Comparing Telephone Survey Responses to Best-Corrected Visual Acuity to Estimate the Accuracy of Identifying Vision Loss: Validation Study

Behavioral Risk Factor Surveillance System and American Community Survey Estimates of Vision Difficulty Prevalence

Importance

Inconsistent estimates of self-reported vision impairment across survey sources may cause confusion about the true size of the population with vision problems.

Objective

To explain why the American Community Survey (ACS) and Behavioral Risk Factor Surveillance System (BRFSS) produce different prevalence estimates for self-reported vision problems in the US, despite using the same question wording.

Design, Setting, and Participants

This was a cross-sectional analysis of the 2021 ACS and BRFSS using subgroup analysis and decomposition. Respondents were from 49 states and the District of Columbia. Included in the analysis were a civilian noninstitutionalized population 18 years and older. Data were analyzed from August 2022 to October 2023.

Intervention

Aspects of sample design and composition.

Main Outcomes and Measures

Self-reported vision problems.

Results

This study included a weighted sample of 2.8 million individuals (median [IQR] age, 47.7 [32.8-63.1] years; 51% male). The estimate of self-reported vision problems prevalence from the BRFSS (4.89%; 95% CI, 4.73%-5.04%) was 1.7 times as high as the estimate from the ACS (2.95%; 95% CI, 2.92%-2.97%) for similarly defined populations. If the BRFSS sample were weighted to align with the composition of other disability types in the ACS, the prevalence of vision problems would be 3.67% (95% CI, 3.53%-3.80%), closing about 63% of the gap between survey estimates.

Conclusion and Relevance

Results of this survey study suggest that the focus on health may be associated with the higher prevalence in the BRFSS through differential nonresponse or question priming. Differences in other survey operations including frame construction, proxy reporting, and imputation had little impact.

Place-Based Measures of Inequity and Vision Difficulty and Blindness

Importance

Known social risk factors associated with poor visual and systemic health in the US include segregation, income inequality, and persistent poverty.

Objective

To investigate the association of vision difficulty, including blindness, in neighborhoods with measures of inequity (Theil H index, Gini index, and persistent poverty).

Design, Setting, and Participants

This cross-sectional study used data from the 2012-2016 American Community Survey and 2010 US census tracts as well as Theil H index, Gini index, and persistent poverty measures from PolicyMap. Data analysis was completed in July 2023.

Main Outcomes and Measures

The main outcome was the number of census tract residents reporting vision difficulty and blindness (VDB) and the association with the Theil H index, Gini index, or persistent poverty, assessed using logistic regression.

Results

In total, 73 198 census tracts were analyzed. For every 0.1-unit increase in Theil H index and Gini index, there was an increased odds of VDB after controlling for census tract-level median age, the percentage of the population that identified as female sex, the percentage of the population that identified as a member of a racial or ethnic minority group, state, and population size (Theil H index: odds ratio [OR], 1.14 [95% CI, 1.14-1.14; P < .001]; Gini index: OR, 1.15 [95% CI, 1.15-1.15; P < .001]). Persistent poverty was associated with an increased odds of VDB after controlling for census tract-level median age, the percentage of the population that identified as female sex, the percentage of the population that identified as a member of a racial or ethnic minority group, state, and population size compared with nonpersistent poverty (OR, 1.36; 95% CI, 1.35-1.36; P < .001).

Conclusions and Relevance

In this cross-sectional study, residential measures of inequity through segregation, income inequality, or persistent poverty were associated with a greater number of residents living with VDB. It is essential to understand and address how neighborhood characteristics can impact rates of VDB.

Employment and retirement among workers who develop vision loss in midlife

BACKGROUND

Vision loss increases with age and is thus more likely to happen later in one's career. With more individuals working beyond typical retirement age, the possibility of experiencing vision loss while working has increased.

OBJECTIVE

The purpose of this study was to investigate how developing vision loss during midlife affects employment and retirement.

METHOD

Using longitudinal Health and Retirement Study data, we identified a sample of 167 workers, 44 to 64 years old, who developed vision loss and a matched comparison sample of 800 workers who did not. We explored job retention and retirement differences between the groups and differences between people with vision loss who retained jobs versus those who did not.

RESULTS

Vision loss was associated with leaving the labor force, although there was a clear trend over time of increasing likelihood of job retention. Occupational category was associated with job retention and people who continued working had more favorable financial situations. Retirees with vision loss were more likely to report involuntary retirement and dissatisfaction with retirement.

CONCLUSIONS

The decreasing likelihood of leaving the labor market after vision loss is an encouraging finding of this study. Workers who left the labor force after vision loss were more likely to be in precarious financial positions, and retirees did not have positive experiences with retirement. Assisting workers with vision loss to remain in the labor force is of vital importance, and increasing awareness and usage of free services for this population may reduce involuntary retirement and its negative consequences.

Vision Need Profiles for the City of Richmond, Virginia: A Pilot Application of Calibration Methods to Vision Surveillance

Purpose

People with vision problems (VPs) have different needs based on their age, economic resources, housing type, neighborhood, and other disabilities. We used calibration methods to create synthetic data to estimate census tract-level community need profiles (CNPs) for the city of Richmond, Virginia.

Design

Cross-sectional secondary data analysis.

Subjects

Anonymized respondents to the 2015 to 2019 American Community Survey (ACS).

Methods

We used calibration methods to transform the ACS 5-year tabular (2015-2019) and Public Use Microdata estimates into a synthetic data set of person-level records in each census tract, and subset the data to persons who answered yes to the question "Are you blind or do you have serious difficulty seeing even when wearing glasses?" To identify individual need profiles (INPs), we applied divisive clustering to 17 variables measuring individual demographics, nonvision disability status, socioeconomic status (SES), housing, and access and independence. We labeled tracts with CNP names based on their predominant INPs and performed sensitivity analyses. We mapped the CNPs and overlayed information on the number of people with VP, the National Walkability Index, and an uncertainty measure based on our sensitivity analysis.

Main Outcome Measures

Individual need profiles and CNPs.

Results

Compared with people without VP, people with VP exhibited higher rates of disabilities, having low incomes, living alone, and lacking access to the internet or private home vehicles. Among people with VP, we identified 7 INP clusters which we mapped into 6 CNPs: (1) seniors (≥ age 65); (2) low SES younger; (3) low SES older; (4) mixed SES; (5) higher SES; and (6) adults and children in group quarters. Three CNPs had lower-than-average walkability. Community need profile assignments were somewhat sensitive to calibration variables, with 18 tracts changing assignments in 1 sensitivity analysis, and 4 tracts changing assignments in ≥ 2 sensitivity analyses.

Conclusions

This pilot project illustrates the feasibility of using ACS data to better understand the support and service needs of people with VP at the census tract level. However, a subset of categorical CNP assignments were sensitive to variable selection leading to uncertainty in CNP assignment in certain tracts.

Financial Disclosures

The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Association Between Social Determinants of Health and Examination-Based Vision Loss vs Self-reported Vision Measures

Importance

Recent evidence suggests that social determinants of health (SDOH) affect vision loss, but it is unclear whether estimated associations differ between clinically evaluated and self-reported vision loss.

Objective

To identify associations between SDOH and evaluated vision impairment and to assess whether these associations hold when examining self-reported vision loss.

Design, Setting, and Participants

This population-based cross-sectional comparison included participants 12 years and older in the 2005 to 2008 National Health and Nutrition Examination Survey (NHANES), participants of all ages (infants and older) in the 2019 American Community Survey (ACS), and adults 18 years and older in the 2019 Behavioral Risk Factor Surveillance System (BRFSS).

Exposures

Five domains of SDOH that are based on Healthy People 2030: economic stability, education access and quality, health care access and quality, neighborhood and built environment, and social and community context.

Main Outcomes and Measures

Presenting vision impairment of 20/40 or worse in the better-seeing eye (NHANES) and self-reported blindness or serious difficulty seeing, even with glasses (ACS and BRFSS).

Results

Of 3 649 085 included participants, 1 873 893 were female (51.1%) and 2 504 206 were White (64.4%). SDOH across domains of economic stability, educational attainment, health care access and quality, neighborhood and built environment, and social context were significant predictors of poor vision. For example, higher income (poverty to income ratio [NHANES]: OR, 0.91; 95% CI, 0.85-0.98; [ACS]: OR, 0.93; 95% CI, 0.93-0.94; categorical income [BRFSS:<$15 000 reference]: $15 000-$24 999; OR, 0.91; 95% CI, 0.91-0.91; $25 000-$34 999: OR, 0.80; 95% CI, 0.80-0.80; $35 000-$49 999: OR, 0.71; 95% CI, 0.71-0.72; ≥$50 000: OR, 0.49; 95% CI, 0.49-0.49), employment (BRFSS: OR, 0.66; 95% CI, 0.66-0.66; ACS: OR, 0.55; 95% CI, 0.54-0.55), and owning a home (NHANES: OR, 0.85; 95% CI, 0.73-1.00; BRFSS: OR, 0.82; 95% CI, 0.82-0.82; ACS: OR, 0.79; 95% CI, 0.79-0.79) were associated with lower odds of vision loss. The study team identified no differences in the general direction of the associations when using either clinically evaluated or self-reported vision measures.

Conclusions and Relevance

The study team found evidence that associations between SDOH and vision impairment track together when using either clinically evaluated or self-reported vision loss. These findings support the use of self-reported vision data in a surveillance system to track trends in SDOH and vision health outcomes within subnational geographies.