Patterns of consistent retention in HIV care and viral suppression among cis-gender women living with HIV in Florida, 2014-2017: a latent class analysis

Introduction. While increased healthcare engagement and antiretroviral therapy (ART) adherence occurs during pregnancy, women living with HIV (WLWH) are often lost to follow-up after delivery. We sought to evaluate postpartum retention in care and viral suppression and to identify associated factors among WLWH in a large public hospital in Atlanta, Georgia. Methods. Data from the time of entry into prenatal care until 24 months postpartum were collected by chart review from WLWH who delivered with ≥20 weeks gestational age from 2011 to 2016. Primary outcomes were retention in HIV care (two HIV care visits or viral load measurements >90 days apart) and viral suppression (<200 copies/mL) at 12 and 24 months postpartum. Obstetric and contraception data were also collected. Results. Among 207 women, 80% attended an HIV primary care visit in a mean 124 days after delivery. At 12 and 24 months, respectively, 47% and 34% of women were retained in care and 41% and 30% of women were virally suppressed. Attending an HIV care visit within 90 days postpartum was associated with retention in care at 12 months (aOR 3.66, 95%CI 1.72-7.77) and 24 months (aOR 4.71, 95%CI 2.00-11.10) postpartum. Receiving ART at pregnancy diagnosis (aOR 2.29, 95%CI 1.11-4.74), viral suppression at delivery (aOR 3.44, 95%CI 1.39-8.50), and attending an HIV care visit within 90 days postpartum (aOR 2.40, 95%CI 1.12-5.16) were associated with 12-month viral suppression, and older age (aOR 1.09, 95% CI 1.01-1.18) was associated with 24-month viral suppression. Conclusions. Long-term retention in HIV care and viral suppression are low in this population of postpartum WLWH. Prompt transition to HIV care in the postpartum period was the strongest predictor of optimal HIV outcomes. Efforts supporting women during the postpartum transition from obstetric to HIV primary care may improve long-term HIV outcomes in women.

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Toward understanding the impact of mHealth features for people with HIV: a latent class analysis of PositiveLinks usage

Translational Behavioral Medicine ◽

10.1093/tbm/ibz180 ◽

2019 ◽

Author(s):

Chelsea E Canan ◽

Tabor E Flickinger ◽

Marika Waselewski ◽

Alexa Tabackman ◽

Logan Baker ◽

...

Keyword(s):

Latent Class Analysis ◽

Clinical Outcomes ◽

Viral Suppression ◽

Latent Class ◽

Class Analysis ◽

Ryan White ◽

Usage Patterns ◽

Community Board ◽

Living With Hiv ◽

The Impact

Abstract PositiveLinks (PL) is a multi-feature smartphone-based platform to improve engagement-in-care and viral suppression (VS) among clinic patients living with HIV. Features include medication reminders, mood/stress check-ins, a community board, and secure provider messaging. Our goal was to examine how PL users interact with the app and determine whether usage patterns correlate with clinical outcomes. Patients (N = 83) at a university-based Ryan White clinic enrolled in PL from June 2016 to March 2017 and were followed for up to 12 months. A subset (N = 49) completed interviews after 3 weeks of enrollment to explore their experiences with and opinions of PL. We differentiated PL members based on 6-month usage of app features using latent class analysis. We explored characteristics associated with class membership, compared reported needs and preferences by class, and examined association between class and VS. The sample of 83 PL members fell into four classes. “Maximizers” used all app features frequently (27%); “Check-in Users” tended to interact only with daily queries (22%); “Moderate All-Feature Users” used all features occasionally (33%); and “As-Needed Communicators” interacted with the app minimally (19%). VS improved or remained high among all classes after 6 months. VS remained high at 12 months among Maximizers (baseline and 12-month VS: 100%, 94%), Check-in Users (82%, 100%), and Moderate All-Feature Users (73%, 94%) but not among As-Needed Communicators (69%, 60%). This mixed-methods study identified four classes based on PL usage patterns that were distinct in characteristics and clinical outcomes. Identifying and characterizing mHealth user classes offers opportunities to tailor interventions appropriately based on patient needs and preferences as well as to provide targeted alternative support to achieve clinical goals.

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