January 2004
Volume 45, Issue 1
Free
Clinical and Epidemiologic Research  |   January 2004
Impact of Bilateral Visual Impairment on Health-Related Quality of Life: the Blue Mountains Eye Study
Author Affiliations
  • Ee-Munn Chia
    From the Department of Ophthalmology and the
  • Jie Jin Wang
    From the Department of Ophthalmology and the
  • Elena Rochtchina
    From the Department of Ophthalmology and the
  • Wayne Smith
    Centre for Clinical Epidemiology and Biostatistics, University of Newcastle, Newcastle, Australia; and the
  • Robert R. Cumming
    School of Public Health, University of Sydney, Sydney, Australia; the
    Centre for Education and Research, Concord Hospital and University of Sydney, Australia.
  • Paul Mitchell
    From the Department of Ophthalmology and the
Investigative Ophthalmology & Visual Science January 2004, Vol.45, 71-76. doi:10.1167/iovs.03-0661
  • Views
  • PDF
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to Subscribers Only
      Sign In or Create an Account ×
    • Get Citation

      Ee-Munn Chia, Jie Jin Wang, Elena Rochtchina, Wayne Smith, Robert R. Cumming, Paul Mitchell; Impact of Bilateral Visual Impairment on Health-Related Quality of Life: the Blue Mountains Eye Study. Invest. Ophthalmol. Vis. Sci. 2004;45(1):71-76. doi: 10.1167/iovs.03-0661.

      Download citation file:


      © 2015 Association for Research in Vision and Ophthalmology.

      ×
  • Supplements

purpose. To assess the impact of visual impairment on health-related quality of life (HRQOL) in an older population and compare it with the impact of major medical conditions.

methods. Participants of the second cross-sectional Blue Mountains Eye Study (BMES; n = 3509; mean age, 66.7 years; 57% female) were asked to complete the self-administered 36-item Short-Form health survey (SF-36), a comprehensive interview, and an eye examination. Visual impairment was defined as visual acuity less than 20/40 (better eye).

results. Of 3154 (89.9%) participants with complete data, 172 (5.5%) had visual impairment due to refractive errors (correctable visual impairment) and 66 (2.1%) due to eye conditions (noncorrectable visual impairment; 49 mild, 9 moderate, 8 severe). After adjustment for demographic and medical confounders, there was a trend toward lower SF-36 scores in participants with noncorrectable impairment than in those with correctable impairment (physical component score [PCS] P trend = 0.01 and mental component score [MCS] P trend = 0.02). Increasingly severe noncorrectable visual impairment was associated with significantly poorer SF-36 scores in all but two dimensions. The impact of noncorrectable visual impairment was comparable to that from major medical conditions (e.g., stroke) and had a greater impact on mental than physical domains (mean MCS = 46.2, PCS = 41). No significant differences in HRQOL were demonstrated between visual impairment cases caused by age-related maculopathy and cataract, after adjusting for severity of visual impairment.

conclusions. Noncorrectable visual impairment was associated with reduced functional status and well-being, with a magnitude comparable to major medical conditions. These data have implications for disability weights such as those developed by the Global Burden of Disease study.

The age-related increase in the prevalence of visual impairment 1 2 3 and its impact on nursing home placement, 4 falls 5 and hip fractures, 6 increased use of community support services, 7 and mortality 8 9 has been well documented. The shift toward broader health perspectives and the emphasis on the patient’s preferences have resulted in the use of health outcome measures that encompass physical, emotional, and social impacts of a condition. 10 Both generic 11 12 13 and vision-related 14 15 16 health questionnaires are increasingly used as primary and secondary outcome measures in clinical trials and to aid the organization, financing, and delivery of health care services. 
The generic, multidimensional 36-item Short-Form health survey (SF-36) 13 has been used across a range of populations, treatment groups, and diseases, including ophthalmic conditions, such as visual impairment or blurred vision, 17 18 aged-related macular degeneration, 19 20 cataract, 21 glaucoma, 22 23 diabetic retinopathy, 20 uveitis, 24 and corneal transplantation. 25 Although recent reports have shown that generic health outcome measures such as the SF-36 are not as sensitive to changes in vision-related function as vision-related questionnaires, 17 21 23 24 the use of the SF-36 enabled comparison of the impact of visual impairment on health-related quality of life (HRQOL) with the impact from a range of other medical conditions. 
It is clear that visual impairment detrimentally affects HRQOL, resulting in lower self-rated health, 26 decreased physical, 19 27 and emotional functioning, 17 28 and lower socialization. 7 17 However, many issues are yet to be clarified: Is there any impact on HRQOL from visual impairment due to undercorrected refractive error and if so, how does it compare with the impact of visual impairment due to eye conditions? Are there differences in the impact of visual impairment on HRQOL between the two major causes: cataract and age-related maculopathy (ARM)? How does the impact of visual impairment compare with impacts from other major medical conditions? 
Using data from the Blue Mountains Eye Study, this report explores these questions in relation to bilateral visual impairment. It supplements our recent report of the impacts from unilateral visual impairment on HRQOL. 18  
Materials and Methods
Study Population
The Blue Mountains Eye Study (BMES) is a population-based study of visual impairment, common eye diseases, and other health conditions in an older community-living population. 3 Study procedures were approved by the University of Sydney Ethics Committee and were in accordance with the tenets of the Declaration of Helsinki. Informed consent was obtained from all participants. 
In 1991, BMES baseline survey (BMES I) identified 4433 eligible noninstitutionalized permanent residents in a door-to-door private census from a defined area in the Blue Mountains region, west of Sydney, Australia. Of this target population, 3654 (82.4%) participated in detailed examinations from 1992 to 1994. From 1997 to 1999, all surviving participants (n = 3111) were invited to the 5-year follow-up examinations. These were attended by 2335 (75.1%) persons (BMES IIA). In 1999, another door-to-door census conducted in the same area identified 1378 newly eligible residents who had moved into the study area or study age group, of which 1174 (85.2%) were examined from 1999 to 2000 (BMES IIB). The second cross-sectional BMES thus comprised 3509 participants from BMES IIA and IIB. 
Instrument Used
The SF-36 contains 36 items measuring eight dimensions of health and well-being: “physical functioning,” “role limitations due to physical problems,” “bodily pain,” “general health perceptions,” “vitality,” “social functioning,” “role limitations due to emotional problems,” and “mental health.” 13 The Australian adapted version 29 was used in this study. Each dimension was scored from 0 (worst possible health state) to 100 (best possible health state) by coding, summating, and transforming its relevant item scores according to the SF-36 manual. 30 Physical and mental component scores (PCS and MCS, respectively) were summary measures calculated using factor analysis and Australian normalized scores (mean = 50, SD = 10). 31 32  
Data Collection
Participants attended a comprehensive medical interview and eye examination by trained technicians. Self-reported history of angina, heart attack, stroke, arthritis, diabetes, and asthma were collected. The self-reported history of cancer was collected but not included due to the high proportion of nonmelanotic skin cancer. 
Monocular distance logarithm of the minimum angle of resolution (logMAR) visual acuity was measured using forced-choice procedures according to the Early Treatment Diabetic Retinopathy Study (ETDRS) methods with habitual correction and with best correction after subjective refraction. 3 Lens and stereo retinal photographs were taken and graded by trained graders and adjudicated by an ophthalmologist. 
Before their examination, all participants of the second cross-sectional BMES were sent a detailed questionnaire that included the SF-36. Participants were asked to bring the questionnaire booklets to the examination or to return it by reply-paid mail. The SF-36 was not administered in BMES I. 
Definitions
Bilateral visual impairment was defined as visual acuity of less than 20/40 in the better eye. Correctable visual impairment was defined as visual impairment at presentation but improved to no impairment after subjective refraction. Noncorrectable visual impairment was defined as impairment at both presentation and after subjective refraction. The latter was stratified into mild visual impairment defined as visual acuity less than 20/40, but 20/80 or more; moderate as less than 20/80 but 20/200 or more, and severe as less than 20/200. 
Cataract was diagnosed during the slit lamp examination and documented with lens photography (SL-7e camera; Topcon Optical Co., Tokyo, Japan, and retroillumination CT-R cataract camera; Neitz Instrument Co., Tokyo, Japan). Age-related maculopathy (ARM) was diagnosed and confirmed by grading of stereoretinal photographs. Details of the cataract 33 and ARM 34 photography and grading used in the Blue Mountains Eye Study have been reported. The causes that contributed to visual loss were determined by the examining ophthalmologist (PM) during the final stage of dilated eye examination. Only in this portion of analysis, comparing HRQOL associated with the two major causes of visual impairment, were participants with cataract surgery excluded. 
Statistical Analysis
Persons who did not attempt the SF-36 questionnaire or who had incomplete visual acuity data were excluded from analysis, which was performed on computer (SAS 8.2 for Windows; SAS Institute Inc., Cary, NC). Comparison of sociodemographic and medical characteristics and the determination of the trend (P trend) among participants without visual impairment compared with those with correctable and noncorrectable impairment were performed with χ2 analysis. The same analysis was used to calculate the trend (P trend) associated with increasing severity of noncorrectable visual impairment. 
Possible confounding sociodemographic and medical variables were analyzed dichotomously (yes versus no). A stepwise regression model was used to determine the subset of these variables that significantly affected either the physical or mental component scores. Only significant variables were included in multiple linear regression analysis to calculate multivariate-adjusted mean scores by analysis of covariance. Age–sex adjustment was used when the numbers of participants were too few to allow multivariate adjustment. 
The two principal causes of visual impairment in our study were cataract and ARM. Only these two conditions were compared because of the low number of subjects with other conditions. Analyses were conducted in two ways. First, mean physical and mental component scores of participants with cataract and ARM were compared, stratifying by the severity of visual impairment. Second, multiple linear regression models using physical and mental component scores as outcome variables were used to assess the independent effects of each eye condition. 
To compare the SF-36 profiles between participants with certain medical conditions, disease-specific SF-36 scores were age and sex standardized to the BMES population, using the direct method. 
Results
Prevalence of Visual Impairment
Participants had a mean age of 66.7 years (range, 49–98 years) and 57% were female. Complete data sets that included SF-36 questionnaires were available for 3154 (89.9%) participants. Of these, the prevalence of presenting bilateral visual impairment was 7.5% (n = 237) but decreased to 2.1% (n = 66; 49 mild, 9 moderate, and 8 severe) after subjective refraction. Because of the relatively low numbers, cases with moderate and severe visual impairment were pooled for the analysis. 
Correctable and Noncorrectable Visual Impairment
Participants with either correctable or noncorrectable visual impairment were more likely to be older and to receive a government social security pension, but were less likely to be married, to have higher qualifications, to own a home or be currently employed than participants without impairment (Table 1) . They were also more likely to report a history of angina and stroke, but there were no significant differences in the prevalence of other medical conditions. 
After adjustment for demographic and medical confounders, there was a trend toward lower SF-36 scores in participants with noncorrectable impairment than in those with correctable impairment (Table 2) in five dimensions (P trend ≤ 0.05) and in both physical and mental component scores (PCS P trend = 0.01, MCS P trend = 0.02). 
After multivariate adjustment, participants with any noncorrectable visual impairment had significantly poorer SF-36 scores in five dimensions and in the mental component score (Table 3) . Increasingly severe noncorrectable visual impairment was associated with deteriorating SF-36 profiles (the latter was only age–sex adjusted because of low sample numbers). Mental domains were more greatly affected than physical domains, as reflected by the trend in the summary component scores (PCS P trend = 0.25, MCS P trend = 0.001). When compared with participants without visual impairment (excluding participants with correctable visual impairment), participants with mild visual impairment had significantly lower scores in the physical-functioning, vitality, and mental health dimensions (P = 0.02, 0.001, and 0.01 respectively), and participants with moderate to severe visual impairment had lower scores in role limitation due to physical problems, vitality, social functioning, and mental health and in the mental component score (P = 0.02, 0.002, 0.001, 0.005, and 0.001, respectively). Similar results were also obtained when participants with correctable visual impairment were included in the reference group. 
Major Causes of Visual Impairment
Participants with ARM were significantly older (mean age, 76.6 years) and more likely to have more severe levels of visual impairment (8.7% had mild and 4.0% had moderate to severe visual impairment) than participants with cataracts (mean age, 73.3 years; 4.5% had mild and 1.3% had moderate to severe visual impairment). Multiple linear regression models simultaneously adjusting for age, sex, visual impairment, any cataract, and any ARM showed physical component scores were significantly affected by age and sex (P < 0.0001), and mental component scores by age and visual impairment (P = 0.0005 and 0.012, respectively). Participants with cataract and ARM did not have significantly different physical and mental component scores when compared with participants without these two conditions, after adjustment for visual impairment (Table 4) , except among participants without visual impairment. In this group, participants with either or both these eye conditions had significantly poorer physical component scores than those without either condition. 
Comparison of Impacts on HRQOL with Other Major Medical Conditions
Table 5 tabulates both the physical and mental component scores and disability weights 35 in descending order of magnitude. The impact of visual impairment (shown in bold) on mental domains was much greater than that of other medical conditions, whereas its physical impact was milder, as reflected by the respective component scores (PCS = 41.0, MCS = 46.2). Stroke, which had similar disability weights as low vision, had a greater impact on physical domains and a milder impact on mental domains than visual impairment (PCS = 39.3 and MCS = 51.5). 
Discussion
In this older population, both correctable and noncorrectable visual impairment detrimentally affected HRQOL, although the impact from noncorrectable impairment was greater and increased with higher levels of impairment. This impact appeared to be directly related to the severity of visual impairment but not to the underlying eye condition. The impact of visual impairment was comparable with that of major medical conditions and affected mental more than physical domains. 
These findings have important implications. First, the burden from visual impairment will increase as its prevalence increases (expected to double in the next 30 years 36 37 ), due to the aging population. Second, the impact from correctable visual impairment reinforces the benefits of eye care services for older people. 38 Although the impact of correctable visual impairment is not as great as that of noncorrectable visual impairment, its higher prevalence, accounting for one third to two thirds 39 of all visual impairment, substantially increases its overall burden. Possible contributors to the high prevalence of correctable visual impairment in the older population include disabling systemic disorders that mask the cause of difficulties, inability to afford costs of treatment, and perceptions that visual loss is to be expected in later life and cannot be ameliorated. 38 40  
The decrease in function and well-being associated with visual impairment is integrated into a person’s HRQOL and is not easily isolated from other medical conditions. Lower sociodemographic status, medical conditions, and disabilities could confound the relationship between visual impairment and HRQOL. We have adjusted for the potential confounders measured in our study but have excluded factors that were not measured. 
The arbitrary cutoff used to define the severity of visual impairment that predicted significant disability may be insufficient to reflect the gradual deteriorating trend in HRQOL with increasing severity of visual impairment. Our data show that moderate to severe visual impairment had the greatest impact on HRQOL. This is consistent with our previous findings that participants with this level of impairment had significantly reduced ability to maintain mobility and independence 4 and had a significantly greater need for community and family support. 7  
Brown et al. 20 compared utility values of diabetic retinopathy and ARM and found that the impact on HRQOL was related to the degree of the impairment and not the underlying condition causing the impairment. 19 20 Our findings are in keeping with these studies. There was a significant difference in the physical component score among participants without visual impairment between those with cataract and/or ARM and those without either condition, probably because this latter group consisted mainly of participants without any eye condition. Participants with visual impairment but who do not have either cataract or AMD commonly have another eye condition that causes the impairment. 
Some studies have reported that generic health outcome measures such as the SF-36 may not be as sensitive to ocular conditions as vision-related health outcome measures. 17 21 23 41 However, its acceptable validity and reliability and its ability to allow comparison across a wide range of medical conditions made this instrument appropriate for the purposes of this report. 
Our data documented that the impact on HRQOL of visual impairment was comparable with that of major medical conditions and affected mental more than physical domains. Our results are comparable to those of Fryback et al., 42 who compared cataract, glaucoma, and ARM with other medical conditions, using four general health measures including the SF-36. This highlights the importance of the disability weight of visual impairment, which has been overlooked until now. 
Disability weights are an essential parameter for the calculation of the disability-adjusted life year (DALY), a health-economic tool increasingly used for the assessment of the burden of disease and priority setting for health research. 43 It is needed for weighting the years lived with a specific disease by the severity of the disability associated with it, hence combining information on the impact of premature death with that of disability and other nonfatal health outcomes. 35 These weights were developed by the World Health Organization (WHO) Global Burden of Disease (GBD) study, 35 using an internationally representative group of health experts and person tradeoff (PTO) methods. Some of these weights were extended in a Dutch study. 44 However, there remains considerable concern about the accuracy of disability weights assigned to various disease categories and the development of most of these weights. 45 46  
In this study, we attempted to compare the impact of poor vision on HRQOL, with these two commonly used health outcome measures. Although their values are not directly comparable because of their different origins, the ranking of these items should be similar. We used the GBD weights instead of those by the Dutch study, as the latter stratified each condition by severity and we had an insufficient number in our population for direct comparison. The GBD weights are stratified by age (0–4, 5–14, 15–44, 45–59, and 60+ years) and treatment status (treated or untreated). We used the disability weights of the treated group aged 60+ years, as this was most comparable to our population. As shown in Table 5 , the mental impact of visual impairment in an older population is much greater than that of other medical conditions, although its physical impact is milder. Stroke, which had similar disability weights to low vision, had a greater physical impact and a milder mental impact than visual impairment in our study. Assuming equal weighting of physical and mental components, the impact of visual impairment and other conditions could be comparable to the GBD disability weight, despite the differences in their respective developments. 
In conclusion, the increasing prevalence of age-related visual impairment and its associated reduction in well-being, functional status, and independence, will greatly increase the resultant burden of disease. This suggests a greater need for eye care services in older populations, particularly in relation to visual impairment due to undercorrected refractive errors. The impact of visual impairment, which is directly related to its severity but not its underlying condition, stresses the importance of low-vision services. Visual impairment has an impact comparable to that of major medical conditions. These data have implications for disability weights, such as those developed by the Global Burden of Disease study. 
Table 1.
 
Sociodemographic and Self-Reported Medical Characteristics among Participants with No Visual Impairment, Correctable or Noncorrectable Visual Impairment
Table 1.
 
Sociodemographic and Self-Reported Medical Characteristics among Participants with No Visual Impairment, Correctable or Noncorrectable Visual Impairment
None (n = 2916) Correctable (n = 172) Noncorrectable (n = 66)
Mean age (y) 65.9 74.4* 79.2*
Women (%) 57.0 61.6 69.7*
Married (%) 63.9 49.4* 36.4*
Qualifications after high school (%) 65.1 50.0* 50.9*
Receives government pension (%) 57.3 73.8* 87.9*
Owns home (%) 91.0 86.1* 80.3*
Currently employed (%) 24.4 8.1* 6.1*
Hospital admission previous 12 months (%) 23.2 25.6 34.9*
Angina (%) 9.7 15.7* 21.2*
Stroke (%) 4.0 9.9* 10.6*
Diabetes (%) 7.2 8.1 7.6
Arthritis (%) 40.0 47.1 51.5
Asthma (%) 11.9 10.5 16.7
Thyroid disease (%) 9.1 9.9 9.1
Current smoker (%) 9.9 9.3 9.2
Table 2.
 
Multivariate Adjusted Mean Scores (Standard Error) for the Eight Dimensions and Physical and Mental Component Scores among Participants with No Visual Impairment, Correctable or Noncorrectable Visual Impairment
Table 2.
 
Multivariate Adjusted Mean Scores (Standard Error) for the Eight Dimensions and Physical and Mental Component Scores among Participants with No Visual Impairment, Correctable or Noncorrectable Visual Impairment
Visual Impairment Physical Functioning Role Limitation Due to Physical Problems Bodily Pain General Health Vitality Social Functioning Role Limitation Due to Emotional Problems Mental Health Physical Component Score Mental Component Score
None (n = 2916) 72.3 (0.4) 67.0 (0.7) 70.6 (0.5) 68.1 (0.4) 62.0 (0.4) 84.1 (0.4) 81.0 (0.7) 78.8 (0.3) 45.0 (0.2) 52.0 (0.2)
Correctable (n = 172) 68.2 (1.8)* 61.1 (3.1) 68.2 (1.9) 66.9 (1.6) 61.7 (1.7) 79.3 (1.9)* 75.8 (2.8) 77.5 (1.3) 42.7 (0.8)* 51.3 (0.8)
Noncorrectable (n = 66) 66.2 (3.0)* 61.9 (5.4) 66.4 (3.2) 61.1 (2.8)* 50.7 (2.8)* , † 76.9 (3.2)* 77.3 (4.8) 71.8 (2.3)* , † 43.0 (1.4) 48.0 (1.5)* , †
P trend 0.02 0.13 0.23 0.04 0.0004 0.01 0.17 0.01 0.01 0.02
Table 3.
 
Mean Scores (standard error) for the Eight Dimensions and Physical and Mental Components among Participants with Noncorrectable Visual Impairment
Table 3.
 
Mean Scores (standard error) for the Eight Dimensions and Physical and Mental Components among Participants with Noncorrectable Visual Impairment
Noncorrectable Visual Impairment* Physical Functioning Role Limitation Due to Physical Problems Bodily Pain General Health Vitality Social Functioning Role Limitation Due to Emotional Problems Mental Health Physical Component Score Mental Component Score
None (n = 2916) 72.9 (0.4) 67.8 (0.7) 70.8 (0.5) 68.2 (0.4) 62.3 (0.4) 84.2 (0.4) 81.2 (0.6) 78.5 (0.3) 45.3 (0.2) 51.9 (0.2)
Mild (n = 49) 64.9 (3.5) 69.1 (6.2) 66.0 (3.7) 61.8 (3.3) 51.6 (3.2), † 79.9 (3.5) 75.8 (5.3) 72.1 (2.6), † 43.7 (1.7) 48.9 (1.6)
Moderate-severe (n = 17) 64.5 (5.9) 42.7 (10.3), † , ‡ 63.8 (6.2) 58.6 (5.2) 45.9 (5.2), † 65.5 (5.8), † , ‡ 67.9 (8.6) 66.9 (4.1), † 41.3 (2.8) 42.9 (2.6), † , ‡
, § Any (n = 66) 66.2 (3.0), † 62.1 (5.4) 66.3 (3.2) 61.1 (2.8), † 50.5 (2.8), † 76.4 (3.1), † 76.8 (4.8) 71.7 (2.3), † 42.9 (1.4) 47.8 (1.4), †
P trend 0.03 0.05 0.24 0.03 <0.0001 0.003 0.19 0.001 0.25 0.001
, ∥ P trend 0.06 0.06 0.31 0.04 <0.0001 0.01 0.31 0.002 0.35 0.001
Table 4.
 
Comparison of Physical and Mental Component Scores for Participants with Cataract, ARM, Both or Neither Eye Condition in the Better Eye, by Level of Visual Impairment
Table 4.
 
Comparison of Physical and Mental Component Scores for Participants with Cataract, ARM, Both or Neither Eye Condition in the Better Eye, by Level of Visual Impairment
Visual Impairment Physical Component Score Mental Component Score
No Cataract or ARM Cataract ARM Cataract and ARM No Cataract or ARM Cataract ARM Cataract and ARM
None 46.6 43.4* 42.9* 40.2* 51.8 52.3 52.5 53.0
(n = 1937) (n = 520) (n = 94) (n = 65) (n = 1937) (n = 520) (n = 94) (n = 65)
Mild 35.4 44.4 38.8 36.3 48.2 54.8 50.9 47.9
(n = 7) (n = 16) (n = 2) (n = 12) (n = 7) (n = 16) (n = 2) (n = 12)
Moderate to severe 54.3 28.0 25.3 41.6 48.3 36.0 43.3 49.9
(n = 2) (n = 3) (n = 3) (n = 5) (n = 2) (n = 3) (n = 3) (n = 5)
Table 5.
 
Comparison of Age- and Sex-Standardized Physical and Mental Component Scores for Visual Impairment and Certain Medical Conditions, with the WHO Disability Weights
Table 5.
 
Comparison of Age- and Sex-Standardized Physical and Mental Component Scores for Visual Impairment and Certain Medical Conditions, with the WHO Disability Weights
Physical Component Score Mental Component Score Disability Weights*
Arthritis 41.0 Arthritis 51.7 Diabetes 0.033–0.129, †
Asthma 41.0 Stroke 51.5 Asthma 0.059
Bilateral visual impairment 41.0 Heart attack 51.4 Angina 0.095
Diabetes 40.7 Asthma 50.8 Arthritis 0.108–0.174, ‡
Stroke 39.3 Diabetes 50.6 Low vision 0.245 , §
Angina 37.6 Angina 49.9 Stroke 0.258, ∥
Heart attack 37.3 Bilateral visual impairment 46.2 Heart attack 0.395
 
Tielsch JM, Sommer A, Witt K, Katz J, Royall RM. Blindness and visual impairment in an American urban population: the Baltimore Eye Survey. Arch Ophthalmol. 1990;108:286–290. [CrossRef] [PubMed]
Klein R, Klein BE, Linton KL, De Mets DL. The Beaver Dam Eye Study: visual acuity. Ophthalmology. 1991;98:1310–1315. [CrossRef] [PubMed]
Attebo K, Mitchell P, Smith W. Visual acuity and the causes of visual loss in Australia: the Blue Mountains Eye Study. Ophthalmology. 1996;103:357–364. [CrossRef] [PubMed]
Wang JJ, Mitchell P, Cumming RG, Smith W. Visual impairment and nursing home placement in older Australians: the Blue Mountains Eye Study. Ophthalmic Epidemiology. 2003;10:3–13. [CrossRef] [PubMed]
Ivers RQ, Cumming RG, Mitchell P, Attebo K. Visual impairment and falls in older adults: the Blue Mountains Eye Study. J Am Geriatr Soc. 1998;46:58–64. [PubMed]
Ivers RQ, Norton R, Cumming RG, Butler M, Campbell AJ. Visual impairment and risk of hip fracture. Am J Epidemiol. 2000;152:633–639. [CrossRef] [PubMed]
Wang JJ, Mitchell P, Smith W, Cumming RG, Attebo K. Impact of visual impairment on use of community support services by elderly persons: the Blue Mountains Eye Study. Invest Ophthalmol Vis Sci. 1999;40:12–19. [PubMed]
Klein R, Klein BE, Moss SE. Age-related eye disease and survival: the Beaver Dam Eye Study. Arch Ophthalmol. 1995;113:333–339. [CrossRef] [PubMed]
Wang JJ, Mitchell P, Simpson JM, Cumming RG, Smith W. Visual impairment, age-related cataract, and mortality. Arch Ophthalmol. 2001;119:1186–1190. [CrossRef] [PubMed]
Gill TM, Feinstein AR. A critical appraisal of the quality of quality-of-life measurements. JAMA. 1994;272:619–626. [CrossRef] [PubMed]
Bergner M, Bobbitt RA, Carter WB, Gilson BS. The Sickness Impact Profile: development and final revision of a health status measure. Med Care. 1981;19:787–805. [CrossRef] [PubMed]
Hunt SM, McKenna SP, McEwen J, Williams J, Papp E. The Nottingham Health Profile: subjective health status and medical consultations. Soc Sci Med [A]. 1981;15:221–229. [PubMed]
Ware JE, Jr, Sherbourne CD. The MOS 36-item short-form health survey (SF-36).I. Conceptual framework and item selection. Med Care. 1992;30:473–483. [CrossRef] [PubMed]
Mangione CM, Phillips RS, Seddon JM, et al. Development of the “Activities of Daily Vision Scale”: a measure of visual functional status. Med Care. 1992;30:1111–1126. [CrossRef] [PubMed]
Steinberg EP, Tielsch JM, Schein OD, et al. The VF-14: an index of functional impairment in patients with cataract. Arch Ophthalmol. 1994;112:630–638. [CrossRef] [PubMed]
Mangione CM, Lee PP, Pitts J, Gutierrez P, Berry S, Hays RD. Psychometric properties of the National Eye Institute Visual Function Questionnaire (NEI-VFQ).NEI-VFQ Field Test Investigators. Arch Ophthalmol. 1998;116:1496–1504. [CrossRef] [PubMed]
Scott IU, Smiddy WE, Schiffman J, Feuer WJ, Pappas CJ. Quality of life of low-vision patients and the impact of low-vision services. Am J Ophthalmol. 1999;128:54–62. [CrossRef] [PubMed]
Chia EM, Mitchell P, Rochtchina E, Foran S, Wang JJ. Unilateral visual impairment and health related quality of life: the Blue Mountains Eye Study. Br J Ophthalmol. 2003;87:392–395. [CrossRef] [PubMed]
Mangione CM, Gutierrez PR, Lowe G, Orav EJ, Seddon JM. Influence of age-related maculopathy on visual functioning and health-related quality of life. Am J Ophthalmol. 1999;128:45–53. [CrossRef] [PubMed]
Brown MM, Brown GC, Sharma S, Landy J, Bakal J. Quality of life with visual acuity loss from diabetic retinopathy and age-related macular degeneration. Arch Ophthalmol. 2002;120:481–484. [CrossRef] [PubMed]
Mangione CM, Phillips RS, Lawrence MG, Seddon JM, Orav EJ, Goldman L. Improved visual function and attenuation of declines in health-related quality of life after cataract extraction. Arch Ophthalmol. 1994;112:1419–1425. [CrossRef] [PubMed]
Wilson MR, Coleman AL, Yu F, et al. Functional status and well-being in patients with glaucoma as measured by the Medical Outcomes Study Short Form-36 questionnaire. Ophthalmology. 1998;105:2112–2116. [CrossRef] [PubMed]
Parrish RK, Gedde SJ, Scott IU, et al. Visual function and quality of life among patients with glaucoma. Arch Ophthalmol. 1997;115:1447–1455. [CrossRef] [PubMed]
Schiffman RM, Jacobsen G, Whitcup SM. Visual functioning and general health status in patients with uveitis. Arch Ophthalmol. 2001;119:841–849. [CrossRef] [PubMed]
Musch DC, Farjo AA, Meyer RF, Waldo MN, Janz NK. Assessment of health-related quality of life after corneal transplantation. Am J Ophthalmol. 1997;124:1–8. [CrossRef] [PubMed]
Wang JJ, Mitchell P, Smith W. Vision and low self-rated health: the Blue Mountains Eye Study. Invest Ophthalmol Vis Sci. 2000;41:49–54. [PubMed]
Rubin GS, Munoz B, Bandeen-Roche K, West SK. Monocular versus binocular visual acuity as measures of vision impairment and predictors of visual disability. Invest Ophthalmol Vis Sci. 2000;41:3327–3334. [PubMed]
Rovner BW, Zisselman PM, Shmuely-Dulitzki Y. Depression and disability in older people with impaired vision: a follow-up study. J Am Geriatr Soc. 1996;44:181–184. [PubMed]
Sanson-Fisher RW, Perkins JJ. Adaptation and validation of the SF-36 Health Survey for use in Australia. J Clin Epidemiol. 1998;51:961–967. [CrossRef] [PubMed]
Ware JE, Snow KK, Kosinski M, Gande B. SF-36 Health Survey. Manual and Interpretation Guide. 1993; Health Institute, New England Medical Center Boston.
Ware JE, Kosinski M, Keller SD. SF-36 Physical and Mental Health Summary Scales. 1994; The Health Institute, New England Medical Center Boston.
Australian Bureau of Statistics. National Health Survey.SF-36 Population Norms. 1997; ABS Canberra, Australia. Publication no. 4399.0
Mitchell P, Cumming RG, Attebo K, Panchapakesan J. Prevalence of cataract in Australia: the Blue Mountains eye study. Ophthalmology. 1997;104:581–588. [CrossRef] [PubMed]
Mitchell P, Smith W, Attebo K, Wang JJ. Prevalence of age-related maculopathy in Australia.The Blue Mountains Eye Study. Ophthalmology. 1995;102:1450–1460. [CrossRef] [PubMed]
Murray CJ, Lopez AD. The Global Burden of Disease: a Comprehensive Assessment of Mortality and Disability from Diseases, Injuries and Risk Factors in 1990 and Projected to 2020. Global Burden of Disease and Injury Series. 1996;1 Harvard School of Public Health Cambridge, MA.
National Advisory Eye Council. Vision research: a National Plan 1994–1998. 1993;305–321. National Eye Institute, National Institutes of Health Bethesda, MD.
Foran S, Wang JJ, Rochtchina E, Mitchell P. Projected number of Australians with visual impairment in 2000 and 2030. Clin Exp Ophthalmol. 2000;28:143–145. [CrossRef]
Sinclair AJ, Bayer AJ, Girling AJ, Woodhouse KW. Older adults, diabetes mellitus and visual acuity: a community-based case-control study. Age Ageing. 2000;29:335–339. [CrossRef] [PubMed]
Foran S, Rose K, Wang JJ, Mitchell P. Correctable visual impairment in an older population: the Blue Mountains Eye Study. Am J Ophthalmol. 2002;134:712–719. [CrossRef] [PubMed]
Smeeth L, Iliffe S. Effectiveness of screening older people for impaired vision in community setting: systematic review of evidence from randomised controlled trials. BMJ. 1998;316:660–663. [CrossRef] [PubMed]
Boisjoly H, Gresset J, Fontaine N, et al. The VF-14 index of functional visual impairment in candidates for a corneal graft. Am J Ophthalmol. 1999;128:38–44. [CrossRef] [PubMed]
Fryback DG, Dasbach EJ, Klein R, et al. The Beaver Dam Health Outcomes Study: initial catalog of health-state quality factors. Med Decis Making. 1993;13:89–102. [CrossRef] [PubMed]
Mathers C, Vos T, Stevenson C. The Burden of Disease and Injury in Australia. 1999; Australian Institute of Health and Welfare Canberra, Australia. Publication no. PHE 17
Stouthard MEA, Essink-Bot ML, Bonsel GJ, et al. Disability Weights for Diseases in the Netherlands. 1997; Department of Public Health, Erasmus University Rotterdam, The Netherlands.
Ustun TB, Chatterji S, Villanueva M, Bendib L, Celik C, Sadana R. WHO Multi-Country Survey Study on Health and Responsiveness 2000–2001. GPE Discussion Paper No. 37. 2000;1–146. WHO Geneva.
Salomon JA, Murray CJL. Estimating health state valuations using a multiple-method. Murray CJL Salomon JA Mathers CD Lopez AD eds. Summary Measures of Population Health: Concepts, Ethics.Measurement and Applications. 2002;487–500. World Health Organization Geneva.
×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×