Estimating healthcare resource needs for COVID-19 patients in Nigeria
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Abstract
Background
Predicting potential healthcare resource use under different scenarios will help to prepare the healthcare system for a surge in COVID-19 patients. In this study, we aim to predict the effect of COVID-19 on hospital resources in Nigeria.
Method
We adopted a previously published discrete-time, individual-level, health-state transition model of symptomatic COVID-19 patients to the Nigerian healthcare system and COVID-19 epidemiology. We simulated different combined scenarios of epidemic trajectories and acute care capacity. Primary outcomes included expected cumulative number of cases, days until depletion resources, and number of deaths associated with resource constraints. Outcomes were predicted over a 60-day time horizon.
Results
In our best-case epidemic trajectory, which implies successful implementation of public health measures to control COVID-19 spread, the current number of ventilator resources in Nigeria (conservative resources scenario), were expended within five days, and 901 patients may die while waiting for hospital resources in conservative resource scenario. In our expanded resource scenarios, ventilated ICU beds were depleted in all three epidemic trajectories within 60 days. Acute care resources were only sufficient in the best-case and intermediate epidemic scenarios, combined with a substantial increase in healthcare resources.
Conclusion
Current hospital resources are inadequate to manage the COVID-19 pandemic in Nigeria. Given Nigeria’s limited resources, it is imperative to increase healthcare resources and maintain aggressive public health measures to reduce COVID-19 transmission.
KEY QUESTIONS
What is already known on this subject?
While western countries seem to be recovering from the COVID-19 pandemic, there is an increasing community spread of the virus in many African countries.
The limited healthcare resources available in the region may not be sufficient to cope with increasing numbers of COVID-19 cases.
What this study adds?
Using the COVID-19 Resource Estimator (CORE) model, we demonstrate that implementing and maintaining aggressive public health measures to keep the epidemic growth at a low rate, while simultaneously substantially increasing healthcare resources is critical to minimize the impact of COVID-19 on morbidity and mortality.
The impact of COVID-19 in low resource settings will likely overwhelm health system capacity if aggressive public health measures are not implemented. To mitigate the impact of COVID-19 in these settings, it is essential to develop strategies to substantially increase health system capacities, including hospital resources, personal protective equipment and trained healthcare personnel and to implement and maintain aggressive public health measures.
Article activity feed
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SciScore for 10.1101/2020.08.19.20178434: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study has limitations. Our estimated number of ward bed was based on a national survey by a pharmaceutical company published in 2007 and might not represent the country’s current capacity. While keeping with the current literature, we assumed that death would only occur in critically ill patients, which may underestimate mortality. …
SciScore for 10.1101/2020.08.19.20178434: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study has limitations. Our estimated number of ward bed was based on a national survey by a pharmaceutical company published in 2007 and might not represent the country’s current capacity. While keeping with the current literature, we assumed that death would only occur in critically ill patients, which may underestimate mortality. Since the start of the pandemic, recruitment and training of new hospital staff have been ongoing, but actual data on the number of recruited staff was not available at the time of modelling, limiting our knowledge on the effect of vast expansion of hospital resources on staff capacity. Our study relies on reported cases to forecast future epidemic trajectories. The model does not account for underreporting of daily cases and long-time lag in case reports, which are common problems that occur in low resource settings due to limited testing capacity.6 Due to the unavailability of detailed COVID-19 data, some parameters included in the model were estimates obtained from a study on ARDS, a disease with similar clinical manifestations with COVID-19. Our healthcare resource utilization probabilities parameters (i.e. length of hospital stay and probability for need of ward, ICU and ventilator admissions and probability of death) were estimates from Canadian setting. While these parameter values arguably deviate from observed data in Africa, they are likely estimates for COVID-19 disease severity. Our study has several strengths. We incorporated obser...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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