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The economic cost of treatment for patients with severe COVID-19 in Maputo Province, Mozambique


Neide Canana
Janet Dula
Nelmo Manjate
Igor Capitine
Ana Olga Mocumbi
Sérgio Chicumbe

Abstract

BACKGROUND
Mozambique has an under-resourced and fragile healthcare system that is already facing a high burden of comorbidities. Determining the cost of COVID-19 treatment is therefore an early priority to inform the resource-constrained pandemic response. This study aimed to estimate the economic cost of treating patients with severe COVID-19 in Mozambique.


METHODOLOGY
Cost data were collected retrospectively from the provider perspective. A “one-way” deterministic sensitivity analysis was performed to test the robustness of the assumptions.


RESULTS
The total economic cost of treatment for inpatients with severe COVID-19 at Centro da Matola 1 (CM1) was 21,157,159 MZN, corresponding to US$334,501. The economic cost per capita per day was 22,039 MZN, corresponding to US$348. The costs of treatment for patients with tuberculosis and patients with pneumonia did not vary considerably, ranging between 176,122 MZN (US$2,785) and 176,113 MZN (US$2,784) for the affected comorbidities; without co-morbidities, 176,105 MZN (US$2,784) to 176,087 MZN (US $2,784), respectively. COVID-19 patients co- infected with bronchopneumonia and HIV were the ones with the least costly morbidities, ranging from 88,085 MZN (US$1,393) to 88,053 MZN (US$1,392).


CONCLUSION
The results show a substantial cost to treat COVID-19 inpatients in a resource-constrained context. The course of the pandemic substantially impacted the total costs and consequently, there has been an increase in the demand for resources. Revising priority setting values and resource allocation shall be taken into consideration for timely adjustments to save lives, considering the scenario of a high burden of commodities versus resource constraints in Mozambican public health services. Additionally, it is highly recommended that the cost analysis be regularly updated to illustrate the current cost and contribute to informing the efficient allocation of resources.


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eISSN: 1022-9272