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Record W4392212683 · doi:10.2196/40796

Attrition Rates in HIV Viral Load Monitoring and Factors Associated With Overdue Testing Among Children Within South Africa’s Antiretroviral Treatment Program: Retrospective Descriptive Analysis

2024· article· en· W4392212683 on OpenAlexvenueno aff
Ahmad Haeri Mazanderani, Lebohang Radebe, Gayle Sherman

Bibliographic record

VenueJMIR Public Health and Surveillance · 2024
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsAttritionViral loadHuman immunodeficiency virus (HIV)MedicineEnvironmental healthAntiretroviral therapyAntiretroviral treatmentVirologyFamily medicinePediatrics

Abstract

fetched live from OpenAlex

BACKGROUND: Numerous studies in South Africa have reported low HIV viral load (VL) suppression and high attrition rates within the pediatric HIV treatment program. OBJECTIVE: Using routine laboratory data, we evaluated HIV VL monitoring, including mobility and overdue VL (OVL) testing, within 5 priority districts in South Africa. METHODS: We performed a retrospective descriptive analysis of National Health Laboratory Service (NHLS) data for children and adolescents aged 1-15 years having undergone HIV VL testing between May 1, 2019, and April 30, 2020, from 152 facilities within the City of Johannesburg, City of Tshwane, eThekwini, uMgungundlovu, and Zululand. HIV VL test-level data were deduplicated to patient-level data using the NHLS CDW (Corporate Data Warehouse) probabilistic record-linking algorithm and then further manually deduplicated. An OVL was defined as no subsequent VL determined within 18 months of the last test. Variables associated with the last VL test, including age, sex, VL findings, district type, and facility type, are described. A multivariate logistic regression analysis was performed to identify variables associated with an OVL test. RESULTS: Among 21,338 children and adolescents aged 1-15 years who had an HIV VL test, 72.70% (n=15,512) had a follow-up VL test within 18 months. Furthermore, 13.33% (n=2194) of them were followed up at a different facility, of whom 3.79% (n=624) were in a different district and 1.71% (n=281) were in a different province. Among patients with a VL of ≥1000 RNA copies/mL of plasma, the median time to subsequent testing was 6 (IQR 4-10) months. The younger the age of the patient, the greater the proportion with an OVL, ranging from a peak of 52% among 1-year-olds to a trough of 21% among 14-year-olds. On multivariate analysis, 2 consecutive HIV VL findings of ≥1000 RNA copies/mL of plasma were associated with an increased adjusted odds ratio (AOR) of having an OVL (AOR 2.07, 95% CI 1.71-2.51). Conversely, patients examined at a hospital (AOR 0.86, 95% CI 0.77-0.96), those with ≥2 previous tests (AOR 0.78, 95% CI 0.70-0.86), those examined in a rural district (AOR 0.63, 95% CI 0.54-0.73), and older age groups of 5-9 years (AOR 0.56, 95% CI 0.47-0.65) and 10-14 years (AOR 0.51, 95% CI 0.44-0.59) compared to 1-4 years were associated with a significantly decreased odds of having an OVL test. CONCLUSIONS: Considerable attrition occurs within South Africa's pediatric HIV treatment program, with over one-fourth of children having an OVL test 18 months subsequent to their previous test. In particular, younger children and those with virological failure were found to be at increased risk of having an OVL test. Improved HIV VL monitoring is essential for improving outcomes within South Africa's pediatric antiretroviral treatment program.

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)0.003
metaresearch head score (Gemma)0.008
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 score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.062
GPT teacher head0.342
Teacher spread0.280 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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