ArticleHIV/AIDS (Auckland, N.Z.)2023
Predictors of Viral Load Status Over Time Among HIV Infected Adults Under HAART in Zewditu Memorial Hospital, Ethiopia: A Retrospective Study.
Article in HIV/AIDS (Auckland, N.Z.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- Article
- CD4 cell count and viral load count association and its joint risk factors among adult TB/HIV co-infected patients: a retrospective follow-up study.BMC research notes · 2025Article
- Prevalence of detectable viral load and its associated factors among adult patients receiving ART in Choma District, Zambia.PloS one · 2025Article
- Predictors of loss to follow-up among people living with HIV on antiretroviral therapy in a rural health facility using paper-based records.Frontiers in public health · 2025Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: HIV attacks the CD4 cells which are responsible for the body's immune response to infectious agents. The main objective of this study was to identify predictors of viral load status over time among HIV patients under HAART in Zewditu Memorial Hospital. Methods: A retrospective institutional-based cohort study design was conducted on 161 HIV-infected adults under HAART whose follow-ups were from January 2014 up to December 2017. A generalized linear mixed-effects model was conducted to infer predictors of the status of viral load at 95% of CI). Results: The descriptive statistics revealed that about 55.9% of the adults under treatment had a detected viral load status. Among the potential predictors, visiting time of patients (AOR = 0.731, 95%: (0.634,0.842) and p-value <0.01), age of patients (AOR = 1.0666, 95% CI: (1.0527,1.0917) and p-value <0.01), weight (AOR=. 0.904, 95% CI: (0.862, 0.946) and p-value <0.01), baseline CD4 cell count (AOR = 0.996, 95% CI: (0.994, 0.998) and P-value <0.01), educated patients (AOR = 0.030, 95% CI: (0.002, 0.385) and p-value=0.0053), rural patients (AOR = 6.30,95% CL: (1.78, 2.25) and p-value=0.0043), working status patients (AOR = 0.5905, 95% CI: (0.547,0.638), p-value <0.01), poor adherent patients (AOR = 1.120, 95% CI; (1.035,1.391) and p-value = 0.016) and patients disclosed the disease status (AOR = 0.195, 95% CI: (0.023, 0.818) and p-value=0.0134) significantly affected the detection status of viral loads, keeping all other covariates constant. Conclusion: The predictor variables; visiting times, the weight of patients, residence area, age of patients, educational level, clinical stages, functional status, baseline CD4 cell count, adherence status, and disclosure status of the disease statistically and significantly affected the status of viral load. Hence, health-related education should be given for patients to disclose their disease status, to be good adherents based on the prescription given to the health staff. Due attentions should be given for rural and uneducated patients. Attention should be forwarded to for non-adherent patients to follow the instruction given by the health staff.
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