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Predictors of CD4 cell count progression over time among adolescents and young adults transitioning to adult-oriented HIV care in Southern Ethiopia from 2017 to 2021: a retrospective cohort study. | LitMetric

AI Article Synopsis

  • The study focuses on identifying factors that influence CD4 cell count progression in adolescents and young adults transitioning to adult-oriented HIV care in Ethiopia, filling a research gap in this area.
  • A retrospective cohort study involving 206 participants analyzed CD4 count data over time, employing statistical methods to identify significant predictors of count changes.
  • Key findings indicated that factors such as transitioning at age 18 or older, rural residency, and higher WHO HIV stages negatively affected CD4 cell count trends, while the average CD4 cell count increased by 6.7 cells/mm every six months among participants.

Article Abstract

Background: Human immunodeficiency virus (HIV), a viral infection impacting CD4 cells, requires an understanding of factors influencing CD4 cell counts to reduce acquired immunodeficiency syndrome(AIDS)-related morbidity and mortality. Despite its importance, no studies have examined predictors of CD4 cell count progression post-transition to adult-oriented HIV care in Ethiopia. This study aimed to identify predictors affecting CD4 cell count trends among adolescents and young adults transitioning to adult-oriented HIV care. Therefore, this study aimed to identify the predictors that affect the trends of CD4 cell count over time among adolescents and young adults who transitioned to adult-oriented HIV care.

Methods: A retrospective cohort study was conducted among 206 adolescents and young adults who transitioned to adult-oriented HIV care and had at least two CD4 cell counts in eight selected health facilities in Southern Ethiopia. These facilities were chosen due to their high volume of people living with HIV. A multivariable linear mixed effect model was fitted to identify the predictors of CD4 cell count progression through time. Multi-collinearity between variables was checked using the variance inflation factor. Model comparisons were done using Akaike and Bayesian information criteria. Regression coefficients with 95% confidence intervals (CI) and P-value ≤ 0.05 were used to measure the strength of association and significance level.

Results: The average CD4 cell count was raised by 6.7 cells/mm every six months among the study participants. Transitioning at the age of 18 or older (β = 204.71; 95% CI = 81.86347, 327.5574; P < 0.001), being rural residents (β= -119.776; 95% CI = -215.362, -24.189; P < 0.014), having World Health Organization (WHO) stage III & IV HIV (β= -182.161; 95% CI = -318.475, -45.847; P < 0.009), were the predictors for CD4 cell count progression over time.

Conclusion: Based on the identified predictors of CD4 cell count progression over time among HIV-positive adolescents and young adults transitioning to adult-oriented HIV care in southern Ethiopia, such as observation time, age at transition, residence, and WHO staging, the study highlights the need for interventions focused on tailored support. Specifically, these interventions should target individuals transitioning with less ART time, those younger than 18, those residing in rural areas, and those with advanced WHO staging, to improve their CD4 cell counts over time.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11491030PMC
http://dx.doi.org/10.1186/s12889-024-20396-xDOI Listing

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