Cost-analysis of COVID-19 sample collection, diagnosis, and contact tracing in low resource setting: The case of Addis Ababa, Ethiopia.

BACKGROUND: Ethiopia has been responding to the COVID-19 pandemic through a combination of interventions, including non-pharmaceutical interventions, quarantine, testing, isolation, contact tracing, and clinical management. Estimating the resources consumed for COVID-19 prevention and control could inform efficient decision-making for epidemic/pandemic-prone diseases in the future. This study aims to estimate the unit cost of COVID-19 sample collection, laboratory diagnosis, and contact tracing in Addis Ababa, Ethiopia. METHODS: Primary and secondary data were collected to estimate the costs of COVID-19 sample collection, diagnosis, and contact tracing. A healthcare system perspective was used. We used a combination of micro-costing (bottom-up) and top-down approaches to estimate resources consumed and the unit costs of the interventions. We used available cost and outcome data between May and December 2020. The costs were classified into capital and recurrent inputs to estimate unit and total costs. We identified the cost drivers of the interventions. We reported the cost for the following outcome measures: (1) cost per sample collected, (2) cost per laboratory diagnosis, (3) cost per sample collected and laboratory diagnosis, (4) cost per contact traced, and (5) cost per COVID-19 positive test identified. We conducted one-way sensitivity analysis by varying the input parameters. All costs were reported in US dollars (USD). RESULTS: The unit cost per sample collected was USD 1.33. The unit cost of tracing a contact of an index case was USD 0.66. The unit cost of COVID-19 diagnosis, excluding the cost for sample collection was USD 3.91. The unit cost of sample collection per COVID-19 positive individual was USD 11.63. The unit cost for COVID-19 positive test through contact tracing was USD 54.00. The unit cost COVID-19 DNA PCR diagnosis for identifying COVID-19 positive individuals, excluding the sample collection and transport cost, was USD 37.70. The cost per COVID-19 positive case identified was USD 49.33 including both sample collection and laboratory diagnosis costs. Among the cost drivers, personnel cost (salary and food cost) takes the highest share for all interventions, ranging from 51-76% of the total cost. CONCLUSION: The costs of sample collection, diagnosis, and contact tracing for COVID-19 were high given the low per capita health expenditure in Ethiopia and other low-income settings. Since the personnel cost accounts for the highest cost, decision-makers should focus on minimizing this cost when faced with pandemic-prone diseases by strengthening the health system and using digital platforms. The findings of this study can help decision-makers prioritize and allocate resources for effective public health emergency response.

Diagnostic accuracy of three commercially available one step RT-PCR assays for the detection of SARS-CoV-2 in resource limited settings.

BACKGROUND: COVID-19 is an ongoing public health pandemic regardless of the countless efforts made by various actors. Quality diagnostic tests are important for early detection and control. Notably, several commercially available one step RT-PCR based assays have been recommended by the WHO. Yet, their analytic and diagnostic performances have not been well documented in resource-limited settings. Hence, this study aimed to evaluate the diagnostic sensitivities and specificities of three commercially available one step reverse transcriptase-polymerase chain reaction (RT-PCR) assays in Ethiopia in clinical setting. METHODS: A cross-sectional study was conducted from April to June, 2021 on 279 respiratory swabs originating from community surveillance, contact cases and suspect cases. RNA was extracted using manual extraction method. Master-mix preparation, amplification and result interpretation was done as per the respective manufacturer. Agreements between RT-PCRs were analyzed using kappa values. Bayesian latent class models (BLCM) were fitted to obtain reliable estimates of diagnostic sensitivities, specificities of the three assays and prevalence in the absence of a true gold standard. RESULTS: Among the 279 respiratory samples, 50(18%), 59(21.2%), and 69(24.7%) were tested positive by TIB, Da An, and BGI assays, respectively. Moderate to substantial level of agreement was reported among the three assays with kappa value between 0 .55 and 0.72. Based on the BLCM relatively high specificities (95% CI) of 0.991(0.973-1.000), 0.961(0.930-0.991) and 0.916(0.875-0.952) and considerably lower sensitivities with 0.813(0.658-0.938), 0.836(0.712-0.940) and 0.810(0.687-0.920) for TIB MOLBIOL, Da An and BGI respectively were found. CONCLUSIONS: While all the three RT-PCR assays displayed comparable sensitivities, the specificities of TIB MOLBIOL and Da An were considerably higher than BGI. These results help adjust the apparent prevalence determined by the three RT-PCRs and thus support public health decisions in resource limited settings and consider alternatives as per their prioritization matrix.

Prevalence of SARS-CoV-2 in urban and rural Ethiopia: Randomized household serosurveys reveal level of spread during the first wave of the pandemic.

BACKGROUND: The spread of SARS-CoV-2 in Sub-Saharan Africa is poorly understood and to date has generally been characterised by a lower number of declared cases and deaths as compared to other regions of the world. Paucity of reliable information, with insights largely derived from limited RT-PCR testing in high-risk and urban populations, has been one of the biggest barriers to understanding the course of the pandemic and informed policy-making. Here we estimate seroprevalence of anti-SARS-CoV-2 antibodies in Ethiopia during the first wave of the pandemic. METHODS: We undertook a population-based household seroprevalence serosurvey based on 1856 participants in Ethiopia, in the capital city Addis Ababa, and in Jimma, a middle-sized town in the Oromia region, and its rural surroundings (districts of Seka and Mana), between 22 July and 02 September 2020. We tested one random participant per household for anti-SARS-CoV-2 antibodies using a high specificity rapid diagnostic tests (RDTs) and evaluated population seroprevalence using a Bayesian logistic regression model taking into account test performance as well as age and sex of the participants. FINDINGS: In total, 2304 random households were visited, with 1856 individuals consenting to participate. This produced a sample of 956 participants in Addis Ababa and 900 participants in Jimma. IgG prevalence was estimated at 1.9% (95% CI 0.4-3.7%), and combined IgM/IgG prevalence at 3.5% (95% CI 1.7-5.4%) for Addis Ababa in early August 2020, with higher prevalence in central sub-cities. Prevalence in Jimma town was lower at 0.5% (95% CI 0-1.8%) for IgG and 1.6% (95%CI 0-4.1%) for IgM/IgG, while in rural Jimma IgG prevalence was 0.2% and IgM/IgG 0.4% in early September. INTERPRETATION: More than four months after the first cases were detected in Ethiopia, Addis Ababa displayed a prevalence under 5% and likely as low as 2%, while rural Jimma displayed a prevalence of 0.2%. A 2% seroprevalence figure for the capital translated to a number of cases at least five times larger than those reported for the country as a whole. At the same time, it contrasts with significantly higher seroprevalence figures in large cities in Europe and America only two to three months after the first cases. This population-based seroepidemiological study thus provides evidence of a slower spread of SARS-CoV-2 in the Ethiopian population during the first wave of the pandemic and does not appear to support the notion that lower case numbers were simply a reflection of limited testing and surveillance. FUNDING: Schmidt Family Foundation, Joachim Hertz Foundation, Nespresso, Peet's and Smuckers.

Establishment of COVID-19 testing laboratory in resource-limited settings: challenges and prospects reported from Ethiopia.

The Coronavirus pandemic is recording unprecedented deaths worldwide. The temporal distribution and burden of the disease varies from setting to setting based on economic status, demography and geographic location. A rapid increase in the number of COVID-19 cases is being reported in Africa as of June 2020. Ethiopia reported the first COVID-19 case on 13 March 2020. Limited molecular laboratory capacity in resource constrained settings is a challenge in the diagnosis of the ever-increasing cases and the overall management of the disease. In this article, the Ethiopian Public Health Institute (EPHI) shares the experience, challenges and prospects in the rapid establishment of one of its COVID-19 testing laboratories from available resources. The first steps in establishing the COVID-19 molecular testing laboratory were i) identifying a suitable space ii) renovating it and iii) mobilizing materials including consumables, mainly from the Malaria and Neglected Tropical Diseases (NTDs) research team at the EPHI. A chain of experimental design was set up with distinct laboratories to standardize the extraction of samples, preparation of the master mix and detection. At the commencement of sample reception and testing, laboratory contamination was among the primary challenges faced. The source of the contamination was identified in the master mix room and resolved. In summary, the established COVID-19 testing lab has tested more than 40,000 samples (August 2020) and is the preferred setting for research and training. The lessons learned may benefit the further establishment of emergency testing laboratories for COVID-19 and/or other epidemic/pandemic diseases in resource-limited settings.