Judith E. Goldstein

The activity inventory: An adaptive visual function questionnaire

Purpose. The Activity Inventory (AI) is an adaptive visual function questionnaire that consists of 459 Tasks nested under 50 Goals that in turn are nested under three Objectives. Visually impaired patients are asked to rate the importance of each Goal, the difficulty of Goals that have at least some importance, and the difficulty of Tasks that serve Goals that have both some importance and some difficulty. Consequently, each patient responds to an individually tailored set of questions that provides both a functional history and the data needed to estimate the patients visual ability. The purpose of the present article is to test the hypothesis that all combinations of items in the AI, and by extension all visual function questionnaires, measure the same visual ability variable. Methods. The AI was administered to 1880 consecutively-recruited low vision patients before their first visit to the low vision rehabilitation service. Of this group, 407 were also administered two other visual function questionnaires randomly chosen from among the Activities of Daily Living Scale (ADVS), National Eye Institute Visual Functioning Questionnaire (NEI VFQ), 14-item Visual Functioning Index (VF-14), and Visual Activities Questionnaire (VAQ). Rasch analyses were performed on the responses to each VFQ, on all responses to the AI, and on responses to various subsets of items from the AI. Results. The pattern of fit statistics for AI item and person measures suggested that the estimated visual ability variable is not unidimensional. Reading-related and other items requiring high visual resolution had smaller residual errors than expected and mobility-related items had larger residual errors than expected. The pattern of person measure residual errors could not be explained by the disorder diagnosis. When items were grouped into subsets representing four visual function domains (reading, mobility, visual motor, visual information), and separate person measures were estimated for each domain as well as for all items combined, visual ability was observed to be equivalent to the first principal component and accounted for 79% of the variance. However, confirmatory factor analysis showed that visual ability is a composite variable with at least two factors: one upon which mobility loads most heavily and the other upon which reading loads most heavily. These two factors can account for the pattern of residual errors. High product moment and intraclass correlations were observed when comparing different subsets of items within the AI and when comparing different VFQs. Conclusions. Visual ability is a composite variable with two factors; one most heavily influences reading function and the other most heavily influences mobility function. Subsets of items within the AI and different VFQs all measure the same visual ability variable.

Responsiveness of the EQ-5D to the effects of low vision rehabilitation.

Purpose This study is an evaluation of the responsiveness of preference-based outcome measures to the effects of low vision rehabilitation (LVR). It assesses LVR-related changes in EQ-5D utilities in patients who exhibit changes in Activity Inventory (AI) measures of visual ability. Methods Telephone interviews were conducted on 77 low-vision patients out of a total of 764 patients in the parent study of “usual care” in LVR. Activity Inventory results were filtered for each patient to include only goals and tasks that would be targeted by LVR. Results The EQ-5D utilities have weak correlations with all AI measures but correlate best with AI goal scores at baseline (r = 0.48). Baseline goal scores are approximately normally distributed for the AI, but EQ-5D utilities at baseline are skewed toward the ceiling (median, 0.77). Effect size for EQ-5D utility change scores from pre- to post-LVR was not significantly different from zero. The AI visual function ability change scores corresponded to a moderate effect size for all functional domains and a large effect size for visual ability measures estimated from AI goal ratings. Conclusions This study found that the EQ-5D is unresponsive as an outcome measure for LVR and has poor sensitivity for discriminating low vision patients with different levels of ability.

Comparison of clinician-predicted to measured low vision outcomes

Purpose To compare low-vision rehabilitation (LVR) clinicians’ predictions of the probability of success of LVR with patients’ self-reported outcomes after provision of usual outpatient LVR services and to determine if patients’ traits influence clinician ratings. Methods The Activity Inventory (AI), a self-report visual function questionnaire, was administered pre-and post-LVR to 316 low-vision patients served by 28 LVR centers that participated in a collaborative observational study. The physical component of the Short Form-36, Geriatric Depression Scale, and Telephone Interview for Cognitive Status were also administered pre-LVR to measure physical capability, depression, and cognitive status. After patient evaluation, 38 LVR clinicians estimated the probability of outcome success (POS) using their own criteria. The POS ratings and change in functional ability were used to assess the effects of patients’ baseline traits on predicted outcomes. Results A regression analysis with a hierarchical random-effects model showed no relationship between LVR physician POS estimates and AI-based outcomes. In another analysis, kappa statistics were calculated to determine the probability of agreement between POS and AI-based outcomes for different outcome criteria. Across all comparisons, none of the kappa values were significantly different from 0, which indicates that the rate of agreement is equivalent to chance. In an exploratory analysis, hierarchical mixed-effects regression models show that POS ratings are associated with information about the patient’s cognitive functioning and the combination of visual acuity and functional ability, as opposed to visual acuity or functional ability alone. Conclusions Clinicians’ predictions of LVR outcomes seem to be influenced by knowledge of patients’ cognitive functioning and the combination of visual acuity and functional ability—information clinicians acquire from the patient’s history and examination. However, clinicians’ predictions do not agree with observed changes in functional ability from the patient’s perspective; they are no better than chance.

Interpretation of health and vision utilities in low vision patients.

Purpose. The purpose of the study is to evaluate the relationship between time trade off (TTO) and standard gamble (SG) estimates of health and vision utilities in a low vision patient sample. Methods. Telephone surveys were conducted on 74 low vision patients. All study participants were administered utility questionnaires that used the TTO and SG methods as they relate to health and vision. Results. There is high between-person variability in the relationship of TTO- to SG-estimated utilities for both vision and health. However, when transformed to logits, differences between TTO and SG utilities for health are equal to differences between TTO and SG utilities for vision. These differences are symmetrically distributed around the origin. The data were consistent with a model that includes both health or vision state and personal response criteria. The model explains between-person variability in the relationship of TTO to SG utilities as idiosyncratic differences within people between response criteria for making TTO and SG judgments. Conclusions. The large between-person variability in the relation of utilities estimated from TTO to those estimated from SG can be explained by large between- and within-person variability in personal TTO and SG response criteria. However, within each person, the response criteria used to judge health state are the same as the response criteria used to judge vision state. This observation leads to the conclusion that health and vision states are in the same units when estimated from utilities. A meta-analysis of published studies that compared TTO with SG utilities for different health states confirms the conclusion of the model that average utilities across people are criterion-free estimates of average health-related states on a common logit scale.

Leveraging Electronic Health Records to Identify and Characterize Patients with Low Vision

ABSTRACT Purpose: To use electronic health record (EHR) data to estimate the prevalence and characteristics of low-vision (LV) patients. Methods: EHR data were obtained for all patients at the nine clinical locations of the Wilmer Eye Institute in 2014. LV status at each visit was defined as visual acuity (VA) worse than 20/40 in the better-seeing eye. Prevalence and incidence estimates were determined over a 12-month period. Demographic and clinical data were used to compare the characteristics of patients with and without LV. Logistic regression analyses were used to determine prevalence and incidence estimates adjusted for age, sex, race, and ethnicity. Results: A total of 100,755 patients were included in the analysis. There were 7752 (7.7%) prevalent and 1962 (2.1%) incident cases of LV. Among patients with LV, 55% had VA between 20/40 and 20/60. Outside of LV clinics, retina and glaucoma clinics had the highest prevalence (18% and 14%, respectively) and incidence (5% and 4%, respectively) of LV. The urban hospital center had twice the prevalence of LV than suburban clinics (11.5% vs. 5.6%). The odds of prevalent LV was greatest among patients 80 years and older (odds ratio = 6.18; 95% confidence interval: 5.62–6.80) as compared to those 20–39 years old. Conclusions: EHR can be used to estimate the prevalence and describe the characteristics of patients with LV seeking ophthalmic care. The highest prevalence rates of LV are observed in the urban setting and among patients obtaining retina and glaucoma care.

Multivariable Regression Model of the EuroQol 5-Dimension Questionnaire in Patients Seeking Outpatient Low Vision Rehabilitation

ABSTRACT Purpose: To understand the source of between-person variance in baseline health utilities estimated from EuroQol 5-dimension questionnaire (EQ-5D) responses of a representative sample of the US low vision outpatient population prior to rehabilitation. Methods: A prospective, observational study of 779 new low vision patients at 28 clinic centers in the US. The EQ-5D, Activity Inventory (AI), Telephone Interview for Cognitive Status (TICS), Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36) physical functioning component, and Geriatric Depression Scale (GDS) were administered by telephone interview prior to rehabilitation. EQ-5D responses were transformed into health utilities, which served as the dependent variable in all analyses. Data were then analyzed to determine how much overall visual ability, functional domains of visual ability, and comorbidities (e.g. physical functioning, depression, cognition) independently contribute to the EQ-5D-based health utility index. Results: Multivariable regression analyses showed that the GDS and SF-36 physical account for nearly 40% of the variance observed in health utilities estimated from EQ-5D responses of low vision patients. Age was also a significant predictor of health utilities, but accounted for very little variance. None of the other variables were significant predictors. Conclusions: Health utilities of low vision patients estimated from the EQ-5D primarily are associated with comorbid factors that are not likely to be responsive to low vision rehabilitation, thereby rendering the EQ-5D an unsuitable outcome measure for this population. However, because the EQ-5D is responsive to comorbid states, it could be a useful tool for evaluating the impact of comorbidities on low vision patient quality of life.

Visual Disability in the Elderly: Implications for Visual Rehabilitation

When visual system disorders result in bilateral visual impairments, patients have difficulty performing their customary activities and experience a diminished quality of life (West et al. 2002). Visual impairments increase patients’ risk of falling (Ivers et al. 1998), injury (Salive et al. 1994), poor general health (Crews and Campbell 2001), depression (Casten et al. 2004), and even death (Pedula et al. 2006). Activity-limiting chronic visual impairments, collectively called “low vision,” most often are caused by age-related visual system disorders, with age-related macular degeneration, glaucoma, diabetic retinopathy, and cataract leading the list (Congdon et al. 2004). Some visual system disorders, such as diabetic retinopathy, are manifestations of more general disorders that frequently produce co-disabilities. But most low vision patients are elderly, so comorbidities and co-disabilities from diseases unrelated to their visual system disorders are common (Ahmadian and Massof 2008). Thus, for a large portion of the low vision population, activity limitations from visual impairments are superimposed on and worsen activity limitations from comorbidities (Langelaan et al. 2009).