Introduction
Methods
Search strategy
Study selection
Exclusion criteria
Data extraction
Data analysis
Results
Characteristics of included studies
Author | Year | Country | Age | Male | Female | Number of tibia fracture | Number of nonunion | Prevalence |
---|---|---|---|---|---|---|---|---|
2018 | USA | 40.4 | 225 | 102 | 284 | 19 | 0.067 | |
2018 | USA | 35.2 | 29 | 11 | 40 | 4 | 0.100 | |
2018 | USA | 43.5 | 20 | 12 | 32 | 6 | 0.188 | |
Chang BS [34] | 2018 | China | 23-57 | 38 | 26 | 60 | 7 | 0.117 |
Liu BQ [35] | 2018 | China | 36.1 | 46 | 5 | 51 | 3 | 0.059 |
Zhang JS [36] | 2018 | China | 49.4 | 60 | 34 | 94 | 5 | 0.053 |
Zhang QL [37] | 2018 | China | 35 | 50 | 36 | 86 | 0 | 0.000 |
Yu JQ [38] | 2018 | China | 42.4 | 65 | 39 | 94 | 5 | 0.053 |
Jin PF [39] | 2018 | China | 57.6 | 90 | 107 | 197 | 26 | 0.132 |
Ge Y [40] | 2018 | China | 39.3 | 50 | 42 | 92 | 2 | 0.022 |
Fang YS [41] | 2018 | China | 45.2 | 49 | 13 | 62 | 1 | 0.016 |
Li J [42] | 2018 | China | 35.5 | 46 | 39 | 70 | 2 | 0.029 |
Xu DY [43] | 2018 | China | 40.9 | 38 | 26 | 64 | 3 | 0.047 |
Li ZT [44] | 2018 | China | 52.4 | 48 | 42 | 90 | 1 | 0.011 |
2018 | UK | 739 | 264 | 1003 | 121 | 0.121 | ||
2018 | Singapore | 38.2 | 101 | 2 | 103 | 44 | 0.427 | |
2018 | Egypt | 37.2 | 52 | 8 | 60 | 2 | 0.033 | |
Javdan M[48] | 2017 | USA | 231 | 12 | 0.052 | |||
2017 | USA | 42 | 184 | 131 | 315 | 17 | 0.054 | |
2017 | USA | 18-63 | 6273 | 6535 | 12808 | 944 | 0.074 | |
Thakore RV [15] | 2017 | USA | 36 | 364 | 102 | 486 | 56 | 0.115 |
2017 | USA | 44 | 82 | 32 | 114 | 24 | 0.211 | |
Xiong SR [52] | 2017 | China | 42.5 | 82 | 66 | 148 | 8 | 0.054 |
2017 | Iran | 35.9 | 45 | 4 | 49 | 3 | 0.061 | |
2017 | Turkey | 40.6 | 52 | 21 | 73 | 1 | 0.014 | |
2017 | India | 37.14 | 32 | 10 | 42 | 3 | 0.071 | |
2017 | India | 38.9 | 5 | 31 | 36 | 4 | 0.111 | |
Mukherjee S [56] | 2017 | India | 40.3 | 26 | 14 | 40 | 3 | 0.075 |
2016 | USA | 42.2 | 156 | 28 | 184 | 16 | 0.087 | |
2016 | USA | 8132 | 6506 | 14,638 | 1758 | 0.120 | ||
2016 | USA | 40.6 | 162 | 54 | 216 | 29 | 0.134 | |
2016 | USA | 39.3 | 93 | 289 | 382 | 56 | 0.147 | |
2016 | USA | 64 | 5 | 0.078 | ||||
2016 | China | 45 | 54 | 71 | 125 | 0 | 0.000 | |
2016 | China | 36.8 | 40 | 16 | 56 | 2 | 0.036 | |
Hao LS [62] | 2016 | China | 19-67 | 67 | 15 | 82 | 2 | 0.024 |
Hu H [63] | 2016 | China | 36.7 | 30 | 22 | 52 | 1 | 0.019 |
Liu JQ [64] | 2016 | China | 43.2 | 44 | 16 | 60 | 1 | 0.017 |
Rao HR [65] | 2016 | China | 35.7 | 35 | 15 | 50 | 2 | 0.040 |
Bai T [66] | 2016 | China | 36.8 | 43 | 17 | 60 | 4 | 0.067 |
Zhao KP [67] | 2016 | China | 35.6 | 41 | 17 | 58 | 1 | 0.017 |
Uchiyama Y [68] | 2016 | Japan | 41.9 | 77 | 8 | 85 | 3 | 0.035 |
2016 | India | 42.7 | 22 | 8 | 30 | 1 | 0.033 | |
2015 | USA | 49.5 | 24 | 17 | 45 | 12 | 0.267 | |
Sun KF [71] | 2015 | China | 43.1 | 32 | 20 | 115 | 7 | 0.061 |
Sun JQ [72] | 2015 | China | 48 | 35 | 21 | 56 | 7 | 0.125 |
Ma N [73] | 2015 | China | 45.4 | 334 | 246 | 580 | 82 | 0.141 |
Huang H [74] | 2015 | China | 17-65 | 52 | 44 | 96 | 5 | 0.052 |
Huang PZ [75] | 2015 | China | 32 | 43 | 13 | 56 | 1 | 0.018 |
Zhang YH [76] | 2015 | China | 36.5 | 49 | 21 | 70 | 2 | 0.029 |
Luo BX [77] | 2015 | China | 38.5 | 47 | 31 | 78 | 1 | 0.013 |
Wang B [78] | 2015 | China | 41.2 | 39 | 33 | 72 | 2 | 0.028 |
Cui LH [79] | 2015 | China | 37.5 | 53 | 21 | 74 | 2 | 0.027 |
Meng YH [80] | 2015 | China | 31.6 | 19 | 35 | 54 | 1 | 0.019 |
Gong Y [81] | 2015 | China | 16-39 | 38 | 32 | 70 | 11 | 0.157 |
Lian HK [82] | 2015 | China | 35.1 | 51 | 43 | 94 | 4 | 0.043 |
2015 | India | 37.5 | 32 | 12 | 44 | 2 | 0.045 | |
2014 | USA | 37.5 | 63 | 30 | 93 | 17 | 0.183 | |
2014 | China. | 43.3 | 116 | 5 | 121 | 2 | 0.017 | |
Dai QH [86] | 2014 | China | 34.5 | 23 | 19 | 42 | 0 | 0.000 |
Wu ZH [87] | 2014 | China | 48.5 | 32 | 18 | 50 | 1 | 0.020 |
Li ZZ [88] | 2014 | China | 43.8 | 76 | 44 | 60 | 5 | 0.083 |
Ren Y [89] | 2014 | China | 34.7 | 49 | 21 | 70 | 4 | 0.057 |
Luan HX [90] | 2014 | China | 37.1 | 78 | 20 | 98 | 6 | 0.061 |
Zhang WJ [91] | 2014 | China | 44 | 43 | 25 | 68 | 3 | 0.044 |
Heng WX [92] | 2014 | China | 18-79 | 45 | 23 | 68 | 4 | 0.059 |
2014 | Turkey | 42 | 32 | 23 | 55 | 3 | 0.055 | |
2014 | USA | 45 | 92 | 71 | 163 | 13 | 0.080 | |
Berlusconi M [95] | 2014 | Italy | 45 | 42 | 18 | 60 | 5 | 0.083 |
2013 | USA | 52.5 | 378 | 475 | 853 | 99 | 0.116 | |
Huang Q [96] | 2013 | China | 36.9 | 80 | 40 | 120 | 3 | 0.025 |
Gong M [97] | 2013 | China | 40.3 | 41 | 11 | 52 | 2 | 0.038 |
Lv YM [98] | 2013 | China | 39.1 | 77 | 34 | 111 | 6 | 0.054 |
Xu YD [99] | 2013 | China | 39 | 105 | 58 | 163 | 2 | 0.012 |
2013 | UK | 77.9 | 63 | 170 | 233 | 23 | 0.099 | |
2013 | Belarus | 43 | 54 | 26 | 80 | 7 | 0.088 | |
2013 | Malaysia | 24.5 | 52 | 6 | 58 | 10 | 0.172 | |
2012 | USA | 32 | 1 | 0.031 | ||||
Lin ZF [104] | 2012 | China | 36.6 | 222 | 194 | 416 | 33 | 0.079 |
Zhang H [105] | 2012 | China | 39.6 | 58 | 38 | 96 | 1 | 0.010 |
Jia QT [106] | 2012 | China | 36 | 61 | 27 | 88 | 4 | 0.045 |
Zhou JL [107] | 2012 | China | 53 | 43 | 9 | 52 | 10 | 0.192 |
2012 | Iran | 26.4 | 45 | 8 | 54 | 3 | 0.056 | |
2011 | USA | 38.3 | 85 | 19 | 114 | 6 | 0.053 | |
Zhu DK [110] | 2011 | China | 18-76 | 53 | 31 | 84 | 3 | 0.036 |
Zhao DL [111] | 2011 | China | 37.8 | 54 | 26 | 80 | 1 | 0.013 |
Liu F [112] | 2011 | China | 32.6 | 32 | 14 | 46 | 4 | 0.087 |
2011 | Australia | 42.4 | 66 | 23 | 89 | 26 | 0.292 | |
Xu JQ [114] | 2009 | China | 36.3 | 121 | 49 | 170 | 8 | 0.047 |
Li ZG [115] | 2009 | China | 35.8 | 71 | 56 | 127 | 3 | 0.024 |
Mahmudi N [116] | 2009 | China | 37 | 34 | 10 | 44 | 3 | 0.068 |
Deng HP [117] | 2009 | China | 40.3 | 51 | 34 | 85 | 4 | 0.047 |
Dong JH [118] | 2009 | China | 18-74 | 77 | 51 | 128 | 2 | 0.016 |
Fu KL [119] | 2009 | China | 112 | 11 | 0.098 | |||
Zhou L [120] | 2009 | China | 37.9 | 52 | 41 | 93 | 5 | 0.054 |
Lang ZY [121] | 2009 | China | 33.6 | 51 | 16 | 67 | 2 | 0.030 |
Wu C [122] | 2009 | China | 19-71 | 25 | 12 | 37 | 2 | 0.054 |
Li QM [123] | 2009 | China | 37.6 | 168 | 51 | 219 | 6 | 0.027 |
2008 | Japan | 34.6 | 70 | 14 | 84 | 17 | 0.202 | |
2008 | UK | 54 | 3 | 0.056 | ||||
Lu HY [126] | 2007 | China | 34.5 | 158 | 98 | 256 | 9 | 0.035 |
Hu GZ [127] | 2007 | China | 33.4 | 301 | 116 | 396 | 11 | 0.028 |
Zeng CJ [128] | 2006 | China | 30.7 | 390 | 264 | 541 | 14 | 0.026 |
Zhang YL [129] | 2006 | China | 35 | 73 | 25 | 98 | 9 | 0.092 |
Zhao XZ [130] | 2006 | China | 43.8 | 52 | 26 | 78 | 5 | 0.064 |
Zhu GH [131] | 2005 | China | 34 | 55 | 23 | 78 | 5 | 0.064 |
2005 | Australia | 34 | 124 | 39 | 163 | 13 | 0.080 | |
2004 | USA | 89 | 2 | 0.022 | ||||
2003 | France | 40.8 | 34 | 15 | 49 | 8 | 0.163 | |
2002 | Canada | 110 | 13 | 0.118 | ||||
1997 | USA | 112 | 9 | 0.080 |
Pooled results, sensitive analysis, publication bias of the prevalence of tibia fracture nonunion
Number of study | N | n | Prevalence rate | Heterogeneity | Model | |||||
---|---|---|---|---|---|---|---|---|---|---|
effect size | lower limit | upper limit | I2 | p | ||||||
Total | 111 | 41429 | 3817 | 0.068 | 0.060 | 0.077 | 86.60% | < 0.01 | Random | |
1. Age (year) | < 60 | 3 | 545 | 60 | 0.125 | 0.060 | 0.189 | 77.50% | 0.012 | Random |
> 60 | 3 | 316 | 65 | 0.204 | 0.160 | 0.249 | 0.00% | 0.689 | Fixed | |
2. Gender | Male | 11 | 8186 | 790 | 0.131 | 0.104 | 0.159 | 77.80% | < 0.01 | Random |
Female | 11 | 8123 | 618 | 0.118 | 0.085 | 0.150 | 84.50% | < 0.01 | Random | |
3. Tobacco smoker | Yes | 8 | 2263 | 299 | 0.173 | 0.119 | 0.226 | 91.80% | < 0.01 | Random |
No | 8 | 12177 | 888 | 0.111 | 0.072 | 0.150 | 87.30% | < 0.01 | Random | |
4. Drink | Yes | 2 | 348 | 42 | 0.136 | 0.036 | 0.235 | 82.50% | 0.017 | Random |
No | 2 | 12842 | 958 | 0.098 | 0.043 | 0.152 | 86.90% | 0.006 | Random | |
5. Body mass index | < 30 | 2 | 24466 | 2257 | 0.091 | 0.049 | 0.133 | 99.30% | < 0.01 | Random |
> 30 | 2 | 3790 | 451 | 0.119 | 0.109 | 0.129 | 0.00% | 0.557 | Fixed | |
30–40 | 2 | 2507 | 236 | 0.094 | 0.083 | 0.105 | 0.00% | 0.441 | Fixed | |
< 40 | 2 | 26973 | 2493 | 0.091 | 0.053 | 0.128 | 99.20% | < 0.01 | Random | |
> 40 | 2 | 1283 | 215 | 0.160 | 0.020 | 0.218 | 87.80% | 0.004 | Random | |
6. Diabetes | Yes | 4 | 347 | 73 | 0.221 | 0.178 | 0.267 | 8.50% | 0.335 | Fixed |
No | 4 | 984 | 103 | 0.102 | 0.065 | 0.139 | 67.50% | 0.046 | Random | |
Yes | 3 | 371 | 58 | 0.153 | 0.116 | 0.189 | 0.00% | 0.420 | Fixed | |
No | 3 | 1197 | 144 | 0.117 | 0.099 | 0.135 | 59.90% | 0.083 | Random | |
8. Opioids user | Yes | 3 | 1035 | 145 | 0.140 | 0.118 | 0.161 | 0.00% | 0.694 | Fixed |
No | 3 | 522 | 58 | 0.097 | 0.031 | 0.164 | 78.40% | 0.010 | Random | |
9. Fracture site | Proximal | 7 | 586 | 30 | 0.043 | 0.027 | 0.06 | 26.50% | 0.254 | Fixed |
Middle | 7 | 724 | 115 | 0.146 | 0.080 | 0.211 | 84.60% | < 0.01 | Random | |
Distal | 7 | 614 | 88 | 0.139 | 0.104 | 0.178 | 24.10% | 0.253 | Fixed | |
10. Injury energy | High | 4 | 710 | 105 | 0.149 | 0.083 | 0.241 | 83.60% | < 0.01 | Random |
Low | 4 | 298 | 22 | 0.065 | 0.007 | 0.175 | 87.30% | < 0.01 | Random | |
11.Open fracture | Yes | 10 | 14037 | 916 | 0.062 | 0.049 | 0.074 | 56.20% | 0.015 | Random |
On | 10 | 1985 | 390 | 0.197 | 0.145 | 0.294 | 84.80% | < 0.01 | Random | |
12. Gustilo-Anderson gradea | I or II | 9 | 680 | 57 | 0.070 | 0.051 | 0.089 | 31.30% | 0.168 | Fixed |
IIIA | 9 | 394 | 55 | 0.130 | 0.097 | 0.163 | 0.00% | 0.686 | Fixed | |
IIIB or IIIC | 9 | 220 | 89 | 0.382 | 0.198 | 0.566 | 88.90% | < 0.01 | random | |
13.Müller AO Classification of Fractures (AO) classificationb | A | 7 | 1039 | 69 | 0.059 | 0.027 | 0.090 | 68.90% | 0.004 | Random |
B | 7 | 600 | 103 | 0.140 | 0.086 | 0.204 | 65.90% | 0.007 | Random | |
C | 7 | 285 | 54 | 0.158 | 0.078 | 0.260 | 74.50% | 0.001 | Random | |
14. Debride time | < 6 h | 2 | 138 | 41 | 0.302 | 0.074 | 0.530 | 89.10% | 0.002 | Random |
> 6 h | 2 | 49 | 20 | 0.405 | 0.268 | 0.541 | 0.00% | 0.411 | Fixed | |
15. Open reduction | Yes | 9 | 573 | 48 | 0.075 | 0.043 | 0.107 | 52.40% | 0.032 | Random |
No | 9 | 606 | 26 | 0.043 | 0.028 | 0.060 | 42.10% | 0.086 | Fixed | |
16. Fixation modec | ORIF | 41 | 6216 | 703 | 0.081 | 0.058 | 0.107 | 82.10% | < 0.01 | Random |
IMN | 51 | 12642 | 1326 | 0.054 | 0.040 | 0.070 | 77.30% | < 0.01 | Random | |
MIPPO | 25 | 988 | 18 | 0.023 | 0.015 | 0.032 | 0.00% | 0.835 | Fixed | |
External fixation | 680 | 33 | 0.055 | 0.023 | 0.098 | 76.90% | < 0.01 | Random | ||
Conservative treatment | 4 | 116 | 22 | 0.134 | 0.003 | 0.409 | 92.10% | < 0.01 | Random | |
17. Fibula fixed | Yes | 7 | 166 | 11 | 0.073 | 0.027 | 0.140 | 53.20% | 0.046 | Random |
No | 7 | 538 | 69 | 0.122 | 0.094 | 0.149 | < 0.01 | 0.611 | Fixed | |
18. Osteofascial compartment syndrome | Yes | 3 | 210 | 31 | 0.134 | 0.088 | 0.179 | 61.90% | 0.072 | Fixed |
No | 3 | 1359 | 162 | 0.105 | 0.058 | 0.151 | 85.40% | 0.001 | Random | |
19. Infection | Yes | 2 | 217 | 84 | 0.510 | 0.155 | 0.866 | 93.80% | < 0.01 | Random |
No | 2 | 1366 | 119 | 0.076 | 0.022 | 0.129 | 92.80% | < 0.01 | Random |
Subgroup analysis of prevalence of tibia fracture nonunion and comparison results
Study | Comparison results | Heterogeneity | Model | ||||||
---|---|---|---|---|---|---|---|---|---|
p | OR | lower limit | upper limit | I2 | p | ||||
1. Age (year) | > 60 vs. < 60 | 3 | < 0.05 | 2.602 | 1.686 | 4.016 | 48.70% | 0.142 | Fixed |
2. Gender | Male vs. Female | 11 | < 0.05 | 1.256 | 1.122 | 1.407 | 14.00% | 0.311 | Fixed |
3. Tobacco smoker | Yes vs. No | 8 | < 0.05 | 1.692 | 1.458 | 1.964 | 49.30% | 0.055 | Fixed |
4. Drink | Yes vs. No | 2 | 0.083 | 1.367 | 0.960 | 1.947 | 0.00% | 0.518 | Fixed |
5. Body mass index (BMI) | 30 < BMI < 40 vs. BMI < 30 | 2 | 0.801 | 1.085 | 0.575 | 2.050 | 93.70% | < 0.05 | Random |
BMI > 40 vs. BMI < 30 | 2 | < 0.05 | 1.874 | 1.607 | 2.185 | 0.00% | 0.660 | Fixed | |
BMI > 30 vs. BMI < 30 | 2 | 0.189 | 1.351 | 0.862 | 2.119 | 93.00% | < 0.05 | Random | |
BMI > 40 vs. 30 < BMI < 40 | 2 | 0.045 | 1.773 | 1.014 | 3.102 | 84.30% | 0.012 | Random | |
BMI > 40 vs. BMI < 40 | 2 | < 0.05 | 1.899 | 1.630 | 2.212 | 0.00% | 0.892 | Fixed | |
6. Diabetes | Yes vs. No | 3 | < 0.05 | 2.731 | 1.857 | 4.014 | 32.20% | 0.229 | Fixed |
7. Nonsteroidal anti-inflammatory drugs user | Yes vs. No | 3 | 0.018 | 1.536 | 1.076 | 2.194 | 0.00% | 0.384 | Fixed |
8. Opioids user | Yes vs. No | 3 | 0.012 | 2.010 | 1.166 | 3.468 | 0.00% | 0.370 | Fixed |
9. Fracture site | Middle vs. Proximal | 7 | < 0.05 | 3.152 | 2.019 | 4.922 | 0.00% | 0.788 | Fixed |
Distal vs. Proximal | 7 | < 0.05 | 2.877 | 1.822 | 4.543 | 0.00% | 0.911 | Fixed | |
Distal vs. Middle | 7 | 0.670 | 0.932 | 0.673 | 1.290 | 0.00% | 0.650 | Fixed | |
10. Injury energy | High vs. Low | 4 | 0.001 | 2.602 | 1.484 | 4.562 | 35.90% | 0.182 | Fixed |
11. Open fracture | Yes vs. No | 9 | < 0.05 | 2.846 | 1.700 | 4.202 | 16.50% | 0.296 | Fixed |
12. Gustilo-Anderson gradea | IIIA vs. I or II | 9 | 0.005 | 1.831 | 1.204 | 2.784 | 0.00% | 0.847 | Fixed |
IIIB or IIIC vs. I or II | 9 | < 0.05 | 7.202 | 4.781 | 10.848 | 4.60% | 0.394 | Fixed | |
IIIB or IIIC vs. IIIA | 9 | < 0.05 | 3.695 | 2.422 | 5.639 | 32.60% | 0.168 | Fixed | |
13. Müller AO Classification of Fractures (AO) classificationb | B vs. A | 7 | 0.010 | 2.522 | 1.249 | 5.930 | 54.20% | 0.041 | Random |
C vs. A | 7 | < 0.05 | 3.685 | 2.405 | 5.648 | 37.00% | 0.160 | Fixed | |
C vs. B | 7 | < 0.05 | 3.569 | 2.428 | 5.325 | 39.60% | 0.142 | Fixed | |
14. Debride time | < 6 h vs. > 6 h | 2 | 0.631 | 1.190 | 0.585 | 2.419 | 0.00% | 0.520 | Fixed |
15. Open reduction | Yes vs. No | 9 | < 0.05 | 2.887 | 1.715 | 4.861 | 26.20% | 0.220 | Fixed |
16. Fixation modec | IMN vs. MIPPO | 15 | 0.003 | 2.681 | 1.397 | 5.146 | 0.00% | 0.980 | Fixed |
IMN vs. ORIF | 28 | 0.020 | 1.127 | 1.019 | 1.247 | 54.10% | <0.05 | Random | |
ORIF vs. MIPPO | 7 | 0.010 | 3.495 | 1.351 | 9.045 | 0.00% | 0.859 | Fixed | |
External vs. ORIF | 10 | 0.115 | 0.506 | 0.217 | 1.182 | 54.00% | 0.016 | Random | |
Conservative vs. ORIF | 4 | 0.264 | 1.496 | 0.737 | 3.035 | 64.10% | 0.062 | Fixed | |
External vs. IMN | 10 | 0.993 | 1.006 | 0.266 | 3.806 | 55.40% | 0.022 | Random | |
17. Fibula fixed | Yes vs. No | 7 | 0.435 | 1.317 | 0.659 | 2.634 | 47.60% | 0.075 | Random |
18. Osteofascial compartment syndrome | Yes vs. No | 3 | 0.106 | 1.420 | 0.968 | 2.173 | 80.30% | 0.006 | Fixed |
19. Infection | Yes vs. No | 2 | < 0.05 | 11.877 | 7.461 | 18.906 | 52.10% | 0.149 | Fixed |
Number of study | N | n | Prevalence rate | Heterogeneity | Model | ||||
---|---|---|---|---|---|---|---|---|---|
Effect size | Lower limit | Upper limit | I2 | p | |||||
USA | 19 | 30167 | 3083 | 0.094 | 0.075 | 0.114 | 93.40% | < 0.01 | Random |
China | 68 | 7550 | 396 | 0.047 | 0.039 | 0.057 | 69.50% | < 0.01 | Random |
Australia | 2 | 252 | 39 | 0.182 | 0.026 | 0.389 | 93.90% | < 0.01 | Random |
Belarus | 1 | 80 | 7 | 0.088 | – | – | – | – | – |
Canada | 1 | 110 | 13 | 0.118 | – | – | – | – | – |
Charlotte | 1 | 163 | 13 | 0.08 | – | – | – | – | – |
Egypt | 1 | 60 | 2 | 0.033 | – | – | – | – | – |
France | 1 | 49 | 8 | 0.162 | – | – | – | – | – |
India | 5 | 150 | 10 | 0.059 | 0.026 | 0.092 | 0 | 0.73 | Fixed |
Iran | 3 | 152 | 9 | 0.059 | 0.022 | 0.097 | 0 | 0.99 | Fixed |
Italy | 1 | 60 | 5 | 0.083 | – | – | – | – | – |
Japan | 2 | 169 | 20 | 0.114 | 0.049 | 0.278 | 91.70% | 0.001 | Random |
Malaysia | 1 | 58 | 10 | 0.172 | – | – | – | – | – |
Singapore | 1 | 103 | 44 | 0.427 | – | – | – | – | – |
Turkey | 1 | 73 | 1 | 0.014 | – | – | – | – | – |
UK | 4 | 1042 | 156 | 0.108 | 0.092 | 0.124 | 47.60% | 0.126 | Fixed |