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)

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) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article 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2025) Robert Baluhya, Ally K. Hussein, Richard Buretta, Peter Richard Torokaa, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8647781/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version 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. HIV viral load Turnaround time Pre-analytical delay RNA degradation Antiretroviral therapy monitoring Laboratory workflow Resource-limited settings Sub-Saharan Africa Figures Figure 1 Figure 2 Background Accurate HIV viral load (VL) measurement is a cornerstone of antiretroviral therapy (ART) monitoring and essential for detecting treatment failure (Kippen et al. 2024 ). The accuracy of viral load testing is therefore fundamental to both clinical care and HIV prevention (WHO 2024; UNAIDS 2024). However, in resource-limited settings, which bear the highest global HIV burden, laboratory systems are frequently strained, leading to prolonged turnaround times (TAT) that often exceed the recommended 14 days (Mbiva et al. 2021 ; Mnzava et al. 2023 ). According to World Health Organization (WHO) guidance, plasma samples should be centrifuged and frozen within 24 hours (WHO 2024). For HIV VL testing kits and testing guidelines generally recommend storage and transport at room temperature (≤ 24 hours), 2–8°C (≤ 5 days), or − 20°C to − 80°C for up to six weeks (WHO 2024) Furthermore, experimental evidence suggests that delays in sample transport or laboratory processing can lead to RNA degradation, potentially resulting in underestimated VL results (Gutema et al. 2022 ; Ethel et al. 2021 ). Laboratory stability studies and systematic review have reported substantial VL declines in stored plasma: 81.8% and 97.5% after 7 and 14 days at 30°C, and 52.6% and 25% after one month at − 20°C and − 80°C, respectively (Ethel et al. 2021 ; Hardie et al. 2024 ; Lesesa et al. 2025 ). While these experimental studies establish biological plausibility, they do not fully capture the complexity of routine laboratory workflows (Hardie et al. 2024 ). In HIV VL testing, delays may occur at multiple points under variable storage and handling conditions (Gutema et al. 2022 ). Evidence quantifying the real-world impact of such operational delays on reported VL results remains limited (Hardie et al. 2024 ). When VL results are systematically underestimated, such errors may carry serious clinical and programmatic consequences, including delayed regimen switching, ongoing viral replication, and inaccurate ART program performance metrics (Lubega et al. 2022 ; NASHCOP 2024). Studies from sub-Saharan Africa indicate that over 50% of VL samples are processed beyond recommended timelines in many settings, highlighting the operational challenges facing high-burden regions (Mbiva et al. 2021 ; Moirana et al. 2022 ; Mnzava et al. 2023 ; Kippen et al. 2024 ; UNAIDS 2024). Against this backdrop, we aimed to quantify the association between turnaround time and HIV viral load result distribution in a high-volume regional laboratory in mainland Tanzania. The novelty of this study lies in its ability to link routine operational delays to systematic shifts in VL result categories, thereby identifying actionable laboratory bottlenecks with direct clinical and programmatic relevance. Centralized VL monitoring systems in sub-Saharan Africa are known to face bottlenecks such as stockouts and testing backlogs that delay clinical decisions; nonetheless, there is a lack of quantitative data on how these delays systematically alter VL result distributions at the population level (Moirana et al. 2022 ; Hardie et al. 2024 ; Kippen et al. 2024 ). Methodology Study Design and Setting We conducted a cross-sectional secondary analysis of HIV viral load (VL) monitoring data from the Njombe Town Council Hospital Laboratory. This facility serves as a regional referral laboratory for a population of approximately 889,946 across the Njombe Region, which spans 24,994 km² % (National Bureau of Statistics [NBS] 2022). The region has an HIV prevalence of 12.7% (National Bureau of Statistics [NBS] 2022). The analysis included all individuals living with HIV whose samples were received and tested at the laboratory from October 2024 to March 2025 (N = 18,027). A total of 18,271 records were extracted from electronic laboratory records. No sampling was performed, minimizing selection bias at the data extraction stage. Data Sources and Variables Of the 18,271 records extracted, 244 (1.3%) were excluded due to missing critical date variables required for turnaround time calculation, resulting in a final analytic sample of 18,025 unique patient samples. Extracted data included VL results and dates for sample collection, laboratory receipt, sample registration, testing, and result authorization, as well as patient age and sex. The primary exposure variables were three pre-analytical turnaround time intervals representing distinct operational stages within the testing pathway: Collection-to-reception (clinic to laboratory), Reception-to-registration (laboratory receipt to system entry), Registration-to-testing (system entry to assay performance). Each interval was categorized into quartiles (Q1–Q4), with Q4 representing the slowest and Q1 the fastest interval. The primary outcome was the categorical VL result as reported by the assay, reflecting routine clinical interpretation. Categories included Target Not Detected (TND), < 40 copies/mL, 40–199 copies/mL, 200–999 copies/mL, and ≥ 1000 copies/mL. Sample Storage and Handling During Turnaround Time Intervals During the study period, HIV VL samples were managed according to national guidelines (URT, 2025) and manufacturers’ instructions (cobas® HIV-1 Test Nucleic acid test for use on the cobas® 4800 system IVD) , which recommend storage and transport at room temperature for ≤ 24 hours, 2–8°C for up to 3 days, or − 20°C to − 80°C for up to six weeks (WHO 2024) In routine practice at the study laboratory, samples awaiting testing after registration were stored within available laboratory cold-chain infrastructure, including refrigeration and freezer storage, in line with these standard operating procedures. However, this study did not directly measure actual storage temperatures, durations at each temperature level, or freeze–thaw cycles at the individual sample level. Consequently, while turnaround time intervals reflect operational delays within the testing pathway, they serve as proxy indicators of potential prolonged storage or delayed processing. Turnaround time intervals were therefore used as proxy indicators of potential prolonged storage or delayed processing, rather than as direct measures of RNA degradation or storage conditions. Data Analysis Data management and statistical analyses were performed using Stata/SE version 17.0 (StataCorp, College Station, TX, USA). Data visualizations and graphical representations were generated using R version 4.3.2 within the RStudio integrated development environment (Posit Software, PBC, Boston, MA, USA). Descriptive statistics were used to summarize sample and patient characteristics. Categorical variables were reported as frequencies and proportions, and distributions across VL categories were compared using Pearson’s chi-square test. Associations between turnaround time components and VL categories were examined using multinomial logistic regression, with TND as the reference outcome. For each turnaround time component, quartiles Q2–Q4 were compared against the fastest quartile (Q1). Model results are presented as relative risk ratios (RRRs) with corresponding 95% confidence intervals (CIs). Although VL categories are ordinal, the proportional odds assumption was not imposed because covariate effects were expected to differ across clinically distinct thresholds of viremia. Moreover, ART regimen and adherence were considered unlikely to satisfy the proportional odds assumption in routine programmatic settings (Dessie et al. 2020 ). Multinomial logistic regression was therefore selected to allow category-specific estimation of effects. Variables with a p-value < 0.20 in bivariable analyses, together with covariates identified a priori based on existing literature (age group, sex, ART regimen, and ART adherence), were included in multivariable models. Multicollinearity was assessed using variance inflation factors, with values < 5 considered acceptable (de Winter 2025 ). Because ART regimen and adherence data were missing for approximately 38% of records, multiple imputation by chained equations was used under a missing-at-random assumption (Junaid et al. 2025 ). Twenty imputed datasets were generated and combined using Rubin’s rules for pooling estimates and standard errors across imputations (Elasra et al. 2022). The number of imputations was selected based on methodological guidance indicating that 20 imputations are sufficient to achieve stable parameter estimates and standard errors, even with moderate to high levels of missingness (StataCorp LLC 2025). A complete-case analysis was conducted as a sensitivity analysis. Adjusted predicted probabilities for each VL category were estimated using marginal standardization at the fastest (Q1) and slowest (Q4) turnaround time quartiles. Marginal estimates after multiple imputation were obtained using the mimrgns module in Stata, which extends the margins framework to multiply imputed datasets. Absolute differences (percentage points) and risk ratios with 95% CIs were calculated based on these predicted probabilities. Confidence intervals for the calculated risk ratios were estimated using the delta method. Results Turnaround Time and Sample Characteristics A total of 18,025 HIV viral load (VL) results were included in the analysis after excluding 244 records (1.3%) with missing date variables. The median total turnaround time (TAT) from sample collection to result authorization was 22 days (IQR 13–36), while the median intra-laboratory TAT (receipt-to-authorization) was 19 days (IQR 10–31). The registration-to-testing interval showed the widest variability (IQR 1–20 days), indicating a major bottleneck in laboratory operations. Median durations for other intervals were: collection-to-receipt 2 days (IQR 1–4), receipt-to-registration 3 days (IQR 1–6), and testing-to-authorization 4 days (IQR 2–8). Viral Load Result Distribution Overall, 94.5% of results were virally suppressed (< 200 copies/mL). This included 13,339 (73.0%) TND results, 2,354 (12.9%) detectable VL < 40 copies/mL, and 1,575 (8.6%) low-level viremia (40–199 copies/mL). Among unsuppressed results, 392 (2.1%) were in the actionable viremia range (200–999 copies/mL) and 611 (3.3%) indicated virological failure (≥ 1000 copies/mL). Baseline characteristics across VL categories are shown in Suplimentary Table 1 . Associations Between Turnaround Time Intervals and Viral Load The registration-to-testing interval was the strongest predictor of shifts in VL results (Fig. 1 , Supplementary Table 2). Samples in the slowest quartile of registration-to-testing interval (Q4, ≥ 21 days) had a higher probability of TND results (85.8%) than the fastest quartile (Q1, ≤ 1 day, 71.2%), with concomitant reductions in all detectable VL categories (Fig. 2 , Table 1 ). Collection-to-reception and reception-to-registration intervals showed weaker and less consistent effects, collection-to-reception delays were associated with slightly higher very low-level viremia (< 40 copies/mL) (Fig. 1 , Supplementary Table 2) Table 1 Adjusted probabilities and risk ratios of HIV viral load categories by registration-to-testing TAT quartile (Q1 ≤ 1 day vs Q4 ≥ 21 days). Viral Load Category (Q1, ≤1day days) Probability (95% CI) (Q4, ≥ 21 days) Probability (95% CI) Absolute Probability Difference Risk Ratio Q4 vs Q1 (95% CI) P-value Target Not Detected 71.22% (70.77–71.67) 85.79% (85.46–86.13) + 14.57 pp 1.205 (1.196–1.214) < 0.001 Detected < 40 cp/mL 14.54% (14.19–14.89) 5.09% (4.88–5.30) -9.45 pp 0.350 (0.334–0.367) < 0.001 Detected 40–199 cp/mL 7.99% (7.72–8.25) 5.68% (5.46–5.91) -2.30 pp 0.712 (0.676–0.750) < 0.001 Detected 200–999 cp/mL 2.77% (2.60–2.93) 1.40% (1.29–1.50) -1.37 pp 0.505 (0.457–0.557) < 0.001 Detected ≥ 1000 cp/mL 3.49% (3.30–3.68) 2.04% (1.91–2.17) -1.45 pp 0.584 (0.537–0.635) < 0.001 Sensitivity Analysis A complete-case analysis (N = 11,050) confirmed the robustness of the inverse association between longer registration-to-testing intervals and viral load detectability. Effects remained consistent with primary analyses using multiple imputation (Supplementary Table 3). Discussion Key Findings and Interpretation This study demonstrates a strong and consistent association between prolonged registration-to-testing intervals and systematically lower reported HIV viral load (VL) results. Samples processed after ≥ 21 days were significantly more likely to be classified as target not detected (TND) compared with those processed within one day, indicating an operational rather than biological effect (Ethel et al. 2021 ; Hardie et al. 2024 ). The most plausible explanation is time-dependent degradation of HIV RNA during extended pre-analytical and in-laboratory holding periods, particularly under high-throughput conditions with batching and workflow congestion. Although temperature and storage conditions were not directly measured, the observed dose-response across delay quartiles and persistence after adjustment for demographic and clinical covariates support a biologically credible, non-causal mechanism rather than confounding by patient factors (Hardie et al 2024 ). Delays of five or more days between sample collection and laboratory reception were also associated with increased detection of very low-level viremia (< 40 copies/mL), likely representing a technical artifact rather than a true biological elevation in VL (Hardie et al. 2024 ). Notably, the downward effect of prolonged delays was substantially larger than any upward artifact from short pre-analytical delays (Fig. 1 ) underscoring the importance of timely sample processing to avoid underestimation of true viral burden (Hardie et al., 2024 ). Clinical and Programmatic Implications Systematic underestimation of VL may delay recognition of low-level viremia and early treatment failure, potentially postponing timely ART regimen adjustments (Rosen et al. 2022 ; Hardie et al. 2024 ). At the program level, this bias may artificially inflate viral suppression rates, affecting progress toward UNAIDS 95-95-95 targets (UNAIDS 2023 ). In high-burden regions such as Tanzania, even modest upward shifts in TND classification could overestimate suppression rates by several percentage points, impacting thousands of individuals and masking early treatment failure signals (Moirana et al. 2022 ; Kathinzi et al. 2025 ). Patients in the slowest registration-to-testing quartile had an 85.8% probability of being classified as TND versus 71.2% in the fastest quartile, representing a 20% absolute increase and highlighting the magnitude of operational bias (Lemma et al. 2020 ; UNAIDS 2024). Laboratory Bottlenecks in HIV Viral Load Testing The registration-to-testing interval emerged as the dominant operational bottleneck, driven by equipment breakdowns, inadequate maintenance, reagent stock-outs, centralized testing, inefficient sample transport, staffing shortages, and high workloads (Mbiva et al. 2021 ; Kathinzi et al. 2025 ; Mnzava et al. 2023 ; Mpofu et al. 2024 ; Fonjungo et al.). These challenges underscore the need for targeted interventions to streamline workflows, improve equipment reliability, optimize human resources, and strengthen specimen logistics (Mnzava et al. 2023 ; Mpofu et al. 2024 ; Fonjungo et al). Potential Interventions A multi-pronged approach is required to address these delays. Automated sample tracking systems can flag bottlenecks in real time (Bowie et al. 2024 ). Decentralization of testing to peripheral or district-level laboratories can reduce transport-related delays and alleviate congestion at central labs (Mnzava et al. 2023 ). Ensuring adequate staffing, training, and workflow optimization, including sample triaging and prioritization protocols, can minimize backlogs (Cherie et al. 2024 ; Lubega et al.). Investment in equipment maintenance, backup instruments, forecasting and supply planning and clinical sample cold-chain monitoring reduces downtime and prevents RNA degradation (Williams et al. 2020 ; Fonjungo et al.). Finally, integrating these interventions with routine quality assurance and data-driven monitoring allows laboratories and HIV programs to continuously evaluate performance and improve turnaround times (WHO 2024). Limitations This study is subject to several limitations. The cross-sectional design limits causal inference (Savitz & Wellenius 2023 ). Storage temperature, plasma separation timing, and freeze–thaw cycles were not directly measured; turnaround time intervals were used as proxies for pre-analytical degradation (Moirana et al. 2022 ; Hardie et al. 2024 ). Residual confounding cannot be entirely excluded, including potential day-of-week effects, staff workload variations, or subtle differences in sample quality (Mesganaw et al. 2024 ; VanderWeele 2023). ART regimen and adherence data were missing for 38% of samples, though multiple imputation was applied. Despite these limitations, the large sample size, real-world setting, and robust sensitivity analyses enhance generalizability. Conclusion Prolonged in-laboratory processing delays, particularly between registration and testing, are associated with systematically lower HIV VL results, reflecting operational bias rather than true virological suppression. This bias can compromise patient management, obscure early detection of treatment failure, and distort national HIV program metrics. Implementing workflow optimization, adequate staffing, equipment maintenance, cold-chain monitoring, decentralization, and real-time sample tracking should be prioritized to ensure accurate viral load testing and reliable monitoring of HIV program performance. Abbreviations ART Antiretroviral Therapy CI Confidence Interval EDTA Ethylenediaminetetraacetic Acid HIV Human Immunodeficiency Virus IQR Interquartile Range MDH Management and Development for Health MI Multiple Imputation MOH Ministry of Health NACP National AIDS Control Programme NASHCOP National AIDS Control Programme (Tanzania) NBS National Bureau of Statistics PPT Plasma Preparation Tube Q1 First Quartile (Fastest Turnaround Time) Q2 Second Quartile Q3 Third Quartile Q4 Fourth Quartile (Slowest Turnaround Time) RNA Ribonucleic Acid RR Risk Ratio RRR Relative Risk Ratio SOP Standard Operating Procedure SSA Sub–Saharan Africa TAT Turnaround Time TND Target Not Detected UNAIDS Joint United Nations Programme on HIV/AIDS VL Viral Load WHO World Health Organization Declarations Ethics approval and consent to participate The study was approved by the Tanzania Ministry of Health as part of a data quality assessment exercise (Reference number Kumb.Na.MA.155/174/01/973). Ethical approval was waived in view of the retrospective nature of the study and because all procedures were performed as part of routine care. Client-identifying information was anonymized to safeguard privacy. Consent for publication Not applicable Competing Interests The authors declare no conflict of interest associated with this work. Funding Statement No formal funding was received for this work. Author Contribution RB led the conceptualization of the study, conducted data analysis, and prepared the original manuscript draft. E.M, R.B, A.Y, J.N, P.N, and Z.C curated and prepared the dataset for analysis, while R.BU, R.Y.M, A.Y, A.S.M, A.M, and M.M provided administrative coordination and oversight. The first draft underwent critical review by M.I.M, A.K.H, R.N, P.R.T, R.B.U, and F.M, who contributed substantial improvements. Further writing, critical review, and editorial revisions were carried out by R.B, A.K.H, R.N, R.BU, R.Y.M, A.K, P.R.T, F.M, A.M, A.S.M, and M.I.M. All authors approved the final manuscript. Acknowledgement The authors thank Directorate of Diagnostics and Health Care Technical Services at the Ministry of Health, Tanzania Field Epidemiology and Laboratory Training Program (FELTP), Management and Development for Health (MDH), Njombe Regional Medical Office, Njombe District Medical Office, and the staff of Njombe Town Council Hospital for their support and cooperation. We also thank Franc Katherine (CDC), Hayes Kristy (CDC), and Mukurasi Kokuhabwa (CDC) for their valuable input on the presentation of this work. Data Availability The datasets analyzed during the current study are not publicly available due to ethical restrictions but may be available from the corresponding author on reasonable request, subject to approval from the relevant ethics committee and data custodians. Baseline characteristics across VL categories; Supplementary table 1 and detailed regression results; Supplementary table 2 and Supplementary table 3, are provided as Supplementary Materials. References Bowie C, Ali E, Kruse G et al (2024) Laboratory data timeliness and completeness improves following implementation of an electronic laboratory information system in Côte d’Ivoire: quasi experimental study on 21 clinical laboratories, 2014–2020. JMIR Public Health Surveill 10:e50407. 10.2196/50407 Cherie N, Berta DM, Tamir M et al (2024) Improving laboratory turnaround times in clinical settings: a systematic review of the impact of lean methodology application. PLoS ONE 19(10):e0312033. 10.1371/journal.pone.0312033 de Winter J (2025) Multicollinearity in statistical modeling: a review. Available from: https://www.researchgate.net/publication/395360487_Multicollinearity_in_Statistical_Modeling_A_Review (accessed Jan 2026) Dessie ZG, Zewotir T, Mwambi H et al (2020) Multilevel ordinal model for CD4 count trends in seroconversion among South Africa women. BMC Infect Dis 20:447. 10.1186/s12879-020-05159-4 Elasra A (2022) Multiple imputation of missing data in educational production functions. Computation 10(4):49. 10.3390/computation10040049 Ethel S, Noumi T, Ngom J (2021) Effects of long-term storage of plasma samples from HIV positive patients on the quality of HIV-1 viral load results. Available from: https://www.researchgate.net/publication/355999297_effects_of_long-term_storage_of_plasma_samples_from_hiv_positive_patients_on_the_quality_of_hiv-1_viral_load_results_references (accessed Jan 2026) Fonjungo PN, Lecher S, Zeh C et al (2023) Progress in scale up of HIV viral load testing in select sub-Saharan African countries 2016–2018. PLoS ONE 18(3):e0282652. 10.1371/journal.pone.0282652 Gutema G, Hailu H, Abdela S et al (2022) Effect of sample management on quantitative HIV-1 viral load measurement at Saint Paul’s Hospital Millennium Medical College, Addis Ababa, Ethiopia. PLoS ONE 17(6):e0269943. 10.1371/journal.pone.0269943 Hardie DR, Newman H, Reid J et al (2024) Field study to determine the reliability of HIV viral load results shows minimal impact of delayed testing in South Africa. Afr J Lab Med 13(1):2364. 10.4102/ajlm.v13i1.2364 Joint United Nations Programme on HIV/AIDS (UNAIDS) (2024) Global AIDS Monitoring 2024: indicators for monitoring the 2021 United Nations Political Declaration on HIV and AIDS. Geneva: UNAIDS. Available from: https://www.unaids.org/sites/default/files/media_asset/global-aids-monitoring_en.pdf (accessed Jan 2026) Junaid K, Kiran T, Gupta M et al (2025) How much missing data is too much to impute for longitudinal health indicators? A preliminary guideline for the choice of the extent of missing proportion to impute with multiple imputation by chained equations. Popul Health Metrics 23:2. 10.1186/s12963-025-00364-2 Kathinzi CM, Njenga PK, Mutai JK (2025) Barriers to effective sample management in viral load testing in Machakos County, Kenya: a convergent parallel mixed-method study. Pan Afr Med J 51:26. 10.11604/pamj.2025.51.26.46991 Kippen A, Nzimande L, Gareta D et al (2024) The viral load monitoring cascade in HIV treatment programmes in sub-Saharan Africa: a systematic review. BMC Public Health 24:2603. 10.1186/s12889-024-20013-x Lemma S, Janson A, Persson LÅ et al (2020) Improving quality and use of routine health information system data in low- and middle-income countries: a scoping review. PLoS ONE 15(10):e0239683. 10.1371/journal.pone.0239683 Lesesa N, Makhoahle PM, Mapolosi K (2025) Determining the stability of HIV-1 RNA in plasma samples stored at various temperatures in primary tubes of centrifuged whole blood. 10.64252/zmcngb63 (accessed Jan 2026) Lubega P, Nalugya SJ, Kimuli AN et al (2022) Adherence to viral load testing guidelines, barriers, and associated factors among persons living with HIV on ART in Southwestern Uganda: a mixed-methods study. BMC Public Health 22:1268. 10.1186/s12889-022-13674-z Mbiva F, Tweya H, Satyanarayana S et al (2021) Long turnaround times in viral load monitoring of people living with HIV in resource-limited settings. J Glob Infect Dis 13(2):85–90. 10.4103/jgid.jgid_172_19 Mesganaw B, Hassen F, Molla H et al (2024) Laboratory specimen rejection rate and associated factors among referred specimens for human immunodeficiency virus viral load testing at Debre Markos Referral Hospital, Ethiopia. Pan Afr Med J 47:112. 10.11604/pamj.2024.47.112.33795 Mnzava D, Okuma J, Ndege R et al (2023) Decentralization of viral load testing to improve HIV care and treatment cascade in rural Tanzania: observational study from the Kilombero and Ulanga Antiretroviral Cohort. BMC Infect Dis 23:222. 10.1186/s12879-023-08155-6 Moirana EL, Muro EP, Kiwelu IE et al (2022) Evaluation of HIV viral load turnaround time in Moshi, Tanzania. J Infect Dev Ctries 16(09):1500–1505. 10.3855/jidc.14145 Mpofu C et al (2024) The potential promise and pitfalls of point of care viral load monitoring to expedite HIV treatment decision making in rural Uganda: a qualitative study. BMC Health Serv Res 24:1265. 10.1186/s12913-024-11747-w National Bureau of Statistics (NBS) [Tanzania] (2022) Census. Dodoma, Tanzania: NBS. Available from: https://www.nbs.go.tz/statistics/topic/census-2022 (accessed Jan 2026) National Bureau of Statistics (NBS) [Tanzania] (2023) Tanzania HIV impact survey (THIS) 2022–2023. Dar es Salaam: NBS. Available from: https://microdata.nbs.go.tz/index.php/catalog/53/related-materials (accessed Jan 2026) Rosen S, Maskew M, Fox MP et al (2022) Implications of delayed viral load monitoring on detection of treatment failure in resource-limited settings. AIDS 36(3):399–407. 10.1097/QAD.0000000000003145 Savitz DA, Wellenius GA (2023) Can cross sectional studies contribute to causal inference? It depends. Am J Epidemiol 192(4):514–516. 10.1093/aje/kwac037 StataCorp (2025) Stata multiple-imputation reference manual release 19. College Station, Texas: StataCorp LLC. Available from: https://www.stata.com/manuals/mi.pdf (accessed Jan 2026) The United Republic of Tanzania, Ministry of Health (2025) National Sample Referral System Guidelines. Dar es Salaam, Tanzania: Ministry of Health. Available from: https://www.ntlp.go.tz/site/assets/files/1138/final_sample_referral_guideline.pdf UNAIDS (2023) Global HIV & AIDS statistics — Fact sheet. Geneva: UNAIDS. Available from: https://www.unaids.org/en/resources/fact-sheet VanderWeele TJ (2019) Principles of confounder selection. Eur J Epidemiol 38:109–122. 10.1007/s10654-019-00494-6 Williams J, Edgil D, Wattleworth M et al (2020) The network approach to laboratory procurement and supply chain management: addressing the system issues to enhance HIV viral load scale-up. Afr J Lab Med 9(1):a1022. https://doi.org/10.4102/ajlm.v9i1.1022 World Health Organization (2024) Module for assessing and strengthening the quality of viral load testing data within HIV programmes and patient monitoring systems: second edition. Geneva: WHO. Available from: https://www.who.int/publications/i/item/9789240085046 World Health Organization (2024) Public report for cobas HIV-1 nucleic acid test for use on the cobas 4800 system (version 2.0). Geneva: WHO Prequalification Programme. https://extranet.who.int/prequal/prequalification-reports/pqdx-0710-118-00 (accessed Jan 2026) World Health Organization (2024) Xpert® HIV-1 viral load XC public report (sample collection, transport, and storage requirements). Geneva: WHO Prequalification Programme. Available from: https://extranet.who.int/prequal/sites/default/files/whopr_files/xpert-hiv-1-viral-load-xc-pqdx-0691-070-00-public-report.pdf (accessed Jan 2026) Additional Declarations No competing interests reported. Supplementary Files SuplimentaryMaterialClinicalSampleswithDelayedProcessingTimeareAssociatedwithLowerHIVViralLoadResultsinaHighVolumeRegionalLaboratoryNjombeRegionTanzaniaOct2024Mar2025.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 16 Mar, 2026 Reviews received at journal 07 Mar, 2026 Reviewers agreed at journal 25 Feb, 2026 Reviews received at journal 09 Feb, 2026 Reviewers agreed at journal 05 Feb, 2026 Reviewers agreed at journal 05 Feb, 2026 Reviewers invited by journal 05 Feb, 2026 Editor assigned by journal 26 Jan, 2026 Submission checks completed at journal 21 Jan, 2026 First submitted to journal 20 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Hussein","email":"","orcid":"","institution":"Tanzania Field Epidemiology and Laboratory Training Program","correspondingAuthor":false,"prefix":"","firstName":"Ally","middleName":"K.","lastName":"Hussein","suffix":""},{"id":580501482,"identity":"cb25cd19-4737-4f0b-80a7-1d7b4bd424c1","order_by":2,"name":"Richard Buretta","email":"","orcid":"","institution":"Management and Development for Health","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"","lastName":"Buretta","suffix":""},{"id":580501483,"identity":"71cc5408-2143-406f-add3-6a482bfebd91","order_by":3,"name":"Peter Richard Torokaa","email":"","orcid":"","institution":"Ministry of Health","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"Richard","lastName":"Torokaa","suffix":""},{"id":580501484,"identity":"1ce54035-992c-4d76-802b-df045209a899","order_by":4,"name":"Robert Ndege","email":"","orcid":"","institution":"Ifakara Health Institute","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"Ndege","suffix":""},{"id":580501485,"identity":"711d3d56-204c-4860-9f39-d83c98d1bc7a","order_by":5,"name":"Ritha Yodosi Masawe","email":"","orcid":"","institution":"Ministry of Health","correspondingAuthor":false,"prefix":"","firstName":"Ritha","middleName":"Yodosi","lastName":"Masawe","suffix":""},{"id":580501486,"identity":"0434d33b-8be3-43ea-8ad9-38df667276f4","order_by":6,"name":"Anthony Kavindi","email":"","orcid":"","institution":"Kilimanjaro Christian Medical University College","correspondingAuthor":false,"prefix":"","firstName":"Anthony","middleName":"","lastName":"Kavindi","suffix":""},{"id":580501487,"identity":"e94bb775-f0aa-4fe0-bfb9-3cf0e61becf1","order_by":7,"name":"Esther Mwavika","email":"","orcid":"","institution":"National Public Health Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Esther","middleName":"","lastName":"Mwavika","suffix":""},{"id":580501493,"identity":"ce72829d-d310-4c74-9656-b54e4214727c","order_by":8,"name":"Ahmad Yusuf","email":"","orcid":"","institution":"Management and Development for Health","correspondingAuthor":false,"prefix":"","firstName":"Ahmad","middleName":"","lastName":"Yusuf","suffix":""},{"id":580501495,"identity":"b15a3b68-4190-4df1-ab09-301505cd054e","order_by":9,"name":"Joel Nikupala","email":"","orcid":"","institution":"Njombe Town Council Hospital","correspondingAuthor":false,"prefix":"","firstName":"Joel","middleName":"","lastName":"Nikupala","suffix":""},{"id":580501496,"identity":"8e2333fa-78a8-4078-9655-e206eabaf673","order_by":10,"name":"Perfecter Nchumbuka","email":"","orcid":"","institution":"Njombe Town Council Hospital","correspondingAuthor":false,"prefix":"","firstName":"Perfecter","middleName":"","lastName":"Nchumbuka","suffix":""},{"id":580501497,"identity":"43a97b40-104b-463b-9545-6c904dda96bd","order_by":11,"name":"Ziada Chwaya","email":"","orcid":"","institution":"Njombe Town Council Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ziada","middleName":"","lastName":"Chwaya","suffix":""},{"id":580501502,"identity":"349f1a64-cf2a-4ad7-8b77-5cfeca207b66","order_by":12,"name":"Frank Msafiri","email":"","orcid":"","institution":"Muhimbili University of Health and Allied Sciences","correspondingAuthor":false,"prefix":"","firstName":"Frank","middleName":"","lastName":"Msafiri","suffix":""},{"id":580501504,"identity":"763a0b4f-6652-4117-9598-dff9d6941946","order_by":13,"name":"Ambwene Mwakalobo","email":"","orcid":"","institution":"Ministry of Health","correspondingAuthor":false,"prefix":"","firstName":"Ambwene","middleName":"","lastName":"Mwakalobo","suffix":""},{"id":580501506,"identity":"456d8655-4f73-4a3b-ab91-471311fc6738","order_by":14,"name":"Alex S. Magesa","email":"","orcid":"","institution":"Ministry of Health","correspondingAuthor":false,"prefix":"","firstName":"Alex","middleName":"S.","lastName":"Magesa","suffix":""},{"id":580501507,"identity":"b890765f-22a4-495e-90a5-33f0c56a2002","order_by":15,"name":"Mecky Isaac Matee","email":"","orcid":"","institution":"Muhimbili University of Health and Allied Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mecky","middleName":"Isaac","lastName":"Matee","suffix":""}],"badges":[],"createdAt":"2026-01-20 10:14:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8647781/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8647781/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101365994,"identity":"225c874a-38d2-434c-8a71-ffb31c4a13a6","added_by":"auto","created_at":"2026-01-29 00:59:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":21271,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage change in the relative risk ratio [(RRR-1) ×100%] for viral load categories from multiply imputed multinomial logistic regression, with target-not-detected (TND) as the base outcome category comparing slowest (Q4) to fastest (Q1) turnaround time.\u003c/p\u003e\n\u003cp\u003eDashed line indicates no change (RRR = 1). Negative values: decreased relative risk of detectable VL relative to TND; positive values: increased relative risk relative to TND. Registration-to-testing interval shows the largest percentage reduction toward TND status.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8647781/v1/94e4123e594fe3140fdcfe30.png"},{"id":101365992,"identity":"781c38f6-7c59-472e-ae36-806fa2017c80","added_by":"auto","created_at":"2026-01-29 00:59:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":59862,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage change in relative risk [(RR-1) ×100%] of each HIV viral load category comparing slowest (Q4, ≥21 days) to fastest (Q1, ≤1 day) registration-to-testing interval.\u003c/p\u003e\n\u003cp\u003eDashed line indicates no change (RR = 1). Values\u0026gt;0 indicate increased likelihood; \u0026lt;0 indicate decreased likelihood. RRs were derived from adjusted predicted probabilities from multiply imputed multinomial logistic regression model. Error bars: 95% CI.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8647781/v1/95715d0158daf52cb002a9a2.png"},{"id":101398578,"identity":"2556ab2b-f94a-4e2f-86fc-980fbdd7c347","added_by":"auto","created_at":"2026-01-29 09:42:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":974290,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8647781/v1/0c7a6267-1d6f-4af7-84e2-2614b62893a9.pdf"},{"id":101365993,"identity":"d8aa49d6-7f6c-4bc2-a84c-43614745e90a","added_by":"auto","created_at":"2026-01-29 00:59:18","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":34410,"visible":true,"origin":"","legend":"","description":"","filename":"SuplimentaryMaterialClinicalSampleswithDelayedProcessingTimeareAssociatedwithLowerHIVViralLoadResultsinaHighVolumeRegionalLaboratoryNjombeRegionTanzaniaOct2024Mar2025.docx","url":"https://assets-eu.researchsquare.com/files/rs-8647781/v1/111aa97cde6a6c31316d0b3b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"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)","fulltext":[{"header":"Background","content":"\u003cp\u003eAccurate HIV viral load (VL) measurement is a cornerstone of antiretroviral therapy (ART) monitoring and essential for detecting treatment failure (Kippen et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The accuracy of viral load testing is therefore fundamental to both clinical care and HIV prevention (WHO 2024; UNAIDS 2024). However, in resource-limited settings, which bear the highest global HIV burden, laboratory systems are frequently strained, leading to prolonged turnaround times (TAT) that often exceed the recommended 14 days (Mbiva et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mnzava et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). According to World Health Organization (WHO) guidance, plasma samples should be centrifuged and frozen within 24 hours (WHO 2024). For HIV VL testing kits and testing guidelines generally recommend storage and transport at room temperature (\u0026le;\u0026thinsp;24 hours), 2\u0026ndash;8\u0026deg;C (\u0026le;\u0026thinsp;5 days), or \u0026minus;\u0026thinsp;20\u0026deg;C to \u0026minus;\u0026thinsp;80\u0026deg;C for up to six weeks (WHO 2024)\u003c/p\u003e \u003cp\u003eFurthermore, experimental evidence suggests that delays in sample transport or laboratory processing can lead to RNA degradation, potentially resulting in underestimated VL results (Gutema et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ethel et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Laboratory stability studies and systematic review have reported substantial VL declines in stored plasma: 81.8% and 97.5% after 7 and 14 days at 30\u0026deg;C, and 52.6% and 25% after one month at \u0026minus;\u0026thinsp;20\u0026deg;C and \u0026minus;\u0026thinsp;80\u0026deg;C, respectively (Ethel et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hardie et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lesesa et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). While these experimental studies establish biological plausibility, they do not fully capture the complexity of routine laboratory workflows (Hardie et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In HIV VL testing, delays may occur at multiple points under variable storage and handling conditions (Gutema et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Evidence quantifying the real-world impact of such operational delays on reported VL results remains limited (Hardie et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhen VL results are systematically underestimated, such errors may carry serious clinical and programmatic consequences, including delayed regimen switching, ongoing viral replication, and inaccurate ART program performance metrics (Lubega et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; NASHCOP 2024). Studies from sub-Saharan Africa indicate that over 50% of VL samples are processed beyond recommended timelines in many settings, highlighting the operational challenges facing high-burden regions (Mbiva et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Moirana et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mnzava et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kippen et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; UNAIDS 2024).\u003c/p\u003e \u003cp\u003eAgainst this backdrop, we aimed to quantify the association between turnaround time and HIV viral load result distribution in a high-volume regional laboratory in mainland Tanzania. The novelty of this study lies in its ability to link routine operational delays to systematic shifts in VL result categories, thereby identifying actionable laboratory bottlenecks with direct clinical and programmatic relevance. Centralized VL monitoring systems in sub-Saharan Africa are known to face bottlenecks such as stockouts and testing backlogs that delay clinical decisions; nonetheless, there is a lack of quantitative data on how these delays systematically alter VL result distributions at the population level (Moirana et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hardie et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kippen et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Setting\u003c/h2\u003e \u003cp\u003eWe conducted a cross-sectional secondary analysis of HIV viral load (VL) monitoring data from the Njombe Town Council Hospital Laboratory. This facility serves as a regional referral laboratory for a population of approximately 889,946 across the Njombe Region, which spans 24,994 km\u0026sup2; % (National Bureau of Statistics [NBS] 2022). The region has an HIV prevalence of 12.7% (National Bureau of Statistics [NBS] 2022). The analysis included all individuals living with HIV whose samples were received and tested at the laboratory from October 2024 to March 2025 (N\u0026thinsp;=\u0026thinsp;18,027). A total of 18,271 records were extracted from electronic laboratory records. No sampling was performed, minimizing selection bias at the data extraction stage.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Sources and Variables\u003c/h3\u003e\n\u003cp\u003eOf the 18,271 records extracted, 244 (1.3%) were excluded due to missing critical date variables required for turnaround time calculation, resulting in a final analytic sample of 18,025 unique patient samples. Extracted data included VL results and dates for sample collection, laboratory receipt, sample registration, testing, and result authorization, as well as patient age and sex.\u003c/p\u003e \u003cp\u003eThe primary exposure variables were three pre-analytical turnaround time intervals representing distinct operational stages within the testing pathway: Collection-to-reception (clinic to laboratory), Reception-to-registration (laboratory receipt to system entry), Registration-to-testing (system entry to assay performance). Each interval was categorized into quartiles (Q1\u0026ndash;Q4), with Q4 representing the slowest and Q1 the fastest interval.\u003c/p\u003e \u003cp\u003eThe primary outcome was the categorical VL result as reported by the assay, reflecting routine clinical interpretation. Categories included Target Not Detected (TND), \u0026lt;\u0026thinsp;40 copies/mL, 40\u0026ndash;199 copies/mL, 200\u0026ndash;999 copies/mL, and \u0026ge;\u0026thinsp;1000 copies/mL.\u003c/p\u003e\n\u003ch3\u003eSample Storage and Handling During Turnaround Time Intervals\u003c/h3\u003e\n\u003cp\u003eDuring the study period, HIV VL samples were managed according to national guidelines (URT, 2025) and manufacturers\u0026rsquo; instructions \u003cem\u003e(cobas\u0026reg; HIV-1 Test Nucleic acid test for use on the cobas\u0026reg; 4800 system IVD)\u003c/em\u003e, which recommend storage and transport at room temperature for \u0026le;\u0026thinsp;24 hours, 2\u0026ndash;8\u0026deg;C for up to 3 days, or \u0026minus;\u0026thinsp;20\u0026deg;C to \u0026minus;\u0026thinsp;80\u0026deg;C for up to six weeks (WHO 2024)\u003c/p\u003e \u003cp\u003eIn routine practice at the study laboratory, samples awaiting testing after registration were stored within available laboratory cold-chain infrastructure, including refrigeration and freezer storage, in line with these standard operating procedures. However, this study did not directly measure actual storage temperatures, durations at each temperature level, or freeze\u0026ndash;thaw cycles at the individual sample level. Consequently, while turnaround time intervals reflect operational delays within the testing pathway, they serve as proxy indicators of potential prolonged storage or delayed processing. Turnaround time intervals were therefore used as proxy indicators of potential prolonged storage or delayed processing, rather than as direct measures of RNA degradation or storage conditions.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eData management and statistical analyses were performed using Stata/SE version 17.0 (StataCorp, College Station, TX, USA). Data visualizations and graphical representations were generated using R version 4.3.2 within the RStudio integrated development environment (Posit Software, PBC, Boston, MA, USA). Descriptive statistics were used to summarize sample and patient characteristics. Categorical variables were reported as frequencies and proportions, and distributions across VL categories were compared using Pearson\u0026rsquo;s chi-square test. Associations between turnaround time components and VL categories were examined using multinomial logistic regression, with TND as the reference outcome. For each turnaround time component, quartiles Q2\u0026ndash;Q4 were compared against the fastest quartile (Q1). Model results are presented as relative risk ratios (RRRs) with corresponding 95% confidence intervals (CIs).\u003c/p\u003e \u003cp\u003eAlthough VL categories are ordinal, the proportional odds assumption was not imposed because covariate effects were expected to differ across clinically distinct thresholds of viremia. Moreover, ART regimen and adherence were considered unlikely to satisfy the proportional odds assumption in routine programmatic settings (Dessie et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Multinomial logistic regression was therefore selected to allow category-specific estimation of effects. Variables with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.20 in bivariable analyses, together with covariates identified a priori based on existing literature (age group, sex, ART regimen, and ART adherence), were included in multivariable models. Multicollinearity was assessed using variance inflation factors, with values\u0026thinsp;\u0026lt;\u0026thinsp;5 considered acceptable (de Winter \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBecause ART regimen and adherence data were missing for approximately 38% of records, multiple imputation by chained equations was used under a missing-at-random assumption (Junaid et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Twenty imputed datasets were generated and combined using Rubin\u0026rsquo;s rules for pooling estimates and standard errors across imputations (Elasra et al. 2022). The number of imputations was selected based on methodological guidance indicating that 20 imputations are sufficient to achieve stable parameter estimates and standard errors, even with moderate to high levels of missingness (StataCorp LLC 2025). A complete-case analysis was conducted as a sensitivity analysis.\u003c/p\u003e \u003cp\u003eAdjusted predicted probabilities for each VL category were estimated using marginal standardization at the fastest (Q1) and slowest (Q4) turnaround time quartiles. Marginal estimates after multiple imputation were obtained using the \u003cem\u003emimrgns\u003c/em\u003e module in Stata, which extends the margins framework to multiply imputed datasets. Absolute differences (percentage points) and risk ratios with 95% CIs were calculated based on these predicted probabilities. Confidence intervals for the calculated risk ratios were estimated using the delta method.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eTurnaround Time and Sample Characteristics\u003c/h2\u003e \u003cp\u003eA total of 18,025 HIV viral load (VL) results were included in the analysis after excluding 244 records (1.3%) with missing date variables. The median total turnaround time (TAT) from sample collection to result authorization was 22 days (IQR 13\u0026ndash;36), while the median intra-laboratory TAT (receipt-to-authorization) was 19 days (IQR 10\u0026ndash;31). The registration-to-testing interval showed the widest variability (IQR 1\u0026ndash;20 days), indicating a major bottleneck in laboratory operations. Median durations for other intervals were: collection-to-receipt 2 days (IQR 1\u0026ndash;4), receipt-to-registration 3 days (IQR 1\u0026ndash;6), and testing-to-authorization 4 days (IQR 2\u0026ndash;8).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eViral Load Result Distribution\u003c/h3\u003e\n\u003cp\u003eOverall, 94.5% of results were virally suppressed (\u0026lt;\u0026thinsp;200 copies/mL). This included 13,339 (73.0%) TND results, 2,354 (12.9%) detectable VL\u0026thinsp;\u0026lt;\u0026thinsp;40 copies/mL, and 1,575 (8.6%) low-level viremia (40\u0026ndash;199 copies/mL). Among unsuppressed results, 392 (2.1%) were in the actionable viremia range (200\u0026ndash;999 copies/mL) and 611 (3.3%) indicated virological failure (\u0026ge;\u0026thinsp;1000 copies/mL). Baseline characteristics across VL categories are shown in Suplimentary Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003eAssociations Between Turnaround Time Intervals and Viral Load\u003c/h3\u003e\n\u003cp\u003eThe registration-to-testing interval was the strongest predictor of shifts in VL results (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Supplementary Table\u0026nbsp;2). Samples in the slowest quartile of registration-to-testing interval (Q4, \u0026ge;\u0026thinsp;21 days) had a higher probability of TND results (85.8%) than the fastest quartile (Q1, \u0026le;\u0026thinsp;1 day, 71.2%), with concomitant reductions in all detectable VL categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Collection-to-reception and reception-to-registration intervals showed weaker and less consistent effects, collection-to-reception delays were associated with slightly higher very low-level viremia (\u0026lt;\u0026thinsp;40 copies/mL) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Supplementary Table\u0026nbsp;2)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAdjusted probabilities and risk ratios of HIV viral load categories by registration-to-testing TAT quartile (Q1\u0026thinsp;\u0026le;\u0026thinsp;1 day vs Q4\u0026thinsp;\u0026ge;\u0026thinsp;21 days).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eViral Load Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(Q1, \u0026le;1day days) Probability (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Q4, \u0026ge;\u0026thinsp;21 days) Probability (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbsolute Probability Difference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRisk Ratio Q4 vs Q1\u003c/p\u003e \u003cp\u003e(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTarget Not Detected\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71.22% (70.77\u0026ndash;71.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85.79% (85.46\u0026ndash;86.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;14.57 pp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.205 (1.196\u0026ndash;1.214)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDetected\u0026thinsp;\u0026lt;\u0026thinsp;40 cp/mL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.54% (14.19\u0026ndash;14.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.09% (4.88\u0026ndash;5.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-9.45 pp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.350 (0.334\u0026ndash;0.367)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDetected 40\u0026ndash;199 cp/mL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.99% (7.72\u0026ndash;8.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.68% (5.46\u0026ndash;5.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.30 pp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.712 (0.676\u0026ndash;0.750)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDetected 200\u0026ndash;999 cp/mL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.77% (2.60\u0026ndash;2.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.40% (1.29\u0026ndash;1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.37 pp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.505 (0.457\u0026ndash;0.557)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDetected\u0026thinsp;\u0026ge;\u0026thinsp;1000 cp/mL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.49% (3.30\u0026ndash;3.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.04% (1.91\u0026ndash;2.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.45 pp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.584 (0.537\u0026ndash;0.635)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity Analysis\u003c/h2\u003e \u003cp\u003eA complete-case analysis (N\u0026thinsp;=\u0026thinsp;11,050) confirmed the robustness of the inverse association between longer registration-to-testing intervals and viral load detectability. Effects remained consistent with primary analyses using multiple imputation (Supplementary Table\u0026nbsp;3).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eKey Findings and Interpretation\u003c/h2\u003e \u003cp\u003eThis study demonstrates a strong and consistent association between prolonged registration-to-testing intervals and systematically lower reported HIV viral load (VL) results. Samples processed after \u0026ge;\u0026thinsp;21 days were significantly more likely to be classified as target not detected (TND) compared with those processed within one day, indicating an operational rather than biological effect (Ethel et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hardie et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The most plausible explanation is \u003cb\u003etime-dependent degradation of HIV RNA\u003c/b\u003e during extended pre-analytical and in-laboratory holding periods, particularly under high-throughput conditions with batching and workflow congestion. Although temperature and storage conditions were not directly measured, the observed dose-response across delay quartiles and persistence after adjustment for demographic and clinical covariates support a biologically credible, non-causal mechanism rather than confounding by patient factors (Hardie et al \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDelays of five or more days between sample collection and laboratory reception were also associated with increased detection of very low-level viremia (\u0026lt;\u0026thinsp;40 copies/mL), likely representing a technical artifact rather than a true biological elevation in VL (Hardie et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Notably, the downward effect of prolonged delays was substantially larger than any upward artifact from short pre-analytical delays (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) underscoring the importance of timely sample processing to avoid underestimation of true viral burden (Hardie et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eClinical and Programmatic Implications\u003c/h2\u003e \u003cp\u003eSystematic underestimation of VL may \u003cb\u003edelay recognition of low-level viremia\u003c/b\u003e and early treatment failure, potentially postponing timely ART regimen adjustments (Rosen et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hardie et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). At the program level, this bias may artificially inflate viral suppression rates, affecting progress toward UNAIDS 95-95-95 targets (UNAIDS \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In high-burden regions such as Tanzania, even modest upward shifts in TND classification could overestimate suppression rates by several percentage points, impacting thousands of individuals and masking early treatment failure signals (Moirana et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kathinzi et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Patients in the slowest registration-to-testing quartile had an 85.8% probability of being classified as TND versus 71.2% in the fastest quartile, representing a 20% absolute increase and highlighting the magnitude of operational bias (Lemma et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; UNAIDS 2024).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLaboratory Bottlenecks in HIV Viral Load Testing\u003c/h2\u003e \u003cp\u003eThe registration-to-testing interval emerged as the dominant operational bottleneck, driven by equipment breakdowns, inadequate maintenance, reagent stock-outs, centralized testing, inefficient sample transport, staffing shortages, and high workloads (Mbiva et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kathinzi et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mnzava et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mpofu et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Fonjungo et al.). These challenges underscore the need for targeted interventions to streamline workflows, improve equipment reliability, optimize human resources, and strengthen specimen logistics (Mnzava et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mpofu et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Fonjungo et al).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePotential Interventions\u003c/h2\u003e \u003cp\u003eA multi-pronged approach is required to address these delays. Automated sample tracking \u003cb\u003esystems\u003c/b\u003e can flag bottlenecks in real time (Bowie et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Decentralization of testing to peripheral or district-level laboratories can reduce transport-related delays and alleviate congestion at central labs (Mnzava et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Ensuring adequate staffing, training, and workflow optimization, including sample triaging and prioritization protocols, can minimize backlogs (Cherie et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lubega et al.). Investment in equipment maintenance, backup instruments, forecasting and supply planning and clinical sample cold-chain monitoring reduces downtime and prevents RNA degradation (Williams et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Fonjungo et al.). Finally, integrating these interventions with routine quality assurance and data-driven monitoring allows laboratories and HIV programs to continuously evaluate performance and improve turnaround times (WHO 2024).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study is subject to several limitations. The cross-sectional design limits causal inference (Savitz \u0026amp; Wellenius \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Storage temperature, plasma separation timing, and freeze\u0026ndash;thaw cycles were not directly measured; turnaround time intervals were used as proxies for pre-analytical degradation (Moirana et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hardie et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Residual confounding cannot be entirely excluded, including potential day-of-week effects, staff workload variations, or subtle differences in sample quality (Mesganaw et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; VanderWeele 2023). ART regimen and adherence data were missing for 38% of samples, though multiple imputation was applied. Despite these limitations, the large sample size, real-world setting, and robust sensitivity analyses enhance generalizability.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eProlonged in-laboratory processing delays, particularly between registration and testing, are associated with systematically lower HIV VL results, reflecting operational bias rather than true virological suppression. This bias can compromise patient management, obscure early detection of treatment failure, and distort national HIV program metrics. Implementing workflow optimization, adequate staffing, equipment maintenance, cold-chain monitoring, decentralization, and real-time sample tracking should be prioritized to ensure accurate viral load testing and reliable monitoring of HIV program performance.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eART\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAntiretroviral Therapy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEDTA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEthylenediaminetetraacetic Acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHIV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHuman Immunodeficiency Virus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterquartile Range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMDH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eManagement and Development for Health\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMultiple Imputation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMOH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMinistry of Health\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNACP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational AIDS Control Programme\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNASHCOP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational AIDS Control Programme (Tanzania)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNBS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Bureau of Statistics\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePPT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePlasma Preparation Tube\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eQ1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFirst Quartile (Fastest Turnaround Time)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eQ2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSecond Quartile\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eQ3\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThird Quartile\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eQ4\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFourth Quartile (Slowest Turnaround Time)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRNA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRibonucleic Acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRisk Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRRR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRelative Risk Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSOP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard Operating Procedure\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSSA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSub\u0026ndash;Saharan Africa\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTAT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTurnaround Time\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTND\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTarget Not Detected\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUNAIDS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eJoint United Nations Programme on HIV/AIDS\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eViral Load\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eThe study was approved by the Tanzania Ministry of Health as part of a data quality assessment exercise (Reference number Kumb.Na.MA.155/174/01/973). Ethical approval was waived in view of the retrospective nature of the study and because all procedures were performed as part of routine care. Client-identifying information was anonymized to safeguard privacy.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors declare no conflict of interest associated with this work.\u003c/p\u003e \u003ch2\u003eFunding Statement\u003c/h2\u003e \u003cp\u003eNo formal funding was received for this work.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eRB led the conceptualization of the study, conducted data analysis, and prepared the original manuscript draft. E.M, R.B, A.Y, J.N, P.N, and Z.C curated and prepared the dataset for analysis, while R.BU, R.Y.M, A.Y, A.S.M, A.M, and M.M provided administrative coordination and oversight. The first draft underwent critical review by M.I.M, A.K.H, R.N, P.R.T, R.B.U, and F.M, who contributed substantial improvements. Further writing, critical review, and editorial revisions were carried out by R.B, A.K.H, R.N, R.BU, R.Y.M, A.K, P.R.T, F.M, A.M, A.S.M, and M.I.M. All authors approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors thank Directorate of Diagnostics and Health Care Technical Services at the Ministry of Health, Tanzania Field Epidemiology and Laboratory Training Program (FELTP), Management and Development for Health (MDH), Njombe Regional Medical Office, Njombe District Medical Office, and the staff of Njombe Town Council Hospital for their support and cooperation. We also thank Franc Katherine (CDC), Hayes Kristy (CDC), and Mukurasi Kokuhabwa (CDC) for their valuable input on the presentation of this work.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets analyzed during the current study are not publicly available due to ethical restrictions but may be available from the corresponding author on reasonable request, subject to approval from the relevant ethics committee and data custodians. Baseline characteristics across VL categories; Supplementary table 1 and detailed regression results; Supplementary table 2 and Supplementary table 3, are provided as Supplementary Materials.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBowie C, Ali E, Kruse G et al (2024) Laboratory data timeliness and completeness improves following implementation of an electronic laboratory information system in C\u0026ocirc;te d\u0026rsquo;Ivoire: quasi experimental study on 21 clinical laboratories, 2014\u0026ndash;2020. JMIR Public Health Surveill 10:e50407. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2196/50407\u003c/span\u003e\u003cspan address=\"10.2196/50407\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCherie N, Berta DM, Tamir M et al (2024) Improving laboratory turnaround times in clinical settings: a systematic review of the impact of lean methodology application. PLoS ONE 19(10):e0312033. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0312033\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0312033\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Winter J (2025) Multicollinearity in statistical modeling: a review. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.researchgate.net/publication/395360487_Multicollinearity_in_Statistical_Modeling_A_Review\u003c/span\u003e\u003cspan address=\"https://www.researchgate.net/publication/395360487_Multicollinearity_in_Statistical_Modeling_A_Review\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed Jan 2026)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDessie ZG, Zewotir T, Mwambi H et al (2020) Multilevel ordinal model for CD4 count trends in seroconversion among South Africa women. BMC Infect Dis 20:447. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12879-020-05159-4\u003c/span\u003e\u003cspan address=\"10.1186/s12879-020-05159-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElasra A (2022) Multiple imputation of missing data in educational production functions. Computation 10(4):49. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/computation10040049\u003c/span\u003e\u003cspan address=\"10.3390/computation10040049\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEthel S, Noumi T, Ngom J (2021) Effects of long-term storage of plasma samples from HIV positive patients on the quality of HIV-1 viral load results. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.researchgate.net/publication/355999297_effects_of_long-term_storage_of_plasma_samples_from_hiv_positive_patients_on_the_quality_of_hiv-1_viral_load_results_references\u003c/span\u003e\u003cspan address=\"https://www.researchgate.net/publication/355999297_effects_of_long-term_storage_of_plasma_samples_from_hiv_positive_patients_on_the_quality_of_hiv-1_viral_load_results_references\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed Jan 2026)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFonjungo PN, Lecher S, Zeh C et al (2023) Progress in scale up of HIV viral load testing in select sub-Saharan African countries 2016\u0026ndash;2018. PLoS ONE 18(3):e0282652. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0282652\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0282652\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGutema G, Hailu H, Abdela S et al (2022) Effect of sample management on quantitative HIV-1 viral load measurement at Saint Paul\u0026rsquo;s Hospital Millennium Medical College, Addis Ababa, Ethiopia. PLoS ONE 17(6):e0269943. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0269943\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0269943\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHardie DR, Newman H, Reid J et al (2024) Field study to determine the reliability of HIV viral load results shows minimal impact of delayed testing in South Africa. Afr J Lab Med 13(1):2364. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4102/ajlm.v13i1.2364\u003c/span\u003e\u003cspan address=\"10.4102/ajlm.v13i1.2364\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJoint United Nations Programme on HIV/AIDS (UNAIDS) (2024) Global AIDS Monitoring 2024: indicators for monitoring the 2021 United Nations Political Declaration on HIV and AIDS. Geneva: UNAIDS. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.unaids.org/sites/default/files/media_asset/global-aids-monitoring_en.pdf\u003c/span\u003e\u003cspan address=\"https://www.unaids.org/sites/default/files/media_asset/global-aids-monitoring_en.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed Jan 2026)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJunaid K, Kiran T, Gupta M et al (2025) How much missing data is too much to impute for longitudinal health indicators? A preliminary guideline for the choice of the extent of missing proportion to impute with multiple imputation by chained equations. Popul Health Metrics 23:2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12963-025-00364-2\u003c/span\u003e\u003cspan address=\"10.1186/s12963-025-00364-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKathinzi CM, Njenga PK, Mutai JK (2025) Barriers to effective sample management in viral load testing in Machakos County, Kenya: a convergent parallel mixed-method study. Pan Afr Med J 51:26. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.11604/pamj.2025.51.26.46991\u003c/span\u003e\u003cspan address=\"10.11604/pamj.2025.51.26.46991\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKippen A, Nzimande L, Gareta D et al (2024) The viral load monitoring cascade in HIV treatment programmes in sub-Saharan Africa: a systematic review. BMC Public Health 24:2603. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12889-024-20013-x\u003c/span\u003e\u003cspan address=\"10.1186/s12889-024-20013-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLemma S, Janson A, Persson L\u0026Aring; et al (2020) Improving quality and use of routine health information system data in low- and middle-income countries: a scoping review. PLoS ONE 15(10):e0239683. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0239683\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0239683\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLesesa N, Makhoahle PM, Mapolosi K (2025) Determining the stability of HIV-1 RNA in plasma samples stored at various temperatures in primary tubes of centrifuged whole blood. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.64252/zmcngb63\u003c/span\u003e\u003cspan address=\"10.64252/zmcngb63\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed Jan 2026)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLubega P, Nalugya SJ, Kimuli AN et al (2022) Adherence to viral load testing guidelines, barriers, and associated factors among persons living with HIV on ART in Southwestern Uganda: a mixed-methods study. BMC Public Health 22:1268. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12889-022-13674-z\u003c/span\u003e\u003cspan address=\"10.1186/s12889-022-13674-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMbiva F, Tweya H, Satyanarayana S et al (2021) Long turnaround times in viral load monitoring of people living with HIV in resource-limited settings. J Glob Infect Dis 13(2):85\u0026ndash;90. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4103/jgid.jgid_172_19\u003c/span\u003e\u003cspan address=\"10.4103/jgid.jgid_172_19\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMesganaw B, Hassen F, Molla H et al (2024) Laboratory specimen rejection rate and associated factors among referred specimens for human immunodeficiency virus viral load testing at Debre Markos Referral Hospital, Ethiopia. Pan Afr Med J 47:112. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.11604/pamj.2024.47.112.33795\u003c/span\u003e\u003cspan address=\"10.11604/pamj.2024.47.112.33795\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMnzava D, Okuma J, Ndege R et al (2023) Decentralization of viral load testing to improve HIV care and treatment cascade in rural Tanzania: observational study from the Kilombero and Ulanga Antiretroviral Cohort. BMC Infect Dis 23:222. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12879-023-08155-6\u003c/span\u003e\u003cspan address=\"10.1186/s12879-023-08155-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoirana EL, Muro EP, Kiwelu IE et al (2022) Evaluation of HIV viral load turnaround time in Moshi, Tanzania. J Infect Dev Ctries 16(09):1500\u0026ndash;1505. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3855/jidc.14145\u003c/span\u003e\u003cspan address=\"10.3855/jidc.14145\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMpofu C et al (2024) The potential promise and pitfalls of point of care viral load monitoring to expedite HIV treatment decision making in rural Uganda: a qualitative study. BMC Health Serv Res 24:1265. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12913-024-11747-w\u003c/span\u003e\u003cspan address=\"10.1186/s12913-024-11747-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Bureau of Statistics (NBS) [Tanzania] (2022) Census. Dodoma, Tanzania: NBS. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nbs.go.tz/statistics/topic/census-2022\u003c/span\u003e\u003cspan address=\"https://www.nbs.go.tz/statistics/topic/census-2022\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed Jan 2026)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Bureau of Statistics (NBS) [Tanzania] (2023) Tanzania HIV impact survey (THIS) 2022\u0026ndash;2023. Dar es Salaam: NBS. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://microdata.nbs.go.tz/index.php/catalog/53/related-materials\u003c/span\u003e\u003cspan address=\"https://microdata.nbs.go.tz/index.php/catalog/53/related-materials\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed Jan 2026)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosen S, Maskew M, Fox MP et al (2022) Implications of delayed viral load monitoring on detection of treatment failure in resource-limited settings. AIDS 36(3):399\u0026ndash;407. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/QAD.0000000000003145\u003c/span\u003e\u003cspan address=\"10.1097/QAD.0000000000003145\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSavitz DA, Wellenius GA (2023) Can cross sectional studies contribute to causal inference? It depends. Am J Epidemiol 192(4):514\u0026ndash;516. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/aje/kwac037\u003c/span\u003e\u003cspan address=\"10.1093/aje/kwac037\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStataCorp (2025) Stata multiple-imputation reference manual release 19. College Station, Texas: StataCorp LLC. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.stata.com/manuals/mi.pdf\u003c/span\u003e\u003cspan address=\"https://www.stata.com/manuals/mi.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed Jan 2026)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThe United Republic of Tanzania, Ministry of Health (2025) National Sample Referral System Guidelines. Dar es Salaam, Tanzania: Ministry of Health. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ntlp.go.tz/site/assets/files/1138/final_sample_referral_guideline.pdf\u003c/span\u003e\u003cspan address=\"https://www.ntlp.go.tz/site/assets/files/1138/final_sample_referral_guideline.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUNAIDS (2023) Global HIV \u0026amp; AIDS statistics \u0026mdash; Fact sheet. Geneva: UNAIDS. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.unaids.org/en/resources/fact-sheet\u003c/span\u003e\u003cspan address=\"https://www.unaids.org/en/resources/fact-sheet\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVanderWeele TJ (2019) Principles of confounder selection. Eur J Epidemiol 38:109\u0026ndash;122. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10654-019-00494-6\u003c/span\u003e\u003cspan address=\"10.1007/s10654-019-00494-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilliams J, Edgil D, Wattleworth M et al (2020) The network approach to laboratory procurement and supply chain management: addressing the system issues to enhance HIV viral load scale-up. Afr J Lab Med 9(1):a1022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4102/ajlm.v9i1.1022\u003c/span\u003e\u003cspan address=\"10.4102/ajlm.v9i1.1022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization (2024) Module for assessing and strengthening the quality of viral load testing data within HIV programmes and patient monitoring systems: second edition. Geneva: WHO. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/publications/i/item/9789240085046\u003c/span\u003e\u003cspan address=\"https://www.who.int/publications/i/item/9789240085046\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization (2024) Public report for cobas HIV-1 nucleic acid test for use on the cobas 4800 system (version 2.0). Geneva: WHO Prequalification Programme. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://extranet.who.int/prequal/prequalification-reports/pqdx-0710-118-00\u003c/span\u003e\u003cspan address=\"https://extranet.who.int/prequal/prequalification-reports/pqdx-0710-118-00\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed Jan 2026)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization (2024) Xpert\u0026reg; HIV-1 viral load XC public report (sample collection, transport, and storage requirements). Geneva: WHO Prequalification Programme. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://extranet.who.int/prequal/sites/default/files/whopr_files/xpert-hiv-1-viral-load-xc-pqdx-0691-070-00-public-report.pdf\u003c/span\u003e\u003cspan address=\"https://extranet.who.int/prequal/sites/default/files/whopr_files/xpert-hiv-1-viral-load-xc-pqdx-0691-070-00-public-report.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed Jan 2026)\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bulletin-of-the-national-research-centre","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnrc","sideBox":"Learn more about [Bulletin of the National Research Centre](https://BNRC.springeropen.com)","snPcode":"42269","submissionUrl":"https://submission.springernature.com/new-submission/42269/3","title":"Bulletin of the National Research Centre","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"HIV viral load, Turnaround time, Pre-analytical delay, RNA degradation, Antiretroviral therapy monitoring, Laboratory workflow, Resource-limited settings, Sub-Saharan Africa","lastPublishedDoi":"10.21203/rs.3.rs-8647781/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8647781/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAccurate 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.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a cross-sectional secondary analysis of VL data from Njombe Town Council Hospital Laboratory (October 2024\u0026ndash;March 2025, N\u0026thinsp;=\u0026thinsp;18,271). After excluding 244 records with missing dates, 18,025 samples were analyzed. Three TAT intervals\u0026mdash;collection-to-reception (clinic to lab), reception-to-registration (lab receipt to system entry), and registration-to-testing (system entry to assay performance)\u0026mdash;were categorized into quartiles. Multivariable multinomial logistic regression with predictive margins estimated associations between TAT delays and VL categories.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMedian total TAT was 22 days (IQR 13\u0026ndash;36), exceeding the recommended\u0026thinsp;\u0026le;\u0026thinsp;14 days. Only the slowest registration-to-testing quartile (Q4: \u0026ge;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\u0026thinsp;=\u0026thinsp;1.205; 95% CI: 1.196\u0026ndash;1.214; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while probabilities for all detectable VL categories declined significantly.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eProlonged 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\u0026mdash;a conclusion likely generalizable to similar centralized HIV viral load laboratories across low- and middle-income countries.\u003c/p\u003e","manuscriptTitle":"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)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-29 00:59:13","doi":"10.21203/rs.3.rs-8647781/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-16T09:22:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-07T14:45:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"119003221130959464249133185937657246375","date":"2026-02-25T13:09:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-09T14:33:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"274131908359945157520614688769877937982","date":"2026-02-05T14:20:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"13254589984090130110857843599383967889","date":"2026-02-05T13:55:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-05T13:35:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-26T11:50:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-22T02:01:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Bulletin of the National Research Centre","date":"2026-01-20T09:34:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bulletin-of-the-national-research-centre","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnrc","sideBox":"Learn more about [Bulletin of the National Research Centre](https://BNRC.springeropen.com)","snPcode":"42269","submissionUrl":"https://submission.springernature.com/new-submission/42269/3","title":"Bulletin of the National Research Centre","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"911cab26-00b2-46d8-8b10-a0c5b545465b","owner":[],"postedDate":"January 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T00:08:22+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-29 00:59:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8647781","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8647781","identity":"rs-8647781","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}