March 2001
Volume 42, Issue 3
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Clinical and Epidemiologic Research  |   March 2001
Psychometric Properties of the 25-Item NEI-VFQ in a Hispanic Population: Proyecto VER
Author Affiliations
  • Aimee T. Broman
    From the Dana Center for Preventive Ophthalmology, Johns Hopkins School of Medicine, Baltimore, Maryland;
  • Beatriz Munoz
    From the Dana Center for Preventive Ophthalmology, Johns Hopkins School of Medicine, Baltimore, Maryland;
  • Sheila K. West
    From the Dana Center for Preventive Ophthalmology, Johns Hopkins School of Medicine, Baltimore, Maryland;
  • Jorge Rodriguez
    Department of Ophthalmology, University of Arizona, Tucson; and
  • Rosario Sanchez
    Department of Ophthalmology, University of Arizona, Tucson; and
  • Robert Snyder
    Department of Ophthalmology, University of Arizona, Tucson; and
  • Ronald Klein
    Department of Ophthalmology, University of Wisconsin, Madison.
Investigative Ophthalmology & Visual Science March 2001, Vol.42, 606-613. doi:
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      Aimee T. Broman, Beatriz Munoz, Sheila K. West, Jorge Rodriguez, Rosario Sanchez, Robert Snyder, Ronald Klein; Psychometric Properties of the 25-Item NEI-VFQ in a Hispanic Population: Proyecto VER. Invest. Ophthalmol. Vis. Sci. 2001;42(3):606-613.

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

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Abstract

purpose. To assess the psychometric properties of the NEI-VFQ-25 in a population-based study of older Hispanic persons living in the United States, explore other demographic factors that affect participant response, and observe the comparability of the Spanish and English versions of the instrument.

methods. A sample of randomly selected block groups in Tucson and Nogales, Arizona, were selected for study. Participants were interviewed at home; a majority of the interviews were conducted in Spanish. The home interview included questions from the NEI-VFQ-25 and HHANES. Presenting acuity was done using ETDRS methodology, followed by a standardized eye examination by an ophthalmologist. The authors analyzed the internal consistency of the NEI-VFQ-25 responses using Cronbach’s α coefficient and the construct validity by assessing the relationship between presenting acuity and scale scores, adjusting for age and gender. A second model was also explored to determine whether other demographic variables affected scale scores; differences in reporting between the Spanish and English versions was observed in this model, used in a subset of the population that minimized interviewer effect.

results. Of the 4774 participants in the study, 99.7% had completed questionnaires, not completed by proxy. The highest nonresponse rate occurred in the Driving scale, with 25% of participants not driving for reasons other than problems with vision. Internal consistency was high, with Cronbach α ranging between 0.65 and 0.86 for scales with multiple items. Adjusting for age and gender, those with presenting acuity worse than 20/40 scored significantly lower than those with presenting acuity 20/40 or better, for all scales. The demographic variables with the most consistent association across the NEI-VFQ-25 scales were presenting acuity, income, and gender. No significant differences in reporting were found between the Spanish and English versions of the questionnaire in the subset of the population.

conclusions. In this study of Hispanic people age 40 years or older, the NEI-VFQ-25 was sensitive to presenting acuity and other demographic variables, such as age, gender, and income. The findings from this psychometric analysis provide evidence of the reliability and validity of some of the scales in the 25-item NEI-VFQ when used among people with a range of visual acuity level, providing other explanatory variables are also considered.

The functional status and quality of life in persons with age-related diseases has assumed more importance as the proportion of the population in the United States age 50 years and older increases. Visual impairment has been shown to have a significant and independent impact on functional status, with older persons reporting more difficulty with mobility and vision-specific tasks as their vision worsens. 1 2  
The Activities of Daily Vision Scale (ADVS) and VF-14 were created to capture the decrements in physical function specifically associated with visual impairment. Recovery of visual function after cataract surgery was associated with improvements in the ADVS, 3 whereas the VF-14 was associated strongly with functional impairment caused by cataract. 4 In previous work we have shown that the ADVS is sensitive not only to vision loss, but also age, gender, and racial variation. 5 As research interest in this area has grown, several “quality of life” instruments have been developed to capture decrements not just in the domain of physical function, but in psychological function, social function, and other domains as well. In this spirit, the National Eye Institute has sponsored research into the development of a vision specific quality of life instrument, the NEI-Visual Function Questionnaire (NEI-VFQ). 6  
In contrast to the ADV and VF-14 instruments, which were developed for use in cataract patients, the NEI-VFQ was developed for use among patients with a range of visual disabilities and impairments, with the goal of creating a comprehensive assessment of quality of life associated with visual function. The 51-item NEI-VFQ has been validated for a group of persons with chronic eye disease. 7 A shortened version of the NEI-VFQ is now in use, which consists of a subset of 25 items from the original 51-item scale. The properties of this shortened version have not been described, but it has been used frequently, in part because of the shortened time for administration. 8 9 10  
The purpose of this study is to assess the psychometric behavior of the NEI-VFQ-25 in a population of older Hispanic people living in the United States, to explore other demographic factors that affect participant response, and to observe the comparability of the Spanish and English versions of the instrument. Our population differs from the population in which the instrument was developed in that our population is relatively healthy and has a common ethnic and cultural background that reflects their Spanish heritage. We present data that examine the reliability and validity of the 25-item NEI-VFQ used among this population. 
Methods
Subjects
Proyecto VER is a population-based survey of visual impairment and blindness among noninstitutionalized Hispanics, age 40 years or older, living in Pima and Santa Cruz counties of southern Arizona. The study was approved by the Johns Hopkins University Joint Committee on Clinical Investigations and follows the tenets of the Declaration of Helsinki. Based on the 1990 census, the total number of Hispanics 40 years or older living in these two counties was 44,657, of whom 14% were residents of Santa Cruz county. 11 The majority of the population in these two counties is concentrated in two major cities: Nogales in Santa Cruz and Tucson in Pima. Selection was done using a stratified random sample of block groups located in Nogales and Tucson. The probability of selection within the strata was proportional to the size of the Hispanic population 40 years or older in each block group. We selected every other household in Nogales block groups, and 2 of every 3 households in Tucson block groups for listing and eligibility determination. After obtaining informed consent for participation, we gave the participant an extensive home interview and set up an appointment for a complete ophthalmic examination at a central clinic site. 
Home Interview and Clinical Measures
The majority of home interviews (80%) were conducted in Spanish. The questionnaire consisted of specific questions on education, socioeconomic and health status, health care utilization, history and duration of diabetes, history of vision problems, visit to an eye care professional, Native-American ancestry, the 25-item NEI-VFQ, and a series of questions from the Hispanic Health and Nutrition Examination Survey (HHANES). 12 The questionnaire was translated in-house from English to Spanish and then was back-translated to verify accuracy. 
Presenting and best corrected visual acuity was measured at the clinic site. The following methods were used for assessing visual acuity for each eye: an autorefractor (Humphrey Auto-Refractor; Humphrey Instruments, San Leandro, CA) was used as a starting point for a full subjective refraction; distance acuity was tested with the Early Treatment Diabetic Retinopathy Study (ETDRS) chart 13 at 3 m, illuminated at 130 cd/m2. Participants who failed to read the largest letters at 3 m were retested at 1.5 m and then at 1 m. Visual acuity was scored as the total number of letters read correctly, transformed to LogMAR units. Failure to read any letters was assigned an acuity of 1.7 LogMAR units, which is equivalent to an acuity of 20/1000. Presenting acuity was measured with the participant’s habitual distance correction. An E chart 13 was used for participants who were illiterate. Data collection started in April of 1997 and ended in September of 1999. 
25-Item NEI-VFQ
The NEI-VFQ-25 was created from a subset of the original 51-item NEI-VFQ. The shortened questionnaire consisted of items from the following scales: General Health, 1 item; General Vision, 1 item; Ocular Pain, 2 items; Near Vision, 3 items; Distance Vision, 3 items; vision-specific Social Functioning, 2 items; vision-specific Mental Health, 4 items; vision-specific Role Functioning, 2 items; Dependency due to Vision, 3 items; Driving, 2 items; Peripheral Vision, 1 item; and Color Vision, 1 item. Ratings for each question consisted of the following types: “amount of difficulty,” “amount of time,”“ amount true,” and “amplitude”. Ocular pain consisted of 1“ amount of time” item and 1 “amplitude of pain” item; mental health consisted of 1 “amount of time” item and 3 “amount true” items. All other scales consisted of items of the same type. 
Item responses were adjusted for directionality (high values reflect participants with good vision or health) and were transformed to a scale of 0 to 100. Adjusted items belonging to a scale were averaged together to create a single scale score. Participants were excluded from the analysis if they had stopped doing the activity for reasons other than poor eyesight or if someone other than the participant had answered their questionnaire. The number of participants responding to items and the distribution of item responses were examined. 
Statistical Analysis
To estimate the internal consistency of the NEI-VFQ-25 for our population, we calculated the Cronbach α for multi-item scales and average inter-item correlation. Cronbach’s α coefficient is the proportion of a scale’s total variance that is attributable to a latent variable underlying the items (e.g., near vision, distance vision). Acceptable coefficient α is between 0.7 and 0.9: an α that is too low signifies low homogeneity among items, whereas a very high coefficient α reflects redundancy. 14 Coefficient α increases not only as the strength of the correlation between items increases, but also as the number of items in the scale increase. Average inter-item correlation is the average of a scale’s item-to-item correlation coefficients. The inter-item correlation will be less than or equal to α and is not influenced by the number of items in a scale. 15 If a scale has high α and low inter-item correlation, it is most likely because of a greater number of items in that scale rather than a high correlation between items. 
Item–scale correlation coefficients were observed for additional information on scale homogeneity. If an item was not part of a subgroup (e.g., reading newsprint and General Health), the correlation was directly calculated; if an item was part of a subgroup (e.g., reading newsprint and Near Vision), the item was correlated with the sum of the other items in that subgroup. In general, we expect that items belonging to scales will show higher correlation with that scale than items not belonging to the scale. Scales with only two items will reflect the degree to which the items correlate with each other; thus, we do not necessarily expect the correlation coefficients to be high for two-item scales. Streiner suggests that an adequate item–scale correlation be above 0.20. 
Statistically, an item correlated with another single item will have lower correlation than an item correlated with the sum of two or more items with similar variance and direction. For more equivalent comparisons between items in and out of the scale, we calculate an inflation factor for the within-scale item correlations. The derivation of this inflation factor is shown in the Appendix. 
Construct validity is concerned with the theoretical relationship between variables. A valid construct will behave in an expected manner, with regard to another variable. 16 17 We assess validity by estimating the statistical significance of presenting acuity in the better eye (hereafter referred to as “presenting acuity”) and the NEI-VFQ-25 scales. In our model, presenting acuity was represented by a binary variable: acuity of 20/40 or better and acuity worse than 20/40. We also account for age and gender. The linear model is as follows:  
\[scale_{ij}{=}{\beta}_{0}{+}{\beta}_{1}{\cdot}age_{i}{+}{\beta}_{2}{\cdot}gender_{i}{+}{\beta}_{3}{\cdot}acuity_{i}{+}{\epsilon}_{i}\]
 
\[i\ {=}\ participant\ i\]
 
\[j\ {=}\ scale\ j\ {=}\ {\{}gh,\ gv,\ nv,\ etc.{\}}\]
 
\[\mathit{scale}\ {=}\ score\ on\ NEI-VFQ-25\ scale\]
 
\[\mathit{age}\ {=}\ age\ of\ the\ participant\]
 
\[\mathit{gender}\ {=}\ 0\ {=}\ male,\ 1\ {=}\ female\]
 
\[\mathit{acuity}\ {=}\ 0\ {=}\ {\geq}20/40,\ 1\ {=}\ {<}20/40\]
 
We expect participants with worse than 20/40 presenting acuity to have significantly lower scores in NEI-VFQ-25 scales than those with better acuity. Scales that describe activities that require central visual acuity, such as Near Vision and Driving, should have stronger responses than scales that describe other aspects of vision-related quality of life, such as Ocular Pain or Color Vision. 7  
Although the item content of the NEI-VFQ-25 focuses on distinguishing vision-related problems, we were interested in how the scales were related to other demographic variables. The variables we thought would be associated with scale scores included age, gender, acculturation, English or Spanish questionnaire, income, level of education, and whether the participant had medical insurance. Age is the only continuous variable in the model. 
The acculturation index is taken from the Hispanic Health and Nutrition Examination Survey (HHANES). 18 Acculturation, as used in HHANES, refers to two components of adaptation to an English-speaking culture in the United States. The first component measures the trend toward increasing use of English for reading, writing, and conversing; the second accounts for the number of generations of family born in the United States. The acculturation score ranges from 1 to 5, where 1 is low acculturation and 5 is high acculturation. Dividing the score into low (1–1.5), middle (1.5–2), and high (2+) acculturation levels creates our acculturation variable. The income variable was created by dividing income into two levels: family income less than $20,000 (low income) and family income $20,000 or higher (high income). Similarly, the education variable was divided into two levels: those who had completed high school and those who had not. The full model is as follows:  
\[scale_{ij}{=}{\beta}_{0}{+}{\beta}_{1}{\cdot}age_{i}{+}{\beta}_{2}{\cdot}gender_{i}{+}{\beta}_{3}{\cdot}acuity_{i}{+}{\beta}_{4}{\cdot}acc_{i1}\]
 
\[{+}{\beta}_{5}{\cdot}acc_{i2}{+}{\beta}_{6}{\cdot}hqtype_{i}{+}{\beta}_{7}{\cdot}income_{i}{+}{\beta}_{8}{\cdot}edu_{i}\]
 
\[{+}{\beta}_{9}{\cdot}ins_{i}{+}{\epsilon}_{i}\]
 
\[i\ {=}\ Participant\ i\]
 
\[j\ {=}\ scale\ j\ {=}\ {\{}gh,\ gv,\ nv,\ etc.{\}}\]
 
\[\mathit{scale}\ {=}\ score\ on\ NEI-VFQ-25\ scale\]
 
\[\mathit{age}\ {=}\ age\ of\ the\ participant\]
 
\[\mathit{gender}\ {=}\ 0\ {=}\ male,\ 1\ {=}\ female\]
 
\[\mathit{acuity}\ {=}\ 0\ {=}\ better\ than\ or\ equal\ to\ 20/40,\ 1\ {=}\ worse\ than\ 20/40\]
 
\[\mathit{acc}_{1}\ {=}\ 1.5\ {\leq}\ acculturation\ index\ {<}\ 2.0\]
 
\[\mathit{acc}_{2}\ {=}\ 2.0\ {\leq}\ acculturation\ index\]
 
\[\mathit{hqtype}\ {=}\ 0\ {=}\ Spanish\ Questionnaire,\ 1\ {=}\ English\ Questionnaire\]
 
\[\mathit{income}\ {=}\ 0\ {=}\ income\ {\geq}\ \$20,000/y,\ 1\ {=}\ income\ {<}\ \$20,000/y\]
 
\[\mathit{edu}\ {=}\ 0\ {=}\ did\ not\ complete\ high\ school,\ 1\ {=}\ completed\ high\ school\]
 
\[\mathit{ins}\ {=}\ 0\ {=}\ no\ medical\ insurance,\ 1\ {=}\ medical\ insurance\]
 
In addition to using this model in the full population, we used the model in two subgroups of the population: those with impaired presenting acuity and those with nonimpaired presenting acuity. This subdivision allowed us to explore the possible interaction between presenting acuity and the demographic variables. 
We explored the possible nonlinear relationship of scale scores and age using descriptive plots of the data. Plots of scale score against age were plotted, and a smoothed spline was calculated in S-plus, along with 95% confidence intervals. 
To assess the comparability of the Spanish and English versions of the questionnaire, we selected responses from interviewers who each gave 60 or more interviews and whose proportion of English interviews was 30% or more. The goal was to obtain a roughly equal amount of participants taking the Spanish and English questionnaires and to remove a possible interviewer effect. We adjusted scores for the demographic variables that were hypothesized to affect the score: age, gender, acculturation, income, education, and medical insurance. If the Spanish and English versions of the interview are equivalent, we expect to see no differences between reporting on the different versions of the questionnaire. 
Results
Of the 4774 participants in the study, 4761 (99.7%) had completed questionnaires, not completed by proxy. 
The number of responding participants and distribution of item responses is shown in Table 1 . The lowest number of responses occurred in Driving (day and night), with a nonresponse (for reasons other than eyesight) rate of approximately 25%. Going to movies had the next highest nonresponse, occurring at 10%. The rest of the items ranged between 0% and 3% nonresponse. 
A majority of the nondrivers were women and in the low-income category. Drivers had more even distributions among gender and income levels. Of the participants who did not go to movies, a larger proportion was in the low-income category, compared with those who did go to movies. 
Items rated for “amount of difficulty” and “amount of time” were generally skewed, with a high proportion of responses in the “no difficulty” and “not often” categories (ceiling effect). Exceptions occurred in general health and general vision, where responses were more normally distributed, and in “amount of time” worrying about eyesight, which was close to uniformly distributed.“ Amount true” items tended to be bimodally distributed, with the lowest proportion of responses in the middle category (“don’t know” response). 
Internal consistency statistics are shown in Table 2 . Cronbach α scores are highest for the Dependency scale, at 0.86, and lowest for the Driving scale, at 0.65. The Driving and Ocular Pain scales, which have α < 0.7, consist of only two items. This paucity of items results in a lower estimate of α. Note that although the Mental Health scale has a high Cronbach α coefficient, it also has a relatively low average inter-item correlation. The high α for the Mental Health scale most likely occurs because it contains four items, the highest number of any of the scales. 
A description of the strength of association between items and scales is shown in Table 3 , where item–scale correlation coefficients are presented. The three-item scales, Near Vision, Distance Vision, and Dependency, had high item–scale correlation with their member items, ranging between 0.58 and 0.80. The four-item scale, Mental Health, had high correlation with three of its items. Also, Mental Health tended to have high correlation with items in the Role Difficulties and Dependency scales. Otherwise, item–scale correlation was generally consistent, where items within a scale tended to have higher correlation with the scale than items outside. This consistency was true for all scales except Social Function, where Going to Movies seemed to have higher association with Social Function than its own items. 
We corrected the low item–scale correlation with an inflation factor that adjusts the correlation on the basis of the number of items within the scale. Table 4 shows inflation factors and inflated item–scale correlation coefficients. Scales with two items had inflation factors on the order of 1.13 ± 0.03, whereas three- and four-item scales had inflation factors on the order of 1.05 ± 0.01. Note that the inflated item–scale correlation coefficients for the Social Function scale are 0.63: although this does not exceed the correlation of 0.65 for Going to Movies and Social Function, the coefficients are now fairly equivalent. 
The results of the linear regression from Eq. 1 are shown in Table 5 . The estimate of the intercept represents the average scale score for men age 57 years, with presenting acuity 20/40 or better. The number of participants responding to each scale is shown, as well as the proportion of the participants with a score of 100. For those with presenting acuity worse than 20/40, we present the average amount their scores differ from the reference group. Values are means ± SE. 
Those with impaired acuity scored significantly lower than those with nonimpaired acuity, in all scales, adjusting for age and gender. Participants with impaired acuity reported the most difficulty with Distance Vision and Driving. 
A summary of the association of other demographic variables is shown in Table 6 . The variables that have significant and consistent directionality across the NEI-VFQ-25 scales are income, gender, and presenting acuity. Those with high yearly income had better scores in all scales. Women had lower scores than men, except in Color Vision. Participants with middle acculturation generally had better scores than both those with low acculturation and those with high acculturation, except in Ocular Pain. There was no association between age and scores in General Vision and Mental Health. Near Vision and Ocular Pain showed a positive association with age. Those with medical insurance tended to have lower scores in General Health and Ocular Pain scales. 
There appeared to be a significant difference in response between those who took the English questionnaire and those who took the Spanish questionnaire; however, we undertook analyses to be certain this was not due to differences in the questionnaire itself. The subgroup analysis, in which interviewers administered sizable numbers of both versions, was based on 845 participants using the Spanish questionnaire and 691 using the English version. There were no differences in reporting for all scales except General Health (higher scores, on average, for those taking the English version) and Color Vision (lower scores, on average, for those taking the English version). The version of the questionnaire was administered at the choice of the participant and was highly tied to the city in which the participant resided: 29% of those residing in Tucson took the English version, compared with only 3% of those residing in Nogales. 
When analyzing the impaired and nonimpaired subgroups separately, we found that parameter estimates that were significantly associated with scale scores from the total group continued to be significantly associated in the nonimpaired group. In the impaired subgroup, however, some of the associations became nonsignificant because of the few number of participants in this group (only 8.1% with worse than 20/40 presenting acuity). However, we can evaluate the influence of the demographic variables on the basis of the magnitude and direction of the estimates. 
In a majority of the scales (Distance Vision, Driving, Role Function, Dependency, Social Function, Mental Health, Color Vision, and Peripheral Vision) the directionality of the parameter estimates in the impaired group was similar to estimates in the nonimpaired group. In these scales, the magnitude of the estimates from the impaired group was equal to the corresponding estimates in the nonimpaired group. In other scales (General Health, General Vision, Near Vision, Ocular Pain, and Peripheral Vision) the magnitude of the estimates from the impaired group was equal to the corresponding estimates in the nonimpaired group but with opposite directionality. Thus, the influence of the demographic variables is not diminished in the group with visual impairment and is important in determining selfreported function. 
The behavior of age and the different NEI-VFQ-25 scales varies, with evidence of a threshold effect in some scales and a general linear decline in others. Figure 1 shows smoothed plots of some scale scores against age: we observe some nonlinear trends as age changes. Scales such as Driving, Distant Vision, and Role Difficulties are flat and then decline after a certain age. In the Near Vision scale, the scores start high at age 40 years, slope downward at about age 50 years, and then climb back by age 70 years. This nonlinear association may better explain the relationship of age and near vision than a linear association and present a more understandable picture. 
Discussion
In this study, we undertook an analysis of the properties of the NEI-VFQ-25 when administered in a Hispanic population. In general, the population was able to respond to all the items, except for Driving, and Going to Movies, which were less commonly performed activities. 
Comparing scores from participants with nonimpaired and impaired presenting acuity showed evidence for validity of the questionnaire. Impaired presenting acuity was significantly associated with decrements in all scales in the NEI-VFQ-25, accounting for age and gender. Ideally, this instrument should be sensitive only to different levels and types of visual impairment. The value of testing the NEI-VFQ-25 in a population-based study is to determine non-vision related variables that may influence item responses. We found that among both visually impaired and non–visually impaired groups, demographic variables such as age, gender, and income play an important role in selfreported function. However, we also found a limitation to its use in this type of study: we were not able to capture as wide a distribution of selfreported values as might exist in visually impaired groups with large numbers of persons with severe impairments. This truncation in the distribution can be seen in the high ceiling effects generated by this generally healthy population. 
We also found some limitations in the scale properties of the NEI-VFQ-25. First, we could not determine the internal reliability of several scales, because the scales consisted of only one item. These items include Peripheral Vision, Color Vision, General Health, and General Vision. Although we may explore the validity of these scales, there is no way to determine internal consistency, because there are no other items measuring a similar variable with which to compare. These items should probably not be classified as scales. 
Second, the nature of the response to items differed, with some responses being “amount of difficulty,” some being “amount of truth,” and others being “amount of time.” Combining such disparate responses into an overall score assumes some underlying rationale for equivalencies of these responses, which is unknown and likely not valid. 19 It is for this reason that we chose to work only within the scales and across the scales without presuming a summary score of all the scales (overall score). This issue also presents a problem in the mental health scale, where one of the four items is a “how often” question, whereas the other items were “amount true” items. 
Third, some of the item–scale scores correlated better with other scales than the scales to which they were assigned. This was especially true for Social Function scale, which has a higher correlation with“ going to see movies, etc.” than with items in the scale. The Mental Health scale was highly correlated with items from the Dependency and Role Function scales, suggesting that the Mental Health scale might be absorbed, rather than be a scale unto itself. The high correlation may also be attributed to the scale construct: both Mental Health and Dependency items use “amount true” type response constructs. 
Differences in response between the Spanish and English translations of the home questionnaire are not likely due to differences in translation, given our rigorous methods of translating and back-translating the questions. Also, we found that the majority of scales showed no differences in reporting between the Spanish and English versions in the subset of participants who had interviewers administering large numbers of both versions. A possible explanation for the differences we did find is that those who selected the Spanish version were inherently different from those who selected the English version, even after accounting for a measurement of“ acculturation.” Because most of those who exclusively chose the Spanish version resided in Nogales, compared with Tucson, we cannot rule out an effect of living in a border community with strong cultural ties to Mexico. 
It is important to note that we only performed certain analyses of the psychometric properties of this instrument. Reliability was only assessed as internal consistency and item–scale correlation. Also, we examined the validity of the instrument in relation to visual acuity loss only and to no other measure of visual loss. Clearly, visual loss may have multiple dimensions, such as loss of contrast sensitivity, peripheral field, stereo acuity, and other measures, some of which may be more closely correlated with decrements in the selfreported scores. However, our intent was to show general validity, which was demonstrated with the decrements in acuity. 
We collected information on the existence of diabetes and high blood pressure, which we found were associated with scale score, diabetes more so than high blood pressure. However, other co-morbidity information was not collected and was most likely absorbed in the age and possibly income variables. Therefore, further refinement by consideration of other co-morbid conditions is warranted. Finally, this study presents results of the NEI-VFQ-25 performance in a population-based study consisting of Mexican Americans; we realize that different responses and interpretations of the questions may occur in other population groups with a different cultural framework. It is important that such studies be carried out to show the utility of the NEI-VFQ in heterogeneous populations. 
In summary, in this study of Hispanic people age 40 years or older, the NEI-VFQ-25 was sensitive to presenting acuity and to other demographic variables, such as age, gender, and income. The findings from this psychometric analysis provide evidence of the reliability and validity of some of the scales in the 25-item NEI-VFQ when used among people with a range of visual acuity levels. Because demographic properties of a population are associated with response to scale items, explanatory variables other than visual function should be considered when creating hypotheses about the scales. A summary score across all scales is not justified for this instrument. 
Appendix 1
Inflation Factor Derivation
In standard psychometric theory each item response is modeled as a“ true score” plus some measurement error:  
\[I_{j}{=}T{+}{\epsilon}_{j}\]
where I j is the jth item in a give scale, T is the underlying true score (contributing equally to all items in the scale), andε j is the random error in item j
We make the traditional assumptions about ε j : cov(T, ε j ) = 0 and cov(ε j k ) = 0, for jk. In addition, we assume that var(T) =γ 2 and var(ε j ) =ν 2, independent of j. Then, we obtain the following:  
\[\mathrm{var}\ (I_{j}){=}\mathrm{var}\ (T){+}\mathrm{var}\ ({\epsilon}_{j}){=}\ {\nu}^{2}\ {+}\ {\gamma}^{2}\ {=}\ {\varsigma}^{2}\]
 
\[cov\ (I_{j},\ I_{k})\ {=}\ ;cov\ (T\ {+}\ {\epsilon}_{j},\ T\ {+}\ {\epsilon}_{k})\ {=}\ var\ (T)\ {=}\ {\gamma}^{2}\]
 
\[corr\ (I_{j},\ I_{k})\ {=}\ \frac{cov\ (I_{j},\ I_{k})}{\sqrt{var\ (I_{j})\ var\ (I_{k})}}\ {=}\ \frac{{\gamma}^{2}}{{\varsigma}^{2}}\ {=}\ {\rho}\]
 
The correlation between item 1 and the average of items 2 and 3 is then  
\[\mathrm{corr}\ \left(I_{1},\ \frac{I_{2}{+}I_{3}}{2}\right)\ {=}\ \frac{\mathrm{cov}\ \left(I_{1},\ \frac{I_{2}{+}I_{3}}{2}\right)}{\sqrt{\mathrm{var}\ (I_{1})\ \mathrm{var}\ \left(\frac{I_{2}{+}I_{3}}{2}\right)}}\ {=}\ \frac{\frac{1}{2}\ \mathrm{cov}\ (I_{1},\ I_{2}){+}\ \frac{1}{2}\ \mathrm{cov}\ (I_{1},\ I_{3})}{\sqrt{\frac{{\varsigma}^{2}}{4}\ (2{\varsigma}^{2}{+}2\ \mathrm{cov}\ (I_{2},\ I_{3}))}}\]
 
\[{=}\ \frac{{\gamma}^{2}}{\sqrt{\frac{{\varsigma}^{2}}{4}\ (2{\varsigma}^{2}{+}2{\gamma}^{2})}}{=}\ \frac{{\gamma}^{2}\sqrt{2}}{{\varsigma}^{2}\sqrt{1{+}\ \frac{{\gamma}^{2}}{{\varsigma}^{2}}}}\ {=}\ \frac{{\rho}\sqrt{2}}{\sqrt{1{+}{\rho}}}\]
By similar reasoning,  
\[\mathrm{corr}\ \left(I_{1},\ \frac{1}{n-1}\ {{\sum}_{j{=}2}^{n}}\ I_{j}\right)\ {=}\ \frac{{\rho}\sqrt{n-1}}{\sqrt{1{+}(n-2){\rho}}}\]
Thus, the inflation factor for a 3-item scale is  
\[\frac{\mathrm{corr}\ \left(I_{1},\ \frac{I_{2}{+}I_{3}{+}I_{4}}{3}\right)}{\mathrm{corr}\ \left(I_{1},\ \frac{I_{2}{+}I_{3}}{2}\right)}{=}\sqrt{\frac{3}{2}\ \left(\frac{1{+}{\rho}}{1{+}2{\rho}}\right)}\]
This inflation factor will range from \(\sqrt{^{\underline{3}}_{2}}\) when ρ = 0, to 1 when ρ = 1. We can estimate ρ with the average inter-item correlation for a given scale. 
Table 1.
 
Distribution of Responses
Table 1.
 
Distribution of Responses
Type of Question NEI-VFQ Items N Rating*
0–20 21–40 41–60 61–80 81–100 Abbr.
Rating (high = good) Overall health (gh) 4759 7.1 38.0 33.4 13.3 8.2 ghealth
Overall vision (gv) 4758 1.2 8.2 39.2 41.6 9.9 gvision
Difficulty (high = not difficult) Reading ordinary print in newspapers (nv) 4716 1.6 14.4 11.5 24.1 48.4 newsp
Work or hobbies that require you to see well up close (nv) 4732 0.9 8.6 9.2 21.7 59.7 upclose
Finding something on a crowded shelf (nv) 4747 0.4 5.1 5.0 14.5 75.0 crowded
Reading street or shop signs (dv) 4734 0.3 4.6 5.5 15.0 74.7 stsigns
Going down stairs in dim light (dv) 4707 0.7 6.2 6.6 16.1 70.5 stairs
Going out to see movies, plays, sports events (dv) 4296 1.1 2.0 2.6 7.1 87.1 movies
Driving during the day (dr) 3615 2.6 0.4 0.8 3.8 92.4 daydrive
Driving during the night (dr) 3555 5.2 6.2 10.0 24.8 53.7 ngtdrive
Amount of time (high = low amount) Eye pain keeps you from activities (op) 4757 1.9 3.8 10.6 15.5 68.1 tmpain
Amplitude of eye pain (op) 4760 1.4 4.6 17.0 26.9 50.1 ampain
Amount of time (high = not often) Accomplish less because of vision (rd) 4757 2.8 5.1 12.3 11.6 68.2 accomp
Limited in length of activities because of vision (rd) 4755 2.2 3.3 6.7 8.2 79.5 lcando
Amount true (high = don’t agree) Stay home because of eyesight (dp) 4756 2.0 2.9 0.6 3.5 91.0 sthome
Rely on others because of eyesight (dp) 4756 2.1 4.4 0.6 3.8 89.2 relyot
Need help from others because of eyesight (dp) 4757 1.6 4.1 0.5 3.9 90.0 ndhelp
Difficulty Seeing how people react to things you say (sf) 4750 0.1 1.3 2.2 6.5 89.9 preact
Visiting with people (sf) 4628 0.6 1.1 1.8 4.5 92.0 visito
Amount of time Worry about eyesight (mh) 4760 13.9 15.8 25.5 23.4 21.5 worryv
Amount true Worry about embarrassing myself because of eyesight (mh) 4757 2.1 3.7 0.6 2.5 91.1 worrye
Feel frustrated because of eyesight (mh) 4757 4.0 8.7 1.2 3.3 82.7 frusta
Have less control of what I do because of eyesight (mh) 4756 3.1 8.2 1.3 4.7 82.7 cntrol
Difficulty Picking out and matching your clothes (cv) 4751 0.2 1.5 1.3 5.0 92.0 colorv
Noticing objects off to the side (pv) 4743 0.2 3.0 4.0 9.7 83.1 periv
Table 2.
 
Internal Consistency
Table 2.
 
Internal Consistency
Scale n Cronbach α
Near vision 4694 0.77 0.54
Distant vision 4267 0.76 0.51
Driving 3554 0.65 0.54
Ocular pain 4759 0.68 0.52
Role difficulties 4758 0.81 0.69
Dependency 4758 0.86 0.67
Social function 4625 0.72 0.56
Mental health 4758 0.74 0.45
Table 3.
 
Item-to-Scale Pearson Correlation Coefficients
Table 3.
 
Item-to-Scale Pearson Correlation Coefficients
NEI-VFQ Items N General Health General Vision Near Vision Distant Vision Driving Ocular Pain Role Diff Depend Social Fncn Mental Health Color Vision Periph Vision
Reading ordinary print in newspapers (nv) 4716 0.17 0.37 0.59 0.43 0.32 0.32 0.43 0.36 0.30 0.41 0.23 0.33
Work or hobbies that require you to see well up close (nv) 4732 0.17 0.35 0.67 0.53 0.36 0.35 0.50 0.41 0.40 0.47 0.31 0.42
Finding something on a crowded shelf (nv) 4747 0.16 0.30 0.58 0.59 0.37 0.36 0.50 0.44 0.50 0.47 0.37 0.51
Reading street or shop signs (dv) 4734 0.16 0.31 0.50 0.60 0.47 0.32 0.46 0.45 0.51 0.45 0.36 0.51
Going down stairs in dim light (dv) 4704 0.21 0.29 0.54 0.58 0.50 0.40 0.55 0.50 0.54 0.53 0.37 0.59
Going out to see movies, plays, sports events (dv) 4296 0.14 0.26 0.47 0.58 0.49 0.34 0.52 0.53 0.65 0.50 0.44 0.51
Driving during the day (dr) 3615 0.12 0.20 0.30 0.45 0.54 0.22 0.40 0.44 0.42 0.35 0.28 0.36
Driving during the night (dr) 3555 0.22 0.31 0.41 0.57 0.54 0.37 0.47 0.43 0.39 0.43 0.25 0.42
Eye pain keeps you from activities (op) 4757 0.19 0.28 0.42 0.46 0.39 0.52 0.59 0.48 0.37 0.51 0.26 0.39
Amplitude of eye pain (op) 4760 0.17 0.21 0.30 0.28 0.23 0.52 0.32 0.24 0.20 0.32 0.14 0.24
Accomplish less because of vision (rd) 4757 0.22 0.34 0.53 0.54 0.44 0.49 0.69 0.54 0.42 0.61 0.30 0.43
Limited in length of activities because of vision (rd) 4755 0.20 0.32 0.52 0.58 0.48 0.47 0.69 0.61 0.50 0.63 0.36 0.47
Stay home because of eyesight (dp) 4756 0.17 0.26 0.38 0.50 0.42 0.37 0.54 0.64 0.51 0.62 0.38 0.43
Rely on others because of eyesight (dp) 4756 0.17 0.29 0.45 0.53 0.42 0.37 0.57 0.80 0.48 0.68 0.38 0.47
Need help from others because of eyesight (dp) 4757 0.16 0.30 0.43 0.52 0.41 0.36 0.54 0.77 0.49 0.66 0.37 0.44
Seeing how people react to things you say (sf) 4750 0.17 0.26 0.43 0.59 0.37 0.29 0.44 0.47 0.56 0.43 0.51 0.57
Visiting with people (sf) 4628 0.15 0.23 0.40 0.59 0.41 0.28 0.45 0.54 0.56 0.45 0.52 0.48
Worry about eyesight (mh) 4760 0.17 0.26 0.28 0.27 0.18 0.26 0.29 0.25 0.19 0.29 0.13 0.21
Worry about embarrassing myself b/c of eyesight (mh) 4757 0.15 0.27 0.41 0.51 0.38 0.36 0.52 0.72 0.49 0.58 0.35 0.43
Feel frustrated because of eyesight (mh) 4757 0.18 0.33 0.47 0.53 0.39 0.43 0.61 0.67 0.44 0.68 0.32 0.44
Have less control of what I do because of eyesight (mh) 4756 0.20 0.33 0.49 0.54 0.43 0.44 0.67 0.72 0.45 0.68 0.34 0.44
Table 4.
 
Inflated Item-to-Scale Correlation Coefficients
Table 4.
 
Inflated Item-to-Scale Correlation Coefficients
NEI-VFQ Items Inflation Factor Inflated Correlation
Reading ordinary print in newspapers (nv) 1.05 0.62
Work or hobbies that require you to see well up close (nv) 1.05 0.71
Finding something on a crowded shelf (nv) 1.05 0.61
Reading street or shop signs (dv) 1.06 0.64
Going down stairs in dim light (dv) 1.06 0.62
Going out to see movies, plays, sports events (dv) 1.06 0.62
Driving during the day (dr) 1.14 0.62
Driving during the night (dr) 1.14 0.62
Eye pain keeps you from activities (op) 1.15 0.60
Amplitude of eye pain (op) 1.15 0.60
Accomplish less because of vision (rd) 1.09 0.75
Limited in length of activities because of vision (rd) 1.09 0.75
Stay home because of eyesight (dp) 1.04 0.66
Rely on others because of eyesight (dp) 1.04 0.83
Need help from others because of eyesight (dp) 1.04 0.80
Seeing how people react to things you say (sf) 1.13 0.63
Visiting with people (sf) 1.13 0.63
Worry about eyesight (mh) 1.04 0.30
Worry about embarrassing myself b/c of eyesight (mh) 1.04 0.60
Feel frustrated because of eyesight (mh) 1.04 0.71
Have less control of what I do because of eyesight (mh) 1.04 0.71
Table 5.
 
Presenting Acuity Effect on NEI-VFQ Scales
Table 5.
 
Presenting Acuity Effect on NEI-VFQ Scales
NEI-VFQ Presenting Acuity (PA)
Reference Difference from Reference
PA Better than or Equal to 20/40* N % Score 90–100 PA Worse than 20/40* N % Score 90–100
General health 47.4 ± 0.6 4367 8.3 −4.8 ± 1.4 385 6.5
General vision 72.0 ± 0.4 4365 10.3 −8.2 ± 0.9 386 5.7
Near vision 85.4 ± 0.5 4367 56.2 −14.6 ± 1.1 382 37.1
Distance vision 93.5 ± 0.4 4366 75.6 −15.4 ± 0.9 381 43.6
Driving 90.4 ± 0.5 3440 55.2 −21.0 ± 1.5 174 29.9
Peripheral vision 94.8 ± 0.4 4358 84.9 −10.6 ± 0.9 380 62.6
Color vision 97.0 ± 0.3 4365 93.1 −6.6 ± 0.7 381 79.8
Ocular pain 86.3 ± 0.5 4368 42.6 −8.5 ± 1.1 386 36.3
Vision specific
Role difficulties 90.2 ± 0.5 4368 68.1 −16.9 ± 1.2 382 41.1
Dependency 96.5 ± 0.4 4368 89.8 −14.7 ± 0.9 382 66.0
Social functioning 97.6 ± 0.3 4366 88.6 −9.0 ± 0.6 382 66.8
Mental health 85.3 ± 0.5 4368 41.0 −15.6 ± 1.1 386 27.7
Table 6.
 
Relationship of NEI-VFQ Scales to Demographic Variables
Table 6.
 
Relationship of NEI-VFQ Scales to Demographic Variables
NEI-VFQ Age Sex Mid-Acult High-Acult HQType Edu Income Ins
General health + 0 + +
General vision 0 + 0 0 + 0
Near vision + + + + 0
Distance vision + 0 0 0
Driving + 0 0 0
Peripheral vision 0 0 + 0
Color vision 0 0 0 0 0
Ocular pain + 0 + +
Role difficulties + 0 0 0
Dependency + 0 0 0
Social functioning 0 0 0 0
Mental health 0 0 0 0 + 0
Figure 1.
 
Nonlinear relationship of scale scores and age.
Figure 1.
 
Nonlinear relationship of scale scores and age.
 
The authors thank Karen Bandeen-Roche and Karl Broman for their advice and input and are indebted to the Team of Proyecto VER for their skill and support. 
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