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.

Maximising mentorship: Variations in laboratory mentorship models implemented in Zimbabwe.

BACKGROUND: Laboratory mentorship has proven to be an effective tool in building capacity and assisting laboratories in establishing quality management systems. The Zimbabwean Ministry of Health and Child Welfare implemented four mentorship models in 19 laboratories in conjunction with the Strengthening Laboratory Management Toward Accreditation (SLMTA) programme. OBJECTIVES: This study outlines how the different models were implemented, cost involved per model and results achieved. METHODS: Eleven of the laboratories had been trained previously in SLMTA (Cohort I). They were assigned to one of three mentorship models based on programmatic considerations: Laboratory Manager Mentorship (Model 1, four laboratories); One Week per Month Mentorship (Model 2, four laboratories); and Cyclical Embedded Mentorship (Model 3, three laboratories). The remaining eight laboratories (Cohort II) were enrolled in Cyclical Embedded Mentorship incorporated with SLMTA training (Model 4). Progress was evaluated using a standardised audit checklist. RESULTS: At SLMTA baseline, Model 1-3 laboratories had a median score of 30%. After SLMTA, at mentorship baseline, they had a median score of 54%. At the post-mentorship audit they reached a median score of 75%. Each of the three mentorship models for Cohort I had similar median improvements from pre- to post-mentorship (17 percentage points for Model 1, 23 for Model 2 and 25 for Model 3; p > 0.10 for each comparison). The eight Model 4 laboratories had a median baseline score of 24%; after mentorship, their median score increased to 63%. Median improvements from pre-SLMTA to post-mentorship were similar for all four models. CONCLUSION: Several mentorship models can be considered by countries depending on the available resources for their accreditation implementation plan.

Weighing the costs: Implementing the SLMTA programme in Zimbabwe using internal versus external facilitators.

BACKGROUND: In 2010, the Zimbabwe Ministry of Health and Child Welfare (MoHCW) adopted the Strengthening Laboratory Management Toward Accreditation (SLMTA) programme as a tool for laboratory quality systems strengthening. OBJECTIVES: To evaluate the financial costs of SLMTA implementation using two models (external facilitators; and internal local or MoHCW facilitators) from the perspective of the implementing partner and to estimate resources needed to scale up the programme nationally in all 10 provinces. METHODS: The average expenditure per laboratory was calculated based on accounting records; calculations included implementing partner expenses but excluded in-kind contributions and salaries of local facilitators and trainees. We also estimated theoretical financial costs, keeping all contextual variables constant across the two models. Resource needs for future national expansion were estimated based on a two-phase implementation plan, in which 12 laboratories in each of five provinces would implement SLMTA per phase; for the internal facilitator model, 20 facilitators would be trained at the beginning of each phase. RESULTS: The average expenditure to implement SLMTA in 11 laboratories using external facilitators was approximately US$5800 per laboratory; expenditure in 19 laboratories using internal facilitators was approximately $6000 per laboratory. The theoretical financial cost of implementing a 12-laboratory SLMTA cohort keeping all contextual variables constant would be approximately $58 000 using external facilitators; or $15 000 using internal facilitators, plus $86 000 to train 20 facilitators. The financial cost for subsequent SLMTA cohorts using the previously-trained internal facilitators would be approximately $15 000, yielding a break-even point of 2 cohorts, at $116 000 for either model. Estimated resources required for national implementation in 120 laboratories would therefore be $580 000 using external facilitators ($58 000 per province) and $322 000 using internal facilitators ($86 000 for facilitator training in each of two phases plus $15 000 for SLMTA implementation in each province). CONCLUSION: Investing in training of internal facilitators will result in substantial savings over the scale-up of the programme. Our study provides information to assist policy makers to develop strategic plans for investing in laboratory strengthening.