
==== Front
Innov Aging
Innov Aging
innovateage
Innovation in Aging
2399-5300
Oxford University Press US

10.1093/geroni/igae071
igae071
Original Report
AcademicSubjects/SOC02600
The Influence of Vision Impairment on the Measurement of Cognition in Older Adults in India: Findings From LASI-DAD
Ehrlich Joshua R MD Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan, USA
Institute for Social Research, University of Michigan, Ann Arbor, Michigan, USA

https://orcid.org/0000-0002-9005-3872
Nichols Emma PhD Center for Economic and Social Research, University of Southern California, Los Angeles, California, USA

Chen Yizhou PhD Center for Economic and Social Research, University of Southern California, Los Angeles, California, USA

https://orcid.org/0000-0003-2443-7058
Nagarajan Niranjani MD Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan, USA

Zeki Al Hazzouri Adina PhD Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, USA

https://orcid.org/0000-0003-0410-6067
Reed Nicholas S PhD Optimal Aging Institute, NYU Grossman School of Medicine, New York, New York, USA

Lee Jinkook PhD Center for Economic and Social Research, University of Southern California, Los Angeles, California, USA
Department of Economics, University of Southern California, Los Angeles, California, USA

Gross Alden L PhD Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA

Albert Steven M PhD, MS, FGSA Decision Editor
Address correspondence to: Joshua R. Ehrlich, MD. E-mail: joshre@umich.edu
2024
09 8 2024
09 8 2024
8 9 igae07126 2 2024
17 7 2024
17 9 2024
© The Author(s) 2024. Published by Oxford University Press on behalf of The Gerontological Society of America.
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com

Abstract

Background and Objectives

Prior investigations have not considered whether poor vision biases cognitive testing. However, such research is vital given increasing evidence that vision impairment (VI) may be an important modifiable risk factor for dementia, particularly in low- and middle-income settings where the prevalence of VI is high.

Research Design and Methods

This study employed data from 3 784 participants in Wave 1 of the Longitudinal Aging Study in India-Diagnostic Assessment of Dementia (LASI-DAD) who underwent both visual acuity and cognitive function testing. We used multiple indicators and multiple causes models to assess differential item functioning (DIF; eg, bias) in cognitive testing by objectively measured distance and near VI. Multivariable linear regression was used to model the association between VI and cognitive factor scores before and after DIF adjustment. Analyses were performed for general cognition and separate cognitive domains, corresponding to memory, language/fluency, executive functioning, and visuospatial performance. Models were adjusted for demographic, health, and socioeconomic covariates.

Results

Participants in our sample were 60 and older. Most participants with VI were 60–69 years old (59.6%) and 50.8% were female. Individuals experiencing both distance and near VI tended to be older, have lower educational attainment, be married, reside in rural settings, and belong to lower consumption and BMI categories. Both distance and near VI were associated with poorer cognition before and after DIF-adjustment. Differences between DIF-unadjusted and -adjusted scores were small compared to the standard error of measurement, indicating no evidence of meaningful measurement differences by VI.

Discussion and Implications

In well-conducted large-scale surveys, bias in cognitive testing due to VI is likely minimal. Findings strengthen previous evidence on the association between VI and dementia by showing that such associations are unlikely to be attributable to vision-related measurement error in the assessment of cognitive functioning.

Cognitive test
Dementia
Differential item functioning
Low-and-middle income country
Population aging
National Institute on Aging 10.13039/100000049 R01AG042778 R01AG051125 RF1AG055273 U01AG065958 Gateway to Global Aging Data R01AG030153
==== Body
pmc Translational Significance: There is a growing literature on the association of vision impairment with cognitive health and dementia. The authors reviewed the literature using PubMed/MEDLINE and found little data on whether poor vision affects the validity of vision-dependent cognitive tests. Our study showed that vision impairment was not associated with any salient bias in cognitive test results. This finding supports using the harmonized cognitive assessment protocol in older adults in India regardless of their level of visual function. Findings from this study reinforce existing evidence on the relationship of vision impairment and cognitive function.

Background and Objectives

Vision impairment has emerged as a modifiable risk factor for cognitive decline and dementia. Numerous cross-sectional and longitudinal studies and meta-analyses have confirmed this association (1), and the modifiable nature of vision impairment is underscored by data showing that at least 80% of vision impairment globally is preventable or curable (2). However, one concern regarding studies of vision and cognition is whether cognitive tests with visual stimuli are biased by participants’ ability to see the cognitive tests (3).

To address the issue of potential measurement bias in cognitive testing, some prior studies have omitted cognitive tests with visual stimuli (4). However, in doing so, potentially important information from these items is lost. Rarely, studies may provide accommodations to those with sensory impairments. A systematic review of research protocols of longitudinal cohort studies of older adults that measured cognitive function described the number and proportion of cohorts that offered accommodations for each type of sensory impairment during neurocognitive testing. The study found that only 22% of the studies offered hearing accommodations and 38% offered vision accommodations during cognitive testing (5).

To our knowledge, only a single prior study directly quantified bias in cognitive testing due to sensory function, and it found no evidence of meaningful measurement differences (3). However, the studies included in that analysis (The Atherosclerosis Risk in Communities Study [ARIC] and Baltimore Longitudinal Study of Aging [BLSA]) were conducted in well-controlled, environments in high-income settings, and were not population representative. Thus, the extent to which such findings are generalizable to low- and middle-income settings with considerably higher rates of vision impairment, illiteracy, and low educational attainment is unclear.

The relevance of this issue to low- and middle-income countries (LMICs) is driven by a comparatively high prevalence of vision impairment in LMICs (6) and rapid population aging that will lead to precipitous increases in the number of older adults with myriad age-related health conditions, including both vision impairment and dementia. For example, in India, an LMIC that is the most populous country in the world, the prevalence of dementia among adults aged 60 and older in 2020 was 7.4% (8.8 million people) (7) and it is projected to increase by 197% between 2019 and 2050 (8). The prevalence of vision problems in India is also high; 33.8% of those aged 50 and older have distance vision impairment or blindness and 76.3% have near vision impairment (9). An estimated 89.5% of cases of distance vision impairment in India could be addressed with cataract surgery or provision of eyeglasses (10), 2 highly cost-effective interventions in India, even when accounting for the higher costs associated with case detection in rural settings (11). Accordingly, though additional interventional research is needed, vision impairment may prove to be an attractive target for future dementia prevention strategies.

The purpose of this study was to investigate whether visual function biases cognitive test results in an LMIC population-based study. To examine this issue, we used nationally representative data from India. We applied item response theory methods that characterize the measurement of latent traits, like cognitive function, that cannot be directly observed (12), and we tested for the presence of differential item functioning (DIF). In this study, DIF can be interpreted as bias in cognitive tests by objective vision impairment status. We hypothesized there is detectable bias in cognitive testing due to visual function, but it is unclear whether this will have an impact on the association between vision and overall or domain-specific cognitive function in India.

Research Design and Methods

Data Source

The Longitudinal Aging Study in India (LASI) includes over 72 000 community-dwelling adults ages 45 and older and their spouses. It is a nationally representative sample of the country, as well as each state and union territory in India and is harmonized with the Health and Retirement Study (HRS) in the United States and other global HRS studies. LASI and other studies in the HRS network launched in-depth sub-studies of late-life cognition and dementia using a harmonized cognitive assessment protocol (HCAP) (13). The LASI-Diagnostic Assessment of Dementia (LASI-DAD), one of the HCAP studies, consists of 4 096 participants age 60+ from 18 states and union territories that are representative of the Indian population, recruited through a stratified random sampling process. The LASI-DAD study protocol has been described in detail elsewhere (13,14). Of the 4 096 LASI-DAD study participants, 312 observations were dropped due to missing vision data in the LASI Core study (15).

Assessment of Cognitive Performance Measures

The LASI-DAD cognitive test protocol was developed with consideration of illiteracy and innumeracy in the population. The test battery included a variety of cognitive tests that were deemed culturally relevant in India and attentive to HCAP harmonization efforts. Factor analysis was conducted consistent with Cattell–Horn–Carroll theory and resulted in factors for general/overall cognition and domain-specific factor scores for orientation, executive functioning, language/fluency, memory, and visuospatial performance. The structure of the confirmatory factor analysis model used here follows the previously published LASI-DAD factor analysis model (16).

Assessment of Vision Impairment

Distance and near visual acuity were measured in the LASI core survey using a tumbling E chart displayed on a laptop. Each eye was tested separately using the participant’s habitual refractive correction (e.g., eyeglasses and contact lenses), if available. Categorization of vision impairment was based on World Health Organization definitions using measured visual acuity in the better-seeing eye (17): mild impairment (<6/12–6/18), moderate impairment (<6/18–6/60), severe impairment (<6/60–3/60), and blindness (<3/60); due to the relatively small number of participants with severe VI and blindness, these were collapsed into a single category for analyses; the same category thresholds were used for both distance and near visual acuity impairments. Logarithm of the minimum angle of resolution (logMAR) values was calculated (−log10 [Snellen acuity]) to transform visual acuity values to a continuous scale. If a participant reported that they could not see light or count fingers held at a distance of 2 m from either eye, then they were considered blind and assigned a logMAR value of 2.0.

Covariates

We included covariates that were considered potential confounders of the relationship between vision impairment and cognition, based on scientific knowledge and prior literature. All LASI-DAD participants self-reported age, gender, highest level of education completed (illiterate, primary school, secondary school, or more), and marital status (married, widowed, and other). Residence in urban versus rural areas was based on classifications from the 2011 Indian Census. Household consumption was calculated and categorized in quartiles as a proxy for income and socioeconomic status. Participants also reported diagnoses of diabetes, hypertension, stroke, and cardiovascular disease, as well as smoking status (ever vs never). We also included body mass index (BMI), which was calculated for all participants based on measured weight and height.

Statistical Analysis

We used descriptive statistics to calculate the demographic and clinical characteristics of the sample overall and stratified by vision impairment status. Chi-squared tests were used to assess the statistical significance of comparisons. We used linear regression to examine the association between general and domain-specific cognitive functioning and near and distance vision impairment severity prior to any adjustment for DIF. For each association of interest, we estimated associations between vision impairment and cognition using 4 models: Model 1 was unadjusted, Model 2 adjusted for age and gender, Model 3 adjusted for variables in Model 2 plus education, marital status, urbanicity, and consumption quartile. Model 4 adjusted for variables in Model 3 plus BMI, and self-reported diabetes, heart disease, hypertension, stroke, and smoking status.

To examine bias in cognitive testing, we used the Multiple Indicators, Multiple Causes (MIMIC) model framework for identifying DIF (18,19). In MIMIC models, DIF is operationalized as a direct effect between vision impairment status and an individual cognitive test item after controlling for the association between vision impairment status and latent cognitive status; such direct effects can be thought of as evidence of test bias insofar as they represent differences in test performance, adjusting for underlying cognitive performance (20). We tested for the presence of uniform DIF in cognitive test items with some dependency on vision (the list of vision-dependent test items is presented in Supplementary Table 1); all other items were treated as anchor items, which are invariant by vision impairment status, and direct effects were constrained to zero across all models. We used a forward stepwise procedure to identify significant direct effects for items with some dependency on vision. In the first step, we estimated a MIMIC model with all direct effects constrained to zero between vision impairment and individual cognitive test items. We then identified the model constraint with the largest model modification index (an estimate of the improvement in model fit if a constraint is freed) and used a robust chi-squared model difference test to assess the statistical significance of freeing the parameter for a direct effect. If this test was statistically significant, we concluded the item in question demonstrated detectable DIF. We then repeated this process after removing the model constraint for the item with DIF. We continued this procedure until the chi-squared model difference test for the item with the largest modification index was not statistically significant. Additional details on this procedure can be found elsewhere (20).

All MIMIC models controlled for the effects of age, gender, and years of education on cognition. All continuous cognitive items were discretized using equal interval discretization and were modeled using 2-parameter logistic item response functions. In addition to the hypothesized underlying latent trait, we added latent methods factors to the models to explain shared variability between cognitive test items that are closely related (e.g., immediate and delayed logical memory test) and to improve model fit (a full list of methods factors is presented in Supplementary Table 2) (21). These methods factors were considered nuisance parameters that serve to better explain item-level intercorrelations, but which are not of substantive interest. Therefore, we do not evaluate DIF for these methods factors. All models were estimated using a weighted least squares mean, and variance-adjusted estimator. We repeated the DIF detection procedure separately for each combination of the 5 cognitive variables (general cognitive functioning and each cognitive domain except orientation, which contained no vision-dependent items) and four binary vision variables (any distance vision impairment vs no distance vision impairment; any near vision impairment vs no near vision impairment; moderate or worse distance vision impairment vs mild or no distance vision impairment; moderate or worse near vision impairment vs mild or no near vision impairment) considered, for a total of 20 sets of DIF models. Analyses for any distance vision impairment and any near vision impairment were considered primary; analyses for moderate or worse distance or near vision impairment were included as sensitivity analyses. In primary analyses, we excluded 2 cognitive test items in the language domain (say/write a sentence and read/follow a command/follow an example) which had differences in administration for literate and illiterate populations. Because either one (in illiterates) or both (in literates) of these items rely on vision, we conducted sensitivity analyses repeating DIF testing for general cognitive status and the language domain stratified by literacy status to assess DIF in these items.

We estimated DIF-adjusted factor scores for general cognitive functioning and each domain by reestimating factor scores after allowing for direct effects between vision variables and cognitive test items for the items identified to have DIF. To assess the salience of DIF on cognitive scores, we compared the difference between DIF-unadjusted and DIF-adjusted factor scores to the median of the standard error of measurement (SEM), which has been used as a threshold to define a meaningful difference in prior studies (22). Finally, we reestimated regression models assessing the association between vision impairment and cognition using DIF-adjusted cognitive factor scores.

Although item-level DIF detection and regression models required a large number of statistical tests, potentially inflating Type I error, to avoid erroneous conclusions potentially due to change findings, we focus our results and interpretations on broader patterns of findings and interpret results with respect to patterns that are consistent across items or across different cognitive domains and vision variables.

MIMIC models were estimated in Mplus Version 8. All other statistical analysis was conducted using STATA version 16.1 or R version 4.2.2.

Results

Sample Characteristics

Table 1 presents characteristics of the study sample (n = 3,784) stratified by vision impairment status (characteristics stratified by distance and near vision status are presented in Supplementary Tables 3 and 4, respectively). Overall, most participants (59.6%) fell within the 60–69-year age category, 50.8% were female, and 21% had educational attainment beyond secondary school.

Table 1. Weighted Sample Characteristics by Vision Impairment Status (n = 3 784)

Characteristics	n	%a	Vision Impairment Status (%)b	p Valuec	
No VI	Any VI	
	13.9	86.1		
Age	<.001	
 60–69	2 211	59.6	16.8	83.2		
 70–79	1 162	29.6	11.2	88.8	
 80+	411	10.8	5.4	94.6	
Gender	<.001	
 Male	1 745	49.2	17.2	82.8		
 Female	2 039	50.8	10.8	89.2	
Education	<.001	
 None	1 852	54.3	9.1	90.9		
 Primary School	1 003	24.6	14.4	85.6	
 ≥Secondary school	929	21.1	25.7	74.3	
Marital status	<.001	
 Married	2 493	67.2	16.0	84.0		
 Widowed	1 222	31.0	9.6	90.4	
 Other	69	1.8	10.6	89.4	
Urbanicity	<.001	
 Urban	1 437	29.3	18.4	81.6		
 Rural	2 347	70.7	12.1	87.9	
Consumption quartile	<.001	
 1st	942	26.8	10.3	89.7		
 2nd	957	25.4	10.0	90.0	
 3rd	948	24.7	17.2	82.8	
 4th	935	23.1	19.0	81.0	
BMI (kg/m2)	<.001	
 <18.5	850	24.7	9.5	90.5		
 18.5–24.9	1 837	49.9	15.1	84.9	
 25.0–29.9	762	18.9	16.9	83.1	
 ≥30.0	287	6.5	14.2	85.8	
Diabetes	.53	
 No	3 115	84.0	13.6	86.4		
 Yes	669	16.0	15.7	84.3	
Heart disease	.30	
 No	3 528	93.7	13.7	86.3		
 Yes	256	6.3	16.9	83.1	
Hypertension	.79	
 No	2 298	62.6	14.1	85.9		
 Yes	1 486	37.4	13.6	86.4	
Stroke	.74	
 No	3 680	97.2	14.0	86.1		
 Yes	104	2.8	13.5	86.5	
Ever smoked	.37	
 No	2 949	75.5	13.5	86.5		
 Yes	835	24.5	15.1	84.9	
Notes: BMI = body mass index; VI = vision impairment.

aTable contains raw counts and survey-weighted percentages, so percentages may not sum to 100%.

bBased on visual acuity in the better-seeing eye.

cPearson chi-squared test.

Notably, individuals experiencing both distance and near VI tended to be older, have lower educational attainment, be married, reside in rural settings, and belong to a lower consumption and BMI categories. Those with near VI alone were more likely to be female and those with distance VI were less likely to self-report diabetes. No significant differences by VI status in the proportion of individuals with heart disease, hypertension, history of stroke, and smoking status.

Associations Between Vision Impairment and Cognitive Functioning

A summary of the cognitive outcome variables categorized by distance and near vision impairment status is presented in Supplementary Tables 5 and 6. Cognitive scores were highest among participants with no vision impairment, with a clear and stepwise decrease observed as the degree of vision impairment worsened.

The results from linear regression models testing the association between distance and near vision impairments and cognitive function are presented in Tables 2 and 3, respectively. Models demonstrated a significant association between both distance and near vision impairment and cognition scores and associations followed a stepwise pattern across vision impairment severity levels. All associations remained statistically significant in models progressively adjusted for larger sets of potential confounders.

Table 2. Association of Distance Vision Impairment With Total and Domain-Specific Cognitive Function Before Differential Item Functioning Adjustment

Variable	Model 1a	Model 2b	Model 3c	Model 4d	
β (95% CI)	β (95% CI)	β (95% CI)	β (95% CI)	
Total cognitione					
No VI	Reference	Reference	Reference	Reference	
Mild VI	−0.28 (−0.37, −0.20)**	−0.22 (−0.30, −0.14)**	−0.10 (−0.16, −0.04)**	−0.08 (−0.14, −0.03)*	
Moderate VI	−0.52 (−0.59, −0.45)**	−0.42 (−0.48, −0.35)**	−0.21 (−0.26, −0.16)**	−0.20 (−0.24, −0.15)**	
Severe VI/Blind	−0.92 (−1.08, −0.76)**	−0.71 (−0.86, −0.56)**	−0.37 (−0.48, −0.26)**	−0.31 (−0.42, −0.20)**	
Orientation					
No VI	Reference	Reference	Reference	Reference	
Mild VI	−0.28 (−0.37, −0.20)**	−0.21 (−0.28, −0.13)**	−0.08 (−0.13, −0.03)*	−0.08 (−0.12, −0.03)*	
Moderate VI	−0.52 (−0.59, −0.45)**	−0.40 (−0.46, −0.34)**	−0.19 (−0.23, −0.15)**	−0.19 (−0.23, −0.15)**	
Severe VI/Blind	−0.95 (−1.11, −0.79)**	−0.71 (−0.85, −0.56)**	−0.36 (−0.45, −0.27)**	−0.33 (−0.42, −0.24)**	
Memory					
No VI	Reference	Reference	Reference	Reference	
Mild VI	−0.26 (−0.35, −0.18)**	−0.22 (−0.30, −0.14)**	−0.12 (−0.18, −0.05)**	−0.10 (−0.17, −0.04)*	
Moderate VI	−0.49 (−0.56, −0.43)**	−0.38 (−0.45, −0.31)**	−0.20 (−0.26, −0.15)**	−0.19 (−0.24, −0.13)**	
Severe VI/Blind	−0.81 (−0.97, −0.65)**	−0.61 (−0.76, −0.45)**	−0.32 (−0.45, −0.19)**	−0.25 (−0.38, −0.12)**	
Language/fluency					
No VI	Reference	Reference	Reference	Reference	
Mild VI	−0.32 (−0.40, −0.23)**	−0.25 (−0.33, −0.17)**	−0.14 (−0.21, −0.08)**	−0.13 (−0.20, −0.07)**	
Moderate VI	−0.51 (−0.58, −0.44)**	−0.41 (−0.47, −0.34)**	−0.23 (−0.28, −0.17)**	−0.21 (−0.26, −0.16)**	
Severe VI/Blind	−0.98 (−1.14, −0.81)**	−0.76 (−0.91, −0.61)**	−0.47 (−0.59, −0.35)**	−0.43 (−0.56, −0.31)**	
Executive function					
No VI	Reference	Reference	Reference	Reference	
Mild VI	−0.26 (−0.35, −0.17)**	−0.20 (−0.28, −0.12)**	−0.07 (−0.12, −0.01)*	−0.05 (−0.11, 0)	
Moderate VI	−0.50 (−0.57, −0.44)**	−0.41 (−0.48, −0.35)**	−0.20 (−0.25, −0.16)**	−0.19 (−0.23, −0.14)**	
Severe VI/Blind	−0.83 (−0.99, −0.67)**	−0.63 (−0.78, −0.48)**	−0.29 (−0.39, −0.18)**	−0.23 (−0.34, −0.13)**	
Notes: CI = confidence interval; VI = vision impairment.

aModel 1: unadjusted.

bModel 2: age and sex adjusted.

cModel 3: Model 2 and education, marital status, urbanicity, and consumption quartile.

dModel 4: Model 3 and body mass index, diabetes, heart disease, hypertension, stroke, and smoking status.

eTotal cognition includes all cognitive tests.

* p < .05.

** p < .01.

Table 3. Association of Near Vision Impairment With Total and Domain-Specific Cognitive Function Before Differential Item Functioning Adjustment

Variable	Model 1a	Model 2b	Model 3c	Model 4d	
β (95% CI)	β (95% CI)	β (95% CI)	β (95% CI)	
Total cognitione					
No VI	Reference	Reference	Reference	Reference	
Mild VI	−0.35 (−0.46, −0.25)**	−0.26 (−0.36, −0.16)**	−0.05 (−0.12, 0.03)	−0.06 (−0.13, 0.02)	
Moderate VI	−0.54 (−0.62, −0.46)**	−0.41 (−0.48, −0.33)**	−0.14 (−0.20, −0.09)**	−0.14 (−0.19, −0.08)**	
Severe VI/Blind	−1.26 (−1.41, −1.11)**	−0.94 (−1.09, −0.80)**	−0.49 (−0.59, −0.38)**	−0.45 (−0.55, −0.34)**	
Orientation					
No VI	Reference	Reference	Reference	Reference	
Mild VI	−0.37 (−0.48, −0.26)**	−0.26 (−0.35, −0.16)**	−0.03 (−0.09, 0.03)	−0.03 (−0.09, 0.03)	
Moderate VI	−0.60 (−0.68, −0.52)**	−0.44 (−0.51, −0.37)**	−0.16 (−0.20, −0.11)**	−0.15 (−0.20, −0.11)**	
Severe VI/Blind	−1.25 (−1.40, −1.10)**	−0.87 (−1.01, −0.74)**	−0.39 (−0.48, −0.30)**	−0.37 (−0.46, −0.28)**	
Memory					
No VI	Reference	Reference	Reference	Reference	
Mild VI	−0.26 (−0.37, −0.15)**	−0.20 (−0.30, −0.09)**	−0.01 (−0.10, 0.08)	−0.02 (−0.10, 0.07)	
Moderate VI	−0.45 (−0.53, −0.37)**	−0.35 (−0.42, −0.27)**	−0.12 (−0.18, −0.05)**	−0.12 (−0.18, −0.05)**	
Severe VI/Blind	−1.08 (−1.23, −0.93)**	−0.79 (−0.93, −0.64)**	−0.40 (−0.52, −0.27)**	−0.36 (−0.49, −0.24)**	
Language/fluency					
No VI	Reference	Reference	Reference	Reference	
Mild VI	−0.34 (−0.45, −0.23)**	−0.25 (−0.35, −0.15)**	−0.05 (−0.13, 0.03)	−0.06 (−0.14, 0.02)	
Moderate VI	−0.51 (−0.59, −0.43)**	−0.37 (−0.44, −0.29)**	−0.12 (−0.18, −0.06)**	−0.12 (−0.18, −0.06)**	
Severe VI/Blind	−1.20 (−1.35, −1.04)**	−0.86 (−1.00, −0.72)**	−0.45 (−0.57, −0.34)**	−0.43 (−0.54, −0.31)**	
Executive function					
No VI	Reference	Reference	Reference	Reference	
Mild VI	−0.41 (−0.52, −0.30)**	−0.32 (−0.42, −0.22)**	−0.10 (−0.17, −0.03)**	−0.11 (−0.18, −0.04)**	
Moderate VI	−0.57 (−0.65, −0.49)**	−0.43 (−0.51, −0.36)**	−0.16 (−0.21, −0.10)**	−0.15 (−0.20, −0.10)**	
Severe VI/Blind	−1.19 (−1.34, −1.04)**	−0.89 (−1.03, −0.75)**	−0.42 (−0.52, −0.32)**	−0.39 (−0.49, −0.29)**	
Notes: CI = confidence interval; VI = vision impairment.

aModel 1: unadjusted.

bModel 2: age and sex adjusted.

cModel 3: Model 2 and education, marital status, urbanicity, and consumption quartile.

dModel 4: Model 3 and body mass index, diabetes, heart disease, hypertension, stroke, and smoking status.

eTotal cognition includes all cognitive tests.

* p < .05.

** p < .01.

Differential Item Functioning by Vision Impairment Status

We found statistically significant evidence of DIF by any distance vision impairment in 4 out of 11 items with visual stimuli measuring general cognition, 1/1 item with visual stimuli measuring memory, 1/3 items for executive functioning, 2/4 items for language/fluency, and 1/3 items for visuospatial functioning (presented in Supplementary Table 7). For any near vision impairment, we also detected statistically significant DIF across a range of items with visual stimuli: 3/11 for general cognition, 0/1 for memory, 1/3 for executive functioning, 3/4 for language fluency, and 0/3 for visuospatial functioning.

We found statistically significant evidence of DIF by moderate or worse distance vision impairment in 3 out of 11 items with visual stimuli measuring general cognition, 0/1 item with visual stimuli measuring memory, 0/3 items for executive functioning, 2/4 items for language/fluency, and 0/3 items for visuospatial functioning (presented in Supplementary Table 7). For moderate or worse near vision impairment, we also detected statistically significant DIF across a range of items with visual stimuli: 6/11 for general cognition, 1/1 for memory, 1/3 for executive functioning, 0/4 for language fluency, and 0/3 for visuospatial functioning. Item characteristic curves for all items with significant DIF, for both distance and near VI, can be found in Supplementary Figure 1.

Across all DIF analyses, nearly all findings were in the expected direction (eg, items were more difficult for those with vision impairment). However, 2 significant findings were in the opposite direction, likely due to chance given the large number of statistical tests conducted. However, despite statistically significant findings, differences between unadjusted and adjusted scores were all smaller than the median of the SEM across the observations, indicating that the magnitude of observed bias was small compared to the uncertainty in estimates of cognitive functioning (Figure 1).

Figure 1. Comparison of DIF-unadjusted and DIF-adjusted cognitive test scores for general cognitive functioning in the LASI-DAD study. DIF = differential item functioning; LASI-DAD = Longitudinal Aging Study in India-Diagnostic Assessment of Dementia. Panel A shows scores adjusted for DIF by near vision impairment and Panel B shows scores adjusted for DIF by distance vision impairment. DIF adjusted models allow for a direct association between cognitive tests and vision impairment status.

Alt Text: A group of graphs showing cognitive scores among those with near vision impairment (A) and those with distance vision impairment (B) before and after adjusting for differential item functioning.

The Impact of Adjusting for Differential Item Functioning in Estimating Associations Between Vision Impairment and Cognitive Functioning

Linear regression models were reestimated with DIF-adjusted cognitive factor scores as outcomes (presented in Supplementary Tables 8 and 9). Results from these models were substantively similar to models estimated using unadjusted cognitive factor scores, as illustrated in Figure 2.

Figure 2. Associations between cognitive functioning and vision impairment before and after adjustment for DIF by near vision impairment (A) and distance vision impairment (B) in the LASI-DAD sample. DIF = differential item functioning; LASI-DAD = Longitudinal Aging Study in India-Diagnostic Assessment of Dementia.

Alt Text: Graphs showing cognitive score estimates divided by domains among those with mild, moderate, and severe vision impairment (panel A is by near vision and panel B is by distance vision impairment) before and after adjusting for differential item functioning.

Sensitivity Analyses

We performed sensitivity analyses stratified by literacy status. Of the items with visual stimuli and differences in administration between literate and illiterate respondents (literate: write a sentence, read and follow command; illiterate: watch and follow command) we found significant evidence of DIF by near vision impairment status for the “write a sentence” item among literate participants. However, there was no evidence of DIF for distance vision impairment nor was there evidence of DIF in these items for the illiterate population. There was also no evidence of salient DIF, and the impact of DIF adjustment on vision-cognition associations remained unchanged compared to primary analyses. Although there were some differences in the statistical significance of DIF across items comparing analyses using the variables for moderate or worse impairment compared to any impairment, in both sets of analyses the magnitude of DIF was less than the SEM; thus, we did not find evidence of meaningful, salient DIF.

Discussion

To our knowledge, this is the first study to assess DIF by sensory function in cognitive testing in an LMIC and in a nationally representative sample from any setting. We found minimal evidence of DIF in cognitive test results as a function of objective vision impairment. This finding is highly relevant to an aging Indian population and may have relevance to cognitive testing across other settings and LMICs. Vision impairment is a potentially modifiable dementia risk factor (17) and up to 90% of cases of vision impairment and blindness are preventable or have yet to be addressed in India (2). Since the prevalence of cognitive impairment and dementia in India is predicted to increase sharply due to population aging and increased life expectancy, it is crucial to collect valid and reliable data on modifiable dementia risk factors. However, since many of the tests that are used to assess cognitive health rely on visual stimuli, it is conceivable that the reported association between vision impairment and dementia could simply be due to an inability to see a test well enough to give an accurate response, rather than actual cognitive status. Notwithstanding, in this nationally representative study we did not find evidence of salient DIF by vision impairment status, suggesting the construct validity of cognitive tests in LASI-DAD is not influenced by vision impairment.

There was evidence of statistically significant DIF by distance VI and near VI for some vision-dependent cognitive test items in the LASI-DAD cognitive battery; however, observed DIF did not lead to meaningful bias in the estimation of cognitive functioning. This was consistent with our hypothesis that there would be a small but detectable influence of visual function of cognitive test performance. Importantly, a statistically significant association between vision impairment and cognitive function was present across cognitive domains when using both DIF-adjusted and unadjusted cognitive factor scores. The differences between unadjusted and DIF-adjusted parameter estimates were considerably smaller than the SEM (22,23), a threshold commonly used to assess the salience of DIF.

Our study findings align with the prior study that analyzed data from the U.S.-based ARIC and BLSA. In that study, the investigators tested for DIF by both vision and hearing impairment status in cognitive assessments (3). As in the current study, all differences in unadjusted and DIF-adjusted model estimates were less than the SEM, though there was some detectable DIF in cognitive tests by sensory status that was independent of administration stimuli (visual or auditory). The authors also acknowledged that neither BLSA nor ARIC are nationally representative samples, and each is uniquely impacted by selection factors. In contrast, LASI-DAD is a nationally representative of India with participants across a wide spectrum of visual and cognitive health. The testing conditions in the LASI-DAD study were also more varied than in BLSA or ARIC; assessments of cognition were conducted in participant homes, which lowers potential selection bias but can lead to logistical challenges in cognitive testing, particularly in LMICs. Results from the current study suggest that this environment did not lead to differences in observed bias due to vision impairment status. Furthermore, the LASI-DAD cognitive battery was designed to be sensitive to issues of illiteracy and innumeracy, so our findings may have greater relevance to aging populations in LMICs where these issues are more common.

This study had several imitations. We excluded 8% of respondents due to missing data on objective vision tests stemming from refusal to complete the exam. However, even if respondents who refused to complete the exam were more likely to be impaired than those who participated, it is unlikely that the results from DIF testing would be biased, as it is improbable that those who were excluded had impairments that were more likely to impact cognitive testing compared to the impairment of those included in this analysis. Due to available data, we only tested for DIF by vision impairment, but it will be important to make a similar assessment of DIF by hearing status when audiometric data become available in subsequent rounds of LASI-DAD. These data were cross-sectional and future studies should assess longitudinal associations between vision impairment and cognitive decline. Finally, this study was carried out in India, a low- and middle-income country with a unique cultural and linguistic context, which may limit generalizability of our findings. Still, as India is home to 18% of the world’s population and is undergoing rapid population aging, these findings have considerable scientific importance for research on cognitive health and dementia, particularly as the first such study in any LMIC.

The study also has important strengths. To our knowledge, this is only the second investigation on the influence of sensory health on measurement of cognition and the first in a nationally representative sample. As the dementia research community moves toward policy and health interventions to address modifiable risk factors, it is vital to understand whether cognitive tests are significantly biased by how well participants can see or hear the tests, or whether they can be used widely across older adults with different levels of sensory function. Additionally, vision was assessed in LASI using an objective clinical assessment, rather than self-reported measures that are commonly used in surveys.

In summary, visual function had only a minimal and nonsalient influence on the measurement of cognitive function among older adults in India. Accordingly, our findings support the use of the standard HCAP in older Indians regardless of the presence or absence of vision impairment. Future work should aim to understand whether these findings generalize to other settings and other cognitive test batteries. Findings from this study reinforce existing evidence on the relationship of vision impairment and cognitive function.

Supplementary Material

igae071_suppl_Supplementary_Material

Funding

The Longitudinal Study of Aging in India is supported by the National Institute on Aging (NIA) (R01AG042778); the Longitudinal Aging Study in India, Diagnostic Assessment of Dementia data (doi.org/10.25549/5hhxs820) is sponsored by the NIA (grant numbers R01AG051125, RF1AG055273, and U01AG065958) and is conducted by the University of Southern California; this work was also supported by the Gateway to Global Aging Data (R01AG030153), and additional funding from the NIA (R01AG070953 and R61AG089063).

Conflict of Interest

None.

Data Availability

The data underlying this article can be accessed for free at the Gateway to Global Aging website (g2aging.org)

Statement of Ethics

This analysis was exempt from IRB approval as it was a secondary analysis of publicly available data. Ethics approval for the collection of data in the LASI-DAD study was obtained from the Indian Council of Medical Research and all collaborating institutions, including the University of Southern California; University of Michigan; the All India Institute of Medical Sciences, New Delhi; the International Institute of Population Sciences, Mumbai; All India Institute of Medical Sciences, Bhubaneshwar; Dr. SN Medical College, Jodhpur; Government Medical College, Thiruvananthapuram; Grant Medical College and J.J. Hospital, Mumbai; Guwahati Medical College, Guwahati; Institute of Medical Sciences, BHU, Varanasi; Madras Medical College, Chennai; Medical College, Kolkata; National Institute of Mental Health and Neurosciences, Bengaluru; Nizam’s Institute of Medical Sciences, Hyderabad; and Sher-e-Kashmir Institute of Medical Sciences, Srinagar; Indira Gandhi Institute of Medical Sciences, Patna; Gwalior Medical College, Madhya Pradesh; All India Institute of Medical Sciences, Rishikesh; and Government Medical College, Chandigarh. Informed consent was obtained from all study participants.
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