Highlights
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African Americans endure a disproportionate burden of visual impairment.
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Vision quality of life was characterized by daily tasks and emotional well-being.
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Field loss had the greatest impact on completing daily visual tasks.
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A meaningful change in visual task was associated with 6 dB lower visual field.
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Preventing visual field loss is important for preserving vision quality of life.
Purpose
To elucidate how visual field loss (VFL) impacts self-reported vision-specific quality of life (VSQOL) in African Americans, who experience a disproportionate burden of visual impairment.
Design
Cross-sectional, population-based cohort.
Methods
Eligible participants (n = 7,957) were recruited who self-identified as African American, were aged 40 years or older, and resided in Inglewood, California, USA. A total of 6,347 participants (80.0%) completed clinical eye examinations. Total mean deviation (MD) of VFL was measured bilaterally as decibels (dB) using the Humphrey SITA Standard 24-2 test. VSQOL was measured using the National Eye Institute Visual Function Questionnaire (NEI-VFQ 25) and scored using item response theory (IRT).
Results
Participants with reliable data (n = 5,121) had a mean age of 60.7 years (standard deviation 11.0); those with worse VFL were older; had more comorbidities, lower income, less education, and worse visual acuity; and were more likely to be unemployed and depressed. Using IRT analysis, a change in VF of 6.2 (95% confidence interval [CI]: 5.3, 7.7) dB and 9.2 (95% CI: 7.5, 11.9) dB was necessary to observe a meaningful (5-point) difference in vision-related task and emotional well-being scores, respectively. VFL had the greatest impact on self-reported driving ability (6.0 dB [95% CI: 5.2, 7.1]), followed by satisfaction with general vision, near vision, vision-related mental health, and peripheral vision.
Conclusions
The strongest impact of VFL reported by African Americans was on their ability to complete visual tasks, especially for driving. An effect of VFL on emotional well-being also was observed, but the magnitude of association was about 50% lower for well-being compared to that of task.
T T he number of people impacted by eye disease is expected to increase in the next 3 decades with the aging of the United States (US) population. The total US economic burden of vision loss and eye disorders in 2013 was an estimated $139 billion. In 2015, the number of US adults aged 40 years and older that were visually impaired reached 3.22 million, which is expected to double by 2050. In the US, older adults, women, and African Americans endure a disproportionate amount of visual impairment (VI). Research is necessary to investigate how VI affects physical and mental health including vision-specific quality of life (VSQOL). Although it is established that VI reduces self-reported health outcomes, investigating specific domains of VSQOL impacted and the magnitude of effects for different US subpopulations may refine our knowledge of how people experience vision loss.
Population-based studies have not evaluated how VI—encompassing both visual field loss (VFL) and visual acuity (VA)—affects VSQOL in African Americans exclusively. But VFL has also been related to worse VSQOL in multicultural populations that include African Americans, as well as in population-based cohorts of Latinos. Existing literature has demonstrated that VFL is associated with reduced physical activity and travel from home as well as more frequent falls, , hip fractures, and automobile accidents. Additional studies have found similar associations in glaucoma patients identified in clinics, , but these results may not represent the impact VFL has on VSQOL in the broader population.
To the best of our knowledge this is the first study to assess how VFL impacts VSQOL in a population-based sample composed entirely of African Americans. We studied participants in the African American Eye Disease Study (AFEDS), a cohort of African Americans 40 years and older residing in Inglewood, California, USA. We used item response theory (IRT) to propose loadings for survey items onto 2 domains of VSQOL—task and well-being. We hypothesized that VSQOL would be inversely related with mild VFL both as a continuous measure in the better-seeing eye (BSE) and as categories of bilateral VFL severity. Finally, we considered how more granular domains of VSQOL are impacted by VFL.
Methods
AFEDS is a population-based, cross-sectional cohort of 6,347 subjects aged 40 years and older residing in 32 US census tracts in the city of Inglewood, California, USA. Data were collected from 2014 to 2018. A detailed description of data collection methods has been published elsewhere. In brief, eligible residents were identified by door-to-door census. Participants were interviewed in their homes and completed a comprehensive clinical eye examination at the local eye clinic. Home interviewing was conducted after informed consent to gather demographic factors and access to medical services. A comorbidity score was calculated as the sum of 12 self-reported medical conditions. Information on visual function and QOL was collected during the clinical eye examination. The University of Southern California Institutional Review Board approval was obtained prospectively before collecting data. All study procedures adhered to the recommendations of the Declaration of Helsinki.
VA was measured in each eye with presenting correction at 4 meters using standard Early Treatment Diabetic Retinopathy Study protocols with a modified distance chart illuminator (Precision Vision, Woodstock, Illinois, USA). , VA loss was defined as presenting VA of 20/40 or worse based on the US definition of VI. Visual fields for each eye were assessed using the Swedish Interactive Threshold Algorithm (SITA) Standard C24-2 test (Humphrey Field Analyzer II 750; Carl Zeiss, Dublin, California, USA). VFL was measured as mean deviation (MD) in decibels (dB), where more negative scores indicated worse VFL. Unreliable measurements with more than 15% false-negatives or false-positives were excluded ; fixation losses were not used as reliability criteria. A sensitivity analysis was performed to investigate the impact of reliability on visual field by relaxing cutoffs to 20% false-positives and 25% false-negatives. Continuous VFL was assessed as unilateral MD in the BSE, which has been shown to be as strong an indicator of VSQOL as integrated and binocular VFL. , VFL categories were based on patterns of laterality (unilateral or bilateral) and severity (mild or moderate-to-severe). , Participants were classified as having no VFL (MD > -2 dB in both eyes), unilateral mild VFL (-6 dB ≤ MD ≤ -2 dB and MD > -2 dB), unilateral moderate-to-severe VFL (MD < -6 and MD > -2 dB), bilateral mild VFL (-6 dB ≤ MD ≤ -2 dB; or MD < -6 dB and -6 dB ≤ MD ≤ -2), and bilateral moderate-to-severe VFL (MD < -6 dB). , We collapsed clinical subdivisions of moderate and severe VFL into a single category owing to low power in these groups individually; this is expected, as both moderate and severe VFL are rare in the population, contrary to what is expected in the clinical setting.
The National Eye Institute Visual Function Questionnaire-25 (NEI-VFQ 25) and the 12-Item Short Form Health Survey (SF-12) were administered by trained interviewers before the clinical examination. VSQOL was measured using the NEI-VFQ 25. , Classical test theory (CTT) analysis of the NEI-VFQ 25 has been validated for various eye diseases in numerous populations. , CTT was completed in the current analysis to allow comparisons with existing literature. Each item was scaled from 0 to 100, with 100 representing maximum VSQOL. Twenty-five items were grouped into 11 vision-specific subscales; a CTT composite score was produced from the mean of all subscales. The NEI-VFQ 25 was also analyzed using the graded response model, a 2-parameter IRT model for ordinal items on a Likert scale. , IRT models classified people with varying VSQOL scores along a linear continuum of item difficulty. , IRT was used to produce 2 VSQOL composites from NEI-VFQ 25 items—task and well-being. , The task composite score was calculated from 13 items belonging to subscales for vision-related role function, distance vision, driving difficulties, peripheral vision, near vision, and color vision. The well-being composite was calculated from 12 items for dependency, general vision, mental health, ocular pain, and social functioning. Task and well-being composite scores were calculated using SAS PROC IRT. Health-related quality of life (HRQOL) was measured as physical and mental component summary scores calculated for the standard US norm-based SF-12 ; a score of 50 (standard deviation [SD] 10) was the average score among US adults.
Differences in covariables were compared among nonexcluded and excluded participants using Wilcoxon rank sum and Fisher exact tests. Continuous covariates were compared across VFL severity categories using analysis of variance, and categorical variables were compared using Bonferroni-adjusted χ 2 tests; all covariables were evaluated using Tukey pairwise comparisons. Tests for trend were performed using the Wilcoxon rank sum test for continuous variables and the 2-sided, exact Cochran-Armitage test for categorical variables.
The generalized linear model was used to assess the relationship between VFL and VSQOL. Conceptual models of VI were developed during discussions with the AFEDS External Advisory Committee. Multivariable linear regression models were adjusted for age, number of comorbidities, sex (female), education (<4 years of college), working status (unemployed), income (≤$20,000), health and vision insurance (yes), VA loss (20/40 or worse), and depression in the last 4 weeks (yes). Missing covariates were imputed by multiple imputation with chained equations. Locally weighted scatterplot smoothing (LOWESS) plots with 95% confidence intervals (CI) were produced for predicted VSQOL outcomes. A 5-unit change in the NEI-VFQ 25 has been associated with a 2-line deficit in VA, which is considered a clinically important change in visual function ; β coefficients from linear regression models were multiplied by 5 units of VSQOL to obtain the corresponding change in VFL. Analysis of covariance (ANCOVA) was used to calculate adjusted mean scores of VFL categories based on VFL severity categories. Effect sizes (ES) were calculated as the difference in adjusted mean scores of each VFL severity level from those without VFL divided by the SD for those without VFL. ES from 0.20 to 0.50 were considered small, from 0.50 to 0.80 were medium, and ≥0.80 were large effects.
All analyses were performed using SAS software 9.4 (SAS Institute, Cary, North Carolina, USA). Data visualization was produced using ggplot2 package for R.
Results
Cohort Description
Of 7,957 identified as eligible, 6,347 (80.0%) participants were included in the final AFEDS cohort. The analytic cohort used for linear regression modeling was composed of 5,121 participants after exclusion of those with incomplete QOL scores or unreliable VFL measurements in both eyes (Supplemental Figure 1; Supplemental Material available at AJO.com). Compared to those excluded (Supplemental Table 1; Supplemental Material available at AJO.com), participants in the analytic cohort were younger (mean age 60.7 vs 62.0 years), had fewer comorbidities (mean 2.3 vs 2.7), and were less likely to be unemployed (54% vs 62%), to earn less than $20,000 per year (28% vs 39%), and to have VA loss (7% vs 17%) ( P < .001). However, these differences were small for income (ES = 0.24) and VA loss (ES = 0.30) and negligible for age, comorbidities, and unemployment (ES < 0.20). There were no statistically significant differences among the 2 groups in sex, education, health insurance, vision insurance, or depression status. The VFL severity cohort used for ANCOVA analyses was composed of 4,207 participants after further exclusion of participants with unreliable measurements in 1 eye (Supplemental Table 2; Supplemental Material available at AJO.com).
More than 95% of the analytic cohort had complete sociodemographic and clinical characteristics, except for 994 (19%) participants with missing income ( Table 1 ). The analytic cohort had a mean age of 60.7 years (SD 11.0 years) and 2.3 comorbidities on average (SD 1.9). Most were women (63%), did not complete college (65%), and had health (92%) and vision insurance (68%). Trends over VFL categories of increasing severity ( P -trend < .01) were observed for older age, more comorbidities, lower income, greater unemployment, VA loss, and depression; there was also a trend with lower education ( P = .04). There were no significant trends between VFL severity categories and sex, health insurance, or vision insurance.
| Sociodemographic and Clinical Characteristics | Analytic Cohort (n = 5,121) a | Visual Field Loss Severity Categories (n = 4,207) b | ||||||
|---|---|---|---|---|---|---|---|---|
| Unilateral | Bilateral | |||||||
| No VFL (n = 2,348) | Mild (n = 848) | Moderate to Severe (n = 92) | Mild (n = 792) | Moderate to Severe (n = 127) | P Value c | Trend Test d | ||
| Age, mean years (SD) | 60.68 (11.00) | 58.26 (9.83) a | 60.92 (10.78) b | 62.86 (9.48) b , c | 64.25 (12.10) c , d | 66.91 (12.11) d | <.001 | <.001 |
| Comorbidities, mean (SD) e | 2.34 (1.90) | 2.12 (1.79) a | 2.39 (1.86) b | 2.43 (1.95) a , b , c | 2.58 (1.96) b , c | 3.02 (2.12) c | <.001 | <.001 |
| Female sex | 3,205 (63%) | 1,456 (62%) a | 532 (63%) a | 53 (58%) a | 507 (64%) a | 71 (56%) a | 1.000 | .897 |
| Unemployed | 2,698 (54%) | 1,050 (46%) a | 458 (56%) a , b | 51 (57%) a , b | 486 (63%) b | 99 (79%) c | <.001 | <.001 |
| Income ≤$20,000 | 1,165 (28%) | 436 (23%) a | 186 (27%) a , b | 18 (24%) a | 223 (35%) a , b | 36 (39%) b | <.001 | <.001 |
| Education <16 years | 3,260 (65%) | 1,452 (63%) a , b | 546 (66%) a , b | 50 (56%) b | 517 (67%) a , b | 89 (71%) a | .484 | .038 |
| Health insurance: yes | 4,592 (92%) | 2,090 (91%) a | 764 (92%) a | 83 (92%) a | 717 (92%) a | 119 (95%) a | 1.000 | .078 |
| Vision insurance: yes | 3,323 (68%) | 1,504 (67%) a | 572 (71%) a | 64 (73%) a | 520 (68%) a | 84 (70%) a | 1.000 | .415 |
| Visual acuity loss: yes f | 366 (7%) | 71 (3%) a | 48 (6%) a , b | 7 (8%) b , c | 114 (14%) c | 34 (27%) d | <.001 | <.001 |
| Depressed: yes g | 303 (6%) | 108 (5%) a | 54 (6%) a | 7 (8%) a | 64 (8%) a | 10 (8%) a | .029 | <.001 |
a The number missing is 144 (2.8%) for unemployment, 994 (19.4%) for income, 139 (2.7%) for education, 101 (2.0%) for insurance, 252 (4.9%) for vision insurance, and 6 (0.1%) for visual acuity loss.
b The analytic cohort had reliable visual field measurements in both eyes for 4,207 (81.3%) of participants and could be categorized into VFL severity categories. Analysis of variance (ANOVA) least square means are shown across VFL severity categories with Tukey-Kramer-adjusted pairwise comparisons. Owing to missing covariate data, not all VFL severity categories sum to the total (n = 4,207); the number missing is 108 (2.6%) for unemployment, 811 (19.3%) for income, 101 (2.4%) for education, 73 (1.7%) for insurance, 193 (4.6%) for vision insurance, and 2 (0.0%) for visual acuity loss.
c P values based on ANOVA type 3 sums of squares for continuous variables and χ 2 tests for categorical variables (Bonferroni-adjusted for multiple comparisons). ANOVA revealed significant differences across the visual field loss categories for both age and number of comorbidities. Bonferroni-adjusted χ 2 tests revealed significant differences for unemployment, annual income, visual acuity loss, and depression.
d Test for trend was performed by the nonparametric Wilcoxon rank sum test for continuous variables, and 2-sided, exact Cochran-Armitage test for categorical variables.
e Number of self-reported comorbidities (diabetes, arthritis, stroke/brain hemorrhage, high blood pressure, angina, heart attack, heart failure, asthma, skin cancer, other cancer, back problems, hearing problems, and other major health problems).
f Visual acuity loss was defined as presenting visual acuity 20/40 or worse.
g Depression was scored using the 12-Item Short-Form Health Survey item “Have you felt downhearted or blue a good bit of the time or more during the past 4 weeks?” Participants were considered depressed if they reported “A good bit of the time,” “Most of the time,” or “All of the time.”
NEI-VFQ 25 Analysis
AFEDS participants tended to report high VSQOL, leading to a ceiling effect in the distribution of item responses on the NEI-VFQ 25 (Supplemental Figure 2; Supplemental Material available at AJO.com). High measures of internal consistency were observed in IRT graded response models for both the task and well-being composites (Supplemental Table 3; Supplemental Material available at AJO.com). Cronbach’s alpha was 0.86 for task and 0.78 for well-being, indicating high inter-item correlation. Latent traits were unidimensional for both IRT composites; a single factor explained 66.9% of the variance for the task composite and 64.8% for the well-being composite. Test information curves demonstrated the NEI-VFQ 25 was most informative for task and well-being VSQOL scores 2 SDs below the mean.
Association of Vision-Specific Quality of Life and Visual Field Loss
In this cohort of African Americans, VSQOL was inversely related to VFL after adjusting for covariables. LOWESS plots demonstrated strong, linear associations between predicted NEI-VFQ 25 IRT scores and VFL in the BSE ( Figure 1 ). Predicted task VSQOL was greater than well-being for VFL ranging from none (0 dB) through severe (-20 dB), after which VSQOL scores were similar for both composites. Both IRT task (β = 0.80, 95% CI: 0.65, 0.95) and well-being (β = 0.54, 95% CI: 0.42, 0.67) composites were more strongly associated with VFL than the CTT composite (β = 0.38, 95% CI: 0.32, 0.44) ( Table 2 ). VFL was most strongly associated with VSQOL subscales including driving difficulties (β = 0.83, 95% CI: 0.70, 0.97), general vision (β = 0.58, 95% CI: 0.44, 0.71), near vision (β = 0.52, 95% CI: 42, 0.62), vision-related mental health (β = 0.48, 95% CI: 0.37, 0.58), and peripheral vision (β = 0.43, 95% CI: 0.34, 0.52). Clinically meaningful (5-point) differences in VSQOL were associated with decrements in VFL of 6.2 dB (95% CI: 5.3, 7.7) for task and 9.2 dB (95% CI: 7.5, 11.9) for well-being composites. Driving difficulties had a clinically important difference of 6.0 dB (95% CI: 5.2, 7.1), the strongest association with VFL of all QOL outcomes (Supplemental Figure 3; Supplemental Material available at AJO.com). The SF-12 scores for HRQOL were either not significantly associated with VFL or the effects were negligible. Associations were attenuated for all QOL scores with VFL in the worse-seeing eye (Supplemental Table 4; Supplemental Material available at AJO.com).
