Scholarlink

Introduction <p>HIV drug resistance (HIVDR) remains a significant challenge in sub-Saharan Africa (SSA) due to limited effective Treatment and healthcare resources vary. Using the first widely available HIVDR surveillance data in SSA, we calculated the prevalence and associated factors of HIVDR amongst the persons that were on ART between 2015 and 2019 using the Population-based HIV Impact Assessment (PHIA).</p> Methods <p>A secondary analysis of the combined PHIA HIVDR data from Cameroon, Malawi, Eswatini, Ethiopia, Namibia, Rwanda, Tanzania, Zambia and Zimbabwe over the 2015–2019 period. All the 1,008 persons with HIVDR information were included in the analysis. We calculated frequencies, proportions, 95% confidence intervals (95%CI), crude/adjusted odds ratios (cOR/aOR), chi-Square statistics using in R. HIVDR was determined through genotypic testing of blood samples from HIV positive individuals with detectable viral load. We examined the prevalence and associated factors of HIVDR in SSA. Standard assays were used to identify mutations linked to resistance to NRTIs, NNRTIs, and PIs. Presence of at least one mutation indicated HIVDR status. Supervised machine learning models were developed in RStudio using the <i>SuperLearner</i> and <i>caret</i> packages, training six algorithms to predict HIVDR. Variable importance was assessed using a random forest model, while predicted probabilities were Generated via Elastic Net LASSO regression with 10-fold cross-validation. Statistical significance was set at <i>P</i> &lt; 0.05.</p> Results <p>An overall prevalence of HIVDR was 35%. Not reaching HIV viral load suppression, experiencing antiretroviral treatment, and certain sociodemographic characteristics including age (35 + years), living in a rural area, and particular national contexts (e.g., higher resistance in Rwanda and Zimbabwe) were important factors linked to higher HIVDR likelihood. Additionally, the study revealed that having viral load suppression was associated with lower HIVDR likelihood (aOR: 0.31, 95% CI: 0.21–0.45, <i>P</i> &lt; 0.001), whereas experiencing antiretroviral treatment was associated with higher HIVDR likelihood (aOR: 2.6, 95% CI: 1.75–3.91, <i>P</i> &lt; 0.001). Machine learning models confirmed that programmatic and contextual factors outweighed individual characteristics in shaping resistance risk. Predicted probabilities were highest among ART-experienced individuals with unsuppressed viral load, reaching up to 45%. While the LASSO model showed moderate accuracy, the Super Learner ensemble outperformed all models.</p> Conclusion <p>This study concludes by highlighting the substantial prevalence of HIVDR in SSA, which varies significantly among nations and sociodemographic characteristics. The results highlight the importance of ART use and viral load suppression in determining HIVDR prevalence, underscoring the necessity of focused interventions to enhance viral load monitoring and ART adherence. To combat the growing threat of HIVDR and guarantee the long-term efficacy of HIV treatment programs in the area, ongoing surveillance and context-specific approaches are crucial.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Prevalence and factors associated with HIV drug resistance among adult persons living with HIV/AIDS in nine countries of Sub-Saharan Africa using population-based HIV impact assessments: 2015–2019

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月14日

查看全文
  • Edson Nsonga,
  • Wingston Felix Ng’ambi,
  • Mtumbi Goma,
  • Cosmas Zyambo

摘要

Introduction

HIV drug resistance (HIVDR) remains a significant challenge in sub-Saharan Africa (SSA) due to limited effective Treatment and healthcare resources vary. Using the first widely available HIVDR surveillance data in SSA, we calculated the prevalence and associated factors of HIVDR amongst the persons that were on ART between 2015 and 2019 using the Population-based HIV Impact Assessment (PHIA).

Methods

A secondary analysis of the combined PHIA HIVDR data from Cameroon, Malawi, Eswatini, Ethiopia, Namibia, Rwanda, Tanzania, Zambia and Zimbabwe over the 2015–2019 period. All the 1,008 persons with HIVDR information were included in the analysis. We calculated frequencies, proportions, 95% confidence intervals (95%CI), crude/adjusted odds ratios (cOR/aOR), chi-Square statistics using in R. HIVDR was determined through genotypic testing of blood samples from HIV positive individuals with detectable viral load. We examined the prevalence and associated factors of HIVDR in SSA. Standard assays were used to identify mutations linked to resistance to NRTIs, NNRTIs, and PIs. Presence of at least one mutation indicated HIVDR status. Supervised machine learning models were developed in RStudio using the SuperLearner and caret packages, training six algorithms to predict HIVDR. Variable importance was assessed using a random forest model, while predicted probabilities were Generated via Elastic Net LASSO regression with 10-fold cross-validation. Statistical significance was set at P < 0.05.

Results

An overall prevalence of HIVDR was 35%. Not reaching HIV viral load suppression, experiencing antiretroviral treatment, and certain sociodemographic characteristics including age (35 + years), living in a rural area, and particular national contexts (e.g., higher resistance in Rwanda and Zimbabwe) were important factors linked to higher HIVDR likelihood. Additionally, the study revealed that having viral load suppression was associated with lower HIVDR likelihood (aOR: 0.31, 95% CI: 0.21–0.45, P < 0.001), whereas experiencing antiretroviral treatment was associated with higher HIVDR likelihood (aOR: 2.6, 95% CI: 1.75–3.91, P < 0.001). Machine learning models confirmed that programmatic and contextual factors outweighed individual characteristics in shaping resistance risk. Predicted probabilities were highest among ART-experienced individuals with unsuppressed viral load, reaching up to 45%. While the LASSO model showed moderate accuracy, the Super Learner ensemble outperformed all models.

Conclusion

This study concludes by highlighting the substantial prevalence of HIVDR in SSA, which varies significantly among nations and sociodemographic characteristics. The results highlight the importance of ART use and viral load suppression in determining HIVDR prevalence, underscoring the necessity of focused interventions to enhance viral load monitoring and ART adherence. To combat the growing threat of HIVDR and guarantee the long-term efficacy of HIV treatment programs in the area, ongoing surveillance and context-specific approaches are crucial.