Dataset Information

COVID-19 Models for Hospital Surge Capacity Planning: A Systematic Review.

ABSTRACT:

Objective

Health system preparedness for coronavirus disease (COVID-19) includes projecting the number and timing of cases requiring various types of treatment. Several tools were developed to assist in this planning process. This review highlights models that project both caseload and hospital capacity requirements over time.

Methods

We systematically reviewed the medical and engineering literature according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We completed searches using PubMed, EMBASE, ISI Web of Science, Google Scholar, and the Google search engine.

Results

The search strategy identified 690 articles. For a detailed review, we selected 6 models that met our predefined criteria. Half of the models did not include age-stratified parameters, and only 1 included the option to represent a second wave. Hospital patient flow was simplified in all models; however, some considered more complex patient pathways. One model included fatality ratios with length of stay (LOS) adjustments for survivors versus those who die, and accommodated different LOS for critical care patients with or without a ventilator.

Conclusion

The results of our study provide information to physicians, hospital administrators, emergency response personnel, and governmental agencies on available models for preparing scenario-based plans for responding to the COVID-19 or similar type of outbreak.

SUBMITTER: Klein MG

PROVIDER: S-EPMC7643009 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

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Publications

COVID-19 Models for Hospital Surge Capacity Planning: A Systematic Review.

Klein Michael G MG Cheng Carolynn J CJ Lii Evonne E Mao Keying K Mesbahi Hamza H Zhu Tianjie T Muckstadt John A JA Hupert Nathaniel N

Disaster medicine and public health preparedness 20200910 1

<h4>Objective</h4>Health system preparedness for coronavirus disease (COVID-19) includes projecting the number and timing of cases requiring various types of treatment. Several tools were developed to assist in this planning process. This review highlights models that project both caseload and hospital capacity requirements over time.<h4>Methods</h4>We systematically reviewed the medical and engineering literature according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (P ...[more]

PMID: 32907668

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Project description:IntroductionThe COVID-19 pandemic revealed an urgent need for analytic tools to help health system leaders plan for surges in hospital capacity. Our objective was to develop a practical and locally informed Tool to help explore the effects of public health interventions on SARS-CoV-2 transmission and create scenarios to project potential surges in hospital admissions and resource demand.MethodsOur Excel-based Tool uses a modified S(usceptible)-E(xposed)-I(nfected)-R(emoved) model with vaccination to simulate the potential spread of COVID-19 cases in the community and subsequent demand for hospitalizations, intensive care unit beds, ventilators, health care workers, and personal protective equipment. With over 40+ customizable parameters, planners can adapt the Tool to their jurisdiction and changes in the pandemic.ResultsWe showcase the Tool using data for Ontario, Canada. Using healthcare utilization data to fit hospitalizations and ICU cases, we illustrate how public health interventions influenced the COVID-19 reproduction number and case counts. We also demonstrate the Tool's ability to project a potential epidemic trajectory and subsequent demand for hospital resources. Using local data, we built three planning scenarios for Ontario for a 3-month period. Our worst-case scenario accurately projected the surge in critical care demand that overwhelmed hospital capacity in Ontario during Spring 2021.ConclusionsOur Tool can help different levels of health authorities plan their response to the pandemic. The main differentiators between this Tool and other existing tools include its ease of use, ability to build scenarios, and that it provides immediate outcomes that are ready to share with executive decision makers. The Tool is used by provincial health ministries, public health departments, and hospitals to make operational decisions and communicate possible scenarios to the public. The Tool provides educational value for the healthcare community and can be adapted for existing and emerging diseases.

Project description:IntroductionThe COVID-19 pandemic will test the capacity of health systems worldwide and especially so in low- and middle-income countries. The objective of this study was to assess the surge capacity of the Kenyan of the Kenyan health system in terms of general hospital and ICU beds in the face of the COVID-19 pandemic.MethodsWe assumed that 2% of the Kenyan population get symptomatic infection by SARS-Cov-2 based on modelled estimates for Kenya and determined the health system surge capacity for COVID-19 under three transmission curve scenarios, 6, 12, and 18 months. We estimated four measures of hospital surge capacity namely: 1) hospital bed surge capacity 2) ICU bed surge capacity 3) Hospital bed tipping point, and 5) ICU bed tipping point. We computed this nationally and for all the 47 county governments.ResultsThe capacity of Kenyan hospitals to absorb increases in caseload due to COVID-19 is constrained by the availability of oxygen, with only 58% of hospital beds in hospitals with oxygen supply. There is substantial variation in hospital bed surge capacity across counties. For example, under the 6 months transmission scenario, the percentage of available general hospital beds that would be taken up by COVID-19 cases varied from 12% Tharaka Nithi county, to 145% in Trans Nzoia county. Kenya faces substantial gaps in ICU beds and ventilator capacity. Only 22 out of the 47 counties have at least 1 ICU unit. Kenya will need an additional 1,511 ICU beds and 1,609 ventilators (6 months transmission curve) to 374 ICU beds and 472 ventilators (18 months transmission curve) to absorb caseloads due to COVID-19.ConclusionSignificant gaps exist in Kenya's capacity for hospitals to accommodate a potential surge in caseload due to COVID-19. Alongside efforts to slow and supress the transmission of the infection, the Kenyan government will need to implement adaptive measures and additional investments to expand the hospital surge capacity for COVID-19. Additional investments will however need to be strategically prioritized to focus on strengthening essential services first, such as oxygen availability before higher cost investments such as ICU beds and ventilators.

Project description:BackgroundSeasonal and regional surges in COVID-19 have imposed substantial strain on healthcare systems. Whereas sharp inclines in hospital volume were accompanied by overt increases in case fatality rates during the very early phases of the pandemic, the relative impact during later phases of the pandemic are less clear. We sought to characterize how the 2020 winter surge in COVID-19 volumes impacted case fatality in an adequately-resourced health system.MethodsWe performed a retrospective cohort study of all adult diagnosed with COVID-19 in a large academic healthcare system between August 25, 2020 to May 8, 2021, using multivariable logistic regression to examine case fatality rates across 3 sequential time periods around the 2020 winter surge: pre-surge, surge, and post-surge. Subgroup analyses of patients admitted to the hospital and those receiving ICU-level care were also performed. Additionally, we used multivariable logistic regression to examine risk factors for mortality during the surge period.ResultsWe studied 7388 patients (aged 52.8 ± 19.6 years, 48% male) who received outpatient or inpatient care for COVID-19 during the study period. Patients treated during surge (N = 6372) compared to the pre-surge (N = 536) period had 2.64 greater odds (95% CI 1.46-5.27) of mortality after adjusting for sociodemographic and clinical factors. Adjusted mortality risk returned to pre-surge levels during the post-surge period. Notably, first-encounter patient-level measures of illness severity appeared higher during surge compared to non-surge periods.ConclusionsWe observed excess mortality risk during a recent winter COVID-19 surge that was not explained by conventional risk factors or easily measurable variables, although recovered rapidly in the setting of targeted facility resources. These findings point to how complex interrelations of population- and patient-level pandemic factors can profoundly augment health system strain and drive dynamic, if short-lived, changes in outcomes.

Dataset Information

COVID-19 Models for Hospital Surge Capacity Planning: A Systematic Review.

Objective

Methods

Results

Conclusion

Publications

COVID-19 Models for Hospital Surge Capacity Planning: A Systematic Review.

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