Sarcouncil Journal of Medical Sciences

Abstract: Background: As historically witnessed, HIV surveillance in the United States has often been dependent on traditional case reporting and demographic factors, but the existing disparities within underserved communities, such as rural areas, racial/ ethnic minorities, and people who inject drugs, continue to be a barrier to the efficacy of standard systems. Purpose: This narrative review is a synthesis of the recent developments (2020-present) in epidemiologic and data-driven methods of HIV surveillance, particularly in the underserved U.S. communities, with a critical analysis of how the advances can transform the way the population receives interventions. Method: We rely on peer-reviewed articles, policy reports, and health data on the population to infer the major emerging trends in molecular cluster detection, machine learning (ML) in incidence prediction, equity-oriented spatial mapping, and integration of social determinants. Rather than generalizing results from study after study, we portray the review in the shape of thematic trends without summarizing the strengths, limitations, and opportunities. Findings: Molecular cluster identification has become a national system, allowing quick detection of cross-jurisdictional transmission networks, and 404 clusters were detected during 2018-2023. The prediction of HIV incidence with high precision is now possible with the use of ML models trained on publicly available health databases of STIs, partly due to the efficacy of this feature as the social vulnerability index (SVI). Visual representation of surveillance data identifies locations with racial, geographic, and socioeconomic disparities. Nonetheless, there are still gaps in terms of delayed reporting of the sequence, low representation of the rural and marginalized populations, and a lack of integration of behavioral data. Inequalities in access to care and subjugation remain, particularly in rural counties in the group among patients who inject drugs (PWID). Summary: Integration of molecular epidemiology, ML, and equity-oriented data tools represents the beginning of a new era of HIV surveillance in the U.S. It will, however, be imperative to invest in capacity building, data infrastructure, and community engagement to ensure that the impact of these tools is maximized by ensuring that these underserved populations can access these tools. Improved surveillance can educate more objectively, expediently, and fair preventive activities and aid in eradicating chronic deficiencies in HIV care.