Influence of Rheumatoid Arthritis on Employment, Function, and Productivity in a Nationally Representative Sample in the United States
Bibliographic record
Abstract
OBJECTIVE: The Medical Expenditure Panel Survey (MEPS) was used to estimate the national influence of rheumatoid arthritis (RA) on employment, limitations in work or housework, inability to work or do housework, missed work days, days spent sick in bed, and annual wages. METHODS: MEPS is a nationally representative survey of the US population. Multiple logistic, negative binomial, and Heckman selection regression methods were used, controlling for age, sex, race, ethnicity, smoking status, income, education, and chronic comorbidity. RA was identified using International Classification of Diseases-9 code 714. RESULTS: In unadjusted descriptive statistics, individuals with RA were older, had more chronic conditions, missed more work days, spent more days sick in bed, had lower employment rates, had higher rates of limitations and inability to work, and received disability benefits at higher rates. After adjustment, multiple regression analyses showed individuals with RA were 53% less likely to be employed [OR 0.47, 95% CI 0.34-0.65], 3.3 times more likely to have limitations in work or housework (95% CI 2.35-4.64), 2.3 times more likely to be unable to work or do housework (95% CI 1.55-3.53), and spent 3.6 times as many days sick in bed as those without RA (95% CI 2.32-5.53). RA was associated with an expected loss of $8957 in annual earnings (95% CI 1881-15,937 dollars). There was no statistically significant difference in missed work days or the level of wages. CONCLUSION: In the most recent available national data for adults, RA was associated with reductions in employment, productivity, and function.
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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".