Minimizing Population Health Loss in Times of Scarce Surgical Capacity During the Coronavirus Disease 2019 Crisis and Beyond: A Modeling Study

OBJECTIVES

Coronavirus disease 2019 has put unprecedented pressure on healthcare systems worldwide, leading to a reduction of the available healthcare capacity. Our objective was to develop a decision model to estimate the impact of postponing semielective surgical procedures on health, to support prioritization of care from a utilitarian perspective.

METHODS

A cohort state-transition model was developed and applied to 43 semielective nonpediatric surgical procedures commonly performed in academic hospitals. Scenarios of delaying surgery from 2 weeks were compared with delaying up to 1 year and no surgery at all. Model parameters were based on registries, scientific literature, and the World Health Organization Global Burden of Disease study. For each surgical procedure, the model estimated the average expected disability-adjusted life-years (DALYs) per month of delay.

RESULTS

Given the best available evidence, the 2 surgical procedures associated with most DALYs owing to delay were bypass surgery for Fontaine III/IV peripheral arterial disease (0.23 DALY/month, 95% confidence interval [CI]: 0.13-0.36) and transaortic valve implantation (0.15 DALY/month, 95% CI: 0.09-0.24). The 2 surgical procedures with the least DALYs were placing a shunt for dialysis (0.01, 95% CI: 0.005-0.01) and thyroid carcinoma resection (0.01, 95% CI: 0.01-0.02).

CONCLUSION

Expected health loss owing to surgical delay can be objectively calculated with our decision model based on best available evidence, which can guide prioritization of surgical procedures to minimize population health loss in times of scarcity. The model results should be placed in the context of different ethical perspectives and combined with capacity management tools to facilitate large-scale implementation.

The potential effectiveness of the WHO International Health Regulations capacity requirements on control of the COVID-19 pandemic: a cross-sectional study of 114 countries

OBJECTIVES

We examined if the WHO International Health Regulations (IHR) capacities were associated with better COVID-19 pandemic control.

DESIGN

Observational study.

SETTING

Population-based study of 114 countries.

PARTICIPANTS

General population.

MAIN OUTCOME MEASURES

For each country, we extracted: (1) the maximum rate of COVID-19 incidence increase per 100,000 population over any 5-day moving average period since the first 100 confirmed cases; (2) the maximum 14-day cumulative incidence rate since the first case; (3) the incidence and mortality within 30 days since the first case and first COVID-19-related death, respectively. We retrieved the 13 country-specific International Health Regulations capacities and constructed linear regression models to examine whether these capacities were associated with COVID-19 incidence and mortality, controlling for the Human Development Index, Gross Domestic Product, the population density, the Global Health Security index, prior exposure to SARS/MERS and Stringency Index.

RESULTS

Countries with higher International Health Regulations score were significantly more likely to have lower incidence (β coefficient -24, 95% CI -35 to -13) and mortality (β coefficient -1.7, 95% CI -2.5 to -1.0) per 100,000 population within 30 days since the first COVID-19 diagnosis. A similar association was found for the other incidence outcomes. Analysis using different regression models controlling for various confounders showed a similarly significant association.

CONCLUSIONS

The International Health Regulations score was significantly associated with reduction in rate of incidence and mortality of COVID-19. These findings inform design of pandemic control strategies, and validated the International Health Regulations capacities as important metrics for countries that warrant evaluation and improvement of their health security capabilities.

Estimated surge in hospital and intensive care admission because of the coronavirus disease 2019 pandemic in the Greater Toronto Area, Canada: a mathematical modelling study

BACKGROUND

In pandemics, local hospitals need to anticipate a surge in health care needs. We examined the modelled surge because of the coronavirus disease 2019 (COVID-19) pandemic that was used to inform the early hospital-level response against cases as they transpired.

METHODS

To estimate hospital-level surge in March and April 2020, we simulated a range of scenarios of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread in the Greater Toronto Area (GTA), Canada, using the best available data at the time. We applied outputs to hospital-specific data to estimate surge over 6 weeks at 2 hospitals (St. Michael's Hospital and St. Joseph's Health Centre). We examined multiple scenarios, wherein the default (R0 = 2.4) resembled the early trajectory (to Mar. 25, 2020), and compared the default model projections with observed COVID-19 admissions in each hospital from Mar. 25 to May 6, 2020.

RESULTS

For the hospitals to remain below non-ICU bed capacity, the default pessimistic scenario required a reduction in non-COVID-19 inpatient care by 38% and 28%, respectively, with St. Michael's Hospital requiring 40 new ICU beds and St. Joseph's Health Centre reducing its ICU beds for non-COVID-19 care by 6%. The absolute difference between default-projected and observed census of inpatients with COVID-19 at each hospital was less than 20 from Mar. 25 to Apr. 11; projected and observed cases diverged widely thereafter. Uncertainty in local epidemiological features was more influential than uncertainty in clinical severity.

INTERPRETATION

Scenario-based analyses were reliable in estimating short-term cases, but would require frequent re-analyses. Distribution of the city's surge was expected to vary across hospitals, and community-level strategies were key to mitigating each hospital's surge.

The Surge After the Surge: Cardiac Surgery Post-COVID-19

Background

The coronavirus disease 2019 (COVID-19) pandemic has dramatically reduced adult cardiac surgery case volumes as institutions and surgeons curtail nonurgent operations. There will be a progressive increase in deferred cases during the pandemic that will require completion within a limited time frame once restrictions ease. We investigated the impact of various levels of increased postpandemic hospital operating capacity on the time to clear the backlog of deferred cases.

Methods

We collected data from 4 cardiac surgery programs across 2 health systems. We recorded case rates at baseline and during the COVID-19 pandemic and created a mathematical model to quantify the cumulative surgical backlog based on the projected pandemic duration. We then used the model to predict the time required to clear the backlog depending on the level of increased operating capacity.

Results

Cardiac surgery volumes fell to 54% of baseline after restrictions were implemented. Assuming a service restoration date of either June 1 or July 1, we calculated the need to perform 216% or 263% of monthly baseline volume, respectively, to clear the backlog in 1 month. The actual duration required to clear the backlog highly depends on hospital capacity in the post-COVID period, and ranges from 1 to 8 months, depending on when services are restored and the degree of increased capacity.

Conclusions

Cardiac surgical operating capacity during the COVID-19 recovery period will have a dramatic impact on the time to clear the deferred cases backlog. Inadequate operating capacity may cause substantial delays and increase morbidity and mortality. If only prepandemic capacity is available, the backlog will never clear.

Covid-19: implications for prehospital, emergency and hospital care in patients with acute coronary syndromes

Hospitals play a critical role in providing communities with essential medical care during all types of disaster. Depending on their scope and nature, disasters can lead to a rapidly increasing service demand that can overwhelm the functional capacity and safety of hospitals and the healthcare system at large. Planning during the community outbreak of coronavirus disease 2019 (Covid-19) is critical for maintaining healthcare services during our response. This paper describes, besides general measures in times of a pandemic, also the necessary changes in the invasive diagnosis and treatment of patients presenting with different entities of acute coronary syndromes including structural adaptations (networks, spokes and hub centres) and therapeutic adjustments.

Declining trends in local health department preparedness capacities

OBJECTIVES

We examined local health department (LHD) preparedness capacities in the context of participation in accreditation and other performance improvement efforts. MetHODS: We analyzed preparedness in 8 domains among LHDs responding to a preparedness capacity instrument from 2010 through 2012. Study groups included LHDs that (1) were exposed to a North Carolina state-based accreditation program, (2) participated in 1 or more performance improvement programs, and (3) had not participated in any performance improvement programs. We analyzed mean domain preparedness scores and applied a series of nonparametric Mann-Whitney Wilcoxon tests to determine whether preparedness domain scores differed significantly between study groups from 2010 to 2012.

RESULTS

Preparedness capacity scores fluctuated and decreased significantly for all study groups for 2 domains: surveillance and investigation and legal preparedness. Significant decreases also occurred among participants for plans and protocols, communication, and incident command. Declines in capacity scores were not as great and less likely to be significant among North Carolina LHDs.

CONCLUSIONS

Decreases in preparedness capacities over the 3 survey years may reflect multiple years of funding cuts and job losses, specifically for preparedness. An accreditation program may have a protective effect against such contextual factors.

Health systems' "surge capacity": state of the art and priorities for future research

CONTEXT

Over the past decade, a number of high-impact natural hazard events, together with the increased recognition of pandemic risks, have intensified interest in health systems' ability to prepare for, and cope with, "surges" (sudden large-scale escalations) in treatment needs. In this article, we identify key concepts and components associated with this emerging research theme. We consider the requirements for a standardized conceptual framework for future research capable of informing policy to reduce the morbidity and mortality impacts of such incidents. Here our objective is to appraise the consistency and utility of existing conceptualizations of health systems' surge capacity and their components, with a view to standardizing concepts and measurements to enable future research to generate a cumulative knowledge base for policy and practice.

METHODS

A systematic review of the literature on concepts of health systems' surge capacity, with a narrative summary of key concepts relevant to public health.

FINDINGS

The academic literature on surge capacity demonstrates considerable variation in its conceptualization, terms, definitions, and applications. This, together with an absence of detailed and comparable data, has hampered efforts to develop standardized conceptual models, measurements, and metrics. Some degree of consensus is evident for the components of surge capacity, but more work is needed to integrate them. The overwhelming concentration in the United States complicates the generalizability of existing approaches and findings.

CONCLUSIONS

The concept of surge capacity is a useful addition to the study of health systems' disaster and/or pandemic planning, mitigation, and response, and it has far-reaching policy implications. Even though research in this area has grown quickly, it has yet to fulfill its potential to generate knowledge to inform policy. Work is needed to generate robust conceptual and analytical frameworks, along with innovations in data collection and methodological approaches that enhance health systems' readiness for, and response to, unpredictable high-consequence surges in demand.

Prediction of unmet primary care needs for the medically vulnerable post-disaster: an interrupted time-series analysis of health system responses

Disasters serve as shocks and precipitate unanticipated disturbances to the health care system. Public health surveillance is generally focused on monitoring latent health and environmental exposure effects, rather than health system performance in response to these local shocks. The following intervention study sought to determine the long-term effects of the 2005 chlorine spill in Graniteville, South Carolina on primary care access for vulnerable populations. We used an interrupted time-series approach to model monthly visits for Ambulatory Care Sensitive Conditions, an indicator of unmet primary care need, to quantify the impact of the disaster on unmet primary care need in Medicaid beneficiaries. The results showed Medicaid beneficiaries in the directly impacted service area experienced improved access to primary care in the 24 months post-disaster. We provide evidence that a health system serving the medically underserved can prove resilient and display improved adaptive capacity under adverse circumstances (i.e., technological disasters) to ensure access to primary care for vulnerable sub-groups. The results suggests a new application for ambulatory care sensitive conditions as a population-based metric to advance anecdotal evidence of secondary surge and evaluate pre- and post-health system surge capacity following a disaster.