Attrition one year after starting antiretroviral therapy before and after the programmatic implementation of HIV “Treat All” in Sub-Saharan Africa: a systematic review and meta-analysis

Makurumidze, Richard; Decroo, Tom; Jacobs, Bart K. M.; Rusakaniko, Simbarashe; Van Damme, Wim; Lynen, Lutgarde; Gils, Tinne

doi:10.1186/s12879-023-08551-y

Cited by 3 publications

(1 citation statement)

References 53 publications

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“…While several models include basic demographic characteristics such as age and sex and clinical history such as baseline CD4 count to predict risk, the mechanisms driving risk within demographic subgroups at higher risk of disengagement than their age/sex peers remain unclear. Other characteristics that predict risk may be important to identify because within virtually any "risky" age/sex stratum, such as young men [17,18], a majority of individuals remain low risk and achieve good outcomes without intervention. In at 2018 population survey in KwaZulu Natal South Africa, for example, young men aged 15-29 were the highest risk age/sex group identified, but more than half of them (51.5%) were virally suppressed [19].…”

Section: Introductionmentioning

confidence: 99%

Triaging clients at risk of disengagement from HIV care: Application of a predictive model to clinical trial data in South Africa

Maskew,

Smith,

De Voux

et al. 2024

Preprint

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BackgroundTo reach South Africa’s targets for HIV treatment and viral suppression, retention on antiretroviral therapy (ART) must increase. Much effort and resources have been invested in tracing those already disengaged and returning them to care programs with mixed success. Here we aim to successfully identify ART clients at risk of loss from care prior to disengagement.Methods and FindingsWe applied a previously developed machine learning and predictive modelling algorithm (PREDICT) to routinely collected ART client data from the SLATE I and SLATE II trials, which evaluated same-day ART initiation in 2017-18. Using a primary outcome of an interruption in treatment (IIT), defined as missing the next scheduled clinic visit by >28 days, we investigated the reproducibility of PREDICT in SLATE datasets. We also tested two risk triaging approaches: 1) threshold approach classifying individuals into low, moderate, or high risk of IIT; and 2) archetype approach identifying subgroups with characteristics associated with risk of ITT. We report associations between risk category groups and subsequent IIT at the next scheduled visit using crude risk differences and relative risks with 95% confidence intervals. SLATE datasets included 7,199 client visits for 1,193 clients over ≤14 months of follow-up. The algorithm achieved 63% accuracy, 89% negative predictive value, and an area under the curve of 0.61 for attendance at next scheduled visit, similar to previous results using only medical record data. The threshold approach consistently and accurately assigned levels of IIT risk for multiple stages of the care cascade. The archetype approach identified several subgroups at increased risk of IIT, including those late to previous appointments, those returning after a period of disengagement, those living alone or without a treatment supporter. Behavioural elements of the archetypes tended to drive risk of treatment interruption more consistently than demographics; e.g. adolescent boys/young men who attended visits on time experienced lowest rates of treatment interruption (10%, PREDICT datasets and 7% SLATE datasets), while adolescent boys/young men returning after previously disengaging from care had highest rates of subsequent treatment interruption (31%, PREDICT datasets and 40% SLATE datasets).ConclusionRoutinely collected medical record data can be combined with basic demographic and socioeconomic data to assess individual risk of future treatment disengagement using machine learning and predictive modelling. This approach offers an opportunity to intervene prior to and potentially prevent disengagement from HIV care, rather than responding only after it has occurred.

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Section: Introductionmentioning

confidence: 99%

Triaging clients at risk of disengagement from HIV care: Application of a predictive model to clinical trial data in South Africa

Maskew,

Smith,

De Voux

et al. 2024

Preprint

View full text Add to dashboard Cite

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TREAT-B or Treat All: time for a broader vision on hepatitis B elimination – Author's reply

Luong Nguyen,

Lemoine,

Ndow

et al. 2024

The Lancet Global Health

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Drivers of disengagement from care during the first six months on antiretroviral therapy for HIV in South Africa: a mixed-methods study

Maskew,

Mutanda,

Scott

et al. 2024

Preprint

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Introduction: For clients on HIV treatment in sub-Saharan Africa, disengagement from HIV care in the early treatment period is a critical obstacle to achieving UNAIDS's second 95 target. While South Africa's Differentiated Service Delivery Guideline on Fast Track Initiation and Counseling (FTIC) define normative procedures, the effect of guideline implementation and the underlying drivers of disengagement remain unclear. Methods: The PREFER mixed-methods study enrolled a prospective cohort of adult clients initiating ART, returning to care after a period of disengagement or already on ART for <6 months at 18 public sector healthcare facilities in South Africa. A survey collected data on demographic and clinical characteristics and preferences of participants who were followed up through routinely-collected medical visit records for up to 7 months after treatment initiation to estimate continuity of HIV care (attended all scheduled visits within 28 days). Focus group discussions (FGDs) were conducted approximately 12 months after enrollment among a subset of participants who had expressed concerns about treatment retention. Results: During the study period 7/9/2022-30/6/2023, PREFER-SA enrolled 1,049 participants (72% female, median age=33 years, 24% with CD4 count<200 cells/mm3); 122 of whom also participated in focus group discussions. By 6 months on ART, 23% were not continuously in HIV care. Those newly initiating ART at study enrollment were more likely to have disengaged or interrupted treatment by 6 months as those who had remained in care for >1 visit. Disengagement was also more likely among men and younger (18-24 years) participants, those reporting food scarcity, and those initiated on regimens other than Dolutegravir. Disengagement did not differ by gender, relationship status, or CD4 count. Qualitative FGD results suggest participants experience barriers to adherence within and between all levels of the socio-ecologic model. Challenges related to the facility experience were most prominent. Conclusions: Among adults initiating or re-initiating ART in South Africa, the highest risk of disengagement is immediately after initiation. We identified several potentially modifiable individual and social characteristics associated with early disengagement. Improving the patient facility experience and strengthening implementation of SA's Service Delivery Guidelines may increase retention during the early treatment period.

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Attrition one year after starting antiretroviral therapy before and after the programmatic implementation of HIV “Treat All” in Sub-Saharan Africa: a systematic review and meta-analysis

Cited by 3 publications

References 53 publications

Triaging clients at risk of disengagement from HIV care: Application of a predictive model to clinical trial data in South Africa

Triaging clients at risk of disengagement from HIV care: Application of a predictive model to clinical trial data in South Africa

TREAT-B or Treat All: time for a broader vision on hepatitis B elimination – Author's reply

Drivers of disengagement from care during the first six months on antiretroviral therapy for HIV in South Africa: a mixed-methods study

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