| dc.description.abstract | Recent HIV research has predominantly employed Single Measure Frameworks (SMF), relying on the latest viral load data while ignoring missing values, despite criticisms for information loss and neglecting correlations. This study aimed at assessing repeated measures framework and single measures framework by examining factors affecting viral load reduction while accounting for missing values. The analysis was conducted on data from Mukono health facilities providing HIV treatment and care. A total of 1670 records of ART patients, was used in the analysis and generalized linear (mixed) models (GLMM) was adopted at the modeling stage. All variables included in this analysis were recorded upon patients’ enrollment on treatment, with the exception of change in the treatment regimen. A GLMM was applied to the data both before and after imputation under the RMF framework. The best-fitting model, selected for evaluating factors influencing viral load copies, was the GLMM fitted to multiply imputed data. The analysis indicated that gender and adherence rating did not have a significant effect on viral load copies. Additionally, patient age, marital status, duration on treatment, WHO clinical stages, and ownership of the facility were included in the analysis. The results showed that marital status, duration on treatment, and the type of health facility had a significant effect on viral load copies. Specifically, viral load copies were higher among those who were currently or formerly married (𝛽 = 0 49 0 30; 𝑆𝐸 = 0 042 0 052; 𝑝 = 0 0000). However, viral load copies were found to be lower among patients who had a longer duration on treatment (𝛽 = 0 01; 𝑆𝐸 = 0 001; 𝑝 = 0 0000) and were receiving treatment at a private facility (𝛽 = 0 196; 𝑆𝐸 = 0 077; 𝑝 = 0 0000). The study highlights the significance of recognizing repeated data patterns in longitudinal settings and addressing missing values in health research. It proposes a similar investigation in controlled environments to evaluate SMF and RMF in presence of missing values. | en_US |
