The financial repercussions of rheumatoid arthritis and determinants of catastrophic healthcare expenditure: insights from the Karnataka chapter of the Indian rheumatology association
Bibliographic record
Abstract
To estimate the financial burden and the determinants of catastrophic healthcare expenditure(CHE) in patients with rheumatoid arthritis(RA) residing in the state of Karnataka, India. This was a cross-sectional, questionnaire-based study carried out by the practicing rheumatologists across 17 centers in Karnataka, India. Patients with RA diagnosed as per ACR classification criteria and on follow-up for at least 1-year were interviewed regarding disease-related expenditures which included direct medical and non-medical costs. CHE defined as > 20% of annual family income was analysed for various sociodemographic and clinical variables. Results are presented in Indian currency (INR), wherein 100 INR = 1.17 USD = 1.06 EURO. We included 2141 patients with RA (M: F::11:89), mean age 50.9 ± 12 years. The median annual expenditure towards treatment of RA including all direct medical and non-medical costs was ₹32200(IQR 21600,45200), the largest proportion (41.0%) being for RA medications. More than 10% annual income was being spent for treatment of RA by 48.1%(n = 1029)] of patients while CHE (> 20%) was noted in 582(27.1%) patients. Longer time taken for referral to rheumatologist [β = 1.28 (1.15,1.43)], longer duration of illness [β = 1.002(1.001,1.003)], presence of comorbidity [β = 1.12(1.04,1.22)] and disability HAQ-DI > 2 [β = 1.37(1.20,1.56)] had significant association with higher direct expenditure. Patients belonging to lower SES [AOR 2.66(1.99,3.56)], primary and middle level of patient education [AOR 1.57(1.05,2.36) & 2.01(1.32,3.07)] and hospitalisation [β = 9.20(6.25,13.6)] incurred CHE. The primary drivers of high direct expenditure in patients with RA in Karnataka, India are cost of medications, delayed specialist referral, high disease activity, disability and comorbidities. Additionally, hospitalization significantly contributes to CHE. 1. Nearly one-quarter of patients with RA in Karnataka, India experience catastrophic healthcare expenditure (CHE), spending over 20% of their annual income for their treatment. 2. Medications account for the largest share (41%) of RA related costs, making treatment affordability a key concern. 3. Patients who take longer time to diagnose and those with higher disease activity, disability, comorbidities and hospitalisations have higher medical expenses. 4. Lower socio economic strata patients and those with lower education levels are at higher risk of catastrophic expenses. 5. Improving access to rheumatologists, early diagnosis, and better disease control can help lower long-term medical expenses and prevent severe disability.
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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 | 0.001 |
| 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".