Evidence map›Paper›PMID 34105865›Full record

ArticleJournal of the International AIDS Society2021

Predicting the individualized risk of poor adherence to ART medication among adolescents living with HIV in Uganda: the Suubi+Adherence study.

Rachel Brathwaite, Fred M Ssewamala, Torsten B Neilands, Moses Okumu, Massy Mutumba, Christopher Damulira, Proscovia Nabunya, Samuel Kizito, Ozge Sensoy Bahar, Claude A Mellins and 1 more

Abstract read
In one paragraph

Article in Journal of the International AIDS Society, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 5 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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  3. Application and impact of Lasso regression in gastroenterology: A systematic review.Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Rachel BrathwaiteInternational Center for Child Health and Development, Brown School, Washington University in St. Louis, St. Louis, MO, USA.ORCID 0000-0002-9363-3581
Fred M SsewamalaInternational Center for Child Health and Development, Brown School, Washington University in St. Louis, St. Louis, MO, USA.
Torsten B NeilandsDivision of Prevention Science, Department of Medicine, University of California, San Francisco, CA, USA.
Moses OkumuSchool of Social Work, University of Illinois at Urbana-Champaign, Champaign, IL, USA.ORCID 0000-0003-2555-3077
Massy MutumbaDepartment of Health Behavior and Biological Sciences, School of Nursing, University of Michigan, Ann Arbor, MI, USA.
Christopher DamuliraInternational Center for Child Health and Development, Brown School, Washington University in St. Louis, St. Louis, MO, USA.
Proscovia NabunyaInternational Center for Child Health and Development, Brown School, Washington University in St. Louis, St. Louis, MO, USA.
Samuel KizitoDepartment of Global Health, School of Public Health, Boston University, Boston, MA, USA.
Ozge Sensoy BaharInternational Center for Child Health and Development, Brown School, Washington University in St. Louis, St. Louis, MO, USA.
Claude A MellinsHIV Center for Clinical and Behavioral Studies, New York State Psychiatric Institute and Columbia University Medical Center, New York, NY, USA.
Mary M McKayBrown School, Washington University in St. Louis, St. Louis, MO, USA.

Funding

Ujima Mentoring ProgramP30MH062246 · NIMH · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI MALLORY O JOHNSON · 2001 to 2026
$57.4M
Suubi+Adherence-R2: Examining the longitudinal HIV treatment adherence among youth living with HIV (YLHIV) transitioning into young adulthoodR01HD074949 · NICHD · WASHINGTON UNIVERSITY · PI NABUNYA, PROSCOVIA, SENSOY BAHAR, OZGE · 2012 to 2024
$7.7M
National Survey of State Policy Decision-making and SimulationP50MH113662 · NIMH · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI HOAGWOOD, KIMBERLY E. · 2019 to 2022
$6.9M
Training Program for Scientists Conducting Research to Reduce HIV Health DisparitR25MH067127 · NIMH · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Emily A Arnold, Torsten Brian Neilands · 2003 to 2026
$5.4M
Strengthening Child Health Research Capacity in Resource Constrained Settings: Researcher Resilience Training (RRT)R25MH118935 · NIMH · WASHINGTON UNIVERSITY · PI SEAN JOE, Mary McKernan McKay · 2018 to 2026
$1.5M
Training LEADers to Accelerate Global Mental Health Disparities in Research (LEAD)T37MD014218 · NIMHD · WASHINGTON UNIVERSITY · PI CAVAZOS-REHG, PATRICIA A, SSEWAMALA, FRED M · 2019 to 2023
$1.3M
NICHD NIH HHS R01 HD074949NIMHD NIH HHS T37 MD014218NIMH NIH HHS P30 MH062246NIMH NIH HHS P50 MH113662NIMH NIH HHS R25 MH067127NIMH NIH HHS R25 MH118935
6 · The paper itself

Abstract

introductionAchieving optimal adherence to antiretroviral therapy (ART) among adolescents living with HIV (ALWHIV) is challenging, especially in low-resource settings. To help accurately determine who is at risk of poor adherence, we developed and internally validated models comprising multi-level factors that can help to predict the individualized risk of poor adherence among ALWHIV in a resource-limited setting such as Uganda.

methodsWe used data from a sample of 637 ALWHIV in Uganda who participated in a longitudinal study, "Suubi+Adherence" (2012 to 2018). The model was developed using the Least Absolute Shrinkage and Selection Operator (LASSO) penalized regression to select the best subset of multi-level predictors (individual, household, community or economic-related factors) of poor adherence in one year's time using 10-fold cross-validation. Seventeen potential predictors included in the model were assessed at 36 months of follow-up, whereas adherence was assessed at 48 months of follow-up. Model performance was evaluated using discrimination and calibration measures.

resultsFor the model predicting poor adherence, five of the 17 predictors (adherence history, adherence self-efficacy, family cohesion, child poverty and group assignment) were retained. Its ability to discriminate between individuals with and without poor adherence was acceptable; area under the curve (AUC) = 69.9; 95% CI: 62.7, 72.8. There was no evidence of possible areas of miscalibration (test statistic = 1.20; p = 0.273). The overall performance of the model was good.

conclusionsOur findings support prediction modelling as a useful tool that can be leveraged to improve outcomes across the HIV care continuum. Utilizing information from multiple sources, the risk prediction score tool applied here can be refined further with the ultimate goal of being used in a screening tool by practitioners working with ALWHIV. Specifically, the tool could help identify and provide early interventions to adolescents at the highest risk of poor adherence and/or viral non-suppression. However, further fine-tuning and external validation may be required before wide-scale implementation.

Indexed as

HIV InfectionsAdolescentFemaleHumansLongitudinal StudiesMaleMedication AdherencePovertyUgandaadolescentsART adherenceHIV/AIDSprediction modellingviral load

Identifiers

PMID34105865
PMCPMC8188571

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.