October 2011
Volume 52, Issue 11
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Clinical and Epidemiologic Research  |   October 2011
Effect of Glaucoma on the Quality of Life of Young Patients
Author Affiliations & Notes
  • Viney Gupta
    From the Dr Rajendra Prasad Centre for Ophthalmic Sciences and
  • Paromita Dutta
    From the Dr Rajendra Prasad Centre for Ophthalmic Sciences and
  • Mary OV
    From the Dr Rajendra Prasad Centre for Ophthalmic Sciences and
  • Kulwant Singh Kapoor
    the Department of Biostatistics, All India Institute of Medical Sciences, New Delhi, India.
  • Ramanjit Sihota
    From the Dr Rajendra Prasad Centre for Ophthalmic Sciences and
  • Guresh Kumar
    the Department of Biostatistics, All India Institute of Medical Sciences, New Delhi, India.
  • Corresponding author: Viney Gupta, Dr Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences, New Delhi 110029, India; gupta_v20032000@yahoo.com
Investigative Ophthalmology & Visual Science October 2011, Vol.52, 8433-8437. doi:10.1167/iovs.11-7551
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      Viney Gupta, Paromita Dutta, Mary OV, Kulwant Singh Kapoor, Ramanjit Sihota, Guresh Kumar; Effect of Glaucoma on the Quality of Life of Young Patients. Invest. Ophthalmol. Vis. Sci. 2011;52(11):8433-8437. doi: 10.1167/iovs.11-7551.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose.: To evaluate the quality of life (QOL) of juvenile-onset primary open-angle glaucoma (JOAG) patients by using a utility-based assessment.

Methods.: QOL using time-tradeoff utility values was analyzed in 70 JOAG patients and compared with 108 adult-onset POAG patients. The relationships of utility values to parameters like age at diagnosis, visual acuity, mean deviation, number of medications and surgery, duration of the disease, level of education, and socioeconomic status were assessed.

Results.: The mean age at diagnosis of JOAG patients was 26 ± 9.8 years, whereas that of the adult onset POAG patients was 62 ± 11.2 years. Overall, there was a decrease in utility values with increasing age (r = −0.3; P < 0.001). The mean utility score among JOAG patients was (0.80 ± 0.18) significantly greater than among adult POAG patients (0.64 ± 0.28; P < 0.001). The differences in utility scores between JOAG and adult POAG patients were significant when adjusted for differences in better eye visual acuity, mean deviation, and the presence of systemic comorbidity among adults (P = 0.02). Among JOAG patients, those needing topical antiglaucoma medications and those with visual acuity worse than 6/12 in the better eye had lower utility values (P = 0.008 and P = 0.002, respectively).

Conclusions.: Utility values among glaucoma patients in the juvenile age group are better than those of adult POAG patients. Decreasing vision in the better eye and having to use medication decrease the utility scores among young patients with glaucoma.

Once diagnosed with glaucoma, patients have the constant anxiety of tedious investigations, need for life-long therapy, cost constraints, side effects of medications, surgery, and the necessity for regular follow-ups. The effects are compounded by the fear of losing their eyesight. Priorities of patients are important in assessing the difficulties they face. 1 The severity of glaucoma further influences the relative importance of their priorities. 
Quality of life (QOL) can be defined as subjective well-being, which depends on a patient's values, priorities, experiences, and aspirations and reflects the difference between the hopes and expectations of a person and his experiences. Assessment of QOL has been used to understand the impact of various factors linked to the disease process on a patient's life. It is also of benefit in micro and macro level health care decision-making. 2 Different measures for assessing health-related QOL covering both the physical and emotional impacts of the disease have been put forth. Some such QOL instruments have been used for assessment of glaucoma patients. 3,4 One of these measures is, utility analysis which uses time tradeoff, standard gamble method, or willingness to pay (also called preference measures). 5 7 Of these, the time-tradeoff method has been found to be easier to understand and more satisfactory. 8 10 Froberg and Kane 11 found the time-tradeoff method to have greater test–retest reliability as well as greater intra- and interrater reliability compared with the standard gamble method. 
The manner in which younger patients view their disease is seldom mentioned in the medical literature. This gap is more obvious when we look for studies on young patients affected with ocular disorders. In this study, we sought to determine the impact of glaucoma on the QOL of patients in the juvenile age group and compare it with adult-onset primary open-angle glaucoma patients, using utility-based analysis. 
Patients and Methods
The study was performed by using a standard questionnaire for patients with juvenile- and adult-onset primary open-angle glaucoma (POAG) managed in a tertiary care center. The study was initiated after approval of our institutional ethics committee and was conducted in accordance with the Declaration of Helsinki. Patients were recruited from Rajendra Prasad Centre for the Ophthalmic Sciences, New Delhi, a tertiary eye care center in Northern India. The inclusion criterion for JOAG was established POAG between 15 to 40 years of age, whereas for adult POAG it was diagnosis after the age of 40 years. Patients with any other coexistent ocular condition that could impair vision (e.g., visually significant cataract, retinal or neural pathology, secondary glaucoma, and angle closure glaucoma) were excluded. 
General medical data including any self-reported systemic comorbidities were recorded. All patients underwent a complete ophthalmic examination, including best corrected Snellen visual acuity, gonioscopy, standard achromatic automated perimetry (Humphrey field analyzer; Carl Zeiss Meditec, Dublin, CA), and Goldmann applanation tonometry. Mean deviation values were used to analyze the severity of the visual field defect (VFD). After a complete clinical examination, each patient was asked questions relating to utility values, and their educational and socioeconomic status was ascertained. 
In the time-tradeoff utility analysis, patients are asked the number of years of their presumed remaining years of life they are willing to trade off for perfect vision and a glaucoma-free life. The time-tradeoff utility values (UVs) are calculated by dividing the number of years traded by the number of expected remaining years of life and subtracting this proportion from 1.0. A utility value of 1.0 indicates a state of perfect health, whereas a utility value of 0 indicates death (Table 1). The questionnaire was administered by an examiner masked to the severity of the disease. Interviews were conducted in the language understood by the patient. 
Table 1.
 
How to Calculate a Utility Score Using the Time-Tradeoff Method: An Example
Table 1.
 
How to Calculate a Utility Score Using the Time-Tradeoff Method: An Example
Age of the respondent: 50 years
Age the subject expects to live: 70 years
Response to the time-tradeoff question above: 10 years
Step 1: Determine the number of additional years the patient expects to live.
    70–50 = 20 additional years
Step 2: Divide the number of years the respondent is willing to give up to spend the rest of his/her living years with perfect vision and free of glaucoma, from the value obtained in step 1.
    10/20 = 0.50
Step 3: Subtract the value obtained in step 2 from 1.0
    1.0–0.50 = 0.50
Interpretation: The respondent is willing to give up 50% of his/her remaining life years in a trade off for life without glaucoma. The utility value is calculated by subtracting the percentage of remaining years traded (0.50, i.e. 50%) from the state of perfect health (1.0; 100%).
The following parameters were recorded: logMAR visual acuity in the better and the worse eyes, mean deviation in the better and worse eyes, total number of surgeries the patient had undergone, total number of topical medications, duration of disease, educational level (categorized as: no formal education, primary school, high school, graduate, and post graduate) and monthly family income (converted to U.S. dollars [USD]). 
Statistical analysis: a sample size of 14 in each group would be necessary to detect a 20% difference in the mean utility values, considering a mean of 0.8 among adults and an SD of 0.15, 12 if the power was 0.8 and type 1 α error was 0.05. The means, standard deviations (SDs), and 95% confidence intervals were calculated for utility values. Pearson's coefficient analysis was used to find the correlation between the study's variables and utility values. Differences of means between groups were analyzed using Student's independent t-test. A stepwise linear regression analysis was conducted to examine the relationship between utility values and the potential predictors in either of the two groups. Analysis of covariance (ANCOVA) was used to determine whether the difference in utility values between the two groups remained significant after adjustment for other variables. P < 0.05 was considered significant (SPSS, ver. 11.5; SPSS, Chicago, IL). 
Results
A total of 76 JOAG patients between 15 and 40 years of age were identified to participate in the study. Six patients were not comfortable answering the questions. Of 110 adult POAG patients, 2 were not comfortable answering the questions and were excluded. 
The mean age at diagnosis of JOAG patients was 26 ± 9.8 years, and the mean age of POAG patients was 62 ± 11.2 years (Table 2). Overall, there was a decrease in utility values with increasing age (r = −0.3, P < 0.001). The mean utility scores among the JOAG patients were significantly better than those of the adult POAG patients (0.80 ± 0.18 vs. 0.64 ± 0.28, respectively; P < 0.001). 
Table 2.
 
Demographic and Clinical Data of Patients
Table 2.
 
Demographic and Clinical Data of Patients
Parameter JOAG Adult POAG P
Age, y 26 ± 9.8 62 ± 11.2 <0.0001
Male: female 61:9 80:28 0.02
Avg. monthly family income in USD 460 ± 239 308 ± 290 0.1
Presence of systemic disease 0 (0%) 42 (38.8%) <0.0001
Avg. mean deviation in better eye, dB −11 ± 10.3 −10.5 ± 8.5 0.7
Avg. mean deviation in worse eye, dB −23 ± 12.4 −20.6 ± 10.5 0.1
Avg. logMAR visual acuity in better eye 0.17 ± 0.26 0.19 ± 0.19 0.6
Avg. logMAR visual acuity in worse eye 0.7 ± 0.7 0.69 ± 0.63 0.9
Avg. number of antiglaucoma medications 1.2 ± 0.8 1.5 ± 0.5 0.01
Avg. number of surgeries 0.9 ± 0.9 0.4 ± 0.6 <0.0001
Duration of the disease, y 7.1 ± 5.8 6 ± 5.2 0.94
Among the JOAG patients, the utility scores were not significantly different between the males and females (Table 3). The JOAG patients in our study expected to live another 44.5 ± 11.6 years on an average. They were willing to give up 8 ± 7.7 years on an average, for perfect vision. Twelve patients (20%) were not willing to trade off any years of live, whereas the remaining (80%) were willing to trade off some years for perfect vision. The mean age of those willing to trade off some years of their remaining lives (36.2 ± 9.4 years) was not significantly different from that of those not willing to trade off any years (30.1 ± 2.5 years; P = 0.12). On average, the adult POAG patients expected to live 19 ± 10 0.4 years and were willing to trade off 6.6 ± 5.6 years for perfect vision. Sixteen adult POAG patients (15%) were not willing to trade off any years of life. 
Table 3.
 
Utility Scores in Different Subgroups of JOAG Patients
Table 3.
 
Utility Scores in Different Subgroups of JOAG Patients
n = 70 Mean Utility Value (SD), 95% CI P
Sex
    Male 61 0.783 (0.184), 0.736 to 0.829 0.171
    Female 9 0.871 (0.123), 0.790 to 0.951
Age at diagnosis, y 0.012
    <20 18 0.875 (0.107), 0.834 to 0.916
    20–30 26 0.738 (0.219), 0.653 to 0.822
    31–40 26 0.759 (0.163), 0.683 to 0.834
Educational status 0.645
    No formal education 5 0.750 (0.190), 0.513 to 0.987
    Primary school 11 0.819 (0.179), 0.690 to 0.948
    High school 18 0.836 (0.118), 0.770 to 0.895
    Graduate 30 0.760 (0.207), 0.683 to 0.837
    Postgraduate 6 0.805 (0.187), 0.608 to 1.002
Family income, USD/mo 0.41
    <500 48 0.766 (0.190), 0.711 to 0.820
    ≥500 12 0.812 (0.151), 0.722 to 0.901
Better eye logMAR visual acuity 0.008
    <0.1 39 0.820 (0.202), 0.755 to 0.886
    0.1–0.3 21 0.85 (0.180), 0.646 to 0.925
    >0.3 10 0.741 (0.127), 0.650 to 0.832
Worse eye logMAR visual acuity 0.463
    <0.1 20 0.827 (0.236), 0.716 to 0.937
    0.1–0.3 17 0.876 (0.125), 0.779 to 0.972
    >0.3 33 0.798 (0.126), 0.754 to 0.843
Better eye mean deviation, dB* 0.67
    <6 26 0.807 (0.147), 0.747 to 0.866
    6–12 9 0.729 (0.324), 0.479 to 0.978
    12–18 11 0.783 (.098), 0.680 to 0.887
    >18 14 0.755 (0.142), 0.673 to 0.837
Worse eye mean deviation, dB* 0.16
    <6 10 0.838 (0.140), 0.754 to 0.923
    6–12 2 0.375 (0.530), −4.390 to 5.14
    12–18 6 0.897 (0.122), 0.768 to 1.025
    >18 36 0.781 (0.148), 0.732 to 0.829
Number of medications 0.002
    0 16 0.891 (0.092), 0.842 to 0.941
    1 17 0.720 (0.213), 0.610 to 0.830
    2 30 0.818 (0.158), 0.757 to 0.878
    ≥3 7 0.629 (0.162), 0.478 to 0.779
Number of surgeries 0.547
    0 30 0.777 (0.235), 0.689 to 0.865
    1 18 0.834 (0.097), 0.786 to 0.883
    ≥2 22 0.785 (0.139), 0.723 to 0.847
Among the patients in this study, utility values showed a positive correlation with the number of years the patients expected to live (r = 0.3, P < 0.001), showing that those expecting to live longer did not necessarily want to give up more years of the life for perfect vision. 
Among the JOAG patients, factors including age at diagnosis (r = 0.19, P = 0 0.34), duration of disease (r = 0.01, P = 0.9), family income or severity of visual field defect did not correlate with the utility score. Those patients not using topical medication had a better utility score (0.89 ± 0.09) than did those using one or more topical antiglaucoma drugs (0.77 ± 0.19; P = 0.002) to control their IOP (Table 3). 
None of the JOAG patients had associated systemic diseases. Among the adult POAG patients, 42 (39%) had some coexisting systemic disorder. These included hypertension (n = 24), diabetes mellitus (n = 10), ischemic heart disease (n = 9), urinary bladder carcinoma (n = 1), asthma (n = 1), and rheumatoid arthritis (n = 1). Utility values of adult POAG patients with coexisting systemic disease (0.62 ± 0.3) were not significantly different from those without any systemic disease (0.66 ± 0.28; P = 0.58). 
Visual Acuity
The average best corrected visual acuity was similar for both juvenile and adult patients (Table 2). Sixty (85%) of the JOAG patients had a logMAR visual acuity of less than 0.3 (6/12 or better) in their better eye. Those with a visual acuity of worse than 6/12 in their better eye had worse utility (0.74 ± 0.12) values compared with those with 6/12 or better in the better eye (0.83 ± 0.2; P = 0.008), as shown in Table 3
Fourteen (20%) of the JOAG patients had their visual acuity reduced to less than 3/60 in one eye due to end-stage glaucoma (monocular blindness). The mean utility score in those with visual acuity worse than 3/60 in the worse eye was lower (0.78 ± 0.16) but not significantly different from those with better than 3/60 (0.81 ± 0.18) in the worse eye (P = 0.5). Among those with monocular blindness (n = 14), utility values correlated with the logMAR visual acuity in the better eye (r = −0.16; P = 0.004). 
Visual Field Defect
The average mean deviation in the worse eye among the JOAG patients was −23 ± 12.44 dB. Visual field data (mean deviation) were not available for eyes with poor visual acuity. Fifty-six (80%) of 70 JOAG patients had advanced VFD (worse than −12 dB) in at least one eye (including those who had poor vision due to glaucoma). There was no evidence of a relationship between the visual field defect in either eye among JOAG patients and utility values (r = 0.069, P = 0.65, and r = 0.048, P = 0.76, respectively). 
Literacy and Socioeconomic Status
Thirty-four (48.5%) JOAG patients in our study had a high school education or less. Educational status did not affect the utility scores among the JOAG patients in this study. Average monthly family income in USD of the JOAG patients was 460 ± 739 (median, 200 USD; range, 40–5000). We correlated the mean deviation in the worse eye at presentation with monthly family income. Apart from the fact that those with lower family incomes tended to have greater visual field defect at presentation (r = 0.41, P = 0.005), there was no relation between family income and utility scores. 
The variables thought to have an association with utility values were chosen for a stepwise regression model in either group. These included age, sex, better eye visual acuity, better eye mean deviation, duration of the disease, and having to use medication. Among the JOAG patients, decreasing visual acuity in the better eye (β coefficient = −0.2) and the need for medication (β coefficient = −0.26) were associated with poorer utility values (r 2 = 0.09, F (2,61) = 6.1; P = 0.04). Among the adult POAG patients, decreasing visual acuity in the better eye (β coefficient = −0.34) was significantly associated with poorer utility values (r 2 = 0.05, F (1,98) = 7.6; P = 0.02). The other variables were excluded from the model. Visual acuity in the better eye was thus an important variable in determining the utility values among both JOAG and adult POAG patients. Test for multicollinerity of the variables revealed low levels of multicollinearity (tolerance = 0.94, 0.95, 0.98, 0.97, and 0.91 among JOAG and 0.98, 0.75, 0.99, 0.96, and 0.90 among POAG) for age, better eye visual acuity, better eye mean deviation, duration of the disease and having to use medication, respectively. 
An analysis of covariance (ANCOVA) was conducted to determine whether the differences in utility values between the JOAG and adult POAG patients remained significant after adjustment for the differences in sex, better eye visual acuity, better eye mean deviation, duration of the disease, having to use medication, and presence of systemic comorbidities. The estimated marginal mean of UV among JOAG was 0.77 (95% CI, 0.69–0.85) and for adult POAG was 0.65 (95% CI, 0.6–0.71). The differences were found to be significant, even after adjustment for the covariates (F (1,144) = 4.8, P = 0.02; r 2 = 0.14). The presence of systemic comorbidities among adults did not affect the differences in utility scores between juvenile and adult patients. 
Discussion
Utility values have been used to assess the QOL of patients suffering from different ocular and systemic morbidities among different populations (Table 4). Diseases that impair QOL minimally, like systemic hypertension, cause minimal decrease in utility values, 18 whereas those that affect a patient's lifestyle significantly, such as metastatic carcinoma or blindness, may markedly decrease the utility values. 6,19 There are few studies on QOL of glaucoma patients from the developing world, 15,16 and none to our knowledge that have assessed the QOL among glaucoma patients with early onset of the disease. 
Table 4.
 
Ocular Morbidity and Utility Scores from Different Parts of the World
Table 4.
 
Ocular Morbidity and Utility Scores from Different Parts of the World
Disease Country Sample Size Utility Value Author
ARMD (mild) United States 115 0.83 Stein et al. 2
Dry eye (severe) United States 56 0.72 Schiffman et al. 13
Glaucoma United States 237 (adult POAG and POAG suspects) 0.9 Jampel 14
India 105 (Adult POAG and PACG) 0.64 Gupta et al. 15
India 70 (JOAG) 0.80 Present study
China 106 (Adult PAC/PACG) 0.75 Sun et al. 16
Singapore 213 (Adult POAG & PACG) 0.88 Saw et al. 17
Bilateral blindness United States 15 0.26 Brown et al. 6
The mean utility values among the juvenile glaucoma patients in our study were greater than those among the adults of a similar ethnic and cultural background and even similar severity of glaucoma. Utility values among adult glaucoma patients from the developed world have been reported to be higher. Jampel 14 reported utility values of 0.91 among adult glaucoma patients in the United States, whereas Saw et al. 17 reported mean utility values of 0.88 in their Chinese patients from Singapore. Most of their patients had early glaucoma or were glaucoma suspects, which could be one of the reasons among others, for the higher utility scores. 
Among our cohort of patients, there was an overall trend of worsening utility values with increasing age. This decreasing QOL with increasing age was also observed by Sun et al. 16 in their Chinese patients with angle closure glaucoma. Increasing age is known to decrease the QOL. 20,21 Younger adults have different perceptions of life and different priorities that determine their QOL than do older adults. 22 The reasons that younger patients perceive a better QOL than do older glaucoma patients could be related to their greater adaptability to changing needs, especially with the social support systems available, better cognitive abilities, and probably their greater will power. Also, younger patients may not fully understand the long-term disability associated with the disease as long as their visual acuity is good in at least one eye, especially so in our cohort of JOAG patients. The absence of systemic illnesses and other concomitant ocular problems among younger compared with older glaucoma patients could also be contributory. However, best corrected visual acuity between the two groups was similar in our study, and we did not find an absence of systemic disease to be associated with better utility values among older glaucoma patients in our study. Neither did the presence of systemic disease among the elderly or the differences in better eye visual acuity, significantly affect the differences in utility values found between the adult and younger patients. 
Eighty percent of the JOAG patients were willing to trade off at least some years of life for perfect vision and a glaucoma free life in the present study. This is in contrast to the studies among Chinese 16 and American 14 glaucoma patients, in which only a third were willing to trade off some years of life for perfect vision. This could be because more than three fourths of our patients presented with advanced glaucoma, whereas less than a third of the patients in the latter studies presented with advanced disease. However, despite advanced field defects and the fact that one fifth of our patients had lost vision in one eye due to glaucoma, JOAG patients were not willing to give up many years of their remaining life for perfect vision. Patients who lose vision in one eye remain in fear of losing sight altogether. 23 Surprisingly, however, even those JOAG patients who had lost vision due to glaucoma in one eye, did not have significantly worse utility values. This could be because these patients maintained reasonably good vision in their better eye. It is known that visual acuity in the better eye correlates more closely with utility values. 24 Brown et al. 6 found that utility values decline drastically only when visual acuity worsens to counting fingers or worse in the better eye. The impact of these findings is that, if the QOL among younger glaucoma patients is not significantly affected, despite advanced disease then there is a greater likelihood that it may impair compliance and be the reason for nonadherence to glaucoma therapy. This is supported by the study of Rees et al. 25 who found nonadherence to be more common among younger patients. 
Utility scores in our study did not correlate with the severity of VFD (mean deviation). Saw et al., 17 in their study found that those with a pattern standard deviation worse than 10 dB had worse utility scores; however, utility scores in their study were not related with the overall worsening of the mean deviation values. Jampel 14 also found poor correlation of utility values with Esterman visual field score. Sun et al. 16 found poorer utility scores among those with advanced field defects, although the number of such patients was small. Vision-related QOL is poorer among those with greater visual field defect. 26,27 Although increasing field defect may impair the activities of daily living and thus vision-related QOL, it may not, depending on the population studied, significantly affect the overall QOL of glaucoma patients. 
Having to use topical medications among juvenile glaucoma patients, was associated with poorer utility scores. The economic burden and the inconvenience of life-long therapy are important determinants of the QOL of glaucoma patients from the developing world with poorer socioeconomic status. 
Using time-tradeoff utility analysis has an advantage, in that the QOL affected by glaucoma can be compared with that affected by other disease states. It also can take into account the economic and psychosocial state of the patient. For those afflicted with diseases at a relatively younger age, the economic burden of health-related disability is expected to be immense. Knowing the impact of the disease on a patient subgroup in a broader sense can help guide decisions on allocation of health resources especially in context of a developing society. 28 QOL affected by glaucoma has to be taken in perspective of patient's overall health-related QOL. QOL questionnaires specific to glaucoma or those that are vision specific have the disadvantage in that they cannot be interpolated to the overall health of the patient, which may be of more importance to him or her. Utility-based analysis is an indirect measure of the overall health-related QOL. However, the disadvantage of the utility analysis, as with all such questionnaires, is that they are dependent on the patient's personality. Another inherent problem with the utility-based questionnaire is the inability of patients to comprehend the questions. 29 One of the limitations of the present study was that it was conducted in a hospital setting. It is likely that the results may have been different if the questionnaire had been administered in more relaxed surroundings. 
The findings of this study represent the perceptions of young patients with eye disease from developing countries. Further studies among similar age group from other populations would be useful. It would also be interesting to know, in future, from a longitudinal study whether these perceptions change with time. 
Footnotes
 Disclosure: V. Gupta, None; P. Dutta, None; M. OV, None; K.S. Kapoor, None; R. Sihota, None; G. Kumar, None
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Table 1.
 
How to Calculate a Utility Score Using the Time-Tradeoff Method: An Example
Table 1.
 
How to Calculate a Utility Score Using the Time-Tradeoff Method: An Example
Age of the respondent: 50 years
Age the subject expects to live: 70 years
Response to the time-tradeoff question above: 10 years
Step 1: Determine the number of additional years the patient expects to live.
    70–50 = 20 additional years
Step 2: Divide the number of years the respondent is willing to give up to spend the rest of his/her living years with perfect vision and free of glaucoma, from the value obtained in step 1.
    10/20 = 0.50
Step 3: Subtract the value obtained in step 2 from 1.0
    1.0–0.50 = 0.50
Interpretation: The respondent is willing to give up 50% of his/her remaining life years in a trade off for life without glaucoma. The utility value is calculated by subtracting the percentage of remaining years traded (0.50, i.e. 50%) from the state of perfect health (1.0; 100%).
Table 2.
 
Demographic and Clinical Data of Patients
Table 2.
 
Demographic and Clinical Data of Patients
Parameter JOAG Adult POAG P
Age, y 26 ± 9.8 62 ± 11.2 <0.0001
Male: female 61:9 80:28 0.02
Avg. monthly family income in USD 460 ± 239 308 ± 290 0.1
Presence of systemic disease 0 (0%) 42 (38.8%) <0.0001
Avg. mean deviation in better eye, dB −11 ± 10.3 −10.5 ± 8.5 0.7
Avg. mean deviation in worse eye, dB −23 ± 12.4 −20.6 ± 10.5 0.1
Avg. logMAR visual acuity in better eye 0.17 ± 0.26 0.19 ± 0.19 0.6
Avg. logMAR visual acuity in worse eye 0.7 ± 0.7 0.69 ± 0.63 0.9
Avg. number of antiglaucoma medications 1.2 ± 0.8 1.5 ± 0.5 0.01
Avg. number of surgeries 0.9 ± 0.9 0.4 ± 0.6 <0.0001
Duration of the disease, y 7.1 ± 5.8 6 ± 5.2 0.94
Table 3.
 
Utility Scores in Different Subgroups of JOAG Patients
Table 3.
 
Utility Scores in Different Subgroups of JOAG Patients
n = 70 Mean Utility Value (SD), 95% CI P
Sex
    Male 61 0.783 (0.184), 0.736 to 0.829 0.171
    Female 9 0.871 (0.123), 0.790 to 0.951
Age at diagnosis, y 0.012
    <20 18 0.875 (0.107), 0.834 to 0.916
    20–30 26 0.738 (0.219), 0.653 to 0.822
    31–40 26 0.759 (0.163), 0.683 to 0.834
Educational status 0.645
    No formal education 5 0.750 (0.190), 0.513 to 0.987
    Primary school 11 0.819 (0.179), 0.690 to 0.948
    High school 18 0.836 (0.118), 0.770 to 0.895
    Graduate 30 0.760 (0.207), 0.683 to 0.837
    Postgraduate 6 0.805 (0.187), 0.608 to 1.002
Family income, USD/mo 0.41
    <500 48 0.766 (0.190), 0.711 to 0.820
    ≥500 12 0.812 (0.151), 0.722 to 0.901
Better eye logMAR visual acuity 0.008
    <0.1 39 0.820 (0.202), 0.755 to 0.886
    0.1–0.3 21 0.85 (0.180), 0.646 to 0.925
    >0.3 10 0.741 (0.127), 0.650 to 0.832
Worse eye logMAR visual acuity 0.463
    <0.1 20 0.827 (0.236), 0.716 to 0.937
    0.1–0.3 17 0.876 (0.125), 0.779 to 0.972
    >0.3 33 0.798 (0.126), 0.754 to 0.843
Better eye mean deviation, dB* 0.67
    <6 26 0.807 (0.147), 0.747 to 0.866
    6–12 9 0.729 (0.324), 0.479 to 0.978
    12–18 11 0.783 (.098), 0.680 to 0.887
    >18 14 0.755 (0.142), 0.673 to 0.837
Worse eye mean deviation, dB* 0.16
    <6 10 0.838 (0.140), 0.754 to 0.923
    6–12 2 0.375 (0.530), −4.390 to 5.14
    12–18 6 0.897 (0.122), 0.768 to 1.025
    >18 36 0.781 (0.148), 0.732 to 0.829
Number of medications 0.002
    0 16 0.891 (0.092), 0.842 to 0.941
    1 17 0.720 (0.213), 0.610 to 0.830
    2 30 0.818 (0.158), 0.757 to 0.878
    ≥3 7 0.629 (0.162), 0.478 to 0.779
Number of surgeries 0.547
    0 30 0.777 (0.235), 0.689 to 0.865
    1 18 0.834 (0.097), 0.786 to 0.883
    ≥2 22 0.785 (0.139), 0.723 to 0.847
Table 4.
 
Ocular Morbidity and Utility Scores from Different Parts of the World
Table 4.
 
Ocular Morbidity and Utility Scores from Different Parts of the World
Disease Country Sample Size Utility Value Author
ARMD (mild) United States 115 0.83 Stein et al. 2
Dry eye (severe) United States 56 0.72 Schiffman et al. 13
Glaucoma United States 237 (adult POAG and POAG suspects) 0.9 Jampel 14
India 105 (Adult POAG and PACG) 0.64 Gupta et al. 15
India 70 (JOAG) 0.80 Present study
China 106 (Adult PAC/PACG) 0.75 Sun et al. 16
Singapore 213 (Adult POAG & PACG) 0.88 Saw et al. 17
Bilateral blindness United States 15 0.26 Brown et al. 6
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