Estimation of excess deaths associated with the COVID-19 pandemic in the United States, March to May 2020
Open accessAbstract
Importance: Efforts to track the severity and public health impact of coronavirus disease 2019 (COVID-19) in the United States have been hampered by state-level differences in diagnostic test availability, differing strategies for prioritization of individuals for testing, and delays between testing and reporting. Evaluating unexplained increases in deaths due to all causes or attributed to nonspecific outcomes, such as pneumonia and influenza, can provide a more complete picture of the burden of COVID-19. Objective: To estimate the burden of all deaths related to COVID-19 in the United States from March to May 2020. Design, Setting, and Population: This observational study evaluated the numbers of US deaths from any cause and deaths from pneumonia, influenza, and/or COVID-19 from March 1 through May 30, 2020, using public data of the entire US population from the National Center for Health Statistics (NCHS). These numbers were compared with those from the same period of previous years. All data analyzed were accessed on June 12, 2020. Main Outcomes and Measures: Increases in weekly deaths due to any cause or deaths due to pneumonia/influenza/COVID-19 above a baseline, which was adjusted for time of year, influenza activity, and reporting delays. These estimates were compared with reported deaths attributed to COVID-19 and with testing data. Results: There were approximately 781 000 total deaths in the United States from March 1 to May 30, 2020, representing 122 300 (95% prediction interval, 116 800-127 000) more deaths than would typically be expected at that time of year. There were 95 235 reported deaths officially attributed to COVID-19 from March 1 to May 30, 2020. The number of excess all-cause deaths was 28% higher than the official tally of COVID-19-reported deaths during that period. In several states, these deaths occurred before increases in the availability of COVID-19 diagnostic tests and were not counted in official COVID-19 death records. There was substantial variability between states in the difference between official COVID-19 deaths and the estimated burden of excess deaths. Conclusions and Relevance: Excess deaths provide an estimate of the full COVID-19 burden and indicate that official tallies likely undercount deaths due to the virus. The mortality burden and the completeness of the tallies vary markedly between states.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Several other studies have shown potential misclassified COVID-19 deaths in non-COVID-19 respiratory conditions, which might be amplified in locations with scarce or inadequate SARS-CoV-2 diagnostic capabilities.
“Several other studies have also shown potential misclassified COVID-19 deaths in non-COVID-19 respiratory conditions,82, 83 which might be amplified in locations with scarce or inadequate SARS-CoV-2 diagnostic capabilities.84”
These outcomes may reflect attributions of some COVID-19 deaths to lower respiratory tract infections generally, or may indicate the contribution of secondary respiratory infections such as mucormycosis to fatal outcomes among individuals with confirmed or unconfirmed SARS-CoV-2 infection, especially in healthcare settings.
“These outcomes may reflect attributions of some COVID-19 deaths to lower respiratory tract infections generally, as reported in other settings46, or may indicate the contribution of secondary respiratory infections such as mucormycosis to fatal outcomes among individuals with confirmed or unconfirmed SARS-CoV-2 infection, especially in healthcare settings.”
From Attributed causes of excess mortality during the COVID-19 pandemic in a south Indian city (2023) · cites this paper as [46]
Excess mortality approaches have been used for over a century to capture the full scope of large-scale infectious disease events, heatwaves, and earthquakes, by measuring the rise in mortality over a historical baseline.
“Excess mortality approaches have been used for over a century to capture the full scope of large-scale infectious disease events, heatwaves, and earthquakes, by measuring the rise in mortality over a historical baseline”
From Direct and indirect mortality impacts of the COVID-19 pandemic in the United States, March 1, 2020 to January 1, 2022 (2023) · cites this paper as Weinberger et al., 2020
In the early phase of the pandemic, excess mortality approaches highlighted substantial underestimation in official statistics of COVID-19 deaths due to limited viral testing.
“In the early phase of the pandemic, these approaches highlighted substantial underestimation in official statistics of COVID-19 deaths due to limited viral testing”
From Direct and indirect mortality impacts of the COVID-19 pandemic in the United States, March 1, 2020 to January 1, 2022 (2023) · cites this paper as Weinberger et al., 2020
Analyses of COVID-19-related deaths as well as excess mortality provide an alternative and potentially less biased picture of epidemic intensity.
“Analyses of COVID-19–related deaths as well as excess mortality provide an alternative and potentially less biased picture of epidemic intensity (7, 8).”
From Socioeconomic status determines COVID-19 incidence and related mortality in Santiago, Chile (2021) · cites this paper as [7]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Li, R., Pei, S., Chen, B., Song, Y., Zhang, T., Yang, W., & Shaman, J. (2020) · Science · doi:10.1126/science.abb3221 · Cited by 3,633 · see citing works
Kucirka, L. M., Lauer, S. A., Laeyendecker, O. B., Boon, D., & Lessler, J. (2020) · Annals of Internal Medicine · doi:10.7326/m20-1495 · Cited by 1,414 · see citing works
Murray, C. J., Lopez, A. D., Chin, B., Feehan, D., & Hill, K. H. (2006) · The Lancet · doi:10.1016/s0140-6736(06)69895-4 · Cited by 605 · see citing works
Methods for current statistical analysis of excess pneumonia-influenza deaths
Serfling, R. E. (1963) · Public Health Reports (1896-1970) · doi:10.2307/4591848 · Cited by 597 · see citing works
Excess deaths from COVID-19 and other causes, March–April 2020.
Woolf, S. H., Chapman, D. A., Sabo, R. T., Weinberger, D. M., & Hill, L. (2020) · JAMA · doi:10.1001/jama.2020.11787 · Cited by 541 · see citing works
Self-citationGlobal Mortality Estimates for the 2009 Influenza Pandemic from the GLaMOR Project: A Modeling Study
Simonsen, L., Spreeuwenberg, P. M. M., Lustig, R. L., Taylor, R. J., Fleming, D., Kroneman, M. W., Kerkhove, M. D. V., Mounts, A. W., Paget, W. J., & Teams, T. G. C. (2013) · PLoS Medicine · doi:10.1371/journal.pmed.1001558 · Cited by 486 · see citing works
Self-citationOlson, D. R., Heffernan, R. T., Paladini, M., Konty, K., Weiss, D., & Mostashari, F. (2007) · PLoS Medicine · doi:10.1371/journal.pmed.0040247 · Cited by 197 · see citing works
Self-citationOlson, D. R., Huynh, M., Fine, A. D., Baumgartner, J., Castro, A., Chan, H. T., Daskalakis, D., Devinney, K., Guerra, K., Harper, S. A., et al. (2020) · MMWR Morbidity and Mortality Weekly Report · doi:10.15585/mmwr.mm6919e5 · Cited by 176 · see citing works
Self-citationNowcasting by Bayesian Smoothing: A flexible, generalizable model for real-time epidemic tracking
McGough, S. F., Johansson, M. A., Lipsitch, M., & Menzies, N. A. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007735 · Cited by 121 · see citing works
Impact of the human immunodeficiency virus epidemic on mortality trends in young men, United States.
Buehler, J. W., Devine, O. J., Berkelman, R. L., & Chevarley, F. M. (1990) · American Journal of Public Health · doi:10.2105/ajph.80.9.1080 · Cited by 83 · see citing works
Prospective forecasts of annual dengue hemorrhagic fever incidence in Thailand, 2010–2014
Lauer, S. A., Sakrejda, K., Ray, E. L., Keegan, L. T., Bi, Q., Suangtho, P., Hinjoy, S., Iamsirithaworn, S., Suthachana, S., Laosiritaworn, Y., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1714457115 · Cited by 62 · see citing works
Self-citationCharu, V., Simonsen, L., Lustig, R., Steiner, C., & Viboud, C. (2013) · Influenza and Other Respiratory Viruses · doi:10.1111/irv.12096 · Cited by 40 · see citing works
Self-citationState‐level estimates of excess hospitalizations and deaths associated with influenza
Czaja, C. A., Miller, L. A., Colborn, K. L., Cockburn, M., Alden, N. B., Herlihy, R., & Simões, E. A. F. (2019) · Influenza and Other Respiratory Viruses · doi:10.1111/irv.12700 · Cited by 19 · see citing works
U.S. Pneumonia and Influenza Mortality Surveillance: A New Era
Kniss, K. L., Malcolm, B. L., Sutton, P., et al. (2014) · Online Journal of Public Health Informatics · doi:10.5210/ojphi.v6i1.5188 · Cited by 2 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- Yang Y, Yang M, Shen C, et al. Evaluating the accuracy of different respiratory specimens in the laboratory diagnosis and monitoring the viral shedding of 2019-nCoV infections. medRxiv. Preprint posted February 17, 2020.
- Kucirka L, Lauer S, Laeyendecker O, Boon D, Lessler J. Variation in false negative rate of RT-PCR based SARS-CoV-2 tests by time since exposure. medRxiv. Preprint posted April 10, 2020.
Cited by
Sorted by citations
Karlinsky, A., & Kobak, D. (2021) · eLife · doi:10.7554/elife.69336 · Cited by 772 · see citing works
Association of Social and Demographic Factors with COVID-19 Incidence and Death Rates in the US
Karmakar, M., Lantz, P. M., & Tipirneni, R. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.36462 · Cited by 580 · see citing works
Excess deaths from COVID-19 and other causes, March–April 2020.
Woolf, S. H., Chapman, D. A., Sabo, R. T., Weinberger, D. M., & Hill, L. (2020) · JAMA · doi:10.1001/jama.2020.11787 · Cited by 541 · see citing works
Self-citationSocioeconomic status determines COVID-19 incidence and related mortality in Santiago, Chile
Mena, G. E., Martinez, P. P., Mahmud, A. S., Marquet, P. A., Buckee, C. O., & Santillana, M. (2021) · Science · doi:10.1126/science.abg5298 · Cited by 431 · see citing works
Chen, Y.-H., Glymour, M., Riley, A., Balmes, J., Duchowny, K., Harrison, R., Matthay, E., & Bibbins-Domingo, K. (2021) · PLOS ONE · doi:10.1371/journal.pone.0252454 · Cited by 267 · see citing works
Aburto, J. M., Kashyap, R., Schöley, J., Angus, C., Ermisch, J., Mills, M. C., & Dowd, J. B. (2021) · Journal of Epidemiology and Community Health · doi:10.1136/jech-2020-215505 · Cited by 159 · see citing works
Ledesma, J. R., Ma, J., Zhang, M., Basting, A. V. L., Chu, H. T., Vongpradith, A., Novotney, A., LeGrand, K. E., Xu, Y. Y., Dai, X., et al. (2024) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(24)00007-0 · Cited by 146 · see citing works
Under-reporting of deaths limits our understanding of true burden of covid-19
Whittaker, C., Walker, P. G. T., Alhaffar, M., Hamlet, A., Djaafara, B. A., Ghani, A., Ferguson, N., Dahab, M., Checchi, F., & Watson, O. J. (2021) · BMJ · doi:10.1136/bmj.n2239 · Cited by 122 · see citing works
Predictive performance of international COVID-19 mortality forecasting models
Friedman, J., Liu, P., Troeger, C. E., Carter, A., Reiner, R. C., Barber, R. M., Collins, J., Lim, S. S., Pigott, D. M., Vos, T., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22457-w · Cited by 118 · see citing works
A comparison of COVID-19 epidemiological indicators in Sweden, Norway, Denmark, and Finland
Yarmol-Matusiak, E. A., Cipriano, L. E., & Stranges, S. (2021) · Scandinavian Journal of Public Health · doi:10.1177/1403494820980264 · Cited by 111 · see citing works
Every Body Counts: Measuring Mortality From the COVID-19 Pandemic
Kiang, M. V., Irizarry, R. A., Buckee, C. O., & Balsari, S. (2020) · Annals of Internal Medicine · doi:10.7326/m20-3100 · Cited by 96 · see citing works
Estimation of the fraction of COVID-19 infected people in U.S. states and countries worldwide
Noh, J., & Danuser, G. (2021) · PLOS ONE · doi:10.1371/journal.pone.0246772 · Cited by 104 · see citing works
Lee, W.-E., Woo Park, S., Weinberger, D. M., Olson, D., Simonsen, L., Grenfell, B. T., & Viboud, C. (2023) · eLife · doi:10.7554/elife.77562 · Cited by 90 · see citing works
“By the end of January 2023, official statistics reported 1,056,000 deaths in the United States alone (Johns Hopkins University, 2022), although burden estimates that do not rely on official tallies suggest a higher death toll (Weinberger et al., 2020; Karlinsky and Kobak, 2021).”
BackgroundMethodInfluentialSelf-citationGilstrap, L., Zhou, W., Alsan, M., Nanda, A., & Skinner, J. S. (2022) · JAMA Neurology · doi:10.1001/jamaneurol.2022.0010 · Cited by 77 · see citing works
All-cause mortality during the COVID-19 pandemic in Chennai, India: an observational study
Lewnard, J. A., Mahmud, A., Narayan, T., Wahl, B., Selvavinayagam, T. S., Mohan B, C., & Laxminarayan, R. (2022) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(21)00746-5 · Cited by 41 · see citing works
Laxminarayan, R., B, C. M., G, V. T., Arjun Kumar, K. V., Wahl, B., & Lewnard, J. A. (2021) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(21)00393-5 · Cited by 43 · see citing works
Excess cerebrovascular mortality in the united states during the COVID-19 pandemic
Sharma, R., Kuohn, L. R., Weinberger, D. M., Warren, J. L., Sansing, L. H., Jasne, A., Falcone, G., Dhand, A., & Sheth, K. N. (2021) · Stroke · doi:10.1161/strokeaha.120.031975 · Cited by 46 · see citing works
Robustness and bias of European excess death estimates in 2020 under varying model specifications
Schöley, J. (2021) · doi:10.1101/2021.06.04.21258353 · Cited by 42 · see citing works
Attributed causes of excess mortality during the COVID-19 pandemic in a south Indian city
Lewnard, J. A., B, C. M., Kang, G., & Laxminarayan, R. (2023) · Nature Communications · doi:10.1038/s41467-023-39322-7 · Cited by 23 · see citing works
A Flexible Statistical Framework for Estimating Excess Mortality
Acosta, R. J., & Irizarry, R. A. (2020) · doi:10.1101/2020.06.06.20120857 · Cited by 52 · see citing works
- Authors
- Weinberger, D. M., Chen, J., Cohen, T., Crawford, F. W., Mostashari, F., Olson, D., Pitzer, V. E., Reich, N. G., Russi, M., Simonsen, L., Watkins, A., & Viboud, C.
- Affiliations
- Yale University (US)
- National Institutes of Health (US)
- Aledade Inc, Bethesda, Maryland
- Department of Health and Mental Hygiene, New York, New York
- University of Massachusetts Amherst (US)
- Roskilde University (DK)
- Corresponding author
- Weinberger, Daniel M.
- ORCID
- Venue
- JAMA Internal Medicine
- Publisher
- American Medical Association (AMA)
- Published
- 2020-10-01
- Volume
- 180
- Issue
- 10
- Pages
- 1336
- ISSN
- 2168-6106 (ISSN-L)
- PMC
- PMC7330834
- OpenAlex
- W3038501713
- Cited by
- 485
- References
- 14
- Citations by year
- 2026: 13 · 2025: 28 · 2024: 44 · 2023: 69 · 2022: 79 · 2021: 169 · 2020: 83
- FWCI
- 15.89
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- JAMA Internal Medicine (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- RUCforsk (Roskilde University) (submittedVersion)
- Funders
- Pfizer
- New York City Department of Health and Mental Hygiene
- National Institutes of Health (UL1TR001863)
- Centers for Disease Control and Prevention (U01IP001122, 1U01IP001122)
- National Institute of General Medical Sciences (1U01IP001122, R35GM119582)
- National Institute of Allergy and Infectious Diseases (R01AI146555, R01AI123208, R01AI112970, R01AI137093)
- National Institute of Child Health and Human Development
- National Center for Advancing Translational Sciences
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (DP2HD091799)
- Topics
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Health Sciences › Medicine › Oncology › COVID-19 and healthcare impacts
- Keywords
- COVID-19 pandemic, excess mortality, all-cause mortality, underreporting of deaths, pneumonia mortality, influenza mortality, diagnostic test availability, state-level variation, United States, influenza surveillance, delayed reporting, SARS-CoV-2
- MeSH terms
- Betacoronavirus, COVID-19, SARS-CoV-2, COVID-19 Testing, Adult, Cause of Death, Female, Humans, Influenza, Human, Male, Mortality, Pneumonia, Pneumonia, Viral, Cost of Illness, Coronavirus Infections, Clinical Laboratory Techniques, Pandemics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review