Sex-Specific Trends in the Prevalence of Osteoarthritis and Rheumatoid Arthritis From 2005 to 2021 in South Korea: Nationwide Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Osteoarthritis and rheumatoid arthritis (RA) are prevalent chronic joint disorders, with prevalence rates varying by sex. However, few studies have comprehensively documented the factors contributing to the sex-specific prevalence of osteoarthritis and RA, including sociological factors and the impact of the COVID-19 pandemic. OBJECTIVE: This study aims to identify long-term trends in the sex-specific prevalence of osteoarthritis and RA from 2005 to 2021 while examining the factors that serve as vulnerabilities specific to each sex within the context of the COVID-19 pandemic. METHODS: Data were collected from a nationally representative sample of 110,225 individuals through the Korea National Health and Nutrition Examination Survey from 2005 to 2021. The study included patients aged 19 years and older diagnosed with osteoarthritis or RA in South Korea. Data were analyzed using weighted trends to accurately represent the sample population, with a 95% CI. Weighted logistic and regression models were used to identify vulnerable groups at risk of osteoarthritis or RA during the pandemic to assess sex-specific trends. RESULTS: In total, 110,225 individuals (n=48,410, 43.92% male participants) were analyzed from 2005 to 2021, with prevalence rates remaining stable over time and higher in female than in male participants. Notably, during the pandemic, female participants aged 60 years and older exhibited a prevalence of osteoarthritis that was 4.92 times greater than male participants and a prevalence of RA that was 6.44 times greater (osteoarthritis: prevalence ratio [PR] 69.78, 95% CI 41.66-116.88; RA: PR 17.27, 95% CI 8.75-34.07). In terms of osteoarthritis, male participants did not show a significant association with BMI (PR 1.40, 95% CI 1.21-1.61; P=.47), whereas female participants exhibited a significantly higher vulnerability within the obese group (PR 1.68, 95% CI 1.55-1.83; P<.001). Regarding RA, lower education levels were associated with increased vulnerability, with male participants showing a greater risk than female participants (male participants: PR 2.29, 95% CI 1.61-3.27 and female participants: PR 1.50, 95% CI 1.23-1.84). CONCLUSIONS: This study reveals that women in South Korea have a higher prevalence of osteoarthritis and RA than men. Understanding these sex-specific trends and identifying vulnerability factors can enhance preventive efforts and patient care.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".