RES E A R C H A R T I C L E Open Access

Balancing revenue generation with capacity

generation: case distribution, financial

impact and hospital capacity changes from

cancelling or resuming elective surgeries in

the US during COVID-19

Joseph E. Tonna

, Heidi A. Hanson

1,2

, Jessica N. Cohan

1,2

, Marta L. McCrum

, Joshua J. Horns

Benjamin S. Brooke

1,2

, Rupam Das

, Brenna C. Kelly

, Alexander John Campbell

and James Hotaling

Abstract

Background: To increase bed capacity and resources, hospitals have postponed elective surgeries, although the

financial impact of this decision is unknown. We sought to report elective surgical case distribution, associated

gross hospital revenue and regional hospital and intensive care unit (ICU) bed capacity as elective surgical cases are

cancelled and then resumed under simulated trends of COVID-19 incidence.

Methods: A retrospective, cohort analysis was performed using insurance claims from 161 million enrollees from

the MarketScan database from January 1, 2008 to December 31, 2017. COVID-19 cases were calculated using

Institute for Health Metrics and Evaluation models. Centers for Disease Control (CDC) reports on the number of

hospitalized and intensive care patients by age estimated the number of cases seen in the ICU, the reduction in

elective surgeries and the financial impact of this from historic claims data, using a denominator of all inpatient

revenue and outpatient surgeries.

Results: Assuming 5% infection prevalence, cancelling all elective procedures decreases ICU overcapacity from 160

to 130%, but these elective surgical cases contribute 78% (IQR 74, 80) (1.1 trillion (T) US dollars) to inpatient hospital

plus outpatient surgical gross revenue per year. Muscul oskeletal, circulatory and digestive category elective surgical

cases compose 33% ($447B) of total revenue.

Conclusions: Procedures involving the musculoskeletal, cardiovascular and digestive system account for the largest

loss of hospital gross revenue when elective surgery is postponed. As hospital bed capacity increases following the

COVID-19 pandemic, restoring volume of these elective cases will help maintain revenue. In these estimates,

adopting universal masking would help to avoid overcapacity in all states.

Keywords: COVID-19 pandemic, Critical care capacity, Resource allocation, Available hospital beds, Overcapacity

which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give

appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if

changes were made. The images or other third party material in this article are included in the article's Creative Commons

licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons

licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain

permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the

data made available in this article, unless otherwise stated in a credit line to the data.

* Correspondence: joseph.ton[email protected]

Surgical Population Analytic Research Core (SPARC), Department of Surgery,

University of Utah Health, Salt Lake City, UT, USA

Full list of author information is available at the end of the article

Tonna et al. BMC Health Services Research (2020) 20:1119

https://doi.org/10.1186/s12913-020-05975-z

Background

The novel Coronavirus Infectious Disease 2019

(COVID-19) is a highly transm ittable virus that has re-

sulted in over 16 million infections worldwide [1].

United States (US) health systems are designed for

stable, predictable utilization patterns; they are generally

unprepared for surge medical need as is requ ired with

the COVID-19 pandemic. Surge situations have historic-

ally been addressed by deploying temporary medical

teams and tent facilities, or have overwhelmed the avail-

able resources with resultant excess morbidity and mor-

tality [2]. Current estimates suggest at least 5% of the US

population will contract COVID-19, of whom 15% will

require hospitalization, and 5% will require intensive

care [3–5]. At this rate, there will be 5 infected patients

requiring hospitalization for every existing US hospital

bed in addition to concomitant hospitalized non-COVID

patients [6].

In order to decrease the resources needed during the

peak of COVID-19, many countries have adopted phys-

ical distancing to attempt to flatten the curve of

COVID-19 [7]. Some nations, such as China, have taken

extreme measures such as forced quarantines and a

complete shutdown of all aspects of society whereas

other countries such as Sweden have chosen to remain

largely open. The United States has emphasized physical

distancing and suspension of non-essential business op-

erations which have, until now, included elective surgical

procedures. This has resulted in hospitals in areas with

relatively few COV ID-19 patients, such as Utah, func-

tioning at only 30% of their typical inpatient and surg ical

capacity. The financial implications of cancelling elective

surgical procedures has been devastating for health care

systems with some systems currently losing upwards of

$25 million USD per week [8].

However, to mitigate large anticipated surges in hospi-

talizations and deaths over the coming months, US hos-

pitals need to sustainably absorb increases in patients

requiring hospitalization and intensive care, while con-

tinuing to care for non-COVID patients. After a state-

ment from the American College of Surgeons (ACS),

many hospit als across the country ceased elective sur-

gery to free beds and limit patient exposure. Recent

models have suggested that the surgical backlog from

these cancellations will be significant [9]. While cancel-

ling elective surgical cases has inc reased capacity, the fi-

nancial impact of these surgeries, including relative

financial contribution by case type, has not been de-

scribed. Furthermore, the epidemiology of the deferred

elective surgical cases, case counts, and their resultant

contribution to bed capacity has not been described.

We estimate the financial implications of cancelling

elective surgeries using a national insurance claim data-

base, Truv en MarketScan (MS). This information can be

used to inform strategies for resuming elective surgical

procedures. We describe regional differences in capacity

of US hospitals to absorb COVID-related inpatient

surges if measures are not taken to slow the spread, de-

scribe elective case distribution, and present the relative

financial impact and case counts of resumption of elect-

ive surgical cases.

Methods

Our analysis is reported according to the Strengthening

the Reporting of Observational Studies in Epidemiology

(STROBE) Guidelines [10]. Thi s study did not meet cri-

teria for review by the Institutional Review Board at the

University of Utah, Salt Lake City, Utah, USA according

to United States law under the Health Insurance Port-

ability and Accountab ility Act (HIPAA) as all data was

fully de-identified.

Data source and study population

Gross inpatient hospital revenue, outpatient surgical rev-

enue, and inpatient and ICU (I/ICU) beds were esti-

mated using Truven MarketScan (MS). MS billing data

captured 8–16% of the entire US population from 2013

to 2017. For every MS patient, we determined the total

number of I/ICU days from 2008 to 2017 for 161 million

(M) enrollees and aggregated visit counts by month,

state, and major diagnostic category (MDC). A person

who switches insurance providers will appear serially in

MS with two different IDs, thus the overall population is

likely to be slightly smaller. Elective surgery admissions

were identified by surgery revenue codes not associated

with emergency revenue or provider codes. Surgical ad-

missions were based on the presence of a surgical rev-

enue code (REVCODE = 0360, 0361, 0362, 0367, 0369,

0975) or admission type (ADM TYP = 1) associated with

the admission. Admissions occurring within 30 days

post-elective (including outpatient) surgery were consid-

ered complications and categorized as resulting from

elective surgery. Gross revenue was aggregated by MDC

codes and surgical (elective vs non-elective) vs non-

surgical. Analysis by MDC excluded eye, human im-

munodeficiency virus, health status and missing

categories.

Study variables and outcomes

MS data were used to report gross hospital revenue de-

rived from inpatient admissions and outpatient surgeries

for each MDC category by elective vs non-elective surgi-

cal and non-surgical. We calculated proportion of gross

revenue from elective surg eries by state, and the propor-

tion of I/ICU beds unoccupied, and occupied by elective

and emergent cases by state. The average number of un-

occupied beds was defined as the difference between the

average number of I/ICU days and the maximum

Tonna et al. BMC Health Services Research (2020) 20:1119 Page 2 of 7

number of I/ICU days in a given state. We used data

from the Harvard Global Health Institute (HG HI) to

identify the total number of I/ICU patie nt beds per state

and applied the proportion from the MS data to obtain

estimates of occupied beds [11].

The Centers for Disease Control (CDC) Morbidity and

Mortality Weekly Report (MMWR) was used to calcu-

late the number of expected I/ICU beds occupied by age

group [12]. We applied these age-specific rates to 5% of

each state’s population using 2018 U.S. Census Bureau

age-specific population estimates to calculate the num-

ber of expected hospitalizations and inpatient cases by

state [13]. We calculated lower bound (LB) and upper

bound (UB) estimates based on the CDC’s reported

uncertainty.

Statistical analysis

In order to estimate the financial implications of cancel-

ling or resuming elective surgical cases, we calculated

the distribution of gross revenue by MDC code and then

by elective and non-elective surgical, and non-surgical

classifications. To inform the balance of revenue and

capacity generation, we illustrate the effect of

cancellation of elective surgical cases on hospital cap-

acity using the projections from the Institute for Health

Metrics and Evaluation [14]. The models incorporate

data on observed COVID-19 deaths, hospitalizations,

and cases and are updated daily. Three scenarios are es-

timated: 1) The current projection assuming social dis-

tancing when deaths are higher than 0.8 per 100,000

people, 2) Continued easing of social distancing

mandates, and 3) Universal Masks (95% use in public).

We assumed the incidence density curves were single

modal, with the highest peak being the first spike in

cases. We assumed that patients occupied a hospital or

ICU bed for an average of 9 days. Capacity was calcu-

lated as the difference between bed occ upancy at the

state level during the highest peak of cases and the num-

ber of hospital or ICU beds in the state.

We illustrated gross revenue by MDC code and by

classification. We calculated the number of hospitalized

patients in each state durin g the simulated pandemic

peak using 11-day inpatient and 9-day ICU length of

stay [4, 5, 15]. We then calculated the ratio of cases to

total number of available beds per state with and with-

out removal of elective surgery.

Results

Across the U.S. there were an average of 1,442,013

(Interquartile range [IQR]: 1,378,039, 1,507,994) in-

patient days per month and 104,265 ([IQR] 101,961,

104,842) ICU days per month in the MS sample.

Roughly 30% of these days were associated with elective

surgery (Fig. 1). We applied these percentages to US

hospital data and classified beds as emergent, elective,

and available. Among 735,996 hospital beds in the US,

351,369 were emergent, 136,264 were elective, and 248,

363 were available.

Financial impact

Elective surgical cases contribute 78% (IQR of statewide

variation 74, 80) to of the gross inpatient and outpatient

Fig. 1 Average Proportion of Inpatient and ICU Person Days per State Resulting from Elective Surgery. Data from 161 million Marketscan patients

from 2008 to 2017 displaying aggregated counts of hospital and ICU beds in total and those resulting from elective surgery averaged across all

states. For each month

Tonna et al. BMC Health Services Research (2020) 20:1119 Page 3 of 7

surgical revenue per year for hospitals represented in

our sample, or 1.1 trillion US dollars. This value varies by

MDC from $488 M to $231 billion (B) (median 39B; IQR

16B, 59B) (Fig. 2). Within each MDC, the percent of rev-

enue from elective surgical cases varies from < 27 to 97%

(median 68%; IQR 53, 86). When restricting our analyses to

inpatient-derived revenue, elective surgery accounted for

43% (IQR of statewide variation 40, 45) of total gross rev-

enue, or $254B. This value varies by MDC from < 1 to 88%,

($25 M to $80B). The relative financial contribution of

elective surgical cases varies regionally and has important

financial implications for hospital specific decisions on re-

sumption of these elective procedures (Fig. 3).

Case counts

With the cancellation of elective surgery, there is a need

to balance revenue generation with capacity increases.

Elective surgical procedures contribute 3B cases in the

MS population, of which 7.9 M are inpatient. This varies

by MDC (median 96 M [IQR 47 M, 224 M], range 1.9 M

– 515 M), of which 197,396 (IQR 86781, 372,544; range

915–2.4 M) are inpatient. Elective musculoskeletal, cir-

culatory and digestive categories comprise 1.1B cases, of

which 3.8 M are inpatient. These three categories to-

gether compose 13% of hospital admissions, but 23%

($142B) of inpatient gross revenue. Including outpatient

surgical cases, they compose 33% ($447B) of total in-

patient and surgical-outpatient hospital revenue.

Hospital and ICU capacity

Without cancelling elective surgery, median state hospital

and ICU capacity was 4967 (IQR 1867, 6100) beds, and

743 (IQR 254.94, 944.37) respectively. This increased to

7692 (IQR 2553, 9679) hospital beds, and 991 (IQR 298,

1197) ICU beds if elective surgeries were cancelled.

There is variability in hospital capacity by state (Fig. 4 ).

As modeled, states would have mean 3.98 patients re-

quiring admission per available ICU bed (Median 1.67;

IQR: 0.89–3.79) (Fig. 4a, c). This distribution is highly

skewed, with Ne w York, New Jersey, Massachusetts,

Michigan, and Pennsylvania having the highest patient

to bed ratios. If mandates continue to be lifted, this in-

creases to mean 5.25 patients per bed (Median 2.80;

IQR: 1.59–5.53) (Fig. 4e). If Universal Mask wearing is

followed in all public locations, states do not reach over-

capacity levels on average. Removing elective surgeries

will alleviate some of this stress (Fi g. 4d), with LB esti-

mates of an average of 2.37 ICU patients per bed IQR

(0–1.98) (Fig. 4b) and UB estimates of an average 3.92

(IQR 1.29–4.03) (Fig. 4f).

Fig. 2 Financial contribution of major diagnostic categories (MDC) to gross hospital revenue. Data from 161 million Marketscan patients from

2008 to 2017 displaying aggregated gross hospital revenue by surgery type, separated by major diagnostic category (MDC), across the US. Levels

are listed in descending order the percentage of each MDC category contributed by elective inpatient cases. Level width is proportional to the

absolute value in US dollars

Tonna et al. BMC Health Services Research (2020) 20:1119 Page 4 of 7

Discussion

Using billing and utilization data to model the financial

contribution of elective surgical cases to hospital gross

revenue, we demonstrate that cessation of elective mus-

culoskeletal, cardiovascular and digestive cases account

for the largest loss of hospital gross revenue, at $447B or

33% of all inpatient and surgical-outpa tient revenue. In

contrast, by case count, musculoskeletal, pregnancy, and

circulatory categories account for the greatest contribu-

tion of elective inpatient cases. Applying case counts to

the availab le hospital beds, cancelling elective surgeries

at all US hospitals will decrease ICU overcapacity from

327 to 237% (lower bound) or from 525 to 392% (upper

bound) assuming 5% of the U.S. population is infected

as shown in our models, but at a financial cost. In light

of the significant contribution of elective surgeries to

gross hospital revenue, selective resumption of high con-

tributing elective MDC categories, in patients who are

COVID-19 negat ive, may be a way to resume surgery in

a financially sustainable way when deemed safe. Out-

comes from operative procedures in COVID-19 positive

patients were recently demonstrated to be significantly

worse, emphasizing the importance of careful patient se-

lection and pre-operative COVID-19 testing for risk

stratification in all patients, most especially for elective

surgery [16].

Continued provision of care to patients with COVID-

19 involves balancing increasing healthcare capacity with

sustaining revenue generation. Previous models have ad-

dressed the expected healthcare burden of COVID-19 in

various ways, but have not addressed revenue gener-

ation. To our knowledge, ours is the first to report case

distribution and financial contribution of elective surger-

ies. We incorporated state specific billing data, reporting

the relative contribution of elective surgeries to gross

hospital revenue, and case counts, by MDC code. Our

study informs the relative financial and capacity impact

from selective resumption of elective cases. Further, we

model the expected increase in bed capacity from

cancelling these surgeries. Tsai et al previously estimated

1.7 excess COVID-19 patients per hospital bed and 4 ex-

cess patients per critical care bed assum ing a 40% cumu-

lative infection rate over 12 and 18 months, and an

aggressive 50% bed availability [ 17 ]. Similarly, Murry

used occupancy data from Medicare and Medicaid pa-

tients to estimate a capacity gap of 17,000 ICU beds and

64,000 hospital beds at peak infection assuming some

measure of social distancing [18]. Our study differs from

these by modeling disease transmission under a lower

infection rate (5%) but faster course (100 days) than Tsai

et al, and in a worse-case scenario without complete

shelter in place orders, which 9 states do not currently

have [19]. We also incorporated state-specific data for

elective case volume and temporal variation. We demon-

strated substantial state-level variation in overcapacity.

This suggests an opportunity for regional cooperation

and resource redistribution.

Our results are generated using a financia l denomin-

ator of total inpatient revenue plus outpatient surgical

cases, and do not account for the Medicare population

that is not insured by Part C. We determined overcap-

acity values using consistent rates of infection across the

population in less than 100 days and assumption s that

regional rates are equal [6, 17, 18]. It is important to

note that we applied rates of disease progression from

regions prior to enforced distancing. Consistent with

this, we assumed a stable rate of disease progression of

100 days since first case and do not account for policy

responses to the pandemic, such as closure of non-

essential service and stay-at-home orders, which are

highly variable across states. It is important to note that

patterns of gross hospital revenue and ICU bed use were

based on the MS population which is not necessarily re-

flective of the U.S. population as a whole. In particular,

patients included in MS carry private insurance through

employers or employer-sponsored Medicare. Thus our

cohort may be biased towards younger, health ier, and

more affluent individuals and patt erns of healthcare

Fig. 3 Regional variation by state in percentage financial contribution of non-elective, elective inpatient and outpatient surgeries. Panel a shows

percent financial contribution to gross hospital revenue by state for non-elective cases. Panel b shows elective inpatient cases. Panel c shows

outpatient cases

Tonna et al. BMC Health Services Research (2020) 20:1119 Page 5 of 7

usage are likely to vary for the overall population. Our

models assumed that all non-emergent cases would be

postponed, however the degree to which a non-

emergent procedure can be considered elective varies by

patient age, comorbidities, etc. and our values may over-

estimate the proportion of surgeries that would truly be

delayed.

Conclusions

Elective inpatient surgeries account for 27% of hospital

and ICU beds and 43% of gross revenue, which varies

substantially by specialty. Among elective cases, muscu-

loskeletal, cardiovascular and digestive MDC categories

account for the largest contribution to hospital gross

revenue, at 33%. The greatest contribution of bed cap-

acity comes from musculoskeletal, pregnancy and circu-

latory categories. The cancellatio n of elective surgery

will result in a substantial increase in hospital and ICU

bed capacity, though this will vary between states, and at

significant financial cost. If Universal Mask wearing is

followed in all public locations, states do not reach over-

capacity levels on average.

Fig. 4 ICU Capacity across the US with and without cancelling elective surgeries. a-f Impact of Cancelling All Elective OR Cases on ICU Bed

Availability if 5% of U.S. Population Infected with COVID-19. Estimates of low (a), mean (c) and high (e) IHME models. Additional capacity through

cancellation of elective cases (b, d, f) was determined by applying estimates of the occupied and unoccupied beds resulting from elective

surgery from the Marketscan database to the Harvard Global Health Institute (HGHI) estimates of total inpatient and ICU beds in each state

Tonna et al. BMC Health Services Research (2020) 20:1119 Page 6 of 7

Abbreviations

ACS: American College of Surgeons; B: Billion; CDC: Centers for Disease

Control; COVID-19: Coronavirus Diseases 2019; DOI: Digital Object Identifier;

HGHI: Harvard Global Health Institute; HIPAA: Health Insurance Portability and

Accountability Act; ICU: Intensive care unit; IHME: Institute for Health Metrics

and Evaluation; I/ICU: Inpatient and intensive care unit; LB: Lower bound;

M: Million; MDC: Major diagnostic category; MMWR: Morbidity and Mortality

Weekly Report; MS: Truven MarketScan; NHLBI: National Heart, Lung, And

Blood Institute; NIH: National Institutes of Health; OSF: Open Science

Foundation; STROBE: Strengthening the Reporting of Observational Studies

in Epidemiology; UB: Upper bound; US: United States

Acknowledgements

Not applicable.

Authors’ contributions

JET, JH had full access to all the data in the study, takes responsibility for the

integrity of the data, the accuracy of the data analysis, and the integrity of

the submission as a whole, from inception to published article. JET, HH, BSB,

JC, MM, JJH, JH conceived study design; JET, HH, BSB, JC, MM, JJH, RD, BK,

AJC, JH contributed to data acquisition and analysis; JET, HH, JJH, JH drafted

the work; all authors revised the article for important intellectual content,

had final approval of the work to be published, and agree to be accountable

to for all aspects of the work.

Funding

JET is supported by a career development award (K23HL141596) from the

National Heart, Lung, And Blood Institute (NHLBI) of the National Institutes of

Health (NIH). The content is solely the responsibility of the authors and does

not necessarily represent the official views of the National Institutes of

Health. None of the funding sources were involved in the design or conduct

of the study, collection, management, analysis or interpretation of the data,

or preparation, review or approval of the manuscript.

Availability of data and materials

To facilitate research reproducibility, replicability, accuracy and transparency,

the associated analytic code is available on the Open Science Foundation

[20] (OSF) repository, [DOI https://doi.org/10.17605/OSF.IO/U53M4]at

[https://osf.io/u53m4]. The data that support the findings of this study were

obtained under license from Truven. Data were received de-identified in ac-

cordance with Section 164.514 of the Health Insurance Portability and Ac-

countability Act (HIPAA).

Ethics approval and consent to participate

This study did not meet criteria for review by the Institutional Review Board

at the University of Utah, Salt Lake City, Utah, USA according to United

States law under the Health Insurance Portability and Accountability Act

(HIPAA) as all data was fully de-identified.

Consent for publication

Not applicable.

Competing interests

JET received modest financial support for speakers fees from LivaNova and

from Philips Healthcare, outside of the work. The other authors declare that

they have no competing interests.

Author details

Surgical Population Analytic Research Core (SPARC), Department of Surgery,

University of Utah Health, Salt Lake City, UT, USA.

Department of Population

Health Sciences, University of Utah Health, Salt Lake City, UT, USA.

Received: 14 May 2020 Accepted: 25 November 2020

References

1. Coronavirus disease (COVID-19) situation report-189. World Health

Organization. https://www.who.int/docs/default-source/coronaviruse/

situation-reports/20200727-covid-19-sitrep-189.pdf?sfvrsn=b93a6913_2.

Accessed 27 Jul 2020.

2. Dosa D, Feng Z, Hyer K, Brown LM, Thomas K, Mor V. Effects of hurricane

Katrina on nursing facility resident mortality, hospitalization, and functional

decline. Disaster Med Public Health Prep. 2010;4(Suppl 1):S28–32.

3. Wu Z, McGoogan JM. Characteristics of and important lessons from the

coronavirus disease 2019 (COVID-19) outbreak in China: summary of a

report of 72314 cases from the Chinese Center for Disease Control and

Prevention. JAMA. 2020;323(13):1239–42.

4. ZhouF,YuT,DuR,FanG,LiuY,LiuZ,XiangJ,WangY,SongB,GuX,etal.

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in

Wuhan, China: a retrospective cohort study. Lancet. 2020;395(10229):1054–62.

5. Guan WJ, Ni ZY, Hu Y, Liang WH, Ou CQ, He JX, Liu L, Shan H, Lei CL, Hui

DSC, et al. Clinical characteristics of coronavirus disease 2019 in China. N

Engl J Med. 2020;382(18):1708–20.

6. Emanuel EJ, Persad G, Upshur R, Thome B, Parker M, Glickman A, Zhang C,

Boyle C, Smith M, Phillips JP. Fair allocation of scarce medical resources in

the time of Covid-19. N Engl J Med. 2020;382(21):2049–55.

7. Social distancing, quarantine and isolation. https://www.cdc.gov/

coronavirus/2019-ncov/prevent-getting-sick/social-distancing.html. Accessed

23 Apr 2020.

8. Werner E, Harris S, Goldstein A. Hospital relief money slow to reach places that

need it most, lawmakers and industry groups say. Washington post; 2020.

https://www.washingtonpost.com/us-policy/2020/04/16/bailout-money-

hospitals-slow-get-out-missing-some-places-that-need-it-most-lawmakers-

industrygroups-say/. Accessed 23 Apr 2020.

9. Collaborative C. Elective surgery cancellations due to the COVID-19

pandemic: global predictive modelling to inform surgical recovery plans. Br

J Surg. 2020. https://doi.org/10.1002/bjs.11746.

10. von Elm E, Altman DG, Egger M, Pocock SJ, Gotzsche PC, Vandenbroucke

JP, Initiative S. The Strengthening the Reporting of Observational Studies in

Epidemiology (STROBE) statement: guidelines for reporting observational

studies. PLoS Med. 2007;4(10):e296.

11. Can hospitals around the nation keep up? Harvard Global Health Institute.

https://globalepidemics.org/our-data/hospital-capacity/. Accessed 4 Apr 2020.

12. Team CC-R. Severe outcomes among patients with coronavirus disease

2019 (COVID-19) - United States, February 12-march 16, 2020. MMWR Morb

Mortal Wkly Rep. 2020;69(12):343–6.

13. Annual county resident population estimates by age, sex, race, and Hispanic

origin: April 1, 2010 to July 1, 2018. United States Census Bureau. United States

Census Bureau. 2018 ed. https://www.census.gov/newsroom/press-kits/2019/

detailed-estimates.html. Accessed 5 Apr 2020.

14. Institute for Health Metrics and Evaluation (IHME). COVID-19 Projections.

University of Washington. https://covid19.healthdata.org/projections.

Accessed 12 July 2020.

15. Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, Wang B, Xiang H, Cheng Z,

Xiong Y, et al. Clinical characteristics of 138 hospitalized patients with 2019

novel coronavirus-infected pneumonia in Wuhan, China. JAMA. 2020;

323(11):1061–9.

16. Nepogodiev D, Bhangu A, Glasbey JC, Li E, Omar OM, Simoes JFF, Abbott TEF,

Alser O, Arnaud AP, Bankhead-Kendall BK, et al. Mortality and pulmonary

complications in patients undergoing surgery with perioperative SARS-CoV-2

infection: an international cohort study. Lancet. 2020;396(10243):27–38.

17. American hospital capacity and projected need for COVID-19 patient care.

Health Affairs. 2020. https://doi.org/10.1377/hblog20200317.457910.

Accessed 4 Apr 2020.

18. IHME COVID-19 health service utilization forecasting team, Murray C.

Forecasting COVID-19 impact on hospital bed-days, ICU-days, ventilator-

days and deaths by US state in the next 4 months. medRxiv. https://doi.org/

10.1101/2020.03.27.20043752.

19. Ortiz JL, Hauck G. Coronavirus in the US: how all 50 states are responding –

and why nine still refuse to issue stay-at-home orders. USA Today; 2020.

https://www.usatoday.com/story/news/nation/2020/03/30/coronavirus-stay-

home-shelter-in-place-orders-by-state/5092413002/. Accessed 6 Apr 2020.

20. Foster ED, Deardorff A. Open Science Framework (OSF). J Med Libr Assoc.

2017;105(2):203–6.

Publisher’sNote

Springer Nature remains neutral with regard to jurisdictional claims in

published maps and institutional affiliations.

Tonna et al. BMC Health Services Research (2020) 20:1119 Page 7 of 7