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Arch Phys Med Rehabil. 2017 Oct 16. pii: S0003-9993(17)31267-4. doi: 10.1016/j.apmr.2017.09.119. [Epub ahead of print]

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

Author information

1
Department of Rehabilitation Medicine, University of Washington, Seattle, WA. Electronic address: dagmara@uw.edu.
2
Food and Drug Administration, Washington, DC.
3
Department of Rehabilitation Medicine, University of Washington, Seattle, WA.
4
Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, MA.

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:

Burns; Quality of life; Rehabilitation; Statistical models

PMID:
29050789
DOI:
10.1016/j.apmr.2017.09.119

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