Publications by authors named "E Mbunge"
Article Synopsis
- HIV drug resistance (HIVDR) is a major issue affecting treatment effectiveness for people with HIV, and this study aimed to identify key predictors of HIVDR based on data from a national survey in Zimbabwe.
- The study found that 44.9% of participants had HIVDR, with higher rates among those with previous virological failures and various factors linked to increased risk, like more lifetime sexual partners and longer time on antiretroviral therapy (ART).
- Interventions are necessary to tackle HIVDR, as the findings provide valuable insights for developing targeted strategies to improve patient outcomes and prevent resistance.
HIV/AIDS remains one of the world's most significant public health and economic challenges, with approximately 36 million people currently living with the disease. Considerable progress has been made to reduce the impact of HIV/AIDS in the past years through successful multiple HIV/AIDS prevention and treatment interventions. However, barriers such as lack of engagement, limited availability of early HIV-infection detection tools, high rates of HIV/sexually transmitted infections (STIs), barriers to access antiretroviral therapy, lack of innovative resource optimisation and distribution strategies, and poor prevention services for vulnerable populations still exist and substantially affect the attainment of the UNAIDS 95-95-95 targets.
View Article and Find Full Text PDFCaesarean sections (CSs) have increased globally, with concerns being raised involving overutilisation and inequalities in access. In Zimbabwe, where healthcare access varies greatly, we aimed to analyse factors associated with ever having a CS using the 2019 National Multiple Indicator Cluster Survey. The weighted national CS rate was 10.
View Article and Find Full Text PDFThere is a substantial increase in sexually transmitted infections (STIs) among men who have sex with men (MSM) globally. Unprotected sexual practices, multiple sex partners, criminalization, stigmatisation, fear of discrimination, substance use, poor access to care, and lack of early STI screening tools are among the contributing factors. Therefore, this study applied multilayer perceptron (MLP), extremely randomized trees (ExtraTrees) and XGBoost machine learning models to predict STIs among MSM using bio-behavioural survey (BBS) data in Zimbabwe.
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