Taylor McLinden

PLOS ONE, Aug 2023 Introduction Case-finding algorithms can be applied to administrative healthcare records to identify people with diseases, including people with HIV (PWH). When supplementing an existing registry of a low prevalence disease, near-perfect specificity helps minimize impacts of adding in algorithm-identified false positive cases. We evaluated the performance of algorithms applied to healthcare records to supplement an HIV registry in British Columbia (BC), Canada.

Acknowledgements The authors would like to sincerely thank all of the LISA participants who took the time to share their experiences with the study. We would also like to acknowledge the LISA Community Advisory Committee: Terry Howard, Rosa Jamal, Isabella Kirchner, Sandy Lambert, Kecia Larkin, Steve Levine, Melissa Medjuck, Stacie Migwans, Sam Mohan, Lori Montgomery, Glynn Townson, Michelle Webb, and Sarah White.

Our study aims to define and identify correlates of social isolation among people living with HIV (PLHIV). The Longitudinal Investigation into Supportive and Ancillary health services (LISA) study provided a cross-sectional analytic sample of 996 PLHIV in British Columbia, Canada (sampled between 2007 and 2010). Individuals marginalized by socio-structural inequities were oversampled; sampling bias was addressed through inverse probability of participation weighting.