Geographic Variation in Employment for U.S. Adults by Visual Impairment Status

Abstract

Keywords

visual impairment blindness employment geographic variation disparity

In the United States, approximately 3.2 million adults have a visual impairment (i.e., blindness or low vision; Varma et al., 2016). These individuals face disproportionately lower levels of employment compared to those without disabilities—2017 national employment rates estimated at 44.2% and 79.4%, respectively, for these two groups (Dunn & Blank, 2018; Erickson et al., 2017; McDonnall & Sui, 2019). Although some studies have examined employment rates of adults with visual impairments at the national level in the United States, less is known about whether the disparity in employment by visual impairment status varies geographically (Bell & Silverman, 2018; McDonnall & Sui, 2019). State- and regional-level employment rates for those with visual impairments mainly comes from American Community Survey analyses, which has small sample sizes in some states compared to other nationally representative surveys such as the Behavioral Risk Factor Surveillance System (BRFSS; Centers for Disease Control and Prevention, 2019; United States Census Bureau, 2020). Using BRFSS data, we determined whether contemporary employment rates for U.S. adults with and without visual impairments differed at the national, regional, and state levels.

Methods

Data Source

The BRFSS has been conducted by the Centers for Disease Control and Prevention (CDC) since 1984 as a means to collect information on the medical conditions and health behaviors of Americans (Centers for Disease Control and Prevention, 2014). These annual surveys include a sample of non-institutionalized individuals aged 18 years and older and are conducted via landline telephone and cell phone in all 50 U.S. states, Washington, D.C., and U.S. territories. A combination of oversampling in certain areas and survey weighting in the BRFSS allows results from survey analyses to be generalized to the entire U.S. population. Since all BRFSS data is deidentified, research using these surveys is exempt from informed consent and institutional review board approval. BRFSS surveys are freely available at the CDC’s website: https://www.cdc.gov/brfss/data_documentation/index.htm.

Our study included 2016, 2017, and 2018 BRFSS survey participants aged 18–64 years. We combined these particular years because they are the latest BRFSS surveys with visual impairment and employment information and three survey years were needed to obtain a large enough sample of individuals with visual impairments for well-powered analyses (Centers for Disease Control and Prevention, 2014). Adults with a “yes” response to the BRFSS question, “Are you blind or do you have serious difficulty seeing, even when wearing glasses?” were categorized as having visual impairments, while those who responded “no” were considered not to have impaired vision. In keeping with CDC analyses of BRFSS surveys, individuals who responded “don’t know/not sure” or “refused” to the visual impairment question, less than 0.5% of the study sample, were excluded from the study (Alaska Department of Health and Social Services, 2018; Montana Department of Public Health and Human Services, 2015).

Defining Employment Rate

We assessed the employment rate through the BRFSS’s employment question, “Are you currently employed?” (Centers for Disease Control and Prevention, 2014). Individuals were categorized as “employed” if their response to the employment question was either “employed for wages” or “self-employed”; “unemployed” if their response was either “out of work for 1 year or more” or “out of work for less than 1 year”; “in the labor force” if their response was either “a homemaker,” “a student,” “unable to work,” or “retired” (Centers for Disease Control and Prevention, 2014). As with the visual impairment question, “do not know/not sure” or “refused” responses to the BRFSS employment question, less than 2% of the study sample, were excluded from the study. To calculate the employment rate, we used an equation from the Bureau of Labor Statistics where $E m p l o y m e n t r a t e = \frac{E m p l o y e d p e o p l e}{E m p l o y e d p e o p l e + U n e m p l o y e d p e o p l e + P e o p l e n o t i n l a b o r f o r c e}$ × (United States Bureau of Labor Statistics, 2020).

Statistical Analyses

The survey-weighted employment rate and accompanying margins of error (Wald type 95% confidence intervals) by visual impairment status were calculated at the national, regional, and state levels using the equation mentioned previously. All analyses were conducted using the R “survey” package Version 4.0 (Lumley, 2020).

Results

The study included 840,759 individuals, of which 4.4% reported having a visual impairment (Table 1). Nationally, the employment rate for people with visual impairments was 40.5% (95% CI: 39.9–40.9%) compared to 69.4% (95% CI: 69.3–69.5%) for those without visual impairments. Of the U.S. regions, adults with visual impairments had the highest rate of employment in the West (45.3%, 95% CI: 40.9–49.7%) and the lowest in the South (37.5%, 95% CI: 34.1–40.8%). State-level employment rates for those with visual impairments varied considerably, with the highest and lowest employment rates observed in Utah (55.8%, 95% CI: 51.9–59.8%) and Alabama (27.6%, 95% CI: 24.7–30.5%), respectively. U.S. regions and states with high employment rates for individuals with visual impairments tended to be areas with high employment rates for individuals without visual impairments.

Table 1.

National-, regional-, and state-level employment rates by visual impairment status.

Variable	Employment rate (%)^a(95% confidence interval)
	Adults with visual impairment	Adults without visual impairment
	(Weighted N = 3,572,464)^b	(Weighted N = 81,166,940)
United States	40.5 (39.9–40.9)	69.4 (69.3–69.5)
Regions
Northeast	41.6 (37.4–45.7)	71.3 (70.5–72.5)
Midwest	43.5 (39.4–47.5)	73.4 (72.7–74.1)
South	37.5 (34.1–40.8)	67.6 (66.7–68.4)
West	45.3 (40.9–49.7)	70.6 (69.8–71.4)
States
Alabama	27.6 (24.7–30.5)	64.1 (63.2–65.0)
Alaska	41.9 (35.3–48.6)	71.7 (70.5–72.8)
Arizona	39.7 (36.6–42.9)	66.1 (65.4–66.8)
Arkansas	34.8 (30.8–38.7)	64.1 (63.0–65.2)
California	44.5 (41.4–47.6)	68.2 (67.5–68.8)
Colorado	46.2 (42.1–50.3)	73.3 (72.7–73.9)
Connecticut	42.8 (39.0–46.6)	72.3 (71.7–73.0)
Delaware	43.7 (39.2–48.2)	71.2 (70.2–72.2)
District of Columbia	46.5 (41.6–51.3)	73.6 (72.6–74.6)
Florida	44.2 (42.4–46.0)	67.4 (67.0–67.9)
Georgia	39.9 (36.4–43.5)	66.7 (65.9–67.5)
Hawaii	55.5 (51.5–59.5)	75.7 (75.0–76.4)
Idaho	44.6 (38.8–50.4)	69.9 (68.9–70.9)
Illinois	40.4 (35.3–45.5)	71.1 (70.2–71.9)
Indiana	38.0 (34.8–41.2)	69.4 (68.7–70.0)
Iowa	38.5 (33.8–43.2)	76.1 (75.4–76.8)
Kansas	44.9 (41.7–48.0)	73.7 (73.1–74.2)
Kentucky	30.9 (28.4–33.4)	65.0 (64.3–65.7)
Louisiana	30.5 (26.9–34.1)	64.9 (63.9–65.9)
Maine	37.1 (33.2–41.0)	72.0 (71.3–72.6)
Maryland	42.0 (38.9–45.2)	71.8 (71.3–72.3)
Massachusetts	41.0 (36.8–45.3)	70.8 (70.1–71.6)
Michigan	34.3 (31.3–37.4)	67.0 (66.3–67.6)
Minnesota	55.1 (52.0–58.2)	77.0 (76.6–77.5)
Mississippi	34.1 (31.0–37.2)	64.0 (63.0–65.0)
Missouri	39.6 (35.7–43.4)	71.8 (71.0–72.7)
Montana	45.7 (41.2–50.2)	72.4 (71.5–73.2)
Nebraska	44.4 (41.2–47.6)	76.4 (75.9–76.9)
Nevada	43.9 (38.8–49.0)	70.1 (69.0–71.2)
New Hampshire	49.1 (43.7–54.4)	74.4 (73.5–75.2)
New Jersey	45.4 (41.5–49.3)	69.8 (69.0–70.6)
New Mexico	39.9 (36.3–43.5)	65.0 (64.1–65.9)
New York	40.2 (38.2–42.2)	68.8 (68.3–69.2)
North Carolina	37.5 (33.5–41.5)	69.8 (68.9–70.6)
North Dakota	45.7 (39.4–52.0)	76.8 (76.0–77.6)
Ohio	40.6 (37.6–43.5)	70.7 (70.1–71.4)
Oklahoma	38.0 (34.2–41.7)	67.9 (67.0–68.8)
Oregon	42.6 (37.9–47.4)	68.8 (67.9–69.7)
Pennsylvania	35.0 (30.5–39.5)	69.9 (69.1–70.7)
Rhode Island	42.5 (37.7–47.2)	69.3 (68.4–70.2)
South Carolina	36.3 (33.5–39.2)	68.2 (67.5–68.9)
South Dakota	54.8 (50.2–59.3)	77.3 (76.5–78.0)
Tennessee	33.0 (29.7–36.3)	67.3 (66.4–68.2)
Texas	40.9 (38.1–43.8)	68.6 (68.0–69.3)
Utah	55.8 (51.9–59.8)	73.4 (72.8–74.0)
Vermont	40.9 (36.0–45.8)	74.2 (73.4–75.0)
Virginia	44.3 (40.8–47.8)	72.5 (71.9–73.2)
Washington	40.1 (36.8–43.4)	69.6 (69.0–70.2)
West Virginia	33.0 (30.0–36.0)	61.3 (60.4–62.3)
Wisconsin	45.3 (39.4–51.1)	74.0 (73.1–74.9)
Wyoming	48.6 (43.1–54.1)	73.5 (72.5–74.5)

^aSurvey weights in the BRFSS have been used to obtain the weighted percentages.

^bSurvey weights in the BRFSS have been used to obtain the weighted N.

Discussion

In this national study, we assessed geographic variation in employment rates for U.S. adults by visual impairment status using BRFSS data. Regional and state-level employment rates for adults with visual impairments were found to differ from the national employment rate for this population. Additionally, we observed that areas with high employment rates for adults with visual impairments were usually also areas with high employment rates for those without impaired vision.

Accounting for margin of error and that employment data may have been collected at different times, we obtained a comparable national employment rate for U.S. adults with visual impairments (40.5%) as previous studies that have used other surveys to estimate this figure (combined 1999–2008 National Health and Nutrition Examination Surveys: 38.3%, combined 1994 and 1995 National Health Interview Survey-Disability Surveys: 41.0%, 2010 Survey of Income and Program Participation: 41.7%, 2015 ACS: 41.8%, 2011 Adult Rehabilitation and Employment Survey: 37.2%) (Bell & Silverman, 2018; Erickson et al., 2017; McDonnall & Sui, 2019). Additionally, within margin of error the range of state-level employment rates we found (27.6–55.8%) is similar to those from U.S. government analyses of ACS data from 2016 (29.0–70.1%), 2017 (27.8–70.4%), and 2018 (27.4–56.3%) with any remaining slight differences possibly owing to different survey sampling techniques (Centers for Disease Control and Prevention, 2014; Cornell University, 2018).

Identification of specific U.S. regions and states where employment rates are low for adults with visual impairments is a critical component of reducing disparities in employment for these individuals. Particularly low employment rates among adults with visual impairments in certain areas may result from the presence of a higher number of employment barriers related to visual impairment such as discrimination, limited means of transportation to work, lack of accessible assistive technology, and little job training and support from rehabilitation agencies in these places (Crudden et al., 2005; McDonnall, O'Mally, Crudden, et al., 2014; O'Day, 1999). Additional efforts to improve employment rates for adults with visual impairments should seek to mitigate the influence of these barriers and be guided by strategies that have shown some success such as further education and job training opportunities (Bell & Silverman, 2018).

There is evidence from studies of Americans with visual impairments that indicates higher educational attainment is not only associated with lower unemployment rates, but also with employment in more senior positions with higher pay (Bell & Mino, 2015). Compared to the 24% employment rate among U.S. adults with visual impairments who have not completed high school, individuals with visual impairments who have completed high school and college have employment rates of 37% and 65%, respectively (Erickson et al., 2017). In addition, individuals with visual impairments who had a college degree earned $10,800 more annually than their peers who only completed high school (Bell & Mino, 2015). Given that recent estimates from the ACS show that only 15% of individuals with visual impairments have earned a college degree, in comparison to 30% of those without visual impairments, additional programs to improve education levels among those with visual impairments are warranted (Erickson et al., 2017). Although it is difficult to create programs that fully encompass all measures of educational support for those with visual impairments, incorporating strategies such as financial support for assistive technology and a dedicated teacher of students with visual impairments (TVI) for kindergarten–12th grade students in these programs may be helpful in increasing higher educational attainment (Bell & Silverman, 2019; Kelly, 2009; Lusk et al., 2013). Since there are financial and human resource constraints related to the implementation and continuation of educational programs, focusing such programs in regions with the lowest employment rates for those with visual impairments, such as the South, may hold the greatest utility.

We acknowledge the study’s limitations. Since BRFSS surveys consist of self-reported responses, some data misclassification is unavoidable (Centers for Disease Control and Prevention, 2014). However, validation studies demonstrate that measures of diabetes (BRFSS: 9.7%, EHR: 9.4%), smoking (BRFSS: 14.7%, EHR: 13.5%), hypertension (BRFSS: 29.6%, EHR: 26.3%), and obesity (BRFSS: 23.8%, EHR: 22.8%) prevalence from the BRFSS are similar to those obtained from electronic health records (Bowlin et al., 1993; Klompas et al., 2017; Scribani et al., 2014). Although we excluded people with "Don’t know/Not sure” or “refused” responses to the visual impairment and employment questions, these exclusions have precedent in BRFSS analyses conducted by the CDC, as well as these individuals making up a very small percentage of the study sample (Alaska Department of Health and Social Services, 2018; Montana Department of Public Health and Human Services, 2015). Despite some study limitations, using BRFSS surveys to examine geographic variation in employment rates by visual impairment status confers several advantages. The larger size of the BRFSS allows for more precise and stable estimates than can be found in the ACS for states that have small populations (e.g., 2758 Alaskan survey participants in the 2018 BRFSS vs. 550 Alaskan survey participants in the 2018 ACS) (Centers for Disease Control and Prevention, 2019; United States Census Bureau, 2020). Additionally, since most state-level information on employment for individuals with visual impairments comes from ACS data, having employment estimates for those with visual impairments from a different data source such as the BRFSS serves as both a good comparison and external validation that geographic differences in employment by visual impairment status do exist.

Footnotes

Authors' Note

Phoebe Tran originated the idea and the study design. Lam Tran ran the data analyses and interpreted results. Liem Tran assisted with study design. All authors contributed in writing the manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Informed Consent

This article is exempt from needing informed consent as no human participants were involved in the study and the data used has been completely anonymized and approved for public use by the United States Centers for Disease Control and Prevention (CDC).

Data Access

Data used in this study is publicly available and can be found at the United States Centers for Disease Control and Prevention’s (CDC) website: .

Research Involving Human Participants and/or Animals

This article does not contain any studies with human participants or animals performed by any of the authors. The BRFSS data used in this study is a secondary publicly available data source that has been completely anonymized and released for public use by the United States Centers for Disease Control and Prevention (CDC).

ORCID iD

Phoebe Tran

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