Dataset Information

Misdiagnosed HIV infection in pregnant women initiating universal ART in South Africa.

ABSTRACT: Introduction: Rapid diagnostic tests (RDTs) are the primary diagnostic tools for HIV used in resource-constrained settings. Without a proper confirmation algorithm, there is concern that false-positive (FP) RDTs could result in misdiagnosis of HIV infection and inappropriate antiretroviral treatment (ART) initiation, but programmatic data on FP are few.

Methods: We examined the accuracy of RDT diagnosis among HIV-infected pregnant women attending public sector antenatal services in Cape Town, South Africa. We describe the proportion of women found to have started on ART erroneously due to FP RDT results based on pre-ART viral load (VL) testing and enzyme-linked immunosorbent assay (ELISA).

Results: We analysed 952 consecutively enrolled pregnant women diagnosed as HIV infected based on two RDTs per local guideline and found 4.5% (43/952) of pre-ART VL results to be <50 copies/ml. After excluding 6 women who had detectable virus on subsequent VL measurements, ELISA was performed on the 37 remaining women. Of these, 3/952 (0.3%) HIV RDT diagnoses were found to be FP. We estimate that using ELISA to confirm all positive RDTs would cost $1110 (uncertainty interval $381-$5382) to identify one patient erroneously initiated on ART, while it costs $3912 for a lifetime of antiretrovirals with VL monitoring for one person.

Conclusions: Compared to the cost of confirming the RDT-based diagnoses, the cost of HIV misdiagnosis is high. While testing programmes based on RDT should strive for constant quality improvement, where resources permit, laboratory confirmation algorithms can play an important role in strengthening the quality of HIV diagnosis in the era of universal ART.

SUBMITTER: Hsiao NY

PROVIDER: S-EPMC5625589 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Misdiagnosed HIV infection in pregnant women initiating universal ART in South Africa.

Hsiao Nei-Yuan NY Zerbe Allison A Phillips Tamsin K TK Myer Landon L Abrams Elaine J EJ

Journal of the International AIDS Society 20170801 Suppl 6

<h4>Introduction</h4>Rapid diagnostic tests (RDTs) are the primary diagnostic tools for HIV used in resource-constrained settings. Without a proper confirmation algorithm, there is concern that false-positive (FP) RDTs could result in misdiagnosis of HIV infection and inappropriate antiretroviral treatment (ART) initiation, but programmatic data on FP are few.<h4>Methods</h4>We examined the accuracy of RDT diagnosis among HIV-infected pregnant women attending public sector antenatal services in ...[more]

PMID: 28872277

Similar Datasets

Project description:INTRODUCTION:HIV viral load (VL) monitoring is a central tool to evaluate ART effectiveness and transmission risk. There is a global movement to expand VL monitoring following recent recommendations from the World Health Organization (WHO), but there has been little research into VL monitoring in pregnant women. We investigated one important question in this area: when and how frequently VL should be monitored in women initiating ART during pregnancy to predict VL at the time of delivery in a simulated South African population. METHODS:We developed a mathematical model simulating VL from conception through delivery using VL data from the Maternal and Child Health - Antiretroviral Therapy (MCH-ART) cohort. VL was modelled based on three major compartments: pre-ART VL, viral decay immediately after ART initiation and viral maintenance (including viral suppression and viraemic episodes). Using this simulation, we examined the performance of various VL monitoring schema in predicting elevated VL at delivery. RESULTS AND DISCUSSION:If WHO guidelines for non-pregnant adults were used, the majority of HIV-infected pregnant women (69%) would not receive a VL test during pregnancy. Most models that based VL monitoring in pregnancy on the time elapsed since ART initiation (regardless of gestation) performed poorly (sensitivity <50%); models that based VL measures in pregnancy on the woman's gestation (regardless of time on ART) appeared to perform better overall (sensitivity >60%). Across all permutations, inclusion of pre-ART VL values had a negligible impact on predictive performance (improving test sensitivity and specificity <6%). Performance of VL monitoring in predicting VL at delivery generally improved at later gestations, with the best performing option a single VL measure at 36 weeks' gestation. CONCLUSIONS:Development and evaluation of a novel simulation model suggests that strategies to measure VL relative to gestational age may be more useful than strategies relative to duration on ART, in women initiating ART during pregnancy, supporting better integration of maternal and HIV health services. Testing turnaround times require careful consideration, and point-of-care VL testing may be the best approach for measuring VL at delivery. Broadening the scope of this simulation model in the light of current scale up of VL monitoring in high burden countries is important.

Project description:IntroductionNew HIV infection during pre-conception and pregnancy is a significant contributor of mother-to-child transmission of HIV in South Africa. This study estimated HIV incidence (defined as new infection within the last one year from the time of the survey which included both new infections occurred during pregnancy or just before pregnancy) among pregnant women and described the characteristics of recently infected pregnant women at national level.MethodsBetween 1 October and 15 November 2017, we conducted a national cross-sectional survey among pregnant women aged 15-49 years old attending antenatal care at 1,595 public facilities. Blood specimens were collected from pregnant women and tested for HIV in a centralised laboratory. Plasma viral load and Limiting Antigen Avidity Enzyme Immunosorbent Assay (LAg) tests were further performed on HIV positive specimens to differentiate between recent and long-term infections. Recent infection was defined as infection that occurred within one year from the date of collection of blood specimen for the survey. Data on age, age of partner, and marital status were collected through interviews. Women whose specimens were classified as recent by LAg assay and with viral loads >1,000 copies/mL were considered as recently infected. The calculated proportion of HIV positive women with recent infection was adjusted for assay-specific parameters to estimate annual incidence. Survey multinomial logistic regression was used to examine factors associated with being recently infected using HIV negative women as a reference group. Age-disparate relationship was defined as having a partner 5 or more years older.ResultsOf 10,049 HIV positive participants with LAg and viral load data, 1.4% (136) were identified as recently infected. The annual HIV incidence was 1.5% (95% confidence interval (CI): 1.2-1.7). In multivariable analyses, being single (adjusted odds ratio, aOR: 3.4, 95% CI: 1.8-6.2) or cohabiting (aOR: 3.8, 95% CI: 1.8-7.7), compared to being married as well as being in an age-disparate relationship among young women (aOR: 3.1, 95% CI: 2.0-4.7; reference group: young women (15-24years) whose partners were not 5 years or more older) were associated with higher odds of recent infection.ConclusionsCompared to previous studies among pregnant women, the incidence estimated in this study was substantially lower. However, the UNAIDS target to reduce incidence by 75% by 2020 (which is equivalent to reducing incidence to <1%) has not been met. The implementation of HIV prevention and treatment interventions should be intensified, targeting young women engaged in age-disparate relationship and unmarried women to fast track progress towards the UNAIDS target.

Dataset Information

Misdiagnosed HIV infection in pregnant women initiating universal ART in South Africa.

Publications

Misdiagnosed HIV infection in pregnant women initiating universal ART in South Africa.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets