ArticleBMC infectious diseases2026
Modeling antiretroviral therapy adherence among people living with HIV: a cross-sectional study at Pantang Hospital, Ghana.
Article in BMC infectious diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPoor adherence to antiretroviral therapy (ART) continues to undermine HIV treatment outcomes in sub-Saharan Africa, including Ghana.
objectiveThis study aimed to quantify ART treatment adherence among people living with HIV (PLHIV) at Pantang Hospital in Ghana.
methodsThis study took place from July to October 2025 among people living with HIV who receive care at Pantang Hospital in Accra, Ghana. Using random sampling, data were collected from 151 participants through interviewer-administered questionnaires assessing sociodemographic characteristics and ART adherence behaviors. A hold-back validation method was used to split the final dataset, allocating 80% to training and 20% to validation. Six machine learning models were applied to identify and rank key adherence predictors, and model performance was compared.
resultsIn all, 151 participants were recruited. Participants had a median age of 42 years; 75.5% were female, and 39.7% were older than 45 years. Most participants were Christian (88.7%), lived in peri-urban areas (70.9%), and were self-employed (72.2%). Non-adherence behaviors were common: 66.9% reported forgetting to take medication, 64.2% reported carelessness, and 85.4% reported stopping medication when feeling worse. Among the six models, Elastic Net regression demonstrated the best overall performance. The strongest predictors of non-adherence were not taking medication over the past weekend (utility estimate 0.3471, 95% confidence interval 0.2292-0.4650) and missing medication 6-10 times in the past week (utility estimate 0.2693, 95% confidence interval 0.1538-0.3848).
conclusionsThe study showed poor adherence to ART among this cohort of PLHIV which increases the risk of community-level transmission. This study provides quantitative data to help develop effective interventions to address poor adherence to improve health outcomes of PLHIV.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.