Characterising persons diagnosed with HIV as either recent or long-term using a cross-sectional analysis of recent infection surveillance data collected in Malawi from September 2019 to March 2020

Abstract

ObjectivesIn Malawi, a recent infection testing algorithm (RITA) is used to characterise infections of persons newly diagnosed with HIV as recent or long term. This paper shares results from recent HIV infection surveillance and describes distribution and predictors.SettingData from 155 health facilities in 11 districts in Malawi were pooled from September 2019 to March 2020.ParticipantsEligible participants were ≥13 years, and newly diagnosed with HIV. Clients had RITA recent infections if the rapid test for recent infection (RTRI) test result was recent and viral load (VL) ≥1000 copies/mL; if VL was <1000 copies/mL the RTRI result was reclassified as long-term. Results were stratified by age, sex, pregnancy/breastfeeding status and district.Results13 838 persons consented to RTRI testing and 12 703 had valid RTRI test results and VL results after excluding clients not newly HIV-positive, RTRI negative or missing data (n=1135). A total of 12 365 of the 12 703 were included in the analysis after excluding those whose RTRI results were reclassified as long term (n=338/784 or 43.1%). The remainder, 446/12 703 or 3.5%, met the definition of RITA recent infection. The highest percentage of recent infections was among breastfeeding women (crude OR (COR) 3.2; 95% CI 2.0 to 5.0), young people aged 15–24 years (COR 1.6; 95% CI 1.3 to 1.9) and persons who reported a negative HIV test within the past 12 months (COR 3.3; 95% CI 2.6 to 4.2). Factors associated with recent infection in multivariable analysis included being a non-pregnant female (adjusted OR (AOR) 1.4; 95% CI 1.2 to 1.8), a breastfeeding female (AOR 2.2; 95% CI 1.4 to 3.5), aged 15–24 years (AOR 1.6; 95% CI 1.3 to 1.9) and residents of Machinga (AOR 2.0; 95% CI 1.2 to 3.5) and Mzimba (AOR 2.4; 95% CI 1.3 to 4.5) districts.ConclusionsMalawi’s recent HIV infection surveillance system demonstrated high uptake and identified sub-populations of new HIV diagnoses with a higher percentage of recent infections.

Membrane computing simulation of sexually transmitted bacterial infections in hotspots of individuals with various risk behaviors

“…Another factor influencing STI epidemiology is derived from highly interconnected sexual networks, which facilitate the rapid spread of new infections among people with high-risk sexual behaviors (e.g., multiple sexual partners, condomless sex, sex with unknown people, alcohol use, and recreational drug use), revealing the excessive vulnerability to new threats, as we learned with the recent international mpox outbreak in 2022 ( 6 ), which is associated with sexual transmission ( 7 ). High connectivity is facilitated by online dating apps for recreational sex, which expand sexual networks ( 8 ).…”