The Impact of Cataract, Cataract Types, and Cataract Grades on Vision-Specific Functioning Using Rasch Analysis


To determine the impact of cataracts and their types and grades on vision-specific functioning.


Prospective population-based cross-sectional study.


The Singapore Indian Eye Study examined 3400 of 4497 (75.6% response rate) ethnic Indians 40 years of age and older living in Singapore. Three thousand one hundred sixty-eight (93.2%) fulfilled inclusion criteria with complete information for final analysis. Cataracts were assessed on slit-lamp examination and were graded according to the Lens Opacity Classification System III. Vision-specific functioning scores were explored with the Visual Function scale, validated using Rasch analysis.


Two hundred sixty-nine (8.5%) and 740 (23.4%) of the study participants had unilateral and bilateral cataracts, respectively, and 329 (10.4%), 800 (25.2%), and 128 (4.1%) participants had nuclear, cortical, and posterior subcapsular (PSC) cataracts, respectively. In multivariate linear regression models, the presence of bilateral rather than unilateral cataract (β = −0.12; 95% confidence interval, −0.20 to 0.00) was associated independently with poorer vision-specific functioning, even after adjusting for undercorrected refractive error (β = −0.11; 95% confidence interval, −0.21 to 0.00). Bilateral nuclear, cortical, and PSC cataracts also were associated with poorer vision-specific functioning (β = −0.31, −0.15, and −1.15, respectively), with combinations of them having even greater impact. Significantly poorer vision-specific functioning occurred at Lens Opacity Classification System grades 4 (nuclear opalescence), 5 (nuclear color), 3 (cortical), and 1 (PSC) or higher.


People with bilateral but not unilateral cataracts experience difficulty with performing vision-specific daily activities independent of refractive error, with PSC cataracts and cataract combinations having the greatest impact. Cataract types cause poorer vision-specific functioning beginning at different severity grades.

Cataract is the leading cause of visual impairment in the world today and is responsible for up to 50% of blindness globally. It causes visual impairment and leads to a reduction in quality of life, as demonstrated previously. The consequences include decreased participation in social activities or daily tasks, depression, increased likelihood of being placed in a nursing home, increased number of falls and resultant hip fractures, and increased mortality. This patient-centered impact is comparable with that of major systemic conditions including stroke, diabetes, and arthritis.

There is growing evidence advocating the use of vision-specific functioning or other quality-of-life measures to evaluate fully the impact of eye disease. Such an assessment factors in the patient’s perspective, instead of relying solely on traditional and objective measures, thereby allowing more meaningful information to be obtained. The available literature suggests a relationship between cataracts and poorer vision-specific functioning; however, there are limited data on the extent of this association. For example, whether bilateral cataract has a greater impact on vision-specific functioning as compared with unilateral cataract is not clear, but is important for appropriate health care resourcing (eg, funding for bilateral cataract surgery). It is also unknown how the various types and grades or severity of cataract impact vision-specific functioning (eg, posterior subcapsular cataract is thought to impact visual function more than other cataract subtypes).

There are more than 1 billion ethnic Indians in the world who would be susceptible to the implications of cataract, and they account for more than one sixth of the global population. Singapore presents a unique opportunity to examine the impact of cataracts on an urbanized population of Indians living outside the Indian subcontinent. Despite good access to services, data from population studies in Singapore show that cataracts still contributed to approximately 60% of blindness and continue to pose a significant problem.

This study aimed to describe the impact of cataracts and their types and grades on vision-specific functioning in a population-based sample from the Singapore Indian Eye Study, by using the Vision-Specific Functioning Scale (VF-11) validated by Rasch analysis. As we have done in earlier studies, using modern psychometric methods such as Rasch analysis on existing high-quality instruments results in a more useful and precise measure of vision-specific functioning pertaining to cataracts.


Study Population

The Singapore Indian Eye Study is a population-based cross-sectional study of major eye diseases among Singaporean Indians conducted between August 2007 and December 2009. The study was conducted with a sampling area from the southwestern part of Singapore. An age-stratified random sampling strategy was adopted to select 6350 individuals 40 years of age or older from a list of 12 000 ethnic Indians provided by the Ministry of Home Affairs. A person was considered ineligible if he or she had moved from the residing address, had not been living there for 6 months, was deceased, or was terminally ill. Of the initial 6350 selected, 4497 subjects were found to be eligible. A total of 3400 subjects participated, representing a 75.6% participation rate. The detailed methodology of the study has been reported elsewhere. Subjects with incomplete information or other eye conditions such as glaucoma, diabetic retinopathy, and age-related macular degeneration were excluded from the study. Final analyses were performed based on the remaining 3168 subjects.

Cataract Assessment

Cataracts were assessed as part of a detailed slit-lamp examination of both anterior and posterior segments. Pharmacologic (tropicamide 1% and phenylephrine hydrochloride 2.5%) dilation was carried out before cataract assessment. The Lens Opacity Classification System (LOCS) III was used at the slit lamp to grade the lenses of all phakic subjects along a scale (0.1 to 6.9 for nuclear opalescence and color; 0.1 to 5.9 for cortical and posterior subcapsular cataracts). Subcategories of cataract grades also were created (0 = 0.1 to 0.9; 1 = 1.0 to 1.9; 2 = 2.0 to 2.9; 3 = 3.0 to 3.9; 4 = 4.0 to 4.9; 5 = 5.0 to 5.9; 6 = 6.0 to 6.9). The different types of cataract were defined accordingly as nuclear (a score of ≥ 4 for nuclear opalescence or nuclear color), cortical (a score of ≥ 2 for cortical cataract), or posterior subcapsular (a score of ≥ 2 for posterior subcapsular cataract). Any cataract was defined as the presence of either nuclear, cortical, or posterior subcapsular cataract in at least 1 eye based on the LOCS III criteria.

Vision Assessment

Visual acuity was measured using the logarithm of the minimum angle of resolution (logMAR) vision chart (Lighthouse International, New York, New York, USA) at a distance of 4 m. Following the rule of identifying at least 3 of 5 numbers to progress downward to the next line, logMAR scores were estimated through a standardized method ([logMAR value of first line with 3 or more incorrect numbers] + [0.02 × number of numbers that were read incorrectly, up to the nearest line with 5 correct numbers]). If no numbers were read at 4 m, the participant was moved to 3 m, 2 m, and 1 m consecutively. If numbers could not be identified off the chart, visual acuity was assessed as counting fingers, hand movements, perception of light, or no perception of light. Best-corrected visual acuity was determined through subjective refraction with a phoropter (Takagi VT-5 Refractor; Takagi Seiko Co, Ltd, Nagano, Japan) by certified study optometrists, using initial readings from an autorefractor (Canon RK-5 Auto Ref-Keratometer; Canon, Inc, Ltd, Tokyo, Japan). Undercorrected refractive error was defined as a change in logMAR score of 0.2 or more between the subject’s presenting visual acuity and best-corrected visual acuity.

We defined low visual impairment as having a logMAR score of between 0.3 and 1.0, whereas severe visual impairment was a logMAR score of 1.0 or more. We further defined 6 groups in increasing levels of visual impairment: (1) normal vision in both eyes; (2) normal vision in one eye and low visual impairment in the other eye; (3) normal vision in one eye and severe visual impairment in the other eye; (4) low visual impairment in both eyes; (5) low visual impairment in one eye and severe visual impairment in the other eye; and (6) severe visual impairment in both eyes.

Vision-Specific Functioning Assessment

The 11-question VF-11 used in this study has been described and validated comprehensively ( Table 1 ). Trained research assistants conducted the face-to-face interviews in either English, Tamil, or both. Items 1 through 9 used a numeric scale from 0 (no difficulty) to 4 (unable to perform activity), whereas items 10 and 11 provided 3 response options (1 = no difficulty; 2 = a little difficulty; 3 = great difficulty). With the participants’ consent, randomly selected interviews were recorded for periodic review by the investigators for quality control purposes.


Items of the Modified Vision-Specific Functioning Scale Used to Determine Participants’ Levels of Vision-Specific Functioning

1. Difficulty in reading small print in the telephone book even with glasses?
2. Difficulty in reading newspaper-size print even with glasses?
3. Difficulty in recognizing friends when you meet them while shopping even with glasses?
4. Difficulty seeing stairs even with glasses?
5. Difficulty in reading street signs or shop signs even with glasses?
6. Difficulty in filling out 4-D or Toto forms even with glasses?
7. Difficulty in playing games—chess or cards—even with glasses?
8. Difficulty in cooking even with glasses?
9. Difficulty in watching television even with glasses?
10. Difficulty in driving during the day because of vision?
11. Difficulty in driving at night because of vision?

Other Variables

Standardized questionnaires were administered through trained interviewers to obtain information on patient demographics, social history, and medical history. Smoking status was defined in categories of people who currently were smoking, had smoked in the past, or who had never smoked. Education was defined in categories of those who had an elementary school education or less, a secondary school education, or education after completing secondary school. Monthly income was defined in categories of less than S$1000 (Singapore dollars), between S$1000 and S$2000, more than S$2000, or those who have retired. Comorbidities were defined based on the subject’s report of having or having had the systemic conditions of myocardial infarction, stroke, diabetes, hypertension, hypercholesterolemia, or thyroid problems.

Rasch Analysis

Rasch analysis was carried out to determine the reliability, validity, and measurement characteristics of VF-11. It is a modern psychometric method that transforms raw ordinal scores into interval-level measurement (expressed in log of odds units, or logits), permitting the use of parametric statistical techniques. It calculates item difficulty (item measure) in relation to person ability (person measure) by placing both on the same linear continuum. A high person measure (in logits) indicates that a person possesses a high level of the assessed latent trait (eg, vision functioning). Rasch analysis also describes the psychometric properties of a scale, such as how well items fit the underlying latent trait being measured, how well items discriminate between the respondents, how well item difficulty targets person ability, and the appropriateness of the response scale used. Rasch analysis was applied using the Andrich rating scale model with Winsteps software version 3.68 (Chicago, Illinois, USA). The following fundamental indicators were used to assess the validity of the VF-11 in this large population based sample of Indians.

Response category threshold ordering

Response category threshold ordering assesses the appropriateness of the response options. Threshold ordering assesses whether participants have difficulty discriminating between the response options or that they perceive categories as interchangeable, that is, a category expected to be harder than another category was actually easier. Disordered thresholds may affect model fit considerably; however, this can be overcome by collapsing poorly used response categories with adjacent ones.

Person separation indices

Person separation indices provide an estimate of the ability of the VF-11 questionnaire to discriminate between strata or groups of participant ability. A person separation index of more than 2.0 and a person reliability score of more than 0.8 generally are considered to be the minimum requirements for satisfactory discrimination of at least 3 strata of participant’s level of the trait being investigated (ie, vision functioning).


Targeting of items to the sample refers to whether the items are of an appropriate level of difficulty for the ability of the sample. This usually is observed from the person-item map. Persons of higher ability and items of greater difficulty are located at the top of the map, and vice versa. Poor targeting occurs when participants generally have a higher or lower ability than the most or least item difficulty threshold, or when items are clustered at particular levels of difficulty, leaving large gaps. Item difficulty and person ability are measured in logits. Ideally, the mean person score should be approximately 0 logits. In general, a difference between the mean person and item score of more than 1.0 logits indicates notable mistargeting.


Unidimensionality refers to the ability of a scale to measure a single underlying construct (in this case, visual function), in addition to how well each item measures the underlying trait. The 2 key indicators for unidimensionality are item fit statistics and testing the assumption of local independence. Item fit determines how well each item fits the underlying trait, and items with an infit mean square value between 0.7 and 1.3 were considered acceptable in this study. Values of less than 0.7 may indicate redundancy and values of more than 1.3 indicate an unacceptable level of noise in the responses. Such items may be removed iteratively from the scale to improve model fit.

To test for local independence, principal component analysis of the residuals is examined, whereby the variance explained by the measures for the empirical calculation should be comparable with those of the model (> 50% for an acceptable model). Furthermore, the unexplained variance in the first contrast of the residuals can indicate if any patterns in the differences within the residuals exist large enough to suggest the existence of a secondary dimension. The first contrast of the residuals should be less than 2.0 eigenvalue units (< 5%), which is close to that seen with random data. An eigenvalue of more than 2.0 is problematic and indicates that there is another underlying trait being captured by the instrument.

Differential item functioning

Differential item functioning (DIF) indicates whether different groups within the sample are responding differently systematically, despite having equal levels of the trait being assessed. Age and gender were included in the DIF analysis because causes of vision impairment are age and gender specific. Self-reported health also was considered because it could reflect the level of visual function reported. We used the following criteria for DIF assessment: small or absent if the DIF contrast in logits was less than 0.50 logits, minimal DIF if the DIF contrast was 0.5 to 1.0 logits, and notable DIF if the DIF contrast was more than 1.0 logits. Significant and meaningful DIF, if found, may indicate that the interpretation of the scale differs by group and is influenced by confounding factors.

All 5 fundamental indicators determine the validity, reliability, measurement characteristics, and unidimensionality of the VF-11. The overall measure of vision functioning of the VF-11 generated by the Winsteps software, after the data were fitted to the Rasch model, was used for the analysis in this study.

Statistical Analysis

Statistical analyses were performed using STATA Data Analysis and Statistical Software version 11.0 (StataCorp, College Station, Texas, USA). Baseline demographics and cataract characteristics in terms of vision-specific functioning were compared, using the analysis of variance to determine associations and to test for potential confounders. Multivariate analyses of the association between independent variables and vision-specific functioning scores then were carried out using 3 linear regression models. The first model adjusted for age, gender, education, income, smoking, and systemic comorbidities. The second model adjusted further for undercorrected refractive error to determine if the impact on vision-specific functioning from cataracts extended beyond the changes in refractive error in a cataractous eye. The third model also adjusted for best-corrected visual acuity to ascertain if the effect on vision-specific functioning score from cataract came solely from the impairment of central vision.

A subanalysis of the different types of cataracts and their grades based on the LOCS III classification and their relationship with vision-specific functioning in a linear regression model also was carried out. Subjects who had the same grade of a particular type of cataract in both of their eyes were grouped according to that particular grade. The cataract grades then were analyzed as an ordinal variable against vision-specific functioning. Subjects who had undergone cataract surgery or who had different grades of cataract in both eyes were not included in the analysis.


Social Demographics and Clinical Characteristics

The social and clinical demographic data of the 3168 participants are presented in Table 2 . The mean ± standard deviation age of the participants was 57.2 ± 9.8 years with an even gender distribution, and females had a lower mean vision-specific functioning score (4.50 ± 1.23) than the males. Of participants who had elementary or lower education, 54.9% (n = 1736) had lower vision-specific functioning scores (4.43 ± 1.25) than those who had higher levels of education. Those who had an income of more than S$2000 (37.6%; n = 1160) scored better in their vision-specific functioning (4.88 ± 0.89) than the lower-income groups. The prevalence rates of hypercholesterolemia, hypertension, and diabetes in the study population were found to be high at 42.7% (n = 1327), 37.7% (n = 1191), and 28.5% (n = 900), respectively.


Characteristics and Cataract Types of the Study Participants and Their Associations with Vision-Specific Functioning Scores Using Rasch Analysis

Characteristics No. (%) Vision-Specific Functioning Scores (Mean ± SD)
All subjects 3168 (100) 4.68 ± 1.10
Age (y)
40 to 49 868 (27.4) 4.89 ± 0.89 a
50 to 59 1050 (33.1) 4.79 ± 0.94
60 to 69 823 (26.0) 4.60 ± 1.18
> 70 427 (13.5) 4.14 ± 1.47
Male 1593 (50.3) 4.86 ± 0.92 b
Female 1575 (49.7) 4.50 ± 1.23
Elementary or lower 1736 (54.9) 4.43 ± 1.2 5 c
Secondary 784 (24.8) 4.93 ± 0.86
Postsecondary 644 (20.4) 5.06 ± 0.71
Monthly income (S$)
< 1000 963 (31.2) 4.31 ± 1.35 b
1000 to 2000 509 (16.5) 4.60 ± 1.08
> 2000 1160 (37.6) 4.88 ± 0.89
Retired 457 (14.8) 5.05 ± 0.69
Smoking status
Current 475 (15.0) 4.64 ± 1.13 d
Past 371 (11.7) 4.79 ± 0.98
Never 2319 (73.3) 4.79 ± 1.08
Comorbid conditions
Myocardial infarction 278 (8.8) 4.49 ± 1.23 e
Stroke 76 (2.4) 4.28 ± 1.43 e
Thyroid 159 (5.0) 4.58 ± 1.20
Hypercholesterolemia 1327 (42.7) 4.62 ± 1.17 e
Hypertension 1191 (37.7) 4.54 ± 1.21 e
Diabetes 900 (28.5) 4.51 ± 1.26 e
Any cataract f
None 1814 (57.3) 4.83 ± 0.94 g
Unilateral 269 (8.5) 4.71 ± 1.08 g
Bilateral 740 (23.4) 4.43 ± 1.35 g
Nuclear cataract f
None 2476 (78.2) 4.79 ± 0.98 g
Unilateral 43 (1.4) 4.49 ± 1.29 g
Bilateral 286 (9.0) 4.14 ± 1.56 g
Cortical cataract f
None 2005 (63.3) 4.81 ± 0.95 g
Unilateral 181 (5.7) 4.81 ± 0.95
Bilateral 619 (19.5) 4.39 ± 1.39 g
PSC cataract f
None 2677 (84.5) 4.75 ± 1.04 g
Unilateral 75 (2.4) 4.60 ± 1.11 g
Bilateral 53 (1.7) 3.43 ± 1.97 g

PSC = posterior subcapsular; SD = standard deviation; S$ = Singapore dollars.

a Significant difference ( P < .5) in comparisons between all categories except 40 to 49 vs 50 to 59.

b Significant difference ( P < .5) in comparisons between all categories.

c Significant difference ( P < .5) in comparison between elementary and lower vs the other 2 categories.

d Significant difference ( P < .5) in comparison between never smoked and the other 2 categories.

e Significant difference ( P < .5) with specific comorbidity vs without.

f Cataracts compared against controls with normal eyes bilaterally.

g Significant difference ( P < .5) with specific cataract type vs without.

Among the participants, 8.5% (n = 269) had unilateral cataracts and 23.4% (n = 740) had bilateral cataracts. Cortical cataracts were the most common (25.2%; n = 800), followed by nuclear cataracts (10.4%; n = 329), and lastly posterior subcapsular cataracts (4.1%; n = 128). Overall, univariate analysis showed that people with poorer vision-specific functioning tend to be older, to be female, to have an elementary or lower education, to have lower income, or to have smoked.

Rasch Analysis of Vision-Specific Functioning

As demonstrated previously, items 10 (driving during the day) and 11 (driving at night) demonstrated significant misfit and were removed. The rest of the items recorded infit mean square values of less than 1.3. The mean ± standard deviation of person and item fit residual values were 2.81 ± 1.21 and 0.89 ± 0.75, respectively. Ideally, the mean and standard deviation values are expected to approximate 0 and 1, respectively, to prove that this scale has substantial validity. The person separation index and person reliability values were 0.84 and 0.41, respectively, and indicated moderate discrimination ability of the scale. There was no evidence of differential item functioning and multidimensionality, which support the aspect that this scale is measuring one underlying trait.

Participants had a higher level of ability than the average of the scale items, meaning that most items of the questionnaire were too easy to perform. However, this was because 75.2% of the participants had normal visual acuity in both eyes.

The 3 most difficult vision-dependent items were: difficulty in reading small print in the telephone book even with glasses (2.77 logit), difficulty in filling out 4-D or Toto forms even with glasses (1.69 logit), and difficulty in reading newspaper size print even with glasses (1.09 logit). The 3 least difficult items were: difficulty seeing stairs even with glasses (−0.76 logit), difficulty in playing games—chess or cards—even with glasses (−1.52 logit), and difficulty in cooking even with glasses (−1.72 logit).

Relationship Between Cataract and Its Types and Vision-Specific Functioning

In multivariate models adjusting for age, gender, education, income, smoking status, and various comorbidities, any cataract (β [regression coefficient] = −0.12; 95% confidence interval [CI], −0.20 to 0.00) was associated independently (P < .05) with vision-specific functioning when it occurred bilaterally, but not when it was present only unilaterally (P > .05; Table 3 ). In model 1, age, gender, education, income, a history of stroke or diabetes, and bilateral cataracts were associated independently (P < .05) with the measure of vision-specific functioning. Being older than 70 years (β = −0.42; 95% CI, −0.61 to -0.23), being female (β = −0.21; 95% CI, −0.54 to −0.29), having an elementary or lower education (β = −0.41; 95% CI, −0.54 to −0.29), or having a monthly income of less than S$1000 (β = −0.27; 95% CI, −0.38 to −0.15) resulted in poorer vision-specific functioning. Having had a stroke also reduced significantly a subject’s scores (β = −0.61; 95% CI, −0.91 to −0.31; P < .05) as compared with having diabetes (β = −0.10; 95% CI, −0.20 to 0.00; P < .05). Adding undercorrected refractive error to the model had little impact on the effect of bilateral cataracts on vision-specific functioning (β = −0.11; 95% CI, −0.21 to 0.00; P < .05). Likewise, individual cataract types remained statistically significant (P < .05). However, when best-corrected visual acuity was included in the model, there was no longer any significant association (P > .05) between bilateral cataracts and vision-specific functioning. Refractive error and the different categories of visual impairment were associated independently with decreased vision-specific functioning, with the exception of having low vision in both eyes.


Differences in Vision-Specific Functioning Scores Using Rasch Analysis by Age, Gender, Education, Income, Smoking, Comorbidities, Cataract, Undercorrected Refractive Error, and Categories of Vision Impairment with Best-Corrected Visual Acuity in Linear Regression Models

Characteristics Vision-Specific Functioning Scores, β (95% CI)
Model 1 Model 2 Model 3
Age (y)
40 to 49 Reference Reference Reference
50 to 59 −0.02 (−0.12 to 0.08) −0.01 (−0.11 to 0.10) −0.02 (−0.11 to 0.08)
60 to 69 −0.06 (−0.19 to 0.06) −0.02 (−0.15 to 0.11) −0.01 (−0.13 to 0.12)
> 70 −0.42 (−0.61 to −0.23) −0.34 (−0.53 to −0.16) −0.18 (−0.35 to 0.02)
Male Reference Reference Reference
Female −0.21 (−0.32 to −0.10) −0.21 (−0.31 to −0.10) −0.19 (−0.30 to −0.09)
Postsecondary Reference Reference Reference
Secondary −0.06 (−0.18, 0.07) −0.05 (−0.17 to 0.07) −0.06 (−0.18, 0.06)
Elementary or lower −0.41 (−0.54 to −0.29) −0.40 (−0.52 to −0.28) −0.38 (−0.49 to −0.26)
Monthly income (S$)
> 2000 Reference Reference Reference
1000 to 2000 −0.07 (−0.19 to 0.06) −0.08 (−0.20 to 0.04) −0.09 (−0.21 to 0.03)
< 1000 −0.27 (−0.38 to −0.15) −0.26 (−0.37 to −0.15) −0.24 (−0.35 to −0.13)
Retired −0.05 (−0.19 to 0.08) −0.07 (−0.20 to 0.06) −0.06 (−0.19 to 0.07)
Smoking status
Never Reference Reference Reference
Current −0.06 (−0.19 to 0.07) −0.05 (−0.17 to 0.09) −0.03 (−0.16 to 0.09)
Past 0.05 (−0.09 to 0.20) 0.08 (−0.06 to 0.22) 0.09 (−0.05 to 0.23)
Comorbid conditions
Myocardial infarction −0.10 (−0.27 to 0.06) −0.13 (−0.29 to 0.03) −0.10 (−0.26 to 0.06)
Stroke −0.61 (−0.91 to −0.31) −0.61(−0.90 to −0.32) −0.63 (−0.92 to −0.34)
Hypercholesterolemia −0.01 (−0.11 to 0.08) −0.01 (−0.10 to 0.08) −0.02 (−0.11 to 0.07)
Hypertension 0.01 (0.10 to 0.11) −0.04 (−0.13 to 0.06) −0.03 (−0.13 to 0.07)
Diabetes −0.10 (−0.20 to 0.00) −0.07 (−0.17 to 0.02) −0.07 (−0.17 to 0.03)
Any cataract
None Reference Reference Reference
Unilateral a 0.02 (−0.11, 0.14)
Bilateral −0.12 (−0.23 to −0.01) −0.11 (−0.21 to −0.00) −0.03 (−0.13 to 0.07)
Refractive error
None/corrected Reference Reference
Undercorrected −0.16 (−0.24 to −0.08) −0.17 (−0.25 to −0.09)
Vision impairment with BCVA
Normal vision both eyes Reference
Normal vision in 1 eye, low vision in the other −0.28 (−0.48 to −0.07)
Normal vision in 1 eye, severe in the other −1.10 (−1.37 to −0.83)
Low vision in both eyes −0.67 (−1.77 to 0.43)
Severe in 1 eye, low vision in the other −1.03 (−1.67 to −0.39)
Severe in both eyes −3.10 (−4.20, −2.01)

Only gold members can continue reading. Log In or Register to continue

Jan 12, 2017 | Posted by in OPHTHALMOLOGY | Comments Off on The Impact of Cataract, Cataract Types, and Cataract Grades on Vision-Specific Functioning Using Rasch Analysis
Premium Wordpress Themes by UFO Themes