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Updated: Apr 23, 2026
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An Affordable HIV-1 Drug Resistance Monitoring Method for Resource Limited Settings
Published on: March 30, 2014
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Pharmacy refill adherence outperforms self-reported methods in predicting HIV therapy outcome in resource-limited
Raphael Z Sangeda1, Fausta Mosha, Mattia Prosperi
1Department of Pharmaceutical Microbiology, Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania. sangeda@gmail.com.
BMC Public Health
|October 5, 2014
Summary
Pharmacy refill adherence is the best method to predict virological failure in resource-limited settings. This approach helps identify patients needing viral load monitoring and HIV drug resistance testing.
Area of Science:
- Infectious Diseases
- Public Health
- Pharmacology
Background:
- Optimal adherence to antiretroviral therapy (ART) is crucial for preventing HIV drug resistance (HIVDR).
- Accurate adherence assessment is vital for predicting treatment outcomes in resource-limited settings (RLS).
Purpose of the Study:
- To identify the most effective adherence assessment method for predicting virological failure in RLS.
- To compare the performance of different adherence measures in an HIV-infected population.
Main Methods:
- A prospective cohort study of 220 HIV-infected adults in Dar es Salaam, Tanzania.
- Assessed adherence using pharmacy refill, self-report (VAS, questionnaire), pill count, and appointment keeping.
- Employed univariate/multivariate logistic regression, decision trees, random forests, and cross-validation to predict virological failure.
Main Results:
- Pharmacy refill adherence (≥95%) was the optimal predictor of virological failure (AUC=0.61).
- Multivariate models showed improved prediction, with boost stepwise MCV achieving AUC=0.64.
- Lower baseline CD4 count, inability to recall diagnosis date, and higher weight were associated with virological failure.
Conclusions:
- Pharmacy refill adherence is a reliable predictor of virological failure in RLS.
- This method can guide viral load monitoring and HIVDR testing strategies in resource-limited settings.
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