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Disability
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| Population | All Adults |
| Model-based measure | A multi-level regression and post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability among adults who were asked to report on the presence of six disability questions (1) related to serious difficulty including: 1) hearing, 2) vision, 3) concentrating, remembering, or making decisions (i.e., cognition), 4) walking or climbing stairs (i.e., mobility), 5) dressing or bathing (i.e., self-care), and 6) doing errands alone (i.e., independent living). See each disability type (hearing, vision, cognition, mobility, self-care, independent living) and any disability) for related survey questions. The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence. The 95% confidence interval was derived using Monte Carlo simulation. Detailed methods are available here. |
| Measure Type | Prevalence (crude and age-adjusted) |
| Time Period of Case Definition | Current |
| Summary | In 2020, 1 in 4 US adults reported at least one of the following disability types: hearing, vision, cognition, mobility, self-care, and independent living (2). Mobility was the most prevalent disability type reported (11.1%), followed by cognition (10.9%), independent living (6.4%), hearing (5.7%), vision (4.9%), and self-care (3.0%) (2). Prevalence of any disability and specific disability types varies by state, demographic characteristics, and presence of general health conditions and chronic conditions (2). National and state estimates of the prevalence of disability in US adults are available at Disability and Health Data System (DHDS) (2) and BRFSS Prevalence & Trends Data (3).
To be healthy, all people with or without disabilities must have opportunities to take part in meaningful daily activities that add to their growth, development, fulfillment, and community contribution. Assessment of disability highlights opportunities and areas for improvement for people with disabilities, including opportunities to fully participate in and benefit from public health activities, receive well-timed interventions and services, interact with their environment without barriers, and participate in everyday life activities (4). |
| Notes | The six-item disability standard series of questions represents a minimum standard, and the questions and answer categories cannot be changed (1). The six questions provide a conservative estimate in that they emphasize “serious” difficulty doing functions, thus potentially excluding people with less severe impairments (5). As with all self-reported sample surveys, BRFSS data might be subject to systematic error resulting from noncoverage, nonresponse, or measurement bias. Prevalence estimates of disability have been shown to vary across surveys with differences attributed to several factors, including survey context, assessment, design, and administration (6). County estimates of disability prevalence generated using the PLACES approach were moderately correlated with direct estimates from the American Community Survey, which is typical in small-area estimation validation studies when comparing data from different surveys (7). |
| Related Objectives or Recommendations | None |
- Implementation guidance on data collection standards for race, ethnicity, sex, primary language, and disability status. US Department of Health and Human Services; 2011. Accessed March 8, 2023. http://aspe.hhs.gov/datacncl/standards/ACA/4302/index.shtml.
- Centers for Disease Control and Prevention. Disability and Health Data System (DHDS) Data. Accessed February 7, 2023. https://dhds.cdc.gov/.
- Centers for Disease Control and Prevention. BRFSS Prevalence & Trends Data. Accessed December 16, 2022. https://www.cdc.gov/brfss/brfssprevalence/.
- National Center for Health Statistics. Chapter 9: Disability and Health. Healthy People 2020 Midcourse Review; 2016. https://www.cdc.gov/nchs/data/hpdata2020/HP2020MCR-C09-DH.pdf.
- McGuire DO, Watson KB, Carroll DD, Courtney-Long EA, Carlson SA. Using two disability measures to compare physical inactivity among US adults with disabilities. Prev Chronic Dis. 2018;15:E08. doi: https://doi.org/10.5888/pcd15.170261.
- Lauer EA, Houtenville AJ. Estimates of prevalence, demographic characteristics and social factors among people with disabilities in the USA: a cross-survey comparison. BMJ Open. 2018;8:e017828. doi: https://doi.org/10.1136/bmjopen-2017-017828.
- Lu H, Wang Y, Liu Y, Holt JB, Okoro CA, Zhang X, et al. County-Level Geographic Disparities in Disabilities Among US Adults, 2018. Prev Chronic Dis 2023;20:230004. doi: https://doi.org/10.5888/pcd20.230004.
| Population | All Adults |
| Model-based measure | A multi-level regression and post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability of having a hearing disability (reporting ‘yes’ to the question: “Are you deaf or do you have serious difficulty hearing?”). The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence. The 95% confidence interval was derived using Monte Carlo simulation. Detailed methods are available here. |
| Measure Type | Prevalence (crude and age-adjusted) |
| Time Period of Case Definition | Lifetime |
| Population | All Adults |
| Model-based measure | A multi-level regression and post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability of having a vision disability (reporting ‘yes’ to the question: “Are you blind or do you have serious difficulty seeing, even when wearing glasses?”). The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence. The 95% confidence interval was derived using Monte Carlo simulation. Detailed methods are available here. |
| Measure Type | Prevalence (crude and age-adjusted) |
| Time Period of Case Definition | Lifetime |
| Population | All Adults |
| Model-based measure | A multi-level regression and post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability of having a vision disability (reporting ‘yes’ to the question: “Because of a physical, mental, or emotional condition, do you have serious difficulty concentrating, remembering, or making decisions?”). The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence. The 95% confidence interval was derived using Monte Carlo simulation. Detailed methods are available here. |
| Measure Type | Prevalence (crude and age-adjusted) |
| Time Period of Case Definition | Lifetime |
| Population | All Adults |
| Model-based measure | A multi-level regression and post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability of having a mobility disability (reporting ‘yes’ to the question: “Do you have serious difficulty walking or climbing stairs?”). The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence. The 95% confidence interval was derived using Monte Carlo simulation. Detailed methods are available here. |
| Measure Type | Prevalence (crude and age-adjusted) |
| Time Period of Case Definition | Lifetime |
| Population | All Adults |
| Model-based measure | A multi-level regression and post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability of having a self-care disability (reporting ‘yes’ to the question: “Do you have difficulty dressing or bathing?”). The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence. The 95% confidence interval was derived using Monte Carlo simulation. Detailed methods are available here. |
| Measure Type | Prevalence (crude and age-adjusted) |
| Time Period of Case Definition | Lifetime |
| Population | All Adults |
| Model-based measure | A multi-level regression and post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability of having an independent living disability (reporting ‘yes’ to the question: “Because of a physical, mental, or emotional condition, do you have difficulty doing errands alone such as visiting a doctor’s office or shopping?”). The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence. The 95% confidence interval was derived using Monte Carlo simulation. Detailed methods are available here. |
| Measure Type: | Prevalence (crude and age-adjusted) |
| Time Period of Case Definition | Lifetime |
| Population | All Adults |
| Model-based measure | A multi-level regression and post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability among adults who said yes to at least one of six disability questions below.
The probability was then applied to the detailed population estimates at the appropriate geographic level to generate the prevalence. The 95% confidence interval was derived using Monte Carlo simulation. Detailed methods are available here. |
| Measure Type | Prevalence (crude and age-adjusted) |
| Time Period of Case Definition | Current |