| Title |
Examining the health-related quality of life using EQ-5D-5L in patients with four kinds of chronic diseases from specialist outpatient clinics in Hong Kong SAR, China
|
|---|---|
| Published in |
Patient preference and adherence, September 2017
|
| DOI | 10.2147/ppa.s143944 |
| Pubmed ID | |
| Authors |
Richard Huan Xu, Annie Wai Ling Cheung, Eliza Lai-Yi Wong |
| Abstract |
Health-related quality of life (HRQoL) measures have been increasingly employed in the evaluation of health utility. The aim of this study was to evaluate the impact of four different kinds of chronic diseases on the HRQoL of patients from specialist outpatient clinics in Hong Kong (HK) using the 5-level EQ-5D (EQ-5D-5L) value set. Patients who attended one of the 26 Hospital Authority's specialist outpatient clinics and met inclusion criteria were potentially sampled. The value of EQ-5D-5L (HK version) that asks the respondent to describe and rate his or her health status was used as the dependent variable. Four kinds of long-term health conditions and other demographic, socioeconomic, and health service use characteristics have been analyzed as independent variables. Chi-square test, robust one-way ANOVA, and the two-part model have been used to analyze the data. A total of 7,496 (53.7%) patients reported to have at least one kind of chronic diseases. The mean score for hypertension (mean =0.882, standard error [SE] =0.207) is higher than the others, which followed by cancer, diabetes, and heart disease. Patients who were male, young, higher education attainment, and with chronic conditions got a higher EQ-5D score. In the two-part model, all the four long-standing health conditions were statistically significant in logistic model, but the magnitude of coefficients changed significantly in ordinary least squares model. Chronic diseases have a significant negative impact on patients' HRQoL, but there might be a varying effect depending on what kind of chronic diseases patients had. Further research is also needed to plan appropriate strategies to improve patient-centered care. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 60 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 8 | 13% |
| Student > Ph. D. Student | 6 | 10% |
| Lecturer | 4 | 7% |
| Student > Bachelor | 3 | 5% |
| Researcher | 2 | 3% |
| Other | 7 | 12% |
| Unknown | 30 | 50% |
| Readers by discipline | Count | As % |
|---|---|---|
| Nursing and Health Professions | 7 | 12% |
| Medicine and Dentistry | 6 | 10% |
| Pharmacology, Toxicology and Pharmaceutical Science | 4 | 7% |
| Economics, Econometrics and Finance | 3 | 5% |
| Computer Science | 2 | 3% |
| Other | 8 | 13% |
| Unknown | 30 | 50% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 September 2017.
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#20,110,957
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Outputs from Patient preference and adherence
#1,272
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Outputs of similar age from Patient preference and adherence
#34
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