Economic Evaluation of Total Knee Replacement Compared with Non-Surgical Management for Knee Osteoarthritis in India
Bibliographic record
Abstract
OBJECTIVE: This study is an economic evaluation of total knee replacement (TKR) in comparison with non-surgical management in India. METHODS: Cost-utility analysis and budget impact analysis (BIA) were conducted on individuals aged ≥ 50 years with osteoarthritis of the knee (OA knee) Kellgren-Lawrence grades 2 and 3 using a provider's perspective. Three scenarios were considered, varying the age at which TKR is administered while assuming a 20-year lifespan for the implant. A Markov model was used to determine incremental cost-effectiveness ratios (ICERs). Sensitivity analysis was conducted incorporating implant costs and other input parameters. RESULTS: Net quality-adjusted life-years (QALYs) gained per OA knee treated with TKR were superior when performed at the age of 50, regardless of OA severity and across all scenarios. The lowest ICER was 36,107 Indian National Rupees (INR) (USD 482.9)/QALY gained, observed at 50 years, while the highest was INR 61,363 (USD 820.72)/QALY gained at 70 years for grade-2 severity. Sensitivity analysis revealed that the ICER was most sensitive to the cost of non-surgical management, health utility values gained in an improved state, and the cost of TKR across scenarios. For the BIA in Scenario 1, with 40% coverage for TKR, costs reach INR 5013 crores (cr) (USD 670,477,060) in 2023 and INR 8444 cr (USD 1,024,628,736) in 2028 (1% of government budgets). In Scenario 2 (full coverage), costs are INR 12,532 cr (USD 1,520,683,008) (2.7%) in 2023, declining to 2.4% in 2028. In Scenario 3, covering 40% under the National Health Mission (NHM), costs vary from 17% in 2023 to 25% in 2028. CONCLUSION: This study concludes that TKR is a cost-effective treatment option compared with non-surgical management for OA knee in India, irrespective of age, implant types, and severity.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".