Dataset Information

Establishing the Factor Structure of a Health-Related Quality of Life Measurement System for Caregivers of Persons Living With Traumatic Brain Injury.

ABSTRACT:

Objectives

To understand the factor structure of health-related quality of life specific to caregivers of people living with traumatic brain injury (TBI).

Design

Prospective, cross-sectional data collection.

Setting

Three TBI Model Systems rehabilitation hospitals, an academic medical center, and a military medical treatment facility.

Participants

Caregivers (N=558) of people who have sustained a TBI (344 caregivers of civilians and 214 caregivers of service members or veterans; 85% women; 58% spouses; mean age, 46.12±14.07y) who have provided care for an average of 5.82±5.40 years.

Interventions

Not applicable.

Main outcomes measures

The Traumatic Brain Injury Caregiver Quality of Life (TBI-CareQOL) measurement system including 10 Patient-Reported Outcomes Measurement Information System item banks (anger, anxiety, depression, social isolation, sleep disturbance, fatigue, ability to participate in social roles and activities, satisfaction with social roles and activities, emotional support, informational support) and 5 TBI-CareQOL banks (feelings of loss-self, feelings of loss-person with TBI, feeling trapped, caregiver-specific anxiety, caregiver strain).

Results

Confirmatory factor analysis model fit indices were compared for 14 empirically derived and 5 theoretically derived models. Confirmatory factor analysis results indicated that the best model fit was for a 6-factor model with dimensions that included mental health, social support, social participation, social isolation, physical health, and caregiver emotion.

Conclusions

Results indicated that a 6-factor model provided the best model fit for health-related quality of life in caregivers of individuals with TBI. These results have utility for both research and clinical applications. Establishing the TBI-CareQOL's factor structure provides preliminary evidence of the measurement system's construct validity, helps inform the selection of measures for specific research or clinical interventions, and informs the development of composite scores.

SUBMITTER: Raad JH

PROVIDER: S-EPMC7311311 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background Traumatic brain injury (TBI) commonly causes death and disability that can result in productivity loss and economic burden. The health-related quality of life (HRQoL) has been measured in patients suffering from TBI, both in clinical and socioeconomic perspectives. The study aimed to assess the HRQoL in patients following TBI using the European quality of life measure-5 domain-5 level (EQ-5D-5L) questionnaire and develop models for predicting the EQ-5D-5L index score in patients with TBI. Method A cross-sectional study was performed with 193 TBI patients who had completed the EQ-5D-5L questionnaire. The clinical characteristics, Glasgow coma scale (GCS) score, treatment, and Glasgow outcome scale (GOS) were collected. The total data was divided into training data (80%) and testing data (20%); hence, the factors affecting the EQ-5D-5L index scores were used to develop the predictive model with linear and nonlinear regression. The performances of the predictive models were estimated with the adjusted coefficient of determination (R 2 ) and the root mean square error (RMSE). Results A good recovery was found at 96.4%, while 2.1% displayed an unfavorable outcome. Moreover, the mean EQ-5D-5L index scores were 0.91558 (standard deviation [SD] 1.09639). GCS score, pupillary light reflex, surgery, and GOS score significantly correlated with the HRQoL scores. The multiple linear regression model had a high adjusted R 2 of 0.6971 and a low RMSE of 0.06701, while the polynomial regression developed a nonlinear model that had the highest adjusted R 2 of 0.6843 and the lowest RMSE of 0.06748. Conclusions A strong positive correlation between the physician-based outcome as GOS and HRQoL was observed. Furthermore, both the linear and nonlinear regression models were acceptable approaches to predict the HRQoL of patients after TBI. There would be limitations for estimating the HRQoL in unconscious or intubated patients. The HRQoL obtained from the predictive models would be an alternative method to resolve this problem.

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