Prevalence and Cancer-Specific Patterns of Functional Disability Among US Cancer Survivors, 2017-2022
Bibliographic record
Abstract
PURPOSE To examine the prevalence and cancer-specific patterns of functional disabilities among US cancer survivors. METHODS Data from 47,768 cancer survivors and 2,432,754 noncancer adults age 18 years and older from the 2017 to 2022 Behavioral Risk Factor Surveillance System were analyzed. Functional disabilities assessed included mobility disability (ie, serious difficulty walking or climbing stairs) and self-care disability (ie, self-reported difficulty dressing or bathing). Multivariable logistic regression models were used to assess the associations between functional disabilities and sociodemographic, lifestyle, and health-related factors. RESULTS Cancer survivors tended to be older and non-Hispanic White than noncancer adults. The prevalence of mobility disability (27.9% v 13.4%) and self-care disability (7.4% v 3.8%) were higher among cancer survivors compared with noncancer adults. After multivariable adjustments, cancer survivors were more likely to report mobility (odds ratio [OR], 1.21 [95% CI, 1.16 to 1.26]) and self-care (OR, 1.19 [95% CI, 1.10 to 1.29]) disability than noncancer adults. The prevalence of mobility (34.9% v 26.3%) and self-care disability (9.8% v 6.7%) was higher in cancer survivors who were receiving active cancer treatment than in those who had completed cancer treatment. Higher prevalence of mobility and self-care disabilities was observed in cancer survivors who were racial/ethnic minorities and with higher BMI, low physical activity, lower levels of education and/or income, comorbidities, and those experiencing cancer/treatment-related pain. Patterns of mobility and self-care disabilities varied across cancer types. CONCLUSION Over a quarter of US cancer survivors reported mobility disability, and nearly 10% reported self-care disability, with patterns varying across cancer types and treatment status. Racial/ethnic minorities, along with underserved groups and individuals with unhealthy lifestyles or comorbidities, were notably more affected by functional disabilities, underscoring the need for targeted disability prevention efforts.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".