Developing a triage tool for use in identifying people living with HIV who are at risk for non-retention in HIV care

Author:

Gebrezgi Merhawi T1ORCID,Fennie Kristopher P2,Sheehan Diana M134,Ibrahimou Boubakari5,Jones Sandra G6,Brock Petra7,Ladner Robert A7,Trepka Mary Jo14

Affiliation:

1. Department of Epidemiology, Robert Stempel College of Public Health and Social Work, Florida International University, Miami, Florida, USA

2. Division of Natural Sciences, New College of Florida, Sarasota, Florida, USA

3. Center for Research on U.S. Latino HIV/AIDS and Drug Abuse (CRUSADA), Florida International University, Miami, Florida, USA

4. Research Centers in Minority Institutions (RCMI), Florida International University, Miami, Florida, USA

5. Department of Biostatistics, Robert Stempel College of Public Health and Social Work, Florida International University, Miami, Florida, USA

6. Nicole Wertheim College of Nursing & Health Sciences, Florida International University, Miami, Florida, USA

7. Behavioral Science Research Corporation, Miami, Florida, USA

Abstract

Identifying people living with human immunodeficiency virus (PLHIV) in human immunodeficiency virus (HIV) care who are at particular risk of non-retention in care is an important element in improving their HIV care outcomes. The purpose of this study was to develop a risk prediction tool to identify PLHIV at risk of non-retention in care over the course of the next year. We used stepwise logistic regression to assess sociodemographic, clinical and behavioral predictors of non-retention in HIV care. Retention in care was defined as having evidence of at least two encounters with an HIV care provider (or CD4 or viral load lab tests as a proxy measure for the encounter), at least three months apart within a year. We validated the risk prediction tool internally using the bootstrap method. The risk prediction tool included a total of six factors: age group, race, poverty level, homelessness, problematic alcohol/drug use, and viral suppression status. The total risk score ranged from 0 to 17. Compared to those in the lowest quartile (0 risk score), those who were in the middle two quartiles (score 1–4) and those in the upper quartile (>4 risk score) were more likely not to be retained in care (odds ratio [OR] 1.63 [confidence interval, CI: 1.39 – 1.92] and OR 4.82 [CI: 4.04 – 5.78], respectively). The discrimination ability for the prediction model was 0.651. We conclude that increased risk for non-retention in care can be predicted with routinely available variables. Since the discrimination of the tool was low, future studies may need to include more prognostic factors in the risk prediction tool.

Funder

National Institute on Minority Health and Health Disparities

Publisher

SAGE Publications

Subject

Infectious Diseases,Pharmacology (medical),Public Health, Environmental and Occupational Health,Dermatology

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