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Record W2491011421 · doi:10.7448/ias.19.5.20841

Factors associated with long‐term antiretroviral therapy attrition among adolescents in rural Uganda: a retrospective study

2016· article· en· W2491011421 on OpenAlexaff
Stephen Okoboi, Livingstone Ssali, Aisha Yansaneh, Celestin Bakanda, Josephine Birungi, Sophie Nantume, Joanne Okullu, Alana Sharp, David Moore, Samuel Kalibala

Bibliographic record

VenueJournal of the International AIDS Society · 2016
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsAIDS VancouverUniversity of British Columbia
FundersU.S. President’s Emergency Plan for AIDS ReliefPopulation CouncilUniversity of WashingtonUnited States Agency for International Development
KeywordsMedicineAttritionRetrospective cohort studyHazard ratioProportional hazards modelLost to follow-upConfidence intervalAntiretroviral therapyHuman immunodeficiency virus (HIV)PediatricsCohortCohort studyDemographyViral loadFamily medicineInternal medicine

Abstract

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INTRODUCTION: As access to antiretroviral therapy (ART) increases, the success of treatment programmes depends on ensuring high patient retention in HIV care. We examined retention and attrition among adolescents in ART programmes across clinics operated by The AIDS Support Organization (TASO) in Uganda, which has operated both facility- and community-based distribution models of ART delivery since 2004. METHODS: Using a retrospective cohort analysis of patient-level clinical data, we examined attrition and retention in HIV care and factors associated with attrition among HIV-positive adolescents aged 10-19 years who initiated ART at 10 TASO clinics between January 2006 and December 2011. Retention in care was defined as the proportion of adolescents who had had at least one facility visit within the six months prior to 1 June 2013, and attrition was defined as the proportion of adolescents who died, were lost to follow-up, or stopped treatment. Descriptive statistics and Cox proportional hazards regression models were used to determine the levels of retention in HIV care and the factors associated with attrition following ART initiation. RESULTS: A total of 1228 adolescents began ART between 2006 and 2011, of whom 57% were female. The median duration in HIV care was four years (IQR=3-6 years). A total of 792 (65%) adolescents were retained in care over the five-year period; 36 (3%) had died or transferred out and 400 (32%) were classified as loss to follow-up. Factors associated with attrition included being older (adjusted hazard ratio (AHR)=1.38, 95% confidence interval (CI) 1.02-1.86), having a higher CD4 count (250+ cells/mm(3)) at treatment initiation (AHR=0.49, 95% CI 0.34-0.69) and HIV care site with a higher risk of attrition among adolescents in Gulu (AHR=2.26; 95% CI 1.27-4.02) and Masindi (AHR=3.30, 95% CI 1.87-5.84) and a lower risk of attrition in Jinja (AHR=0.24, 95% CI 0.08-0.70). Having an advanced WHO clinical stage at initiation was not associated with attrition. CONCLUSIONS: We found an overall retention rate of 65%, which is comparable to rates achieved by TASO's adult patients and adolescents in other studies in Africa. Variations in the risk of attrition by TASO treatment site and by clinical and demographic characteristics suggest the need for early diagnosis of HIV infection, use of innovative approaches to reach and retain adolescents living with HIV in treatment and identifying specific groups, such as older adolescents, that are at high risk of dropping out of treatment for targeted care and support.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.037
GPT teacher head0.327
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations51
Published2016
Admission routes1
Has abstractyes

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