Dataset Information

Health care utilization and health-related quality of life of injury patients: comparison of educational groups.

ABSTRACT:

Background

Differences in health care utilization by educational level can contribute to inequalities in health. Understanding health care utilization and health-related quality of life (HRQoL) of educational groups may provide important insights into the presence of these inequalities. Therefore, we assessed characteristics, health care utilization and HRQoL of injury patients by educational level.

Method

Data for this registry based cohort study were extracted from the Dutch Injury Surveillance System. At 6-month follow-up, a stratified sample of patients (≥25 years) with an unintentional injury reported their health care utilization since discharge and completed the EQ-5-Dimension, 5-Level (EQ-5D-5L) and visual analogue scale (EQ VAS). Logistic regression analyses, adjusting for patient and injury characteristics, were performed to investigate the association between educational level and health care utilization. Descriptive statistics were used to analyse HRQoL scores by educational level, for hospitalized and non-hospitalized patients.

Results

This study included 2606 patients; 47.9% had a low, 24.4% a middle level, and 27.7% a high level of education. Patients with low education were more often female, were older, had more comorbidities, and lived more often alone compared to patients with high education (p < 0.001). Patients with high education were more likely to visit a general practitioner (OR: 1.38; CI: 1.11-1.72) but less likely to be hospitalized (OR: 0.79; CI: 0.63-1.00) and to have nursing care at home (OR: 0.66; CI: 0.49-0.90) compared to their low educated counterparts. For both hospitalized an non-hospitalized persons, those with low educational level reported lower HRQoL and more problems on all dimensions than those with a higher educational level.

Conclusion

Post-discharge, level of education was associated with visiting the general practitioner and nursing care at home, but not significantly with use of other health care services in the 6 months post-injury. Additionally, patients with a low educational level had a poorer HRQoL. However, other factors including age and sex may also explain a part of these differences between educational groups. It is important that patients are aware of potential consequences of their trauma and when and why they should consult a specific health care service after ED or hospital discharge.

SUBMITTER: van der Vlegel M

PROVIDER: S-EPMC8451142 | 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.