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. 2020 Nov 26;29-30:100587. doi: 10.1016/j.eclinm.2020.100587

Global, regional prevalence, incidence and risk factors of knee osteoarthritis in population-based studies

Aiyong Cui 1,1, Huizi Li 1,1, Dawei Wang 1, Junlong Zhong 1, Yufeng Chen 1, Huading Lu 1,⁎
PMCID: PMC7704420  PMID: 34505846

Abstract

Background

Knee osteoarthritis (OA) is a major cause of disability in the elderly, however, there are few studies to estimate the global prevalence, incidence, and risk factors of knee OA.

Methods

For this study, we searched PUBMED, EMBASE and SCOPUS from inception to April 4, 2020, without language restriction. We identified eligible studies with information on the prevalence or incidence of knee OA in population-based observational studies and extracted data from published reports. We did random-effects meta-analysis to generate estimates. This study was registered with PROSPERO (CRD42020181035).

Findings

Out of 9570 records identified, 88 studies with 10,081,952 participants were eligible for this study. The pooled global prevalence of knee OA was 16⋅0% (95% CI, 14⋅3%-17⋅8%) in individuals aged 15 and over and was 22⋅9% (95% CI, 19⋅8%-26⋅1%) in individuals aged 40 and over. Correspondingly, there are around 654⋅1 (95% CI, 565⋅6–745⋅6) million individuals (40 years and older) with knee OA in 2020 worldwide. The pooled global incidence of knee OA was 203 per 10,000 person-years (95% CI, 106–331) in individuals aged 20 and over. Correspondingly, there are around annual 86⋅7 (95% CI, 45⋅3–141⋅3) million individuals (20 years and older) with incident knee OA in 2020 worldwide. The prevalence and incidence varied substantially between individual countries and increased with age. The ratios of prevalence and incidence in females and males were 1⋅69 (95% CI, 1⋅59–1⋅80, p<0⋅00) and 1⋅39 (95% CI, 1⋅24–1⋅56, p<0⋅00), respectively.

Interpretation

Our study provides the global prevalence (16⋅0% [95% CI, 14⋅3%-17⋅8%]) and incidence (203 per 10,000 person-years [95% CI, 106–331]) of knee OA. These findings can be used to better assess the global health burden of knee OA. Further prospective cohort studies are warranted to identify modifiable risk factors for providing effectively preventive strategies in the early stages of the disease.

Funding

This work was supported by grants from the National Natural Science Foundation of China (nos. 81772384 and 81572174).

Research in context.

Evidence before this study

To our knowledge, only the Global Burden of Disease Study estimated the global prevalence and incidence of osteoarthritis (OA) from 2000 onwards. The latest estimate was in 2017.

Added value of this study

We included 88 studies for this systematic review and meta-analysis, of which 18 studies were published from 2017 to 2020. We found that the global prevalence of knee OA was 16⋅0% (95% CI, 14⋅3%−17⋅8%) in individuals aged 15 and over and was 22⋅9% (95% CI, 19⋅8%−26⋅1%) in individuals aged 40 and over. Correspondingly, there are around 654⋅1 (95% CI, 565⋅6–745⋅6) million individuals (40 years and older) with knee OA in 2020 worldwide. We found that the global incidence of knee OA was 203 per 10,000 person-years (95% CI, 106–331) in individuals aged 20 and over. Correspondingly, there are around annual 86⋅7 (95% CI, 45⋅3–141⋅3) million individuals (20 years and older) with incident knee OA in 2020 worldwide. The ratios of prevalence and incidence in females and males were 1⋅69 (95% CI, 1⋅59–1⋅80, p<0⋅00) and 1⋅39 (95% CI, 1⋅24–1⋅56, p<0⋅00), respectively. We also assessed risk factors, the results showed that the prevalence and incidence increased with age, peaked at the advanced age on prevalence and at 70–79 years old on incidence; increasing education attainment was negatively associated with the prevalence of knee OA.

Implications of all the available evidence

Global health-care providers, policy makers, and the general public should be aware of the high prevalence of knee OA and the modifiable risk factors such as obesity, injury, and education. More efforts should be made to explore the cause of the high prevalence and incidence of knee OA in women and elderly and provide effectively preventive strategies in the early stages of the disease.

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1. Introduction

Knee osteoarthritis (OA) is a common progressive multifactorial joint disease and is characterized by chronic pain and functional disability [1]. Knee OA accounts for almost four fifths of the burden of OA worldwide and increases with obesity and age [2]. Up to now, knee OA is incurable except knee arthroplasty which is considered as an effective treatment at an advanced stage of the disease, however, which is responsible for substantial health costs [3]. Many researchers have shifted their focus to the prevention and treatment in the early stage of the disease [4]. Accordingly, it is essential to understand the prevalence, incidence, and modifiable risk factors of knee OA for providing effectively preventive strategies.

The World Health Organization, the International League Against Rheumatism, and global OA experts have made substantial efforts over the past few decades, many population-based epidemiological studies of knee OA have been conducted worldwide [5]. Exploring the differences of prevalence, incidence, and risk factors of knee OA in age, gender, region, and others can help us better understand the potential etiology of knee OA. However, there is a paucity of the epidemiologic data of knee OA in the global population. The Global Burden of Disease (GBD) study 2017 and recent several reviews assessed the epidemiological characteristics of knee OA, but they still used data published before 2000 [6,7]. Moreover, the main objective of the GBD study was to assess the burden of OA. Furthermore, the GBD study 2017 only included the symptomatic knee OA which was radiologically confirmed, which tended to collect relatively late cases, because there is discordance between symptomatic and radiographic changes. The early painful patients with knee OA may be not suffer from radiographic changes. On the contrary, the patients with severe radiographic changes may not have any symptoms [8]. The possible explanations are that structural changes on radiographs may not have a nociceptive innervation and the experience of pain is related to many factors such as tolerance and susceptibility. In general, the patient with both radiographic changes and symptoms is usually considered to be more severe. Accordingly, only either symptomatic or radiological knee OA may occur earlier than the radiologically confirmed symptomatic knee OA, and the former may provide more opportunities for disease prevention. Even Du H et al. found that almost a quarter of patients with the radiographic OA, but no symptoms, complained of disability [9]. Moreover, with the changes of demographic structure and lifestyle, the epidemiological data of knee OA are also changing. Wallace IJ et al. demonstrated that the prevalence of knee OA has doubled since the mid-20th century in the United States [10]. Consequently, investigating the contemporary epidemiology of knee OA is considerably important. However, it is time-consuming, expensive, and difficult to examine the prevalence or incidence of knee OA in large-scale population.

Therefore, we quantitatively synthesized the published epidemiological data of knee OA in individual country to estimate the global, regional prevalence of knee OA from 2000 to 2020, to estimate the global, regional incidence of knee OA from 1990 to 2020, and to identify risk factors of knee OA, aiming to produce more accurate and up-to-date evidence-based information for health-care providers and policy makers.

2. Methods

2.1. Search strategy and selection criteria

We performed this systematic review and meta-analysis in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline, the Meta-analysis of Observational Studies in Epidemiology (MOOSE) reporting guideline, and Guidelines for Accurate and Transparent Health Estimates Reporting: The GATHER Statement [11], [12], [13]. The protocol of this study was preregistered in the PROSPERO database (CRD42020181035).

The criteria for study inclusion are as follows: an original study reporting the prevalence or incidence of knee OA; a study examining radiographical (femorotibial), symptomatic, or self-report knee OA; a cross-sectional or longitudinal observational study; a population-based study; on prevalence, a study collecting data from 2000 onwards; on incidence, a study collecting data from 1990 onwards. In addition, several studies reporting register primary care data [14], [15], [16] and claims data [17] were included, because those data were a close representation of the national population and had been validated [18,19]. Studies are excluded: examined individuals only with radiographically patellofemoral OA; diagnosed knee OA by MRI and ultrasonography; a hospital-based study; used sample sizes less than 150.

Two researchers (CAY, LHZ) searched PUBMED, EMBASE, and SCOPUS from inception to April 4, 2020 using search terms defined according to PECOS (participants, exposure, control, outcome, study) principle (Appendix 1.1–3), without limitation in language. We also identified reference lists of relevant systematic reviews and included studies, but we did not search gray literature. To select eligible studies, we firstly used endnote software (version 19⋅0) to remove duplicates from different bibliographic databases. Then two investigators (CAY, LHZ) independently screened titles and abstracts of remaining literature search records and assessed all potential eligible articles against the inclusion and exclusion criteria. The two researchers (CAY, LHZ) and another professor (LHD) discussed together to resolve any discrepancy in the process of selecting studies.

2.2. Data analysis

Two investigators (CAY, LHZ) extracted data from published reports. We extracted the data related to prevalence and risk factors of knee OA (sample sizes, the number of cases of knee OA) and the data related to incidence of knee OA (sample sizes at risk, the number of cases of incident knee OA, follow-up time, person-years at risk). Where several studies used data from the same study cohort, we only analyzed one study that presented the most comprehensive and latest data.

The primary outcome of this study are the prevalence and incidence of knee OA. The prevalence of knee OA is defined as a proportion, namely, the number of cases of knee OA divided by the sample sizes. The incidence equals the number of cases of incident knee OA divided by the person-years at risk. Concomitantly, we also estimated the global number of individuals with knee OA (40 years and older) and with incident knee OA (20 years and older) in 2020 according to world population prospects obtained from the UN Population Division [20]. The secondary outcome are risk factors (age, gender, area of residence, cigarettes, and education level) of knee OA, and the relationships between risk factors and the prevalence and incidence of knee OA are expressed by risk ratios (RRs).

We assessed the quality of prevalence studies using the risk of bias tool developed by Hoy D et al., which includes 10 items and a summary assessment [21]. And we assessed the quality of incidence studies using risk of bias tool designed by ourselves according to previously validated quality assessment tools, which includes 6 items and a summary assessment. Two raters (CAY, LHZ) independently assessed each item and any discrepancy was resolved by consensus. Finally, we used Revman software (version 5⋅3) to visualize the results.

We analyzed heterogeneity using the I2 statistic. To explore the source of heterogeneity, we firstly conducted subgroup analysis according to the characteristics of studies, the classification are as follows: location (continent, country); definition (radiological, symptomatic, and self-report knee OA); sex (female, male); age. Then we performed sensitively analysis to explore the impact of individual study on the overall effects. Univariate meta-regression models were further used to assess the associations between the characteristics of studies and the pooled prevalence and incidence of knee OA. We qualitatively and quantitatively detected publication bias using funnel plots and the Egger linear regression test, respectively [22].

We used R software (version 4⋅0) and Stata software (version 15⋅1) to conduct the meta-analysis. Considering the anticipated high heterogeneity of the observational studies, we employed random-effects model to generate estimates. To stabilize variances, study data were transformed using the Freeman-Tukey double arcsine transformation [23]. All results were generated accompanying 95% confidence interval (CI). A 2-sided P value < 0⋅05 was considered statistically significant in all analyses.

2.3. Role of the funding source

The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit for publication.

3. Results

In total, 9570 citations were retrieved, of which 4044 were duplicated articles and 5438 were ineligible for inclusion criteria, of 88 remaining articles were included for this meta-analysis. The process of selecting studies was showed in Fig. 1.

Fig. 1.

Fig 1

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PRISMA flow diagram.

We finally conducted the present meta-analysis based on 88 articles with 10,081,952 participants, of those, 73 prevalence studies were showed in Table 1, 17 incidence studies were showed in Table 2. Those studies were from six continents (47 in Asian (144,838 participants), 23 in Europe (9,042,232 participants), 11 in North America (236,263 participants), five in South America (15,670 participants), one in Oceania (847 participants), one in Africa (400 participants)) and 34 countries. 70 articles were cross-sectional studies, four articles were retrospective cohort studies. 14 articles were prospective cohort studies. On prevalence, 19 studies examined the radiographic knee OA, 56 studies examined the symptomatic knee OA, and 3 studies examined the self-report knee OA; on incidence, ten studies reported the radiological knee OA and seven studies reported the symptomatic knee OA. The number of females was more than that of males in most of the included studies. 16 studies were high risk of bias, 29 were moderate risk of bias, 43 were low risk of bias (Appendix 2.1–4).

Table 1.

The summary characteristics of included prevalence studies.

Countries NO. of Total NO. of knee OA Age (Mean (SD); Rang (years)) BMI (kg/m2) NO. of Female Methods of Diagnosis Time of Data Collection Type of Study Sampling Method Type of Article
Zhang Y et al. 2001 [34] China 1,781 692 ≥60  25⋅7 1,051 KL≥2 2001 Cross-sectional Random sampling Full text
Zhang Y et al. 2001 [34] China 1,781 199 ≥60  25⋅7 1,051 KL≥2 and Symptomatic (pain) 2001 Cross-sectional Random sampling Full text
Minh Hoa TT et al. 2003 [35] Vietnam 2,119 52 40 (17); 16–93 NA 1,138 ACR 2000 Cross-sectional No random Full text
Minaur N 2004 [36] Australia 847 26 35 (13); 15–86 NA 446 Doctor-diagnosed 2002 Cross-sectional All subjects were interviewed Full text
Senna ER et al. 2004 [37] Brazil 3,038 47 36 (16); 16–92 NA 1,929 ACR 2004 Cross-sectional Probabilistic sampling Full text
Du H et al. 2005 [9] China 2,093 934 54; ≥40  22⋅9 1,099 KL≥2 2001 Cross-sectional Random sampling Full text
Du H et al. 2005 [9] China 2,093 150 54; ≥40  22⋅9 1,099 KL≥2 and Symptomatic (pain) 2001 Cross-sectional Random sampling Full text
Haq SA et al. 2005 [38] Bangladesh 5,077 441 >15 NA 2,517 ACR 2001 Cross-sectional Random sampling Full text
Kaçar C et al. 2005 [39] Turkey 655 97 60 (8); >50 NA 306 ACR 2005 Cross-sectional Cluster sampling Full text
Salaffi F et al. 2005 [40] Italy 2,155 116 58 (18); 18–91 25⋅1 1,151 ACR 2004 Cross-sectional Random sampling Full text
Wang W et al. 2006 [41] China 1,450 176 ≥40 NA 746 KL≥2 2005 Cross-sectional Multiple stage cluster sampling Abstract
Zeng QY et al. 2006 [42] China 2,188 244 35–64 23⋅9 1,139 ACR 2005 Cross-sectional House by house Full text
Felson DT et al. 2007 [43] USA 1,128 104 62; 34–90 27⋅4 NA KL≥2 2005 Cross-sectional No random Full text
Tangtrakulwanich B et al. 2007 [44] Thailand 506 234 59; >40 24⋅4 288 KL≥2 2007 Cross-sectional Cluster stratified sampling Full text
Davatchi F et al. 2008 [45] Iran 10,291 1,532 >15 NA 5,413 Doctor-diagnosed 2004–2005 Cross-sectional Multistage sampling Full text
Grotle M et al. 2008 [46] Norway 3,266 232 24–74 NA 1,786 Self-report doctor-diagnosed 2004 Cross-sectional No random Full text
Quintana JM et al. 2008 [47] Spain 7,577 925 71 (7); 60–90 NA 4,264 ACR 2002–2003 Cross-sectional Stratified random sampling Full text
Joshi VL et al. 2009 [48] India 8,145 451 >16 NA NA ACR 2004–2008 cross-sectional House by house Full text
Kang X et al. 2009 [49] China 1,025 155 58 (8); >50 22⋅5 520 KL≥2 2005 Cross-sectional Compact segment sampling Full text
Kang X et al. 2009 [49] China 1,026 109 58 (8); >50 22⋅5 520 KL≥2 and Symptomatic (pain) 2005 Cross-sectional Compact segment sampling Full text
Yoshimura N et al. 2009 [50] Japan 3,040 1,660 70 (11); >40 23⋅0 1,979 KL≥2 2005–2007 Cross-sectional Random sampling Full text
Kim I et al. 2010 [51] Korea 504 188 70 (8); >50 24⋅6 274 KL≥2 2007 Cross-sectional No random Full text
Kim I et al. 2010 [51] Korea 504 121 70 (8); >50 24⋅6 274 KL≥2 and Symptomatic (pain) 2007 Cross-sectional No random Full text
Salve H et al. 2010 [52] India 260 123 >40 NA All female ACR 2009 Cross-sectional House by house Full text
Cibere J et al. 2010 [53] Canada 255 98 40–79 NA 173 K/L ≥ 2 2010 Cross-sectional Multistage random sampling Full text
Guillemin F et al. 2011 [54] France 27,109 1,962 40–75 NA 18,111 KL≥2 and Symptomatic (pain) 2007–2009 Cross-sectional Random sampling Full text
Horváth G et al. 2011 [55] Hungary 672 111 52; 20–67 NA 432 K/L ≥ 2 2011 Cross-sectional No random Full text
Namali H et al. 2011 [56] Sri Lanka 1,750 207 ≥50 NA NA ACR 2006–2007 Cross-sectional Stratified cluster sampling Abstract
Cakır N et al. 2012 [57] Turkey 4,952 265 ≥40 NA 2,575 ARA 2012 Cross-sectional No random Full text
Chaaya M et al. 2012 [58] Lebanon 3,530 106 38(17); 15–90 NA 1,730 ACR 2007 Cross-sectional Multistage cluster sampling Full text
Ajit NE et al. 2012 [59] India 342 58 43 (17) NA NA ACR 2011–2012 Cross-sectional Stratified random sampling Abstract
Jiang L et al. 2012 [60] China 1,196 193 63 (9); 40–84 NA 623 KL≥2 and Symptomatic (pain) 2005 Cross-sectional Stagestratified sampling Full text
Sandoughi M et al. 2013 [61] Iran 2,100 321 33 (15); >15 NA 1,179 ACR 2008–2009 Cross-sectional Multistage cluster randomly sampling Full text
Zhang J et al. 2013 [62] China 7,126 983 44 (17); 16–90 NA 3,517 KL≥2 and Symptomatic (pain) 2013 Cross-sectional Stratified multistage cluster sampling Full text
Yeşil H et al. 2013 [63] Turkey 522 109 54 (9); ≥40 29⋅6 390 ACR 2013 Cross-sectional No random Full text
Xiang Z et al. 2013 [64] China 1,499 499 62 (11); ≥40 NA 790 ACR 2010–2011 Cross-sectional Cluster stratified multistage random sampling Full text
Turkiewicz A et al. 2013 [65] Sweden 7,737 1,965 70 (8); 56–84 27⋅0 947 KL≥2 2007 Cross-sectional Random sampling Abstract
Turkiewicz A et al. 2013 [65] Sweden 7,737 812 70 (8); 56–84 27⋅0 947 KL≥2 and Symptomatic (pain) 2007 Cross-sectional Random sampling Abstract
Turkiewicz A et al. 2013 [65] Sweden 7,737 696 70 (8); 56–84 27⋅0 947 ACR 2007 Cross-sectional Random sampling Abstract
Thomas E et al. 2014 [66] England 26,100 8,000 66 (11); ≥50  NA 14,172 Symptomatic OA 2004 Cross-sectional All subjects were interviewed Full text
Visser AW et al. 2014 [67] Netherlands 5,284 991 57; 45–65 29⋅9 2,794 ACR 2008–2013 Cross-sectional No random Full text
Singh AK et al. 2014 [68] India 496 204 ≥60 NA 238 ACR 2009–2010 Cross-sectional House by house Full text
Tehrani-Banihashemi A et al. 2014 [69] Iran 1,565 303 38 (19); >15 NA 862 ACR 2006 Cross-sectional Random sampling Full text
Ho-Pham LT et al. 2014 [70] Vietnam 658 225 56; 40–98 NA 488 KL≥2 2014 Cross-sectional Simple random sampling Full text
Haouichat C et al. 2014 [71] Algeria 400 84 65 (9); ≥50 30⋅1 All female KL≥2 2014 Cross-sectional Stratified sampling Abstract
Edwards M et al. 2014 [72] Europe countries 2,904 587 74 (5); 65–85 27⋅3 1,507 ACR 2010–2011 Cross-sectional Random sampling Full text
Edwards M et al. 2014 [72] Germany 405 45 74 (5); 65–85 27⋅3 NA ACR 2010–2011 Cross-sectional Random sampling Full text
Edwards M et al. 2014 [72] Italy 467 135 74 (5); 65–85 27⋅3 NA ACR 2010–2011 Cross-sectional Random sampling Full text
Edwards M et al. 2014 [72] Netherlands 558 102 74 (5); 65–85 27⋅3 NA ACR 2010–2011 Cross-sectional Random sampling Full text
Edwards M et al. 2014 [72] Spain 535 129 74 (5); 65–85 27⋅3 NA ACR 2010–2011 Cross-sectional Random sampling Full text
Edwards M et al. 2014 [72] Sweden 506 101 74 (5); 65–85 27⋅3 NA ACR 2010–2011 Cross-sectional Random sampling Full text
Edwards M et al. 2014 [72] England 433 69 74 (5); 65–85 27⋅3 NA ACR 2010–2011 Cross-sectional Random sampling Full text
Ara R et al. 2014 [73] Bangladesh 4,850 401 56 (13); ≥15 NA 3,502 ACR 2011 Cross-sectional House by house and census Abstract
Moghimi N et al. 2015 [74] Iran 5,830 1,096 >15 NA 3,236 Doctor-diagnosed 2011–2012 Cross-sectional Clusters randomly sampling Full text
Granados Y et al. 2015 [75] Venezuela 3,973 218 44 (18); >18 NA 2,367 ACR 2011 Cross-sectional House by house Full text
Zeng SY et al. 2015 [76] China 4,056 421 48; ≥16 NA 2,108 ACR 2012 Cross-sectional Random sampling Full text
Plotnikoff R et al. 2015 [77] Canada 4,733 497 ≥18 NA NA Self-report doctor-diagnosed OA 2015 Cross-sectional Random sampling Abstract
Cho HJ et al. 2015 [78] Korea 696 265 72 (5); 65–91 24⋅0 398 KL≥2 2005–2006 Cross-sectional Simple random sampling Full text
Tang X et al. 2016 [79] China 17,128 1,387 60; ≥45 NA 8,761 Doctor-diagnosed OA 2011–2012 Cross-sectional Multistage probability sampling Full text
Guevara-Pacheco S et al. 2016 [80] Ecuador 4,877 361 43 (18); >18 NA 2,916 ACR 2014 Cross-sectional Mixed multistage and random sampling Full text
Pal CP et al. 2016 [81] India 4,909 1,412 ≥45 NA NA KL≥2 2016 Cross-sectional Simple random sampling Full text
Branco JC et al. 2016 [82] Portugal 7,911 981 >18 NA 4,153 ACR 2011–2013 Cross-sectional Stratified multistage random sampling Full text
Liu Y et al. 2016 [83] China 3,428 568 55 (10); ≥40–74 NA 1,767 KL≥2 and Symptomatic (pain) 2010 Cross-sectional Proportionately stratified random sampling Full text
Deshpande BR et al. 2016 [84] USA 198,600 13,750 ≥25 NA 103,140 Doctor-diagnosed OA 2007–2008 Cross-sectional NA Full text
Al Saleh J et al. 2016 [85] United Arab Emirates 3,984 1,028 42 (16); 18–85 28⋅8 3,084 ACR 2009 Cross-sectional Random sampling Full text
Murphy LB et al. 2016 [86] USA 1,246 192 ≥51 NA 723 KL≥2 2003 Longitudinal Stratified simple random sampling Full text
Murphy LB et al. 2016 [86] USA 1,342 155 ≥51 NA 778 KL≥2 and Symptomatic (pain) 2003 Longitudinal Stratified simple random sampling Full text
Kolahi S et al. 2017 [87] Iran 952 143 50 (9); 35–70 NA 516 ACR 2014–2015 Cross-sectional Random sampling Full text
Ahmed S et al. 2017 [88] Bangladesh 1,843 135 ≥18 NA NA Doctor-diagnosed OA 2017 Cross-sectional Clusters sampling Abstract
Prashansanie Hettihewa A et al. 2018 [89] Sri Lanka 666 134 63 (9); ≥50 NA All female ACR 2013 Cross-sectional Multistage cluster sampling Full text
Venkatachalam J et al. 2018 [90] India 1,986 538 >18 NA 1,260 ACR 2014 Cross-sectional Random sampling Full text
Vega-Hinojosa O et al. 2018 [91] Peru 1,095 17 39; ≥18 NA 614 ARA 2010 Cross-sectional Probabilistic, stratified random sampling Full text
Seoane-Mato D et al. 2018 [92] Spain 4,753 661 ≥20 NA NA ACR 2016 Cross-sectional Stratified cluster sampling Abstract
Kumar P et al. 2018 [93] India 10,171 766 ≥0 NA NA Doctor-diagnosed OA 2004–2007 Cross-sectional Cluster sampling Full text
Gavali M et al. 2018 [94] India 647 84 45; 35–57 NA 386 Doctor-diagnosed OA 2017–2018 Cross-sectional House by house Abstract
Ananto M et al. 2018 [95] Indonesia 2,067 296 ≥15 NA NA Doctor-diagnosed OA 2018 Cross-sectional Multistage random sampling Abstract
Leung YY et al. 2018 [96] Singapore 3,364 370 ≥18 NA 1,822 Symptomatic OA 2013–2014 Cross-sectional A nationally representative sample Full text
Macías-Hernández SI et al. 2018 [97] Mexico 204 52 57 (11); ≥40 NA 124 KL≥2 2018 Cross-sectional No random Full text
Macías-Hernández SI et al. 2018 [97] Mexico 204 36 57 (11); ≥40 NA 124 KL≥2 and Symptomatic (pain) 2018 Cross-sectional No random Full text
Postler A et al. 2018 [17] Germany 2,700,000 329,216 75; >60 NA 1,884,600 ICD-10 codes 2014 Cross-sectional De-identified claims data Full text
Badley EM et al. 2018 [98] Canada 30,097 4,460 45–85 NA NA Self-report doctor-diagnosed OA 2018 Cross-sectional No random Abstract
Rodriguez-Veiga D et al. 2019 [99] Spain 707 207 62; ≥40 NA 398 Doctor-diagnosed OA 2019 Cross-sectional Random sampling Abstract
Guevara SV et al. 2019 [100] Ecuador 2,687 177 44 (20); ≥18 NA 1,690 ACR 2016–2018 Cross-sectional Random and mixed (stratified and conglomerate) Full text
Hvidberg MF et al. 2019 [14] Denmark 4,555,439 177,662 ≥16 NA 2,314,163 ICD-10 codes 2013 Longitudinal NA Full text
Hong JW et al. 2020 [101] Korea 12,287 4,313 63; ≥50 NA 7,056 KL≥2 2010–2013 Cross-sectional Proportional-allocation systematic sampling with multistage stratification Full text
Sasaki E et al. 2020 [102] Japan 1,104 282 54; ≥20 22⋅9 661 KL≥2 2005 Cross-sectional No random Full text
Swain S et al. 2020 [15] England 1,690,618 49,028 ≥20 NA NA ICD-10 codes 2017 Longitudinal NA Full text
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NO=numero. OA=osteoarthiritis. SD=standard deviation. BMI=body mass index. KL=the Kellgren and Lawrence. NA=not applicate. ACR=the American College of Rheumatology classification criteria. ARA=the clinical criteria of the American Rheumatism Association. ICD=International Classification of Disease.

Table 2.

The summary characteristics of included incidence studies.

Countries NO. of knee OA Person- years Time of Follow- up (years) Age at baseline, Mean; Range (years) NO. of Female Methods of Diagnosis Time of Data Collection
Oliveria SA et al. 1995 [103] USA 461 196,990 3⋅0 20–89 NA KL≥2 + symptoms 1988-(1991,1992)
Felson DT et al. 1995 [104] USA 93 4,844 8⋅1 71; (63–91) 381 K/L grade >2 (1983–1985)-(1992–1993)
Slemenda C et al. 1998 [105] USA 27 728 2⋅6 71; (≥65) 113 K/L grade >2 NA
Gelber AC et al. 1999 [106] USA 62 42,480 36⋅0 23; (21–25) All men Self-report NA
Murphy LB et al. 2016 [86] USA 193 6,853 5⋅5 ≥45 723 K/L grade >2 (1991–1997)-(1999–2003)
Cooper C et al. 2000 [107] England 45 1,234 5⋅1 70; (≥55) NA K/L grade >2 (1990–1991)-(1995–1996)
Duncan R et al. 2011 [108] England 55 759 3⋅0 65 (57–73) NA K/L grade >2 (2002–2003)-(2005–2006)
Driban JB et al. 2020 [109] England 93 3,575 15⋅0 45–64 All female K/L grade >2 (1988–1989)−2004
Swain S et al. 2020 [15] England 3,432 1,492,174 1⋅0 ≥20 NA CDI-10 2007
Prieto-Alhambra D et al. 2014 [16] Spain 96,222 3,266,826 4⋅5 ≥40 NA Doctor-diagnosed 2006–2010
Grotle M et al. 2008 [110] Norway 114 16,750 10⋅0 42; (24–66) NA Self-report 1994–2004
Mork PJ et al. 2012 [111] Norway 351 329,527 11⋅0 44; ≥20 15,191 Self-report (1984–1986)-(1995–1997)
Reijman M et al. 2007 [112] Netherlands 76 9,055 6⋅6 66; (≥55) 792 K/L grade >2 (1990–1993)-(1996–1999)
Nishimura A et al. 2011 [113] Japan 57 1,044 4⋅0 71; (65–89) 165 K/L grade >2 (1997,1999,2001,2003)−2007
Muraki S 2012 [114] Japan 107 3,623 3⋅3 69; (23–95) 631 K/L grade >2 (2005–2007)-(2008–2010)
Sasaki E et al. 2015 [115] Japan 129 1,615 5⋅0 55; (25–85) NA K/L grade >2 2008–2013
Toivanen AT et al. 2010 [116] Finland 94 18,106 22⋅0 42; (30–72) 454 Doctor-diagnosed (1978,1980)-(2000,2001)
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NO.=numero. OA=osteoarthiritis. KL=the Kellgren and Lawrence. NA=not applicate. ICD=International Classification of Disease.

We estimated the global prevalence of knee OA based on 9,440,250 participants from 34 countries, most of participants were ≥15 years old (mean 57⋅2 (standard deviation (SD) 6⋅5)). The pooled global prevalence of knee OA was 16⋅0% (95% CI, 14⋅3%−17⋅8%) (Fig. 2) in individuals aged 15 and over and was 22⋅9% (95% CI, 19⋅8%−26⋅1%) (Appendix 3) in individuals aged 40 and over. Correspondingly, there are around 654⋅1 (95% CI, 565⋅6–745⋅6) million individuals (40 years and older) with knee OA in 2020 worldwide. The prevalence was 19⋅2% (95% CI, 15⋅7%−23⋅0%) in Asian, 13⋅4% (95% CI, 10⋅1%−17⋅0%) in Europe, 15⋅8% (95% CI, 11⋅2%−20⋅9%) in North America, 4⋅1% (95% CI, 2⋅1%−6⋅9%) in South America, 3⋅1% (95% CI, 2⋅0%−4⋅4%) in Oceania, 21⋅0% (95% CI, 17⋅1%−25⋅1%) in Africa (Fig. 2). The prevalence in individual country ranged from 1⋅6% (95% CI, 1⋅1%−2⋅0%) to 46⋅3% (95% CI, 41⋅9%−50⋅6%) (Fig. 3 and Appendix 4). The radiological knee OA (28⋅7% [95% CI, 23⋅6%−34⋅1%]) (Appendix 5) was more common than the symptomatic (12⋅5% [95% CI, 10⋅8%−14⋅3%]) (Appendix 6) and self-report (10⋅6% [95% CI, 6⋅5%−15⋅6%]) (Appendix 7) knee OA. The prevalence was higher in women (21⋅7% [95% CI, 19⋅0%−24⋅5%]) (Appendix 8) than in men (11⋅9% [95% CI, 10⋅2%−13⋅8%]) (Appendix 9).

Fig. 2.

Fig 2

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The forest plot of the global prevalence of knee osteoarthritis.

Fig. 3.

Fig 3

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Global distribution of the prevalence of knee osteoarthritis (OA).

We estimated the global incidence of knee OA based on 643,948 participants from 7 countries, most of participants were ≥20 (mean 46⋅0 (SD 5⋅7)) years old at baseline. The global incidence of knee OA was 203 per 10,000 person-years (95% CI, 106–331) in individuals aged 20 and over (Fig. 4). Correspondingly, there are around annual 86⋅7 (95% CI, 45⋅3–141⋅3) million individuals (20 years and older) with incident knee OA in 2020 worldwide. The incidence was 130 per 10,000 person-years (95% CI, 59–228) in the United States, 315 per 10,000 person-years (95% CI, 42–824) in the United Kingdom, 525 per 10,000 person-years (95% CI, 245–902) in the Japan, 52 per 10,000 person-years (95% CI, 42–63) in the Finland, 84 per 10,000 person-years (95% CI, 66–106) in the Netherlands, 33 per 10,000 person-years (95% CI, 1–113) in the Norway, 295 per 10,000 person-years (95% CI, 293–296) in the Spain (Appendix 10). The incidence was higher in the radiological knee OA (373 per 10,000 person-years [95% CI, 248–522]) than in the symptomatic knee OA (50 per 10,000 person-years [95% CI, 1–175]) (Fig. 4).

Fig. 4.

Fig 4

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The forest plot of the global incidence of knee osteoarthritis.

We assessed that the risk factors were age, gender, area of residence, cigarettes, and education level. We found that the risk of prevalence and incidence increased with age, peaked at the advanced age on prevalence (Fig. 5 and Appendix 11) and at 70–79 years old on incidence (Appendix 12 and 13). The risk of prevalence and incidence in females 1⋅69 (95% CI, 1⋅59–1⋅80, p<0⋅00) and 1⋅39 (95% CI, 1⋅24–1⋅56, p<0⋅00) times as much as males, respectively (Appendix 14 and 15). The risk of prevalence was not significantly different in rural and urban (RRs 0⋅97, 95% CI, 0⋅74–1⋅28, p<0⋅00) (Appendix 16). The risk of prevalence was higher in junior and senior high school than in college degree and above (RRs 2⋅01, 95% CI, 1⋅28–3⋅16, p<0⋅00) (Appendix 17) and was higher in elementary school and below than in junior and senior high school (RRs 2⋅14, 95% CI, 1⋅70–2⋅69, p<0⋅00) (Appendix 18). The risk of prevalence was not significantly different in smoking and no-smoking in cross-sectional studies (RRs 0⋅81, 95% CI, 0⋅44–1⋅51, p<0⋅51) (Appendix 19), but the risk of incidence was lower in smoking than in no-smoking in prospective cohort studies (RRs 0⋅60, 95% CI, 0⋅44–0⋅82, p<0⋅00)) (Appendix 20).

Fig. 5.

Fig 5

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The trend of the prevalence of knee osteoarthritis changes with age.

Sensitivity analysis showed that there was no significant change in the estimates of prevalence and incidence after omitting any one of the included studies (Appendix 21 and 22). The univariate meta-regression results could explain some heterogeneity caused by publication year, type of study, sampling method, type of knee OA, mean BMI, but couldn`t explain all heterogeneity (Appendix 23 and 24). The funnel plots showed visually asymmetrical distribution of published studies on prevalence and incidence of knee OA (Appendix 25 and 26). But Egger tests indicated that there was no statistically significant publication bias on prevalence (Egger test: bias=11⋅98; 95% CI −2⋅33 to −26⋅28; p = 0⋅099) and incidence (Egger test: bias= −17⋅35; 95% CI −40⋅45 to 5⋅74; p = 0⋅130).

4. Discussion

The present systematic review and meta-analysis is, to our knowledge, the first study pooling the latest data and the largest sample sizes to estimate the epidemiologic characteristics of knee OA at the beginning of the 21st century. Our main findings were that the global prevalence of knee OA was 16⋅0% (95% CI, 14⋅3%−17⋅8%) in individuals aged 15 and over and was 22⋅9% (95% CI, 19⋅8%−26⋅1%) in individuals aged 40 and over; the incidence was 203 per 10,000 person-years (95% CI, 106–331) in individuals aged 20 and over; the prevalence and incidence increased with age, peaked at the advanced age on prevalence and at 70–79 years old on incidence; and the ratios of prevalence and incidence in females and males were 1⋅69 (95% CI, 1⋅59–1⋅80, p<0⋅00) and 1⋅39 (95% CI, 1⋅24–1⋅56, p<0⋅00), respectively.

The prevalence varied with the characteristics of studies (e.g., age, sex, BMI, etc.). In general, the global prevalence of knee OA was high from 2000 to 2020, which may bring a huge burden to global health care system. At continent-level, the prevalence was higher in Asian (19⋅2% [95% CI, 15⋅7%−23⋅0%]) than in Europe (13⋅4% [95% CI, 10⋅1%−17⋅0%]) and North America (15⋅8% [95% CI, 11⋅2%−20⋅9%]). The prevalence was lowest in South America (4⋅1% [95% CI, 2⋅1%−6⋅9%]). There was not adequate data to draw a conclusion in Oceania and Africa. At country-level, the prevalence ranged from 1⋅6% (95% CI, 1⋅1%−2⋅0%) to 46⋅3% (95% CI, 41⋅9%−50⋅6%). Thailand (46⋅3% [95% CI, 41⋅9%−50⋅6%]), Japan (39⋅6% [95% CI, 14⋅1%−68⋅7%]), Korea (36⋅1% [95% CI, 34⋅1%−38⋅1%]) and Indian (21⋅0% [95% CI, 11⋅0%−34⋅%]) had higher prevalence. Given Asian were relatively thinner than others, however, they had higher prevalence in our study. We analyzed that the number of studies examining radiographic knee OA was more in Asian than in other continents in the meta-analysis, which was the main reason why knee OA was more prevalent in Asian. Other possible reasons were that Asian were used to bending knee and squatting and genetic or environmental factors. In conclusion, we failed to determine accurately which country or continent had a higher prevalence of knee OA based on the existing evidence in our study, because there was no comparability of baseline data between studies. We found that the radiographic knee OA (28⋅7% [95% CI, 23⋅6%−34⋅1%]) was more prevalent than the symptomatic knee OA (12⋅4% [95%CI, 10⋅8%−14⋅2%]), which was consistent with a previous systematic review conducted by Pereira D et al. and they concluded that the prevalence of OA depended on definition used [24]. Accordingly, according to the results of our study, we speculated that the radiographic knee OA may be detected earlier, which can provide more opportunities for prevention of knee OA. We found the positive correlation between the prevalence of knee OA and increased age, similarly, the same result could be seen in GBD 2017 Study [25] and other studies [26,27]. However, the prevalence peaked at around 50 years of age for knee OA in GBD 2014 Study, the potential explanation may be that the GBD 2014 Study used inadequate data. Considering the older people easier encountered with knee OA and according to the current consensus, tailored care should be given to them [3]. We pooled a large sample (3,686,544 participants) to explore the ratio of prevalence and incidence of knee OA in women and men, to our knowledge, which was the strongest evidence so far [28]. Consequently, a more accurate estimate can be obtained. The results showed that prevalence and incidence in females 1⋅69 (95% CI, 1⋅59–1⋅80, p<0⋅00) and 1⋅39 (95% CI, 1⋅24–1⋅56, p<0⋅00) times as much as males, respectively, which was corresponded to established consensus [1,29]. More efforts should be made to explore the cause of the high prevalence of knee OA in women.

There were very rare studies examining the incidence of knee OA, most of the published studies were in Europe and North America. In Asian, only Japan has the published data of incidence of knee OA. The data of incidence was unavailable in South America, Oceania, and Africa. Considering most of whole world population in Asian, Oceania, and Africa, their data may be highly significant to ascertain the etiology and reduce global burden. And in consideration of the economic situation of those areas, initiating new program like Community Oriented for Control of the Rheumatic Diseases (COPCORD) may be a cost-effective strategy for understanding the epidemiological characteristics of knee OA. The data of the United Kingdom and the United States was more comprehensive and the pooled results showed that the incidence was 315 per 10,000 person-years (95% CI, 42–824) in the United Kingdom and 130 per 10,000 person-years (95% CI, 59–228) in the United States. We found that the incidence of knee OA increased with age and peaked at 70–79 years, which was consistent with the latest result of the study in the United Kingdom [15]. This may be an interesting topic, the prospective studies are warranted to validate it in future.

It is well known that knee OA is a multiple factors disease, which include modifiable (obesity, knee injury) and unmodifiable (age, sex) factors [30]. In our meta-analysis, the pooled estimates showed that the prevalence of knee OA was negatively associated with education level, which was in agreement with several previous studies [31,32]. This may be because of the fact that individuals with lower education level frequently involved heavy physical activities or accessed to few the knowledge of prevention for knee OA. We found that there was no statistically significant difference in prevalence between rural and urban (RRs 0⋅97, 95% CI, 0⋅74–1⋅28, p = 0⋅84), which was contradictory to some previous studies included in the meta-analysis. We speculated that the sample size of the individual study may be not enough. However, above estimates should be interpreted with caution because articles included in this meta-analysis were cross-sectional studies, which didn`t allow to draw a causal conclusion. Our pooled estimates showed that the incidence was lower in smokers than in no-smokers in cohort study, which was inconsistent with a previous systematic review and meta-analysis conducted by Hui M et al. [33]. They argued that the relationship between smoking and knee OA may be confounded by body mass index (BMI). Because BMI was a significant risk factor for the occurrence and development of knee OA and smokers were usually thinner than no-smokers. Another possible reason was that smokers often suffered from multiple comorbidities such as lung cancer, cardiovascular disease, so the no-smokers may have a longer life span and they were more likely to develop knee OA. Further prospective studies are needed to verify the dose-response relationship between the incidence of knee OA and cigarettes. Nevertheless, researchers have realized that knee OA is amenable to prevention and treatment at early stages [30]. Increasing education attainment, weight control, injury prevention, improving muscle function, and others may play an important role for early prevention in future.

There are some limitations in our study. Firstly, there is statistically significant heterogeneity in our meta-analysis. However, it is anticipated that the observational studies in epidemiology usually have substantial heterogeneity given established associations between the prevalence and incidence of knee OA and age, sex, BMI, type of knee OA, genetic factors and so on. Secondly, due to included studies had few data directly examining the difference between prevalence of knee OA and ethnicity, we were unable to differentiate it. Thirdly, we couldn`t assess the prevalence of knee OA for individual country accurately owing to the limitations of the number of original studies. Fourthly, despite we assessed the relationships between risk factors (area of residence, cigarettes, and education level) and the prevalence and incidence of knee OA, those evidences were not strong enough because of the limited number of included studies. Fifthly, considering the preference of the journal and individual, the potential publication bias can hardly be excluded.

In conclusion, this study provides the global prevalence (16⋅0% [95% CI, 14⋅3%−17⋅8%]) and incidence (203 per 10,000 person-years [95% CI, 106–331]) of knee OA. These findings can be used to better estimate the global health burden of knee OA. The knee OA is continuing to be prevalent in global adults and over, especially in older and women. Further prospective cohort studies are warranted to identify modifiable risk factors for providing effectively preventive strategies in the early stages of the disease.

Declaration of Competing Interests

We declare that we have no conflicts of interest.

Acknowledgments

Contributors

LHD, CAY, and LHZ conducted the study protocol. CAY and LHZ searched the databases, selected the literature and extracted the data. CAY and LHZ did the statistical analyses. CAY draw figures, CAY and LHZ prepared the first draft under the guidance of LHD. All authors interpreted the results, reviewed drafts of the Article, and approved the final version. CAY and LHZ contributed equally to this work.

Acknowledgments

We would like to thank the National Natural Science Foundation Council of China.

Funding

This work was supported by grants from the National Natural Science Foundation of China (nos. 81772384 and 81572174).

Data sharing statement

The study protocol is available with this publication. No additional unpublished data are available.

Footnotes

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.eclinm.2020.100587.

Appendix. Supplementary materials

Appendix 1.1. Search strategy in PUBMED. Appendix 1.2. Search strategy in EMBASE. Appendix 1.3. Search strategy in SCOPUS.

mmc1.docx (15.4KB, docx)

Appendix 2.1. Risk of bias summary of the prevalence studies

mmc2.pdf (1MB, pdf)

Appendix 2.2. Risk of bias graph of the prevalence studies

Appendix 2.1\elsamp #x2013;2. We used the risk of bias tool for prevalence studies developed by Hoy D et\elsamp #x00A0;al. (2012), which comprises 10 items plus a summary assessment. Items 1 to 4 assess the external validity of the study (domains are selection and nonresponse bias), and items 5 to 10 assess the internal validity (items 5 to 9 assess the domain of measurement bias, and item 10 assesses bias related to the analysis). The summary assessment evaluates the overall risk of study bias and is based on the rater`s subjective judgment given responses to the preceding 10 items. Response options for individual items were either low or high risk of bias; Response options for the summary assessment were low, moderate, or high risk of bias.

mmc3.pdf (114.4KB, pdf)

Appendix 2.3. Risk of bias summary of the incidence studies

mmc4.pdf (412.1KB, pdf)

Appendix 2.4. Risk of bias graph of the incidence studies

Appendix 2.3\elsamp #x2013;4. we assessed the quality of the incidence studies using risk of bias tool designed by ourselves according to previous validated quality assessment tools, which includes 6 items and a summary assessment: (1) Was the target population clearly defined? (2) Was random sampling method used? (3) Were the samples at risk representative of the target population? (4) Were non-responders clearly described? (5) Were data collection methods standardized? (6) Was validated criteria used to assess the presence of disease? (7) Summary item on the overall risk of study bias. The summary assessment evaluates the overall risk of study bias and is based on the rater`s subjective judgment given responses to the preceding 6 items. Response options for individual items were either low (answer YES) or high (answer NO) risk of bias; Response options for the summary assessment were low, moderate, or high risk of bias.

mmc5.pdf (112KB, pdf)

Appendix 3. The forest plot of the global prevalence of knee osteoarthritis in individuals aged 40 and over

mmc6.pdf (8.1KB, pdf)

Appendix 4. The forest plot of the prevalence of knee osteoarthritis in a single country

mmc7.pdf (17.4KB, pdf)

Appendix 5. The forest plot of the prevalence of the radiological knee osteoarthritis

mmc8.pdf (6.5KB, pdf)

Appendix 6. The forest plot of the prevalence of the symptomatic knee osteoarthritis

mmc9.pdf (9.4KB, pdf)

Appendix 7. The forest plot of the prevalence of the self-report knee osteoarthritis

mmc10.pdf (5.2KB, pdf)

Appendix 8. The forest plot of the prevalence of knee osteoarthritis in women

mmc11.pdf (8KB, pdf)

Appendix 9. The forest plot of the prevalence of knee osteoarthritis in men

mmc12.pdf (8KB, pdf)

Appendix 10. The forest plot of the incidence of knee osteoarthritis in a single country

mmc13.pdf (7.6KB, pdf)

Appendix 11. The relationship between age and the prevalence of knee osteoarthritis: to accurately assess the relationship between age in different genders and the prevalence of knee osteoarthritis, we only extracted data from the study which not only reported the prevalence in women but also in men at each age group (such as 30\elsamp #x2013;39, 40\elsamp #x2013;49, \elsamp #x2026;).

mmc14.docx (18KB, docx)

Appendix 12. The relationship between age and the incidence of knee osteoarthritis: because original study didn`t report the incidence in different genders for each age group (such as 40\elsamp #x2013;49, 50\elsamp #x2013;59, \elsamp #x2026;), so we only assessed the relationship between age in all genders and the incidence of knee osteoarthritis.

mmc15.docx (15.8KB, docx)

Appendix 13. The trend of the incidence of knee osteoarthritis changes with age

mmc16.pdf (1.4MB, pdf)

Appendix 14. The forest plot of the ratio of the prevalence of knee osteoarthritis in women and in men

mmc17.pdf (268.6KB, pdf)

Appendix 15. The forest plot of the ratio of the incidence of knee osteoarthritis in women and in men

mmc18.pdf (214.6KB, pdf)

Appendix 16. The forest plot of the ratio of the prevalence of knee osteoarthritis in rural and urban

mmc19.pdf (191.8KB, pdf)

Appendix 17. The forest plot of the ratio of the prevalence of knee osteoarthritis in Junior and Senior high school and College and above

mmc20.pdf (166.6KB, pdf)

Appendix 18. The forest plot of the ratio of the prevalence of knee osteoarthritis in Elementary school and below and Junior and Senior high school

mmc21.pdf (165.7KB, pdf)

Appendix 19. The forest plot of the ratio of the prevalence of knee osteoarthritis in smoking and no-smoking in cross-sectional studies

mmc22.pdf (168KB, pdf)

Appendix 20. The forest plot of the ratio of the incidence of knee osteoarthritis in smoking and no-smoking in prospective cohort studies

mmc23.pdf (159.1KB, pdf)

Appendix 21. Sensitivity analyses for the estimates of the global prevalence of knee osteoarthritis

mmc24.pdf (8.1KB, pdf)

Appendix 22. Sensitivity analyses for the estimates of the global incidence of knee osteoarthritis

mmc25.pdf (5.1KB, pdf)

Appendix 23. The univariate meta-regression results on the prevalence of knee osteoarthritis.

mmc26.docx (16.6KB, docx)

Appendix 24. The univariate meta-regression results on the incidence of knee osteoarthritis.

mmc27.docx (15.8KB, docx)

Appendix 25. The funnel plots for published studies on prevalence of knee osteoarthritis

mmc28.pdf (106.8KB, pdf)

Appendix 26. The funnel plots for published studies on prevalence of knee osteoarthritis

mmc29.pdf (63.4KB, pdf)
mmc30.docx (64.5KB, docx)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Appendix 1.1. Search strategy in PUBMED. Appendix 1.2. Search strategy in EMBASE. Appendix 1.3. Search strategy in SCOPUS.

mmc1.docx (15.4KB, docx)

Appendix 2.1. Risk of bias summary of the prevalence studies

mmc2.pdf (1MB, pdf)

Appendix 2.2. Risk of bias graph of the prevalence studies

Appendix 2.1\elsamp #x2013;2. We used the risk of bias tool for prevalence studies developed by Hoy D et\elsamp #x00A0;al. (2012), which comprises 10 items plus a summary assessment. Items 1 to 4 assess the external validity of the study (domains are selection and nonresponse bias), and items 5 to 10 assess the internal validity (items 5 to 9 assess the domain of measurement bias, and item 10 assesses bias related to the analysis). The summary assessment evaluates the overall risk of study bias and is based on the rater`s subjective judgment given responses to the preceding 10 items. Response options for individual items were either low or high risk of bias; Response options for the summary assessment were low, moderate, or high risk of bias.

mmc3.pdf (114.4KB, pdf)

Appendix 2.3. Risk of bias summary of the incidence studies

mmc4.pdf (412.1KB, pdf)

Appendix 2.4. Risk of bias graph of the incidence studies

Appendix 2.3\elsamp #x2013;4. we assessed the quality of the incidence studies using risk of bias tool designed by ourselves according to previous validated quality assessment tools, which includes 6 items and a summary assessment: (1) Was the target population clearly defined? (2) Was random sampling method used? (3) Were the samples at risk representative of the target population? (4) Were non-responders clearly described? (5) Were data collection methods standardized? (6) Was validated criteria used to assess the presence of disease? (7) Summary item on the overall risk of study bias. The summary assessment evaluates the overall risk of study bias and is based on the rater`s subjective judgment given responses to the preceding 6 items. Response options for individual items were either low (answer YES) or high (answer NO) risk of bias; Response options for the summary assessment were low, moderate, or high risk of bias.

mmc5.pdf (112KB, pdf)

Appendix 3. The forest plot of the global prevalence of knee osteoarthritis in individuals aged 40 and over

mmc6.pdf (8.1KB, pdf)

Appendix 4. The forest plot of the prevalence of knee osteoarthritis in a single country

mmc7.pdf (17.4KB, pdf)

Appendix 5. The forest plot of the prevalence of the radiological knee osteoarthritis

mmc8.pdf (6.5KB, pdf)

Appendix 6. The forest plot of the prevalence of the symptomatic knee osteoarthritis

mmc9.pdf (9.4KB, pdf)

Appendix 7. The forest plot of the prevalence of the self-report knee osteoarthritis

mmc10.pdf (5.2KB, pdf)

Appendix 8. The forest plot of the prevalence of knee osteoarthritis in women

mmc11.pdf (8KB, pdf)

Appendix 9. The forest plot of the prevalence of knee osteoarthritis in men

mmc12.pdf (8KB, pdf)

Appendix 10. The forest plot of the incidence of knee osteoarthritis in a single country

mmc13.pdf (7.6KB, pdf)

Appendix 11. The relationship between age and the prevalence of knee osteoarthritis: to accurately assess the relationship between age in different genders and the prevalence of knee osteoarthritis, we only extracted data from the study which not only reported the prevalence in women but also in men at each age group (such as 30\elsamp #x2013;39, 40\elsamp #x2013;49, \elsamp #x2026;).

mmc14.docx (18KB, docx)

Appendix 12. The relationship between age and the incidence of knee osteoarthritis: because original study didn`t report the incidence in different genders for each age group (such as 40\elsamp #x2013;49, 50\elsamp #x2013;59, \elsamp #x2026;), so we only assessed the relationship between age in all genders and the incidence of knee osteoarthritis.

mmc15.docx (15.8KB, docx)

Appendix 13. The trend of the incidence of knee osteoarthritis changes with age

mmc16.pdf (1.4MB, pdf)

Appendix 14. The forest plot of the ratio of the prevalence of knee osteoarthritis in women and in men

mmc17.pdf (268.6KB, pdf)

Appendix 15. The forest plot of the ratio of the incidence of knee osteoarthritis in women and in men

mmc18.pdf (214.6KB, pdf)

Appendix 16. The forest plot of the ratio of the prevalence of knee osteoarthritis in rural and urban

mmc19.pdf (191.8KB, pdf)

Appendix 17. The forest plot of the ratio of the prevalence of knee osteoarthritis in Junior and Senior high school and College and above

mmc20.pdf (166.6KB, pdf)

Appendix 18. The forest plot of the ratio of the prevalence of knee osteoarthritis in Elementary school and below and Junior and Senior high school

mmc21.pdf (165.7KB, pdf)

Appendix 19. The forest plot of the ratio of the prevalence of knee osteoarthritis in smoking and no-smoking in cross-sectional studies

mmc22.pdf (168KB, pdf)

Appendix 20. The forest plot of the ratio of the incidence of knee osteoarthritis in smoking and no-smoking in prospective cohort studies

mmc23.pdf (159.1KB, pdf)

Appendix 21. Sensitivity analyses for the estimates of the global prevalence of knee osteoarthritis

mmc24.pdf (8.1KB, pdf)

Appendix 22. Sensitivity analyses for the estimates of the global incidence of knee osteoarthritis

mmc25.pdf (5.1KB, pdf)

Appendix 23. The univariate meta-regression results on the prevalence of knee osteoarthritis.

mmc26.docx (16.6KB, docx)

Appendix 24. The univariate meta-regression results on the incidence of knee osteoarthritis.

mmc27.docx (15.8KB, docx)

Appendix 25. The funnel plots for published studies on prevalence of knee osteoarthritis

mmc28.pdf (106.8KB, pdf)

Appendix 26. The funnel plots for published studies on prevalence of knee osteoarthritis

mmc29.pdf (63.4KB, pdf)
mmc30.docx (64.5KB, docx)

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