Black-White Differences in Self-Reported Disability Outcomes in the U.S.: Early Childhood to Older Adulthood
Bibliographic record
Abstract
OBJECTIVE: We examined black-white differences in activities of daily living (ADLs), functional limitations (FLs), vision/hearing/sensory impairment, and memory/learning problems in a large, nationally representative sample of community-dwelling and institutionalized people across the lifespan. METHODS: Data are from the 2006 American Community Survey (n=2,288,800). We included data on non-Hispanic black respondents (125,985 males and 145,780 females) and non-Hispanic white respondents (977,792 males and 1,039,243 females) ≥5 years of age. We used logistic regression to examine the black-white odds for each disability outcome. The overall response rate was 97.5%. RESULTS: For FLs, ADL limitations, and memory/learning problems, black people experienced higher odds of disability across the adult lifespan compared with white people. Black-white differences narrowed in older age. For vision/hearing problems, a black-white crossover was found in older age (≥85 years), where odds of vision/hearing problems were lower among black people than among white people. For all disability outcomes, black-white differences peaked in midlife (50-69 years of age), with black people having approximately 1.5 to two times the odds of disabilities as their white peers. CONCLUSIONS: The study findings suggest the need to address black-white disparities across a range of disability outcomes throughout the lifespan. Future work identifying the factors accounting for this pattern of disparities will help inform the development of appropriate prevention strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".