Planning for the aftershocks: a model of post-acute care needs for hospitalized COVID-19 patients
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Abstract
Since its emergence in late 2019, COVID-19 has caused significant global morbidity and mortality, overwhelming health systems. Considerable attention has been paid to the burden COVID-19 has put on acute care hospitals, with numerous models projecting hospitalizations and ICU needs for the duration of the pandemic. However, less attention has been paid to where these patients may go if they require additional care following hospital discharge. As COVID-19 patients recover from severe infections, many of them require additional care. Yet with post-acute care facilities averaging 85% capacity prior to the pandemic and the significant potential for outbreaks, consideration of the downstream effects of the surge of hospitalized COVID-19 patients is critical. Here, we present a method for projecting COVID-19 post-acute care needs. Our model is designed to take the output from any of the numerous epidemiological models (hospital discharges) and estimate the flow of patients to post-acute care services, thus providing a similar surge planning model for post-acute care services. Using data from the University of Utah Hospital, we find that for those who require specialized post-acute care, the majority require either home health care or skilled nursing facilities. Likewise, we find the expected peak in post-acute care occurs about two weeks after the expected peak for acute care hospitalizations, a result of the duration of hospitalization. This short delay between acute care and post-acute care surges highlights the importance of considering the organization necessary to accommodate the influx of recovering COVID-19 patients and protect non-COVID-19 patients prior to the peak in acute care hospitalizations. We developed this model to guide policymakers in addressing the “aftershocks” of discharged patients requiring further supportive care; while we only show the outcomes for discharges based on preliminary data from the University of Utah Hospital, we suggest alternative uses for our model including adapting it to explore potential alternative strategies for addressing the surge in acute care facilities during future pandemic waves.
Author Summary
COVID-19 has caused significant morbidity and mortality globally, putting considerable strain on healthcare systems as a result of high rates of hospitalization and critical care needs among COVID-19 patients. To address this immediate need, a number of decision support tools have been developed to project hospitalization, intensive care unit (ICU) hospitalizations, and ventilator needs for the COVID-19 pandemic. As COVID-19 patients are discharged from acute care hospitals, many of them will require significant additional post-acute care. However, with post-acute care facilities at high capacity prior to the influx of COVID-19 patients and with significant outbreak potential in long-term care facilities, there is high potential for shortages of post-acute care services. Here, we present a model of COVID-19 post-acute care needs that is analogous to most epidemiological models of COVID-19 hospitalization and ICU care needs. We develop our model on University of Utah Hospital data and demonstrate its utility and its flexibility to be used in other contexts. Our model aims to guide public health policymaking in addressing the “aftershocks” of discharged patients requiring further care, to prevent potential healthcare shortages.
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SciScore for 10.1101/2020.06.12.20129551: (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: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our modeling approach has several limitations. Our initial assumptions about the fraction of patients requiring each post-acute care service are very uncertain, as they are based on aggregated historical University of Utah Health discharges prior to the appearance of COVID-19. However, we account for this by giving relatively low weight to our priors versus observed COVID-19 discharges. Another limitation is that we do not account for …
SciScore for 10.1101/2020.06.12.20129551: (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: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our modeling approach has several limitations. Our initial assumptions about the fraction of patients requiring each post-acute care service are very uncertain, as they are based on aggregated historical University of Utah Health discharges prior to the appearance of COVID-19. However, we account for this by giving relatively low weight to our priors versus observed COVID-19 discharges. Another limitation is that we do not account for uncertainty about the length-of-stay estimates for post-acute care services, although the model could be modified to incorporate this in future work. The largest limitation is that the model does not directly account for characteristics of the patient population, meaning the posterior allocation probabilities may not generalize well to regions or health systems with dissimilar populations. An extension of this work could condition the probability of requiring a post-acute care service on patient characteristics such as age or comorbidities. Although we highlight several limitations, our approach is well-suited to answer policy questions and provides a unique modeling output that can help guide the post-acute care surge. As most epidemiological models focused on acute care services, we concentrate on post-discharge needs, however, as is the case with standard epidemiological models, it is critical for policy makers to consider the range of possible trajectories and the sensitivity of results to different assumptions. Likewise, since we do not mod...
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.
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- No protocol registration statement was detected.
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