Global, regional, and national burden of hip osteoarthritis (1992–2021) and predictions of future disease burden trends: a systematic analysis of the global burden of disease study 2021
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| 出版年: | BMC Musculoskeletal Disorders vol. 26 (2025), p. 1-14 |
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Springer Nature B.V.
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| 045 | 2 | |b d20250101 |b d20251231 | |
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| 100 | 1 | |a Gong, Jichun | |
| 245 | 1 | |a Global, regional, and national burden of hip osteoarthritis (1992–2021) and predictions of future disease burden trends: a systematic analysis of the global burden of disease study 2021 | |
| 260 | |b Springer Nature B.V. |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a BackgroundHip osteoarthritis can lead to significant joint pain and functional impairment, significantly affecting patients' daily life and activity levels. This study presents the latest global, regional, and national estimates for the incidence, prevalence and years Lost due to Disability(YLDs) due to hip OA of the by sex, age, and location, based on the GBD 2021, as well as forecasted incidence to future.MethodsComprehensive data on hip OA from 1992 to 2021 in 204 countries and territories was obtained from the Global Burden of Disease public database. This dataset included information on hip OA incidence, prevalence, and YLDs Projections for hip OA incidence, prevalence, and YLDs were generated using the Norpred model and Bayesian age-period-cohort models. Additionally, age-population cohort(APC) model, decomposition analysis, and health inequality analysis were employed to assess trends and disparities in hip OA. The EPAC and AAPC apply only to rates (crude rates or age standardized rates), and decomposition analysis is used to analyze the number of cases.ResultsThe Age-standardized Incidence Rate (ASIR), Age-standardized Prevalence Rate (ASPR), and Age-standardized YLDs in regions with high Socio-Demographic Index (SDI) were found to be high. In contrast, the ASIR in low SDI regions was relatively low. During the period from 1992 to 2021, the global incidence, prevalence, and YLDs of hip OA have all shown significant increases over the past three decades. Middle SDI countries and Low-middle SDI countries exhibited the highest growth rates. However, the slowdown in this growth trend is mainly reflected in countries in high SDI regions. The growth trend of incidence, prevalence, and YLDs of hip OA in other four SDI region countries is still increasing. In the APC model, the global incidence of hip OA starts at the age of 30, reaches the first peak at the age of 65, and then slowly decreases. After the age of 80, the incidence rises again. The prevalence and YLDs increased with the age of patients and have a growing trend over time. In the decomposition analysis, the factor of population growth has played a more important role in the increase of incidence, prevalence, and YLDs of hip OA. Compared to 1992, the incidence rate, prevalence, concentration index, and slope index of HIP OA in different SDI regions in 2021 are still increasing. According to the results of the prediction model, the incidence, prevalence and YLDs of hip OA may still increase in the future.ConclusionsThe age-standardized incidence, prevalence, and YLDs of global hip OA have been on the rise from 1992 to 2021. While the trend in regions with low SDI continues to increase, the rise in regions with high SDI is slowing down. Predictions for the future indicate that the ASIR, prevalence, and YLDs may continue to significantly increase. Our study brought to light the significant challenges in the control and management of OA, encompassing both the escalating number of cases and global distributive inequalities, which may provide valuable insights for refining public health policies and equitable allocation of medical resources. | |
| 610 | 4 | |a University of Washington | |
| 651 | 4 | |a United States--US | |
| 653 | |a Hip joint | ||
| 653 | |a Hip | ||
| 653 | |a Population | ||
| 653 | |a Osteoarthritis | ||
| 653 | |a Public health | ||
| 653 | |a Bayesian analysis | ||
| 653 | |a Regression analysis | ||
| 653 | |a Age | ||
| 653 | |a Trends | ||
| 653 | |a Disease | ||
| 653 | |a Mortality | ||
| 653 | |a Injuries | ||
| 653 | |a Regions | ||
| 653 | |a Cartilage | ||
| 653 | |a Health disparities | ||
| 653 | |a Arthralgia | ||
| 653 | |a Population growth | ||
| 653 | |a Inequality | ||
| 653 | |a Mathematical models | ||
| 653 | |a Prediction models | ||
| 700 | 1 | |a Xiang, Chenghao | |
| 700 | 1 | |a Zheng, Shuni | |
| 773 | 0 | |t BMC Musculoskeletal Disorders |g vol. 26 (2025), p. 1-14 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
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