Biomédica: revista del Instituto Nacional de Salud (Dec 2017)

Optimizing resources to reduce costs to determine HIV viral load in limited resources settings

  • Amalia Girón-Callejas,
  • Ricardo Mendizabal-Burastero,
  • Elizabeth Yax,
  • Axel Martínez,
  • Carlos Mejía-Villatoro

DOI
https://doi.org/10.7705/biomedica.v37i4.3318
Journal volume & issue
Vol. 37, no. 4
pp. 460 – 465

Abstract

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Introduction: HIV viral load testing is a key factor to evaluate the accomplishment of the UNAIDS target of 90% of viral suppression among people receiving antiretroviral therapy. Pooled samples are a potentially accurate and economic approach in resource-constrained settings, but efficiency can be negatively affected by high prevalence rates of virological failure. Objective: Strategies were assessed to increase the relative efficiency of pooled HIV viral load testing in resource-constrained settings. Materials and methods: We evaluated two strategies: a) plasma samples were not included in pools if patients had 1,000 copies/ml, or were antiretroviral therapy naïve patients, and b) plasma pools were organized separately for first and second-line antiretroviral therapy regimens. Individual viral load tests were used to compare pooled results. Results: Negative predictive values were similar for patients on first (100.0%; 95% CI 99.5 to 100.0) and second-line antiretroviral therapy regimens (99.4%; 95% CI 96.9 to 99.9). However, the incidence of virological failure among individuals on first-line antiretroviral therapy was lower than second-line antiretroviral therapy patients (p <0.01), resulting in greater savings in laboratory tests in patients on first-line antiretroviral therapy (74.0%; 95% CI 71.0 to 76.7) compared with the group of patients on second-line antiretroviral therapy (50.9%; 95% CI 44.4 to 57.3) (p<0.01). Conclusion: Selecting the samples to be included in the pools and selecting the pools according to ART regimens are criteria that could lead to decreased spending on laboratory tests for HIV viral load determination in resource-constrained settings.

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