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Changes in Quality of Life Associated with Complications of Diabetes: Results from the ADVANCE Study

Open ArchivePublished:December 02, 2015DOI:https://doi.org/10.1016/j.jval.2015.10.010

      Abstract

      Objective

      To measure the impact of complications on summary measures of health-related quality of life among people with type 2 diabetes.

      Methods

      Patients participating in the Action in Diabetes and Vascular Disease:Preterax and Diamicron MR Controlled Evaluation (ADVANCE) trial were administered a health-related quality-of-life questionnaire, the three-level EuroQol five-dimensional questionnaire (EQ-5D-3L), on four occasions over a 5-year period. We used two-way fixed-effects longitudinal regression models to investigate the impact of incident diabetes complications (stroke, heart failure, myocardial infarction, ischemic heart disease, renal failure, blindness, and amputation) on EQ-5D-3L utility score (where 1 = perfect health), while controlling for characteristics of individuals that do not vary over time.

      Results

      The effect of having any one of the seven complications was to reduce the EQ-5D-3L utility score by 0.054 (95% confidence interval 0.044–0.064), and this was not significantly affected by baseline age, sex, economic region, or the value set used to derive utilities. The complication with the largest disutility was amputation (0.122), followed by stroke (0.099), blindness (0.083), renal failure (0.049), heart failure (0.045), and myocardial infarction (0.026). Ischemic heart disease did not significantly reduce the utility score. Quality of life also declined with elapsed time—by an average of 0.006 per year, in addition to the effect of complications.

      Conclusions

      Common complications significantly reduce health-related quality of life. Utility scores derived from the EQ-5D-3L provide a potential measure that can be used to summarize patient-reported outcomes and inform health economic models. Prevention of complications is critical to reduce the progressive burden of declining quality of life for people with diabetes.

      Keywords

      Introduction

      The quality-adjusted life-year is one of the most widely used outcome measures used in health economic evaluations [
      • Clarke P.M.
      • Gray A.M.
      • Briggs A.
      • et al.
      Cost-utility analyses of intensive blood glucose and tight blood pressure control in type 2 diabetes (UKPDS 72).
      ,
      • Schmittdiel J.
      • Vijan S.
      • Fireman B.
      • et al.
      Predicted quality-adjusted life years as a composite measure of the clinical value of diabetes risk factor control.
      ] and has been advocated as a patient-reported outcome that can assist in clinical and patient decision making [
      • Schmittdiel J.
      • Vijan S.
      • Fireman B.
      • et al.
      Predicted quality-adjusted life years as a composite measure of the clinical value of diabetes risk factor control.
      ,
      • Kind P.
      • Lafata J.E.
      • Matuszewski K.
      • Raisch D.
      The use of QALYs in clinical and patient decision-making: issues and prospects.
      ]. Many studies using individual participant data therefore aim to estimate the effects of disease events on health-related quality of life (HRQOL) and determine utility weights that can be used in cost-utility analyses. Commonly, HRQOL is measured by a utility score in which full health is assigned a value of 1 and death is assigned a value of 0. Utility scores can be derived from generic quality-of-life instruments, including the three-level EuroQol five-dimensional questionnaire (EQ-5D-3L), and can be used as weights to calculate healthy life-years or quality-adjusted life-years [
      • Scuffham P.A.
      • Whitty J.A.
      • Mitchell A.
      • Viney R.
      The use of QALY weights for QALY calculations: a review of industry submissions requesting listing on the Australian Pharmaceutical Benefits Scheme 2002-4.
      ]. Utility values have also been used as a metric for quantifying quality improvement of diabetes management in a clinical setting [
      • Schmittdiel J.
      • Vijan S.
      • Fireman B.
      • et al.
      Predicted quality-adjusted life years as a composite measure of the clinical value of diabetes risk factor control.
      ].
      Among people with diabetes, a key factor influencing quality-adjusted life-years is the degree and nature of diabetes-related complications experienced by patients over a lifetime [
      • Beaudet A.
      • Clegg J.
      • Thuresson P.O.
      • et al.
      Review of utility values for economic modeling in type 2 diabetes.
      ,
      • Lung T.W.
      • Hayes A.J.
      • Hayen A.
      • et al.
      A meta-analysis of health state valuations for people with diabetes: explaining the variation across methods and implications for economic evaluation.
      ]. Typically, utilities associated with complications of diabetes have been derived from cross-sectional data [
      • Beaudet A.
      • Clegg J.
      • Thuresson P.O.
      • et al.
      Review of utility values for economic modeling in type 2 diabetes.
      ,
      • Clarke P.
      • Gray A.
      • Holman R.
      Estimating utility values for health states of type 2 diabetic patients using the EQ-5D (UKPDS 62).
      ,
      • Bagust A.
      • Beale S.
      Modelling EuroQol health-related utility values for diabetic complications from CODE-2 data.
      ,
      • Huang E.S.
      • Brown S.E.
      • Ewigman B.G.
      • et al.
      Patient perceptions of quality of life with diabetes-related complications and treatments.
      ,
      • Kiadaliri A.A.
      • Gerdtham U.G.
      • Eliasson B.
      • et al.
      Health utilities of type 2 diabetes-related complications: a cross-sectional study in Sweden.
      ,
      • O’Reilly D.J.
      • Xie F.
      • Pullenayegum E.
      • et al.
      Estimation of the impact of diabetes-related complications on health utilities for patients with type 2 diabetes in Ontario, Canada.
      ,
      • Zhang P.
      • Brown M.B.
      • Bilik D.
      • et al.
      Health utility scores for people with type 2 diabetes in U.S. managed care health plans: results from Translating Research Into Action for Diabetes (TRIAD).
      ], by comparing those with and without a history of a complication at a point in time. These values, however, may be substantially different to longitudinal changes in HRQOL for individuals who experience complications. For example, it has been shown that individuals who go on to have complications already have consistently lower quality of life than do those who do not [
      • Hayes A.J.
      • Clarke P.M.
      • Voysey M.
      • Keech A.
      Simulation of quality-adjusted survival in chronic diseases: an application in type 2 diabetes.
      ] and this may contribute to measurement bias [
      • Alva M.
      • Gray A.
      • Mihaylova B.
      • Clarke P.
      The effect of diabetes complications on health-related quality of life: the importance of longitudinal data to address patient heterogeneity.
      ].
      The aim of this study was to determine complication-specific measures of utility change, using a large longitudinal data set of people with type 2 diabetes across 20 countries and over 5 years of follow-up. We examined seven major diabetes-related complications—stroke, heart failure, myocardial infarction (MI), ischemic heart disease, renal failure, blindness, and amputation—and specifically investigated homogeneity in quality-of-life decrements associated with complications in different population subgroups.

      Methods

      Study Population

      We used a longitudinal data set based on the Action in Diabetes and Vascular Disease:Preterax and Diamicron MR Controlled Evaluation (ADVANCE) trial, which involved 11,140 patients with type 2 diabetes from 20 high- and middle-income countries in Australasia, Asia, Europe, and North America [
      • Patel A.
      • MacMahon S.
      • Chalmers J.
      • et al.
      Effects of a fixed combination of perindopril and indapamide on macrovascular and microvascular outcomes in patients with type 2 diabetes mellitus (the ADVANCE trial): a randomised controlled trial.
      ]. The ADVANCE study was a multicountry randomized 2 × 2 factorial trial involving patients with type 2 diabetes and compared 1) routine blood pressure lowering based on a perindopril-indapamide combination with a matching placebo, and 2) intensive gliclazide-based therapy with usual guideline-based glucose control therapy. ADVANCE is registered with ClinicalTrials.gov (number NCT00145925).
      Patients were eligible for the trial if they had been diagnosed with type 2 diabetes mellitus at the age of 30 years or older, were aged 55 years or older at entry to the study, and had a history of major macrovascular disease or at least one other risk factor for macrovascular disease. The eligibility criteria were intentionally designed to enrol a broad cross-section of high-risk patients [
      • Zhang P.
      • Brown M.B.
      • Bilik D.
      • et al.
      Health utility scores for people with type 2 diabetes in U.S. managed care health plans: results from Translating Research Into Action for Diabetes (TRIAD).
      ]. Because this study focuses on quality of life, the analyses involved 11,130 patients who had completed at least one quality-of-life questionnaire.

      Measurement of Quality of Life

      Patient-reported health status was determined using the generic quality-of-life instrument the EQ-5D-3L, which determines functional status across five domains— mobility, self-care, usual activities, anxiety/depression, and pain. Each domain has three possible levels (i.e., 1, 2, or 3), representing “no problems,” “some problems,” and “extreme problems,” respectively. Respondents are asked to choose one level that reflects their “own health state today” for each of the domains. The EQ-5D-3L was administered to all patients at randomization and then on three further occasions: at 2 years and 4 years postrandomization and at trial close-out, representing 5 years of follow-up. Utility scores for all analyses were derived using the UK value set [
      • Dolan P.
      Modeling valuations for EuroQol health states.
      ], but sensitivity analyses were also carried out using region-specific or alternative value sets. These included the use of the US valuation of the EQ-5D-3L [
      • Shaw J.W.
      • Johnson J.A.
      • Coons S.J.
      US valuation of the EQ-5D health states: development and testing of the D1 valuation model.
      ] for “established market economies,” a value set for Poland determined from a time trade-off approach for Eastern Europe [
      • Golicki D.
      • Niewada M.
      • Jakubczyk M.
      • et al.
      Self-assessed health status in Poland: EQ-5D findings from the Polish valuation study.
      ], and a value set derived for China also using time trade-off for Asia [
      • Liu G.G.
      • Wu H.
      • Li M.
      • et al.
      Chinese time trade-off values for EQ-5D health states.
      ].

      Definition of Outcomes

      Nonfatal complications were derived from adverse events records using International Classification of Diseases, Tenth Revision and procedural codes (see Appendix Table 1 in Supplemental Materials found at doi:10.1016/j.jval.2015.10.010). These included the following seven complications: acute MI, stroke, ischemic heart disease (including angina and coronary atherosclerosis), heart failure, blindness, amputation, and renal failure. We also examined quality-of-life changes associated with “any complication,” a composite outcome composed of any of the seven complications above.

      Statistical Analysis

      We used two-way fixed-effects longitudinal regression modeling to investigate changes in EQ-5D-3L utility score associated with incident nonfatal complications of diabetes while controlling for all individual patient characteristics and survey time of the EQ-5D-3L. The analysis thus controls for differences in individual characteristics that may affect utility score before complications occur; for example, women are widely reported to have a lower mean utility than do men [
      • Zhang P.
      • Brown M.B.
      • Bilik D.
      • et al.
      Health utility scores for people with type 2 diabetes in U.S. managed care health plans: results from Translating Research Into Action for Diabetes (TRIAD).
      ]. All analyses were also adjusted for the effect of elapsed time, which represents increasing age or duration of diabetes of participants that is independent of complications. We tested whether time-fixed effects were needed by adding dummies for EQ-5D-3L survey time and testing whether they were significantly different from 0. Details of the fixed-effects models are given in the statistical appendix in Supplemental Materials found at doi:10.1016/j.jval.2015.10.010. The initial analysis focused on change in quality of life associated with any incident complication—a composite outcome of any of the seven defined complications. We then tested whether this change in utility was similar for particular population subgroups defined by sex, baseline age (under or over 65 years), diabetes duration (under or over 7 years), previous complications status, and region (geographic and economic classifications, as used previously: Asia, Eastern Europe, and established market economies) [
      • Salomon J.A.
      • Patel A.
      • Neal B.
      • et al.
      Comparability of patient-reported health status: multicountry analysis of EQ-5D responses in patients with type 2 diabetes.
      ,
      • Woodward M.
      • Patel A.
      • Zoungas S.
      • et al.
      Does glycemic control offer similar benefits among patients with diabetes in different regions of the world? Results from the ADVANCE trial.
      ]. Second, utility changes associated with each of the seven separate complications and with elapsed time were determined. Finally, we investigated, independently of complications, the impact of baseline age on the decline in utility associated with elapsed time. A Hausman test [
      • Hausman J.A.
      Specification tests in econometrics.
      ] was used to test the fixed-effects model against an alternative random effects model. We performed Wald tests to investigate homogeneity of the interaction term dummies.

      Results

      Among the 11,140 participants at baseline, 11,130 (99.9%) had one or more complete EQ-5D-3L questionnaires, and most participants (n = 8723 [78%]) had all four completed EQ-5D-3L questionnaires over the 5 years of follow-up. This amounted to a total of 39,857 patient EQ-5D-3L records for analysis. Patient characteristics for the analysis population are presented in Table 1. Thirty-nine percent of the patients had a microvascular or macrovascular complication at baseline, and 12% of the patients had an incident complication during the 5 years of follow-up. Table 2 describes the EQ-5D-3L utility scores collected at four time points over 5 years of follow-up. The average EQ-5D-3L utility score at baseline was 0.82, and this declined to 0.80 at the 5-year follow-up assessment. In a cross-sectional analysis, EQ-5D-3L utility scores at baseline were significantly different between men and women (P < 0.001), across different regions (P < 0.001), and by baseline microvascular and macrovascular complication status (P < 0.001) (Table 2).
      Table 1Characteristics of study participants
      CharacteristicParticipants (N = 11,130)
      Age at baseline (y)65.8 (6.4)
      Duration of diabetes at baseline (y)7.9 (6.4)
      Sex
       Male6401 (57)
       Female4729 (43)
      Microvascular or macrovascular disease
       History at baseline4349 (39)
       No history at baseline6781 (61)
      Region
       Eastern Europe2142 (19)
       Asia4136 (37)
       Established market economies4852 (44)
      Patients with incident nonfatal events
       Any nonfatal event1366 (12.0)
       MI247 (2.2)
       Stroke335 (3.0)
       Heart failure270 (2.4)
       IHD483 (4.4)
       Blindness44 (0.4)
       Amputation39 (0.3)
       Renal failure89 (0.8)
      Values are mean (SD) or n (%).
      IHD, ischemic heart disease; MI, myocardial infarction.
      Table 2Summary statistics of EQ-5D-3L utility scores at four time assessments, and overall, stratified by sex, economic region, history of macrovascular/microvascular disease, age, and duration of diabetes, using UK valuation of the EQ-5D-3L and sensitivity analysis using region-specific valuations
      Region-specific valuations used were the Polish value set for Eastern Europe, a Chinese value set for Asia, and the US value set for established market economics.
      EQ-5D-3L utility score, mean ± SD
      Population categoryBaseline (N = 11,081)2 y (N = 10,301)4 y (N = 9,532)5 y (N = 8,943)
      All patients0.82 ± 0.190.81 ± 0.220.80 ± 0.220.80 ± 0.22
      Sex
      Mean utilities at baseline survey significantly different (P < 0.001) across categories.
       Men0.84 ± 0.180.83 ± 0.200.82 ± 0.210.82 ± 0.21
       Women0.79 ± 0.200.77 ± 0.230.76 ± 0.230.76 ± 0.23
      Age at baseline
       <65 y0.82 ± 0.190.82 ± 0.210.82 ± 0.210.82 ± 0.21
       ≥65 y0.82 ± 0.200.79 ± 0.220.78 ± 0.230.78 ± 0.23
      Duration of diabetes at baseline
       <7 y0.82 ± 0.190.81 ± 0.210.81 ± 0.210.81 ± 0.21
       ≥7 y0.82 ± 0.200.80 ± 0.220.79 ± 0.220.79 ± 0.23
      Microvascular or macrovascular disease at baseline
      Mean utilities at baseline survey significantly different (P < 0.001) across categories.
       Yes0.80 ± 0.210.79 ± 0.230.78 ± 0.240.78 ± 0.23
       No0.83 ± 0.180.82 ± 0.200.81 ± 0.210.81 ± 0.22
      Region (UK valuation of EQ-5D-3L)
      Mean utilities at baseline survey significantly different (P < 0.001) across categories.
       Eastern Europe0.76 ± 0.210.75 ± 0.230.74 ± 0.230.74 ± 0.24
       Asia0.85 ± 0.160.84 ± 0.190.84 ± 0.200.84 ± 0.20
       Established market economies0.82 ± 0.200.80 ± 0.220.79 ± 0.230.78 ± 0.23
      Region (alternative valuation of EQ-5D-3L)
      Region-specific valuations used were the Polish value set for Eastern Europe, a Chinese value set for Asia, and the US value set for established market economics.
       Eastern Europe0.86 ± 0.210.85 ± 0.230.85 ± 0.230.85 ± 0.24
       Asia0.89 ± 0.160.88 ± 0.190.87 ± 0.200.87 ± 0.20
       Established market economies0.86 ± 0.200.85 ± 0.220.84 ± 0.230.83 ± 0.23
      EQ-5D-3L, three-level, EuroQol five-dimensional questionnaire.
      low asterisk Region-specific valuations used were the Polish value set for Eastern Europe, a Chinese value set for Asia, and the US value set for established market economics.
      Mean utilities at baseline survey significantly different (P < 0.001) across categories.
      In the fixed-effects longitudinal model, the overall effect of having any one of the seven complications during follow-up was a permanent utility decrement of 0.054 (95% confidence interval [CI] 0.044–0.064). There was no significant effect of baseline age (P = 0.362), sex (P = 0.281), diabetes duration (P = 0.351), history of microvascular or macrovascular disease (P = 0.231), or region (P = 0.179) on this overall utility decrement (Fig. 1). The Hausmann test strongly rejected the null hypothesis of equal coefficients in the fixed-effects and random-effects models (P < 0.001). Figure 2 shows the effects of elapsed time and complications on change in EQ-5D-3L utility scores. The complication with the largest decrement in utility score was amputation (0.122), followed by stroke (0.099), blindness (0.083), renal failure (0.049), heart failure (0.045), and MI (0.026). Ischemic heart disease had the smallest decrement (0.01), but this was not significantly different from 0. Across all patients, utility change across time and independent of complications at 2-, 4-, and 5-year follow-up was a reduction of 0.017, 0.026, and 0.030, respectively. Further stratification by baseline age showed that loss of quality of life over time was greater for older participants. Based on 5 years of follow-up, the average (95% CI) utility decrement for patients who were 50 to 59 years old at baseline was 0.018 (0.011–0.026), for those 60 to 69 years old was 0.023 (0.018–0.029), and for those older than 70 years at baseline was 0.043 (0.035–0.05).
      Figure thumbnail gr1
      Fig. 1Two-way fixed-effects model β coefficients for utility change associated with incident complications of diabetes (composite of seven complications), and in five additional models in which complications were stratified by baseline age, sex, diabetes duration, history of microvascular or macrovascular complications, and economic region. All models were adjusted for the time of the EQ-5D-3L survey and individual patient effects. CI, confidence interval; EME, established market economy; EQ-5D-3L, three-level, EuroQol five-dimensional questionnaire.
      Figure thumbnail gr2
      Fig. 2Two-way fixed-effects model β coefficients for utility change associated with elapsed time and seven nonfatal diabetes complications. CHF, congestive heart failure; CI, confidence interval; IHD, ischemic heart disease; MI, myocardial infarction.
      The sensitivity analysis, using region-specific EQ-5D-3L value sets, indicated higher mean EQ-5D-3L utility scores than those using the UK valuation, for all regions (P < 0.001). Using utilities from the alternative value sets in the two-way fixed-effects model, the mean (95% CI) utility decrement associated with “any complication” was 0.046 (0.052–0.039), which was not significantly different (P = 0.182) from the value of 0.054 (0.044–0.064) determined in the main analysis using the UK valuation.

      Conclusions

      A longitudinal regression analysis using up to 5 years of data on more than 11,000 patients with type 2 diabetes from the ADVANCE study indicated that complications had a permanent impact on EQ-5D-3L utility scores, a health-related measure of quality of life. At baseline, the average EQ-5D-3L score was 0.827 (95% CI 0.824–0.830) and for those experiencing events the estimated additional decrement associated with an incident complication was 0.054 (95% CI 0.044–0.064). The EQ-5D-3L utility decrement for incident complications was not significantly affected by the baseline characteristics of patients, nor by the use of region-specific or alternative valuations for the EQ-5D-3L. Our estimate of the utility decrement for any complication was very similar to that determined in a longitudinal analysis of quality-of-life data from the United Kingdom Prospective Diabetes Study [
      • Alva M.
      • Gray A.
      • Mihaylova B.
      • Clarke P.
      The effect of diabetes complications on health-related quality of life: the importance of longitudinal data to address patient heterogeneity.
      ]; however, baseline utility was much lower in that study.
      The individual complication with the largest overall reduction on utility score was amputation (0.122), followed by stroke (0.099), blindness (0.083), renal failure (0.049), heart failure (0.044), and MI (0.025). The nonsignificant impact of ischemic heart disease on quality of life may be attributed to modern treatments (e.g., stents), which are very effective in controlling angina and allowing survivors to quickly get back to normal life. Our results agree broadly with those of many other studies [
      • Lung T.W.
      • Hayes A.J.
      • Hayen A.
      • et al.
      A meta-analysis of health state valuations for people with diabetes: explaining the variation across methods and implications for economic evaluation.
      ,
      • Clarke P.
      • Gray A.
      • Holman R.
      Estimating utility values for health states of type 2 diabetic patients using the EQ-5D (UKPDS 62).
      ,
      • O’Reilly D.J.
      • Xie F.
      • Pullenayegum E.
      • et al.
      Estimation of the impact of diabetes-related complications on health utilities for patients with type 2 diabetes in Ontario, Canada.
      ,
      • Glasziou P.
      • Alexander J.
      • Beller E.
      • Clarke P.
      Which health-related quality of life score? A comparison of alternative utility measures in patients with type 2 diabetes in the ADVANCE trial.
      ] in that the largest changes in quality of life were for amputation, stroke, and loss of vision. The complication-specific utility decrements determined in this study, however, were generally lower than those previously reported [
      • Beaudet A.
      • Clegg J.
      • Thuresson P.O.
      • et al.
      Review of utility values for economic modeling in type 2 diabetes.
      ]—this probably reflects the differing methodologies used (longitudinal vs. cross-sectional). As noted by Alva et al. [
      • Alva M.
      • Gray A.
      • Mihaylova B.
      • Clarke P.
      The effect of diabetes complications on health-related quality of life: the importance of longitudinal data to address patient heterogeneity.
      ], cross-sectional studies are likely to overestimate the impact of complications on utility score, whereas in the fixed-effects model, subjects serve as their own controls, allowing us to isolate the effect of complications from other patient-specific effects.
      We have also shown that elapsed time (which represents aging and increasing duration of diabetes) has a significant effect on quality-of-life changes with an average decrement of around 0.03 over the full 5-year duration of the study, or 0.006 per annum. This effect of declining quality of life with aging is independent of complications and accelerates at older ages. For example, for patients in their fifties, the utility decrement over the 5-year study was 0.018 or 0.003 per annum, whereas for patients in their sixties it was 0.023 over 5 years or 0.005 per annum and for those older than 70 years at baseline the decrement was 0.043 in 5 years, or 0.008 per annum. The age effects appear consistent with reported population norms such as those for the United Kingdom (https://www.york.ac.uk/media/che/documents/papers/discussionpapers/CHE%20Discussion%20Paper%20172.pdf) and provide a useful means to compare the effects of complications. For example, the results of our study suggest that the long-term impact of heart failure on quality of life is equivalent to around 5 years of aging, whereas an MI was equivalent to around 2.5 years and a stroke equivalent to around 10 years of aging for someone in their seventies.
      Our study has also highlighted the issue of which value set to use in determining utility scores from a generic quality-of-life survey instrument. Mean EQ-5D-3L utility scores differed depending on the value set used, most notably for patients from Eastern Europe, where the mean (SD) EQ-5D-3L utility score at baseline was 0.76 (0.21) using the UK value set [
      • Dolan P.
      Modeling valuations for EuroQol health states.
      ] and 0.86 (0.21) using the Polish [
      • Golicki D.
      • Niewada M.
      • Jakubczyk M.
      • et al.
      Self-assessed health status in Poland: EQ-5D findings from the Polish valuation study.
      ] value set. Despite these differences in mean values, the fixed-effects longitudinal model found no significant differences in the change in utility associated with incident complications, regardless of which value set was used, thus reinforcing the finding that there is no effect of geographic region on quality-of-life changes associated with complications.
      A previous analysis based on the ADVANCE study [
      • Salomon J.A.
      • Patel A.
      • Neal B.
      • et al.
      Comparability of patient-reported health status: multicountry analysis of EQ-5D responses in patients with type 2 diabetes.
      ] showed substantial variation across patients from different regions in their responses to the items that make up the EQ-5D-3L, with those in Eastern Europe reporting a higher level of problems on most dimensions of the EQ-5D-3L. Using the UK valuation of the EQ-5D-3L, these translated into lower utility scores of people from Eastern Europe compared with people in Asia or in established market economies. The results in our study suggest that these apparent differences may be ameliorated by using the appropriate value set for the region.
      The major strength of our study is the sophisticated analysis of a large and very rich longitudinal data set, with repeated EQ-5D-3L measurements on the same individuals over 5 years, totaling more than 39,000 records and thus enabling the elucidation of the impact of diabetes complications on HRQOL over any background noise in the data. We used a fixed-effects rather than random-effects model because previous work has indicated that patients who go on to have complications are systematically different from those who do not. We also found an effect of declining utility related to aging, something that has not usually been accounted for in modeled economic evaluations. This aging effect is in addition to the effect of complications, due to the additive specification of the model.
      One limitation of our analysis is that we did not investigate the effects of multiple complications of the same type, but analyzed first events only. Recurrent events were not defined trial outcomes in ADVANCE, but we estimated that only a very small proportion of participants (<0.5%) were likely to have two nonfatal events of the same type during the follow-up period. We were able, however, to investigate the effect of multiple different complications, and the effect on utility was additive. Another limitation is that we were not able to take account of severity of complications, and we did not separate acute changes in utility from long-term changes. Finally, data informing these analyses are from a randomized clinical trial population and may not be representative of the wider population with type 2 diabetes.
      Despite cross-sectional differences in levels of mean utility at baseline across sex, complication status, and economically defined regions of the world, the longitudinal decrements in quality of life related to incident diabetes complications were homogeneous. Longitudinal changes in quality of life were also insensitive to the value set used to derive utility scores. The implication for health economic modeling is that a similar set of utility decrements for complications may be used for patients across different regions, of different ages and with different comorbidities, even though these patients may differ in their baseline level of quality of life. A novel finding was to quantify the loss in utility attributable to aging but independent of complications, which is usually unaccounted for in health economic models.
      Utility scores derived from the EQ-5D-3L provide a potential measure to summarize patient-reported outcomes and inform health economic simulation models [
      • Hayes A.J.
      • Leal J.
      • Gray A.M.
      • et al.
      UKPDS outcomes model 2: a new version of a model to simulate lifetime health outcomes of patients with type 2 diabetes mellitus using data from the 30 year United Kingdom Prospective Diabetes Study: UKPDS 82.
      ]. Prevention of complications is critical to reduce the progressive burden of declining quality of life for people with diabetes.
      Source of financial support: This study was supported by the National Health and Medical Research Council of Australia (NHMRC) (project grant 571372). The ADVANCE trial (clinical trial reg. no. NCT00145925, clinicaltrials.gov) was partially funded by the NHMRC (project grant ID 211086 and program grants 358395 and 571281) and by Servier International.

      Supplemental Materials

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