Abstract
We aimed; 1) to determine the validity of the EuroQol 5D (EQ-5D) for the health-related quality of life (HRQOL) of Korean patients with type 2 diabetes, and 2) to identify associated factors of the HRQOL of these patients. Follow-up surveys were conducted for consecutive patients with type 2 diabetes. HRQOL was assessed using the EQ-5D and the Short Form-36 (SF-36). The validity of EQ-5D was assessed with the perspectives of known group, convergent and discriminant validity. Additionally, a linear mixed model using a backward elimination was used for identify associated factors. Of the 1,072 patients included in the first survey, 858 (80.0%) completed the questionnaires in the follow-up. In the known group validity, the problem rates in each EQ-5D dimension were highest among women, elderly people, and less-educated subjects. The Spearman's ρ between the EQ-5D and the SF-36 scales were larger in the comparable dimensions than those in the less comparable dimensions. In the final model, we found that sex, age, education, body mass index, atrial fibrillation, stroke, and retinopathy were statistically significant. Our data suggest that the EQ-5D is a valid tool for Korean patients with type 2 diabetes and that various factors could affect their HRQOL.
Health policies for non-communicable diseases should include strategies for the management of type 2 diabetes, as this disease has become more common worldwide and remains an important risk factor for cardiovascular diseases. The International Diabetes Federation estimates that 333 million people will have diabetes by 2025 (1). In the USA, the prevalence of diabetes increased from 5.3% to 8.2% between 1980 and 2000 (2). In Korea, the prevalence of diabetes (aged ≥ 30 yr) was estimated to be 9.1% in 2005 (3, 4).
In analyzing the health impact of chronic diseases, health-related quality of life (HRQOL) has been commonly used as an outcome indicator because patient cooperation forms the core of health plans for incurable diseases. The quality-adjusted life years (QALYs) is defined as a measure of a person's length of life weighted by a valuation of their HRQOL and a valuable outcome measure of disease burden. The QALYs can be used in clinical practice, trials, economic evaluation, and health policy formulation.
The EuroQol 5D (EQ-5D), a generic measure of HRQOL, is widely used to determine the utility weight to measure decreases in QALYs due to disease. Some studies have used EQ-5D to estimate the HRQOL of type 2 diabetic patients in Western and Asian countries. Clarke et al. (5) reported that the EQ-5Dindex is an independent predictor of mortality risk, future vascular events, and other complications in patients with type 2 diabetes. Sakamaki et al. (6) reported HRQOL in Japanese patients with type 2 diabetes using EQ-5D, and Sakthong et al. (7) estimated the EQ-5Dindex in Thai patients with type 2 diabetes. However, in these studies, the HRQOL has been assessed by cross-sectional design. Because HRQOL is a time-dependent variable, repeated measurements are required to obtain reliable estimates. In addition, the Korean version of EQ-5D has not been validated for use in patients with type 2 diabetes in Korea.
The aim of this study was to assess the validity of the Korean version of EQ-5D to evaluate the HRQOL of Korean patients with type 2 diabetes, and also to identify associated factors to the HRQOL of these patients during follow-up surveys.
Consecutive patients with type 2 diabetes who routinely visited the outpatient clinics of three university hospitals (i.e., Sanggye Paik Hospital and Konkuk University Hospital in Seoul, and Ilsan Paik Hospital in Gyeonggi Province) were invited to participate in the first survey (October 2007-January 2008). Participants completed all the questionnaires in the clinic on the day of recruitment to the first survey.
Subjects were included in the study if they were > 20-yr-old and had a confirmed diagnosis and/or received ongoing treatment for type 2 diabetes. The exclusion criteria were type 1 diabetes, a history of not taking relevant treatments, and unwillingness to participate in the study. During subsequent follow-up visits, subjects who participated in the first survey were invited to complete a second survey from December 2007 to April 2008 using same methods. All surveys were conducted by well-trained research nurses.
The following information was recorded during interviews and by reviewing the medical charts: general characteristics of the subjects, including sex, age, education, marital status, drinking and smoking status; and clinical factors such as weight, height, treatment modality, comorbidities (e.g., hypertension, hyperlipidemia, atrial fibrillation [AF], and cataracts); macrovascular complications such as acute myocardial infarction, angina pectoris, acute stroke, and transient ischemic attack (TIA); and microvascular complications such as retinopathy and nephropathy. The subjects' HRQOL was assessed using the EQ-5D. In the first survey, the Short Form (SF)-36 was used to validate the EQ-5D. EQ-5Dindex was calculated using Korean valuation set (8).
The known group construct, convergent, and discriminant validities of the EQ-5D were examined. The known group construct validity was determined by comparing the rates of reported problem pertaining to each EQ-5D dimension among different groups based on sex, age, and education. We expected the rates to be higher among women, older individuals, and less-educated patients. To evaluate the convergent and discriminant validity, we examined the correlation coefficients between the EQ-5D and the eight scale scores and two summary scores of the SF-36. Our hypothesis was a greater correlation between the SF-36 scales and the EQ-5D dimensions that represent similar characteristics (e.g., the mobility dimension of EQ-5D and the physical functioning [PF] scale of SF-36) than between less comparable scales and dimensions, such as the mobility of EQ-5D and the mental health (MH) scale of SF-36.
Student's t-tests or ANOVA and chi-square tests were performed to compare the characteristics of study participants of the first and second surveys and HRQOL on general characteristics. In addition, Student's t-test was used to examine the mean difference in SF-36 scale scores with regards to EQ-5D dimensions. Spearman's correlation coefficients were calculated between the EQ-5D and the SF-36. The agreement of problems in the EQ-5D dimension was analyzed by Cohen's kappa value. We examined the effect of each general and clinical characteristic on the HRQOL of patients with type 2 diabetes using a linear mixed model and backward eliminations were used for variable selections. Analyses were performed with PASW statistical software 18.0 (SPSS Inc., Chicago, IL, USA) with a P value < 0.05 considered statistically significant.
The study protocol was approved by the institutional review board of each participating hospital (IRB No. #07-35 in the Sanggye Paik Hospital; KUH 1010058 in the Konkuk University Hospital; and IB-0709-056 in the Ilsan Paik Hospital). And informed consent was obtained from all patients.
The characteristics of the subjects are shown in Table 1. Of the 1,072 patients with type 2 diabetes who completed the first survey, 581 (54.2%) were men. Of the 858 patients who completed the second survey 473 (55.1%) were men. The follow-up rate was 80.0%. The mean ages of the patients who completed the first and second surveys were 57.5 and 57.7 yr, respectively. Education was classified as follows (percentages in the parentheses correspond with the first and second surveys, respectively): "elementary school or below" (25.2% and 25.7%), "middle or high school" (49.6% and 49.7%), and "university or above" (25.1% and 24.6%). The mean BMI was 24.9 kg/m2 in the first survey and 25.0 kg/m2 in the second survey. A total of 431 (40.2%, first survey) and 338 (39.4%, second survey) patients were treated with diet therapy; 864 (80.7%) and 693 (80.8%), with oral hypoglycemic agents; and 282 (26.3%) and 234 (27.3%), with insulin therapy (including patients receiving both insulin and oral hypoglycemic agents). No significant differences in general characteristics were observed between the two groups.
Hypertension (55.3% and 55.8%) and hyperlipidemia (53.5% and 54.1%) were the most frequent comorbidities. Angina pectoris (8.3% and 8.9%) and ischemic stroke (5.6% and 6.1%) were frequent macrovascular complications. Furthermore, 23.6% and 24.4% of the patients had retinopathy and 4.6% and 7.2% of the patients had nephropathy in the first and second surveys, respectively. No significant differences were noted in the proportion of complications between the first and second surveys.
As expected in the known group construct, the HRQOL significantly differed with sex, age, and education (P < 0.01). In addition, the EQ-5Dindex was lower in women than in men, and scores were higher in younger than in older subjects. Patients in the elementary school or below group had lower EQ-5Dindex scores than those in the middle/high school and university or above groups (Table 2).
As results from convergent and discriminant validity analysis, most mean differences of SF-36 scale scores were statistically significant according to problem reporting of each EQ-5D dimension (Fig. 1). The difference of PF scale score in patients on mobility problem reporting (32.2), as determined using EQ-5D, was larger than the difference of MH scale scores (14.2). In addition, the difference of MH scale scores of patients on anxiety/depression of EQ-5D was 25.5, while the difference of PF scale score on problem reporting in anxiety/depression was 14.9. Table 3 showed the spearman's correlation coefficients between the EQ-5D and the SF-36. The coefficients were larger between related dimensions (e.g., -0.462 between mobility in EQ-5D and physical functioning [PF] in SF-36 and -0.652 between pain/discomfort in EQ-5D and bodily pain [BP] in SF-36) than unrelated dimensions (e.g. -0.201 between mobility in EQ-5D and mental health [MH] in SF-36).
Some differences were found in the rates of problems, as assessed according to the EQ-5D dimensions between two surveys (Table 4). We found that 7.0% of the subjects reported a change in the self-care dimension, and 19.2% of people reported a change in the pain/discomfort dimension.
In the univariate model, most variables were significantly related with the HRQOL of Korean type 2 diabetes patients except for hyperlipidemia, MI, and nephropathy. The final multivariate model included sex, age, BMI, AF, ischemic stroke, and retinopathy (Table 5). The absolute values of β coefficients on age groups (0.025 to 0.0597) were larger than those values of sex (0.0225) and education (0.017 to 0.0289). In clinical factors, the β coefficient of AF was the largest (-0.0751) and followed by ischemic stroke (-0.0541), BMI group (0.0315 to 0.0448) and retinopathy (-0.021).
Our findings reveal that the EQ-5D is a useful tool measuring the HRQOL of Korean patients with type 2 diabetes. In addition, we found that comorbidities such as obesity and AF and complications such as ischemic stroke and retinopathy significantly affected the HRQOL of these patients. The sex, age and education were also important factors to HRQOL of Korean type 2 diabetes patients.
Previous studies have reported that certain medical conditions hamper the HRQOL of patients with type 2 diabetes. Redekop et al. (9) and Lee et al. (10) reported that obesity significantly reduces the HRQOL of patients with type 2 diabetes, as measured using the EQ-5D. Using the Self-Administered Quality of Well Being index, Coffey et al. (11) reported that the major complications of diabetes are associated with low HRQOL. In addition, Sakamaki et al. (6) found a negative association between the major complications of type 2 diabetes and HRQOL, but this association was statistically insignificant except in the case of certain subjective symptoms. The authors proposed that this finding resulted from the exclusion of patients with severe disease. Consistent with this suggestion, our patients were also recruited from outpatient clinics; hence, the impacts of comorbidities and complications on EQ-5Dindex were relatively low.
Validity was assessed in terms of three aspects, and was found to match our expectations. With regards to convergent validity, there were considerable differences in the PF, RP, BP, and SF scale scores on the SF-36 compared with subjects' scores on the EQ-5D dimensions of mobility, self-care, routine activity (i.e., all three are related to physical ability). Furthermore, there were notable correlation between BP scores of the SF-36 and pain/discomfort scores of the EQ-5D, as well as between the RE scale scores on the SF-36 and the anxiety/depression dimension of the EQ-5D. Therefore, we could conclude that the convergent validity of the EQ-5D is acceptable. The age and sex distributions of the EQ-5Dindex were also similar to expected values. Most items received lower scores in women and elderly subjects. Therefore, our data suggest that the EQ-5D could be a valid and acceptable method of evaluating the HRQOL of Korean patients with type 2 diabetes.
In this study, there were some differences in the HRQOLs of Korean patients with type 2 diabetes between the surveys. These findings show that the HRQOL is a time-dependent variable, and should be repeatedly measured in type 2 diabetic patients to ensure reliable estimations. In addition, because changes in HRQOL may have caused patients to discontinue follow-up, the actual change in HRQOL may have been greater than estimated in the current study.
There are three valuation sets for the Korean version of the EQ-5D. We used the valuation set reported by Jo et al. (8) because this set was calculated from general populations in Seoul and Gyeonggi province. Although we did not represent the results using the other valuation sets (12-14) in this paper, the coefficients in the models differ according to the valuation sets. These findings indicate that valuation sets could affect the results of utility studies; so sensitivity analyses should be conducted using data from other Korean studies.
This study has limitation in terms of the representativeness of the study population because we did not randomly recruit study participants. However, our study population was derived from three institutes in diverse regions. Also, the mean age and sex distributions of our patients were similar to those in previous population-oriented utilization studies of patients with diabetes in Korea (15).
In conclusion, our findings suggest that EQ-5D is a valid tool for measuring the HRQOL of Korean patients with type 2 diabetes. In the final model for utility-weight prediction, BMI, hypertension, angina pectoris, CHF, AF, stroke, TIA, and retinopathy negatively affected the HRQOL of Korean patients with type 2 diabetes.
Figures and Tables
Fig. 1
Differences in eight SF-36 scales with regards to reports of "no problems" and "some or severe problems" in each EQ-5D domain. *P < 0.05. PF, physical functioning; RP, role-physical; BP, bodily pain; GH, general health; VT, vitality; SF, social functioning; RE, role-emotional; MH, mental health.
![jkms-27-255-g001](/upload/SynapseData/ArticleImage/0063jkms/jkms-27-255-g001.jpg)
ACKNOWLEDGMENTS
This study was funded by Sanofi-Aventis Korea Co. Ltd. Y. J. Choi is an employee of Sanofi-aventis Korea Co. Ltd. Y. J. Choi contributed to study design, study management, interviewer education, data analysis, and review of the manuscript. All other authors contributed to study design, study management, data collection, data analysis, and writing of the manuscript. The fund from Sanovi-Aventis did not influence the conduct of study, data interpretation, and writing of the report. The authors have no other conflict of interest to report.
References
1. King H, Aubert RE, Herman WH. Global burden of diabetes, 1995-2025: prevalence, numerical estimates, and projections. Diabetes Care. 1998. 21:1414–1431.
2. Gregg EW, Cadwell BL, Cheng YJ, Cowie CC, Williams DE, Geiss L, Engelgau MM, Vinicor F. Trends in the prevalence and ratio of diagnosed to undiagnosed diabetes according to obesity levels in the US. Diabetes Care. 2004. 27:2806–2812.
3. Choi YJ, Kim HC, Kim HM, Park SW, Kim J, Kim DJ. Prevalence and management of diabetes in Korean adults: Korean National Health and Nutrition Examination Surveys 1998-2005. Diabetes Care. 2009. 32:2016–2020.
4. Kim DJ. The epidemiology of diabetes in Korea. Diabetes Metab J. 2011. 35:303–308.
5. Clarke PM, Hayes AJ, Glaszou PG, Scott R, Simes J, Keech AC. Using the EQ-5D index score as a predictor of outcomes in patients with type 2 diabetes. Med Care. 2009. 47:61–68.
6. Sakamaki H, Ikeda S, Ikegami N, Uchigata Y, Iwamoto Y, Origasa H, Otani T, Otani Y. Measurement of HRQL using EQ-5D in patients with type 2 diabetes mellitus in Japan. Value Health. 2006. 9:47–53.
7. Sakthong P, Charoenvisuthiwongs R, Shabunthom R. A comparison of EQ-5D index scores using the UK, US, and Japan preference weights in a Thai sample with type 2 diabetes. Health Qual Life Outcomes. 2008. 6:71.
8. Jo MW, Yun SC, Lee SI. Estimating quality weights for EQ-5D health states with time trade-off method in South Korea. Value Health. 2008. 11:1186–1189.
9. Redekop WK, Koopmanschap MA, Stolk RP, Rutten GE, Wolffenbuttel BH, Niessen LW. Health-related quality of life and treatment satisfaction in Dutch patients with type 2 diabetes. Diabetes Care. 2002. 25:458–463.
10. Lee AJ, Morgan CL, Morrissey M, Wittrup-Jensen KU, Kennedy-Martin T, Currie CJ. Evaluation of the association between the EQ-5D (health-related utility) and body mass index (obesity) in hospital-treated people with type 1 diabetes, type 2 diabetes and with no diagnosed diabetes. Diabet Med. 2005. 22:1482–1486.
11. Coffey JT, Brandle M, Zhou H, Marriott D, Burke R, Tabaei BP, Engelgau MM, Kaplan RM, Herman WH. Valuing health-related quality of life in diabetes. Diabetes Care. 2002. 25:2238–2243.
12. Lee YK, Nam HS, Chuang LH, Kim KY, Yang HK, Kwon IS, Kind P, Kweon SS, Kim YT. South Korean time trade-off values for EQ-5D health states: modeling with observed values for 101 health state. Value Health. 2009. 12:1187–1193.
13. Kang EJ, Shin HS, Park HJ, Jo MW, Kim NY. A valuation of health status using EQ-5D. Korea J Health Econ Policy. 2006. 12:19–43.
14. Dolan P. Modeling valuation for EuroQol health states. Med Care. 1997. 35:1095–1108.
15. Diabetes in Korea. 2007. Seoul: Korean Diabetes Association and Health Insurance Review and Assessment Service.