CC BY-NC-ND 4.0 · Methods Inf Med 2023; 62(03/04): 100-109
DOI: 10.1055/a-2015-1244
Original Article

Prehospital Cardiac Arrest Should be Considered When Evaluating Coronavirus Disease 2019 Mortality in the United States

Nick Williams
1   National Library of Medicine, Lister Hill National Center for Biomedical Communications, Bethesda, Maryland, United States
› Author Affiliations


Background Public health emergencies leave little time to develop novel surveillance efforts. Understanding which preexisting clinical datasets are fit for surveillance use is of high value. Coronavirus disease 2019 (COVID-19) offers a natural applied informatics experiment to understand the fitness of clinical datasets for use in disease surveillance.

Objectives This study evaluates the agreement between legacy surveillance time series data and discovers their relative fitness for use in understanding the severity of the COVID-19 emergency. Here fitness for use means the statistical agreement between events across series.

Methods Thirteen weekly clinical event series from before and during the COVID-19 era for the United States were collected and integrated into a (multi) time series event data model. The Centers for Disease Control and Prevention (CDC) COVID-19 attributable mortality, CDC's excess mortality model, national Emergency Medical Services (EMS) calls, and Medicare encounter level claims were the data sources considered in this study. Cases were indexed by week from January 2015 through June of 2021 and fit to Distributed Random Forest models. Models returned the variable importance when predicting the series of interest from the remaining time series.

Results Model r2 statistics ranged from 0.78 to 0.99 for the share of the volumes predicted correctly. Prehospital data were of high value, and cardiac arrest (CA) prior to EMS arrival was on average the best predictor (tied with study week). COVID-19 Medicare claims volumes can predict COVID-19 death certificates (agreement), while viral respiratory Medicare claim volumes cannot predict Medicare COVID-19 claims (disagreement).

Conclusion Prehospital EMS data should be considered when evaluating the severity of COVID-19 because prehospital CA known to EMS was the strongest predictor on average across indices.

Publication History

Received: 31 March 2022

Accepted: 04 January 2023

Accepted Manuscript online:
18 January 2023

Article published online:
27 February 2023

© 2023. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

  • References

  • 1 Le Duc JW, Sorvillo TE. A quarter century of emerging infectious diseases---where have we been and where are we going?. Acta Med Acad 2018; 47 (01) 117-130
  • 2 Polonsky JA, Baidjoe A, Kamvar ZN. et al. Outbreak analytics: a developing data science for informing the response to emerging pathogens. Philos Trans R Soc Lond B Biol Sci 2019; 374 (1776): 20180276
  • 3 Wu JT, Leung K, Lam TTY. et al. Nowcasting epidemics of novel pathogens: lessons from COVID-19. Nat Med 2021; 27 (03) 388-395
  • 4 Bhatia S, Lassmann B, Cohn E. et al. Using digital surveillance tools for near real-time mapping of the risk of infectious disease spread. NPJ Digit Med 2021; 4 (01) 73
  • 5 Leung K, Wu JT, Leung GM. Real-time tracking and prediction of COVID-19 infection using digital proxies of population mobility and mixing. Nat Commun 2021; 12 (01) 1501
  • 6 Redd SC, Frieden TR. CDC's evolving approach to emergency response. Health Secur 2017; 15 (01) 41-52
  • 7 Li BZ, Li MS, Huang JY, Chen YY, Lu YH. [Expanding the pandemic influenza preparedness framework to the epidemic of COVID-19]. Chin J Prev Med 2020; 54 (06) 597-601
  • 8 Tam T. Fifteen years post-SARS: key milestones in Canada's public health emergency response. Can Commun Dis Rep 2018; 44 (05) 98-101
  • 9 Martinello RA. Preparing for avian influenza. Curr Opin Pediatr 2007; 19 (01) 64-70
  • 10 Gibson PJ, Theadore F, Jellison JB. The common ground preparedness framework: a comprehensive description of public health emergency preparedness. Am J Public Health 2012; 102 (04) 633-642
  • 11 Brower JL. The threat and response to infectious diseases (Revised). Microb Ecol 2018; 76 (01) 19-36
  • 12 National Pandemic Influenza Plans | Pandemic Influenza (Flu) | CDC [Internet].. 2018 [cited 2022 Jan 18]. Accessed January 31, 2022 at:
  • 13 Pandemic Influenza Preparedness, Response and Recovery Guide for Critical Infrastructure and Key Resources.:84.
  • 14 Wang Q, Zhang T, Zhu H. et al. Characteristics of and public health emergency responses to COVID-19 and H1N1 outbreaks: a case-comparison study. Int J Environ Res Public Health 2020; 17 (12) E4409
  • 15 Viglione G. How many people has the coronavirus killed?. Nature 2020; 585 7823 22-24
  • 16 Zhu H, Wang Q, Zhang T. et al. Initial public-health emergency response to SARS and COVID-19 pandemics in mainland china: a retrospective comparative study. Risk Manag Healthc Policy 2021; 14: 4199-4209
  • 17 Lee H-Y, Oh M-N, Park Y-S, Chu C, Sona T-J. Public health emergency preparedness and response in Korea. J Korean Med Assoc 2017; 60 (04) 296-299
  • 18 Wang V. . Why China Is the World's Last ‘Zero Covid’ Holdout. The New York Times [Internet]. 2021 Oct 28 [cited 2022 Jan 18]. Accessed January 01, 2023 at:
  • 19 Huang P, Ruwitch J. . What the U.S. can learn from China's response to COVID infections. NPR [Internet]. 2021 Nov 8 [cited 2022 Jan 18]. Accessed January 01, 2023 at:
  • 20 Why China is still trying to achieve zero Covid. BBC News [Internet]. 2021 Nov 15 [cited 2022 Jan 18]. Accessed January 1, 2023 at:
  • 21 Beaney T, Clarke JM, Jain V. et al. Excess mortality: the gold standard in measuring the impact of COVID-19 worldwide?. J R Soc Med 2020; 113 (09) 329-334
  • 22 Iuliano AD, Chang HH, Patel NN. et al. Estimating under-recognized COVID-19 deaths, United States, March 2020-May 2021 using an excess mortality modelling approach. Lancet Reg Health Am 2021; 1: 100019
  • 23 Rossen LM. Notes from the field: update on excess deaths associated with the COVID-19 Pandemic—United States, January 26, 2020–February 27, 2021. MMWR Morb Mortal Wkly Rep 2021;70. Accessed January 1, 2023 at: cited 2021 Dec 15 [Internet]
  • 24 Rossen LM. . Excess Deaths Associated with COVID-19, by Age and Race and Ethnicity — United States, January 26–October 3, 2020. MMWR Morb Mortal Wkly Rep [Internet]. 2020 [cited 2022 Jan 11];69. Accessed January 1, 2023 at:
  • 25 Stokes AC, Lundberg DJ, Elo IT, Hempstead K, Bor J, Preston SH. COVID-19 and excess mortality in the United States: a county-level analysis. PLoS Med 2021; 18 (05) e1003571
  • 26 Woolf SH, Chapman DA, Sabo RT, Weinberger DM, Hill L. Excess deaths from COVID-19 and other causes, March-April 2020. JAMA 2020; 324 (05) 510-513
  • 27 Woolf SH, Chapman DA, Sabo RT, Weinberger DM, Hill L, Taylor DDH. Excess deaths from COVID-19 and other causes, March-July 2020. JAMA 2020; 324 (15) 1562-1564
  • 28 Farrington CP, Andrews NJ, Beale AD, Catchpole MA. A statistical algorithm for the early detection of outbreaks of infectious disease. J R Stat Soc Ser A Stat Soc 1996; 159 (03) 547-563
  • 29 Noufaily A, Enki DG, Farrington P, Garthwaite P, Andrews N, Charlett A. An improved algorithm for outbreak detection in multiple surveillance systems. Stat Med 2013; 32 (07) 1206-1222
  • 30 Dawson DE. National Emergency Medical Services Information System (NEMSIS). Prehosp Emerg Care 2006; 10 (03) 314-316
  • 31 Mann NC, Kane L, Dai M, Jacobson K. Description of the 2012 NEMSIS public-release research dataset. Prehosp Emerg Care 2015; 19 (02) 232-240
  • 32 Handberry M, Bull-Otterson L, Dai M. et al. Changes in Emergency Medical Services Before and during the COVID-19 pandemic in the United States, January 2018-December 2020. Clin Infect Dis 2021; 73 (Suppl (Suppl. 01) S84-S91
  • 33 Distributed Random Forest (DRF)—H2O documentation [Internet].. [cited 2022 Jan 14]. Accessed January 1. 2023 at:
  • 34 Onozuka D, Hagihara A. Extreme influenza epidemics and out-of-hospital cardiac arrest. Int J Cardiol 2018; 263: 158-162
  • 35 Moa A, Tan T, Wei J, Hutchinson D, MacIntyre CR. Burden of influenza in adults with cardiac arrest admissions in Australia. Int J Cardiol 2022; 361: 109-115
  • 36 Čulić V, AlTurki A, Proietti R. Public health impact of daily life triggers of sudden cardiac death: a systematic review and comparative risk assessment. Resuscitation 2021; 162: 154-162
  • 37 Duijster JW, Doreleijers SDA, Pilot E. et al. Utility of emergency call centre, dispatch and ambulance data for syndromic surveillance of infectious diseases: a scoping review. Eur J Public Health 2020; 30 (04) 639-647
  • 38 McVaney KE, Pepe PE, Maloney LM. et al; Writing group on behalf of the Metropolitan EMS Medical Directors Global Alliance. The relationship of large city out-of-hospital cardiac arrests and the prevalence of COVID-19. EClinicalMedicine 2021; 34: 100815
  • 39 Riesgo LGC, Ziemann A, Rosenkoetter N. et al. Use of routinely collected emergency medical data for earlier detection of health threats in Europe: first evaluation results of the SIDARTHa syndromic surveillance system. Resuscitation 2010; 81 (02) S7
  • 40 Yadav R, Bansal R, Budakoty S, Barwad P. COVID-19 and sudden cardiac death: a new potential risk. Indian Heart J 2020; 72 (05) 333-336
  • 41 Brodeur A, Gray D, Islam A, Bhuiyan S. A literature review of the economics of COVID-19. J Econ Surv 2021; 35 (04) 1007-1044
  • 42 Bergquist S, Otten T, Sarich N. COVID-19 pandemic in the United States. Health Policy Technol 2020; 9 (04) 623-638
  • 43 Xu HD, Basu R. How the United States flunked the COVID-19 Test: some observations and several lessons. Am Rev Public Adm 2020; 50 6–7 568-576
  • 44 Data Modernization Initiative [Internet]. [cited 2022 Jan 18]. Accessed January 31, 2023 at: 2021