Laboratory capacity strengthening in Zimbabwe as part of the COVID-19 response: what has worked? What still needs to be done?

The COVID-19 pandemic was declared a Public Health Emergency of International Concern on January 30, 2020. The government of Zimbabwe through the Ministry of Health and Child Care set up the COVID-19 national preparedness and response plan in which the laboratory was a key pillar. The implementation of PCR testing, genomic sequencing, and the establishment of quality management systems during the COVID-19 response strengthened the capacity of the public health laboratory system in responding to the pandemic. Here we present the different strategies taken by the government that strengthened laboratory capacity, the lessons learned during the COVID-19 response, and recommendations on how the capacity can be sustained and leveraged for outbreak response in the future.

Zimbabwe's emergency response to COVID-19: Enhancing access and accelerating COVID-19 testing as the first line of defense against the COVID-19 pandemic.

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) spreads rapidly, causing outbreaks that grow exponentially within a short period before interventions are sought and effectively implemented. Testing is part of the first line of defense against Corona Virus Disease of 2019 (COVID-19), playing a critical role in the early identification and isolation of cases to slow transmission, provision of targeted care to those affected, and protection of health system operations. Laboratory tests for COVID-19 based on nucleic acid amplification techniques were rapidly developed in the early days of the pandemic, but such tests typically require sophisticated laboratory infrastructure and skilled staff. In March 2020, Zimbabwe confirmed its first case of COVID-19; this was followed by an increase in infection rates as the pandemic spread across the country, thus increasing the demand for testing. One national laboratory was set to test all the country's COVID-19 suspect cases, building pressure on human and financial resources. Staff burnout and longer turnaround times of more than 48 h were experienced, and results were released late for clinical relevance. Leveraging on existing PCR testing platforms, including GeneXpert machines, eased the pressure for a short period before facing the stockout of SARs-CoV-2 cartridges for a long time, leading to work overload at a few testing sites contributing to long turnaround times. On September 11, WHO released the interim guidance to use antigen rapid diagnostic test as a diagnostic tool. The Zimbabwe laboratory pillar quickly adopted it and made plans for its implementation. The National Microbiology Reference Laboratory verified the two emergency-listed kits, the Panbio Abbott and the Standard Q, Biosensor, and they met the WHO minimum performance of ≥97% specificity and ≥80% sensitivity. Decentralizing diagnostic testing leveraging existing human resources became a game-changer in improving COVID-19 containment measures. Task shifting through training on Antigen rapid diagnostic tests (Ag-RDT) commenced, and testing was decentralized to all the ten provinces, from 1 central testing laboratory to more than 1,000 testing centers. WhatsApp platforms made it easier for data to be reported from remote areas. Result turnaround times were improved to the same day, and accessibility to testing was enhanced.

Can visual interpretation of NucliSens graphs reduce the need for repeat viral load testing?

BACKGROUND: In Zimbabwe, viral load (VL) testing for people living with HIV on antiretroviral therapy is performed at the National Microbiology Reference Laboratory using a NucliSens machine. Anecdotal evidence has shown that invalid graphs for "Target Not Detectable (TND)" will upon repeat VL testing produce a valid result for virus not detected, therefore removing the need to repeat the test. This needs formal assessment. OBJECTIVES: To determine i) intra- and inter-rater agreement of the visual interpretation of NucliSens graphs (Target Detectable [TD], TND and No Line [NL]) between two laboratory scientists and ii) sensitivity, specificity and predictive values of the NucliSens graphs compared with repeat VL results. METHOD: Cross sectional study using secondary data. Two laboratory scientists independently rated graphs one week apart for intra-rater agreement and compared final ratings with each other for inter-rater agreement. Consensus interpretations of graphs were compared with repeat VL results. Kappa coefficients were used to obtain measures of agreement. RESULTS: There were 562 patients with NucliSens graphs and repeat VL. Kappa scores were: 0.98 (Scientist A); 0.99 (Scientist B); 0.96 (Scientist A versus Scientist B); and 0.65 (NucliSens graphs versus VL). Sensitivity, specificity, positive predictive value and negative predictive value for graphs compared with VL were 71%, 92%, 79% and 89% respectively. CONCLUSION: Intra-and inter-rater agreements were almost perfect. The negative predictive value translates to a false negative rate of 11%. If repeat VL testing is not done, the clinical consequences need to be balanced against cost savings and the risks outweigh the benefits.

HIV Viral Load Estimation Using Hematocrit Corrected Dried Blood Spot Results on a BioMerieux NucliSENS® Platform.

While reporting human immunodeficiency virus (HIV) viral load (VL) using dried blood spot (DBS) in the BioMerieux NucliSENS platform, application of the hematocrit correction factor has been suggested. In this cross-sectional study from the National Microbiology Reference Laboratory of Zimbabwe, we assessed whether hematocrit correction (individual and/or mean) in DBS results improved the correlation with plasma VL and prediction of VL non-suppression (≥1000 copies per ml in plasma). Of 517 specimens during August-December 2018, 65(12.6%) had non-suppressed plasma VL results. The hematocrit correction factor ranged from 1.3 to 2.0 with a mean of 1.6, standard deviation (SD: 1.5, 1.7). The intraclass correlation (ICC) for mean (0.859, 95% CI: 0.834, 0.880) and individual (0.809, 95% CI: 0.777, 0.837) hematocrit corrected DBS results were not significantly different. The uncorrected DBS results had a significantly lower ICC (0.640, 95% CI: 0.586, 0.688) when compared to corrected DBS results. There were no significant differences in validity, predictive values, and areas under the receiver operating characteristics curves for all three DBS results when predicting VL non-suppression. To conclude, hematocrit correction of DBS VL results improved agreement with the plasma results but did not improve prediction of VL non-suppression. The results were not significantly different for individual and mean corrected results.