Blindness or vision impairment has been among the top 10 disabilities of people aged 18 or older for 20 years. In 2015, there were 1.02 million blind and 3.22 million visually impaired individuals in the United States.1 Among these, many have been adversely impacted by their visual impairment in terms of employment. In fact, only 46.2% of those with visual impairments were employed in 2019 compared to 78.6% of people without disabilities.2 This disparity highlights the need to better understand the potential modifiable variables between visual impairment and employment.
The impact of visual impairment on employment has been broadly investigated in a systematic review that found over 50 studies involving more than 1.3 million participants.3 These studies have generally come from high-income, industrialized countries and revealed several consistent findings. Unemployment tends to be associated with lower levels of education, non-white race, female gender, and severity of visual impairment.3 The methodologic approaches of these studies, however, have varied considerably and are further confounded by differences in sociodemographic variables among study populations. In addition, the systematic review contained a subset of studies that failed to restrict their study to the working-age population, including individuals who were not part of the working-age population.3 While variables such as severity of visual difficulty were studied, the review did not address all degrees of visual difficulty. Rather, it compared only those with low vision with those who are completely blind. Such issues make it difficult to draw meaningful conclusions. Our examination of the 2022 National Health Interview Survey (NHIS) conducted by the National Center for Health Statistics (NCHS) allows for an updated perspective on how visual impairment has affected employment following the COVID-19 pandemic.
Data were accessed from the publicly available NCHS website.4 The study was exempted from Northwestern University Institutional Review Board as survey results are de-identified collective data and publicly available. Demographic and survey data were download, including survey weights. A multivariable logistic regression model was created to evaluate the odds of unemployment (“laid off” and ““looking for work”), as the main reason for not working. Persons over the age of 65, as well as persons retired, going to school, self-employed, seasonal or contract works were excluded. Independent variables selected for the model included gender, race, Hispanic ethnicity, urban residency, level of education, citizenship, and self-reported vision. The latter variable was categorized as seeing with “some” difficulty, with “severe” difficulty, “can’t see at all,” and “a lot of difficulty.” Education was categorized as less than high school, high school graduation, some college, and college or higher. Living site was classified as non-metropolitan, fringe metropolitan, small-medium metropolitan, and large central metropolitan. SAS version 9.4 was used for estimation of odd ratio (OR) with 95% confidence intervals (CI).
In 2022, there were 27,651 adult NHIS respondents. The total employed and unemployed respondents (unweighted) comprised 16,838 individuals out of which 440 were unemployed. The resulting unemployment rate of 2.61% was slightly lower than the national unemployment rate that year of 3.6%.5 Table 1 shows the results of the multivariate logistic regression model. Relevant associations with unemployment were lower levels of education, particularly less than high school (OR 6.05, 95% CI: 3.98–9.18) and high school (OR 3.80, 95% CI: 2.78–5.21); severe vision difficulty (OR 3.68 95% CI: 1.73–7.86); Asian race (2.53, 95% CI: 1.64–3.89); and Black race (1.78, 95% CI: 1.31–2.41) (Table 1). Female gender was not significantly associated with unemployment (OR 0.93, 95% CI 0.74–1.17). The odds of unemployment were marginally elevated for those living in large metropolitan areas, while being born in the United States had a modest protective effect (OR 0.53, 95% CI: 0.42–0.66). (Table 1).
Table 1.
Odds Ratios of Unemployed to Employed.
| Unemployed vs Employment | Confidence Interval | |
|---|---|---|
|
| ||
| Sex | ||
| Male | Reference | |
| Female | 0.93 | (0.74–1.17) |
| Birthplace | ||
| Born Abroad | Reference | |
| Natural-Born | 0.53 | (0.42–0.66)* |
| Urban-rural classification | ||
| Non-metropolitan | Reference | |
| Large fringe metropolitan | 1.15 | (0.76–1.74) |
| Medium and small metropolitan | 1.03 | (0.69–1.53) |
| Large central metropolitan | 1.58 | (1.06–2.33)* |
| Difficulty Seeing | ||
| No Difficulty | Reference | |
| Moderate Vision Difficulty | 1.32 | (0.98–1.80) |
| Serve Vision Difficulty | 3.69 | (1.73–7.86)* |
| Hispanic Origin | ||
| Non-Hispanic | Reference | |
| Hispanic | 1.44 | (0.99–2.08) |
| Race | ||
| White | Reference | |
| Black | 1.78 | (1.31–2.41)* |
| Asian | 2.53 | (1.64–3.89)* |
| Other Races | 1.06 | (0.61–1.85) |
| Unknown Races | 0.75 | (0.43–1.33) |
| Education | ||
| College or Higher | Reference | |
| Less than High School | 6.05 | (3.98–9.18)* |
| High School Grad | 3.80 | (2.78–5.21)* |
| Some college/associates degree | 1.47 | (1.06–2.04)* |
| Unknown Education | 2.27 | (0.48–10.84) |
P<0.05
Previous research has examined the impact of visual impairment on employment prior to the COVID-19 pandemic.3 This study examined its impact on employment following the pandemic. Given the changes in the post-pandemic economy, it may serve as a benchmark for future inquiries. This large-scale household interview survey coordinated by the NCHC has been conducted since 1957, and has been continuously improved upon to best represent a cross-section of the U.S. population.4 Unlike surveys performed on the visually impaired, the NCHC has been developed and vetted over decades. The large sample size gives it statistical power.6 Assessment of the most recent NCHC confirms that the severity of visual impairment and level of education, particularly less than college, is strongly linked to unemployment. These associations are plausible and have consistently appeared in the literature, even among populations with different socioeconomic characteristics.3,6
The single most predictive factor for unemployment was having an education of less than high school. (OR 6.05 95% CI: 3.98–9.18); This is consistent with previous research showing that lower education levels correlate to higher rates of unemployment.7 The association was documented at various levels of education: high school grads were more likely to be employed than those with an education of less than high school; those with some college/associates degree were more likely to be employed than those with lesser levels of education. Given that the association between employment and education has been documented consistently in the past yet persists, indicated that strategies in the past have yet to make an impact in the U.S.3,6 This study has several limitations. Notably, visual acuity was not objectively measured. Since some causes of self-reported visual impairment are easily remediated (e.g., uncorrected refractive error), their impact on gainful employment can vary greatly. The possibility of misclassification is also greater when having to rely of self-reporting. This is particularly problematic in assessing severity of visual impairment when personal assessment can diverge substantially from objective definitions.
Targeting the educational and vocational training needs of those with visual impairment may be the most effective means of improving employment among those with subnormal vision. The National Institutes of Health recently designated people with disabilities including visual impairments as a population for studying health disparities. Future research is needed to understand the barriers that the visual impaired have in obtaining ophthalmic care, formal education, and employment.8
Financial Support:
This research was supported by a grant from the National Eye Institute 1R01EY034444–01 Health Disparities in Utilization, Quality, and Outcomes for Three Common Ocular Conditions (HealthDOC) and an unrestricted grant from Research to Prevent Blindness, New York, NY (French DD and Kanwar K).
Footnotes
Conflict of Interest Disclosure: None of the following authors have any proprietary interests or conflicts of interest related to this submission.
References
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