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HIV infection is a significant independent risk factor for severe COVID-19 disease and death. We summarize COVID-19 vaccine responses in people living with HIV (PLHIV). A systematic literature review of studies from 1 January 2020 to 31 March 2022 of COVID-19 vaccine immunogenicity in PLHIV from multiple databases was performed. Twenty-eight studies from 12 countries were reviewed. While twenty-two (73%) studies reported high COVID-19 vaccine seroconversion rates in PLHIV, PLHIV with lower baseline CD4 count, CD4/CD8 ratio, or higher baseline viral load had lower seroconversion rates and immunologic titers. Data on vaccine induced seroconversion in PLHIV are reassuring, but more research is needed to evaluate the durability of COVID-19 vaccine responses in PLHIV.

Background: Virologic suppression (VS) has been defined using an HIV viral load (VL) of <1,000 copies/mL. Low-level viremia (51-999 copies/mL) is associated with an increased risk of virologic failure and HIV drug resistance. Methods: Retrospective data from persons with HIV (PWH) who initiated ART between January 2016–September 2022 in Nigeria were analyzed for VS at cut-off values <1000 copies/mL. Results: In 2022, VS at <1000 copies/mL was 95.7%. Using cut-off values of <400, <200 and <50 copies/mL, VS was 94.2%, 92.5%, and 87.0%, respectively. Discussion: Monitoring VS using lower cut-off values, alongside differentiated management of low-level viremia, may help Nigeria achieve HIV epidemic control targets.

Diagnosis of tuberculosis (TB) among young children (<5 years) is challenging due to the paucibacillary nature of clinical disease and clinical similarities to other childhood diseases. We used machine learning to develop accurate prediction models of microbial confirmation with simply defined and easily obtainable clinical, demographic, and radiologic factors. We evaluated eleven supervised machine learning models (using stepwise regression, regularized regression, decision tree, and support vector machine approaches) to predict microbial confirmation in young children (<5 years) using samples from invasive (reference-standard) or noninvasive procedure. Models were trained and tested using data from a large prospective cohort of young children with symptoms suggestive of TB in Kenya. Model performance was evaluated using areas under the receiver operating curve (AUROC) and precision-recall curve (AUPRC), accuracy metrics. (i.e., sensitivity, specificity), F-beta scores, Cohen’s Kappa, and Matthew’s Correlation Coefficient. Among 262 included children, 29 (11%) were microbially confirmed using any sampling technique. Models were accurate at predicting microbial confirmation in samples obtained from invasive procedures (AUROC range: 0.84–0.90) and from noninvasive procedures (AUROC range: 0.83–0.89). History of household contact with a confirmed case of TB, immunological evidence of TB infection, and a chest x-ray consistent with TB disease were consistently influential across models. Our results suggest machine learning can accurately predict microbial confirmation of M. tuberculosis in young children using simply defined features and increase the bacteriologic yield in diagnostic cohorts. These findings may facilitate clinical decision making and guide clinical research into novel biomarkers of TB disease in young children.