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Analysis of regional vulnerabilities to US COVID-19 hospital capacity

Eric Dorré

Capital Markets and Digital Assets: Strategy | Product Development | Operations | Business and Corporate Development | Regulatory Change | Technology

发布日期: 2020年3月18日

Purpose

The following short paper is written to provoke discussion and action among public health officials, medical professionals and government officials who have the means to mobilize facilities, materials and people. We must prepare for the likely large influx of medical caseload that may overwhelm our current capacity to serve it. If Italy’s experience is our canary in a coalmine, US healthcare service complications with the potential for patient triage, selective treatment, and unnecessary death will be most prominent in regions with disproportionately elderly populations relative to hospital bed capacity and physicians, with higher incidences of underlying health conditions acting a multiplier of the problems. Using publicly available information, this paper highlights those areas. The paper does not focus on the reduction of spread of the virus, but rather it emphasizes the need to anticipate and prepare for our capacity to deal with it.

Summary

There is widely available data that foretells where demand for care and availability to provide it will be constrained. The data will alarm you. You might live in a region identified as high risk. We are all walking into an unknown healthcare future in the next few weeks and months. I urge you to read and help take action. Public data reveals concerning similarities between the situation in US regions and the drivers of medical delivery failure in Italy. Actions should be taken now to retool those regions in order to provide care for those affected by Covid-19, as well as maintain capacity to treat other life threatening conditions for which the health system is geared to serve.

Drivers of imminent healthcare demand and supply

Demographics will drive demand for care in different regions of the United States

Age greater than 60 is not evenly dispersed
Specific underlying health conditions are regional (Heart Disease, Diabetes, others)

High level capacity constraints to deliver that care regionally in the United States

Supply of community hospital beds
Number of professional healthcare personnel
Availability of medical supplies (out of scope for this paper)

Implications at the regional level could be significant for all, and severe for some

Regions of concern illustrate an imbalanced supply and demand curve of resources required to treat Covid-19

South Carolina and Nevada materially lack hospital beds and physicians to serve their population weighted to 65 and older, with populations exhibiting materially higher than US national average [“USNA”] incidence of underlying health factors. 7.9m total people, equivalent to over 2% of the US population lives in these states
16 other states carry 2 red flags, defined here as >10% below UNSA hospital beds per 65 and older, >10% below UNSA physicians per 65 and older, and/or 110% or greater incidence of underlying health factors such as Heart Disease or Diabetes. 78m people, or 25% of the US population lives in these states
53% the US population lives in one of 21 states carrying one risk factor
20% live in 11 states with near or better than US average bed capacity, physician availability and health metrics

Suggested actions

Build temporary facilities, positioned to ensure health care professionals can access while they maintain health and effectiveness

Use of military locations, personnel and material may be required. Locations could include bases and vessels, hotel/convention facilities and universities with hospitals and housing. Where necessary, isolated vacation spots such as ranches, islands, recreation facilities could be used.
Bias towards locations nearer to larger, existing hospital facilities to keep caregivers for Covid-19 and other non-related emergency procedures accessible
Triage and care for Covid-19 in these temporary facilities, thus maintaining to the extent possible capacity for other specialized needs with quicker turnaround, and reducing contamination risk
Provide caregivers rest facilities at nearby hotels to, reduce the likelihood of extreme fatigue, falling severely ill and unable to care for others

Other actions

Immediately reduce time-discretionary specialty medical services, affording specialty Physicians and their departments time to prepare and plan for the logistical shift (training, set up, materials sourcing, etc).
Relax traditional red tape to ensure some level of care can be delivered, rather than no level of care. For example, while maintaining standards, streamline certifications of facilities and material/supplies production.

Key questions from others’ experience

In the coming paragraphs, we’ll build a case for action by answering the following questions:

What problems are other countries facing who are a few days ahead of us on the curve?
What should we do now to learn from their experiences of those further ahead of the curve?
Where are these risks related to demand for care, and inability to supply it likely to emerge in the United States?

Remarks on data, methodology, and rate of response

This paper will use the observed metrics in Wuhan and constraints in Italy to provide insight into the latter two questions. There are many acknowledged reasons to debate the data I’ve used, such as the Case Fatality Rate [“CFR”] and the applicability of Wuhan’s early cases; however, they are adequately indicative of the large-scale differences in severity based upon identified risk factors; I’m not building fancy epidemiological models. International benchmarks are sourced from the World Health Organization via the World Bank, as I found them to be most normalized there. That’s all we’ve got at the moment, and I bias towards speed of action versus analytical perfection. Similarly, we use US state data as it is available from public sources including the Kaiser Family Foundation State Facts website. I realize that a more granular level of data, including geographic and hospital bed type, would be more transparent and actionable; but again, I don’t have that data, the American Hospital Association was unwilling to give it to me without a significant fee. If this inspires someone who has better data to use it and add to this analysis, then that is a “win”. Or if people are biased to action with more experience in the space and just decide to move, perhaps even better. I’m happy to help with that.

Demand for care is driven by age and underlying health conditions

The Case Fatality Rate/CFR in Wuhan was 0.9% in persons with no underlying health conditions (period leading up to 11 Feb 2020), with increased age and presence of underlying health conditions dramatically changing the profile of severity of the disease, and the demand for health services with it:

Over 60 years old - Wuhan CFR by age:

14.8%: 80+
8.0%: 70-79
3.6%: 60-69
<0.4% for those under 50

Underlying health issues - Wuhan CFR by underlying health condition:

Cardiovascular disease: 10.5%
Diabetes: 7.3%
Chronic respiratory disease: 6.3%
Hypertension: 6.0%
Cancer: 5.6%

Sources: Novel Coronavirus Pneumonia Emergency Response Epidemiology Teamexternal iconexternal icon. [The Epidemiological Characteristics of an Outbreak of 2019 Novel Coronavirus Diseases (COVID-19) in China]. Zhonghua Liu Xing Bing Xue Za Zhi. 2020;41(2):145–151. DOI:10.3760/cma.j.issn.0254-6450.2020.02.003

Constraints to provide care are simple to understand, and manageable with creativity

In Western Europe, and Italy to-date, there are acute shortages of:

People - Healthy, qualified medical professionals
Places - Hospital beds
Things - Medical supplies

Further complicating matters is that medical professionals in Italy and China have been more prone to higher severity cases, including death, absent of other risk factors. This is likely due to elevated exposure and extreme fatigue.

As we explore more thoroughly the geographic distribution of the risk factors driving demand and constraints on supply, we will see where those two influences in concert may foretell elevated risk to healthcare delivery.

As I do not have access to medical supplies data, it is out of scope of this discussion. It is, however, very relevant. The logistics of medical supply chains may very well be an influencer of temporary hospital location analysis.

Italy’s problem: unavailability or misalignment of resources required to address the demand driven by unhealthy persons over 65 years of age

Italy’s key problem is the disease and its targeting of the disproportionately elderly and infirm population, placing strains on a system designed for other purposes, specifically exposing a shortage of hospital beds to attend the infirm of all ages. While Italy also lacks healthy-enough physicians to attend to the caseload, as you can see below, the USA may actually be worse-off in that regard. It must be noted that both beds and doctors are necessary (but not sufficient) conditions to provide adequate care; one without the other will still result in an inability to serve.

Selected countries Capacity of Physicians and Community Hospital Beds Relative to Over 65 Population

Sources: World Bank Group/IBRD-IDA: World Health Organization, supplemented by country data.; World Health Organization's Global Health Workforce Statistics, OECD, supplemented by country data; Highbury Associates, LLC analysis

Chart note: In this chart, “Physicians” are considered Generalist and Specialist Doctors. Later analysis in this paper examines US-state data and will also include Nurse Practitioners and Physician Assistants, as they will perform important capacity systemically in the crisis in the US. I made a choice to include other professional caregivers in US State data as I felt it would improve the quality of the analysis and conclusions. The differences in this metric for international Physicians does not dilute the analysis or conclusions of this paper.

Bottom line. Italy needs more beds and doctors and healthcare providers. The United States is no better off as a whole. With respect to Physicians, we are even shorter-staffed than Italy. The problem is more acute in different regions in the USA. The following graphs and heat maps illustrate the potential problems.

Relative vulnerabilities in the USA

Capacity constraints are very real

The following chart illustrates the imbalanced distribution of resources across the United States, measured in terms of Community Hospital Beds and Physicians (General Medicine and Specialty doctors, Physician Assistants and Nurse Practitioners). No doubt the data is even more differentiated at more granular geographic and bed-type levels (example, ICU beds). On the left hand side and in blue are states where the number of infirm seniors may quickly overwhelm the bed capacity. In orange (and below the 0% line) are states with below USNA number of physicians per person over the age of 65. At any age, you don’t want to be in a state where both blue and orange lines are below average, as services will be constrained. You want a proactive plan and action from officials in your state.

Remember, on this Physician-based metric, the USA average is at a 10% deficit to Italy on an apples-to-apples basis. Many US states are far worse off than Italy.