Sociocultural and Economic Disparities in Physical Therapy Utilization Among Insured Older Adults With Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To examine influences of sociocultural and economic determinants on physical therapy (PT) utilization for older adults with rheumatoid arthritis (RA). METHODS: In these annual cross-sectional analyses between 2012 and 2016, we accessed Medicare enrollment data and fee-for-service claims. The cohort included Medicare beneficiaries with RA based on 3 diagnosis codes or 2 codes plus a disease-modifying antirheumatic drug medication claim. We defined race and ethnicity and dual Medicare/Medicaid coverage (proxy for income) using enrollment data. Adults with a Current Procedural Terminology code for PT evaluation were classified as utilizing PT services. Associations between race and ethnicity and dual coverage and PT utilization were estimated with logistic regression analyses. Potential interactions between race and ethnicity status and dual coverage were tested using interaction terms. RESULTS: Of 106,470 adults with RA (75.1% female; aged 75.8 [SD 7.3] years; 83.9% identified as non-Hispanic White, 8.8% as non-Hispanic Black, 7.2% as Hispanic), 9.6-12.5% used PT in a given year. Non-Hispanic Black (adjusted odds ratio [aOR] 0.77, 95% CI 0.73-0.82) and Hispanic (aOR 0.92, 95% CI 0.87-0.98) individuals had lower odds of PT utilization than non-Hispanic White individuals. Adults with dual coverage (lower income) had lower odds of utilization than adults with Medicare only (aOR 0.44, 95% CI 0.43-0.46). There were no significant interactions between race and ethnicity status and dual coverage on utilization. CONCLUSION: We found sociocultural and economic disparities in PT utilization in older adults with RA. We must identify and address the underlying factors that influence these disparities in order to mitigate them.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".