Monitoring pre-exposure prophylaxis (PrEP) metrics can guide service delivery yet does not occur routinely. We developed a survey to understand current practices for monitoring PrEP at PrEP-providing organizations in Illinois and Missouri. The survey was distributed from September through November 2020; 26 organizations participated.
View Article and Find Full Text PDFBackground: To End the HIV Epidemic and reduce the number of incident HIV infections in the United States by 90%, pre-exposure prophylaxis (PrEP) uptake and persistence among cisgender women, particularly racial and ethnic minority women, must be increased. Medical providers play a pivotal role across the PrEP care continuum.
Methods: In this qualitative study, guided by the Consolidated Framework for Implementation Research, we explored health care provider perspectives on facilitators and barriers to PrEP implementation strategies for Black cisgender women in the Midwest United States.
Due to improved efficiency and reduced cost of viral sequencing, molecular cluster analysis can be feasibly utilized alongside existing human immunodeficiency virus (HIV) prevention strategies. The goal of this paper is to elucidate how HIV molecular cluster and social network analyses are being integrated to implement HIV response interventions. We searched PubMed, Scopus, PsycINFO, and Cochrane Library databases for studies incorporating both HIV molecular cluster and social network data.
View Article and Find Full Text PDFJ Racial Ethn Health Disparities
June 2022
Background: Disparities in HIV incidence and PrEP use among Black ciswomen remain. We examine factors associated with PrEP persistence using mixed methods.
Setting: Black ciswomen in Chicago, IL, prescribed PrEP at a federally qualified health center (FQHC).
Background: Mental illness and substance use are prevalent among people living with HIV and often lead to poor health outcomes. Electronic medical record (EMR) data are increasingly being utilized for HIV-related clinical research and care, but mental illness and substance use are often underdocumented in structured EMR fields. Natural language processing (NLP) of unstructured text of clinical notes in the EMR may more accurately identify mental illness and substance use among people living with HIV than structured EMR fields alone.
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