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(Investigative Ophthalmology and Visual Science. 2005;46:4016-4023.)
© 2005 by The Association for Research in Vision and Ophthalmology, Inc.
DOI:  10.1167/iovs.05-0072
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The Impact of Age-Related Macular Degeneration on Health Status Utility Values

Mireia Espallargues,1,2 Carolyn J. Czoski-Murray,1 Nicholas J. Bansback,1 Jill Carlton,3 Grace M. Lewis,3 Lindsey A. Hughes,3 Christopher S. Brand,4 and John E. Brazier1

1From the Health Economics and Decision Science, University of Sheffield, Sheffield, United Kingdom; the 2Catalan Agency for Health Technology Assessment and Research, Catalan Health Service, Catalan, Spain; and the 3Departments of Orthoptics and 4Ophthalmology, Sheffield Teaching Hospitals NHS Trust, Sheffield, United Kingdom.


   Abstract
Top
 Abstract
Methods
 Results
 Discussion
 References
 
PURPOSE. To estimate health status utility values in patients with age-related macular degeneration (ARMD) associated with visual impairments, by using preference-based measures of health.

METHOD. This was a cross-sectional study involving patients with unilateral or bilateral ARMD who attended a large teaching hospital. Patients underwent visual tests (near and distant visual acuity [VA] and contrast sensitivity [CS]) and completed health status questionnaires including the Index of Visual Function (VF)-14 and three preference-based measures (the Health Utilities Index Mark III [HUI-3], the EuroQoL Health Questionnaire [EQ-5D], and the Short Form 6D Health Status Questionnaire [SF-6D]) and the time tradeoff (TTO). The mean health status is presented for five groups, defined according to the VA in the better-seeing eye and for four CS groups.

RESULTS. Two hundred nine patients were recruited with substantial loss of visual function as obtained by visual tests (mean decimal VA in the better-seeing eye: 0.2) and self-report (mean VF-14 score: 41.5). The mean (±SD) utilities were 0.34 ± 0.28 for HUI-3, 0.66 ± 0.14 for SF-6D, 0.72 ± 0.22 for EQ-5D, and 0.64 ± 0.31 for TTO. The HUI-3 had the highest correlation with VA and CS (0.40 and –0.34), followed by TTO (0.25 and –0.21). Across the VA and CS groups, only HUI3 and TTO had a significant linear trend (P < 0.05). In a regression model with CS and VA as explanatory variables, only the coefficient on CS was statistically significant.

CONCLUSIONS. ARMD is associated with a substantial impact on patients’ health status, but this was not reflected in two of the generic preference-based measures used. The HUI-3 seems to be the instrument of choice for use in economic evaluations in which community data are needed. It may be more appropriate to base economic models on CS or some combination of CS and VA rather than on VA alone.

With the development of photodynamic therapy and other therapies for age-related macular degeneration (ARMD), there has been increasing interest in assessing the impact on quality of life of this condition, to establish the clinical and cost effectiveness of new interventions. There are several well-established objective tests of visual function used in clinical trials, but these do not assess quality of life nor do they provide the measures needed to inform cross program resource allocation decisions.

During the past two decades, economic evaluation has become increasingly important as a tool to aid decision-makers concerning the allocation of scarce resources within healthcare. Several authoritative guidelines for the conduct of economic evaluations in healthcare have been produced around the world, including the United States,1 Canada,2 Australia,3 The Netherlands,4 and England and Wales.5 A central component in all guidelines concerns the use of Quality Adjusted Life Years (QALYs) as the measure of effectiveness. The QALY approach combines the value of health-related quality-of-life (HRQoL) with the value of length of life into a single index. It does this by assigning a value to each state of health, using a scale with 0 for states regarded as bad as being dead, 1 for states of perfect health, and <0 for states regarded as worse than being dead.6 A person’s time profile of health status can be valued and summed to a total number of QALYs. The difference in QALYs with and without an intervention provides a measure of the benefit. Interventions can then be compared in terms of their incremental cost per QALY ratio.

A key challenge in ARMD and other ophthalmic diseases is to find a suitable measure for obtaining these health status values. Brown et al.,7 in a seminal study, have estimated utility values for different levels of VA in patients with ARMD. The study recruited 80 white patients with unilateral or bilateral ARMD and visual loss to a minimum of 20/40 in at least one eye. Mean health status values have been published across five levels of VA in the better-seeing eye, using the techniques of time-tradeoff (TTO) and standard-gamble (SG). Aside from the small sample size, one concern with the study by Brown et al. is it obtained data directly from patients. Currently, most public agencies recommend that general population values be used to derive QALYs (Canada, Australia, The Netherlands, and England and Wales), though there are strong arguments for using patient data in cost-effectiveness analysis.8 Furthermore, the Brown study only examined the effect of visual acuity (VA) on HRQoL. There has been an increasing interest in the relationship between health state values with other visual functioning measures such as contrast sensitivity (CS).

In the current study, we sought to assess the impact of visual impairment due to ARMD on three generic preference-based measures (utility valuation by the EuroQoL Health Questionnaire [EQ-5D], the Health Utilities Index Mark III [HUI-3], and the Short Form 6D Health Status Questionnaire [SF-6D]), along with a patient’s own state-of-health valuation (direct elicitation with the TTO), and assessment of visual function using a vision-specific questionnaire of HRQoL (the Index of Visual Function [VF]-14).


   Methods
Top
 Abstract
 Methods
Results
 Discussion
 References
 
Participants and Setting
This was a cross-sectional study of patients with unilateral or bilateral ARMD from a large Sheffield Teaching Hospital (UK) who attended either the Ophthalmic Clinic or the Low Vision Training Service (from October 2003 to March 2004). All patients diagnosed with ARMD were eligible, provided they were able and willing to respond to the interview protocol. Both atrophic (dry), characterized by geographic atrophy, and exudative (wet), characterized by choroidal neovascularization, ARMD were considered. Patients known to have other ocular comorbidities (e.g., glaucoma, uveitis, cataract, amblyopia, corneal scarring, vitreous hemorrhage, optic neuropathy, or other eye conditions that could cause visual impairment) were excluded.

Data Collection
Patients recruited from ophthalmic clinics were asked to attend a special session to undertake additional examination. At the routine clinic visit, patients had slit-lamp biomicroscopy, dilated fundus examination, and fluoroscopy (fluorescein angiography or scanning laser ophthalmoscope [SLO], if required). At the special session, measurements of VA and CS were obtained by two senior orthoptists seconded to the study. No measurements involved dilated fundus examinations. Participants recruited from the low-vision database attended the same special sessions and were not reexamined by an ophthalmologist. The low-vision register comprises most patients previously diagnosed with ARMD at the hospital.

Trained interviewers administered the VF-14, a number of preference-based measures (HUI3, EQ-5D, and SF-6D), a visual analog scale (VAS), and TTO by direct elicitation. Sociodemographic data, health, and social services utilization and participation in support groups, were also ascertained. Clinical information including time since diagnosis of ARMD, subtype, previous, or planned photodynamic therapy, previous cataract extraction, training in or use of different low vision aids/techniques for ARMD patients and, the presence of a chronic illness or disability and limitation other than vision problems were obtained as well.

The visual tests and questionnaires were administered by a standard protocol. The study was approved by the South Sheffield Research Ethics Committee and followed the tenets of the Declaration of Helsinki. Patients gave informed written consent before taking part in the study.

Measures
Visual Tests.
LogMAR best corrected distance VA in left and right eyes and binocular near VA were measured with a Bailey-Lovie chart by the letter-by-letter scoring method. Counting fingers, hand motion, no light perception, or unable to see were assigned the worst possible value in the logMAR (logarithm of the minimum angle of resolution scale: 1 letter at 10 cm is 2.86 logMAR or 0.001 in the decimal scale). CS was measured binocularly with a Pelli-Robson chart (in log units), by the triplet scoring method,9 10 at 1-meter distance. Those patients unable to score at all on CS were assigned the minimum value on the test (0 log units).

Visual Function Index VF-14.
The VF-14 is a patient-reported measure of functional disability related to vision based on 14 everyday activities that can be affected by cataracts (e.g., recognizing people, seeing steps and curbs, performing certain manual tasks, filling in forms, cooking, watching TV, engaging in two leisure activities and four reading activities, and driving during the day and night). Only activities that the patient considers relevant to his or her situation are scored (with five possible responses from no difficulty to unable to perform the activity). The final score ranges from 0 (maximum disability) to 100 (no disability).11 The validity and internal consistency had been assessed in patients with cataract12 13 and also in patients with ARMD.14

Preference-Based Measures.
Three generic preference-based measures were selected (EQ-5D, SF-6D, and HUI-3), because of their common usage and because they met the requirements for a reference case analysis recommended by the Washington Panel1 and requested by the National Institute for Clinical Excellence (NICE).5 They obtain general population values by using a choice-based method of preference elicitation. This was supplemented by the TTO for obtaining patient values and to allow comparison with the study by Brown et al.7

The EQ-5D has five dimensions: mobility, self-care, usual activities, pain and discomfort, and anxiety and depression, and each dimension has three levels. The values used were obtained from a large-scale survey of the general population undertaken in the United Kingdom.15 The EQ-5D has become one of the most widely used generic measures of health in Europe and is commonly used in economic evaluation. It is accompanied by a VAS on which the respondent marks an assessment of their overall health between the best imaginable state of health at the top of the scale and the worst imaginable health at the bottom.16

The SF-6D is derived from the SF-36, a generic measure of health.17 18 It is composed of six multilevel dimensions of health: physical functioning, role limitation, social functioning, bodily pain, mental health, and vitality. It was constructed from the SF-36 and valued using the SG valuation technique from a sample of the U.K. general population.18 The resultant algorithm can be used to convert SF-36 data at the individual level to a preference-based index.

The HUI3 has eight attributes: hearing, vision, speech, ambulation, dexterity, emotion, cognition, and pain. It has been evaluated by using VAS and SG techniques on a Canadian sample of the general population.19

Patients’ own assessment of utility was elicited with the variant of TTO used to value the EQ-5D developed at the University of York, which has been shown to be reliable and consistent.15 It asks patients how many years they would trade in return for full health (including perfect vision). Patients have to choose between two alternatives: 10 years in their current health or x years in full health (where x is less than or equal to 10) and then death. The value of x is varied until the respondents are indifferent between the alternatives. The health status value is calculated as x/10.6 20 21

Statistical Analysis
Descriptive analyses were used to characterize the sociodemographic, clinical characteristics, and the health status of the sample. Health status values were compared with those of community samples of elderly individuals and others with chronic illnesses. Patients were divided into different groups according to their visual function. The cutoffs were for logMAR VA at 2, 1.3, 0.6, and 0.3 and for log CS at 0.30, 0.90, and 1.30. For the VF-14 categories were defined at 50, 75, and 95 points.22 The better- and worse-seeing eyes were considered to be that with better and worse distant VA, respectively.

The statistical comparisons of means were made with ANOVA, using the F test to check differences between groups and to test for linear trend. Variability in health status scores explained by visual parameters (VA, CS, and VF-14) was assessed by eta2 as the sum of squares between groups divided by the total sum of squares from ANOVA results multiplied by 100. Pearson’s product moment correlation coefficients were calculated to examine the relationship between health status measures. The relative contributions of VA and CS were examined by regression.


   Results
Top
 Abstract
 Methods
 Results
Discussion
 References
 
Four hundred fifty-one patients who met the criteria were invited to participate. Of those, 209 completed the visual tests and interviews, and 242 eligible patients declined to participate or did not reply. Item completion rates exceeded 95% in all health measures. Comparison between patients with complete data and those who were excluded because of missing data showed no relevant differences on the rest of the variables in the study.

Mean age was 79.6 years (range, 43–96), and 121 (57.9%) patients were women. Only 3.4% were currently employed, the majority (88%) had private households, and almost half (46%) of the sample was living alone. The majority (83%) had a long-standing illness or disability unrelated to their vision, and it limited their activities in 71.6% of the cases (Table 1) . On average, ARMD was diagnosed 3.7 years ago, and one fifth had dry ARMD. Less than 10% had received photodynamic therapy (PDT; Table 1 ). The mean logMAR VA in the better- and worse-seeing eyes was 1.0 (0.24 in decimal scale) and 1.68 (0.08 decimal), respectively. Mean CS was also low at 0.7 log units, most of the patients being below 1.3.


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  		TABLE 1. Sociodemographic and Clinical Characteristics of Participants with ARMD

 
The mean (SD) utility values were 0.34 (0.28) for HUI-3, 0.66 (0.14) for SF-6D, and 0.72 (0.22) for EQ-5D (Table 2) . Direct utility elicitation by means of TTO resulted in a value of 0.64 (0.31) and by VAS was 65.0. The score ranged from 1.0 at the upper end to between 0.3 and –0.6 at the lower end. The equivalent mean scores in the general population for 75- to 85-year-olds was 0.71 for the EQ-5D23 and 0.65 for SF-6D.24 There are no U.K. norms for the HUI-3. The mean (SD) VF-14 score was 41.5 (28.4).


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  		TABLE 2. Summary Statistics of Patient-Based Health Status and Preference-Based Measures in Patients with ARMD

 
The HUI-3 had larger and more significant correlations with tests of visual function and the VF-14 than either of the other preference-based measures (Table 3) . There was a similar linear trend of increasing utility and health status values as VA and CS improved (Table 4) . The highest percentages of explained variability (eta2) were observed for HUI-3 (VA = 13%, CS = 14%, VF-14 = 25%). TTO showed the next highest percentages of variability (VA = 4%, CS = 9%, VF-14 =11%). This was followed by SF-6D and VAS and finally EQ-5D. These relationships were slightly stronger for CS than for VA in terms of variability in health status scores explained by the visual tests (eta2). However, perceived visual disability as assessed by the VF-14 explained the greatest variance in all these measures. Box plots (box-and-whisker charts with the medians, interquartile ranges, and full ranges) and scatterplots are shown for HUI-3 and TTO in Figures 1 and 2 , respectively, in relation to tests of visual function (VA and CS) and self-reported visual functioning (VF-14).


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  		TABLE 3. Pearson’s Correlations and Significance Level between CS, VA, VF-14, and the Preference-Based Measures

 

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  		TABLE 4. Mean (SD) Scores of Preference-Based Utilities According to VA and CS

 


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  		FIGURE 1. Box plots and scatterplots between HUI-3 and visual function as measured by visual tests (VA, CS) and self-reported measures (VF-14).

 


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  		FIGURE 2. Box plots and scatterplots between TTO and visual function as measured by visual tests (VA, CS) and self-reported measures (VF-14).

 
When we applied the VA categories in Brown et al.7 to our sample, a different distribution emerged, with more people in the worst VA categories and fewer in the best ones when compared with the data of Brown et al. (Tables 5 and 6) . The range of mean TTO scores in the VA group was 0.58 to 0.81 in our study compared to 0.40 to 0.89 in Brown et al. (Table 5) . In our study, both mean TTO and HUI-3 utility values across these groups presented some inconsistencies, although there was an overall linear trend of increasing utility values as visual function improved. The cross tabulation of better-seeing eye VA with CS showed that for each VA group there was a range of possible CS scores, but extreme combinations of good CS and poor VA, and vice versa, were rare.


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  		TABLE 5. Mean (SD) TTO Values According to VA and CS Groups

 

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  		TABLE 6. Mean (SD) HUI-3 Values According to VA and CS Groups

 
The combined impact of VA and CS on TTO and HUI-3 scores is shown in Tables 5 and 6 and graphically for the HUI-3 in Figure 3 . The relative contribution of CS and VA was also examined by multiple regression where CS and VA were entered as explanatory variables at the same time (Table 7) . These analyses showed a significant relationship between HUI-3 and CS (P < 0.005) but not with VA in the better-seeing eye (P = 0.226). The relationship between TTO and CS just reached significance at the 5% level (P = 0.046) and was not near significance for TTO and VA (P = 0.686).



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  		FIGURE 3. Mean utility values (HUI-3) according to VA (better-seeing eye, distant, logMAR) and CS (binocular, log units).

 

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  		TABLE 7. Multiple Linear Regression Results for HUI-3 and TTO against CS and VA

 

   Discussion
Top
 Abstract
 Methods
 Results
 Discussion
References
 
ARMD is associated with reductions in patients’ health status in terms of visual impairment (VA and CS) and patient-reported visual function (VF-14). This impact was not reflected in two of the generic preference-based measures (EQ-5D and SF-6D) nor in the VAS, to any great extent. It was best reflected in the HUI-3, which showed utility values much lower than the other measures and had the strongest relationship with VA, CS, and VF-14. Directly elicited TTO values from patients were significantly related to VA, CS, and VF-14, with the VA result supporting earlier findings of Brown et al.7 These results have important implications for the estimation of QALYs and the conduct of economic evaluation. This study also provides evidence for the relative importance of CS in people’s HRQoL compared with VA.

Previous studies have found a stronger relationship between various measures of HRQoL and visual impairment. High levels of disability have been observed in patients with ARMD that were similar to levels in patients with cancer and stroke.25 There are also studies that show ARMD is associated with significant psychological distress26 27 28 and elevated risks of depression,29 with scores of generic and specific scales of emotional distress being worse than those observed in a similar-aged population without this disease and comparable to those obtained in patients with other chronic conditions (such as arthritis, chronic obstructive pulmonary disease, acquired immunodeficiency, and bone marrow transplantation).26 Depression has been found to be twice the rate (32.5%) observed in other studies of community-dwelling elderly.25 These differences with our findings require some explanation.

A part of the explanation seems to lie in the descriptive systems of the EQ-5D and the SF-6D (along with the SF-36 from which it is derived). The lack of sensitivity does not seem to have been due to floor effects in either instrument, a problem observed in the SF-6D in heart disease and rheumatoid arthritis,30 31 32 rather the problem seems to be due to the lack of relevance of the descriptive systems. The main effect of ARMD is to reduce the ability of the individual to engage in everyday activities that require clear central vision (such as reading, writing, recognizing people, driving, and watching TV). Peripheral vision is not affected by ARMD. The generic health status measures of EQ-5D and SF-6D seem to fail to capture the impact on activities of daily living that depend on central vision.33 The HUI-3 is different from the other two generic preference-based measures, in that it includes a vision dimension. Self-reported TTO reflects the impact from a patient’s perspective.

Most ophthalmologists have relied on measures such as VA and visual field assessment, but other tests such as contrast sensitivity, glare testing, color vision testing, stereoscopic testing, reading speed, and light and dark adaptation, have shown that VA is a descriptor of a single aspect of vision rather than comprehensive assessment of visual function.34 In our study, CS showed a slightly better relationship and explained more variation in health status than VA. The regression results of CS and VA together seem to support the view that CS better reflects the impact on HRQoL than does VA.

Elicitation of utility values in patients with ARMD by Brown et al.7 resulted in higher mean scores than our study, on the basis of their patients’ completion of TTO of 0.72 compared with our mean of 0.64. This must be in part because the patients in our study had worse VA overall. However, the relationship between TTO and VA was weaker in our study than in Brown et al. The mean utility scores in our study were lower in the better VA categories (0.81 vs. 0.89) and higher in the worse VA groups (0.58 vs. 0.40). This difference may have occurred by chance because of the comparatively small numbers in some of the VA groups, particularly in Brown et al. where the number in the group was between 5 and 23. Even in our study with more than twice the number of respondents, this still could be a factor. Another explanation may be the significant degree of CS variation in a given VA group in our study. The best VA category in our study, for example, had 5 of 11 cases in the worst two CS categories with health status values of between 0.3 and 0.68. Brown et al. did not measure CS, and so we cannot adjust for the likely impact of CS variation. A more recent and larger study by Brown et al.35 also showed that the worse the VA the greater the impact of ARMD on health status; but, again, CS was not measured.

The differences between our study and that of Brown et al.7 may also have occurred because our patients had had known ARMD on average for a long time. Brown et al. found that the mean TTO values in patients who had had the condition for less than a year was significantly lower than those who had had it for more than a year, with a mean of 0.63 compared with 0.8, respectively. The cohort in Brown et al. had the condition for less time than ours, with 49% having had the condition for less than 1 year, compared with just 24% in our study. Another reason for the difference may be the different variants used in our respective studies. Brown et al. used an open-ended variant of TTO, whereas ours was an interactive variant developed at York University and subsequently used to value the EQ-5D.

Another explanation for the differences with Brown et al. could be related to subject selection and participation rates. All patients with diagnosed ARMD who attended a clinic or were on the low-vision register were invited to take part. However, there may have been a degree of bias in the self-selection that is a concern when the response rate is just 50%. Because of ethical restrictions it was not possible to compare those who agreed to participate with those who declined or did not respond. Nevertheless, most of the reasons given by refusals in a free text box do not seem to be related to ocular disease but to such impediments as the weather, time of year (Christmas), dark nights, not wanting to leave the dog, being a caregiver, and so forth. These limitations, however, should not invalidate our results regarding the relationship between visual tests, VF-14 score, and generic health status measures.

The results suggest that the HUI-3 is a better generic measure than the EQ-5D and SF-6D in this group of patients. This study presents HUI-3 health status utility values for various VA and CS groups that can be used to populate cost-effectiveness models of new interventions in patients with ARMD, for agencies requiring community values. The results also suggest that utility values seem to be more dependent on CS than VA, although perceived visual disability is the parameter that best explains results in preference-based measures. Thus, valuable instruments such as CS and perceived visual function, will be helpful to supplement VA measurement in patients with ARMD, along with the HUI-3 or TTO for those studies seeking to inform economic evaluations.


   Footnotes
 
Supported by Novartis, Basel Switzerland; Grants 2003BE-00127 and 2004BE-00219 DURSI (ME) from the Government of Catalonia, Spain; and funding from the Medical Research Council Health Services Research Collaboration (JEB).

Submitted for publication January 21, 2005; revised May 19, 2005; accepted September 19, 2005.

Disclosure: M. Espallargues, None; C.J. Czoski-Murray, Novartis (F); N.J. Bansback, None; J. Carlton, None; G.M. Lewis, None; L.A. Hughes, None; C.S. Brand, Novartis (C), Pfizer (C); J.E. Brazier, Novartis (C), Pfizer (C)

The publication costs of this article were defrayed in part by page charge payment. This article must therefore be marked "advertisement" in accordance with 18 U.S.C. §1734 solely to indicate this fact.

Corresponding author: John E. Brazier, Health Economics and Decision Science, ScHARR [School of Health and Related Research], The University of Sheffield, Regent Court, 30 Regent Street, Sheffield S1 4DA, UK; j.e.brazier{at}sheffield.ac.uk.


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 Discussion
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