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Record W4362522113 · doi:10.1371/journal.pone.0283991

Prevalence and factors associated with HIV treatment non-adherence among people living with HIV in three regions of Cameroon: A cross-sectional study

2023· article· en· W4362522113 on OpenAlexaff
Amos Buh, Raywat Deonandan, James Gomes, Alison Krentel, Olanrewaju Oladimeji, Sanni Yaya

Bibliographic record

VenuePLoS ONE · 2023
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineCross-sectional studyFamily medicineHuman immunodeficiency virus (HIV)Multivariate analysisOddsAntiretroviral therapyInterviewDemographyLogistic regressionViral loadInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: In Cameroon, HIV care decentralization is enforced as a national policy, but follow-up of people living with HIV (PLWH) is provider-driven, with little patient education and limited patient participation in clinical surveillance. These types of services can result in low antiretroviral therapy (ART) adherence. The objective of this study was to assess the prevalence and predictors of ART non-adherence among PLWH in Cameroon. METHODS: A cross-sectional descriptive study of PLWH in HIV treatment centres in Cameroon was conducted. Only PLWH, receiving treatment in a treatment centre within the country, who had been on treatment for at least six months and who were at least 21 years old were included in the study. Individuals were interviewed about their demographics and ART experiences. Data were collected using a structured interviewer-administered questionnaire and analyzed using STATA version 14. RESULTS: A total of 451 participants participated in this study, 33.48% were from the country's Southwest region. Their mean age was 43.42 years (SD: 10.42), majority (68.89%) were females. Overall proportion of ART non-adherence among participants was 37.78%, 35.88% missed taking ART twice in the last month. Reasons for missing ART include forgetfulness, business and traveling without drugs. Over half of participants (54.67%) know ART is life-long, 53.88% have missed ART service appointments, 7.32% disbelieve in ART benefits, 28.60% think taking ART gives unwanted HIV Status reminder and 2.00% experienced discrimination seeking ART services. In the multivariate analysis, odds of ART non-adherence in participants aged 41 and above was 0.35 times (95%CI: 0.14, 0.85) that in participants aged 21-30 years, odds of ART non-adherence comparing participants who attained only primary education to those who attained higher than secondary education was 0.57 times (95%CI: 0.33, 0.97) and the odds of ART non-adherence in participants who are nonalcohol consumers was 0.62 times (95%CI: 0.39, 0.98) that in alcohol consumers. CONCLUSION: High proportion of participants are ART non-adherent, and the factors significantly associated with ART non-adherence include age, education and alcohol consumption. However, some reasons for missing ART are masked in participants' limited knowledge in taking ART, disbelief in ART benefits, feelings that ART gives unwanted HIV status reminder and experiencing discrimination when seeking ART services. These underscores need to improve staff (health personnel) attitudes, staff-patient-communication, and proper ART prior initiation counselling of patients. Future studies need to focus on assessing long-term ART non-adherence trends and predictors using larger samples in many treatment centres and regions.

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)0.001
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 score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.084
GPT teacher head0.321
Teacher spread0.238 · 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

Citations24
Published2023
Admission routes1
Has abstractyes

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