Medicine:Case fatality rate

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Short description: Medical measurement formula

In epidemiology, case fatality rate (CFR) – or sometimes more accurately case-fatality risk – is the proportion of people diagnosed with a certain disease, who end up dying of it. Unlike a disease's mortality rate, the CFR does not take into account the time period between disease onset and death. A CFR is generally expressed as a percentage. It represents a measure of disease lethality and may change with different treatments.[1] CFRs are most often used for with discrete, limited-time courses, such as acute infections.


The mortality rate – often confused with the CFR – is a measure of the relative number of deaths (either in general, or due to a specific cause) within the entire population per unit of time.[2] A CFR, in contrast, is the number of deaths among the number of diagnosed cases only, regardless of time or total population.[3]

From a mathematical point of view, by taking values between 0 and 1 or 0% and 100%, CFRs are actually a measure of risk (case fatality risk) – that is, they are a proportion of incidence, although they do not reflect a disease's incidence. They are neither rates, incidence rates, nor ratios (none of which are limited to the range 0–1). They do not take into account time from disease onset to death.[4][5]

Sometimes the term case fatality ratio is used interchangeably with case fatality rate, but they are not the same. A case fatality ratio is a comparison between two different case fatality rates, expressed as a ratio. It is used to compare the severity of different diseases or to assess the impact of interventions.[6]

Because the CFR is not an incidence rate by not measuring frequency, some authors note that a more appropriate term is case fatality proportion.[7]

Example calculation

If 100 people in a community are diagnosed with the same disease, and 9 of them subsequently die from the effects of the disease, the CFR would be 9%. If some of the cases have not yet resolved (neither died nor fully recovered) at the time of analysis, a later analysis might take into account additional deaths and arrive at a higher estimate of the CFR, if the unresolved cases were included as recovered in the earlier analysis. Alternatively, it might later be established that a higher number of people were subclinically infected with the pathogen, resulting in an IFR below the CFR.[citation needed]

A CFR may only be calculated from cases that have been resolved through either death or recovery. The preliminary CFR, for example, of a newly occurring disease with a high daily increase and long resolution time would be substantially lower than the final CFR, if unresolved cases were not excluded from the calculation, but added to the denominator only.

[math]\displaystyle{ \text{CFR in }{\%} = \frac\text{Number of deaths from disease}\text{Number of confirmed cases of disease}\times100 }[/math][8]

Infection fatality rate

Like the case fatality rate, the term infection fatality rate (IFR) also applies to infectious diseases, but represents the proportion of deaths among all infected individuals, including all asymptomatic and undiagnosed subjects. It is closely related to the CFR, but attempts to additionally account for inapparent infections among healthy people.[9] The IFR differs from the CFR in that it aims to estimate the fatality rate in both sick and healthy infected: the detected disease (cases) and those with an undetected disease (asymptomatic and not tested group).[10] Individuals who are infected, but show no symptoms, are said to have inapparent, silent or subclinical infections and may inadvertently infect others. By definition, the IFR cannot exceed the CFR, because the former adds asymptomatic cases to its denominator.

[math]\displaystyle{ \text{IFR in }{\%} = \frac\text{Number of deaths from disease}\text{Number of infected individuals}\times100 }[/math][8]


Main page: Medicine:List of human disease case fatality rates

Some examples will suggest the range of possible CFRs for diseases in the real world:

See also


  1. Rebecca A. Harrington, Case fatality rate at the Encyclopædia Britannica
  2. For example, a diabetes mortality rate of 5 per 1,000 or 500 per 100,000 characterizes the observation of 50 deaths due to diabetes in a population of 10,000 in a given year, resulting in a yearly diabetes mortality rate of 0.5%, far below the actual diabetic individual's fatality risk. (See Harrington, Op. cit..)
  3. "Coronavirus: novel coronavirus (COVID-19) infection". 2020-03-25. 
  4. Entry "Case fatality rate" in Last, John M. (2001), A Dictionary of Epidemiology, 4th edition; Oxford University Press, p. 24. ISBN:0-19-514168-7
  5. Hennekens, Charles H. and Julie E. Buring (1987), Epidemiology in Medicine, Little, Brown and Company, p. 63. ISBN:0-316-35636-0
  6. Bosman, Arnold (2014-05-28). "Attack rates and case fatality". ECDC. 
  7. Peter Cummings: Analysis of Incidence Rates. In: CRC Press (2019).
  8. 8.0 8.1 "Estimating mortality from COVID-19" (in en). 
  9. "Infection fatality rate". DocCheck Medical Services GmbH. 
  10. "Global Covid-19 Case Fatality Rates". Centre for Evidence-Based Medicine. 
  11. "Report of the Review Committee on the Functioning of the International Health Regulations (2005) in relation to Pandemic (H1N1) 2009". 2011-05-05. p. 37. 
  12. Taubenberger, Jeffery K.; David M. Morens (January 2006). "1918 influenza: the mother of all pandemics". Emerging Infectious Diseases (Coordinating Center for Infectious Diseases, Centers for Disease Control and Prevention) 12 (1): 15–22. doi:10.3201/eid1201.050979. PMID 16494711. PMC 3291398. Retrieved 2009-04-17. 
  13. Li, F C K; B C K Choi; T Sly; A W P Pak (June 2008). "Finding the real case-fatality rate of H5N1 avian influenza". Journal of Epidemiology and Community Health 62 (6): 555–559. doi:10.1136/jech.2007.064030. ISSN 0143-005X. PMID 18477756. Retrieved 2009-04-29. 
  14. Template:COVID-19 data/cite
  15. Chan-Yeung, Moira; Xu, Rui-Heng (November 2003). "SARS: epidemiology" (in en). Respirology 8 (s1): S9–S14. doi:10.1046/j.1440-1843.2003.00518.x. ISSN 1323-7799. PMID 15018127. PMC 7169193. 
  16. "MERS situation update, January 2020" (in en-gb). 
  17. "Yellow fever". Fact sheets. World Health Organization. 7 May 2019. 
  18. Johansson, Michael A.; Vasconcelos, Pedro F.C.; Staples, J. Erin (June 30, 2014). "The whole iceberg: estimating the incidence of yellow fever virus infection from the number of severe cases". Transactions of the Royal Society of Tropical Medicine and Hygiene 108 (8): 482–487. doi:10.1093/trstmh/tru092. PMID 24980556. PMC 4632853. 
  19. Servadio, Joseph L.; Muñoz-Zanzi, Claudia; Convertino, Matteo (August 16, 2021). "Estimating case fatality risk of severe Yellow Fever cases: systematic literature review and meta-analysis". BMC Infectious Diseases 21 (819): 819. doi:10.1186/s12879-021-06535-4. PMID 34399718. 
  20. Heymann, David L., ed (2008). Control of Communicable Diseases Manual (19th ed.). Washington, D.C.: American Public Health Association. ISBN 978-0-87553-189-2. 
  21. USAMRIID (2011). USAMRIID's Medical Management of Biological Casualties Handbook (7th ed.). U.S. Government Printing Office. ISBN 9780160900150. Retrieved 2021-11-25. 
  22. "Antibiotics for treating plague: A systematic review (Executive summary)". WHO guidelines for plague management: revised recommendations for the use of rapid diagnostic tests, fluoroquinolones for case management and personal protective equipment for prevention of post-mortem transmission [Internet].. World Health Organization. 2021. 
  23. Prentice, Michael B.; Rahalison, Lila (April 7, 2007). "Plague". Lancet 369 (9568): 1196–1207. doi:10.1016/S0140-6736(07)60566-2. PMID 17416264. 
  24. Lozano, Rafael et al. (December 2012). "Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010". The Lancet 380 (9859): 2095–2128. doi:10.1016/s0140-6736(12)61728-0. ISSN 0140-6736. PMID 23245604. PMC 10790329. 
  25. Tiemersma, Edine W.; van der Werf, Marieke J.; Borgdorff, Martien W.; Williams, Brian G.; Nagelkerke, Nico J. D. (4 April 2011). "Natural History of Tuberculosis: Duration and Fatality of Untreated Pulmonary Tuberculosis in HIV Negative Patients: A Systematic Review" (in en). PLOS ONE 6 (4): e17601. doi:10.1371/journal.pone.0017601. ISSN 1932-6203. PMID 21483732. Bibcode2011PLoSO...617601T. 
  26. King, John W (April 2, 2008). "Ebola Virus". eMedicine. WebMd. 
  27. "Rabies Fact Sheet N°99". World Health Organization. July 2013. 

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