Predicting HIV Drug Resistance among Persons Living with HIV/AIDS in Sub-Saharan Africa using Population-based HIV Impact Assessment Surveys: 2015-2019

Edson Nsonga, Mtumbi Goma, Wingston Felix Ng’ambi,Cosmas Zyambo

crossref(2024)

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摘要
Introduction HIV drug resistance (HIVDR) remains a significant challenge in sub-Saharan Africa (SSA), where access to effective treatment and healthcare resources varies widely. Socioeconomic status, demographic factors, clinical parameters, and regional disparities have been associated with patterns of HIVDR across SSA. Understanding the interplay of these factors is crucial for designing effective interventions to mitigate the impact of HIVDR and improve treatment outcomes in the region. Methods We conducted a secondary analysis of the Population-based HIV Impact Assessment (PHIA) HIV drug resistance datasets from Cameroon, Malawi, Eswatini, Ethiopia, Namibia, Rwanda, Tanzania, Zambia and Zimbabwe. All recipients of care aged between 15+ years were included in this analysis. The outcome of interest was whether a person had HIVDR resistant strains or no HIVDR resistant strains. Predictive analysis, chi-square test, univariable and multivariable logistic regression analyses were conducted in R. Statistical significance was set at P<0.05. Results The total sample size across the nine countries was 1008. Tanzania had the highest representation (16.8%), followed by Zambia (16.3%) and Zimbabwe (14.2% while Rwanda had the lowest representation (5.1%). Significant associations were observed between ARV status, viral suppression, country of residence and HIVDR in SSA. Individuals residing in Rwanda had significantly higher odds of HIVDR (adjusted OR = 3.63, 95% CI: 1.22-11.0, p = 0.021) compared to other countries. Additionally, individuals with suppressed viral loads had significantly lower odds of HIVDR (adjusted OR = 0.31, 95% CI: 0.21-0.45, p < 0.001), while those on ART exhibited higher odds of HIVDR (adjusted OR = 2.6, 95% CI: 1.75-3.91, p < 0.001). Conclusion This study focused on how clinical and sociodemographic factors influence HIVDR patterns in SSA. To mitigate the effects of HIVDR and improve treatment outcomes in the region, it is critical to address barriers to treatment access and adherence and upgrade the healthcare system.
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