CROI 2024 Abstract eBook

Abstract eBook

Poster Abstracts

experienced one and 2 gaps in care, whereas the lowest was observed among those who drooped-out of care (Figure, A); CD4 restoration after reengagement in care, especially after the second time, was notably worse compared to the corresponding CD4 increase after ART initiation before disengagement, regardless of the baseline CD4 levels (Figure B). Conclusion: While CD4 counts tend to increase after reengagement in care, it is evident that multiple disengagements cumulatively impact negatively CD4 evolution; young IVDUs and those of non-Greek origin are the most vulnerable for disengagement from care.

1043 Rethinking the Definition of Late HIV Diagnosis Using Florida Surveillance Data, 2015-2021

Christina E Parisi , Robert Cook, Zhigang Li, Shantrel Canidate, Awewura Kwara, Zhi Zhou, Natalie Chichetto University of Florida, Gainesville, FL, USA Background: Late HIV diagnosis is a barrier to ending the epidemic as it may be associated with prolonged transmission risk and worse HIV-related outcomes. The CDC defines late HIV diagnosis as a CD4+ T-cell count<200 cells/mm 3 at diagnosis. It is unclear if an expanded clinically relevant definition of CD4<350 would better represent those in need of resources to prevent poor outcomes. We aimed to examine trends in the annual proportion of different definitions (late/ delayed) of late HIV diagnosis between 2015-2021 in Florida, characteristics associated with late/delayed diagnosis, and the association between diagnosis status and mortality. Methods: Data included laboratory results for HIV care recipients, diagnosed 2015-2021, in the Enhanced HIV/AIDS Reporting System. Diagnosis status was categorized as late (CD4<200), delayed (200≤CD4<350), and timely (CD4≥350). Characteristics of interest were age, gender, race/ethnicity, birth region, and diagnosis facility. Multivariable multinomial logistic regression models examined characteristics by diagnosis status. Logistic regression, adjusting for characteristics, estimated risk of death during the study period. Results: Of 27,460 individuals (21% female, 39% non-Hispanic [NH] Black, 35% Hispanic, 2% NH Other, mean age 38.3 years [SD 12.9]), 23% had a late and 18% had a delayed diagnosis overall, and this proportion was consistent annually. Older adults (ref=18-24; 25-34 [OR 1.6, 95% CI 1.4-1.8], 35-49 [2.4, 2.1-2.7], 50+ [2.7, 2.3-3.0]) and those of Caribbean birth region (ref=North America; 1.1, 1.0-1.3) were more likely to have a late diagnosis. Late and delayed diagnosis were similarly associated with female (ref=male; delayed: 0.9, 0.8-0.96, late: 0.8, 0.7-0.9), NH Black (ref=NH White; delayed: 1.6, 1.4-1.7, late: 1.5, 1.4-1.6) Hispanic (delayed: 1.4, 1.2-1.5, late: 1.1, 1.0-1.2), and inpatient diagnosis (ref=outpatient; delayed: 1.6, 1.4-1.7, late: 5.4, 5.0-5.9) characteristics. There were 1,176 deaths and delayed (1.3, 1.04-1.5) and late (1.9, 1.6-2.2) diagnosis were associated with death. Conclusion: Expanding the late diagnosis definition captured an additional 18% of HIV diagnoses that may be at greater risk of poor outcomes such as death. Characteristics associated with late/delayed diagnoses were consistent and both were associated with greater mortality risk. Those with late/delayed diagnosis would benefit from enhanced intervention. Future research can target other late/delayed diagnosis factors to address missed HIV testing opportunities.

Poster Abstracts

1042 A Multistate Analysis of Longitudinal Care Outcomes Among People Newly-Linked to Care in Missouri Aditi Ramakrishnan , Aaloke Mody, Daniel Vo, Ernie-Paul Barrette, Rachel Presti, William G. Powderly, Anne Trolard, Catherine Schwarz, Elvin H. Geng, Lindsey M. Filiatreau Washington University in St Louis, St Louis, MO, USA Background: Traditional metrics of retention in HIV care fall short of quantifying the dynamic process of engagement and disengagement from care over time. Novel longitudinal analytic approaches reveal more nuanced care trajectories for people living with HIV (PLWH), thus providing stronger evidence to improve service delivery. We conducted a multistate analysis to describe transitions in and out of care and comprehensively represent longitudinal outcomes in Missouri, an Ending the HIV Epidemic priority state. Methods: We analyzed electronic health record data collected from PLWH ≥ 18 years who were newly or presumed newly linked to care at a large Ryan White-funded HIV primary care clinic in St. Louis, Missouri between January 1, 2015 and March 15, 2020. We estimated the prevalence of 9 mutually exclusive and exhaustive care states over the 3 years following linkage to clinic based on 1) retention status (retained, disengaged, re-engaged) and 2) viral load (VL) status (virally suppressed (VS), viremic, unknown/late VL). Disengaged was defined as > 90 days late for a scheduled appointment. VS was defined as a VL < 200 copies/mL ≤ 200 days; viremic as VL > 200 copies/mL ≤ 200 days; and unknown/late VL as a gap in VL measurement > 200 days. Results: 1,110 patients were newly linked to the clinic during the observation period, contributing 2255.4 person-years of follow-up. Median age was 34 years (IQR 25-47), 865 (77.9%) were male, 745 (67.1%) identified as non-Hispanic Black, and 229 (20.6%) were 18-24 years at entry. Three years following linkage to clinic, 53.1% (95% CI: 50.0, 56.2) were in care (22.3% continuously retained, 30.8% re-engaged, Figure). Regarding VS, 92.7% of those continuously retained were VS compared to 78.7% who had re-engaged in care. Although 69.4% were retained with VS at 6 months, 29.2% and 46.2% disengaged at 1- and 3 years, respectively. Among those who disengaged, 61.2% (95% CI: 57.4, 65.0) re-engaged and 40.2% (95% CI: 36.4, 44.0) were VS after one year. Conclusion: Disengagement, re-engagement, and viremia over time are far more common than previously reported in Missouri. While engagement is initially strong, subsequent disengagement and re-engagement are high in the first year following linkage to care, with uncertain outcomes among those who do not re-engage. Multistate analyses can strengthen our understanding of such tenuous timepoints in the care continuum, highlighting important targets for optimizing lost-to-care and re-engagement services to end the epidemic.

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