Abstract
Background:
The study assesses the ‘health-related quality of life’ (HRQoL) in patients with cataract and glaucoma, examines its determinants through both generic and vision-specific instruments, and evaluates the degree of agreement between the two HRQoL assessment tools.
Methods:
A facility-based survey was carried out among 541 participants (297 glaucoma patients and 244 cataract patients) in outpatient settings of tertiary facility from January to June 2024. After obtaining informed consent, the participants were interviewed about their sociodemographic characteristics, medical history, and HRQoL. The mean utility value for the ‘EuroQol five dimensions five levels’ (‘EQ-5D-5L’) and the composite score for the ‘National Eye Institute Visual Function Questionnaire-25’ (‘NEI-VFQ-25’) were calculated. Factors influencing HRQoL were analyzed through a generalized linear regression model. Additionally, Pearson’s correlation coefficient was determined to evaluate the correlation between the HRQoL derived through vision-specific and generic measures.
Results:
The ‘EQ-5D-5L’ utility values were 0.74 (0.71–0.77) and 0.87 (0.85–0.89) in glaucoma and cataracts, respectively. The ‘NEI-VFQ-25’ composite score was 74.8 (72.2–77.2) in glaucoma and 79.3 (77.3–81.2) in cataract. Visual acuity was significantly associated with HRQoL in both disorders. ‘NEI-VFQ-25’ score had a strong and moderate correlation with EQ-5D-5L value in glaucoma and cataract, respectively.
Conclusion:
The decrement in HRQoL in patients with glaucoma and cataract underscores the need to prioritize policies for improving awareness, early detection, and management of these diseases. While the ‘EQ-5D-5L’ adequately captures HRQoL in glaucoma, the incorporation of vision-specific bolt-on dimension to the generic instrument in cataract should be explored.
Keywords: Cataract, economic evaluation, glaucoma, HRQoL, patient-reported outcomes, quality of life
INTRODUCTION
Worldwide, more than 1.1 billion individuals are affected by vision impairment, with 90% of this population residing in developing countries.[1] India is home to 23.5% of the global blind and visually impaired population.[2] Among various eye disorders, cataract and glaucoma are significant causes of vision loss and are responsible for 72% of the blind cases in the country.[2] While glaucoma results in progressive optic nerve damage and irreversible peripheral vision loss, cataract is characterized by lens opacity and gradual decline in visual clarity. Both the conditions impose substantial physical, psychological, and socioeconomic burdens, adversely affecting functionality and productivity.[3,4]
Aligning health system priorities with the needs of patients and their families is crucial to ensure patient-centric care. ‘Health-related quality of life’ (HRQoL) has emerged as a critical construct for capturing these broader consequences of disease as it quantifies individuals’ subjective perceptions of health and incorporates both clinical and nonclinical determinants.[5] HRQoL assessments are therefore of importance to the patients, health care practitioners, policymakers, and researchers.[5,6] However, despite its impressive utility, the use of HRQoL is divided over fundamental methodological and feasibility issues. With the advancement of the field, researchers have a wide choice of generic as well as disease-specific instruments to measurement of HRQoL. Generic tools facilitate comparisons across diseases and populations, whereas disease-specific instruments provide greater sensitivity to condition-related aspects but lack cross-disease comparability.[7] The inconsistent application of these tools undermines reproducibility and constrains the cumulative progress of public health science.
In India, HRQoL among patients with cataract and glaucoma has predominantly been assessed through vision-specific tools, such as ‘National Eye Institute Visual Function Questionnaire-25’ (‘NEI-VFQ-25’),[8,9] ‘Glaucoma Quality of Life,’[8,9,10,11,12,13,14,15] ‘Surgery Specific Questionnaire,’[9] ‘Indian Visual Functioning Questionnaire,’[16,17,18,19] ‘Indian Compressed Assessment of Ability Related to Vision,’[16] ‘Glaucoma Activity Limitation,’[13] ‘Viswanathan questionnaire,’[13] and ‘WHO/prevention of blindness and deafness 20-item visual functioning questionnaire.’[20] Contrarily, only a few studies used the generic measures of ‘time-trade off,’[15,21] and ‘standard gamble,’[21] ‘WHOQOL-BREF questionnaire,’[22] and ‘EuroQol-five dimensions-five levels tool’ (‘EQ-5D-5L’).[19] This imbalance is of concern as generic measures are used for the calculation of ‘quality-adjusted life years’ (QALYs), which constitute the standard metric for outcome evaluation in health technology assessments (HTA).[23] Given India’s increasing reliance on HTA to inform benefit package design and resource allocation, the scarcity of evidence on generic HRQoL measures in ophthalmic disorders represents a significant gap.
Furthermore, while vision-related measures are responsive to variations in HRQoL, their length and design for research purposes often limit feasibility in routine clinical settings.[6] Generic tools, such as ‘EQ-5D-5L,’ are shorter and could provide a feasible alternative in busier clinical environments. However, the degree of concordance between ‘EQ-5D-5L’ and vision-specific measures remains largely unexplored in India.
In order to address these evidence gaps, the study aims to assess the HRQoL of patients with glaucoma and cataract using vision-specific measure (‘NEI-VFQ-25’) and generic measure (‘EQ-5D-5L’), identify the determinants of HRQoL, and examine the level of concordance between these two widely used tools in assessing HRQoL in ophthalmic disorders for clinical relevance in India. The choice of ‘EQ-5D-5L’ generic tool was based on Indian HTA guidelines.[23] Moreover, the availability of ‘EQ-5D-5L’ Indian value set in the public domain makes it the preferred generic instrument. On similar lines, the preference for ‘NEI-VFQ-25’ was guided by its wide use and its psychometric properties for the assessment of QoL in glaucoma and cataracts.[24,25,26,27]
METHODOLOGY
The study is a part of a larger cross-sectional analytical research project aimed at measuring patient-reported health status in patients with ophthalmic diseases at a government tertiary care institution in northern India.
Study participants
Based on reported standard deviations of 0.19 for glaucoma and 0.17 for cataract and assuming a margin of error of 5%, the required sample sizes were calculated as 277 for glaucoma and 222 for cataract.[28,29] The study included 18 years and above aged individuals with confirmed diagnosis of glaucoma or cataracts and who attended the outpatient department (OPD) of the government tertiary healthcare institution.
The exclusion criteria were outpatients who reported with surgical or laser procedures on eyes in the past 3 months, patients with any retinal pathology that could significantly reduce visual acuity, patients with hearing loss, and refusal of written consent for participation in the study. The participants were selected through consecutive sampling technique, following the exclusion and inclusion criteria.
The study participants underwent comprehensive ocular evaluations, including detailed ocular history, refraction tests, tonometry, dilated fundus examination and bio-microscopy through a slit lamp, visual field testing, along with gonioscopy by ophthalmic specialists. Visual acuity in both eyes was classified according to the World Health Organization criteria.[30] Visual field defects were categorized using the Hodapp–Anderson–Parrish grading scale.[31] Medication adherence was self-reported and defined as not missing more than one eye drop in the past 15 days.
Data collection
A semistructured tool was used by trained research assistants to capture sociodemographic details, clinical history, disease-specific attributes, and HRQoL through interviews with the included participants. Data collection was completed in 6 months spanning from January to June 2024. The participants were explained about the objectives of the study and data collection process. The interviews were conducted in Hindi, with each interview lasting approximately 30 minutes. Clinical details of each participant were acquired by the research assistants through appraisal of the medical records, which were validated by the ophthalmic specialists.
Quality of life tools
Two assessment tools were utilized to evaluate HRQoL: the ‘NEI-VFQ-25’ and the ‘EQ-5D-5L’.
NEI-VFQ-25
The ‘NEI-VFQ-25’ is a vision-specific QoL measure, which comprises of ‘25 questions’ that cover ‘11 domains’ focused on vision-related issues and one domain on general health.[32] The responses of each question are converted on a scale of 0 to 100 using a standardized method. The composite score is an average of scores from the vision-related domains and ranges from 0 to 100, with 100 indicating perfect heath.
EQ-5D-5L
The ‘EQ-5D-5L’ is a generic measure and is constituted by two components: the ‘EQ-5D-5L descriptive system’ and ‘EuroQoL- visual analogue system’ (‘EQ-VAS’). The descriptive system comprises dimensions of ‘mobility,’ ‘self-care,’ ‘usual activities,’ ‘pain/discomfort,’ and ‘anxiety/depression.’[33] Respondents choose from five levels of functioning for each dimension, ranging from no to extreme difficulties. Once responses for all five dimensions are collected, nation-specific value sets are applied to compute the ‘EQ-5D-5L’ utility value.[34] A higher utility score denotes better HRQoL.
Statistical analysis
Sociodemographic as well as clinical factors were evaluated using descriptive analyses in Stata 13. Each patient’s descriptive health condition’s utility values were derived through the Indian ‘EQ-5D-5L’ value set.[34] Subsequently, we used a multiple linear regression model with parameter estimates based on the ordinary least squares method. Individual multivariable regression models were used to assess the determinants of ‘EQ-5D-5L’ utility values and ‘NEI-VFQ-25’ composite scores.
The sociodemographic and clinical characteristics of the patients were used as independent variables to determine their impact on the utility score (composite scores). The assumptions of the multiple linear regression model, including linearity, error term normality, homoscedasticity, and multicollinearity, were verified for model selection. Due to violation of assumptions related to normality, we employed the maximum likelihood estimation strategy for the generalized linear model method to estimate associations in order to prevent biased and inconsistent values. This method gives consistent estimators for future usage and relaxes the assumptions of response and residual normality.
Last, the concordance between the ‘NEI-VFQ-25 composite score’ and ‘EQ-5D-5L value’ was evaluated using Pearson’s correlation coefficient. We applied Evan’s classification to classify the strength of correlation, ranging from extremely weak to very strong.[35]
RESULTS
A total of 541 participants (297 glaucoma patients and 244 cataract patients) were interviewed for the study. The mean age of participants with glaucoma and cataract was 52.4 and 60.9 years, respectively, with males comprising almost half of the sample [Table 1]. More than 80% respondents with both diseases reported having formal education. Approximately, 35% glaucoma and 27% cataract patients were employed in paid occupations. 21% of glaucoma patients and 34% of cataract patients presented with diabetes, while nearly 40% of patients in both the subsamples had hypertension [Tables 2A and 2B].
Table 1.
Sociodemographic characteristics and quality of life scores among patients with glaucoma and cataract from different sociodemographic groups
| Demographic factors | Glaucoma |
Cataract |
||||
|---|---|---|---|---|---|---|
| Number of patients n (%) | Mean EQ-5D-5L score (SD) | Mean NEI-VFQ-25 composite score (SD) | Number of patients n (%) | Mean EQ-5D-5L score (SD) | Mean NEI-VFQ-25 composite score (SD) | |
| Mean | 297 | 0.741 (0.32) | 74.75 (21.98) | 244 | 0.867 (0.17) | 79.3 (15.54) |
| Age (years) | ||||||
| <40 | 73 (24.6%) | 0.760 (0.27) | 76.53 (20.98) | 14 (5.7%) | 0.721 (0.34) | 80.44 (14.74) |
| 40-60 | 108 (36.4%) | 0.726 (0.32) | 70.44 (24.66) | 85 (34.8%) | 0.867 (0.15) | 76.53 (15.36) |
| >60 | 116 (39.1%) | 0.743 (0.34) | 77.63 (19.36) | 145 (59.4%) | 0.882 (0.16) | 80.77 (15.61) |
| Gender | ||||||
| Male | 174 (58.6%) | 0.763 (0.32) | 76.31 (21.96) | 127 (52.0%) | 0.898 (0.12) | 81.26 (15.21) |
| Female | 123 (41.4%) | 0.710 (0.32) | 72.55 (21.91) | 117 (48.0%) | 0.835 (0.21) | 77.12 (15.67) |
| Area | ||||||
| Rural | 161 (54.2%) | 0.689 (0.35) | 72.46 (22.7) | 101 (41.4%) | 0.876 (0.13) | 76.97 (16.08) |
| Urban | 136 (45.8%) | 0.803 (0.26) | 77.46 (20.86) | 143 (58.6%) | 0.861 (0.19) | 80.91 (15) |
| Education | ||||||
| Illiterate | 36 (12.1%) | 0.664 (0.26) | 62.37 (22.18) | 39 (16.0%) | 0.824 (0.23) | 72.69 (16.05) |
| Literate | 261 (87.9%) | 0.752 (0.32) | 76.46 (21.44) | 205 (84.0%) | 0.876 (0.16) | 80.53 (15.16) |
| Employment | ||||||
| Employed | 104 (35.0%) | 0.802 (0.33) | 77.88 (19.37) | 66 (27.0%) | 0.895 (0.14) | 80.15 (14.62) |
| Unemployed | 193 (65.0%) | 0.708 (0.31) | 73.06 (23.14) | 178 (73.0%) | 0.857 (0.18) | 78.95 (15.90) |
| Caste | ||||||
| General | 191 (64.3%) | 0.743 (0.30) | 75.74 (22.20) | 134 (54.9%) | 0.870 (0.17) | 80.87 (15.31) |
| Nongeneral | 106 (35.7%) | 0.737 (0.35) | 72.97 (21.58) | 110 (45.1%) | 0.864 (0.18) | 77.32 (15.67) |
| Marital status | ||||||
| Unmarried | 53 (17.8%) | 0.665 (0.45) | 67.8 (24.70) | 38 (15.6%) | 0.856 (0.09) | 76.17 (18.5) |
| Married | 244 (82.2%) | 0.757 (0.28) | 76.26 (21.10) | 206 (84.4%) | 0.869 (0.18) | 79.85 (14.91) |
| Income-based economic status | ||||||
| Poorest | 59 (19.9%) | 0.596 (0.45) | 65.82 (24.48) | 50 (20.5%) | 0.835 (0.19) | 71.97 (18.05) |
| Poor | 66 (22.2%) | 0.705 (0.27) | 68.19 (21.48) | 49 (20.1%) | 0.890 (0.10) | 79.97 (14.56) |
| Middle | 70 (23.6%) | 0.789 (0.23) | 76.39 (18.43) | 74 (30.3%) | 0.883 (0.16) | 81.26 (12.07) |
| Rich | 48 (16.2%) | 0.872 (0.19) | 88.36 (8.56) | 24 (9.8%) | 0.850 (0.27) | 80.60 (13.71) |
| Richest | 54 (18.2%) | 0.765 (0.31) | 78.29 (25.37) | 47 (19.3%) | 0.862 (0.17) | 82.52 (17.49) |
| Insurance | ||||||
| Not insured | 151 (50.8%) | 0.749 (0.32) | 76.12 (20.13) | 108 (44.3%) | 0.851 (0.20) | 80.51 (16.18) |
| Insured in a public scheme | 125 (42.1%) | 0.720 (0.31) | 71.29 (24.12) | 126 (51.6%) | 0.877 (0.14) | 78.05 (15.22) |
| Insured in a private scheme | 21 (7.1%) | 0.808 (0.31) | 85.49 (17.2) | 10 (4.1%) | 0.919 (0.08) | 81.42 (12.71) |
EQ-5D-5L: EuroQoL-five dimensions-five levels, NEI-VFQ-25: National Eye Institute-Visual Function Questionnaire-25, SD: Standard Deviation
Table 2A.
Clinical history and quality of life of patients with glaucoma and cataract with respect to differences in clinical characteristics
| Clinical factors | Glaucoma |
Cataract |
||||
|---|---|---|---|---|---|---|
| Number of patients n (%) | Mean EQ-5D-5L score (SD) | Mean NEI-VFQ-25 composite score (SD) | Number of patients n (%) | Mean EQ-5D-5L score (SD) | Mean NEI-VFQ-25 composite score (SD) | |
| Presence of Diabetes | ||||||
| Yes | 62 (20.9%) | 0.699 (0.32) | 75.16 (22.85) | 82 (33.6%) | 0.869 (0.18) | 81.95 (15.44) |
| No | 235 (79.1%) | 0.752 (0.32) | 74.64 (21.80) | 162 (66.4%) | 0.866 (0.17) | 77.93 (15.46) |
| Presence of Hypertension | ||||||
| Yes | 117 (39.4%) | 0.762 (0.27) | 77.27 (21.18) | 94 (38.5%) | 0.863 (0.21) | 82.46 (13.76) |
| No | 180 (60.6%) | 0.728 (0.34) | 73.11 (22.40) | 150 (61.5%) | 0.870 (0.14) | 77.29 (16.29) |
| Family history of glaucoma/cataract | ||||||
| Yes | 30 (10.1%) | 0.842 (0.13) | 69.99 (23.41) | 78 (32.0%) | 0.865 (0.17) | 81.28 (15.49) |
| No | 267 (89.9%) | 0.730 (0.33) | 75.28 (21.80) | 166 (68.0%) | 0.868 (0.17) | 78.34 (15.52) |
| Type of glaucoma | ||||||
| Primary open angle glaucoma (POAG) | 155 (52.2%) | 0.734 (0.33) | 75.12 (21.31) | |||
| Primary angle closure disease (PACG) | 67 (22.6%) | 0.725 (0.28) | 75.03 (21.45) | |||
| Secondary glaucoma | 75 (25.2%) | 0.770 (0.32) | 73.74 (24) | |||
| Type of cataract | ||||||
| Nuclear sclerotic (NS) only | 103 (42.4%) | 0.876 (0.16) | 81.03 (15.47) | |||
| Cortical (C) only | 12 (4.9%) | 0.828 (0.16) | 76.97 (19.93) | |||
| Posterior subcapsular (PSC) only | 29 (11.9%) | 0.852 (0.13) | 77.62 (15.57) | |||
| NS and C | 32 (13.2%) | 0.869 (0.25) | 82.01 (12.56) | |||
| NS and PSC | 46 (18.9%) | 0.878 (0.13) | 77.42 (15.72) | |||
| C and PSC | 6 (2.5%) | 0.885 (0.11) | 73 (14.04) | |||
| NS, PSC and C | 15 (6.2%) | 0.850 (0.23) | 77.09 (16.15) | |||
| Duration of disease | ||||||
| <2 years | 73 (24.6%) | 0.682 (0.38) | 69.02 (25.83) | 49 (20.2%) | 0.878 (0.12) | 75.92 (15.13) |
| 2-4 years | 65 (21.9%) | 0.774 (0.25) | 74.91 (21.02) | 38 (15.6%) | 0.882 (0.09) | 79.34 (15.87) |
| 5-7 years | 57 (19.2%) | 0.820 (0.23) | 80.30 (18.22) | 28 (11.5%) | 0.871 (0.15) | 80.50 (14.72) |
| >7 years | 102 (34.3%) | 0.718 (0.34) | 75.65 (20.85) | 128 (52.7%) | 0.858 (0.21) | 80.27 (15.77) |
C: Cortical, EQ-5D-5L: EuroQoL-five dimensions-five levels, NEI-VFQ-25: National Eye Institute-Visual Function Questionnaire-25, NS: Nuclear sclerotic, PSC: Post subcapsular, SD: Standard Deviation
Table 2B.
Disease attributes and quality of life of patients with glaucoma and cataract with respect to differences in disease attributes
| Clinical factors | Glaucoma |
Cataract |
||||
|---|---|---|---|---|---|---|
| Number of patients n (%) | Mean EQ-5D-5L score (SD) | Mean NEI-VFQ-25 composite score (SD) | Number of patients n (%) | Mean EQ-5D-5L score (SD) | Mean NEI-VFQ-25 composite score (SD) | |
| Number of eyes affected | ||||||
| One | 92 (31%) | 0.690 (0.39) | 70.71 (26.28) | 130 (53.3%) | 0.879 (0.18) | 80.4 (15.63) |
| Both | 205 (69%) | 0.764 (0.28) | 76.56 (19.56) | 114 (46.7%) | 0.854 (0.17) | 78 (15.41) |
| Visual acuity (Worse eye) | ||||||
| Normal (≥6/12) | 87 (29.3%) | 0.848 (0.21) | 85.24 (14.02) | 7 (2.9%) | 0.921 (0.09) | 87.47 (18.97) |
| Mild (<6/12-6/18) | 56 (18.9%) | 0.833 (0.23) | 86.47 (13.17) | 58 (23.8% | 0.900 (0.11) | 85.83 (12.51) |
| Moderate (<6/18-6/60) | 77 (25.9%) | 0.750 (0.31) | 72.65 (19.24) | 81 (33.2%) | 0.891 (0.12) | 81.24 (13.50) |
| Severe (<6/60-3/60) | 7 (2.4%) | 0.751 (0.47) | 67.52 (29.36) | 46 (18.9%) | 0.874 (0.14) | 78.67 (14.40) |
| Blind (<3/60) | 70 (23.6%) | 0.525 (0.37) | 55.37 (23.78) | 52 (21.3%) | 0.781 (0.27) | 68.34 (16.69) |
| Intraocular pressure (mm Hg) | ||||||
| ≤21 | 252 (84.8%) | 0.752 (0.31) | 77.13 (20.54) | |||
| >21 | 45 (15.2%) | 0.682 (0.36) | 61.40 (25.09) | |||
| Visual field defect (Worse eye) | ||||||
| Mild | 106 (35.7%) | 0.764 (0.29) | 78.97 (20.27) | |||
| Moderate | 147 (49.5%) | 0.705 (0.35) | 72.05 (22.03) | |||
| Severe | 44 (14.8%) | 0.576 (0.51) | 63.14 (23.97) | |||
| Type of treatment received | ||||||
| None | 5 (1.7%) | -0.963 (0.00) | 22.38 (2.24) | 179 (0.73) | 0.867 (0.17) | 78.49 ((15.28) |
| Surgery in one eye | 98 (33.0%) | 0.681 (0.38) | 70.26 (20.81) | 65 (0.27) | 0.868 (0.18) | 81.44 (16.16) |
| Surgery in both eyes | 42 (14.1%) | 0.754 (0.23) | 71.90 (21.80) | - | - | - |
| Eye drops and oral drugs | 152 (51.2%) | 0.803 (0.24) | 80.16 (20.04) | - | - | - |
| Number of eye drops | ||||||
| 1 | 49 (16.8%) | 0.869 (0.19) | 83.30 (17.5) | |||
| 2-3 | 183 (62.7%) | 0.766 (0.31) | 76.11 (21.8) | |||
| >3 | 60 (20.5%) | 0.630 (0.29) | 67.97 (18.7) | |||
| Adherence to treatment | ||||||
| Yes | 77 (50.7%) | 0.830 (0.23) | 82.60 (18.30) | |||
| No | 75 (49.3%) | 0.777 (0.26) | 77.69 (21.43) | |||
| Complications associated with treatment | ||||||
| Present | 78 (26.3%) | 0.606 (0.34) | 66.69 (23.45) | |||
| None | 219 (73.7%) | 0.789 (0.29) | 77.62 (20.74) | |||
EQ-5D-5L: EuroQoL-five dimensions-five levels, NEI-VFQ-25: National Eye Institute-Visual Function Questionnaire-25, SD: Standard Deviation
Tables 1, 2A, and 2B present the comprehensive demographic and clinical characteristics with disease-specific attributes of the study participants.
Health-related quality of life
The mean ‘EQ-5D-5L’ derived HRQoL in patients with glaucoma was 0.74 (0.71–0.77), while for those with cataracts, it was 0.87 (0.85–0.89). Additionally, the mean NEI-VFQ-25 composite score for glaucoma patients was 74.8 (72.2–77.2), compared to 79.28 (77.32–81.24) for cataract patients [Table 3]. The most reported problems by the patients of glaucoma and cataract in the ‘EQ-5D-5L’ interview were mobility (46%) and difficulties in usual activities (59%), respectively, followed by pain. The least reported issue for both the diseases was difficulties with self-care activities, as reported by 19% glaucoma and 6% cataract patients. When enquired about questions in ‘NEI-VFQ-25,’ glaucoma patients reported least scores for domains of general vision (60.34), driving (62.53), and role difficulties (62.75). On the other hand, patients with cataract reported facing higher issues in mental health (66.05), general vision (66.95), and role difficulties (68.03).
Table 3.
NEI-VFQ-25 subscale-wise scores in patients with glaucoma and cataract
| Glaucoma | Cataract | |
|---|---|---|
| General Health | 55.56 (27.85) | 64.24 (19.89) |
| General Vision | 60.34 (25.23) | 66.95 (18.34) |
| Ocular Pain | 83.00 (21.16) | 84.36 (19.56) |
| Near activities | 69.44 (34.99) | 75.46 (23.03) |
| Distance activities | 77.01 (28.36) | 77.82 (20.74) |
| Social Functioning | 83.80 (22.11) | 89.02 (17.00) |
| Mental Health | 74.01 (30.19) | 66.05 (30.29) |
| Role Difficulties | 62.75 (33.94) | 68.03 (32.11) |
| Dependency | 74.27 (34.90) | 75.43 (29.32) |
| Driving | 62.53 (32.55) | 75.92 (21.33) |
| Colour vision | 90.82 (21.51) | 95.59 (13.55) |
| Peripheral vision | 79.63 (27.29) | 95.70 (14.76) |
Predictors of HRQoL
Visual acuity in worse eye was a significant predictor of HRQoL in both glaucoma and cataract, after adjusting for all sociodemographic and clinical variables [Table 4]. However, ‘EQ-5D-5L’-derived utility value was not statistically different in glaucoma patients with severe visual impairment and blindness in worse eye. Quality of life in glaucoma patients was influenced by the number of eyes affected, presence of treatment-related complications, type of glaucoma, age, area of residence, and education. While in patients with cataract, age, education, and type of cataract had significant effect on quality-of-life values.
Table 4.
Determinants of quality of life of patients with glaucoma and cataract
| Glaucoma |
Cataract |
||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| EQ-5D-5L |
NEI-VFQ-25 Composite Score |
EQ-5D-5L |
NEI-VFQ-25 Composite Score |
||||||||||
| Coef. | SE | P | Coef. | SE | P | Coef. | SE | P | Coef. | SE | P | ||
| Age Group | |||||||||||||
| <40 years | Reference | ||||||||||||
| 40-60 years | -0.081 | 0.04 | 0.068 | -7.677** | 2.76 | 0.005 | 0.180** | 0.05 | <0.001 | -1.628 | 4.24 | 0.701 | |
| >60 years | -0.066 | 0.04 | 0.123 | -0.711 | 2.66 | 0.79 | 0.194** | 0.05 | <0.001 | -0.760 | 4.28 | 0.859 | |
| Gender | |||||||||||||
| Male | Reference | ||||||||||||
| Female | -0.056 | 0.03 | 0.105 | -2.747 | 2.14 | 0.199 | -0.027 | 0.03 | 0.287 | -1.606 | 1.98 | 0.418 | |
| Area of residence | |||||||||||||
| Rural | Reference | ||||||||||||
| Urban | 0.081* | 0.03 | 0.014 | 1.573 | 2.07 | 0.447 | -0.013 | 0.02 | 0.556 | 2.279 | 1.95 | 0.243 | |
| Education | |||||||||||||
| Illiterate | Reference | ||||||||||||
| Literate | 0.047 | 0.05 | 0.365 | 8.689** | 3.24 | 0.007 | 0.056 | 0.03 | 0.085 | 6.521* | 2.79 | 0.021 | |
| Health Insurance | |||||||||||||
| Not insured | Reference | ||||||||||||
| Public | 0.087* | 0.04 | 0.015 | 3.754 | 2.24 | 0.094 | 0.033 | 0.02 | 0.136 | -0.949 | 1.90 | 0.617 | |
| Private | 0.086 | 0.07 | 0.190 | 9.118 | 4.09 | 0.026 | 0.064 | 0.06 | 0.253 | -1.636 | 4.74 | 0.730 | |
| Wealth Quintile | |||||||||||||
| Poorest | Reference | ||||||||||||
| Poor | -0.008 | 0.05 | 0.873 | -7.288 | 3.28 | 0.026 | 0.033 | 0.03 | 0.323 | 2.918 | 2.89 | 0.313 | |
| Middle | 0.107* | 0.05 | 0.041 | 2.667 | 3.25 | 0.412 | 0.017 | 0.03 | 0.594 | 4.097 | 2.71 | 0.132 | |
| Rich | 0.112 | 0.06 | 0.056 | 6.362 | 3.65 | 0.081 | -0.013 | 0.04 | 0.765 | 3.304 | 3.67 | 0.369 | |
| Richest | 0.031 | 0.06 | 0.592 | 0.898 | 3.61 | 0.803 | -0.014 | 0.04 | 0.696 | 3.616 | 3.10 | 0.244 | |
| Number eyes affected | |||||||||||||
| One | Reference | ||||||||||||
| Both | -0.143** | 0.05 | 0.005 | -6.519* | 3.15 | 0.038 | -0.024 | 0.03 | 0.365 | -2.243 | 2.26 | 0.322 | |
| Presence of Diabetes | |||||||||||||
| No | Reference | ||||||||||||
| Yes | -0.042 | 0.04 | 0.328 | 1.377 | 2.66 | 0.605 | -0.017 | 0.02 | 0.480 | 3.556 | 1.98 | 0.074 | |
| Presence of Hypertension | |||||||||||||
| No | Reference | ||||||||||||
| Yes | -0.029 | 0.04 | 0.431 | -2.998 | 2.31 | 0.195 | 0.038 | 0.02 | 0.115 | 0.453 | 2.04 | 0.824 | |
| Family History of diseases | |||||||||||||
| No | Reference | ||||||||||||
| Yes | 0.084 | 0.06 | 0.129 | -5.557 | 3.43 | 0.106 | 0.01 | 0.02 | 0.682 | 2.141 | 2.01 | 0.289 | |
| Vision (worse eye) | |||||||||||||
| Blind | Reference | ||||||||||||
| Normal | 0.222** | 0.05 | <0.001 | 23.934** | 3.09 | <0.001 | 0.166* | 0.07 | 0.014 | 17.750 | 5.70 | 0.002 | |
| Mild | 0.219** | 0.05 | <0.001 | 27.221** | 3.25 | <0.001 | 0.139** | 0.03 | <0.001 | 17.135 | 2.80 | <0.001 | |
| Moderate | 0.164** | 0.05 | <0.001 | 14.710** | 2.9 | <0.001 | 0.121** | 0.03 | <0.001 | 12.341 | 2.54 | <0.001 | |
| Severe | 0.167 | 0.11 | 0.121 | 15.594* | 6.67 | 0.019 | 0.124** | 0.04 | 0.001 | 11.015 | 2.98 | <0.001 | |
| Type of Treatment Received | |||||||||||||
| None | Reference | ||||||||||||
| Surgery (One eye) | 0.771** | 0.13 | <0.001 | 38.369** | 8.15 | <0.001 | -0.001 | 0.03 | 0.985 | 4.051 | 2.60 | 0.120 | |
| Surgery (Both eyes) | 0.776** | 0.14 | <0.001 | 37.657** | 8.68 | <0.001 | |||||||
| Oral Drugs and Eye Drops | 0.817** | 0.14 | <0.001 | 43.703** | 8.38 | <0.001 | |||||||
| Self-reported Complications | |||||||||||||
| Absent | Reference | ||||||||||||
| Present | -0.208** | 0.04 | <0.001 | -12.743** | 2.39 | <0.001 | |||||||
| Type of Glaucoma | |||||||||||||
| POAG | Reference | ||||||||||||
| PACG | 0.142** | 0.04 | 0.001 | 11.656** | 2.74 | <0.001 | |||||||
| Secondary | 0.248** | 0.05 | <0.001 | 8.281* | 3.37 | 0.014 | |||||||
| Type of cataract | |||||||||||||
| Nuclear sclerotic (NS) | Reference | ||||||||||||
| Cortical (C) | -0.014 | 0.05 | 0.788 | -2.560 | 4.38 | 0.559 | |||||||
| Post subcapsular (PSC) | 0.004 | 0.04 | 0.922 | -3.535 | 3.10 | 0.256 | |||||||
| NS & C | 0.002 | 0.03 | 0.962 | 0.140 | 2.86 | 0.961 | |||||||
| NS & PSC | 0.009 | 0.03 | 0.756 | -4.940 | 2.51 | 0.049 | |||||||
| C & PSC | 0.003 | 0.07 | 0.963 | -10.269 | 6.07 | 0.092 | |||||||
| NS & C & PSC | -0.049 | 0.05 | 0.296 | -7.312 | 3.98 | 0.068 | |||||||
| Constant | -0.305 | 0.15 | 0.042 | 11.761 | 9.34 | 0.208 | 0.549 | 0.08 | <0.001 | 61.449 | 6.22 | <0.001 | |
| R 2 | 0.4247 | 0.5401 | 0.215 | 0.2986 | |||||||||
| Adjusted R2 | 0.367 | 0.4939 | 0.1162 | 0.2139 | |||||||||
| Kolmogorov–Smirnov Test (Error) | 0.012 | 0.049 | 0.031 | 0.002 | |||||||||
| Breusch–Pagan Test | 0.051 | 0.162 | 0.105 | 0.079 | |||||||||
| VIF | 1.26-4.89 | 1.26-4.89 | 1.15-2.06 | 1.15-2.06 | |||||||||
Bold numbers present statistically significant values at P<0.05; *statistically significant at P<0.05; **statistically significant at P<0.01; Coef.: coefficient; SE: Standard error. C: Cortical, Coef: Coefficient, EQ-5D-5L: EuroQoL-five dimensions-five levels, NEI-VFQ-25: National Eye Institute- Visual Function Questionnaire-25, NS: Nuclear sclerotic, PACG: Primary Angle Closure Glaucoma, PSC: Post subcapsular, SE: Standard Error
Correlation between EQ-5D-5Land NEI-VFQ-25
‘NEI-VFQ-25’ composite score exhibited significant strong (r = 0.738) and moderate (r = 0.509) correlation with ‘EQ-5D-5L’-derived HRQoL value in glaucoma and cataract, respectively. ‘EQ-5D-5L’ had moderate to strong concurrence with subscales of ‘NEI-VFQ-25’ composite scores in glaucoma. On the other hand, there was very weak to moderate correlation between ‘NEI-VFQ-25’ subscales and the ‘EQ-5D-5L’ utility values.
DISCUSSION
We measured HRQoL in patients with glaucoma and cataracts using the ‘EQ-5D-5L’ and ‘NEI-VFQ-25’ questionnaires. Considering a perfect quality of life (an ‘EQ-5D-5L’ value of 1 and an ‘NEI-VFQ-25’ composite score of 100), both the eye disorders resulted in decrements, with a greater decrement in glaucoma. The mean ‘NEI-VFQ-25’ score (74.8) in glaucoma patients was comparable to that observed by Muralidharan et al. (72) but lower than that informed by Kumar et al. (88).[8,9] Limited reporting of sociodemographic and clinical parameters in these studies constrained interpretation of these findings.
This study is the first from India to report ‘EQ-5D-5L’ utility values in glaucoma patients, precluding validation against domestic literature. The observed utility value (0.74) aligns with findings from a multicountry European study but is lower than those reported in Thailand.[36,37] However, the Thai study acknowledged potential selection bias in their sample as a limitation, which could have resulted in an overestimation of HRQoL.
The ‘NEI-VFQ-25’ score (79) for cataract patients was consistent with findings from China (76) and California (78).[27,38] The ‘EQ-5D-5L’ utility score of 0.87 for patients with cataracts aligned with results from two Indian tertiary institutions.[19] However, this score was higher compared to that reported from other tertiary institutions in India.[19] While age and gender distributions were comparable with the present study, lack of detailed sociodemographic and clinical characteristics of the sample limited the exploration of these differences. This underscores the need for systematic reporting of sociodemographic and disease-specific variables in HRQoL studies to enable robust evidence synthesis and nationally representative estimates.
During the ‘EQ-5D-5L’ interviews, participants frequently reported issues related to dimensions of ‘mobility’ and ‘difficulties in usual activities’ in both the disorders. The findings from ‘NEI-VFQ-25’ further highlighted general vision, role difficulties, and mental health as key concerns. The results emphasize be importance of sensitizing the healthcare providers to address patient-reported concerns in the clinical practice, ensuring that treatments are patient-centric.
Among glaucoma patients, those residing in rural areas reported lower HRQoL, likely reflecting disparities in healthcare access, disease awareness, and timely treatment. This underscores the need for targeted interventions and policy measures to strengthen eye care delivery among sociodemographically disadvantaged groups. Education was positively associated with perceived health, potentially due to greater awareness and higher expectations from treatment. The findings are particularly relevant in the context of low awareness about glaucoma in India, which ranges from 0.32% to 27% across regions.[39] Although the awareness of cataracts has increased over the years, delays in treatment persist due to misconceptions around treatment and fears surrounding surgery.[40]
These factors contribute to an increased risk of vision loss in patients with cataract and glaucoma. Consistent with the literature, our study observed a decrement in HRQoL with increasing severity of vision loss, reaffirming visual acuity as the determinant of perceived health status in patients of glaucoma and cataracts.[21,26,41,42] Collectively, the results emphasize the importance of public health strategies focused on improved awareness, early detection, and stronger linkages between diagnosed patients and the formal healthcare system to prevent avoidable vision loss and enhance QoL.
The Government of India has expanded its comprehensive primary healthcare program to incorporate systematic screening for eye disorders, including cataract and glaucoma, through the Ayushman Arogya Mandirs.[43] The integration of vision-related questions into the community-based assessment checklist, alongside annual screening by frontline health workers, is expected to facilitate earlier detection of these conditions. Additionally, health promotion activities by health workers, envisioned under this program, hold the potential to strengthen health literacy, thereby improving awareness, timely care-seeking, and overall health outcomes.
Complementing these efforts, the ‘Ayushman Bharat-Pradhan Mantri Jan Aarogya Yojana’ (AB-PMJAY) also provides financial protection for diagnostics and inpatient treatments for glaucoma and cataract, thereby improving access to care.[44] Such initiatives are expected to promote early diagnosis and timely treatment, ultimately contributing to better quality of life at the population level. However, the existing evidence indicates that anti-glaucoma medications account for the majority of the expenditure among glaucoma patients, and financial hardship remains a key barrier to adherence.[45] To address this gap, integrating ambulatory care for glaucoma within the AB-PMJAY framework or including the anti-glaucoma medications in the essential medicine list at primary care level would be beneficial. Such measures are expected to improve continuity of treatment, reduce the risk of progression of disease, and prevent further decline in HRQoL.
From a methodological viewpoint, the study identified strong correlation between ‘EQ-5D-5L’ utility value and ‘NEI-VFQ-25’ composite score in patients with glaucoma and moderate correlation in patients with cataracts. These findings contribute to the ongoing global discourse on whether incorporating a vision-specific dimension as a bolt-on to the ‘EQ-5D-5L’ could improve its sensitivity and responsiveness in capturing the unique impact of visual disorders on HRQoL.[46] In line with this, the study results suggest that future research should explore the integration of “vision bolt-on” to the ‘EQ-5D-5L’ for assessing HRQoL in individuals with cataracts, where moderate correlation with vision-specific tools indicates potential gaps in measurement.
In contrast, the strong concurrence between ‘EQ-5D-5L’ and ‘NEI-VFQ-25’ in glaucoma patients suggests that ‘EQ-5D-5L’ alone may serve as a pragmatic and efficient tool for assessing HRQoL in this population. This alignment may be attributed to the chronic, progressive nature of glaucoma, which exerts a broader impact on overall health and daily activities, when compared to cataracts, which are typically acute, surgically treatable, and less likely to influence overall health status once treated. Similar findings have been highlighted in previous studies, where EQ-5D demonstrated greater responsiveness to chronic eye conditions such as diabetic retinopathy, and age-related macular degeneration,[47,48] while showing limited sensitivity in short-term, curable conditions like cataracts.[49]
Nonetheless, the hypothesis that disease chronicity influences the degree of alignment between generic and disease-specific HRQoL instruments requires further empirical investigation. Such evidence would be critical to guide methodological choices in HTA, particularly in developing nations where the feasibility and cost-efficiency of using shorter generic tools, such as ‘EQ-5D-5L,’ is highly relevant.
STRENGTHS AND LIMITATIONS
Our study is unique in its determination of HRQoL through the ‘EQ-5D-5L’ tool along with the Indian value set, as well as in evaluating the correlation between vision-specific and generic instruments specifically for glaucoma and cataract. By applying country-specific utility weights, our study provides more contextually relevant and accurate estimates of HRQoL for Indian patients with glaucoma and cataract, enhancing the applicability of the findings for policy and clinical decision-making in the local context.
However, there are certain limitations. Our study was limited to one tertiary facility. Although the hospital caters to population from six states of the country, certain demographic groups, such as individuals without formal education or patients with severe visual impairment, may be under-represented, due to care seeking behaviors. Future studies should consider multisite, community-based designs for HRQoL evaluation to ensure broader sociodemographic representation. Second, no visual support was used for administering ‘EQ-5D-5L’ since this could have led to bias in responses due to the differential level of visual acuity in the sample. Additionally, the cross-sectional nature of the data collection restricts our ability to assess changes in HRQoL over time or to evaluate the sensitivity of the instruments to temporal changes in perceived quality of life. Future research could develop algorithms to create crosswalks between instruments for glaucoma patients and examine the impact of incorporating a vision-specific dimension into the ‘EQ-5D-5L’ to enhance sensitivity for assessing HRQoL in cataract patients.
CONCLUSION
The study demonstrates that cataracts and glaucoma substantially impair quality of life and impact daily functioning. Clinically, the findings emphasize the importance of early detection, timely management, and patient-centred care addressing functional, psychological, and daily living challenges through effective counseling. From a policy perspective, integration of eye care into primary care, enhancing health literacy and early detection of diseases, is likely to improve the quality of life. Enhancing access to ophthalmic medicines through Ayushman Bharat can further support adherence, prevent vision loss, and optimize population-level outcomes. For researchers, the ‘EQ-5D-5L’ may serve as a practical and efficient tool for HRQoL assessment in glaucoma, while future research should focus on validating and refining measurement tools with incorporation of vision ‘bolt-on’ to improve sensitivity in cataract patients.
Ethical approval
The research study was approved by the Institutional Ethics Committee, Post Graduate Institute of Medical Education and Research, Chandigarh (Reference number: IEC- 06/2023-2804)
Conflicts of interest
There are no conflicts of interest.
Funding Statement
The work is supported by grant (INV-064844) from the Bill and Melinda Gates Foundation. The funder played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; nor in the decision to submit the article for publication. The study was independently investigator-led, and all authors had full access to all data (including statistical reports and tables) in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
REFERENCES
- 1.Burton MJ, Ramke J, Marques AP, Bourne RRA, Congdon N, Jones I, et al. The Lancet Global Health Commission on Global Eye Health: Vision beyond 2020. Lancet Glob Health. 2021;9:e489–551. doi: 10.1016/S2214-109X(20)30488-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Thomas R, Paul P, Rao GN, Muliyil JP, Mathai A. Present status of eye care in India. Surv Ophthalmol. 2005;50:85–101. doi: 10.1016/j.survophthal.2004.10.008. [DOI] [PubMed] [Google Scholar]
- 3.Glen FC, Crabb DP. Living with glaucoma: A qualitative study of functional implications and patients’ coping behaviours. BMC Ophthalmol. 2015;15:128. doi: 10.1186/s12886-015-0119-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Yamada M, Mizuno Y, Miyake Y; Cataract Survey Group of the National Hospital Organization of Japan A multicenter study on the health-related quality of life of cataract patients: Baseline data. Jpn J Ophthalmol. 2009;53:470–6. doi: 10.1007/s10384-009-0709-0. [DOI] [PubMed] [Google Scholar]
- 5.Lapin BR. Considerations for Reporting and Reviewing Studies Including Health-Related Quality of Life. Chest. 2020;158:S49–56. doi: 10.1016/j.chest.2020.03.007. [DOI] [PubMed] [Google Scholar]
- 6.Higginson IJ, Carr AJ. Measuring quality of life: Using quality of life measures in the clinical setting. BMJ. 2001;322:1297–300. doi: 10.1136/bmj.322.7297.1297. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Parkerson GR, Jr, Connis RT, Broadhead WE, Patrick DL, Taylor TR, Tse CK. Disease-specific versus generic measurement of health-related quality of life in insulin-dependent diabetic patients. Med Care. 1993;31:629–39. doi: 10.1097/00005650-199307000-00005. [DOI] [PubMed] [Google Scholar]
- 8.Kumar S, Ichhpujani P, Singh R, Thakur S, Sharma M, Nagpal N. The impact of primary open-angle glaucoma: Quality of life in Indian patients. Indian J Ophthalmol. 2018;66:416–9. doi: 10.4103/ijo.IJO_847_17. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Muralidharan S, Kumar S, Ichhpujani P, Dhillon HK. Quality of life in glaucoma patients: Comparison of medical therapy, trabeculectomy, and glaucoma drainage device surgery. Indian J Ophthalmol. 2022;70:4206–11. doi: 10.4103/ijo.IJO_667_22. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Kusumgar P, Bhandary SV, Rao L, Abbagani S, Addoor KR. Assessment of quality of life in glaucoma patients using the glaucoma Quality of life-15 questionnaire. Asian J Ophthalmol. 2020;17:273–84. [Google Scholar]
- 11.Dhawan M, Hans T, Sandhu PS, Midha N. Evaluation of vision-related quality of life in patients with glaucoma: A hospital-based Study. J Curr Glaucoma Pract. 2019;13:9–15. doi: 10.5005/jp-journals-10078-1242. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Dubey S, Bedi H, Bedi M, Matah P, Sahu J, Mukherjee S, et al. Impact of visual impairment on the wellbeing and functional disability of patients with glaucoma in India. J Curr Ophthalmol. 2020;32:14–8. doi: 10.1016/j.joco.2019.09.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Nayyar S, Kumar S, Rehman O, Ichhpujani P, Singla E. Impact of moderate and severe primary open-angle glaucoma on quality of life due to activity limitation. Indian J Ophthalmol. 2022;70:552–7. doi: 10.4103/ijo.IJO_1687_21. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Ajith BS, Najeeb N, John A, Anima VN. Cross sectional study of depression, anxiety and quality of life in glaucoma patients at a tertiary centre in North Kerala. Indian J Ophthalmol. 2022;70:546–51. doi: 10.4103/ijo.IJO_1389_21. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Gothwal VK, Bagga DK, Rao HL, Bharani S, Sumalini R, Garudadri CS, et al. Is utility-based quality of life in adults affected by glaucoma? Invest Ophthalmol Vis Sci. 2014;55:1361–9. doi: 10.1167/iovs.13-13729. [DOI] [PubMed] [Google Scholar]
- 16.Reighard CL, Pillai MR, Shroff S, Spaeth GL, Schilling SG, Wizov SS, et al. Glaucoma-associated visual task performance and vision-related quality of life in South India. Ophthalmol Glaucoma. 2019;2:357–63. doi: 10.1016/j.ogla.2019.06.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Bandhu SD, Vabale YG, Sambarey PP, Walke AB, Raje S. Impact of cataract on the quality of life of rural patients in India. J Clin Ophthalmol Res. 2016;4:75–81. [Google Scholar]
- 18.Finger RP, Kupitz DG, Holz FG, Balasubramaniam B, Ramani RV, Lamoureux EL, et al. The impact of the severity of vision loss on vision-related quality of life in India: An evaluation of the IND-VFQ-33. Invest Ophthalmol Vis Sci. 2011;52:6081–8. doi: 10.1167/iovs.11-7388. [DOI] [PubMed] [Google Scholar]
- 19.Jain S, Chauhan A, Rajshekar K, Vashist P, Gupta P, Mathur U, et al. Generic and vision related quality of life associated with different types of cataract surgeries and different types of intraocular lens implantation. PLoS One. 2020;15:e0240036. doi: 10.1371/journal.pone.0240036. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Kurian E, Vishwakarma P, Deenadalyalan J, Mondal A, Ranpise D, Chavan S, et al. Determinants of vision related quality of life of patients undergoing unilateral cataract surgical services in charitable hospitals in India: A multicentre prospective cohort study. Int J Community Med Public Health. 2018;5:1138–48. [Google Scholar]
- 21.Gupta V, Srinivasan G, Mei SS, Gazzard G, Sihota R, Kapoor KS. Utility values among glaucoma patients: An impact on the quality of life. Br J Ophthalmol. 2005;89:1241–4. doi: 10.1136/bjo.2005.068858. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Sen S, Mishra A, Das M, Iyer V, Sethi M. Assessment of quality of life in glaucoma patients in a tertiary care center in Eastern India. Indian J Ophthalmol. 2023;71:2767–72. doi: 10.4103/IJO.IJO_3050_22. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Sharma D, Prinja S, Aggarwal AK, Rajsekar K, Bahuguna P. Development of the Indian Reference Case for undertaking economic evaluation for health technology assessment. Lancet Reg Health Southeast Asia. 2023;16:100241. doi: 10.1016/j.lansea.2023.100241. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Quaranta L, Riva I, Gerardi C, Oddone F, Floriani I, Konstas AG. Quality of life in glaucoma: A review of the literature. Adv Ther. 2016;33:959–81. doi: 10.1007/s12325-016-0333-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Clemons TE, Chew EY, Bressler SB, McBee W; Age-Related Eye Disease Study Research Group National eye institute visual function questionnaire in the age-related eye disease study (AREDS): AREDS Report No. 10. Arch Ophthalmol. 2003;121:211–7. doi: 10.1001/archopht.121.2.211. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.To KG, Meuleners LB, Fraser ML, Do DV, Duong DV, Huynh VA, et al. The impact of cataract surgery on vision-related quality of life for bilateral cataract patients in Ho Chi Minh City, Vietnam: A prospective study. Health Qual Life Outcomes. 2014;12:16. doi: 10.1186/1477-7525-12-16. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Mangione CM, Lee PP, Gutierrez PR, Spritzer K, Berry S, Hays RD, et al. Development of the 25-item National Eye Institute Visual Function Questionnaire. Arch Ophthalmol. 2001;119:1050–8. doi: 10.1001/archopht.119.7.1050. [DOI] [PubMed] [Google Scholar]
- 28.Aspinall PA, Johnson ZK, Azuara-Blanco A, Montarzino A, Brice R, Vickers A. Evaluation of quality of life and priorities of patients with glaucoma. Invest Ophthalmol Vis Sci. 2008;49:1907–15. doi: 10.1167/iovs.07-0559. [DOI] [PubMed] [Google Scholar]
- 29.Dixon P, Hollingworth W, Sparrow J. Mapping to quality of life and capability measures in cataract surgery patients: From Cat-PROM5 to EQ-5D-3L, EQ–5D-5L, and ICECAP-O using mixture modelling. MDM Policy Pract. 2020;5:2381468320915447. doi: 10.1177/2381468320915447. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.World Health Organization Blindness and visual impairment. Available from: https://www.who.int/news-room/fact-sheets/detail/blindness-and-visual-impairment#:~:text=Mild%20%E2%80%93%20visual%20acuity%20worse%20than,acuity%20worse%20than%203%2F60 . [Last assessed on 2024 Jul 09]
- 31.Budenz DL, Rhee P, Feuer WJ, McSoley J, Johnson CA, Anderson DR. Comparison of glaucomatous visual field defects using standard full threshold and Swedish interactive threshold algorithms. Arch Ophthalmol. 2002;120:1136–41. doi: 10.1001/archopht.120.9.1136. [DOI] [PubMed] [Google Scholar]
- 32.National Eye Institute . National Eye Institute; United States of America: Visual Function Questionnaire- 25. 2000 version. Available from: nei.nih.gov/learn-about-eye-health/outreach-resources/outreach-materials/visual-function-questionnaire-25 . [Last assessed on 2024 May 28] [Google Scholar]
- 33.EUROQOL EQ-5D-5L instruments. Available from: https://euroqol.org/information-and-support/euroqol-instruments/eq-5d-5l/ . [Last accessed on 2024 Jul 09]
- 34.Jyani G, Sharma A, Prinja S, Kar SS, Trivedi M, Patro BK, et al. Development of an EQ-5D-5L value set for india using an extended design (DEVINE) study: The Indian 5-level version EQ-5D-5L value set. Value Health. 2022;25:1218–26. doi: 10.1016/j.jval.2021.11.1370. [DOI] [PubMed] [Google Scholar]
- 35.Papageorgiou SN. On correlation coefficients and their interpretation. J Orthod. 2022;49:359–61. doi: 10.1177/14653125221076142. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Thygesen J, Aagren M, Arnavielle S, Bron A, Fröhlich SJ, Baggesen K, et al. Late-stage, primary open-angle glaucoma in Europe: Social and health care maintenance costs and quality of life of patients from 4 countries. Curr Med Res Opin. 2008;24:1763–70. doi: 10.1185/03007990802111068. [DOI] [PubMed] [Google Scholar]
- 37.Rojananuangnit K, Sudjinda N. Utility index and patient-reported outcome measures in glaucomatous patients comparing with normal participants. Clin Ophthalmol. 2021;15:835–43. doi: 10.2147/OPTH.S300226. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Wan Y, Zhao L, Huang C, Xu Y, Sun M, Yang Y, et al. Validation and comparison of the National Eye Institute Visual Functioning Questionnaire-25 (NEI VFQ-25) and the Visual Function Index-14 (VF-14) in patients with cataracts: A multicentre study. Acta Ophthalmol. 2021;99:e480–8. doi: 10.1111/aos.14606. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Maharana PK, Rai VG, Pattebahadur R, Singhi S, Chauhan AK. Awareness and knowledge of glaucoma in Central India: A hospital-based study. Asia Pac J Ophthalmol (Phila) 2017;6:243–9. doi: 10.22608/APO.2015166. [DOI] [PubMed] [Google Scholar]
- 40.Dharmadhikari S, Bakare PN, Deshpande M, Hegade A, Kesari A. Study on barriers causing delay in cataract surgery of bilateral cataract blind at a tertiary eye care center in urban India. J Clin Ophthalmol Res. 2017;5:69–72. [Google Scholar]
- 41.Kobelt G, Jonsson B, Bergstrom A, Chen E, Lindén C, Alm A. Cost-effectiveness analysis in glaucoma: What drives utility? Results from a pilot study in Sweden. Acta Ophthalmol Scand. 2006;84:363–71. doi: 10.1111/j.1600-0420.2005.00621.x. [DOI] [PubMed] [Google Scholar]
- 42.Lamoureux EL, Fenwick E, Pesudovs K, Tan D. The impact of cataract surgery on quality of life. Curr Opin Ophthalmol. 2011;22:19–27. doi: 10.1097/ICU.0b013e3283414284. [DOI] [PubMed] [Google Scholar]
- 43.Ministry of Health and Family Welfare. Government of India Training manual on eye care for Community Heath Officer at Ayushman Bharat- Health and Wellness Centres. 2021. Available from: https://nhsrcindia.org/sites/default/files/2021-12/Eye%20Care%20Training%20Manual%20for%20CHO.pdf . [Last accessed on 2024 Jul 26]
- 44.Ayushman Bharat Package Master- HBP 2022. Available from: https://ayushmanup.in/assets/doc/HBP-2022.pdf . [Last accessed on 2024 Jul 26]
- 45.Nayak B, Gupta S, Kumar G, Dada T, Gupta V, Sihota R. Socioeconomics of long-term glaucoma therapy in India. Indian J Ophthalmol. 2015;63:20–4. doi: 10.4103/0301-4738.151458. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Gandhi M, Ang M, Teo K, Wong CW, Wei YC, Tan RL, et al. A vision ‘bolt-on’ increases the responsiveness of EQ-5D: Preliminary evidence from a study of cataract surgery. Eur J Health Econ. 2020;21:501–11. doi: 10.1007/s10198-019-01156-w. [DOI] [PubMed] [Google Scholar]
- 47.Evans K, Law SK, Walt J, Buchholz P, Hansen J. The quality of life impact of peripheral versus central vision loss with a focus on glaucoma versus age-related macular degeneration. Clin Ophthalmol. 2009;3:433–45. doi: 10.2147/opth.s6024. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Purohit N, Goyal A, Gupta V, Gupta PC, Singh P, Prinja S. Measuring the quality of life of patients with diabetic retinopathy in northern India: A comparison of generic and vision specific instruments. Health Qual Life Outcomes. 2025;23:17. doi: 10.1186/s12955-025-02340-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Gandhi M, Ang M, Teo K, Wong CW, Wei YC, Tan RL, et al. EQ-5D-5L is more responsive than EQ-5D-3L to treatment benefit of cataract surgery. Patient. 2019;12:383–92. doi: 10.1007/s40271-018-00354-7. [DOI] [PubMed] [Google Scholar]