Disability

What to know

PLACES measure types capture the prevalence of reported disabilities among U.S. populations at four geographic levels. This information can be used to make informed decisions about programs and resources for improving the health, quality of life, and accessibility of services for individuals with disabilities.
Man in a wheelchair sitting at a long table working on a laptop. Woman in background also working.

Disability measure definitions

Population
All Adults
Model-based measure
A multi-level regression, 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 types of disability1 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).
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 2022, 1 in 4 U.S. adults reported at least one of the following disability types: hearing, vision, cognition, mobility, self-care, and independent living2. Cognition was the most prevalent disability type reported (13.9%), followed by mobility (12.2%), independent living (7.7%), hearing (6.2%), vision (5.5%), and self-care (3.6%2). Prevalence of any disability and specific disability types vary by state, demographic characteristics, and presence of general health conditions and chronic conditions2. National and state estimates of the prevalence of disability among U.S. adults are available at Disability and Health Data System (DHDS)2 and BRFSS Prevalence & Trends Data3.

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 activities4.
Notes
The six-item disability standard series of questions represents a minimum standard, and the questions and answer categories cannot be changed1. The six questions provide a conservative estimate in that they emphasize “serious” difficulty doing functions, thus potentially excluding people with less severe impairments5. 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 administration6 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 surveys7.
Related Objectives or Recommendations
None

Hearing disability among adults

Population
All Adults
Model-based measure
A multi-level regression, 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

Vision disability among adults

Population
All Adults
Model-based measure
A multi-level regression, 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

Cognitive disability among adults

Population
All Adults
Model-based measure
A multi-level regression, post-stratification approach was applied to BRFSS and ACS data to compute a detailed probability of having a cognitive 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

Mobility disability among adults

Population
All Adults
Model-based measure
A multi-level regression, 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

Self-care disability among adults

Population
All Adults
Model-based measure
A multi-level regression, 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

Independent living disability among adults

Population
All Adults
Model-based measure
A multi-level regression, 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

Any disability among adults

Population
All Adults
Model-based measure
A multi-level regression, 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:

  • “Are you deaf or do you have serious difficulty hearing?”
  • “Are you blind or do you have serious difficulty seeing, even when wearing glasses?”
  • “Because of a physical, mental, or emotional condition, do you have serious difficulty concentrating, remembering, or making decisions?”
  • “Do you have serious difficulty walking or climbing stairs?”
  • “Do you have difficulty dressing or bathing?”
  • “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
Current
  1. Implementation guidance on data collection standards for race, ethnicity, sex, primary language, and disability status. US Department of Health and Human Services; 2011. Accessed October 22, 2024. http://aspe.hhs.gov/datacncl/standards/ACA/4302/index.shtml
  2. Centers for Disease Control and Prevention. Disability and Health Data System (DHDS) Data. Accessed October 22, 2024. https://dhds.cdc.gov/.
  3. Centers for Disease Control and Prevention. BRFSS Prevalence & Trends Data. Accessed October 22, 2024. https://www.cdc.gov/brfss/brfssprevalence/.
  4. 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
  5. 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
  6. 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
  7. 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