Health Care Access and Use Among Adults With and Without Vision Impairment: Behavioral Risk Factor Surveillance System, 2018

Qi Cheng, PhD, MPH¹; Catherine A. Okoro, PhD, MS¹; Isabel Mendez, MS²; Elizabeth A. Lundeen, PhD, MPH, MNSP²; Jinan B. Saaddine, MD, MPH²; Renee Stein, PhD³; Joseph Holbrook, PhD, MPH¹ (View author affiliations)

Suggested citation for this article: Cheng Q, Okoro CA, Mendez I, Lundeen EA, Saaddine JB, Stein R, et al. Health Care Access and Use Among Adults With and Without Vision Impairment: Behavioral Risk Factor Surveillance System, 2018. Prev Chronic Dis 2022;19:220066. DOI: http://dx.doi.org/10.5888/pcd19.220066.

PEER REVIEWED

Abstract

Introduction

Adults with vision impairment may have unique needs when accessing health care to maintain good health. Our study examined the relationship between vision status and access to and use of health care.

Methods

We analyzed data on adults aged 18 years or older who participated in the 2018 Behavioral Risk Factor Surveillance System. Vision impairment was identified by a yes response to the question “Are you blind or do you have serious difficulty seeing, even when wearing glasses?” Survey questions assessed health care access over the past year (having health insurance coverage, a usual health care provider, or unmet health care needs because of cost) and use of health care during that period (routine checkup and dental visit). We estimated age-adjusted prevalence of our outcomes of interest and used bivariate analyses to compare estimates of the outcomes by vision impairment status.

Results

The prevalence of self-reported vision impairment was 5.3%. Compared with adults without impaired vision, adults with vision impairment had a lower prevalence of having health insurance coverage (80.6% vs 87.6%), a usual health care provider (71.9% vs 75.7%), or a dental visit in the past year (52.9% vs 67.2%) and a higher prevalence of having an unmet health care need in the past year because of cost (29.2% vs 12.6%).

Conclusion

Adults with vision impairment reported lower access to and use of health care than those without. Further research can better identify and understand barriers to care to improve access to and use of health care among this population.

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Introduction

People with disabilities such as vision impairment often face challenges in receiving health care (1). In 2010, approximately 4 million US adults aged 40 years or older had impaired vision, and this number is projected to increase with the aging of the population and an increase in chronic conditions that lead to vision impairment (2,3). Vision impairment ranks among the top 10 disabilities in the US, and people with this disorder are more likely to have chronic conditions such as diabetes, heart disease, hypertension, injuries, depression, and premature death (4–11). They therefore have a particular need for increased access to general health care services to increase their use of those services.

High-quality and timely health care is essential to maintain good general health and prevent health disorders. Studies of people with impaired vision have focused on their access to and use of vision care; however, a better understanding is needed of how they access and use general health care services (12,13). A key objective of Healthy People 2030 is to increase the proportion of people with disabilities, including vision impairment, who have appropriate access to care and to eliminate disparities between people with and without disabilities (14). Gaps exist not only in our understanding of access to and use of general health care services but also of disparities in access and use between those with and without vision impairment (15,16). Our study aimed to describe the prevalence estimates of several measures of access to and use of general health care among US adults with and without impaired vision.

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Methods

The Behavioral Risk Factor Surveillance System (BRFSS) is an annual, state-based random–digit-dial landline and cellular telephone survey administered by states in collaboration with the Centers for Disease Control and Prevention (CDC). The survey is designed to collect information on health-related risk behaviors, chronic health conditions, and use of preventive services among noninstitutionalized adults aged 18 years or older residing in the US, the District of Columbia, and selected territories (ie, Guam and Puerto Rico). BRFSS uses 3 modules of questions: a core module that all jurisdictions collect, optional modules that focus on specific health issues that jurisdictions may choose to collect, and modules that jurisdictions can add according to their needs (11). The design, methodology, random sampling procedures, weighting strategies, and validity of measures for BRFSS have been previously published (17). We used the 2018 core module in our study, and the median survey response rate for all jurisdictions — 50 states, the District of Columbia, Guam, and Puerto Rico — was 49.9% (18). BRFSS data collection protocols are reviewed by CDC’s institutional review board (Protocol Number 2988) and the Office of Management and Budget (OMB No. 0920–1061, expiration date 3/31/2021). Secondary analyses are not subject to approval because data are de-identified. The overall sample consisted of 437,436 adults aged 18 years or older. We characterized respondents as having self-reported vision impairment if they answered yes to the question “Are you blind or do you have serious difficulty seeing, even when wearing glasses?” We excluded respondents who answered, “don’t know/not sure” or “refused” or had missing data in answers to this question (n = 11,134, 2.5%), yielding a sample of 426,302 adults.

Our outcome variables included several measures of health care access and use. Access was measured by using 3 variables: having health insurance coverage, having a usual health care provider, and having an unmet health care need. Having health insurance coverage was characterized as a yes response to the question “Do you have any kind of health insurance coverage, including health insurance, prepaid plans such as HMOs, or government plans such as Medicare or Indian Health Service?” Having a usual health care provider was indicated by a yes response to the question “Do you have one person you think of as your personal doctor or health care provider?” Having an unmet health care need because of cost in the past year was characterized as a yes response to the question, “Was there a time in the past 12 months when you needed to see a doctor but could not because of cost?” Health care use was measured by using 2 variables: having a routine checkup and having a dental visit. Receipt of a routine checkup within the past year was assessed by the question, “About how long has it been since you last visited a doctor for a routine checkup?” Having a dental visit in the past year was assessed by the question, “Including all types of dentists, such as orthodontists, oral surgeons, and all other dental specialists, as well as dental hygienists, how long has it been since you last visited a dentist or a dental clinic for any reason?” Responses to the 2 questions were dichotomized into in the past year or not in the past year.

Sociodemographic and geographic variables were age, sex, race or ethnicity, ratio of annual household income to the federal poverty level (FPL), and US region or territory of residence. We used the following categories to classify respondents: age (18–44, 45–64, or ≥65 y), sex, race or ethnicity (non-Hispanic White, non-Hispanic Black, Hispanic, and other races [American Indian/Alaska Native, Native Hawaiian or Pacific Islander, Asian, and multiracial]), and US region or territory (Northwest, Midwest, South, West, Guam, and Puerto Rico). FPL categories are based on the ratio of the respondent’s annual household income (given family size by numbers: 1–14 adults or children ≥0 y in the household) to the designated 2017 federal poverty threshold as defined by the US Census Bureau. This ratio was multiplied by 100 and expressed as a percentage, and federal poverty thresholds were then used to categorize respondents into 4 FPL categories: <100% of FPL, 100%–199% of FPL, ≥200% of FPL, or unknown (19). General health status was assessed by asking the question “Would you say that in general your health is?” They were coded into 3 categories: excellent/very good, good, or fair/poor. Health behavior variables of cigarette smoking and leisure-time physical activity were included. Cigarette smoking status was assessed by asking “Have you smoked at least 100 cigarettes in your entire life?” and “Do you now smoke cigarettes every day, some days, or not at all?” Respondents who reported smoking at least 100 cigarettes in their lifetime and who now smoke either every day or some days were coded as a “current smoker”; those who smoked at least 100 cigarettes in their lifetime and who now do not smoke at all were coded as “former smoker”; those who reported never having smoked 100 cigarettes in their lifetime were coded as “never smoker.” Leisure-time physical activity was assessed by asking “During the past month, other than your regular job, did you participate in any physical activities or exercises such as running, calisthenics, golf, gardening, or walking for exercise?” If respondents reported participating in any of these activities, they were coded as “engaging in leisure-time physical activity.” The metropolitan status classification was determined by applying the National Center for Health Statistics 2013 Urban−Rural Classification Scheme for Counties (20) to the BRFSS data using state and county FIPS (Federal Information Processing Standards) codes (https://www.nist.gov/standardsgov/compliance-faqs-federal-information-processing-standards-fips). Counties are considered metropolitan areas if they are classified as large central, large fringe, medium, or small metropolitan; counties are considered nonmetropolitan areas if they are classified as micropolitan or noncore. This classification was based on population density and metropolitan statistical areas (with an urbanized population ≥50,000 residents), micropolitan statistical areas (population cluster of between 10,000 and 49,999 residents), and noncore areas in the 50 states and District of Columbia (20). In addition to vision impairment, BRFSS included questions about 5 other disability types. We used these types as covariates and included hearing (“Are you deaf or do you have serious difficulty hearing?”), cognition (“Because of a physical, mental, or emotional condition, do you have serious difficulty concentrating, remembering, or making decisions?”), mobility (“Do you have serious difficulty walking or climbing stairs?”), self-care (“Do you have difficulty dressing or bathing?”), and independent living (“Because of a physical, mental, or emotional condition, do you have difficulty doing errands alone such as visiting a doctor’s office or shopping?”). Respondents who reported yes to any one of these questions were classified as having that disability. If a respondent answered yes to at least one disability other than vision impairment, they were classified as having any other disability; a respondent who answered no to all 5 other disabilities was classified as not having any other disability.

Sample sizes were computed by using unweighted numbers. Age-adjusted prevalence estimates and 95% CIs were calculated. Ninety-five percent CIs were calculated as the prevalence ±1.96 × standard error. Age standardization was performed by using the population distribution for the 3 age groups according to the 2000 US Census (18–44, 45–64, and ≥65 y), which remains the standard for the National Center for Health Statistics (21). SAS-callable SUDAAN, version 9.4 (Research Triangle Institute) was used to account for the multistage, complex sampling design.

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Results

Among surveyed adults, 23,545 reported having vision impairment, and the weighted prevalence of vision impairment was 5.3% (95% CI, 5.1%–5.4%). The characteristics of adults by self-reported vision impairment status are presented (Table 1).Respondents who were more likely to report having vision impairment were those aged 45 years and older (≥65 years, 31.5% vs 20.5%; 45–64 years, 39.8% vs 32.7%), women (56.2% vs 51.0% ), Hispanic respondents (25.3% vs 16.5%), non-Hispanic Black respondents (15.7% vs 11.5%), those living in lower-income households (<100% of FPL, 26.8% vs 12.1%; 100%–199% of FPL, 27.2% vs 18.8%), and those residing in the South US census region (43.3% vs 37.3%) and in the territories of Guam and Puerto Rico (3.9% vs 1.0%). Compared with respondents without vision impairment, those with impaired vision had a higher percentage of fair or poor general health (50.2% vs 16.8%), current cigarette smoking (24.7% vs 15.0%), and not engaging in leisure-time physical activity (41.1% vs 23.5%). Having 1 or more other disability was reported by a higher proportion of persons with vision impairment (68.2%) compared with those without vision impairment (23.2%). Compared with people without vision impairment, those with vision impairment reported a higher percentage of disability across all disability types (hearing: 22.6% vs 5.7%; cognition: 37.3% vs 10.0%; mobility: 44.9% vs 12.0%; self-care: 16.5% vs 3.1%; independent living: 29.4% vs 5.8%).

Measures of health care access differed by vision impairment status (Table 2). Respondents with vision impairment had a lower prevalence of having health insurance coverage (80.6%; 95% CI, 78.9%–82.1% vs 87.6%; 95% CI, 87.3%–87.9%) and a usual health care provider (71.9%; 95% CI, 70.2%–73.5% vs 75.7%; 95% CI, 75.4%–76.0%), and a higher prevalence of reporting cost as a reason for unmet health care need (29.2%; 95% CI, 27.6%–31.0% vs 12.6%; 95% CI, 12.3%–12.8%), compared with those without vision impairment. The prevalence of these indicators for health care access also varied by vision impairment status within some sociodemographic and geographic subgroups. For example, among non-Hispanic White adults, those with vision impairment had a lower prevalence of having health insurance coverage and a usual health care provider compared with those without vision impairment; among Hispanic and non-Hispanic Black adults, we found no difference in the prevalence of having health insurance coverage or having a usual health care provider by vision impairment status. Respondents with vision impairment and 1 or more other disability types had a lower prevalence of having health insurance coverage. Compared with those without vision impairment, those with vision impairment reported a higher prevalence of cost as a reason for unmet health care needs in all subgroups.

Prevalence of receiving health and dental care in the past year differed by vision impairment status (Table 3). We found no difference in the receipt of a routine health checkup in the past year between respondents with or without vision impairment (75.6%; 95% CI, 73.9%–77.2% vs 75.2%; 95% CI, 74.9%–75.5%). Among those with vision impairment, just over half (52.9%; 95% CI, 51.2%–54.7%) reported that they had a dental visit in the past year, which was lower than those without vision impairment (67.2%; 95% CI, 66.9%–67.6%). The prevalence of having had a dental visit in the past year was lower among those with vision impairment, compared with those without vision impairment; this was the case in all subgroup comparisons with the exception of Hispanic respondents and adults with a disability affecting self-care or independent living.

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Discussion

Our results indicate that BRFSS respondents with impaired vision had a lower prevalence of having health insurance coverage, a usual health care provider, or a dental visit in the past year and a higher prevalence of having unmet health care needs because of cost. Also, we found little to no difference in the prevalence of having had a routine checkup in the past year between adults with and without vision impairment. These findings suggest that people with impaired vision face greater barriers to health care access and use than those without. Results of our analysis may contribute to a further understanding of these barriers. A previous study on this topic used 2002–2004 Medical Expenditure Panel Survey data (22,23), and as in our study, found that people with vision impairment had more access problems related to cost of care than those without. Importantly, that study found adults with vision impairment were more likely to have insurance coverage whereas we found that adults aged younger than 65 years with vision impairment were less likely to have insurance coverage. Although our study found that a similar percentage of adults aged 65 years or older reported having insurance coverage, we could not rule out the possibility that Medicare coverage would contribute to this observation. Thus, further investigation is warranted on this topic. Sufficient access to and use of health care services, including preventive care, is especially important for people with vision impairment to maintain health, because they are more likely to have other health disorders or comorbidities (5,24–26). Previous study findings indicated that older US adults (≥50 y) with vision impairment may be less likely to use cancer-related preventive services than those without (27). That study found the prevalence of self-reported poor health status was 3 times higher among people with vision impairment compared with those without vision impairment. Although that cross-sectional study did not allow for examination of causality, this disparity may be partially due to people with vision impairment receiving inadequate medical treatment and preventive care.

A recent study that focused on US adults aged 40 years or older found that vision-impaired people received fewer regular dental preventive care and treatment services (28). Poor dental care can have a negative impact on self-esteem, quality of life, nutrition, communication, and general health. However, that study did not assess whether other factors, such as comorbidities or low-income status, accounted for some of the association between vision impairment and less use of dental care.

Respondents with vision impairment were more than twice as likely to report unmet health care needs in the past year because of cost than those without impaired vision. In addition, a household income below the federal poverty threshold was reported more often by respondents with vision impairment than those without. Taken together, these data suggest that cost may pose a substantial health care barrier for people with vision impairment. Spencer and colleagues reported that transportation issues and refusal of services by health care providers were primary barriers among people aged 40 years or older with vision impairment (22). Challenges associated with transportation are likely to affect people with vision impairment more than others, and people with disabilities have limited access to health care facilities because of a lack of assistive technology (29). The use of assistive technology, such as application of the sighted guide technique and screen magnifiers for low-vision computer users, could help reduce this disparity. Additionally, social support is an important consideration in obtaining access to health care among people with vision impairment (30). In all, the factors associated with barriers to access and use of health and dental care services among people with impaired vision are complex and multidimensional and can exist at the individual, population, and societal level. Our study considered broad associations, whereas future studies could examine how specific factors interact with characteristics of vision-impaired people. Understanding how such factors are associated with barriers to access and use of health and dental care among people with vision impairment requires further study.

Our study had limitations that should be considered when interpreting its findings. Health care access and use among people with impaired vision may be affected by many factors, and causality cannot be inferred from our analysis. The question defining vision disability asked in the BRFSS survey may capture only serious forms of vision impairment; therefore, any associations found in our study may not represent associations among people with mild or moderate vision impairment. Because BRFSS data are cross-sectional, causality among sociodemographic characteristics, health care access and use, and vision impairment cannot be inferred. Similarly, longitudinal information (eg, length of time the person had impaired vision, cause of vision impairment) was not included in our analyses. Lastly, because BRFSS data are self-reported, they may be subject to recall error.

In conclusion, by using a nationally drawn sample of US noninstitutionalized adults, we found that people with vision impairment had lower access to and use of health care services than people without vision impairment, specifically a lower prevalence of having health insurance coverage, a usual health care provider, and receiving dental care, and a higher prevalence of having unmet health care needs because of cost within the past year. Further work would better elucidate barriers to access and use of health care services to remove these barriers and improve the health of people with impaired vision.

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Acknowledgments

We thank state Behavioral Risk Factor Surveillance System coordinators for their assistance. The authors have no financial disclosures or conflicts of interest to report. The findings and conclusions of this report are those of the authors and do not necessarily represent the official position of CDC. No copyrighted materials or tools were used in this research.

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Author Information

Corresponding Author: Qi Cheng, PhD, MPH, Division of Human Development and Disability, National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, 4770 Buford Hwy NE, MS S106-4, Atlanta, GA 30341-3717. Telephone: 404-639-8775. Email: ycx6@cdc.gov.

Author Affiliations: ¹Division of Human Development and Disability, National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, Atlanta, Georgia. ²Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, Georgia. ³Community Guide Office, Office of the Associate Director for Policy and Strategy, Centers for Disease Control and Prevention, Atlanta, Georgia.

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References

1. Horner-Johnson W, Dobbertin K, Lee JC, Andresen EM; Expert Panel on Disability and Health Disparities. Disparities in health care access and receipt of preventive services by disability type: analysis of the Medical Expenditure Panel Survey. Health Serv Res 2014;49(6):1980–99. CrossRef PubMed
2. Varma R, Vajaranant TS, Burkemper B, Wu S, Torres M, Hsu C, et al. Visual impairment and blindness in adults in the United States: demographic and geographic variations from 2015 to 2050. JAMA Ophthalmol 2016;134(7):802–9. CrossRef PubMed
3. Zheng DD, Christ SL, Lam BL, Feaster DJ, McCollister K, Lee DJ. Patterns of chronic conditions and their association with visual impairment and health care use. JAMA Ophthalmol 2020;138(4):387–94. CrossRef PubMed
4. Jones GC, Rovner BW, Crews JE, Danielson ML. Effects of depressive symptoms on health behavior practices among older adults with vision loss. Rehabil Psychol 2009;54(2):164–72. CrossRef PubMed
5. Crews JE, Chou CF, Sekar S, Saaddine JB. The prevalence of chronic conditions and poor health among people with and without vision impairment, aged ≥ 65 years, 2010–2014. Am J Ophthalmol 2017;182:18–30. CrossRef PubMed
6. Kulmala J, Era P, Törmäkangas T, Pärssinen O, Rantanen T, Heikkinen E. Visual acuity and mortality in older people and factors on the pathway. Ophthalmic Epidemiol 2008;15(2):128–34. CrossRef PubMed
7. Dhital A, Pey T, Stanford MR. Visual loss and falls: a review. Eye (Lond) 2010;24(9):1437–46. CrossRef PubMed
8. Patel N, Stagg BC, Swenor BK, Zhou Y, Talwar N, Ehrlich JR. Association of co-occurring dementia and self-reported visual impairment with activity limitations in older adults. JAMA Ophthalmol 2020;138(7):756–63. CrossRef PubMed
9. Swenor BK, Wang J, Varadaraj V, Rosano C, Yaffe K, Albert M, et al. Vision impairment and cognitive outcomes in older adults: the Health ABC study. J Gerontol A Biol Sci Med Sci 2019;74(9):1454–60. PubMed
10. Saydah S, Gerzoff RB, Taylor CA, Ehrlich JR, Saaddine J. Vision impairment and subjective cognitive decline-related functional limitations — United States, 2015–2017. MMWR Morb Mortal Wkly Rep 2019;68(20):453–7. CrossRef PubMed
11. Centers for Disease Control and Prevention (CDC). Prevalence of disabilities and associated health conditions among adults—United States, 1999. MMWR Morb Mortal Wkly Rep 2001;50(7):120–5. PubMed
12. McGwin G, Khoury R, Cross J, Owsley C. Vision impairment and eye care utilization among Americans 50 and older. Curr Eye Res 2010;35(6):451–8. CrossRef PubMed
13. Varadaraj V, Frick KD, Saaddine JB, Friedman DS, Swenor BK. Trends in eye care use and eyeglasses affordability: the US National Health Interview Survey, 2008–2016. JAMA Ophthalmol 2019;137(4):391–8. CrossRef PubMed
14. US Department of Health and Human Services. Healthy people 2030. Objectives and data. People with disabilities. 2020. Accessed August 23, 2022. https://health.gov/healthypeople/objectives-and-data/browse-objectives/people-disabilities
15. Zhang X, Cotch MF, Ryskulova A, Primo SA, Nair P, Chou CF, et al. Vision health disparities in the United States by race/ethnicity, education, and economic status: findings from two nationally representative surveys. Am J Ophthalmol 2012;154(6 Suppl):S53–62.e1, S62.e1. CrossRef PubMed
16. Zambelli-Weiner A, Crews JE, Friedman DS. Disparities in adult vision health in the United States. Am J Ophthalmol 2012;154 (6 Suppl):S23–30.e1, S30.e1. CrossRef PubMed
17. Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System. 2019. Accessed August 23, 2022. https://www.cdc.gov/brfss/
18. Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System 2018 summary data quality report. 2019. Accessed August 23, 2022. https://www.cdc.gov/brfss/annual_data/2018/pdf/2018-sdqr-508.pdf
19. Okoro CA, Hollis ND, Cyrus AC, Griffin-Blake S. Prevalence of disabilities and health care access by disability status and type among adults — United States, 2016. MMWR Morb Mortal Wkly Rep 2018;67(32):882–7. CrossRef PubMed
20. Ingram DD, Franco SJ. 2013 NCHS urban-rural classification scheme for counties. Vital Health Stat 2 2014;(166):1–73. PubMed
21. Cano M, Gelpí-Acosta C. Risk of drug overdose mortality for island-born and US-born Puerto Ricans, 2013–2019. J Racial Ethn Health Disparities 2022;9(4):1403-14 . CrossRef PubMed
22. Spencer C, Frick K, Gower EW, Kempen JH, Wolff JL. Disparities in access to medical care for individuals with vision impairment. Ophthalmic Epidemiol 2009;16(5):281–8. CrossRef PubMed
23. Klein RJ, Schoenborn CA. Age adjustment using the 2000 projected U.S. population. Healthy People 2010 Stat Notes 2001;(20):1–10. PubMed
24. Frank CR, Xiang X, Stagg BC, Ehrlich JR. Longitudinal associations of self-reported vision impairment with symptoms of anxiety and depression among older adults in the United States. JAMA Ophthalmol 2019;137(7):793–800. CrossRef PubMed
25. Christ SL, Lee DJ, Lam BL, Zheng DD, Arheart KL. Assessment of the effect of visual impairment on mortality through multiple health pathways: structural equation modeling. Invest Ophthalmol Vis Sci 2008;49(8):3318–23. CrossRef PubMed
26. McCarty CA, Nanjan MB, Taylor HR. Vision impairment predicts 5 year mortality. Br J Ophthalmol 2001;85(3):322–6. CrossRef PubMed
27. Assi L, Varadaraj V, Shakarchi AF, Sheehan OC, Reed NS, Ehrlich JR, et al. Association of vision impairment with preventive care use among older adults in the United States. JAMA Ophthalmol 2020;138(12):1298–306. CrossRef PubMed
28. Crews JE, Chou CF, Naavaal S, Griffin SO, Saaddine JB. Self-reported oral health status among adults aged 40+ years with and without vision impairment: National Health Interview Study, 2008. Am J Ophthalmol 2020;210:184–91. CrossRef PubMed
29. Kirschner KL, Breslin ML, Iezzoni LI. Structural impairments that limit access to health care for patients with disabilities. JAMA 2007;297(10):1121–5. CrossRef PubMed
30. Nyman SR, Innes A, Heward M. Social care and support needs of community-dwelling people with dementia and concurrent visual impairment. Aging Ment Health 2017;21(9):961–7. CrossRef PubMed

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Tables

^a Vision impairment was assessed by asking respondents, “Are you blind or do you have serious difficulty seeing, even when wearing glasses?” Answers were coded as yes or no. Data are weighted.
^b American Indian or Alaska Native, Native Hawaiian or Pacific Islander, Asian, non-Hispanic, or multiracial.
^c Federal poverty level (FPL) percentage was calculated as the ratio of the respondent’s annual household income to the appropriate simplified 2017 federal poverty threshold defined by the US Census Bureau (given family size: number of adults [from 1 to 14] in the household and number of children [≥0] in the household). This ratio is multiplied by 100 and expressed as a percentage, and FPL was then used to categorize respondents into 4 FPL groups: 1) <100% of FPL, 2) 100%–199% of FPL, 3) ≥200% of FPL, and 4) unknown.
^d Determined by asking respondents, “Would you say that in general your health is — ?” Answers were categorized as excellent/very good, good, or fair/poor.
^e Current smoker, smoked at least 100 cigarettes in their lifetime and smoked either every day or some days; former smoker, smoked at least 100 cigarettes in their lifetime and no longer smoked at all; never smoker, never smoked a total of 100 cigarettes in their lifetime.
^f Categorized as participating in a leisure-time physical activity if they responded yes to the question “During the past month, other than your regular job, did you participate in any physical activities or exercises such as running, calisthenics, golf, gardening, or walking for exercise?”
^g Categorized as have one or more other disabilities if response was yes to any 1 or more of 5 questions on hearing disability, cognition disability, mobility disability, self-care disability, or independent living disability.
^h Categorized as metropolitan for respondents in counties classified as large central, large fringe, medium, or small metropolitan versus nonmetropolitan for respondents in counties classified as micropolitan or noncore, according to the National Center for Health Statistics 2013 Urban−Rural Classification Scheme (20).

^a Age adjustment for prevalence standardized based on the population breakdown for the following age groups according to the 2000 US Census: 18–44, 45–64, and ≥65 years (http://www.cdc.gov/nchs/data/statnt/statnt20.pdf). Values are weighted percentage (95% CI).
^b Health care access measures include 3 measures of “Had health insurance coverage,” “Had usual health care provider,” and “Had unmet health care need because of cost in the past year.” Responses for each health care access outcome were dichotomized into yes or no respectively.
^c Vision impairment was assessed by asking respondents, “Are you blind or do you have serious difficulty seeing, even when wearing glasses?” Answers were coded as yes or no.
^d American Indian or Alaska Native, Native Hawaiian or Pacific Islander, Asian, non-Hispanic, or multiracial.
^e Federal poverty level (FPL) percentage was calculated as the ratio of the respondent’s annual household income to the appropriate simplified 2017 federal poverty threshold defined by the US Census Bureau (given family size: number of adults [from 1 to 14] in the household and number of children [≥0] in the household). This ratio is multiplied by 100 and expressed as a percentage, and FPL was then used to categorize respondents into 4 FPL groups: 1) <100% of FPL, 2) 100%–199% of FPL, 3) ≥200% of FPL, and 4) unknown.
^f Assessed by asking “Would you say that in general your health is excellent/very good, good, or fair/poor?”
^g Current smoker, smoked at least 100 cigarettes in their lifetime and smoked either every day or some days; former smoker, smoked at least 100 cigarettes in their lifetime and no longer smoked at all; never smoker, never smoked a total of 100 cigarettes in their lifetime.
^h Respondents were categorized as participating in a leisure-time physical activity if they responded yes to a question of “During the past month, other than your regular job, did you participate in any physical activities or exercises such as running, calisthenics, golf, gardening, or walking for exercise?”
ⁱ Adults were categorized as have one or more other disabilities if they responded yes to any 1 or more of 5 questions on hearing disability, cognition disability, mobility disability, self-care disability, or independent living disability.
^j Metropolitan status was categorized as a Metropolitan area for respondents in counties classified as large central, large fringe, medium, or small metropolitan versus Nonmetropolitan area for respondents in counties classified as micropolitan or noncore, according to the National Center for Health Statistics 2013 Urban−Rural Classification Scheme (20).

^a Age adjustment for prevalence was standardized based on the population breakdown for the following age groups according to the 2000 US Census: 18–44, 45–64, and ≥65 years (http://www.cdc.gov/nchs/data/statnt/statnt20.pdf).
^b Use of health care included 2 measures, having a routine checkup within the past year and having a dental visit within the past year. Responses to each were dichotomized as yes or no.
^c Vision impairment was assessed by asking respondents, “Are you blind or do you have serious difficulty seeing, even when wearing glasses?” Answers were coded as yes or no.
^d American Indian or Alaska Native, Native Hawaiian or Pacific Islander, Asian, non-Hispanic, and multiracial.
^e Federal poverty level (FPL) percentage was calculated as the ratio of the respondent’s annual household income to the appropriate simplified 2017 federal poverty threshold defined by the US Census Bureau (given family size: number of adults [from 1 to 14] in the household and number of children [≥0] in the household). This ratio is multiplied by 100 and expressed as a percentage, and FPL was then used to categorize respondents into 4 FPL groups: 1) <100% of FPL, 2) 100%–199% of FPL, 3) ≥200% of FPL, and 4) unknown.
^f Assessed by asking “Would you say that in general your health is excellent/very good, good, or fair/poor?”
^g Current smoker, smoked at least 100 cigarettes in their lifetime and smoked either every day or some days; former smoker, smoked at least 100 cigarettes in their lifetime and no longer smoked at all; never smoker, never smoked a total of 100 cigarettes in their lifetime.
^h Categorized as participating in a leisure-time physical activity if they responded yes to the question “During the past month, other than your regular job, did you participate in any physical activities or exercises such as running, calisthenics, golf, gardening, or walking for exercise?”
ⁱ Categorized as having one or more other disabilities if response was yes to any 1 or more of 5 questions on hearing disability, cognition disability, mobility disability, self-care disability, or independent living disability.
^j Categorized as metropolitan for respondents in counties classified as large central, large fringe, medium, or small metropolitan versus nonmetropolitan for respondents in counties classified as micropolitan or noncore, according to the National Center for Health Statistics 2013 Urban−Rural Classification Scheme (20).

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