Temporal trends and projections of the global burden of osteoarthritis derived from the Global Burden of Disease 2021 study

Temporal trends and projections of the global burden of osteoarthritis derived from the Global Burden of Disease 2021 study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Temporal trends and projections of the global burden of osteoarthritis derived from the Global Burden of Disease 2021 study Wentao Zhao, Feng Li, Yunfei Hou, Guoxin Zhang, Xing Sun, Tianlang Xie, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7057886/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: This study updates the assessment of the global, regional, and national burden of osteoarthritis (OA) from 1990 to 2021, focusing on trends in incidence, prevalence, and years lived with disability (YLDs), and explores the sociodemographic factors influencing these trends. Methods: Data from the Global Burden of Disease Study 2021 were used to estimate OA incidence, prevalence, and YLDs at global, regional, and national levels. Joinpoint regression analysis was employed to assess temporal trends, while age-period-cohort models were used to examine the effects of age, period, and birth cohort on OA trends. The Slope Index of Inequality and Concentration Index were applied to analyze cross-country health inequities in OA burden. Results: Between 1990 and 2021, the global OA case count increased from 20.9 million to 46.6 million (122.7%), with the ASIR (age-standardized incidence rate rising) from 489.78 to 535 per 100,000 (9.25%; EAPC(estimated annual percentage change): 0.33, 95% CI: 0.31–0.35). Global prevalence increased from 256 million to 606 million cases, with ASPR (age-standardized prevalence rates) rising from 6393.12 to 6967.29 per 100,000. Regionally, low SDI (sociodemographic index) regions exhibited a 156.23% increase in incidence compared to 73.26% in high SDI regions. At the country level, China’s OA cases increased by 150.38% (ASIR: 487.11 to 554.61 per 100,000; EAPC: 0.58, 95% CI: 0.51–0.66). The BAPC model forecasts an ASIR of approximately 535 per 100,000 by 2030. Conclusion: The burden of OA has significantly increased over the past three decades, with notable regional disparities. This rise is primarily attributed to population aging and increasing obesity rates. Targeted prevention, early diagnosis, and enhancements in healthcare services are crucial to addressing the growing global impact of OA. Osteoarthritis Global Burden of Disease Study Public health Risk factors Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Background OA is a common condition that primarily affects the hands, knees, hips, and feet, leading to pain, disability, and reduced mobility, especially in older adults.( 1 ) OA significantly contributes to health and economic burdens, including direct medical costs, lost productivity, and the intangible impacts of fatigue and reduced quality of life( 2 ). With an aging global population and rising obesity rates, OA is expected to become even more prevalent in the coming years.( 3 ) In high-income countries, OA-related medical expenses account for a substantial portion of GDP(Gross domestic product).( 4 ) The burden of OA exhibits significant disparities across regions and countries. Beyond high-income countries, OA is increasingly affecting low- and middle-income nations, where demographic shifts and lifestyle factors such as poor diet and sedentary behavior contribute to rising incidence rates.( 5 ) OA has become a leading cause of disability worldwide, with notable increases not only in Europe, North America, and Asia, but also in regions such as Africa and Latin America, where healthcare resources are limited. While many studies have examined the burden of OA, they often rely on outdated data, focus on specific regions, or address only certain aspects of the disease. Given the chronic and progressive nature of OA, early intervention is crucial, however, challenges such as the absence of established diagnostic criteria, insufficient treatment options, and underdeveloped healthcare infrastructure, particularly in low-income countries impede effective management and prevention efforts.( 6 ) This research utilizes data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 to provide a comprehensive and current assessment of the global burden and trends related to OA. ( 7 )The study includes analyses at global, regional, and national levels, dissects the burden by demographic and epidemiological factors, explores cross-country health inequities.( 8 ) 2. Methods Data sources The annual counts of incident cases, prevalent cases, and years lived with disability (YLDs), along with age-standardized rates by sex, age, region, and country, were derived from the Global Burden of Disease study 2021. The DisMod-MR 2.1 model, a Bayesian mixed-effects meta-regression tool, estimated incidence, prevalence, mortality, years of life lost, YLDs, and disability-adjusted life years (DALYs) for 371 diseases and injuries across 204 countries and territories from 1990 to 2021. The SDI, combining fertility rate, mean years of schooling, and income per capita, was obtained from the Institute for Health Metrics and Evaluation. Based on 2021 SDI estimates, countries were classified into five quintiles: high, high-middle, middle, low-middle, and low SDI. The world was divided into 21 regions to ensure epidemiological homogeneity, determined by factors such as mortality rates and health determinants. Statistical analyses The Joinpoint regression model was used to analyze the log-linear trends of ASIR for osteoarthritis across global and regional levels from 1990 to 2021. This model detects significant trend shifts, referred to as joinpoints, which divide the overall trend into segments. The slope for each segment is calculated as the annual percent change (APC). The average annual percent change (AAPC) is calculated as the geometrically weighted average of individual APC values. An increasing trend is defined when both the APC or AAPC and its lower 95% confidence interval (CI) exceed zero. A decreasing trend is recognized when the APC or AAPC and its upper 95% CI are both below zero. If neither condition is satisfied, the trend is considered stable. Two standard measures of inequality—The Slope Index of Inequality and Concentration Index were used to assess the distribution of osteoarthritis burden across countries. The slope index of inequality was calculated by regressing national incidence and YLD rates for the entire population on a relative position scale linked to sociodemographic development. The concentration index was obtained by numerically integrating the area under the Lorenz concentration curve, which was created using the cumulative YLD distribution and the cumulative relative distribution of the population ranked by the SDI. Age-period-cohort models were used to evaluate the combined effects of age, period, and cohort on osteoarthritis incidence trends, allowing for the disaggregation and analysis of underlying physiological, social, historical, and environmental determinants. The linear correlation between age, period, and cohort introduces the nonidentification problem, where unique parameter estimates cannot be obtained. To resolve this, incidence was categorized into consecutive 5-year age groups (ranging from 5–9 years to 95–99 years), 5-year periods (from 1990–1994 to 2020–2021), and corresponding 5-year birth cohort groups (spanning from 1895–1899 to 1985–1989). The median birth and diagnosis dates were used as reference points for cohort and period, respectively. Relative risks (RRs) were estimated using the maximum likelihood method within the age-period-cohort Poisson regression model with a log(Y) link, incorporating natural spline functions to model the effects. To forecast the global burden of OA to 2030, the Bayesian Age-Period-Cohort (BAPC) model was employed, assuming that the effects of age, period, and cohort remain consistent within temporal proximity. Bayesian inference in the BAPC model utilized a second-order stochastic excursion to smooth the prior values for age, period, and cohort, enabling the prediction of posterior rates. To address the challenges of mixing and convergence typically associated with traditional Bayesian methods, such as Markov chain Monte Carlo, the model incorporated Integrated Nested Laplace Approximations (INLA) to approximate the marginal posterior distributions. This approach has become widely recognized in the analysis of chronic disease trends and the forecasting of future disease burdens. 3. Results Trends in the burden of OA The global burden of OA increased significantly from 1990 to 2021. The total number of OA cases rose from 20.9 million in 1990 to 46.6 million in 2021, a 122.7% increase. The age-standardized incidence rate (ASIR) grew from 489.78 per 100,000 in 1990 to 535 per 100,000 in 2021, representing a 9.25% increase. The estimated annual percent change (EAPC) for global OA incidence from 1990 to 2019 was 0.33 (95% CI: 0.31–0.35). (Table 1 ) The global prevalence of OA also increased from 256 million in 1990 to 606 million in 2021, with the age-standardized prevalence rate (ASR) rising from 6393.12 per 100,000 to 6967.29 per 100,000.(Table S1 ) The total years lived with disability (YLDs) related to OA increased from 8.92 million in 1990 to 21.3 million in 2021, with the age-standardized YLD rate rising from 222.8 per 100,000 in 1990 to 244.5 per 100,000 in 2021. The EAPC for YLDs from 1990 to 2019 was 0.37 (95% CI: 0.33–0.4), highlighting the growing disability burden. (Table S2) Table 1 The case number and ASR of incidence of OA in 1990 and 2021 for both sexes by SDI quintiles and by GBD regions, with EAPC from 1990 to 2021. location 1990 2021 EAPC (95 % CI) 1990–2019 Number (95 % UIs) ASR (95 % UIs) Number (95 % UIs) ASR (95 % UIs) Global 20900510.49 (18467652.64-23104315.92) 489.78 (433.1-541.51) 46632144.37 (41122052.73-51644430.77) 535 (472.38-591.97) 0.33 (0.31-0.35) SDI quintiles Low SDI 1094622.36 (965733.07-1219371.2) 408.88 (362.19-452.55) 2804976.17 (2479497.58-3122531.67) 447.12 (395.36-493.37) 0.29 (0.28-0.31) Low-middle SDI 3035072.54 (2675262.62-3375779.45) 423.51 (374.68-469.5) 7812257.58 (6892042.84-8676931.11) 480.13 (424.96-533.01) 0.42 (0.4-0.44) Middle SDI 5750921.82 (5058231.66-6410278.28) 475.42 (419.58-527.87) 15437968.58 (13569373.27-17143283.15) 536.49 (473.16-595.02) 0.48 (0.44-0.51) High-middle SDI 5133784.52 (4537917.93-5675082.8) 498.45 (441.11-549.27) 10350051.4 (9099041.92-11468241.96) 548.07 (481.66-608.49) 0.38 (0.35-0.42) High SDI 5864566.47 (5225613.42-6479489.06) 568.82 (505.9-627.74) 10189235.97 (9088793.27-11269284.92) 611.3 (542.71-675.91) 0.21 (0.17-0.24) GBD regions Andean Latin America 121136.54 (107214.18-134640.11) 519.51 (461.53-577.69) 363396.5 (319747.57-402547.11) 578.18 (511.33-641.1) 0.35 (0.34-0.37) Australasia 124185.65 (110781.01-137553.28) 555.78 (493.55-617.22) 269451.73 (239374.24-301169.39) 620.09 (550.76-686.53) 0.34 (0.31-0.36) Caribbean 139108.27 (123460.78-154387.5) 513.03 (455.98-570.5) 296169.9 (262546.75-330668.29) 555.77 (493.24-617.51) 0.29 (0.27-0.3) Central Asia 221868.54 (194411.32-249129.85) 444.99 (392.55-498.51) 475854.12 (413121.22-535995.46) 504.46 (442.21-565.75) 0.43 (0.4-0.46) Central Europe 699280.23 (618327.98-779465.69) 474.27 (419.37-525.88) 960143.69 (849389.5-1070329.04) 522.05 (460.81-580.35) 0.33 (0.32-0.35) Central Latin America 509647.54 (450945.99-568025.64) 527.96 (468.13-587.05) 1561604.8 (1379180.83-1731311.41) 589.49 (521.45-652.52) 0.37 (0.36-0.37) Central Sub-Saharan Africa 122007.42 (106783.23-135467.16) 441.86 (390.35-491.24) 339701.23 (298430.94-378042.51) 463.08 (409.68-515.24) 0.12 (0.08-0.16) East Asia 4836774.22 (4234495.29-5416116.68) 487.3 (428.32-544.04) 12051700.47 (10562111.16-13556244.21) 554.47 (486.91-619.37) 0.58 (0.5-0.65) Eastern Europe 1495247.02 (1312342.06-1682318.8) 550.43 (484.03-614.18) 1833037.93 (1612583.96-2058219.98) 584.97 (515.25-651.42) 0.29 (0.26-0.31) Eastern Sub-Saharan Africa 366642.93 (322665-409381.62) 411.5 (363.2-457.47) 1000971 (885083.79-1115451.19) 461.02 (407.42-509.93) 0.39 (0.37-0.4) High-income Asia Pacific 1345776.3 (1189858.25-1491372.54) 641.16 (568.18-707.78) 2189402.79 (1958142.42-2413152.29) 682.07 (606.06-752.84) 0.35 (0.22-0.49) High-income North America 1899644.64 (1695714.25-2093719.63) 605.72 (535.69-670.62) 3457087.12 (3062826.32-3850765.87) 646.38 (572.29-715.37) 0.07 (-0.07-0.2) North Africa and Middle East 839234.42 (738099.2-934460.23) 424.31 (375.36-470.67) 2730761.38 (2402789.3-3044966.79) 488.31 (433.7-542.33) 0.44 (0.42-0.47) Oceania 16658.74 (14676.47-18606.53) 441.66 (389.8-491.69) 47742.61 (41835-53394.35) 480.95 (423.12-536.36) 0.25 (0.22-0.27) South Asia 2993307.58 (2640026.16-3326475.62) 430.77 (382.03-477.59) 8220377.67 (7241991.21-9115916.28) 495.01 (436.64-548.03) 0.47 (0.45-0.49) Southeast Asia 1128949.85 (993293.25-1259580.33) 376.04 (332.26-418.37) 3261524.6 (2872404.58-3641133.22) 437.13 (386.13-485.01) 0.51 (0.5-0.51) Southern Latin America 252629.79 (224152.65-281234.07) 540.18 (478.83-600.89) 483946.18 (431133.2-536648.45) 596.27 (530.39-660.42) 0.29 (0.26-0.32) Southern Sub-Saharan Africa 157577.09 (139016.14-175111.98) 513.31 (454.83-568.81) 375227.74 (330227.08-417302.16) 557.24 (493.49-618.18) 0.28 (0.27-0.29) Tropical Latin America 551639.12 (485844.29-614415.82) 527.59 (467.44-585.25) 1568533.39 (1385103.16-1733205.81) 589.12 (521.39-650.6) 0.38 (0.37-0.39) Western Europe 2630868.23 (2349666.53-2928270.32) 521.51 (465.66-578.73) 3918887.82 (3498381.84-4363204.13) 557.66 (497.27-618.53) 0.19 (0.17-0.21) Western Sub-Saharan Africa 448326.4 (395307.5-501301.02) 439.61 (387.27-489.07) 1226621.72 (1078016.62-1368996.58) 483.84 (427.08-536.68) 0.32 (0.3-0.33) Regionally, the incidence of OA varied. Low SDI regions saw the largest relative increase, with OA cases rising from 1.1 million in 1990 to 2.8 million in 2021 (a 156.23% increase). The age-standardized incidence rate (ASIR) in these regions rose from 408.88 per 100,000 in 1990 to 447.12 per 100,000 in 2021. High SDI regions experienced a more moderate increase, with cases rising from 5.9 million in 1990 to 10.2 million in 2021 (73.26% increase) and the ASIR rising from 568.82 per 100,000 in 1990 to 611.3 per 100,000 in 2021. (Table 1 ) Prevalence in low SDI regions rose from 11.8 million in 1990 to 29.9 million in 2021, while high SDI regions saw an increase from 79.7 million in 1990 to 152.4 million in 2021. (Table S2) Country-level analysis of OA incidence revealed substantial variability in ASIR between 1990 and 2021. In China, OA cases increased from 4.65 million (ASIR: 487.11 per 100,000) in 1990 to 11.65 million (ASIR: 554.61 per 100,000) in 2021, with an EAPC of 0.58 (95% CI: 0.51–0.66). Similarly, India experienced a rise from 2.47 million to 6.70 million cases, with ASIR increasing from 438.57 to 505 per 100,000 (EAPC: 0.47, 95% CI: 0.45–0.50). In contrast, the Kingdom of Denmark showed minimal change, with ASIR shifting from 542.83 to 539.24 per 100,000 (EAPC: 0.02, 95% CI: -0.01–0.05). Additionally, the Arab Republic of Egypt’s ASIR increased from 426.67 to 485.92 per 100,000 (EAPC: 0.35, 95% CI: 0.32–0.37), and Nigeria’s rose from 450.16 to 498.72 per 100,000 (EAPC: 0.35, 95% CI: 0.31–0.40). These data underscore marked heterogeneity in OA incidence trends across countries. (Table S3, Fig. 1 ) Gender disparities in the burden of OA were also evident, with women consistently experiencing a higher incidence compared to men. Figure 2 showed the prevalence and incidence of osteoarthritis across different age groups in 2021. Osteoarthritis is most prevalent among individuals aged 35 and older, with a marked increase in incidence observed between the ages of 30 and 69. Both males and females are particularly affected between the ages of 50 and 54. A similar pattern is seen in the prevalence rates, with a significant rise in incidence starting at age 50, peaking in the 65–69 age range. Interestingly, women exhibit higher rates of both prevalence and incidence compared to men. Joinpoint regression analysis From 1990 to 1994, the global ASIR of osteoarthritis showed an initial increase with APC of 0.1868 (95% CI: 0.1110 to 0.2626), indicating a rapid rise in OA incidence. However, this growth slowed from 1994 to 2000, with an APC of 0.0851 (95% CI: 0.0655 to 0.1047). A sharp increase occurred from 2000 to 2005, with an APC of 0.5603 (95% CI: 0.5353 to 0.5853), reflecting a significant rise in OA burden globally. After 2005, the trend shifted downward between 2005 and 2010, with an APC of -0.0850 (95% CI: -0.1093 to -0.0606). However, the incidence increased again from 2010 to 2016, with an APC of 0.4505 (95% CI: 0.4335 to 0.4675), and the growth accelerated in the final period analyzed (2016 to 2021) with an APC of 0.6453 (95% CI: 0.5669 to 0.7238).( Fig. 3 , Table S4) In high SDI regions, a notable rise in OA incidence was observed from 1990 to 1995, with an APC of 0.5846 (95% CI: 0.5129 to 0.6563). Between 1995 and 2005, the rate of increase slowed (-0.156, 95% CI: -0.1843 to -0.1279), but after 2005, OA incidence surged again with an APC of 0.9703 (95% CI: 0.8168 to 1.1241) from 2005 to 2009, followed by more moderate increases in subsequent years. In middle SDI regions, the incidence showed fluctuations. A decline in the APC occurred from 1990 to 1995 (-0.2506, 95% CI: -0.3106 to -0.1905), but from 2000 to 2005, the APC increased substantially to 1.0386 (95% CI: 0.9927 to 1.0846). Post-2010, the growth slowed slightly, with an APC of 0.5505 (95% CI: 0.5191 to 0.5819). Low SDI regions saw consistent increases in OA incidence, particularly from 1990 to 1996, with an APC of 0.2260 (95% CI: 0.2098 to 0.2423). The rise in incidence continued steadily across the study period, suggesting a growing burden of OA in low-income countries. Age-period-cohort analysis The results from the age-period-cohort analysis of OA incidence are presented in Fig. 4 . Additional subgroup analyses, stratified by sex. After adjusting for the effects of period and birth cohort, the age factor was found to have a substantial impact on the risk of OA incidence. The relative risk for incidence exhibited a trend of initial increase followed by a subsequent decline, with the peak risk observed in individuals aged 55–59 years. Following the adjustment for age and birth cohort effects, the period effect on OA incidence was also found to be significant. The period effect demonstrated a gradual upward trend, with the relative risk for incidence increasing by 1.1 times from the period 1990 to 2015. The highest incidence risk was observed in the 2015 period. Furthermore, after controlling for the age and period effects, the birth cohort effect revealed a notable influence on the incidence risk of OA. The birth cohort effect indicated a higher risk of incidence in earlier cohorts compared to later cohorts, with the relative risk continuously decreasing from the 1895–1899 cohort to the 1985–1989 cohort. Notably, males exhibited a higher incidence risk than females from the 1895–1899 cohort to the 1935–1939 cohort. Inequality analysis The analysis of OA burden using the slope index of inequality reveals a widening gap in both OA incidence and YLD between countries with the highest and lowest SDI from 1990 to 2021.(Fig. 5 ) For OA incidence, the slope index of inequality increased from 0.18 in 1990 to -0.41 in 2021, indicating a disproportionate increase in the burden in higher SDI countries. A similar trend was observed in YLDs, where the slope index of inequality also showed an upward shift, pointing to a growing disparity in disability due to OA. In contrast, the relative concentration index for both OA incidence and YLDs remained relatively unchanged. For instance, the relative concentration index for OA incidence stood at -0.12 in 1990 and − 0.13 in 2021, while YLDs maintained a steady relative concentration index of -0.19 across both years. Prediction to 2030 The predictions based on the BAPC model indicate that the age-standardized incidence rate (ASIR) for osteoarthritis (OA) is expected to increase from 491.06 per 100,000 in 1990 to around 535 per 100,000 by 2030. Despite stable age-standardized rates, the total burden of OA is expected to significantly increase by 2030. (Fig. 6 ) 4. Discussion OA has become an increasingly significant global public health issue. Over the past three decades, both the incidence and prevalence of OA have risen substantially, driven by demographic factors, particularly population aging, and the growing prevalence of obesity. ( 9 , 10 )This study identifies a consistent rise in the global burden of OA, indicating a critical need for comprehensive interventions to mitigate its effects. A notable sex disparity exists, with females demonstrating increased susceptibility to OA, highlighting the need for gender-specific considerations in the development of public health strategies.( 11 ) Cross-country inequality analysis highlights disparities in the burden of OA, particularly in countries with differing SDI scores. Given current trends, the burden of OA is expected to continue rising through 2030, underscores the increasing pressure on healthcare systems, which must respond effectively through comprehensive prevention, diagnosis, and management strategies. The global obesity epidemic is a critical factor contributing to the OA burden. Obesity, defined by an excessive accumulation of adipose tissue, which presents a significant risk to an individual’s health. It is most commonly assessed through the Body Mass Index (BMI), places significant mechanical stress on weight-bearing joints, particularly the knees and hips, accelerating the onset and progression of OA.( 12 , 13 ) Epidemiological studies have shown a strong association between high BMI and OA, with high BMI being a key modifiable risk factor. The rising rates of obesity, especially among younger populations, suggest that OA incidence will continue to increase in the coming decades, further exacerbating the global health burden. Given the critical role of obesity in the development and progression of OA, weight management should be prioritized in preventive measures.( 14 ) Public health campaigns promoting healthier diets, physical activity, and active lifestyles are essential for reducing the obesity-related burden of OA.( 15 ) Specifically, interventions targeting early prevention of obesity. The World Health Organization (WHO) and other international organization have consistently emphasized the importance of weight management as a key preventive strategy, and these efforts should be scaled up to mitigate the risk of OA associated with obesity.( 16 ) The contrasting OA burden trends between low and high SDI regions arise from distinct socioeconomic and healthcare dynamics. In low-SDI areas, accelerated population aging, urbanization-induced sedentary lifestyles, and occupational physical strain (e.g., agrarian labor) synergistically drive OA incidence, compounded by limited access to diagnostics, rehabilitation, and preventive care—factors exacerbating DALY.( 17 ) Conversely, high-SDI regions exhibit slower burden growth due to established OA management systems, yet sustain large prevalent caseloads from extended longevity and historically high OA prevalence. While advanced therapies (e.g., pharmacologic interventions, joint replacements) in these regions mitigate disease progression, sustainability challenges persist due to aging demographics and escalating costs. To address these disparities, context-specific strategies are imperative: low-SDI regions should prioritize community-based weight management, ergonomic adaptations for laborers, and telehealth-enabled screening to expand primary care access, whereas high-SDI systems require AI-driven early detection and value-based care models to optimize aging population management.( 18 ) Cross-regional collaborations, including technology transfers (e.g., affordable imaging tools) and WHO-aligned policy frameworks integrating OA care into universal health coverage, could reduce equity gaps. Future research must evaluate intervention scalability across SDI gradients while addressing epidemiological data deficiencies in resource-limited settings. The age-stratified burden of OA underscores distinct etiological and management challenges across life stages. In younger populations (< 50 years), rising OA prevalence is driven by obesity, sports-related joint injuries, and delayed diagnosis due to low clinical suspicion, while middle-aged adults (50–65 years) face accelerated progression from metabolic syndrome and occupational repetitive strain.( 19 , 20 ) Older adults (> 65 years) bear the highest disability burden due to degenerative joint changes, sarcopenia, and comorbidities limiting surgical options, though younger patients experience significant productivity losses from prolonged disability. Addressing these disparities requires age-tailored strategies: younger populations benefit from obesity prevention and sports safety initiatives, middle-aged adults require workplace ergonomic adaptations and community-based early screening, and older adults need integrated care combining pharmacologic pain control, low-impact exercise, and fall prevention.( 21 ) Technological innovations, such as wearable sensors for joint load monitoring in youth and AI-assisted radiographic grading for elderly clinics, could enhance precision management.( 22 ) Policy frameworks must align with WHO’s universal health coverage goals to prioritize OA as a public health challenge, particularly in aging societies. Future research should clarify interactions between age-specific risk profiles and emerging environmental determinants, such as sedentarism in digitalized lifestyles, to optimize interventions across age groups. Early detection of OA is critical to prevent disease progression and reduce long-term disability.( 23 ) Screening programs targeting high-risk individuals—such as those with a family history of OA, joint injuries, or occupations involving repetitive movements—can enable timely interventions. Early interventions, including physical therapy and weight management, can slow or prevent the worsening of OA symptoms.( 24 , 25 ) This study provides valuable insights into osteoarthritis, offering a comprehensive analysis of its prevalence and incidence across sex, age, and SDI levels. By examining these factors, the study highlights key demographic groups most affected by the disease and identifies regional disparities in burden. The global scope and longitudinal data make it an essential reference for shaping effective prevention and treatment programs, guiding future public health policies, and addressing the growing osteoarthritis burden worldwide. This study has several limitations that should be considered when interpreting its findings. This study lacks a detailed exploration of the impact of healthcare interventions or policies, which could mitigate the projected trends. Furthermore, the analysis primarily focuses on demographic factors like aging and obesity, excluding other non-medical risk factors such as occupational exposures and environmental influences that could play a role in OA incidence. 5. Conclusions OA has become an increasingly significant global health issue, driven largely by population aging and the rising prevalence of obesity. The growing burden of OA highlights the need for improved healthcare services, with an emphasis on early detection and preventive measures targeting modifiable risk factors, particularly high BMI. Strengthening healthcare systems, providing comprehensive management strategies, and ensuring equitable access to care are crucial for addressing the global OA burden and improving patient outcomes across all regions. Abbreviations ASR Age-standardized rate CI Confidence interval DALY Disability-adjusted life year EAPC Estimated annual percentage change GBD Global Burden of Disease Study SDI Socio-demographic Index UI Uncertainty interval BMI Body Mass Index ASIR Age-standardized incidence rate rising ASPR Age-standardized prevalence rates APC Annual percent change AAPC Average annual percent change BAPC Bayesian Age-Period-Cohort Declarations Ethics declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials All data could be extracted from the Global Health Data Exchange (GHDx) website (http://ghdx.healthdata.org/gbd-results-tool). Competing interests The authors declare that they have no competing interests. Funding This research was conducted without the any funding. Author Contributions: Wentao Zhao and Feng Li had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: Wentao Zhao, Feng Li. Acquisition, interpretation of data: Guoxin Zhang, Xing Sun, Yunlong Wang, Xintong Zhao, Tianlang Xie. Drafting of the manuscript: Wentao Zhao, Feng Li. Critical review of the manuscript for important intellectual content: Yunfei Hou. Statistical analysis: Tianlang Xie, Wankun Zhou, Yanjun Gao, Xiaobo Wang, Yunlong Wang, Jiazheng Duan, Xintong Zhao. Administrative, technical support: Yunfei Hou, Shuncheng Jiao, Guoxin Zhang, Min Zhao. Supervision: Wentao Zhao, Feng Li, Yunfei Hou. All authors participated in drafting the manuscript or critically revising it for substantial intellectual content, and all have provided final approval of the version to be published. Acknowledgements The authors would like to sincerely acknowledge and express their gratitude for the invaluable contributions made by all the collaborators of the Global Burden of Disease Study 2021. Author information Authors and Affiliations Wentao Zhao, MD 1 Feng Li, MD 2 Yunfei Hou, MD 3 Guoxin Zhang, MD 1 Xing Sun, MD 1 Tianlang Xie, MD 1 Yunlong Wang, MD 1 Wankun Zhou,MD 1 Xiaobo Wang, MD 1 Xintong Zhao,MD 1 Yanjun Gao, MD 1 Jiazheng Duan, MD 1 Shuncheng Jiao, MD 1 Min Zhao, MD 1 1.Department of Orthopedic Surgery, Beijing Shunyi District Hospital, No. 3 of Guangming Nanjie, Shunyi District, Beijing, 101300, People's Republic of China. 2.Hangzhou Geriatric Hospital, Department of Orthopedics, Affiliated Hangzhou First People's Hospital Chengbei Campus, School of Medicine, Westlake University, Hangzhou, 310006, China. 3.Arthritis Clinic and Research Center, Peking University People's Hospital, Peking University, No. 11, Xizhimen South Street, Xicheng District, Beijing, 100044, People's Republic of China. Corresponding author name and email address: Feng Li, MD E-mail: [email protected] References Tang Sa, Zhang C, Oo WM, Fu K, Risberg MA, Bierma-Zeinstra SM et al. Osteoarthr Nat Reviews Disease Primers. 2025;11(1). Steinmetz JD, Culbreth GT, Haile LM, Rafferty Q, Lo J, Fukutaki KG, et al. Global, regional, and national burden of osteoarthritis, 1990–2020 and projections to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet Rheumatol. 2023;5(9):e508–22. Courties A, Kouki I, Soliman N, Mathieu S, Sellam J. Osteoarthritis year in review 2024: Epidemiology and therapy. Osteoarthr Cartil. 2024;32(11):1397–404. Vos T, Lim SS, Abbafati C, Abbas KM, Abbasi M, Abbasifard M, et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204–22. Weng Q, Chen Q, Jiang T, Zhang Y, Zhang W, Doherty M, et al. Global burden of early-onset osteoarthritis, 1990–2019: results from the Global Burden of Disease Study 2019. Ann Rheum Dis. 2024;83(7):1–11. Pigeolet M, Jayaram A, Park KB, Meara JG. Osteoarthritis in 2020 and beyond. Lancet. 2021;397(10279):1059–60. Murray CJL. Findings from the Global Burden of Disease Study 2021. Lancet. 2024;403(10440):2259–62. Ferrari AJ, Santomauro DF, Aali A, Abate YH, Abbafati C, Abbastabar H, et al. Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10440):2133–61. Hunter DJ, Bierma-Zeinstra S, Osteoarthritis. Lancet. 2019;393(10182):1745–59. Huang W, Xiao YS, Zou YH, Zhong LQ, Huang GQ. The relationship between visceral adipose tissue and osteoarthritis among older adults: evidence from the NHANES 1999–2018. Front Nutr. 2025;12:1526377. Kreitmaier P, Swift D, Wilkinson JM, Zeggini E. Epigenomic differences between osteoarthritis grades in primary cartilage. Osteoarthritis Cartilage. 2024;32(9):1126–33. Zheng H, Chen C. Body mass index and risk of knee osteoarthritis: systematic review and meta-analysis of prospective studies. BMJ Open. 2015;5(12):e007568. Haslam DW, James WP. Obes Lancet. 2005;366(9492):1197–209. Swinburn BA, Sacks G, Hall KD, McPherson K, Finegood DT, Moodie ML, et al. The global obesity pandemic: shaped by global drivers and local environments. Lancet. 2011;378(9793):804–14. Godziuk K, Hawker GA. Obesity and body mass index: Past and future considerations in osteoarthritis research. Osteoarthritis Cartilage. 2024;32(4):452–9. Gelber AC. Knee Osteoarthritis. Ann Intern Med. 2024;177(9):ITC129–44. Diseases GBD, Injuries C. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204–22. Huang J, Yeung AM, Armstrong DG, Battarbee AN, Cuadros J, Espinoza JC, et al. Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes. J Diabetes Sci Technol. 2023;17(1):224–38. Wei G, Lu K, Umar M, Zhu Z, Lu WW, Speakman JR, et al. Risk of metabolic abnormalities in osteoarthritis: a new perspective to understand its pathological mechanisms. Bone Res. 2023;11(1):63. Sampath SJP, Venkatesan V, Ghosh S, Kotikalapudi N, Obesity. Metabolic Syndrome, and Osteoarthritis-An Updated Review. Curr Obes Rep. 2023;12(3):308–31. Davis MA, Ettinger WH, Neuhaus JM. Obesity and osteoarthritis of the knee: evidence from the National Health and Nutrition Examination Survey (NHANES I). Semin Arthritis Rheum. 1990;20(3 Suppl 1):34–41. Hidaka R, Matsuda K, Igari T, Takeuchi S, Imoto Y, Yagi S, et al. Development and accuracy of an artificial intelligence model for predicting the progression of hip osteoarthritis using plain radiographs and clinical data: a retrospective study. BMC Musculoskelet Disord. 2024;25(1):893. Favero M, Ramonda R, Goldring MB, Goldring SR, Punzi L. Early knee osteoarthritis. RMD Open. 2015;1(Suppl 1):e000062. Messier SP, Beavers DP, Queen K, Mihalko SL, Miller GD, Losina E et al. Effect of Diet and Exercise on Knee Pain in Patients With Osteoarthritis and Overweight or Obesity. JAMA. 2022;328(22). Messier SP, Mihalko SL, Legault C, Miller GD, Nicklas BJ, DeVita P, et al. Effects of intensive diet and exercise on knee joint loads, inflammation, and clinical outcomes among overweight and obese adults with knee osteoarthritis: the IDEA randomized clinical trial. JAMA. 2013;310(12):1263–73. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterials.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7057886","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":498388434,"identity":"2d897ecd-988b-4229-a990-28b47ec5991c","order_by":0,"name":"Wentao Zhao","email":"","orcid":"","institution":"Beijing Shunyi District Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wentao","middleName":"","lastName":"Zhao","suffix":""},{"id":498388435,"identity":"95819e72-7a33-4eed-9149-bd75f542fc67","order_by":1,"name":"Feng Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYBACAziDvQFEWhCvRcKA5wCIK0GKFokEME1Yizn/2WeSPyru1ZlLPr+64UeBBAN/e3cCXi2WDcfNpHnOFEtYzs4pu9kDdJjEmbMb8DvsYBubNGNbgoTB7Zy0GzxALQYSuQS0HGZjk/wJ0nLzTNrNP0RpOcbGJsEL0nKD/dht4mw5w8ZszXMmQXLDmRy22zIGEjyE/XL+GOPNHxUJ/AbHjz+7+eaPjRx/ey9+LUiABxxHPMQqBwH2B6SoHgWjYBSMghEEAHKnQ0CcMs3MAAAAAElFTkSuQmCC","orcid":"","institution":"The Third People’s Hospital of Xiaoshan Hangzhou","correspondingAuthor":true,"prefix":"","firstName":"Feng","middleName":"","lastName":"Li","suffix":""},{"id":498388436,"identity":"364c9e33-9f98-4e08-a11b-4c7bd70e4e64","order_by":2,"name":"Yunfei Hou","email":"","orcid":"","institution":"Peking University People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yunfei","middleName":"","lastName":"Hou","suffix":""},{"id":498388437,"identity":"803fe4f1-8124-4413-aff3-fdccf7845266","order_by":3,"name":"Guoxin Zhang","email":"","orcid":"","institution":"Beijing Shunyi District Hospital","correspondingAuthor":false,"prefix":"","firstName":"Guoxin","middleName":"","lastName":"Zhang","suffix":""},{"id":498388438,"identity":"0dc4df72-a183-4f6a-ba1b-03ad380c5884","order_by":4,"name":"Xing Sun","email":"","orcid":"","institution":"Beijing Shunyi District Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xing","middleName":"","lastName":"Sun","suffix":""},{"id":498388439,"identity":"4eb0afd1-f174-4579-a68e-ef4ce2c3be82","order_by":5,"name":"Tianlang Xie","email":"","orcid":"","institution":"Beijing Shunyi District Hospital","correspondingAuthor":false,"prefix":"","firstName":"Tianlang","middleName":"","lastName":"Xie","suffix":""},{"id":498388440,"identity":"ee785792-750c-4ad7-9699-3d2e29fba9dd","order_by":6,"name":"Yunlong Wang","email":"","orcid":"","institution":"Beijing Shunyi District Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yunlong","middleName":"","lastName":"Wang","suffix":""},{"id":498388441,"identity":"bad98fca-7ec0-42c9-bd0c-6526504aa847","order_by":7,"name":"Wankun Zhou","email":"","orcid":"","institution":"Beijing Shunyi District Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wankun","middleName":"","lastName":"Zhou","suffix":""},{"id":498388442,"identity":"a99dbc66-342d-43e7-bc6a-af191622d991","order_by":8,"name":"Xiaobo Wang","email":"","orcid":"","institution":"Beijing Shunyi District Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaobo","middleName":"","lastName":"Wang","suffix":""},{"id":498388444,"identity":"defaba4a-bb1b-40f3-8b79-cde50abb752e","order_by":9,"name":"Xintong Zhao","email":"","orcid":"","institution":"Beijing Shunyi District Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xintong","middleName":"","lastName":"Zhao","suffix":""},{"id":498388447,"identity":"8cbb192b-5796-4f72-ad98-8e6e78a3c9f7","order_by":10,"name":"Yanjun Gao","email":"","orcid":"","institution":"Beijing Shunyi District Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yanjun","middleName":"","lastName":"Gao","suffix":""},{"id":498388450,"identity":"86f28fea-b4a8-4f6d-9de4-759e777a5f18","order_by":11,"name":"Jiazheng Duan","email":"","orcid":"","institution":"Beijing Shunyi District Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiazheng","middleName":"","lastName":"Duan","suffix":""},{"id":498388453,"identity":"653edfc7-550a-4c87-84c0-8aacc020dc18","order_by":12,"name":"Shuncheng Jiao","email":"","orcid":"","institution":"Beijing Shunyi District Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shuncheng","middleName":"","lastName":"Jiao","suffix":""},{"id":498388456,"identity":"258608cf-c405-4d0b-8b6c-c6b6da0d5073","order_by":13,"name":"Min Zhao","email":"","orcid":"","institution":"Beijing Shunyi District Hospital","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2025-07-06 12:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7057886/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7057886/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88836559,"identity":"75859459-3693-4dfa-ac7a-327a80ea5437","added_by":"auto","created_at":"2025-08-12 01:15:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":61120,"visible":true,"origin":"","legend":"\u003cp\u003eThe global ASIR and ASPR (per 100,000) of Osteoarthritis in 204 countries.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7057886/v1/036ea40df34581d059ebab82.png"},{"id":88836560,"identity":"6d52e530-0b75-482a-be42-e4e9cf9fe070","added_by":"auto","created_at":"2025-08-12 01:15:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":28914,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal age-specific numbers of prevalence and incidence.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7057886/v1/d42572a9910396a0e89d8db0.png"},{"id":88837253,"identity":"d05920df-b239-4caa-a87f-125698cc337a","added_by":"auto","created_at":"2025-08-12 01:23:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1438121,"visible":true,"origin":"","legend":"\u003cp\u003eThe joinpoint regression analysis on the ASR of incidence on SDI regions.\u003c/p\u003e\n\u003cp\u003eAbbreviations: ASR, age-standardized rate; SDI, sociodemographic index.\u003c/p\u003e\n\u003cp\u003e(A) Global. (B) High SDI. (C) High-middle SDI. (D) Middle SDI. (E) Low-middle SDI. (F) Low SDI\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7057886/v1/948576caf077c4cff507cdb9.png"},{"id":88837250,"identity":"1542cd51-cb43-408e-9e13-67ce0c1e7b10","added_by":"auto","created_at":"2025-08-12 01:23:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":317894,"visible":true,"origin":"","legend":"\u003cp\u003eThe effects of age, period, and birth cohort on the relative risk of OA incidence\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7057886/v1/ca286b461341f1976343918c.png"},{"id":88837941,"identity":"818de241-d2b6-4b06-81ae-aaa78f8120c9","added_by":"auto","created_at":"2025-08-12 01:31:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":175021,"visible":true,"origin":"","legend":"\u003cp\u003eHealth inequality regression curves and concentration curves for the DALYs of incidence (A and B), prevalence (C and D) worldwide 1990 and 2021.\u003c/p\u003e\n\u003cp\u003eAbbreviations: DALY, disability-adjusted life-years\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7057886/v1/c869f3e61e052b14d060fa7c.png"},{"id":88836575,"identity":"8f6cb947-f5ac-44ae-83cf-43b918403481","added_by":"auto","created_at":"2025-08-12 01:15:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":39959,"visible":true,"origin":"","legend":"\u003cp\u003eThe prediction of ASIR in Osteoarthritis to 2030 all over the world.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7057886/v1/51300e01b05b6313a2d191ff.png"},{"id":99788080,"identity":"46904084-343e-40df-949c-35c09ffc225a","added_by":"auto","created_at":"2026-01-08 12:44:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2432133,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7057886/v1/269efb1c-adf7-4d0d-a88a-e4fcca301d76.pdf"},{"id":88836561,"identity":"b4c18270-9f62-439c-8137-305a37953c29","added_by":"auto","created_at":"2025-08-12 01:15:00","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":50386,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7057886/v1/371da6317f48559879f7e730.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Temporal trends and projections of the global burden of osteoarthritis derived from the Global Burden of Disease 2021 study","fulltext":[{"header":"1. Background","content":"\u003cp\u003eOA is a common condition that primarily affects the hands, knees, hips, and feet, leading to pain, disability, and reduced mobility, especially in older adults.(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) OA significantly contributes to health and economic burdens, including direct medical costs, lost productivity, and the intangible impacts of fatigue and reduced quality of life(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). With an aging global population and rising obesity rates, OA is expected to become even more prevalent in the coming years.(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) In high-income countries, OA-related medical expenses account for a substantial portion of GDP(Gross domestic product).(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eThe burden of OA exhibits significant disparities across regions and countries. Beyond high-income countries, OA is increasingly affecting low- and middle-income nations, where demographic shifts and lifestyle factors such as poor diet and sedentary behavior contribute to rising incidence rates.(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) OA has become a leading cause of disability worldwide, with notable increases not only in Europe, North America, and Asia, but also in regions such as Africa and Latin America, where healthcare resources are limited.\u003c/p\u003e\u003cp\u003eWhile many studies have examined the burden of OA, they often rely on outdated data, focus on specific regions, or address only certain aspects of the disease. Given the chronic and progressive nature of OA, early intervention is crucial, however, challenges such as the absence of established diagnostic criteria, insufficient treatment options, and underdeveloped healthcare infrastructure, particularly in low-income countries impede effective management and prevention efforts.(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eThis research utilizes data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 to provide a comprehensive and current assessment of the global burden and trends related to OA. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)The study includes analyses at global, regional, and national levels, dissects the burden by demographic and epidemiological factors, explores cross-country health inequities.(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cb\u003eData sources\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe annual counts of incident cases, prevalent cases, and years lived with disability (YLDs), along with age-standardized rates by sex, age, region, and country, were derived from the Global Burden of Disease study 2021. The DisMod-MR 2.1 model, a Bayesian mixed-effects meta-regression tool, estimated incidence, prevalence, mortality, years of life lost, YLDs, and disability-adjusted life years (DALYs) for 371 diseases and injuries across 204 countries and territories from 1990 to 2021. The SDI, combining fertility rate, mean years of schooling, and income per capita, was obtained from the Institute for Health Metrics and Evaluation. Based on 2021 SDI estimates, countries were classified into five quintiles: high, high-middle, middle, low-middle, and low SDI. The world was divided into 21 regions to ensure epidemiological homogeneity, determined by factors such as mortality rates and health determinants.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStatistical analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe Joinpoint regression model was used to analyze the log-linear trends of ASIR for osteoarthritis across global and regional levels from 1990 to 2021. This model detects significant trend shifts, referred to as joinpoints, which divide the overall trend into segments. The slope for each segment is calculated as the annual percent change (APC). The average annual percent change (AAPC) is calculated as the geometrically weighted average of individual APC values. An increasing trend is defined when both the APC or AAPC and its lower 95% confidence interval (CI) exceed zero. A decreasing trend is recognized when the APC or AAPC and its upper 95% CI are both below zero. If neither condition is satisfied, the trend is considered stable.\u003c/p\u003e\u003cp\u003eTwo standard measures of inequality\u0026mdash;The Slope Index of Inequality and Concentration Index were used to assess the distribution of osteoarthritis burden across countries. The slope index of inequality was calculated by regressing national incidence and YLD rates for the entire population on a relative position scale linked to sociodemographic development. The concentration index was obtained by numerically integrating the area under the Lorenz concentration curve, which was created using the cumulative YLD distribution and the cumulative relative distribution of the population ranked by the SDI.\u003c/p\u003e\u003cp\u003eAge-period-cohort models were used to evaluate the combined effects of age, period, and cohort on osteoarthritis incidence trends, allowing for the disaggregation and analysis of underlying physiological, social, historical, and environmental determinants. The linear correlation between age, period, and cohort introduces the nonidentification problem, where unique parameter estimates cannot be obtained. To resolve this, incidence was categorized into consecutive 5-year age groups (ranging from 5\u0026ndash;9 years to 95\u0026ndash;99 years), 5-year periods (from 1990\u0026ndash;1994 to 2020\u0026ndash;2021), and corresponding 5-year birth cohort groups (spanning from 1895\u0026ndash;1899 to 1985\u0026ndash;1989). The median birth and diagnosis dates were used as reference points for cohort and period, respectively. Relative risks (RRs) were estimated using the maximum likelihood method within the age-period-cohort Poisson regression model with a log(Y) link, incorporating natural spline functions to model the effects.\u003c/p\u003e\u003cp\u003eTo forecast the global burden of OA to 2030, the Bayesian Age-Period-Cohort (BAPC) model was employed, assuming that the effects of age, period, and cohort remain consistent within temporal proximity. Bayesian inference in the BAPC model utilized a second-order stochastic excursion to smooth the prior values for age, period, and cohort, enabling the prediction of posterior rates. To address the challenges of mixing and convergence typically associated with traditional Bayesian methods, such as Markov chain Monte Carlo, the model incorporated Integrated Nested Laplace Approximations (INLA) to approximate the marginal posterior distributions. This approach has become widely recognized in the analysis of chronic disease trends and the forecasting of future disease burdens.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003eTrends in the burden of OA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe global burden of OA increased significantly from 1990 to 2021. The total number of OA cases rose from 20.9\u0026nbsp;million in 1990 to 46.6\u0026nbsp;million in 2021, a 122.7% increase. The age-standardized incidence rate (ASIR) grew from 489.78 per 100,000 in 1990 to 535 per 100,000 in 2021, representing a 9.25% increase. The estimated annual percent change (EAPC) for global OA incidence from 1990 to 2019 was 0.33 (95% CI: 0.31\u0026ndash;0.35). (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) The global prevalence of OA also increased from 256\u0026nbsp;million in 1990 to 606\u0026nbsp;million in 2021, with the age-standardized prevalence rate (ASR) rising from 6393.12 per 100,000 to 6967.29 per 100,000.(Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e) The total years lived with disability (YLDs) related to OA increased from 8.92\u0026nbsp;million in 1990 to 21.3\u0026nbsp;million in 2021, with the age-standardized YLD rate rising from 222.8 per 100,000 in 1990 to 244.5 per 100,000 in 2021. The EAPC for YLDs from 1990 to 2019 was 0.37 (95% CI: 0.33\u0026ndash;0.4), highlighting the growing disability burden. (Table S2)\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe case number and ASR of incidence of OA in 1990 and 2021 for both sexes by SDI quintiles and by GBD regions, with EAPC from 1990 to 2021.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003elocation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 293px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1990\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEAPC (95 % CI) 1990\u0026ndash;2019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber (95 % UIs)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eASR (95 % UIs)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber (95 % UIs)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eASR (95 % UIs)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlobal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e20900510.49 (18467652.64-23104315.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e489.78\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(433.1-541.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e46632144.37 (41122052.73-51644430.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e535\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(472.38-591.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.33 (0.31-0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 926px;\"\u003e\n \u003cp\u003eSDI quintiles\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eLow SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e1094622.36 (965733.07-1219371.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e408.88\u003c/p\u003e\n \u003cp\u003e(362.19-452.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2804976.17 (2479497.58-3122531.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e447.12\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(395.36-493.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.29 (0.28-0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eLow-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e3035072.54 (2675262.62-3375779.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e423.51\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(374.68-469.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e7812257.58 (6892042.84-8676931.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e480.13\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(424.96-533.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.42 (0.4-0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eMiddle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e5750921.82 (5058231.66-6410278.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e475.42\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(419.58-527.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e15437968.58 (13569373.27-17143283.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e536.49\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(473.16-595.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.48 (0.44-0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eHigh-middle SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e5133784.52 (4537917.93-5675082.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e498.45\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(441.11-549.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e10350051.4 (9099041.92-11468241.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e548.07\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(481.66-608.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.38 (0.35-0.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eHigh SDI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e5864566.47 (5225613.42-6479489.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e568.82\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(505.9-627.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e10189235.97 (9088793.27-11269284.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e611.3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(542.71-675.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.21 (0.17-0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 926px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGBD regions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eAndean Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e121136.54 (107214.18-134640.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e519.51\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(461.53-577.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e363396.5\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(319747.57-402547.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e578.18\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(511.33-641.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.35 (0.34-0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 123px;\"\u003e\n \u003cp\u003eAustralasia\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e124185.65 (110781.01-137553.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e555.78\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(493.55-617.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e269451.73\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(239374.24-301169.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e620.09\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(550.76-686.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.34 (0.31-0.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eCaribbean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e139108.27 (123460.78-154387.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e513.03\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(455.98-570.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e296169.9\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(262546.75-330668.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e555.77\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(493.24-617.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.29 (0.27-0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eCentral Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e221868.54 (194411.32-249129.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e444.99\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(392.55-498.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e475854.12\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(413121.22-535995.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e504.46\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(442.21-565.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.43 (0.4-0.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eCentral Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e699280.23 (618327.98-779465.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e474.27\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(419.37-525.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e960143.69\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(849389.5-1070329.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e522.05\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(460.81-580.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.33 (0.32-0.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eCentral Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e509647.54 (450945.99-568025.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e527.96\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(468.13-587.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1561604.8\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(1379180.83-1731311.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e589.49\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(521.45-652.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.37 (0.36-0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eCentral Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e122007.42 (106783.23-135467.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e441.86\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(390.35-491.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e339701.23\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(298430.94-378042.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e463.08\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(409.68-515.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.12 (0.08-0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eEast Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e4836774.22 (4234495.29-5416116.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e487.3\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(428.32-544.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e12051700.47 (10562111.16-13556244.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e554.47\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(486.91-619.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.58 (0.5-0.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eEastern Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e1495247.02 (1312342.06-1682318.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e550.43\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(484.03-614.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1833037.93 (1612583.96-2058219.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e584.97\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(515.25-651.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.29 (0.26-0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eEastern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e366642.93 (322665-409381.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e411.5\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(363.2-457.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1000971\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(885083.79-1115451.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e461.02\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(407.42-509.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.39 (0.37-0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eHigh-income Asia Pacific\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e1345776.3 (1189858.25-1491372.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e641.16\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(568.18-707.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2189402.79 (1958142.42-2413152.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e682.07\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(606.06-752.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.35 (0.22-0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eHigh-income North America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e1899644.64 (1695714.25-2093719.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e605.72\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(535.69-670.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e3457087.12 (3062826.32-3850765.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e646.38\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(572.29-715.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.07 (-0.07-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eNorth Africa and Middle East\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e839234.42 (738099.2-934460.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e424.31\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(375.36-470.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e2730761.38\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(2402789.3-3044966.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e488.31\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(433.7-542.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.44 (0.42-0.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eOceania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e16658.74 (14676.47-18606.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e441.66\u003c/p\u003e\n \u003cp\u003e(389.8-491.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e47742.61\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(41835-53394.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e480.95\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(423.12-536.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.25 (0.22-0.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eSouth Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e2993307.58 (2640026.16-3326475.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e430.77\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(382.03-477.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e8220377.67 (7241991.21-9115916.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e495.01\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(436.64-548.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.47 (0.45-0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eSoutheast Asia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e1128949.85 (993293.25-1259580.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e376.04\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(332.26-418.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e3261524.6\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(2872404.58-3641133.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e437.13\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(386.13-485.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.51 (0.5-0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eSouthern Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e252629.79 (224152.65-281234.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e540.18\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(478.83-600.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e483946.18\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(431133.2-536648.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e596.27\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(530.39-660.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.29 (0.26-0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eSouthern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e157577.09 (139016.14-175111.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e513.31\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(454.83-568.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e375227.74\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(330227.08-417302.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e557.24\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(493.49-618.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.28 (0.27-0.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eTropical Latin America\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e551639.12 (485844.29-614415.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e527.59\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(467.44-585.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1568533.39 (1385103.16-1733205.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e589.12\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(521.39-650.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.38 (0.37-0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eWestern Europe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e2630868.23 (2349666.53-2928270.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e521.51\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(465.66-578.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e3918887.82 (3498381.84-4363204.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e557.66\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(497.27-618.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.19 (0.17-0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eWestern Sub-Saharan Africa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e448326.4 (395307.5-501301.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e439.61\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(387.27-489.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 170px;\"\u003e\n \u003cp\u003e1226621.72 (1078016.62-1368996.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e483.84\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(427.08-536.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003e0.32 (0.3-0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eRegionally, the incidence of OA varied. Low SDI regions saw the largest relative increase, with OA cases rising from 1.1\u0026nbsp;million in 1990 to 2.8\u0026nbsp;million in 2021 (a 156.23% increase). The age-standardized incidence rate (ASIR) in these regions rose from 408.88 per 100,000 in 1990 to 447.12 per 100,000 in 2021. High SDI regions experienced a more moderate increase, with cases rising from 5.9\u0026nbsp;million in 1990 to 10.2\u0026nbsp;million in 2021 (73.26% increase) and the ASIR rising from 568.82 per 100,000 in 1990 to 611.3 per 100,000 in 2021. (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) Prevalence in low SDI regions rose from 11.8\u0026nbsp;million in 1990 to 29.9\u0026nbsp;million in 2021, while high SDI regions saw an increase from 79.7\u0026nbsp;million in 1990 to 152.4\u0026nbsp;million in 2021. (Table S2)\u003c/p\u003e\n\u003cp\u003eCountry-level analysis of OA incidence revealed substantial variability in ASIR between 1990 and 2021. In China, OA cases increased from 4.65\u0026nbsp;million (ASIR: 487.11 per 100,000) in 1990 to 11.65\u0026nbsp;million (ASIR: 554.61 per 100,000) in 2021, with an EAPC of 0.58 (95% CI: 0.51\u0026ndash;0.66). Similarly, India experienced a rise from 2.47\u0026nbsp;million to 6.70\u0026nbsp;million cases, with ASIR increasing from 438.57 to 505 per 100,000 (EAPC: 0.47, 95% CI: 0.45\u0026ndash;0.50). In contrast, the Kingdom of Denmark showed minimal change, with ASIR shifting from 542.83 to 539.24 per 100,000 (EAPC: 0.02, 95% CI: -0.01\u0026ndash;0.05). Additionally, the Arab Republic of Egypt\u0026rsquo;s ASIR increased from 426.67 to 485.92 per 100,000 (EAPC: 0.35, 95% CI: 0.32\u0026ndash;0.37), and Nigeria\u0026rsquo;s rose from 450.16 to 498.72 per 100,000 (EAPC: 0.35, 95% CI: 0.31\u0026ndash;0.40). These data underscore marked heterogeneity in OA incidence trends across countries. (Table S3, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003eGender disparities in the burden of OA were also evident, with women consistently experiencing a higher incidence compared to men. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e showed the prevalence and incidence of osteoarthritis across different age groups in 2021. Osteoarthritis is most prevalent among individuals aged 35 and older, with a marked increase in incidence observed between the ages of 30 and 69. Both males and females are particularly affected between the ages of 50 and 54. A similar pattern is seen in the prevalence rates, with a significant rise in incidence starting at age 50, peaking in the 65\u0026ndash;69 age range. Interestingly, women exhibit higher rates of both prevalence and incidence compared to men.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJoinpoint regression analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom 1990 to 1994, the global ASIR of osteoarthritis showed an initial increase with APC of 0.1868 (95% CI: 0.1110 to 0.2626), indicating a rapid rise in OA incidence. However, this growth slowed from 1994 to 2000, with an APC of 0.0851 (95% CI: 0.0655 to 0.1047). A sharp increase occurred from 2000 to 2005, with an APC of 0.5603 (95% CI: 0.5353 to 0.5853), reflecting a significant rise in OA burden globally. After 2005, the trend shifted downward between 2005 and 2010, with an APC of -0.0850 (95% CI: -0.1093 to -0.0606). However, the incidence increased again from 2010 to 2016, with an APC of 0.4505 (95% CI: 0.4335 to 0.4675), and the growth accelerated in the final period analyzed (2016 to 2021) with an APC of 0.6453 (95% CI: 0.5669 to 0.7238).( Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, Table S4)\u003c/p\u003e\n\u003cp\u003eIn high SDI regions, a notable rise in OA incidence was observed from 1990 to 1995, with an APC of 0.5846 (95% CI: 0.5129 to 0.6563). Between 1995 and 2005, the rate of increase slowed (-0.156, 95% CI: -0.1843 to -0.1279), but after 2005, OA incidence surged again with an APC of 0.9703 (95% CI: 0.8168 to 1.1241) from 2005 to 2009, followed by more moderate increases in subsequent years. In middle SDI regions, the incidence showed fluctuations. A decline in the APC occurred from 1990 to 1995 (-0.2506, 95% CI: -0.3106 to -0.1905), but from 2000 to 2005, the APC increased substantially to 1.0386 (95% CI: 0.9927 to 1.0846). Post-2010, the growth slowed slightly, with an APC of 0.5505 (95% CI: 0.5191 to 0.5819). Low SDI regions saw consistent increases in OA incidence, particularly from 1990 to 1996, with an APC of 0.2260 (95% CI: 0.2098 to 0.2423). The rise in incidence continued steadily across the study period, suggesting a growing burden of OA in low-income countries.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAge-period-cohort analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results from the age-period-cohort analysis of OA incidence are presented in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. Additional subgroup analyses, stratified by sex. After adjusting for the effects of period and birth cohort, the age factor was found to have a substantial impact on the risk of OA incidence. The relative risk for incidence exhibited a trend of initial increase followed by a subsequent decline, with the peak risk observed in individuals aged 55\u0026ndash;59 years.\u003c/p\u003e\n\u003cp\u003eFollowing the adjustment for age and birth cohort effects, the period effect on OA incidence was also found to be significant. The period effect demonstrated a gradual upward trend, with the relative risk for incidence increasing by 1.1 times from the period 1990 to 2015. The highest incidence risk was observed in the 2015 period.\u003c/p\u003e\n\u003cp\u003eFurthermore, after controlling for the age and period effects, the birth cohort effect revealed a notable influence on the incidence risk of OA. The birth cohort effect indicated a higher risk of incidence in earlier cohorts compared to later cohorts, with the relative risk continuously decreasing from the 1895\u0026ndash;1899 cohort to the 1985\u0026ndash;1989 cohort. Notably, males exhibited a higher incidence risk than females from the 1895\u0026ndash;1899 cohort to the 1935\u0026ndash;1939 cohort.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInequality analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis of OA burden using the slope index of inequality reveals a widening gap in both OA incidence and YLD between countries with the highest and lowest SDI from 1990 to 2021.(Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e) For OA incidence, the slope index of inequality increased from 0.18 in 1990 to -0.41 in 2021, indicating a disproportionate increase in the burden in higher SDI countries. A similar trend was observed in YLDs, where the slope index of inequality also showed an upward shift, pointing to a growing disparity in disability due to OA. In contrast, the relative concentration index for both OA incidence and YLDs remained relatively unchanged. For instance, the relative concentration index for OA incidence stood at -0.12 in 1990 and \u0026minus;\u0026thinsp;0.13 in 2021, while YLDs maintained a steady relative concentration index of -0.19 across both years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrediction to 2030\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe predictions based on the BAPC model indicate that the age-standardized incidence rate (ASIR) for osteoarthritis (OA) is expected to increase from 491.06 per 100,000 in 1990 to around 535 per 100,000 by 2030. Despite stable age-standardized rates, the total burden of OA is expected to significantly increase by 2030. (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOA has become an increasingly significant global public health issue. Over the past three decades, both the incidence and prevalence of OA have risen substantially, driven by demographic factors, particularly population aging, and the growing prevalence of obesity. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)This study identifies a consistent rise in the global burden of OA, indicating a critical need for comprehensive interventions to mitigate its effects. A notable sex disparity exists, with females demonstrating increased susceptibility to OA, highlighting the need for gender-specific considerations in the development of public health strategies.(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) Cross-country inequality analysis highlights disparities in the burden of OA, particularly in countries with differing SDI scores. Given current trends, the burden of OA is expected to continue rising through 2030, underscores the increasing pressure on healthcare systems, which must respond effectively through comprehensive prevention, diagnosis, and management strategies.\u003c/p\u003e\u003cp\u003eThe global obesity epidemic is a critical factor contributing to the OA burden. Obesity, defined by an excessive accumulation of adipose tissue, which presents a significant risk to an individual\u0026rsquo;s health. It is most commonly assessed through the Body Mass Index (BMI), places significant mechanical stress on weight-bearing joints, particularly the knees and hips, accelerating the onset and progression of OA.(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) Epidemiological studies have shown a strong association between high BMI and OA, with high BMI being a key modifiable risk factor. The rising rates of obesity, especially among younger populations, suggest that OA incidence will continue to increase in the coming decades, further exacerbating the global health burden.\u003c/p\u003e\u003cp\u003eGiven the critical role of obesity in the development and progression of OA, weight management should be prioritized in preventive measures.(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) Public health campaigns promoting healthier diets, physical activity, and active lifestyles are essential for reducing the obesity-related burden of OA.(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) Specifically, interventions targeting early prevention of obesity. The World Health Organization (WHO) and other international organization have consistently emphasized the importance of weight management as a key preventive strategy, and these efforts should be scaled up to mitigate the risk of OA associated with obesity.(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eThe contrasting OA burden trends between low and high SDI regions arise from distinct socioeconomic and healthcare dynamics. In low-SDI areas, accelerated population aging, urbanization-induced sedentary lifestyles, and occupational physical strain (e.g., agrarian labor) synergistically drive OA incidence, compounded by limited access to diagnostics, rehabilitation, and preventive care\u0026mdash;factors exacerbating DALY.(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) Conversely, high-SDI regions exhibit slower burden growth due to established OA management systems, yet sustain large prevalent caseloads from extended longevity and historically high OA prevalence. While advanced therapies (e.g., pharmacologic interventions, joint replacements) in these regions mitigate disease progression, sustainability challenges persist due to aging demographics and escalating costs. To address these disparities, context-specific strategies are imperative: low-SDI regions should prioritize community-based weight management, ergonomic adaptations for laborers, and telehealth-enabled screening to expand primary care access, whereas high-SDI systems require AI-driven early detection and value-based care models to optimize aging population management.(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) Cross-regional collaborations, including technology transfers (e.g., affordable imaging tools) and WHO-aligned policy frameworks integrating OA care into universal health coverage, could reduce equity gaps. Future research must evaluate intervention scalability across SDI gradients while addressing epidemiological data deficiencies in resource-limited settings.\u003c/p\u003e\u003cp\u003eThe age-stratified burden of OA underscores distinct etiological and management challenges across life stages. In younger populations (\u0026lt;\u0026thinsp;50 years), rising OA prevalence is driven by obesity, sports-related joint injuries, and delayed diagnosis due to low clinical suspicion, while middle-aged adults (50\u0026ndash;65 years) face accelerated progression from metabolic syndrome and occupational repetitive strain.(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) Older adults (\u0026gt;\u0026thinsp;65 years) bear the highest disability burden due to degenerative joint changes, sarcopenia, and comorbidities limiting surgical options, though younger patients experience significant productivity losses from prolonged disability. Addressing these disparities requires age-tailored strategies: younger populations benefit from obesity prevention and sports safety initiatives, middle-aged adults require workplace ergonomic adaptations and community-based early screening, and older adults need integrated care combining pharmacologic pain control, low-impact exercise, and fall prevention.(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) Technological innovations, such as wearable sensors for joint load monitoring in youth and AI-assisted radiographic grading for elderly clinics, could enhance precision management.(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) Policy frameworks must align with WHO\u0026rsquo;s universal health coverage goals to prioritize OA as a public health challenge, particularly in aging societies. Future research should clarify interactions between age-specific risk profiles and emerging environmental determinants, such as sedentarism in digitalized lifestyles, to optimize interventions across age groups.\u003c/p\u003e\u003cp\u003eEarly detection of OA is critical to prevent disease progression and reduce long-term disability.(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) Screening programs targeting high-risk individuals\u0026mdash;such as those with a family history of OA, joint injuries, or occupations involving repetitive movements\u0026mdash;can enable timely interventions. Early interventions, including physical therapy and weight management, can slow or prevent the worsening of OA symptoms.(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eThis study provides valuable insights into osteoarthritis, offering a comprehensive analysis of its prevalence and incidence across sex, age, and SDI levels. By examining these factors, the study highlights key demographic groups most affected by the disease and identifies regional disparities in burden. The global scope and longitudinal data make it an essential reference for shaping effective prevention and treatment programs, guiding future public health policies, and addressing the growing osteoarthritis burden worldwide.\u003c/p\u003e\u003cp\u003eThis study has several limitations that should be considered when interpreting its findings. This study lacks a detailed exploration of the impact of healthcare interventions or policies, which could mitigate the projected trends. Furthermore, the analysis primarily focuses on demographic factors like aging and obesity, excluding other non-medical risk factors such as occupational exposures and environmental influences that could play a role in OA incidence.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eOA has become an increasingly significant global health issue, driven largely by population aging and the rising prevalence of obesity. The growing burden of OA highlights the need for improved healthcare services, with an emphasis on early detection and preventive measures targeting modifiable risk factors, particularly high BMI. Strengthening healthcare systems, providing comprehensive management strategies, and ensuring equitable access to care are crucial for addressing the global OA burden and improving patient outcomes across all regions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eASR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAge-standardized rate\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eConfidence interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDALY\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDisability-adjusted life year\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEAPC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEstimated annual percentage change\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGBD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGlobal Burden of Disease Study\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSDI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSocio-demographic Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eUncertainty interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBody Mass Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eASIR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAge-standardized incidence rate rising\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eASPR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAge-standardized prevalence rates\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAPC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAnnual percent change\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAAPC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAverage annual percent change\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBAPC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBayesian Age-Period-Cohort\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data could be extracted from the Global Health Data Exchange (GHDx) website\u003c/p\u003e\n\u003cp\u003e(http://ghdx.healthdata.org/gbd-results-tool).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was conducted without the any funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWentao Zhao and Feng Li had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.\u003c/p\u003e\n\u003cp\u003eConcept and design: Wentao Zhao, Feng Li.\u003c/p\u003e\n\u003cp\u003eAcquisition, interpretation of data: Guoxin Zhang, \u0026nbsp;Xing Sun, Yunlong Wang, \u0026nbsp;Xintong Zhao, \u0026nbsp;Tianlang Xie.\u003c/p\u003e\n\u003cp\u003eDrafting of the manuscript: Wentao Zhao, Feng Li.\u003c/p\u003e\n\u003cp\u003eCritical review of the manuscript for important intellectual content: Yunfei Hou.\u003c/p\u003e\n\u003cp\u003eStatistical analysis: Tianlang Xie, Wankun Zhou, Yanjun Gao, Xiaobo Wang, Yunlong Wang, Jiazheng Duan, Xintong Zhao.\u003c/p\u003e\n\u003cp\u003eAdministrative, technical support: Yunfei Hou, Shuncheng Jiao, Guoxin Zhang, Min \u0026nbsp;Zhao.\u003c/p\u003e\n\u003cp\u003eSupervision: Wentao Zhao, Feng Li, Yunfei Hou.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors participated in drafting the manuscript or critically revising it for substantial intellectual content, and all have provided final approval of the version to be published.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to sincerely acknowledge and express their gratitude for the invaluable contributions made by all the collaborators of the Global Burden of Disease Study 2021.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWentao Zhao, MD\u003csup\u003e1\u003c/sup\u003e\u0026nbsp; Feng Li, MD\u003csup\u003e2\u003c/sup\u003e\u0026nbsp; Yunfei Hou, MD\u003csup\u003e3\u003c/sup\u003e\u0026nbsp; Guoxin Zhang, MD\u003csup\u003e1\u003c/sup\u003e\u0026nbsp; Xing Sun, MD\u003csup\u003e1\u003c/sup\u003e\u0026nbsp; Tianlang Xie, MD\u003csup\u003e1\u003c/sup\u003e\u0026nbsp; Yunlong Wang, MD\u003csup\u003e1\u003c/sup\u003e\u0026nbsp; Wankun Zhou,MD\u003csup\u003e1\u0026nbsp;\u003c/sup\u003e Xiaobo Wang, MD\u003csup\u003e1\u003c/sup\u003e\u0026nbsp; Xintong Zhao,MD\u003csup\u003e1\u003c/sup\u003e\u0026nbsp; Yanjun Gao, MD\u003csup\u003e1\u003c/sup\u003e\u0026nbsp; Jiazheng Duan, MD\u003csup\u003e1\u003c/sup\u003e\u0026nbsp; Shuncheng Jiao, MD\u003csup\u003e1\u003c/sup\u003e\u0026nbsp; Min Zhao, MD\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e1.Department of Orthopedic Surgery, Beijing Shunyi District Hospital, No. 3 of Guangming Nanjie, Shunyi District, Beijing, 101300, People's Republic of China.\u003c/p\u003e\n\u003cp\u003e2.Hangzhou Geriatric Hospital, Department of Orthopedics, Affiliated Hangzhou First People's Hospital Chengbei Campus, School of Medicine, Westlake University, Hangzhou, 310006, China.\u003c/p\u003e\n\u003cp\u003e3.Arthritis Clinic and Research Center, Peking University People's Hospital, Peking University, No. 11, Xizhimen South Street, Xicheng District, Beijing, 100044, People's Republic of China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author name and email address:\u0026nbsp;\u003c/strong\u003eFeng Li, MD\u003c/p\u003e\n\u003cp\u003eE-mail: [email protected]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTang Sa, Zhang C, Oo WM, Fu K, Risberg MA, Bierma-Zeinstra SM et al. Osteoarthr Nat Reviews Disease Primers. 2025;11(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSteinmetz JD, Culbreth GT, Haile LM, Rafferty Q, Lo J, Fukutaki KG, et al. Global, regional, and national burden of osteoarthritis, 1990\u0026ndash;2020 and projections to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet Rheumatol. 2023;5(9):e508\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCourties A, Kouki I, Soliman N, Mathieu S, Sellam J. Osteoarthritis year in review 2024: Epidemiology and therapy. Osteoarthr Cartil. 2024;32(11):1397\u0026ndash;404.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVos T, Lim SS, Abbafati C, Abbas KM, Abbasi M, Abbasifard M, et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWeng Q, Chen Q, Jiang T, Zhang Y, Zhang W, Doherty M, et al. Global burden of early-onset osteoarthritis, 1990\u0026ndash;2019: results from the Global Burden of Disease Study 2019. Ann Rheum Dis. 2024;83(7):1\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePigeolet M, Jayaram A, Park KB, Meara JG. Osteoarthritis in 2020 and beyond. Lancet. 2021;397(10279):1059\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMurray CJL. Findings from the Global Burden of Disease Study 2021. Lancet. 2024;403(10440):2259\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFerrari AJ, Santomauro DF, Aali A, Abate YH, Abbafati C, Abbastabar H, et al. Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990\u0026ndash;2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10440):2133\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHunter DJ, Bierma-Zeinstra S, Osteoarthritis. Lancet. 2019;393(10182):1745\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang W, Xiao YS, Zou YH, Zhong LQ, Huang GQ. The relationship between visceral adipose tissue and osteoarthritis among older adults: evidence from the NHANES 1999\u0026ndash;2018. Front Nutr. 2025;12:1526377.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKreitmaier P, Swift D, Wilkinson JM, Zeggini E. Epigenomic differences between osteoarthritis grades in primary cartilage. Osteoarthritis Cartilage. 2024;32(9):1126\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZheng H, Chen C. Body mass index and risk of knee osteoarthritis: systematic review and meta-analysis of prospective studies. BMJ Open. 2015;5(12):e007568.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHaslam DW, James WP. Obes Lancet. 2005;366(9492):1197\u0026ndash;209.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSwinburn BA, Sacks G, Hall KD, McPherson K, Finegood DT, Moodie ML, et al. The global obesity pandemic: shaped by global drivers and local environments. Lancet. 2011;378(9793):804\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGodziuk K, Hawker GA. Obesity and body mass index: Past and future considerations in osteoarthritis research. Osteoarthritis Cartilage. 2024;32(4):452\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGelber AC. Knee Osteoarthritis. Ann Intern Med. 2024;177(9):ITC129\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDiseases GBD, Injuries C. Global burden of 369 diseases and injuries in 204 countries and territories, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang J, Yeung AM, Armstrong DG, Battarbee AN, Cuadros J, Espinoza JC, et al. Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes. J Diabetes Sci Technol. 2023;17(1):224\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWei G, Lu K, Umar M, Zhu Z, Lu WW, Speakman JR, et al. Risk of metabolic abnormalities in osteoarthritis: a new perspective to understand its pathological mechanisms. Bone Res. 2023;11(1):63.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSampath SJP, Venkatesan V, Ghosh S, Kotikalapudi N, Obesity. Metabolic Syndrome, and Osteoarthritis-An Updated Review. Curr Obes Rep. 2023;12(3):308\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDavis MA, Ettinger WH, Neuhaus JM. Obesity and osteoarthritis of the knee: evidence from the National Health and Nutrition Examination Survey (NHANES I). Semin Arthritis Rheum. 1990;20(3 Suppl 1):34\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHidaka R, Matsuda K, Igari T, Takeuchi S, Imoto Y, Yagi S, et al. Development and accuracy of an artificial intelligence model for predicting the progression of hip osteoarthritis using plain radiographs and clinical data: a retrospective study. BMC Musculoskelet Disord. 2024;25(1):893.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFavero M, Ramonda R, Goldring MB, Goldring SR, Punzi L. Early knee osteoarthritis. RMD Open. 2015;1(Suppl 1):e000062.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMessier SP, Beavers DP, Queen K, Mihalko SL, Miller GD, Losina E et al. Effect of Diet and Exercise on Knee Pain in Patients With Osteoarthritis and Overweight or Obesity. JAMA. 2022;328(22).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMessier SP, Mihalko SL, Legault C, Miller GD, Nicklas BJ, DeVita P, et al. Effects of intensive diet and exercise on knee joint loads, inflammation, and clinical outcomes among overweight and obese adults with knee osteoarthritis: the IDEA randomized clinical trial. JAMA. 2013;310(12):1263\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Osteoarthritis, Global Burden of Disease Study, Public health, Risk factors","lastPublishedDoi":"10.21203/rs.3.rs-7057886/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7057886/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e This study updates the assessment of the global, regional, and national burden of osteoarthritis (OA) from 1990 to 2021, focusing on trends in incidence, prevalence, and years lived with disability (YLDs), and explores the sociodemographic factors influencing these trends.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Data from the Global Burden of Disease Study 2021 were used to estimate OA incidence, prevalence, and YLDs at global, regional, and national levels. Joinpoint regression analysis was employed to assess temporal trends, while age-period-cohort models were used to examine the effects of age, period, and birth cohort on OA trends. The Slope Index of Inequality and Concentration Index were applied to analyze cross-country health inequities in OA burden.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Between 1990 and 2021, the global OA case count increased from 20.9 million to 46.6 million (122.7%), with the ASIR (age-standardized incidence rate rising) from 489.78 to 535 per 100,000 (9.25%; EAPC(estimated annual percentage change): 0.33, 95% CI: 0.31–0.35). Global prevalence increased from 256 million to 606 million cases, with ASPR (age-standardized prevalence rates) rising from 6393.12 to 6967.29 per 100,000. Regionally, low SDI (sociodemographic index) regions exhibited a 156.23% increase in incidence compared to 73.26% in high SDI regions. At the country level, China’s OA cases increased by 150.38% (ASIR: 487.11 to 554.61 per 100,000; EAPC: 0.58, 95% CI: 0.51–0.66). The BAPC model forecasts an ASIR of approximately 535 per 100,000 by 2030.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The burden of OA has significantly increased over the past three decades, with notable regional disparities. This rise is primarily attributed to population aging and increasing obesity rates. Targeted prevention, early diagnosis, and enhancements in healthcare services are crucial to addressing the growing global impact of OA.\u003c/p\u003e","manuscriptTitle":"Temporal trends and projections of the global burden of osteoarthritis derived from the Global Burden of Disease 2021 study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-12 01:14:55","doi":"10.21203/rs.3.rs-7057886/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"20403a4c-acea-46cc-87fc-854f1bb35994","owner":[],"postedDate":"August 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-24T11:39:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-12 01:14:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7057886","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7057886","identity":"rs-7057886","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}