HIV viral load results turn around time and influencing factors in selected districts in Uganda
Matovu, John Kennedy
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Introduction: Uganda adopted a 90-90-90 strategy aimed at 90% of people living with HIV know their status, 90% of all diagnosed receive Anti Retro Viral (ARV) treatment, and 90% on Anti-Retroviral Treatment (ART) have suppressed viral load. Viral load suppression is affected by a number of barriers such as viral load results turnaround time. Longer turnaround time (TAT) delays initiation of treatment, adherence counseling, and/or switch to second-line ART in patients experiencing treatment failure. These delays reduce the advantage of viral load testing over immunological monitoring and lead to poorer health outcomes including increased risk of opportunistic infections, prolonged immune activation, development of drug resistance, and increased mortality. Objective: To evaluate HIV viral load results turnaround time and influencing factors in selected districts in Uganda. Methods: Mixed methods were used in this study. The quantitative study was carried out on viral load samples collected from health facilities in the districts of Mbarara (613 samples), Luwero (513 samples), Lira (554), and Tororo (468 samples). Descriptive statistics were applied to determine the mean TAT, proportions and ranges. Analysis of variance (ANOVA) was used to analyze the differences among mean TAT in districts. The qualitative study was done using in depth interviews among 4 hub coordinators, 8 sample transporters and 16 ART in charges. Thematic analysis was used to describe common terms. Results: Mean TAT was found to be 25.4 days, Confidence interval (CI) 24.9-26.0. The mean TAT within the districts was significantly different (p<0.001). The effect of the different months on TAT was not significantly different (p 0.076). Facility factors influencing TAT included misplacing of results by sample transporters, inadequate completion of request forms, failure to pack samples by staff, documentation gaps. Hub-related factors included inadequate tracking of samples sent and results released, time to download results, motorbike condition, transporter attitude, and availability of resources. UNHLS factors included transporter vehicle condition, time to processing of samples, availability of funding, time to results upload for printing, and supervision of labs. Finally, the MOH-related factors included motorbike tracking, inappropriate tracking on the national dashboard, resource availability, distance to sample processing facility, and MOH guidelines about TAT to health workers. Conclusion: The findings suggest that TAT is still very high compared to the target and it is also different within the districts. There are numerous factors that cause these delays and they can be addressed by multiple stakeholders. Measures to improve TAT need to be multi-faceted and addressed at national, district, and facility level. The bottlenecks to the reduction of TAT lie across the entire cascade of sample and results transportation system.