Time-Dependent Predictors of Loss to Follow-Up in a Large HIV Treatment Cohort in Nigeria. (6th August 2014) - Meloni, Seema Thakore; Chang, Charlotte; Chaplin, Beth; Rawizza, Holly; Jolayemi, Oluwatoyin; Banigbe, Bolanle; Okonkwo, Prosper; Kanki, Phyllis
Abstract : Evaluation of time-dependent predictors of loss to follow-up in a large HIV treatment program revealed that early adherence patterns, in addition to CD4 count and viral load, predicted loss to follow-up and should be used as measures in devising targeted interventions to increase program retention. Abstract: Background.  Most evaluations of loss to follow-up (LTFU) in human immunodeficiency virus (HIV) treatment programs focus on baseline predictors, prior to antiretroviral therapy (ART) initiation. As risk of LTFU is a continuous issue, the aim of this evaluation was to augment existing information with further examination of time-dependent predictors of loss. Methods.  This was a retrospective evaluation of data collected between 2004 and 2012 by the Harvard School of Public Health and the AIDS Prevention Initiative in Nigeria as part of PEPFAR-funded program in Nigeria. We used multivariate modeling methods to examine associations between CD4 + cell counts, viral load, and early adherence patterns with LTFU, defined as no refills collected for at least 2 months since the last scheduled appointment. Results.  Of 51 953 patients initiated on ART between 2004 and 2011, 14 626 (28%) were LTFU by 2012. Factors associated with increased risk for LTFU were young age, having nonincome-generating occupations or no education, being unmarried, World Health Organization (WHO) stage, having a detectable viral load, and lower CD4 + cell counts. In a subset analysis, adherence patterns during the first 3 months of ART were associated with risk of LTFU by month 12. Conclusions.  In settings with limited resources, early adherence patterns, as well as CD4 + cell counts and unsuppressed viral load, at any time point in treatment are predictive of loss and serve as effective markers for developing targeted interventions to reduce rates of attrition.