article · Bulletin of the National Research Centre/Bulletin of the National Research Center
Abstract Background Accurate HIV viral load (VL) testing is essential for monitoring antiretroviral therapy (ART) and detecting treatment failure. In resource-limited settings such as Njombe Region, Tanzania, laboratories often face prolonged turnaround times (TAT) and suboptimal sample transport and storage. While RNA degradation during extended storage is well documented, its impact on routine, high-volume VL testing remains unclear. This study quantified the association between laboratory processing delays and reported HIV VL results. Methods We conducted a cross-sectional secondary analysis of VL data from Njombe Town Council Hospital Laboratory (October 2024–March 2025, N = 18,271). After excluding 244 records with missing dates, 18,025 samples were analyzed. Three TAT intervals—collection-to-reception (clinic to lab), reception-to-registration (lab receipt to system entry), and registration-to-testing (system entry to assay performance)—were categorized into quartiles. Multivariable multinomial logistic regression with predictive margins estimated associations between TAT delays and VL categories. Results Median total TAT was 22 days (IQR 13–36), exceeding the recommended ≤ 14 days. Only the slowest registration-to-testing quartile (Q4: ≥21 days) showed consistently fewer detectable VL results. Adjusted probability of Target Not Detected (TND) increased from 71.2% (fastest quartile) to 85.8% (slowest quartile) (RR = 1.205; 95% CI: 1.196–1.214; p < 0.001), while probabilities for all detectable VL categories declined significantly. Conclusion Prolonged registration-to-testing delays were strongly associated with lower reported HIV VL results, consistent with RNA degradation or pre-analytical bias. Such delays may compromise patient care and program monitoring. Therefore, interventions targeting workflow and laboratory system optimization are warranted—a conclusion likely generalizable to similar centralized HIV viral load laboratories across low- and middle-income countries.
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DOI: 10.1186/s42269-026-01478-9
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