The Association of Low Income with Functional Status and Disease Burden in German Patients with Rheumatoid Arthritis: Results of a Cross-sectional Questionnaire Survey Based on Claims Data
Bibliographic record
Abstract
OBJECTIVE: To assess the influence of income on self-reported disease and work productivity outcomes. METHODS: Persons with rheumatoid arthritis (RA) diagnosis (International Classification of Diseases, 10th ed. codes M05/M06) on health insurance claims data in at least 2 quarters of 2013 were randomly selected. They were mailed questionnaires covering RA diagnosis, household income, functional capacity [Hannover functional status questionnaire (FFbH), 0-100], RA Impact of Disease questionnaire (RAID; 0-10), self-reported swollen joint count (SJC; 0-48), tender joint count (TJC; 0-50), and effect of RA on work productivity (change of work, fewer working hours, sick leave, application for disability pension, and others). Weighted multivariable linear regression models were used to assess the association between income and disease outcomes. RESULTS: A total of 1492 persons of working age who confirmed RA diagnosis were available for analysis. The mean age was 55 years, 82% were women, and 74% were under rheumatologic care. A total of 27%, 52%, and 21% had a low (< €1500), medium (€1500-3200), and high monthly income (> €3200), respectively. Respondents with low income had the worst mean FFbH, RAID, SJC, and TJC values. This was confirmed in the regression model: mean FFbH low versus high income -8.65 (95% CI -9.72 to -7.58), RAID 0.73 (0.59-0.86), and SJC 3.47 (2.86-4.08). Sick leave (8.7%/3.5%/1.8%) and disability pension (18.1%/9.6%/6.9%) were more frequent in patients with low versus medium versus high income (p < 0.05). CONCLUSION: The association of low income with a higher disease burden, more functional disability, and higher rates of work loss emphasizes the need to focus on these outcomes when choosing treatment strategies for patients in the lower income groups.
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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 | 0.000 |
| 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".