Identifying and predicting longitudinal trajectories of care for people newly diagnosed with HIV in South Africa

Abstract

Background Predicting long-term care trajectories at the time of HIV diagnosis may allow targeted interventions. Our objective was to uncover distinct CD4-based trajectories and determine baseline demographic, clinical, and contextual factors associated with trajectory membership. Methods We used data from the Sizanani trial (NCT01188941), in which adults were enrolled prior to HIV testing in Durban, South Africa from August 2010-January 2013. We ascertained CD4 counts from the National Health Laboratory Service over 5y follow-up. We used groupbased statistical modeling to identify groups with similar CD4 count trajectories and Bayesian information criteria to determine distinct CD4 trajectories. We evaluated baseline factors that predict membership in specific trajectories using multinomial logistic regression. We examined calendar year of participant enrollment, age, gender, cohabitation, TB positivity, self-identified barriers to care, and ART initiation within 3 months of diagnosis. Results 688 participants had longitudinal data available. Group-based trajectory modeling identified four distinct trajectories: one with consistently low CD4 counts (21%), one with low CD4 counts that increased over time (22%), one with moderate CD4 counts that remained stable (41%), and one with high CD4 counts that increased over time (16%). Those with higher CD4 counts at diagnosis were younger, less likely to have TB, and less likely to identify

Measuring patient engagement with HIV care in sub‐Saharan Africa: a scoping study

“…Flag patients early for intervention [ 57 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 ]…”

“…Measuring patterns avoids obscuring the individual differences in engagement over time that occurs with single time‐point measures [ 57 , 60 ], potentially useful in understanding engagement dynamics [ 49 , 58 ]. Evaluation of individual‐level longitudinal trajectories of engagement can also uncover “behavioural phenotypes” that identify high‐risk individuals in high‐risk periods of their treatment journey [ 57 , 123 ], offering a novel opportunity to direct interventions to behavioural patterns rather than the demographic categories we traditionally use to target differentiated services (often with poor success) [ 57 ]. These prospects make these measures worth the required longer follow‐up [ 6 ], adjustments to data collection to track patients and link data from different sources [ 157 ] and the added computational complexity [ 5 ].…”

“…• Routine evaluation of engagement in patients in facility care [6,61,62,77,78] • Rationalize resources, such as expensive genotype [33,[79][80][81][82] or viral load [50,[82][83][84][85][86][87] testing and resources to trace those lost to follow-up [88] • Understanding the behaviour of people on ART [15,21,25,63,65, • Routine evaluation of engagement in patients in differentiated service delivery models [49,119] • Flag patients early for intervention [57,[120][121][122][123][124][125][126][127][128][129] • a At facility and population levels for quality improvement and strategic decision-making.…”

Early diagnosis of HIV/aids infection: concept analysis

“…Identifying and predicting longitudinal trajectories of care for people newly diagnosed with HIV in South Africa Randomized controlled trial E14 (22) Spain/2020…”

“…Evaluating the impact of post-trial implementation of RHIVA nurse-led HIV screening on HIV testing, diagnosis and earlier diagnosis in general practice in London, UK Pragmatic study E16 (24) Netherlands/ 2022 Of the sixteen studies selected, eleven refer to homosexual intercourse as one of the factors associated with the risk of infection (11)(12)(13)(14)(15)(16)(17)(18)(19)(20)22,25) .…”