Category Archives: Health Systems

Background HIV/AIDS may cause malnutrition, both directly and indirectly, through common infections. This systematic review and meta-analysis aim to evaluate the effects of nutritional interventions on nutritional status, immunological status,...

BACKGROUND/OBJECTIVES: Access to antiretroviral (ARV) drugs remains a critical challenge in achieving the WHO/UNAIDS 95-95-95 targets, with medication costs representing a substantial barrier. This systematic review evaluates the effect of...

OBJECTIVES: This scoping review aims to synthesize existing evidence on mental health (MH) interventions for people living with HIV/AIDS (PLWHA) in Africa. Given the high prevalence of MH disorders in...

BACKGROUND: Adolescents living with HIV face unique challenges as they gain greater independence while transitioning into adulthood. They must adjust their self-management activities and take primary responsibility for their health,...

The aim is to identify, in the literature, the influence of Global Health Initiatives (GHI) on the HIV combat in Africa. A scoping review was carried out in March 2023...

OBJECTIVE: to analyze clinical trials that assessed the effectiveness of technologies for healthy lifestyles in people with HIV. METHOD: A systematic review, conducted in five databases, with association of controlled...

BACKGROUND: Despite increasing backlash against harm reduction efforts and the need to understand the risk environments encountered by people who inject drugs (PWID), a quantitative systematic review on public injecting...

Background: Adolescents and young adults (AYA) make up a significant share of the world’s burden of HIV and other sexually transmitted infections (STI). Self-testing can increase testing coverage and strengthen the...

BACKGROUND: Hepatitis C virus (HCV) is a genetically diverse blood-borne pathogen causing liver inflammation and damage. It is one of the global public health problems responsible for claiming thousands of...

Background Although a growing body of evidence supports zero risk of sexual HIV transmission from a person with sustained virological suppression, known as U=U (undetectable equals untransmittable), data have been...

Background Machine learning (ML) shows promise for sexually transmitted infection (STI) risk prediction, but systematic evidence of its effectiveness remains fragmented. Methods We systematically searched six electronic databases, three preprint...

Background HIV treatment interruption remains a significant barrier to achieving global HIV/AIDS control goals. Machine learning (ML) models offer potential for predicting treatment interruption by leveraging large clinical data. Understanding...