Burden of osteoarthritis in India and its states, 1990–2019: findings from the Global Burden of disease study 2019
Bibliographic record
Abstract
Objective To describe the burden of osteoarthritis (OA) in India from 1990 to 2019. Design Data from Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 were used. The burden of OA –knee OA, hip OA, hand OA, and other OA– was estimated for India and its states from 1990 to 2019 through a systematic analysis of prevalence, incidence, years lived with disability (YLD), and disability-adjusted life years (DALY) using methods reported in GBD 2019 study. Result Around 23.46 million individuals in India had OA in 1990; this increased to 62.35 million in 2019. The age-standardised prevalence of OA increased from 4,895 (95% uncertainty interval (UI):4,420–5,447) in 1990–5313 (95%UI:4,799–5,898) in 2019, per 100,000 persons. Similarly, DALYs due to OA increased from 0.79 million (95%UI:0.40–1.55) to 2.12 million (95%UI:1.07–4.23); while age-standardised DALYs increased from 164 (95%UI:83–325) to 180 (95%UI:91–361) per 100,000 persons from 1990 to 2019. OA was the 20 th most common cause of YLDs in India in 2019, accounting for 1.48% (95%UI:0.88–2.78) of all YLDs; increasing from 23rd most common cause in 1990 (1.25%(95%UI:0.74–2.34)). Knee OA was the most common form of OA, followed by hand OA. The prevalence, incidence, and DALYs for OA and knee OA were consistently higher in females than males. Conclusion The burden and impact of OA in India are substantial and is increasing. Adopting suitable control and preventive community measures to reduce modifiable risk factors (obesity, injuries, occupational stress) are needed to reduce the current and future burden of OA 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.001 | 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".