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Beyond binary retention in HIV care: predictors of the dynamic processes of patient engagement, disengagement, and re-entry into care in a US clinical cohort.

ABSTRACT: OBJECTIVES:Studies examining engagement in HIV care often capture cross-sectional patient status to estimate retention and identify predictors of attrition, which ignore longitudinal patient care-seeking behaviors. We describe the cyclical nature of (dis)engagement and re-entry into HIV care using the state transition framework. DESIGN:We represent the dynamic patterns of patient care-retention using five states: engaged in care, missed one, two, three, or more expected visits, and deceased. Then we describe various care-seeking behaviors in terms of transitioning from one state to another (e.g. from disengaged to engaged). This analysis includes 31?009 patients enrolled in the Center for AIDS Research Network of Integrated Systems (CNICS) in the United States from 1996 to 2014. METHOD:Multistate models for longitudinal data were used to identify barriers to retention and subgroups at higher risk of falling out of care. RESULTS:The initial 2 years following primary engagement in care were a crucial time for improving retention. Patients who had not initiated antiretroviral therapy, with lower CD4 cell counts, higher viral load, or not having an AIDS-defining illness were less likely to be retained in care. CONCLUSION:Beyond the individual patient characteristics typically used to characterize retention in HIV care, we identified specific periods of time and points in the care continuum associated with elevated risk of transitioning out of care. Our findings can contribute to evidence-based recommendations to enhance long-term retention in CNICS. This approach can also be applied to other cohort data to identify retention strategies tailored to each population.

SUBMITTER: Lee H

PROVIDER: S-EPMC6136972 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Beyond binary retention in HIV care: predictors of the dynamic processes of patient engagement, disengagement, and re-entry into care in a US clinical cohort.

Lee Hana H Wu Xiaotian K XK Genberg Becky L BL Mugavero Michael J MJ Cole Stephen R SR Lau Bryan B Hogan Joseph W JW

AIDS (London, England) 20180901 15

<h4>Objectives</h4>Studies examining engagement in HIV care often capture cross-sectional patient status to estimate retention and identify predictors of attrition, which ignore longitudinal patient care-seeking behaviors. We describe the cyclical nature of (dis)engagement and re-entry into HIV care using the state transition framework.<h4>Design</h4>We represent the dynamic patterns of patient care-retention using five states: engaged in care, missed one, two, three, or more expected visits, an ...[more]

PMID: 30005018

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Project description:IntroductionA broad range of community-centred care models for patients stable on anti-retroviral therapy (ART) have been proposed by the World Health Organization to better respond to patient needs and alleviate pressure on health systems caused by rapidly growing patient numbers. Where available, often a single alternative care model is offered in addition to routine clinical care. We operationalized several community-centred ART delivery care models in one public sector setting. Here, we compare retention in care and on ART and identify predictors of disengagement with care.MethodsPatients on ART were enrolled into three community-centred ART delivery care models in the rural Shiselweni region (Swaziland), from 02/2015 to 09/2016: Community ART Groups (CAGs), comprehensive outreach care and treatment clubs. We used Kaplan-Meier estimates to describe crude retention in care model and retention on ART (including patients who returned to clinical care). Multivariate Cox proportional hazard models were used to determine factors associated with all-cause attrition from care model and disengagement with ART.ResultsA total of 918 patients were enrolled. CAGs had the most participants with 531 (57.8%). Median age was 44.7 years (IQR 36.3 to 54.4), 71.8% of patients were female, and 62.6% fulfilled eligibility criteria for community ART. The 12-month retention in ART was 93.7% overall; it was similar between model types (p = 0.52). A considerable proportion of patients returned from community ART to clinical care, resulting in lower 12 months retention in care model (82.2% overall); retention in care model was lowest in CAGs at 70.4%, compared with 86.3% in outreach and 90.4% in treatment clubs (p < 0.001). In multivariate Cox regression models, patients in CAGs had a higher risk of disengaging from care model (aHR 3.15, 95% CI 2.01 to 4.95, p < 0.001) compared with treatment clubs. We found, however, no difference in attrition in ART between alternative model types.ConclusionsConcurrent implementation of three alternative community-centred ART models in the same region was feasible. Although a considerable proportion of patients returned back to clinical care, overall ART retention was high and should encourage programme managers to offer community-centred care models adapted to their specific setting.

Project description:BackgroundThe use of remote data capture for monitoring blood glucose and supporting digital apps is becoming the norm in diabetes care. One common goal of such apps is to increase user awareness and engagement with their day-to-day health-related behaviors (digital engagement) in order to improve diabetes outcomes. However, we lack a deep understanding of the complicated association between digital engagement and diabetes outcomes.ObjectiveThis study investigated the association between digital engagement (operationalized as tagging of behaviors alongside glucose measurements) and the monthly average blood glucose level in persons with type 2 diabetes during the first year of managing their diabetes with a digital chronic disease management platform. We hypothesize that during the first 6 months, blood glucose levels will drop faster and further in patients with increased digital engagement and that difference in outcomes will persist for the remainder of the year. Finally, we hypothesize that disaggregated between- and within-person variabilities in digital engagement will predict individual-level changes in blood glucose levels.MethodsThis retrospective real-world analysis followed 998 people with type 2 diabetes who regularly tracked their blood glucose levels with the Dario digital therapeutics platform for chronic diseases. Subjects included "nontaggers" (users who rarely or never used app features to notice and track mealtime, food, exercise, mood, and location, n=585) and "taggers" (users who used these features, n=413) representing increased digital engagement. Within- and between-person variabilities in tagging behavior were disaggregated to reveal the association between tagging behavior and blood glucose levels. The associations between an individual's tagging behavior in a given month and the monthly average blood glucose level in the following month were analyzed for quasicausal effects. A generalized mixed piecewise statistical framework was applied throughout.ResultsAnalysis revealed significant improvement in the monthly average blood glucose level during the first 6 months (t=-10.01, P<.001), which was maintained during the following 6 months (t=-1.54, P=.12). Moreover, taggers demonstrated a significantly steeper improvement in the initial period relative to nontaggers (t=2.15, P=.03). Additional findings included a within-user quasicausal nonlinear link between tagging behavior and glucose control improvement with a 1-month lag. More specifically, increased tagging behavior in any given month resulted in a 43% improvement in glucose levels in the next month up to a person-specific average in tagging intensity (t=-11.02, P<.001). Above that within-person mean level of digital engagement, glucose levels remained stable but did not show additional improvement with increased tagging (t=0.82, P=.41). When assessed alongside within-person effects, between-person changes in tagging behavior were not associated with changes in monthly average glucose levels (t=1.30, P=.20).ConclusionsThis study sheds light on the source of the association between user engagement with a diabetes tracking app and the clinical condition, highlighting the importance of within-person changes versus between-person differences. Our findings underscore the need for and provide a basis for a personalized approach to digital health.

Dataset Information

Beyond binary retention in HIV care: predictors of the dynamic processes of patient engagement, disengagement, and re-entry into care in a US clinical cohort.

Publications

Beyond binary retention in HIV care: predictors of the dynamic processes of patient engagement, disengagement, and re-entry into care in a US clinical cohort.

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