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Satisfaction With Life Over Time in People With Burn Injury: A National Institute on Disability, Independent Living, and Rehabilitation Research Burn Model System Study

Published:October 16, 2017DOI:https://doi.org/10.1016/j.apmr.2017.09.119

      Abstract

      Objective

      To examine trajectories of satisfaction with life (SWL) of burn survivors over time and their clinical, demographic, and other predictors.

      Design

      Longitudinal survey.

      Setting

      Not applicable.

      Participants

      Individuals ≥18 years of age who underwent burn-related surgery and met one of the following criteria: (1) >10% total body surface area (TBSA) burn and ≥65 years of age; (2) >20% TBSA burn and 18 to 64 years of age; (3) electrical high voltage/lightning injury; or (4) burn injury to the hands, face, or feet. The participants (N=378) had data on all variables of interest and were included in the analyses.

      Interventions

      Not applicable.

      Main Outcome Measure

      Satisfaction With Life Scale.

      Results

      Growth mixture modeling identified 2 classes with different trajectories of SWL. The mean SWL of the unchanged class (n=224, 60%) was flat over 2 years with high initial SWL scores. The SWL of the dissatisfied class (n=154, 40%) was at the low end of average and got progressively worse over time.

      Conclusions

      SWL after burn injury can be described by 2 different trajectories with substantially different outcomes. Older age, worse mental health, and unemployment prior to injury predicted membership in the dissatisfied class. Additional services could be provided to those at high risk for low SWL to achieve better outcomes.

      Keywords

      List of abbreviations:

      BMS (Burn Model System), GMM (growth mixture modeling), HRQOL (health-related quality of life), MCS (Mental Component Summary), PCS (Physical Component Summary), QOL (quality of life), SF-12 (12-Item Short Form Health Survey), SWAP (Satisfaction With Appearance Scale), SWL (satisfaction with life), SWLS (Satisfaction With Life Scale), TBSA (total body surface area)
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      References

        • Tompkins R.G.
        Survival from burns in the new millennium 70 years' experience from a single institution.
        Ann Surg. 2015; 261: 263-268
        • Esselman P.C.
        Burn rehabilitation: an overview.
        Arch Phys Med Rehabil. 2007; 88: S3-S6
        • Stavrou D.
        • Weissman O.
        • Tessone A.
        • et al.
        Health related quality of life in burn patients – a review of the literature.
        Burns. 2014; 40: 788-796
        • Pavoni V.
        • Gianesello L.
        • Paparella L.
        • Buoninsegni L.T.
        • Barboni E.
        Outcome predictors and quality of life of severe burn patients admitted to intensive care unit.
        Scand J Trauma Resusc Emerg Med. 2010; 18: 24
        • Elsherbiny O.E.
        • Salem M.A.
        • El-Sabbagh A.H.
        • Elhadidy M.R.
        • Eldeen S.M.
        Quality of life of adult patients with severe burns.
        Burns. 2011; 37: 776-789
        • Murphy M.E.
        • Holzer 3rd, C.E.
        • Richardson L.M.
        • et al.
        Quality of life of young adult survivors of pediatric burns using world health organization disability assessment scale ii and burn specific health scale-brief: a comparison.
        J Burn Care Res. 2015; 36: 521-533
        • Herndon D.N.
        Total burn care.
        4th ed. Saunders Elsevier, New York2012
        • Falder S.
        • Browne A.
        • Edgar D.
        • et al.
        Core outcomes for adult burn survivors: a clinical overview.
        Burns. 2009; 35: 618-641
        • Klinge K.
        • Chamberlain D.J.
        • Redden M.
        • King L.
        Psychological adjustments made by postburn injury patients: an integrative literature review.
        J Adv Nurs. 2009; 65: 2274-2292
        • Riis A.
        • Andersen M.
        • Pedersen M.B.
        • Hall K.W.
        Long-term psychosocial adjustment in patients with severe burn injuries: a follow-up study.
        Burns. 1992; 18: 121-126
        • Druery M.
        • Brown T.L.
        • Muller M.
        Long term functional outcomes and quality of life following severe burn injury.
        Burns. 2005; 31: 692-695
        • Patterson D.R.
        • Ptacek J.T.
        • Cromes F.
        • Fauerbach J.A.
        • Engrav L.
        The 2000 Clinical Research Award. Describing and predicting distress and satisfaction with life for burn survivors.
        J Burn Care Rehabil. 2000; 21: 490
        • Goverman J.
        • Mathews K.
        • Nadler D.
        • et al.
        Satisfaction with life after burn: A Burn Model System National Database Study.
        Burns. 2016; 42: 1067-1073
        • Klein M.B.
        • Lezotte D.L.
        • Fauerbach J.A.
        • et al.
        The National Institute on Disability and Rehabilitation Research burn model system database: a tool for the multicenter study of the outcome of burn injury.
        J Burn Care Res. 2007; 28: 84
        • Muthén L.K.
        • Muthén B.O.
        Mplus user's guide.
        7th ed. Muthén & Muthén, Los Angeles1998
        • Diener E.
        • Emmons R.A.
        • Larsen R.J.
        • Griffin S.
        The Satisfaction With Life Scale.
        J Pers Assess. 1985; 49: 71-75
        • Maas A.I.
        • Harrison-Felix C.L.
        • Menon D.
        • et al.
        Common data elements for traumatic brain injury: recommendations from the interagency working group on demographics and clinical assessment.
        Arch Phys Med Rehabil. 2010; 91: 1641-1649
        • Biering-Sorensen F.
        • Alai S.
        • Anderson K.
        • et al.
        Common data elements for spinal cord injury clinical research: a National Institute for Neurological Disorders and Stroke project.
        Spinal Cord. 2015; 53: 265-277
        • Pavot W.
        • Diener E.
        • Colvin C.R.
        • Sandvik E.
        Further validation of the Satisfaction With Life Scale: evidence for the cross-method convergence of well-being measures.
        J Pers Assess. 1991; 57: 149-161
        • Rosengren L.
        • Jonasson S.B.
        • Brogårdh C.
        • Lexell J.
        Psychometric properties of the Satisfaction With Life Scale in Parkinson's disease.
        Acta Neurol Scand. 2015; 132: 164-170
        • Amtmann D.
        • Bocell F.D.
        • Bamer A.
        • et al.
        Psychometric properties of the Satisfaction with Life Scale in people with traumatic brain, spinal cord, or burn injury: a National Institute on Disability, Independent Living, and Rehabilitation Research Model System Study.
        Assessment. 2017; 2: 1-11
        • Lawrence J.W.
        • Heinberg L.J.
        • Roca R.
        • Munster A.
        • Spence R.
        • Fauerbach J.A.
        Development and validation of the Satisfaction With Appearance Scale: assessing body image among burn-injured patients.
        Psychol Assess. 1998; 10: 64-70
        • Ware Jr., J.E.
        • Kosinski M.
        • Keller S.D.
        A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity.
        Med Care. 1996; 34: 220-233
        • Miller T.
        • Bhattacharya S.
        • Zamula W.
        • et al.
        Quality-of-life loss of people admitted to burn centers, United States.
        Qual Life Res. 2013; 22: 2293-2305
        • Druery M.
        • Newcombe P.A.
        • Cameron C.M.
        • Lipman J.
        Factors influencing psychological, social and health outcomes after major burn injuries in adults: cohort study protocol.
        BMJ Open. 2017; 7: e017545
        • Edgar D.
        • Dawson A.
        • Hankey G.
        • Phillips M.
        • Wood F.
        Demonstration of the validity of the SF-36 for measurement of the temporal recovery of quality of life outcomes in burns survivors.
        Burns. 2010; 36: 1013-1020
        • Little T.D.
        Longitudinal structural equation modeling.
        Guilford Press, New York2013
        • Jung T.
        • Wickrama K.A.
        An introduction to latent class growth analysis and growth mixture modeling.
        Soc Personal Psychol Compass. 2008; 2: 302-317
        • Muthén B.O.
        • Shedden K.
        Finite mixture modeling with mixture outcomes using the EM algorithm.
        Biometrics. 1999; 55: 463-469
        • Muthén B.O.
        Latent variable analysis: growth mixture modeling and related techniques for longitudinal data.
        in: Kaplan D. The Sage handbook of quantitative methodology for the social sciences. Sage Publications, Newbery Park2004: 345-368
        • Duncan T.E.
        • Duncan S.C.
        • Strycker L.A.
        An introduction to latent variable growth curve modeling: concepts, issues, and applications.
        2nd ed. Lawrence Erlbaum Associates, Mahwah2006
        • Nylund K.
        • Asparouhov T.
        • Muthén B.
        Deciding on the number of classes in latent class analysis and growth mixture modeling: a Monte Carlo simulation study.
        Struct Equ Modeling. 2007; 14: 535-569
        • Curran P.J.
        • Obeidat K.
        • Losardo D.
        Twelve frequently asked questions about growth curve modeling.
        J Cogn Dev. 2010; 11: 121-136
        • Juengst S.B.
        • Adams L.M.
        • Bogner J.A.
        • et al.
        Trajectories of life satisfaction after traumatic brain injury: influence of life roles, age, cognitive disability, and depressive symptoms.
        Rehabil Psychol. 2015; 60: 353-364
        • Graham J.W.
        Missing data analysis: making it work in the real world.
        Annu Rev Psychol. 2009; 60: 549-576
        • Horton N.J.
        • Kleinman K.P.
        Much ado about nothing: a comparison of missing data methods and software to fit incomplete data regression models.
        Am Stat. 2007; 61: 79-90