Evidence map›Paper›PMID 41575763›Full record

ArticleJAMA health forum2026

Disability Diagnoses Identified by the American Community Survey 6-Question Sequence.

Ari Ne'eman

Abstract read
In one paragraph

Article in JAMA health forum, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Ari Ne'emanDepartment of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.

Funding

UNDERSTANDING AND IMPROVING EPISODE-BASED HOSPITAL CAREP01AG019783 · NIA · DARTMOUTH COLLEGE · PI AMBER E BARNATO · 2001 to 2026
$52.9M
NIA NIH HHS P01 AG019783
6 · The paper itself

Abstract

Importance: Federal survey data collection identifies people with disabilities predominantly by using a 6-question sequence asking about different functional impairments known as the American Community Survey-6 (ACS-6). However, little is known about the specific diagnoses identified by the ACS-6 or whether they vary across demographic subgroups. Objective: To characterize the disability diagnoses identified by the ACS-6 and assess to what extent they identify a consistent population across demographic subgroups. Design, Setting, and Participants: This cross-sectional study among people with disabilities responding to the 2023 or 2024 Survey of Income and Program Participation (SIPP) assessed the prevalence of 36 different diagnosis groupings in the ACS-6 as a whole and within each of the individual questions, as well as how the identified diagnoses varied by age group, race and ethnicity, sex, and educational attainment, with further disaggregation by cognitive disability status. Data were analyzed between August 1 and September 15, 2025. Exposure: Identification as people with disabilities using the ACS-6. Main Outcomes and Measures: Diagnoses reported by survey respondents as causing the functional impairments they listed in the SIPP. Results: A total of 13 341 people with disabilities (52.2% female; mean [SD] age, 53.0 [23.0] years) responding to the SIPP were included. Among people with disabilities aged 22 to 64 years, the most common diagnoses were anxiety or obsessive-compulsive disorders (prevalence, 15.6%; 95% CI, 14.5%-16.9%), depression (15.3%; 95% CI, 14.1%-16.5%), unspecified musculoskeletal issues (13.5%; 95% CI, 12.5%-14.6%), back or spinal problems (11.6%; 95% CI, 10.6%-12.6%), and unspecified neurologic disorders (10.8%; 95% CI, 9.8%-11.8%). The most common disability diagnoses reported by respondents identified by the ACS-6 were different across age groups but similar across demographic groups defined by sex, race and ethnicity, and educational attainment. Conclusions and Relevance: The results of this cross-sectional study suggest that the ACS-6 identifies a similar population across demographic subgroups not characterized by age but highlight substantial heterogeneity in the population of people with disabilities within these subgroups and across age groups. Contemporary debates regarding future revisions to disability data collection in federal population surveys should address the ability to account for this heterogeneity in survey design.

Indexed as

Persons with DisabilitiesAdultAgedCross-Sectional StudiesFemaleHumansMaleMiddle AgedSurveys and QuestionnairesUnited StatesYoung Adult

Identifiers

PMID41575763
PMCPMC12831152

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.