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Quality-of-life loss of people admitted to burn centers, United States

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Abstract

Purpose

To estimate quality-of-life loss per serious burn survivor in a large U.S. cohort.

Methods

Longitudinal functional assessments of all 1,587 people receiving primary treatment in 5 burn centers between 2000 and 2009 included pre-burn (retrospective), at time of discharge, and 6, 12, and 24 months post-injury. We assessed adults with RAND Short Form (SF) 12 and children with SF-10 or Child Health Questionnaire, the child surveys scored using standard norms-based scoring. A literature review identified 20 quality-adjusted life year utility scorings for SF-12 and 27 scorings for EQ-5d response distributions predicted from SF-12 scores. We computed composite scores for each patient and time period by applying 32 scorings that met quality/non-duplication criteria.

Results

Mean quality-of-life scores were 0.805 4 weeks pre-burn, 0.562 at discharge, rebounded through 1 year, and stabilized at 0.735 (0.750 for TBSA burned below 25 %, 0.722 for TBSA burned of 25–50 %, and 0.695 for larger burns). As a percentage of initial levels, burns reduced short-term quality of life by 30 %. Long-term loss averaged 11 %, ranging from 9 % for TBSA burned below 25–13 % for TBSA burned above 50 %. Children recovered faster and more fully.

Conclusion

Burns cause substantial losses in quality of life, with long-term losses comparable to traumatic brain injury.

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References

  1. Whitehead, S., & Ali, S. (2010). Health outcomes in economic evaluation: The QALY and utilities. British Medical Bulletin, 96(1), 5–21.

    Article  PubMed  Google Scholar 

  2. Sanchez, J.-L. A., Pereperez, S. B., Bastida, J. L., & Martinez, M. M. (2007). Cost-utility analysis applied to the treatment of burn patients in a specialized center. Archives of Surgery, 142(1), 50–57.

    Article  PubMed  Google Scholar 

  3. Oster, C., Willebrand, M., Dyster-Aas, J., Kildal, M., & Ekselius, L. (2009). Validation of the EQ-5d questionnaire in burn-injured adults. Burns, 35(5), 723–732.

    Article  PubMed  Google Scholar 

  4. Polinder, S., van Beeck, E. F., Essink-Bot, M. L., Toet, H., Looman, C. W. N., et al. (2007). Functional outcome at 2.5, 5, 9, and 24 months after injury in the Netherlands. Journal of Trauma Nursing, 62(1), 133–141.

    Google Scholar 

  5. Dolan, P. (1997). Modeling valuations for EuroQol health states. Medical Care, 35(11), 1095–1108.

    Article  PubMed  CAS  Google Scholar 

  6. Ware, J. J. E., Kosinski, M., Turner-Bowker, D. M., & Gandek, B. (2002). How to score version 2 of the SF-12 health survey (with a supplement documenting version 1). Lincoln, RI: Quality Metric Incorporated.

    Google Scholar 

  7. Turner-Bowker, D. M., Bayliss, M. S., Kosinski, M., Zhao, J., & Saris-Baglama, R. N. (2003). SF-10 for children™,a user’s guide. Lincoln, RI: QualityMetric Inc.

    Google Scholar 

  8. HealthActCHQ. (2008). Child health questionnaire scoring and interpretation manual. Cambridge MA: HealthActCHQ Inc.

    Google Scholar 

  9. McHorney, C., Ware, J., Rogers, W., Raczek, A. E., Lu, J. F., et al. (1992). The validity and relative precision of MOS short- and long-form health status scales and Dartmouth COOP charts: Results from the Medical Outcomes Study. Medical Care, 30(5), MS253–MS265.

    PubMed  CAS  Google Scholar 

  10. Schofield, M., & Mishra, G. (1998). Validity of the SF-12 compared with the SF-36 Health Survey in pilot studies of the Australian Longitudinal Study on Women’s Health. Journal of Health Psychology, 3, 259–271.

    Article  PubMed  CAS  Google Scholar 

  11. Gray, A., Rivero-Arias, O., & Clarke, P. (2006). Estimating the association between SF-12 reponses and EQ-5D utility values by response mapping. Medical Decision Making, 26, 18–29.

    Article  PubMed  Google Scholar 

  12. Econsys. (2008). A study of compensation payments for service-connected disabilities: Earnings and quality of life loss analysis (Vol. III). Washington, DC: Department of Veterans Affairs.

    Google Scholar 

  13. Grenier, W., Weijnen, T., Nieuwenhuizen, M., Oppe, S., Badia, X., et al. (2003). A single European currency for EQ-5D health states: Results from a six-country study. The European Journal of Health Economics, 4(3), 222–231.

    Article  Google Scholar 

  14. Sengupta, N., Nichol, M. B., Wu, J., & Globe, D. (2004). Mapping the SF-12 to the HU13 and VAS in a managed care population. Medical Care, 42(9), 927–937.

    Article  PubMed  Google Scholar 

  15. Mortimer, D., & Segal, L. (2008). Comparing the incomparable? A systematic review of competing techniques for converting descriptive measures of health status into QALY-weights. Medical Decision Making, 28, 66–89.

    Article  PubMed  Google Scholar 

  16. Lundberg, L., Johannesson, M., Isacson, D. G. I., & Borgquist, L. (1999). The relationship between health-state utilities and the SF-12 in a general population. Medical Decision Making, 19, 128–140.

    Article  PubMed  CAS  Google Scholar 

  17. Shmueli, A. (1999). Subjective health status and health values in the general population. Medical Decision Making, 19, 122–127.

    Article  PubMed  CAS  Google Scholar 

  18. Brazier, J. E., & Roberts, J. (2004). The estimation of a preference-based measure of health from the SF-12. Medical Care, 42(9), 851–859.

    Article  PubMed  Google Scholar 

  19. Fryback, D. G., Lawrence, W. F., Martin, P. A., Klein, R., & Klein, B. E. (1997). Predicting quality of well-being scores from the SF-36: Results from the Beaver Dam Health Outcomes Study. Medical Decision Making, 17, 1–9.

    Article  PubMed  CAS  Google Scholar 

  20. Sullivan, P., & Ghushchyan, V. (2006). Mapping the EQ-5D index from the SF-12: U.S. general population preferences in a nationally representive sample. Medical Decision Making, 26, 401–409.

    Article  PubMed  Google Scholar 

  21. Bult, J. R., Bosch, J., & Hunink, M. (1996). Heterogeneity in the relationship between the standary-gamble utility measure and health-status dimensions. Medical Decision Making, 16, 226–233.

    Article  PubMed  CAS  Google Scholar 

  22. Bult, J. R., Hunick, G. M., Tsevat, J., & Weinstein, M. C. (1998). Heterogeneity in the relationship between the time tradeoff and short form-36 for HIV-infected and primary care patients. Medical Care, 36, 523–532.

    Article  PubMed  CAS  Google Scholar 

  23. Wittrup-Jensen, K. U., & Pedersen, K. M. (2008). Estimation of a preference-based index measure of health for SF-12 by applying multi-attribute utility theory (MAUT)—an experiment: University of Southern Denmark. http://www.sam.sdu.dk/healtheco/publications/20088.pdf. Accessed April 14, 2008.

  24. Drummond, M. F., O’Brien, B., Stoddart, G. L., & Torrance, G. W. (1997). Methods for the economic evaluation of health care programmes (2nd ed.). Oxford: Oxford University Press.

    Google Scholar 

  25. Greene, W. H. (2008). Econometric analysis (6th ed.). New Jersey, NJ: Pearson Education/Prentice Hall.

    Google Scholar 

  26. Ray, D. R., Zamula, W. W., Miller, T. R., Brigham, P., Cohen, M., Douglass, J., Galbraith, M., et al. (1993). Estimating the costs to society of cigarette fire injuries. Report to Congress in response to the Fire Safe Cigarette Act of 1990 (Vol. 6). Washington, DC: U.S. Government Printing Office and Consumer Product Safety Commission.

  27. Ewing, J. A. (1984). Detecting alcoholism: The CAGE questionnaire. Journal of the American Medical Association, 252, 1905–1907.

    Article  PubMed  CAS  Google Scholar 

  28. Walters, S. J., & Brazier, J. E. (2005). Comparison of the minimally important difference for two health state utility measures: EQ-5D and SF-6D. Quality of Life Research, 14(6), 1523–1532.

    Article  PubMed  Google Scholar 

  29. Farivar, S. S., Liu, H., & Hays, R. D. (2004). Half standard deviation estimate of the minimum important difference in HRQOL scores? Expert Review of Pharmacoeconomics and Outcomes Research, 4(5), 515–523.

    Article  PubMed  Google Scholar 

  30. Franks, P., Lubetkin, E., Gold, M., Tancredi, D., & Jia, H. (2004). Mapping the SF-12 to the EuroQol EQ-5D index in a national U.S. sample. Medical Decision Making, 24, 247–254.

    Article  PubMed  Google Scholar 

  31. Franks, P., Lubetkin, E., Gold, M., & Tancredi, D. (2003). Mapping the SF-12 to preference-based instruments convergent validity in a low-income, minority population. Medical Care, 41(11), 1277–1283.

    Article  PubMed  Google Scholar 

  32. Lawrence, W., & Fleishman, J. (2004). Predicting EuroQol EQ-5D preference scores from the SF-12 health survey in a nationally representive sample. Medical Decision Making, 24, 160–169.

    Article  PubMed  Google Scholar 

  33. Nichols, M. B., Sengupta, N., & Globe, D. (2001). Evaluating quality-adjusted life years: Estimation of the Health Utility Index (HU12) from the SF-36. Medical Decision Making, 21, 105–112.

    Google Scholar 

  34. Shmueli, A. (2004). The relationship between the visual analog scale and the SF-36 scales in the general population: An update. Medical Decision Making, 24(1), 61–63.

    Article  PubMed  Google Scholar 

  35. Stratmann-Schoene, D., Kuehn, T., Kreienberg, R., & Leidl, R. (2006). A preference-based index for the SF-12. Health Economics, 15(6), 553–564.

    Article  PubMed  CAS  Google Scholar 

  36. Badia, X., Schiaffino, A., Alonso, J., & Herdman, M. (1998). Using the EuroQol 5-D in the Catalan general population: Feasibility and construct validity. Quality of Life Research, 7(4), 311–322.

    Article  PubMed  CAS  Google Scholar 

  37. Cleemput, I., Kesteloot, K., Moons, P., Vanrenterghem, Y., Van Hooff, J., et al. (2004). The construct and concurrent validity of the EQ-5D in a renal transplant population. Value in Health, 7(4), 499–509.

    Article  PubMed  Google Scholar 

  38. EuroQol Group. (2007). EQ-5D value sets: Inventory, comparative review and user guide. The Netherlands: Springer.

    Google Scholar 

  39. Jelsma, J., Hansen, K., Weerdt, W., de Cock, P., & Kind, P. (2003). How do Zimbabweans value health states? Population Health Metrics, 1(1), 11.

    Article  PubMed  Google Scholar 

  40. Johnson, J., & Coons, S. (1998). Comparison of the EuroQol and the SF-12 in an adult US sample. Quality of Life Research, 7, 155–166.

    Article  PubMed  CAS  Google Scholar 

  41. Lamers, L. M., McDonnell, J., Stalmeier, P. F. M., Krabbe, P. F. M., & Busschbach, J. J. V. (2006). The Dutch tariff: results and arguments for an effective design for national EQ-5D valuation studies. Health Economics Letters, 15(10), 1121–1132.

    Article  CAS  Google Scholar 

  42. Shaw, J., Johnson, J., & Coons, S. J. (2005). U.S. valuation of the EQ-5D health states development and testing of the D1 valuation model. Medical Care, 43(3), 203–220.

    Article  PubMed  Google Scholar 

  43. Tsuchiya, A., Ikeda, S., Ikegami, N., Nishimura, S., Sakai, I., et al. (2002). Estimating an EQ-5D populating value set: The case of Japan. Health Economics, 11(4), 341–353.

    Article  PubMed  Google Scholar 

  44. Hakim, Z., & Pathak, D. S. (1999). Modelling the EuroQol data: A comparison of discrete choice conjoint and conditional preference modeling. Health Economics, 8, 103–116.

    Article  PubMed  CAS  Google Scholar 

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Acknowledgments

This study was funded by a contract with the U.S. Consumer Product Safety Commission (CPSC). This journal article was prepared in part by CPSC staff but has not been reviewed or approved by, and may not necessarily reflect the views of, the Commission. The deidentified data used for this study were obtained from the Burn Model Systems Centers Database which is supported by the U.S. Department of Education, National Institute on Disability and Rehabilitation Research (NIDRR) in collaboration with the BMS Centers and the BMS National Data and Statistical Center. However, these contents may not reflect the opinions or views of the BMS Centers, NIDRR, or the U.S. Department of Education.

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Authors and Affiliations

  1. Pacific Institute for Research and Evaluation, Calverton, MD, USA

    Ted Miller & Soma Bhattacharya

  2. U.S. Consumer Product Safety Commission, Bethesda, MD, USA

    William Zamula

  3. University of Colorado Health Sciences Center, Lakeland, CO, USA

    Dennis Lezotte

  4. University of Texas Southwestern Medical Center, Dallas, TX, USA

    Karen Kowalske

  5. University of Texas Medical Branch and Shriner’s Hospital, Galveston, TX, USA

    David Herndon

  6. Johns Hopkins University, Baltimore, MD, USA

    James Fauerbach

  7. Harborview Medical Center, University of Washington, Seattle, WA, USA

    Loren Engrav

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  1. Ted Miller
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Correspondence to Ted Miller.

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Miller, T., Bhattacharya, S., Zamula, W. et al. Quality-of-life loss of people admitted to burn centers, United States. Qual Life Res 22, 2293–2305 (2013). https://doi.org/10.1007/s11136-012-0321-5

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