Impact of Comorbid Conditions on Healthcare Expenditure and Work-related Outcomes in Patients With Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effect of comorbid conditions on direct healthcare expenditure and work-related outcomes in patients with rheumatoid arthritis (RA). METHODS: This is a retrospective analysis of the Medical Expenditure Panel Survey from 2006 to 2015 in 4967 adults with RA in the United States. Generalized linear models were used for healthcare expenditure and income, logistic models for employment status, and zero-inflated negative binomial models for absenteeism. Thirteen comorbid conditions were included as potential predictors of direct cost- and work-related outcomes. The models were adjusted for sociodemographic factors including sex, age, region, marital status, race/ethnicity, income, education, and smoking status. RESULTS: Patients with RA with heart failure (HF) had the highest incremental annual healthcare expenditure (US$8205, 95% CI $3683-$12,726) compared to those without the condition. Many comorbid conditions including hypertension (HTN), diabetes, depression, chronic obstructive pulmonary disease, cancer, stroke, and HF reduced the chance of patients with RA aged between 18-64 years being employed. Absenteeism of employed patients with RA was significantly affected by HTN, depression, disorders of the eye and adnexa, or stroke. On average, RA patients with HF earned US$15,833 (95% CI $4435-$27,231) per year less than RA patients without HF. CONCLUSION: Comorbid conditions in patients with RA were associated with higher annual healthcare expenditure, lower likelihood of employment, higher rates of absenteeism, and lower income. Despite its low prevalence, HF was associated with the highest incremental healthcare expenditure and the lowest likelihood of being employed compared to other common comorbid conditions.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".