Browsing by Author "Mehrotra M"

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    Novel Longitudinal Methods for Assessing Retention in Care: a Synthetic Review.
    (2021-Aug) Mody A; Tram KH; Glidden DV; Eshun-Wilson I; Sikombe K; Mehrotra M; Pry JM; Geng EH; Centre for Infectious Diseases Research in Zambia, Lusaka, Zambia.; Division of Infectious Diseases, Washington University School of Medicine, Campus Box 8051, 4523 Clayton Avenue, St. Louis, Missouri, 63110, USA. aaloke.mody@wustl.edu.; Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.; Division of Infectious Diseases, Washington University School of Medicine, Campus Box 8051, 4523 Clayton Avenue, St. Louis, Missouri, 63110, USA.; Department of Public Health Environments and Society, Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, UK.; CIDRZ; Centre for Infectious Disease Research in Zambia (CIDRZ)
    PURPOSE OF REVIEW: Retention in care is both dynamic and longitudinal in nature, but current approaches to retention often reduce these complex histories into cross-sectional metrics that obscure the nuanced experiences of patients receiving HIV care. In this review, we discuss contemporary approaches to assessing retention in care that captures its dynamic nature and the methodological and data considerations to do so. RECENT FINDINGS: Enhancing retention measurements either through patient tracing or "big data" approaches (including probabilistic matching) to link databases from different sources can be used to assess longitudinal retention from the perspective of the patient when they transition in and out of care and access care at different facilities. Novel longitudinal analytic approaches such as multi-state and group-based trajectory analyses are designed specifically for assessing metrics that can change over time such as retention in care. Multi-state analyses capture the transitions individuals make in between different retention states over time and provide a comprehensive depiction of longitudinal population-level outcomes. Group-based trajectory analyses can identify patient subgroups that follow distinctive retention trajectories over time and highlight the heterogeneity of retention patterns across the population. Emerging approaches to longitudinally measure retention in care provide nuanced assessments that reveal unique insights into different care gaps at different time points over an individuals' treatment. These methods help meet the needs of the current scientific agenda for retention and reveal important opportunities for developing more tailored interventions that target the varied care challenges patients may face over the course of lifelong treatment.