Expressing Death Risk as Condensed Life Experience and Death Intensity
Abstract
Some risk exposures, including many medical and surgical procedures, typically carry hazards of death that are difficult to convey and appreciate in absolute terms. I propose presenting the death risk as a condensed life experience (i.e., the equivalent amount of life T that would carry the same cumulative mortality hazard for a person of the same age and sex based on life tables). For example, if the risk of death during an elective 1-hour procedure is 0.01%, and same-age and same-sex people have a 0.01% death risk over 1 month, one can inform the patient that “this procedure carries the same death risk as living 1 month of normal life.” Comparative standards from other risky activities or from a person with the same disease at the same stage and same predictive profile could also be used. A complementary metric that may be useful to consider is the death intensity. The death intensity λ is the hazard function that shows the fold-risk estimate of dying compared with the reference person. The death intensity can vary substantially for different phases of the event, operation, or procedure (e.g., intraoperative, early postoperative, late postoperative), and this variability may also be useful to convey. T will vary depending on the time window for which it is computed. I present examples for calculating T and λ using literature data on accidents, ascent to Mount Everest, and medical and surgical procedures.
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References
1. Lipkus IM. Numeric, verbal, and visual formats of conveying health risks: suggested best practices and future recommendations. Med Decis Making. 2007;27:696–713.
2. Woloshin S, Schwartz LM, Byram S, Fischhoff B, Welch HG. A new scale for assessing perceptions of chance: a validation study. Med Decis Making. 2000;20:298–307.
3. Howard RA. Microrisks for medical decision analysis. Int J Technol Assess Health Care. 1989;5:357–70.
4. Howard RA. On making life and death decisions. In: Richard J, Schwing C, Albers WA eds. Societal Risk Assessment: How Safe Is Safe Enough? General Motors Research Laboratories. New York: Plenum; 1980.
5. Halvorsen PA, Wisløff TF, Støvring H, Aasland O, Kristiansen IS. Therapeutic decisions by number needed to treat and survival gains: a cross-sectional survey of lipid-lowering drug recommendations. Br J Gen Pract. 2011;61(589):e477–83.
6. Halvorsen PA, Kristiansen IS, Aasland OG, Førde OH. Medical doctors’ perception of the “number needed to treat” (NNT): a survey of doctors’ recommendations for two therapies with different NNT. Scand J Prim Health Care. 2003;21(3):162–6.
7. Kristiansen IS, Nexoe J, Gyrd-Hansen D, et al. NNT is not easily understood. Fam Pract. 2002;19:566–7.
8. Sandman P, Weinstein N, Hallman W. Communications to reduce risk underestimation and overestimation. Risk Decis Pol. 1998;3:93–108.
9. Ropeik D. How risky is flying? Available from: http://www.pbs.org/wgbh/nova/space/how-risky-is-flying.html. Accessed April 23, 2012.
10. Centers for Disease Control and Prevention. Life tables. Available from: http://www.cdc.gov/nchs/products/life_tables.htm. Accessed April 26, 2012.
11. 10 leading causes of death by age group. Available from: http://www.chdl.org/Projects/10%20Leading%20Causes%20of%20Death%20by%20Age%20Group%202001.pdf. Accessed April 26, 2012.
13. Pitman A, Krysinska K, Osborn D, King M. Suicide in young men. Lancet. 2012;379:2383–92.
14. Noto TJ Jr, Johnson LW, Krone R, et al. Cardiac catheterization 1990: a report of the Registry of the Society for Cardiac Angiography and Interventions (SCA&I). Cathet Cardiovasc Diagn. 1991;24(2):75–83.
15. Krone RJ, Johnson L, Noto T. Five year trends in cardiac catheterization: a report from the Registry of the Society for Cardiac Angiography and Interventions. Cathet Cardiovasc Diagn. 1996;39(1):31–5.
16. Shroyer AL, Coombs LP, Peterson ED, et al. The Society of Thoracic Surgeons: 30-day operative mortality and morbidity risk models. Ann Thorac Surg. 2003;75(6):1856–64; discussion 1864–5.
17. Shahian DM, O’Brien SM, Filardo G, et al. The Society of Thoracic Surgeons 2008 cardiac surgery risk models: part 1—coronary artery bypass grafting surgery. Ann Thorac Surg. 2009;88(1, Suppl):S2–22.
18. Finks JF, Osborne NH, Birkmeyer JD. Trends in hospital volume and operative mortality for high-risk surgery. N Engl J Med. 2011;364:2128–37.
19. Gigerenzer G, Edwards A. Simple tools for understanding risks: from innumeracy to insight. BMJ. 2003;327:741–4.
20. Hoffrage U, Lindsey S, Hertwig R, Gigerenzer G. Communicating statistical information. Science. 2000;290:2261–2.
21. Gigerenzer G, Gaissmaier W, Kurz-Milcke E, Schwartz LM, Woloshin S. Helping doctors and patients make sense of health statistics. Psychol Sci Public Interest. 2007;8:53–96.
22. Ioannidis JP. Adverse events in randomized trials: neglected, restricted, distorted, and silenced. Arch Intern Med. 2009;169:1737–9.
23. Ethgen M, Boutron I, Baron G, Giraudeau B, Sibilia J, Ravaud P. Reporting of harm in randomized, controlled trials of nonpharmacologic treatment for rheumatic disease. Ann Intern Med. 2005;143:20–5.
24. Papanikolaou PN, Ioannidis JP. Availability of large-scale evidence on specific harms from systematic reviews of randomized trials. Am J Med. 2004;117:582–9.
25. Papanikolaou PN, Christidi GD, Ioannidis JP. Comparison of evidence on harms of medical interventions in randomized and nonrandomized studies. CMAJ. 2006;174:635–41.
26. Martin RC II, Brennan MF, Jaques DP. Quality of complication reporting in the surgical literature. Ann Surg. 2002;235:803–13.
27. Yazici Y, Yazici H. A survey of inclusion of the time element when reporting adverse effects in randomised controlled trials of cyclo-oxygenase-2 and tumour necrosis factor alpha inhibitors. Ann Rheum Dis. 2007;66:124–7.
28. Cunningham R, Herzog T, London R. Models for Quantifying Risk. 3rd ed. Winsted, CT: Actex; 2008.
29. Fan Y. Correlated defaults in intensity-based models. Math Finance. 2007;17:155–73.
30. Cont R, Deguest R, Yu HK. Default intensities implied by CDO spreads: inversion formula and model calibration. SIAM J Financial Math. 2010;1:555–85.
31. Siontis GC, Tzoulaki I, Ioannidis JP. Predicting death: an empirical evaluation of predictive tools for mortality. Arch Intern Med. 2011;171:1721–6.
32. Forrow L, Taylor WC, Arnold RM. Absolutely relative: how research results are summarized can affect treatment decisions. Am J Med. 1992;92:121–4.
33. Bobbio M, Demichelis B, Giustetto G. Completeness of reporting trial results: effect on physicians’ willingness to prescribe. Lancet. 1994;343:1209–11.
34. Bucher HC, Weinbacher M, Gyr K. Influence of method of reporting study results on decision of physicians to prescribe drugs to lower cholesterol concentration. BMJ. 1994;309:761–4.
35. Hux JE, Naylor CD. Communicating the benefits of chronic preventive therapy: does the format of efficacy data determine patients’ acceptance of treatment? Med Decis Making. 1995;15:152–7.
36. Cranney M, Walley T. Same information, different decisions: the influence of evidence on the management of hypertension in the elderly. Br J Gen Pract. 1996;46:661–3.
37. McNeil BJ, Pauker SG, Sox HC Jr, Tversky A. On the elicitation of preferences for alternative therapies. N Engl J Med. 1982;306:1259–62.
38. Spiegelhalter D, Pearson M, Short I. Visualizing uncertainty about the future. Science. 2011;333:1393–400.
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Article first published online: April 11, 2013
Issue published: August 2013
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PubMed: 23579043
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