Delayed Lab Processing Linked to Lower HIV Viral Load Results

A cross-sectional secondary analysis of 18,025 HIV viral load samples from a high-volume regional laboratory in Njombe, Tanzania, found that prolonged delays between sample registration and testing were associated with systematically lower reported viral load results. Published in the Bulletin of the National Research Centre in September 2026, the study indicates that operational bottlenecks in laboratory workflows may introduce a pre-analytical bias that affects the accuracy of viral suppression monitoring. The research team, led by Robert Baluhya and colleagues, examined data collected between October 2024 and March 2025 to quantify the relationship between turnaround time intervals and the distribution of viral load categories.

The study focused on the Njombe Town Council Hospital Laboratory, which serves as a regional referral center for a population of approximately 889,946 people across the Njombe Region. This area has an HIV prevalence of 12.7%, nearly three times the national average in Tanzania, making accurate laboratory monitoring critical for clinical care and programmatic oversight. The analysis included all individuals living with HIV whose samples were received and tested at the facility during the specified period, with 18,271 records initially extracted from electronic laboratory systems. After excluding 246 records with missing critical date variables, the final analytic sample comprised 18,025 unique patient samples, allowing for a comprehensive assessment of routine laboratory operations.

Accurate measurement of HIV viral load is essential for monitoring antiretroviral therapy and detecting treatment failure. However, in resource-limited settings, laboratory systems often face prolonged turnaround times that exceed recommended guidelines. The National Guideline for Laboratory Sample Referral System in Tanzania recommends that whole blood reach a hub and undergo plasma separation within six hours of collection, with subsequent transportation to the testing laboratory within 96 hours. The internal laboratory interval from specimen receipt to result release is established at 72 hours, yet the median total turnaround time from sample collection to result release in this study was 22 days, significantly exceeding the recommended timeline.

The researchers categorized three distinct pre-analytical turnaround time intervals into quartiles to evaluate the impact of delay across the entire distribution. These intervals were collection-to-reception, reception-to-registration, and registration-to-testing. The registration-to-testing interval, which represents the time from system entry to assay performance, showed the widest variability and emerged as the strongest predictor of shifts in viral load results. The slowest quartile for this interval, Q4, encompassed delays of 21 to 76 days, indicating a major bottleneck in laboratory operations where samples were stored at -80 degrees Celsius until testing.

Using multivariable multinomial logistic regression, the study found that samples in the slowest registration-to-testing quartile had a significantly higher probability of being classified as Target Not Detected compared to those in the fastest quartile. Conversely, the probabilities for all detectable viral load categories declined significantly with increasing delay. The largest relative decline was observed for the lowest detectable category, with a 65% reduction in relative risk, followed by reductions of 50% for the 200–999 copies/mL category and 42% for the ≥1000 copies/mL category.

This pattern suggests that prolonged storage and processing delays lead to time-dependent degradation of HIV ribonucleic acid, resulting in underestimated viral load results. The consistent dose-response relationship observed across all three turnaround time intervals and quartiles supports a biological mechanism of RNA degradation rather than patient-level confounding. Experimental evidence cited in the study indicates that HIV RNA can degrade substantially under suboptimal storage conditions, with declines of up to 97.5% observed after 14 days at elevated temperatures. Although the samples in this study were frozen at -80 degrees Celsius, the extended duration before testing likely contributed to the observed reduction in detectable viral loads.

The study also examined the impact of other covariates on viral load categories. Male sex was associated with a higher relative risk for all detectable viral load categories, with risk ratios ranging from 1.25 to 1.40. Adolescents aged 10 to 19 years showed increased relative risk for some detectable categories compared to children aged 0 to 9 years, while adult groups aged 20 and older showed reduced relative risk for most detectable categories. These findings align with broader programmatic data indicating that adolescents often face greater challenges in treatment adherence and viral suppression compared to adults.

Alternative first-line antiretroviral regimens were associated with reduced relative risk for low-level viremia and actionable viremia categories compared to the standard adult first-line regimen of tenofovir, lamivudine, and dolutegravir. However, the association for the highest viral load category was borderline, with a confidence interval that included 1.0. The researchers noted that this likely reflects clinical selection bias inherent to alternative regimen prescription, where patients are often monitored more closely, rather than pharmacological superiority of the alternative regimens.

ART adherence data were missing for approximately 38% of records, so the researchers used multiple imputation by chained equations to handle the missing data under a missing-at-random assumption. A complete-case sensitivity analysis restricted to records with no missing covariates confirmed the robustness of the inverse association between longer registration-to-testing intervals and HIV viral load detectability. The effects remained consistent with the primary analyses using multiple imputation, reinforcing the reliability of the findings despite the substantial amount of missing data.

The cross-sectional design of the study limits causal inference, and unmeasured pre-analytical variables such as freeze-thaw cycles and plasma separation timing could potentially confound the relationship between viral load result categories and turnaround time. Additionally, laboratory operational variables such as equipment downtime, reagent stockout frequency, and daily testing volume were not captured in the electronic dataset, preventing a direct evaluation of their contribution to the observed delays. Despite these limitations, the large sample size, real-world setting, and robust sensitivity analyses enhance the generalizability of the findings to similar centralized HIV viral load laboratories in low- and middle-income countries.

The findings have significant clinical and programmatic implications, as systematic underestimation of viral load may delay the recognition of low-level viremia and early treatment failure. This bias could artificially inflate viral suppression rates, affecting progress toward the UNAIDS 95-95-95 targets. The study estimates that approximately 643 patients in the slowest processing quartile may have been reported as Target Not Detected largely as a consequence of delayed processing rather than true virological suppression. This illustrates the potential scale of programmatic impact from registration-to-testing delays and underscores the clinical relevance of addressing this specific bottleneck.

To mitigate these issues, the researchers recommend a multi-pronged approach that includes automated sample tracking systems to flag bottlenecks in real time, decentralization of testing to peripheral laboratories to reduce transport-related delays, and ensuring adequate staffing and workflow optimization. Investment in equipment maintenance, backup instruments, and clinical sample cold-chain monitoring can also reduce downtime and prevent RNA degradation. Integrating these interventions with routine quality assurance and data-driven monitoring will allow laboratories and HIV programs to continuously evaluate performance and improve turnaround times, ensuring accurate viral load testing and reliable program monitoring.

The laboratory’s operational context highlights the logistical challenges inherent in centralized testing systems. During the study period, the facility received 25,043 HIV program samples, of which 19,366 contained results by the end of the analysis window. The remaining 5,677 samples had not yet yielded results, while 1,095 records pertained to early infant diagnosis rather than routine viral load monitoring. This high volume of samples processed through a single regional hub creates a bottleneck where samples are stored at -80 degrees Celsius until testing capacity becomes available. The median time from sample registration to actual testing was three days, but the interquartile range extended to 20 days, indicating that a significant portion of samples waited considerably longer.

The statistical approach employed in this study was designed to isolate the specific impact of processing delays from other patient-level factors. This method generated twenty imputed datasets, allowing for the pooling of estimates and standard errors across imputations. By using marginal standardization, the researchers calculated adjusted predicted probabilities for each viral load category at the fastest and slowest turnaround time quartiles. The use of this robust statistical framework helps ensure that the observed associations are not driven by confounding variables such as age, sex, or treatment regimen.

Subject of Research: Association between laboratory processing delays and HIV viral load results in Tanzania

Article Title: Clinical samples with delayed processing time are associated with lower HIV viral load results in a high-volume regional laboratory, Njombe Region, Tanzania (Oct 2024–Mar 2025)

Article References: Baluhya, R., Hussein, A. K., Buretta, R., Torokaa, P. R., Ndege, R., Masawe, R. Y., Kavindi, A., Mwavika, E., Yusuf, A., Nikupala, J., Nchumbuka, P., Chwaya, Z., Msafiri, F., Mwakalobo, A., Magesa, A. S., & Matee, M. I. (2026). Clinical samples with delayed processing time are associated with lower HIV viral load results in a high-volume regional laboratory, Njombe Region, Tanzania (Oct 2024–Mar 2025). Bulletin of the National Research Centre, 50(1), Article 96. https://doi.org/10.1186/s42269-026-01478-9

Image Credits: AI Generated

DOI: 10.1186/s42269-026-01478-9

Keywords: HIV, viral load, laboratory turnaround time, RNA degradation, Tanzania, antiretroviral therapy, pre-analytical bias, clinical monitoring, Clinical, samples, delayed, processing