Estimating excess 1-year mortality associated with the COVID-19 pandemic according to underlying conditions and age: a population-based cohort study
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Abstract
BACKGROUND: The medical, societal, and economic impact of the coronavirus disease 2019 (COVID-19) pandemic has unknown effects on overall population mortality. Previous models of population mortality are based on death over days among infected people, nearly all of whom thus far have underlying conditions. Models have not incorporated information on high-risk conditions or their longer-term baseline (pre-COVID-19) mortality. We estimated the excess number of deaths over 1 year under different COVID-19 incidence scenarios based on varying levels of transmission suppression and differing mortality impacts based on different relative risks for the disease. METHODS: In this population-based cohort study, we used linked primary and secondary care electronic health records from England (Health Data Research UK-CALIBER). We report prevalence of underlying conditions defined by Public Health England guidelines (from March 16, 2020) in individuals aged 30 years or older registered with a practice between 1997 and 2017, using validated, openly available phenotypes for each condition. We estimated 1-year mortality in each condition, developing simple models (and a tool for calculation) of excess COVID-19-related deaths, assuming relative impact (as relative risks [RRs]) of the COVID-19 pandemic (compared with background mortality) of 1·5, 2·0, and 3·0 at differing infection rate scenarios, including full suppression (0·001%), partial suppression (1%), mitigation (10%), and do nothing (80%). We also developed an online, public, prototype risk calculator for excess death estimation. FINDINGS: We included 3 862 012 individuals (1 957 935 [50·7%] women and 1 904 077 [49·3%] men). We estimated that more than 20% of the study population are in the high-risk category, of whom 13·7% were older than 70 years and 6·3% were aged 70 years or younger with at least one underlying condition. 1-year mortality in the high-risk population was estimated to be 4·46% (95% CI 4·41-4·51). Age and underlying conditions combined to influence background risk, varying markedly across conditions. In a full suppression scenario in the UK population, we estimated that there would be two excess deaths (vs baseline deaths) with an RR of 1·5, four with an RR of 2·0, and seven with an RR of 3·0. In a mitigation scenario, we estimated 18 374 excess deaths with an RR of 1·5, 36 749 with an RR of 2·0, and 73 498 with an RR of 3·0. In a do nothing scenario, we estimated 146 996 excess deaths with an RR of 1·5, 293 991 with an RR of 2·0, and 587 982 with an RR of 3·0. INTERPRETATION: We provide policy makers, researchers, and the public a simple model and an online tool for understanding excess mortality over 1 year from the COVID-19 pandemic, based on age, sex, and underlying condition-specific estimates. These results signal the need for sustained stringent suppression measures as well as sustained efforts to target those at highest risk because of underlying conditions with a range of preventive interventions. Countries should assess the overall (direct and indirect) effects of the pandemic on excess mortality. FUNDING: National Institute for Health Research University College London Hospitals Biomedical Research Centre, Health Data Research UK.
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More recent analyses have examined excess mortality patterns for specific causes of death by age and socio-demographic groups and have compared the COVID-19 death toll between countries.
“More recent analyses have examined excess mortality patterns for specific causes of death by age and socio-demographic groups and have compared the COVID-19 death toll between countries”
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 Banerjee et al., 2020
A decrease in cancer-attributed deaths can be explained by mortality displacement.
“A decrease in the number of cancer-attributed deaths can be explained by mortality displacement (19, 20)”
From Socioeconomic status determines COVID-19 incidence and related mortality in Santiago, Chile (2021) · cites this paper as [19]
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Cites
Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30183-5 · Cited by 48,884 · see citing works
Zhou, F., Yu, T., Du, R., Fan, G., Liu, Y., Liu, Z., Xiang, J., Wang, Y., Song, B., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30566-3 · Cited by 27,263 · see citing works
Yang, J., Zheng, Y., Gou, X., Pu, K., Chen, Z., Guo, Q., Ji, R., Wang, H., Wang, Y., & Zhou, Y. (2020) · International Journal of Infectious Diseases · doi:10.1016/j.ijid.2020.03.017 · Cited by 4,206 · see citing works
Case-fatality rate and characteristics of patients dying in relation to COVID-19 in Italy
Onder, G., Rezza, G., & Brusaferro, S. (2020) · JAMA · doi:10.1001/jama.2020.4683 · Cited by 4,171 · see citing works
Ferguson, N. M., Laydon, D. J., Nedjati-Gilani, G. L., Imai, N., Ainslie, K. E. C., Baguelin, M., Bhatia, S., Boonyasiri, A., Cucunubá, Z. M., Cuomo-Dannenburg, G., et al. (2020) · Spiral (Imperial College London) · doi:10.25561/77482 · Cited by 3,453 · see citing works
Are patients with hypertension and diabetes mellitus at increased risk for COVID-19 infection?
Fang, L., Karakiulakis, G., & Roth, M. (2020) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(20)30116-8 · Cited by 2,947 · see citing works
CDC COVID-19 Response Team, CDC COVID-19 Response Team, Bialek, S. R., Boundy, E. O., Bowen, V. B., Chow, N. A., Cohn, A. C., Dowling, N. F., Ellington, S., Gierke, R., et al. (2020) · MMWR Morbidity and Mortality Weekly Report · doi:10.15585/mmwr.mm6912e2 · Cited by 2,278 · see citing works
Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19)
Gomes, C. A. (2020) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · Cited by 2,226 · see citing works
Rapsomaniki, E., Timmis, A., George, J., Pujades-Rodriguez, M., Shah, A. D., Denaxas, S., White, I. R., Caulfield, M. J., Deanfield, J. E., Smeeth, L., et al. (2014) · The Lancet · doi:10.1016/s0140-6736(14)60685-1 · Cited by 1,643 · see citing works
Self-citationHemingway, H., & Marmot, M. G. (1999) · BMJ · doi:10.1136/bmj.318.7196.1460 · Cited by 1,342 · see citing works
Self-citationReal estimates of mortality following COVID-19 infection
Baud, D., Qi, X., Nielsen‐Saines, K., Musso, D., Pomar, L., & Favre, G. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30195-x · Cited by 1,253 · see citing works
Type 2 diabetes and incidence of cardiovascular diseases: a cohort study in 1.9 million people
Shah, A. D., Langenberg, C., Rapsomaniki, E., Denaxas, S., Pujades-Rodriguez, M., Gale, C. P., Deanfield, J., Smeeth, L., Timmis, A., & Hemingway, H. (2015) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(14)70219-0 · Cited by 1,225 · see citing works
Self-citationYusuf, S., Islam, S., Chow, C. K., Rangarajan, S., Dagenais, G., Diaz, R., Gupta, R., Kelishadi, R., Iqbal, R., Avezum, A., et al. (2011) · The Lancet · doi:10.1016/s0140-6736(11)61215-4 · Cited by 964 · see citing works
Eubank, S., Eckstrand, I. A., Lewis, B. L., Venkatramanan, S., MARATHE, M. V., & Barrett, C. L. (2020) · Bulletin of Mathematical Biology · doi:10.1007/s11538-020-00726-x · Cited by 882 · see citing works
Prognosis research strategy (PROGRESS) 1: A framework for researching clinical outcomes
Hemingway, H., Croft, P. R., Perel, P., Hayden, J. A., Abrams, K. R., Timmis, A., Briggs, A. H., Udumyan, R., Moons, K. G. M., Steyerberg, E. W., et al. (2013) · BMJ · doi:10.1136/bmj.e5595 · Cited by 662 · see citing works
Self-citationExcess winter mortality in Europe: a cross country analysis identifying key risk factors
Healy, J. (2003) · Journal of Epidemiology & Community Health · doi:10.1136/jech.57.10.784 · Cited by 623 · see citing works
Rocklöv, J., Sjödin, H., & Wilder‐Smith, A. (2020) · Journal of Travel Medicine · doi:10.1093/jtm/taaa030 · Cited by 471 · see citing works
Ahmed, F., Zviedrite, N., & Uzicanin, A. (2018) · BMC Public Health · doi:10.1186/s12889-018-5446-1 · Cited by 416 · see citing works
Joint HFSA/ACC/AHA Statement Addresses Concerns Re: Using RAAS Antagonists in COVID-19
Bozkurt, B., Kovacs, R. J., & Harrington, B. (2020) · Journal of Cardiac Failure · doi:10.1016/j.cardfail.2020.04.013 · Cited by 358 · see citing works
Diaz, J. H. (2020) · Journal of Travel Medicine · doi:10.1093/jtm/taaa041 · Cited by 352 · see citing works
Bell, S., Daskalopoulou, M., Rapsomaniki, E., George, J., Britton, A. R., Bobák, M., Casas, J. P., Dale, C. E., Denaxas, S., Shah, A. D., et al. (2017) · BMJ · doi:10.1136/bmj.j909 · Cited by 334 · see citing works
Self-citationKuan, V., Denaxas, S., Gonzalez-Izquierdo, A., Direk, K., Bhatti, O., Husain, S., Sutaria, S., Hingorani, M., Nitsch, D., Parisinos, C. A., et al. (2019) · The Lancet Digital Health · doi:10.1016/s2589-7500(19)30012-3 · Cited by 305 · see citing works
Self-citationUK phenomics platform for developing and validating electronic health record phenotypes: CALIBER
Denaxas, S., González-Izquierdo, A., Direk, K., Fitzpatrick, N. K., Fatemifar, G., Banerjee, A., Dobson, R., Howe, L. J., Kuan, V., Lumbers, R. T., et al. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz105 · Cited by 227 · see citing works
Self-citationEconomic considerations for social distancing and behavioral based policies during an epidemic
Fenichel, E. P. (2013) · Journal of Health Economics · doi:10.1016/j.jhealeco.2013.01.002 · Cited by 173 · see citing works
Rapsomaniki, E., Thuresson, M., Yang, E., Blin, P., Hunt, P. R., Chung, S., Stogiannis, D., Pujades‐Rodríguez, M., Timmis, A., Denaxas, S., et al. (2016) · European Heart Journal - Quality of Care and Clinical Outcomes · doi:10.1093/ehjqcco/qcw004 · Cited by 114 · see citing works
Self-citationWhat China’s coronavirus response can teach the rest of the world
Cyranoski, D. (2020) · Nature · doi:10.1038/d41586-020-00741-x · Cited by 111 · see citing works
van Rossum, C. T. M., Shipley, M. J., Hemingway, H., Grobbee, D. E., Mackenbach, J. P., & Marmot, M. G. (2001) · International Journal of Epidemiology · doi:10.1093/ije/30.5.1109 · Cited by 103 · see citing works
Self-citationPikoula, M., Quint, J. K., Nissen, F., Hemingway, H., Smeeth, L. L., & Denaxas, S. (2019) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-0805-0 · Cited by 88 · see citing works
Self-citationKaura, A., Panoulas, V. F., Glampson, B., Davies, J., Mulla, A., Woods, K., Omigie, J., Shah, A. D., Channon, K. M., Weber, J. N., et al. (2019) · BMJ · doi:10.1136/bmj.l6055 · Cited by 84 · see citing works
Self-citationSallakh, M. A. A., Vasileiou, E., Rodgers, S., Lyons, R. A., Sheikh, A., & Davies, G. A. (2017) · European Respiratory Journal · doi:10.1183/13993003.00204-2017 · Cited by 56 · see citing works
Harris, S. K., Shi, S., Brealey, D. A., MacCallum, N. S., Denaxas, S., Pérez–Suárez, D., Ercole, A., Watkinson, P., Jones, A., Ashworth, S., et al. (2018) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2018.01.006 · Cited by 56 · see citing works
Self-citationGlobal Alliance for Chronic Disease researchers' statement on multimorbidity
Hurst, J. R., Dickhaus, J., Maulik, P. K., et al. (2018) · The Lancet Global Health · doi:10.1016/s2214-109x(18)30391-7 · Cited by 56 · see citing works
Time spent at blood pressure target and the risk of death and cardiovascular diseases
Chung, S.-C., Pujades-Rodriguez, M., Duyx, B., Denaxas, S. C., Pasea, L., Hingorani, A., Timmis, A., Williams, B., & Hemingway, H. (2018) · PLOS ONE · doi:10.1371/journal.pone.0202359 · Cited by 51 · see citing works
Self-citation
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COVID-19 pandemic and admission rates for and management of acute coronary syndromes in england
Mafham, M. M., Spata, E., Goldacre, R., Gair, D., Curnow, P., Bray, M., Hollings, S., Roebuck, C., Gale, C. P., Mamas, M. A., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)31356-8 · Cited by 714 · see citing works
Lai, A. G., Pasea, L., Banerjee, A., Hall, G., Denaxas, S., Chang, W. H., Katsoulis, M., Williams, B., Pillay, D., Noursadeghi, M., et al. (2020) · BMJ Open · doi:10.1136/bmjopen-2020-043828 · Cited by 447 · see citing works
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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
Years of life lost to COVID-19 in 81 countries
Pifarré i Arolas, H., Acosta, E., López-Casasnovas, G., Lo, A., Nicodemo, C., Riffe, T., & Myrskylä, M. (2021) · Scientific Reports · doi:10.1038/s41598-021-83040-3 · Cited by 312 · see citing works
Trends in suicide in Japan by gender during the COVID-19 pandemic, up to September 2020
Nomura, S., Kawashima, T., Yoneoka, D., Tanoue, Y., Eguchi, A., Gilmour, S., Kawamura, Y., Harada, N., & Hashizume, M. (2021) · Psychiatry Research · doi:10.1016/j.psychres.2020.113622 · Cited by 129 · 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
Reimagining global health systems for the 21st century: lessons from the COVID-19 pandemic
Assefa, Y., Gilks, C. F., van de Pas, R., Reid, S., Gete, D. G., & Van Damme, W. (2021) · BMJ Global Health · doi:10.1136/bmjgh-2020-004882 · Cited by 48 · see citing works
Ashofteh, A., Bravo, J. M., & Ayuso, M. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109422 · Cited by 21 · see citing works
- Authors
- Banerjee, A., Pasea, L., Harris, S., Gonzalez-Izquierdo, A., Torralbo, A., Shallcross, L., Noursadeghi, M., Pillay, D., Sebire, N., Holmes, C., Pagel, C., Wong, W. K., Langenberg, C., Williams, B., Denaxas, S., & Hemingway, H.
- Affiliations
- University College London Hospitals NHS Foundation Trust (GB)
- University College London (GB)
- Great Ormond Street Hospital (GB)
- University of Oxford (GB)
- University of Cambridge (GB)
- The Alan Turing Institute (GB)
- Health Data Research UK (GB)
- Corresponding author
- Banerjee, Amitava
- ORCID
- Venue
- The Lancet
- Publisher
- Elsevier BV
- Published
- 2020-05
- Volume
- 395
- Issue
- 10238
- Pages
- 1715-1725
- ISSN
- 0099-5355 (ISSN-L)
- 0140-6736
- PMC
- PMC7217641
- OpenAlex
- W3024344902
- Cited by
- 518
- References
- 33
- Citations by year
- 2026: 12 · 2025: 30 · 2024: 27 · 2023: 42 · 2022: 77 · 2021: 175 · 2020: 154
- FWCI
- 17.81
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- The Lancet (publishedVersion, cc-by-nc-nd)
- medRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Apollo (University of Cambridge) (submittedVersion, cc-by-nc-nd)
- reponame:Expeditio Repositorio Institucional UJTL
- Apollo
- Funders
- Health Data Research UK (grant no LOND1)
- NIHR Clinician Scientist award (CS-2016-007)
- 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, COVID-19 mortality, underlying disease, comorbidities, age, mortality estimation, transmission suppression, relative risk, risk calculator, vulnerable populations, England, public health policy, health disparities, socioeconomic deprivation
- MeSH terms
- Multimorbidity, COVID-19, Adult, Aged, Aged, 80 and over, Female, United Kingdom, Humans, Male, Middle Aged, Mortality, Pneumonia, Viral, Risk Factors, Models, Statistical, Cohort Studies, Coronavirus Infections, Pandemics
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