ECONOMIC BURDEN OF SYSTEMIC LUPUS ERYTHEMATOSUS (SLE) IN INDIA: A SYSTEMATIC LITERATURE REVIEW
Bibliographic record
Abstract
PV077a / #777 Poster Topic: AS11 - Epidemiology and Public Health Background/Purpose While the clinical burden of SLE (systematic lupus erythematosus) is well-recognized, its economic impact in India remains poorly understood. This scoping review aims to assess the current evidence on the economic burden of SLE in India and identify gaps in the existing literature. Methods A systematic literature review was conducted to identify relevant studies reporting on the economic burden of SLE in India, adhering to PRISMA guidelines. PubMed was searched in August 2024 for all studies reporting economic outcomes, healthcare resource utilization (HCRU), or cost-related data in SLE patients among Indian females, without time restrictions. Data on study characteristics, patient demographics, economic analyses, cost outcomes, and HCRU were extracted. Risk of bias was assessed using the Newcastle-Ottawa Scale. Results Out of 701 initial records screened, 4 observational studies met the inclusion criteria (1 prospective, 3 retrospective), with sample sizes ranging from 17 to 1,354 patients (total N=1,628). The female population represented 92.7% (n=1,509) of the total sample. All studies utilized hospital-based data sources from North India (2 studies) and South India (2 studies). No study performed a model-based comprehensive economic evaluation. One retrospective study reported direct costs, noting a 6-month per-patient expenditure of INR 630 (USD 8.85) for IV cyclophosphamide and INR 50 (USD 0.70) for oral therapy. HCRU data indicated higher hospitalization rates and longer hospital stays in SLE patients with infections compared to those without, but the HCRU data were not translated to economic metrics. The risk of bias was rated “good” in 1 study and “fair” in the remaining 3. Conclusions There is an acute paucity of studies evaluating the economic burden of SLE in India. Existing data are limited to direct costs and basic HCRU metrics, with no comprehensive economic evaluations or assessments of indirect costs. This significant gap emphasizes the urgent need for prospective, large-scale health economic studies to inform policy decisions and optimize resource allocation for SLE management in India.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".