Comparative Analysis of Osteoarthritis Prevention and Management Policies: Lessons for Iran from High-income Countries
Bibliographic record
Abstract
Background: Osteoarthritis (OA), as one of the most common chronic joint diseases, significantly contributes to the global burden of disability and imposes considerable financial costs on patients and healthcare systems. The prevalence of this disease is increasing rapidly across various countries. This study aimed to examine and compare national policies on the prevention and management of OA in four high-income countries and Iran. Methods: A comparative review was conducted by searching PubMed, Scopus, Web of Science, Google Scholar, Google, and the websites of the World Bank, the Organization for Economic Co-operation and Development (OECD), and the Ministry of Health of the selected countries from 2000 to 2024. This study utilizes the "Policy Analysis Triangle" framework by Walt and Gilson to analyze OA prevention and management policies in the selected countries. Expert interviews and document analysis were used to collect information in Iran. In the data analysis of this section, framework analysis and content analysis were also used through the software MAXQDA-10. Results: This study examined the policies and measures of 5 countries (the United States, Canada, Australia, the United Kingdom, and Iran) concerning OA prevention and management. In the United States, the focus is on physical activity, weight management, and healthy diets, supported by the Centers for Disease Control programs and the Arthritis Foundation. Canada implements educational programs and national policies through extensive collaboration between government and nongovernmental organizations. Australia emphasizes improving access to healthcare services and care through the cooperation of various organizations. In the UK, the focus is on prevention and public education, alongside strengthening access to health services. Iran primarily focuses on treatment and pain management, facing challenges such as limited financial resources and public awareness. Conclusion: Although there are similarities between OA prevention and management policies in Iran and high-income countries, Iran continues to face significant challenges in primary prevention, policy evaluation, and resource access. By leveraging the experiences of high-income countries and adopting strategies based on assessment and prevention, Iran could significantly improve its policies and reduce the long-term burden of OA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".