameroon

A sourced PLOS One study, published 2026-04-27. Read the researchers’ findings, methods and context, with links to connected African health evidence.

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Published in PLOS One on 2026-04-27, this study is presented as an attributed evidence briefing. The excerpts below are the original researchers’ words, reproduced under their Creative Commons Attribution licence. Health Signal has selected the abstract and, where needed, opening background paragraphs; the complete methods, results, limitations and references remain in the linked paper.

Original authors: Marie Atsama-Amougou, Sabine Ndejo Atsinkou, Yagai Bouba, Hamadou Amadou, Teclaire Elodie Ngo-Malabo, Nadine Nguendjoung Fainguem, Julius Nwobegahay, Comfort Vuchas, Elong Elise, Bertrand Eyoum Bille, David Donchi Nguiala, Emmanuel Biongolo, Boutgam Nadine Lamare, Yakouba Liman, Marcel Tongo, Etienne Mpabuka, Ahidjo Ayouba, Charles Kouanfack, Albert Franck Zeh Meka, Joseph Fokam

Source: PLOS One • DOI 10.1371/journal.pone.0324886

Read the complete primary study ↗ · Creative Commons Attribution licence

From the researchers

Abstract

HIV Viral Load (HIV-VL) and Early Infant Diagnosis (EID) play a pivotal role in the laboratory surveillance, monitoring of HIV/AIDS, and its elimination as a public concern. However, sample rejection due to sample nonconformity (SNC) resulting from inadequate collection, transportation, and management, especially during the pre-analytical phase, negatively affects laboratory performance. This study aimed to characterize errors observed during the pre-analytical phase of HIV-VL and EID testing across national reference laboratories in Cameroon and to identify factors associated with rejection.

A descriptive and quantitative study of the nonconformities (NC) identified was collected from 11/01/2024 to 08/12/2024 in seventeen HIV reference laboratories, which constitute the national network of HIV-VL and EID testing coverage. For this study, the number of rejected samples, the reason for rejection, and the type of test ordered were recorded monthly.

During the study period, 326,885 and 38,354 specimens received for HIV viral load and EID. Of those 12,748 (3.9%) and 2.7% (1,039) were rejected. The SNC analysis indicates the presence of multiple errors or NC in some samples. For HIV viral load, our results indicate that specimen identification errors for viral load were the most common NC (63.14%; n = 8049; P = 0.031), followed by insufficient specimen volume (43.7%; n = 5571; P = 0.049) and quality errors, including hemolyzed specimens (27.8%; n = 3543; P = 0.054), and specimen transport packaging errors (9.1%; n = 1160; P = 0.069). For HIV EID, specimen rejections were primarily attributed to missing or mismatched identification on the request forms (37.12%, n = 386; P = 0.042), sample unavailability (13.4%; n = 139; P = 0.056), and information discrepancies (44.2%; n = 459; P = 0.033). The observed significant rejection rates for both HIV viral load and EID exceeded the established national rejection rate of <2% of errors. Our results suggest that corrective action is critical, along with the establishment of policies to detect and resolve preanalytical errors in Cameroon.

Our findings highlight the high magnitude of preanalytical errors for HIV-VL and EID tests used in the testing and management of people living with HIV/AIDS in Cameroon. Therefore, the laboratory system should be strengthened to ensure high-quality patient services and support optimization. Suggestions for improvement include distributing a validated specimen-collection manual, creating electronic test-request forms, providing staff training, and regularly on-site supervising the use of available resources, all of which are necessary in this country.

Introduction

Laboratory testing encompasses the pre-analytical, analytical, and post-analytical phases [1]. The pre-analytical phase, spanning from the initial test request to specimen preparation for analysis, is widely recognized as the most error-prone stage. Pre-analytical problems account for up to 70% of all laboratory errors [2,3]. This high error rate is largely driven by external factors and the manual handling steps, including test requests, specimen collection, labeling, storage, and transportation, which often occur outside of direct laboratory oversight [4,5]. If not adequately controlled, pre-analytical errors can compromise the reliability and accuracy of test results, delay clinical decision-making, increase healthcare costs, and ultimately undermine patient safety [6,7].

Reading this evidence

Use the full paper to check the study design, participant selection, comparator and outcome definitions before drawing a practical conclusion. An association does not by itself show cause and effect. A reported performance measure needs its denominator and uncertainty. A finding from a particular facility, sample or time period should retain that scope when it is discussed elsewhere.

This briefing makes the source accessible within the wider research & evidence desk. It does not assess an individual patient or certify a product. When considering relevance to another health system, examine the original authors’ account of context and limitations, then seek local implementation evidence. The related reporting below provides other studies to read alongside this one; it is a topic connection, rather than a claim that their results are directly comparable.

© 2026 Marie Atsama-Amougou, Sabine Ndejo Atsinkou, Yagai Bouba, Hamadou Amadou, Teclaire Elodie Ngo-Malabo, Nadine Nguendjoung Fainguem, Julius Nwobegahay, Comfort Vuchas, Elong Elise, Bertrand Eyoum Bille, David Donchi Nguiala, Emmanuel Biongolo, Boutgam Nadine Lamare, Yakouba Liman, Marcel Tongo, Etienne Mpabuka, Ahidjo Ayouba, Charles Kouanfack, Albert Franck Zeh Meka, Joseph Fokam. Excerpts reproduced under CC BY. Selection, formatting and contextual notes by Health Signal. The researchers are the authors of the source study, not members of Health Signal’s staff.

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Health Signal’s publication desk curates source-based health analysis and attributed research briefings. Study authors and originating institutions are credited within each article. Preparation used AI assistance; source and licence checks do not constitute independent replication or clinical endorsement.

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