CROI 2020 Abstract eBook

Abstract eBook

Poster Abstracts

of these programs on the HIV care cascade remains unknown. The objective of this analysis was to evaluate rates of linkage to care and subsequent retention in care associated with an ED-based universal opt-out HIV screening program in San Diego. Methods: All newly HIV diagnosed and known HIV-positive out-of-care (i.e. >12 months without a clinic visit) individuals were identified through EMR-based universal opt-out HIV screening for persons aged 13-64 years at the University of California San Diego EDs between July 2017 and September 2019. Case managers dedicated to the program focused on (re)linking these individuals to care and stopped case management at the time of (re)linkage. Retention in care was assessed at 6 and 12 months following initial (re)linkage to care. Univariate and multivariable binary logistic regression models assessed medical, and social variables (derived from existing literature) as predictors of successful linkage and retention in care (Table). Results: A total of 47 newly diagnosed and 92 known HIV-positive out-of-care persons were identified. 40 of 47 (85%) newly diagnosed individuals were linked to care, and 48 of 92 (52%) known HIV+ out of care individuals were re-linked to care. At 6 months follow-up, 23/33 (70%) of the newly diagnosed individuals were still in care, 5 (15%) were confirmed to be out of care, and 5 (15%) were unable to be contacted. At 6 months follow-up, 14/26 (54%) of the known HIV-positive persons were still retained in care, 11 (42.3%) were confirmed to be out of care, and 1 (4%) was unable to be contacted (p=0.04 vs new diagnoses). Methamphetamine use (within six months of ED screening; 43% of Meth users confirmed out of care) was significantly associated with falling out of care in the multivariable model (p=0.033; Table). Conclusion: While our universal opt-out ED HIV screening program achieved high rates of (re)linkage to care, 37% had (again) fallen out of care within 6 months. In particular, persons using methamphetamine may benefit from continuous case management that goes beyond initial linkage in order to achieve higher rates of retention in care and increase the impact of ED HIV screening programs.

1137 IMPROVING HIV CASE-FINDING WITH MACHINE LEARNING Pavlo Smyrnov 1 , Yulia Sereda 1 , Artem Lytvyn 1 , Olga Denisiuk 1 1 Alliance for Public Health, Kyiv, Ukraine

Background: Alliance for Public Health implements social network strategy for HIV case-finding among key populations. We developed a model to improve recruitment of undiagnosed HIV-positives in the network using machine learning (ML) algorithm. Methods: Since 2016, 130,095 people who inject drugs and their extended risk network peers were recruited in 12 regions of Ukraine. Recruitment starts from HIV positive cases with following criteria: 14+ years, inject drugs. Participants provided with 3 coupons to invite their peers defined as an injecting or sexual partner or somebody from the social network who can be also at risk of HIV. Recruitment stops if there are two HIV-negative cases next to each other in a chain. Data on recruitment chains and participants' characteristics collected in mobile application. Additional coupons are provided to participants with certain characteristics, such as “over 10 years of injecting drugs”, “history of incarceration”, “positive HIV test result”. We implemented a ML algorithm to predict in real-time the probability of recruiting an undiagnosed HIV-positive person within onestep from the participant who receives coupons. Considering the estimated probability, recommendations on a number of additional coupons are provided. Results: Among participants who received coupons, 75.9%(n=35,965/47,404) recruited at least one peer and 15%(n=7,146/47,404) recruited at least one HIV-positive participant within onestep. In comparison with current recruitment algorithmML model is 1.5-2.5 times more efficient (based on GINI index) in predicting chances of undiagnosed positive case (Fig.1). ML model with 42predictors yielded a GINI index of 34%for classification of HIV-positives and negatives. The most informative predictors of recruitment of HIV-positives included “HIV test result”, “Region”, “Years of injecting drugs”, while “Age” and “Marital Status”had the lowest contribution to prediction. Conclusion: Higher level of discriminatory power (an ability to distinguish between successful and not successful recruitment)of ML model suggests that application of ML algorithm during recruitment could improve HIV-positive yield and guide HIV testing to address gap in locating undiagnosed cases. Further steps include piloting of ML algorithmwith randomizing recruiters to evaluate effectiveness of ML in improving case-finding in groups connected in risk networks with high prevalence of HIV.

Poster Abstracts

1139 EXPERIENCE FROM THE LARGEST WESTERN US EMERGENCY DEPARTMENT ON ENDING THE EPIDEMIC

1138 HIV SCREENING IN EMERGENCY DEPARTMENTS: LINKAGE WORKS, BUT WHAT ABOUT RETENTION? Kushagra Mathur 1 , Jill Blumenthal 1 , Gabriel A.Wagner 1 , Lucy Horton 1 , Miriam Zuazo 1 , George Lara-Paez 2 , Megan Lo 1 , Gary M. Vilke 1 , Christopher J. Coyne 1 , Susan J. Little 1 , Martin Hoenigl 1 1 University of California San Diego, La Jolla, CA, USA, 2 University of California San Diego, San Diego, CA, USA Background: Universal opt-out HIV screening programs in emergency department (ED) settings have been successful in linking newly-diagnosed and out-of-care known HIV-positive persons into care. However, most of these programs report linkage to care but not retention rates and so the actual impact

Kathleen Jacobson 1 , Sanjay Aroroa 2 , Chun Nok Lam 2 , Mikheala Go 2 , Mike Menchine 2 1 University of Southern California, Alhmabra, CA, USA, 2 University of Southern California, Los Angeles, CA, USA Background: Emergency Departments (ED) account for 135 million healthcare visits annually. HIV positive patients are 3 times more likely to visit an ED, be a racial minority and lack insurance than their non HIV counter parts. EDs are a safety net for HIV infected individuals, and it is often their sole and only point of entry into the healthcare system. The role out of HIV testing in US emergency departments has paralleled the decline in undiagnosed HIV in the US, potentially contributing substantially to curbing the epidemic. However,

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