HIV risk score and prediction model in the United States: A Scoping Review Protocol

Human immunodeficiency virus (HIV) remains a significant public health concern in the United States, affecting approximately 1.2 million individuals, with a substantial portion unaware of their infection status. Identifying individuals at elevated risk of HIV infection through predictive models holds promise for enhancing public health efforts. A preliminary search from PubMed revealed a handful of studies focused on developing HIV risk prediction models or risk scores, each employing varying methodologies such as logistic regression and machine learning. These studies targeted a diverse population including men who have sex with men, emergency department visitors, and the general population, drawing data from surveys, surveillance, and electronic health records. Despite these individual efforts, there is a notable absence of comprehensive review papers summarizing the outcomes of these studies. Addressing this gap, this scoping review systematically identifies and synthesizes results from existing predictive models for HIV risk. The primary objective is to determine the variables used in HIV risk scoring and prediction models, contributing to a more comprehensive understanding of HIV risk assessment. This protocol describes the thorough procedure for conducting a scoping review. It outlines the inclusion and exclusion criteria for relevant studies and offers the search methods that will be used in PubMed, EMBASE, and CINAHL. Detailed paper screening, data extraction, and risk of bias assessment process were described.