Table 2 Descriptive characteristics and quality assessment of the included publications
From: Health-related quality of life in Chinese workers: a systematic review and meta-analysis
Author(year) | Occupation | Age (mean ± standard deviation, range) | Gender (%male) | Sample size (effective response rate) | Questionnaire | Region of work (province) | Quality assessment score |
|---|---|---|---|---|---|---|---|
Huang et al. (2001) [14] | Nurses | 31.2 ± 8.9, 18–55 | 0% | 522 (94.9%) | 100 | Hubei | 5 |
Liu et al. (2004) [15] | Medical staff | 33.7 ± 9.1 | 37.3% | 807 (89.7%) | 100 | Hunan | 6 |
Wang et al. (2005) [16] | Military personnel | 21.6 ± 3.7, 16–44 | 100% | 612 (96.5%) | BREF | Inner Mongolia | 6 |
Chen et al. (2005) [17] | Nurses | 33.3 ± 8.7, 18–56 | 0% | 1053 (90.0%) | 100 | Jiangsu | 4 |
Li et al. (2005) [18] | Military convalescents | 66.5 ± 9.7, 37–85 | 86.5% | 244 (Unknown) | BREF | Guangdong | 3 |
Jing et al. (2005) [19] | Oculists | 33.3 ± 9.3a | 32.2% | 311 (94.2%) | BREF | Guangdong | 6 |
Zhao et al. (2006) [20] | Military personnel | 21.3 ± 3.0 | 100% | 485 (99.0%) | BREF | Tibet | 5 |
Geng et al. (2006) [21] | Armed polices | 21.2 ± 3.1, 17–33 | 100% | 1283 (100%) | BREF | Guangdong | 4 |
Tang et al. (2006) [22] | Military personnel | 20.8 ± 2.3, 17–33 | 100% | 215 (Unknown) | BREF | Unknown | 4 |
Tang et al. (2006) [23] | Hospital temporary workers | Unknown | Unknown | 562 (93.7%) | 100 | Shenzhen | 4 |
Yang et al. (2006) [24] | Middle school teachers | Unknown | 18.4% | 718 (89.4%) | BREF | Hebei | 5 |
Liu et al. (2007) [25] | Nurses | 29.9 ± 8.6a | Unknown | 96 (96.0%) | 100 | Heilongjiang | 3 |
Liu et al. (2007) [26] | Roadmen | 29.8 ± 9.1 | 100% | 376 (Unknown) | BREF | Hubei | 4 |
Chen et al. (2007) [27] | Nurses | 34.8 ± 9.2 | Unknown | 1648 (92.7%) | BREF | Shandong | 4 |
Zhou et al. (2007) [28] | Middle SchoolTeachers | 36.2 ± 8.0, 19–60 | 45.5% | 622 (95.7%) | BREF | Hunan | 6 |
Liu et al. (2007) [29] | Armed police forces | 19.8 ± 1.9 | 100% | 516 (97.4%) | BREF | Qinghai | 6 |
Yang et al. (2008) [30] | Scientific research personnel | 22–85 | 32.4% | 272 (95.4%) | 100 | Beijing | 5 |
Wang et al. (2008) [31] | Nurses | 31.5 ± 4.9, 21–44 | 0% | 189 (94.5%) | BREF | Guangdong | 4 |
Tang et al. (2008) [32] | Military personnel | Unknown | Unknown | 2581 (92.2%) | BREF | Unknown | 5 |
Tang et al. (2008) [33] | Nurses | 32.5 ± 8.5, 18–53 | 0% | 574 (94.7%) | 100 | Guangdong | 6 |
Du et al. (2008) [34] | Gym coaches | 27.0 ± 5.6a | 64.9% | 97 (75.8%) | BREF | Shanghai, Jiangsu | 5 |
Liu et al. (2008) [35] | Nurses | 36.0, 18–60 | 0% | 479 (95.8%) | BREF | Shandong | 5 |
Yu et al. (2008) [36] | Coal workers | 19–50 | 56.2% | 505 (93.5%) | BREF | Shanxi | 7 |
Zhang et al. (2008) [37] | Furniture maker | 29.5 ± 8.6, 17–52 | 83.5% | 85 (Unknown) | BREF | Beijing | 5 |
Su et al. (2008) [38] | Middle SchoolTeachers | 33.6 ± 7.5, 21–57 | 34.7% | 759 (94.9%) | 100 | Shandong | 6 |
Dong et al. (2008) [39] | Nurses | 34.7 ± 8.3 | Unknown | 115 (76.7%) | 100 | Yunnan | 3 |
Li et al. (2008) [40] | Doctors | 39.7 ± 8.3 | 63.5% | 200 (80.0%) | 100 | Chongqing | 4 |
Liu et al. (2009) [41] | Reconstruction personnel after earthquake | 39.5 ± 6.0 | 96.4% | 112 (Unknown) | BREF | Sichuan | 3 |
Tang et al. (2009) [42] | Military personnel | 22.8 ± 3.8, 16–48 | 99.8% | 2305 (95.8%) | BREF | Shanghai, Jiangsu, Jiangxi, Fujian | 5 |
Gao et al. (2009) [43] | Nurses | 32.9 ± 8.8, 20–52 | Unknown | 1018 (92.5%) | 100 | Yunnan | 5 |
Wan et al. (2009) [44] | Nurses | 31.9 ± 7.5a, 19–48 | 0% | 499 (90.7%) | 100 | Hubei | 5 |
Li et al. (2009) [45] | Nurses | 33.4 ± 7.2a | 0.4% | 560 (94.0%) | BREF | Shaanxi | 6 |
Zhou et al. (2009) [46] | Employees in finance, trading, technology, media, etc | 29.7 ± 7.6, 19–59 | 35.9% | 1001 (95.3%) | BREF | Shanghai | 5 |
Zhang et al. (2009) [47] | Nurses | 31.8 ± 8.1, 18–55 | 2.1% | 610 (87.1%) | 100 | Xinjiang | 7 |
Huang et al. (2009) [48] | Construction workers | Unknown | Unknown | 1035 (Unknown) | BREF | Anhui | 4 |
Huang et al. (2009) [49] | Train drivers | 31.1 ± 6.9, 19–52 | 100% | 230 (100%) | BREF | Guangdong | 5 |
Ding et al. (2009) [50] | Construction workers | 32.5 ± 10.0, 18–50 | 89.1% | 101 (94.4%) | BREF | Shandong | 5 |
Song et al. (2009) [51] | Journalists | Unknown | 0% | 117 (Unknown) | BREF | Unknown | 3 |
Gu et al. (2009) [52] | Electronic enterprise workers | mainly 20–30 (64.9%) | 31.6% | 868 (86.8%) | 100 | Jiangsu | 5 |
Song et al. (2009) [53] | Slaughterhouse workers | Unknown | Unknown | 970 (64.3%) | BREF | Hebei | 4 |
Liu et al. (2009) [54] | Medical staff | 38.7 ± 9.9a | 26.7% | 664 (94.9%) | BREF | Liaoning | 5 |
Wang et al. (2009) [55] | Education, scientific research, administrative management, medical technology and other workers | 48.0 ± 5.5, 40–60 | 52.2% | 1315 (84.3%) | BREF | Guizhou | 6 |
Xing et al. (2010) [56] | Nurses | 31.6 ± 6.9 | 5.1% | 99 (82.5%) | BREF | Shandong | 4 |
Bai et al. (2010) [57] | Civil servants | 36.7 ± 8.4a, 20–60 | 51.3% | 809 (95.2%) | BREF | Xinjiang | 5 |
Wang et al. (2010) [58] | Medical staff | 31.0 ± 9.1, 19–70 | 11.4% | 404 (Unknown) | BREF | Beijing | 4 |
Fu et al. (2010) [59] | Scientific research personnel | 40.0, 27–56 | 72.7% | 260 (Unknown) | BREF | Guangdong | 3 |
Liu et al. (2010) [60] | Emergency nurses | 28.9 ± 5.8, 20–58 | 6.1% | 196 (93.3%) | BREF | Shandong | 5 |
Zhang et al. (2010) [61] | Steel workers | 38.1 ± 6.6, 19–51 | 92.7% | 383 (95.8%) | BREF | Shanxi | 5 |
Liu et al. (2010) [62] | Nurses | 27.5 ± 6.2, 18–50 | 3.6% | 1213 (93.3%) | 100 | Guangxi | 5 |
Jiang et al. (2010) [63] | Construction, service, processing and manufacturing workers | 24.6 ± 4.7a, 16–35 | 28.3% | 265 (75.7%) | BREF | Fujian | 5 |
Tang et al. (2010) [64] | Elementary and middle school teachers | 22–59 | 44.4% | 169 (92.9%) | 100 | Zhejiang | 4 |
Yao et al. (2010) [65] | Medical college teachers | 36.6, 24–59 | 33.6% | 345 (95.8%) | BREF | Shanxi | 5 |
Jin et al. (2011) [66] | Nurses | 31.6 ± 9.1a, 19–53 | 0% | 200 (Unknown) | 100 | Guangdong | 3 |
Xu et al. (2011) [67] | Nurses | 35.0 ± 8.0 | Unknown | 561 (93.5%) | BREF | Beijing | 5 |
Lou et al. (2011) [68] | Medical staff | 34.9 ± 9.1a | 22.3% | 452 (Unknown) | BREF | Shenzhen | 5 |
Wang et al. (2011) [69] | Nurses | 28.4, 19–45 | 0.3% | 385 (96.7%) | BREF | Tianjin | 5 |
Long et al. (2011) [70] | Doctors | 23–60 | 57.0% | 235 (78.3%) | BREF | Guangdong | 4 |
Wei et al. (2011) [71] | Military personnel | 21.2 ± 2.8, 18–34 | 100% | 559 (98.4%) | BREF | Unknown | 5 |
Ye et al. (2011) [72] | Military personnel | 21.5 ± 2.9, 17–33 | 100% | 554 (90.8%) | BREF | Yunnan | 6 |
Wan et al. (2011) [73] | Policemen | Unknown | 62.9% | 70 (Unknown) | BREF | Yunnan | 2 |
Xiong et al. (2011) [74] | Medical staff | 33.4 ± 8.0 | 35.0% | 331 (Unknown) | BREF | Hubei | 5 |
Wang et al. (2011) [75] | Medical staff | 37.0, 21–60 | 26.0% | 672 (97.4%) | WHOQOL-BREF | Beijing | 6 |
Zhang et al. (2011) [76] | Medical college teachers | 37.0, 21–60 | 30.1% | 249 (88.9%) | BREF | Anhui | 5 |
Ma et al. (2012) [77] | Military personnel | 37.6 ± 13.1a | 100% | 181 (90.5%) | BREF | Unknown | 4 |
Ma et al. (2012) [78] | Peasant workers | 26.8 ± 4.8 | 63.1% | 756 (Unknown) | 100 | Hebei | 3 |
Ban et al. (2012) [79] | Special education teachers | Unknown | 35.9% | 131 (87.3%) | BREF | Guizhou | 4 |
Wang et al. (2012) [80] | Nurses | Unknown | Unknown | 290 (96.7%) | 100 | Shenzhen | 3 |
Hu et al. (2012) [81] | Enameled wire workers | 32.5 ± 7.2, 19–55 | 74.3% | 319 (Unknown) | BREF | Anhui | 5 |
Xu et al. (2012) [82] | Nurses | 31.0, 18–54 | Unknown | 287 (88.6%) | BREF | Guangdong | 4 |
Zhang et al. (2012) [83] | Medical staff | > 40 | 21.5% | 536 (97.1%) | BREF | Beijing | 6 |
Liu et al. (2012) [84] | Electronic enterprise workers | 34.9 ± 10.8a | 10.0% | 641 (98.6%) | BREF | Guangdong | 4 |
Zhang et al. (2013) [85] | Service workers | 24.3 ± 6.2a | 0% | 358 (Unknown) | BREF | Hebei | 5 |
Xu et al. (2013) [86] | Nurses | 34.2 ± 10.9a | 2.0% | 256 (88.6%) | BREF | Beijing | 4 |
Wang et al. (2013) [87] | Employees in public places | 30.1 ± 8.0, 19–57 | 27.5% | 200 (Unknown) | BREF | Anhui | 4 |
Hu et al. (2013) [88] | Civil servants | 33.6 ± 10.5 | 55.4% | 514 (93.5%) | BREF | Chongqing | 5 |
Tan et al. (2013) [89] | Medical staff | 39.8 ± 11.1a | Unknown | 273 (Unknown) | BREF | Guangdong | 2 |
Shan et al. (2013) [90] | Medical staff | 37.0 ± 8.6 | 54.9% | 82 (82.0%) | BREF | Zhejiang | 4 |
Wu et al. (2013) [91] | Doctors | 34.9 ± 5.9, 21–48 | 38.1% | 291 (89.8%) | BREF | Fujian | 4 |
Xing et al. (2013) [92] | Manufacturing, food and domestic service, retail sector, construction industry, transportation and other workers | 39.9 ± 12.2a, 20–65 | 48.4% | 1869 (93.5%) | BREF | Zhejiang | 6 |
Yu et al. (2013) [93] | Nurses | 24.4 ± 3.5 | 10.5% | 468 (78.0%) | BREF | Hunan | 6 |
Fu et al. (2013) [94] | Nurses | 27.5 ± 5.0, 19–50 | 0% | 310 (91.2%) | 100 | Henan | 4 |
Zhang et al. (2013) [95] | Nurses | Unknown | 47.1% | 374 (93.5%) | BREF | Shandong | 6 |
Wu et al. (2013) [96] | Foundry enterprise workers | 26.4 ± 2.8, 22–39 | 82.4% | 901 (91.5%) | BREF | Anhui | 6 |
Geng et al. (2013) [97] | Nurses | 43.8 ± 9.1a | 0% | 793 (88.1%) | BREF | Beijing and Tianjin | 5 |
Lin et al. (2014) [98] | Medical staff | 31.2 ± 8.0, 18–57 | 0% | 315 (95.5%) | BREF | Fujian | 6 |
He et al. (2014) [99] | Peasant workers engaged in non-agricultural production work | 39.2 ± 8.8a | 70.6% | 436 (86.7%) | BREF | Unknown | 4 |
Li et al. (2014) [100] | Nurses | 18–30 | 0% | 450 (88.2%) | BREF | Henan | 6 |
Guo et al. (2014) [101] | Network, communications, pharmaceutical, banking and other industries staff; mining workers; construction workers | 28.6 ± 4.9, 20–46 | Unknown | 1165 (Unknown) | BREF | Beijing | 3 |
Li et al. (2014) [102] | Nurses | 34.3 ± 9.3 | 0% | 356 (96.2%) | BREF | Heilongjiang | 6 |
Lao et al. (2014) [103] | Doctors | 29.5 ± 4.0, 19–50 | 77.4% | 1064 (62.6%) | BREF | Hunan | 6 |
Wang et al. (2014) [104] | Military personnel | 34.5 ± 6.8 | 100% | 445 (Unknown) | BREF | Unknown | 4 |
Zhang et al. (2014) [105] | Community nurses | 20.7 ± 3.0 | 8.2% | 232 (96.3%) | BREF | Jiangsu | 5 |
Yang et al. (2014) [106] | Kindergarten teachers | 33.2 ± 5.3, 18–60 | 14.6% | 403 (91.6%) | BREF | Guizhou | 6 |
Han et al. (2014) [107] | Nurses | 28.0 ± 8.0, 16–50 | 0% | 102 (92.7%) | BREF | Shanghai | 4 |
Wu et al. (2014) [108] | Nurses | 28.4 ± 5.5, 22–48 | 0% | 215 (97.7%) | BREF | Henan | 4 |
Zhang et al. (2015) [109] | Nurses | 28.9 ± 7.8, 20–48 | 36.5% | 181 (97.8%) | BREF | Shandong | 5 |
Yang et al. (2015) [110] | HIV / AIDS prevention and control personnel | 28.8, 23–48 | 31.6% | 250 (100%) | BREF | Guangxi | 5 |
Guan et al. (2015) [111] | HIV / AIDS prevention and control personnel | 32.5 ± 8.4, 19–60 | 46.0% | 250 (100%) | BREF | Heilongjiang | 5 |
Li et al. (2015) [112] | Medical staff | 39.7 ± 8.6, 21–63 | 2.6% | 76 (Unknown) | BREF | Henan | 4 |
Jiang et al. (2015) [113] | Railway construction workers | 29.1 ± 10.9, 22–45 | 98.3% | 950 (94.0%) | BREF | Shanxi | 6 |
Miao et al. (2015) [114] | Nurses | 29.4 ± 11.6, 24–44 | Unknown | 268 (95.7%) | BREF | Heilongjiang | 4 |
Tang et al. (2015) [115] | Doctors | 39.9 ± 11.3a, 15–65 | 51.7% | 576 (91.4%) | BREF | Guangdong | 6 |
Kang et al. (2015) [116] | Medical rescuers | 31.4 ± 6.9a | 33.7% | 303 (89.6%) | BREF | Gansu | 7 |
Yan et al. (2015) [117] | Doctors | 40.2 ± 8.5 | 90.0% | 60 (96.8%) | BREF | Guangdong | 4 |
Pan et al. (2015) [118] | Nurses | 32.6 ± 7.3 | 11.8% | 152 (95.0%) | BREF | Guangdong | 4 |
Chen et al. (2016) [119] | Sanitation workers | 32.8 ± 12.9a | 43.8% | 121 (63.0%) | BREF | Ningxia | 4 |
Dai et al. (2016) [120] | Civil servants | 32.7 ± 8.6, 19–54 | 57.5% | 708 (79.8%) | BREF | Jiangsu | 5 |
Hu et al. (2016) [121] | Workers in a chemical enterprise | 51.1 ± 9.7a, 30–70 | 71.4% | 538 (90.7%) | BREF | Anhui | 6 |
Yang et al. (2016) [122] | Workers in nonferrous metal ore concentrator, smelting enterprise, lead acid battery enterprise | 35.8 ± 9.5, 21–59 | 0% | 652 (97.3%) | BREF | Guangdong | 5 |
Zhao et al. (2016) [123] | Military personnel | 40.9 ± 10.1a, 18–59 | 87.5% | 616 (94.8%) | BREF | Unknown | 5 |
Tang et al. (2017) [124] | Nurses | 39.9 ± 9.1a, 22–54 | Unknown | 40 (Unknown) | 100 | Liaoning | 2 |
Zhang et al. (2017) [125] | Medical staff | 22.6 ± 4.9, 17–47 | 37.7% | 239 (95.2%) | BREF | Tibet | 5 |
Lai et al. (2017) [126] | Nurses | 32.1 ± 9.0a | 0% | 100 (Unknown) | BREF | Shenzhen | 3 |
Zhao et al. (2017) [127] | Medical staff | 35.5 ± 5.1, 20–50 | Unknown | 406 (81.2%) | BREF | Shaanixi | 5 |
Xiao et al. (2017) [128] | Seafarers | Unknown | 100% | 917 (98.7%) | BREF | Jiangsu | 6 |
Su et al. (2017) [129] | Armed polices | 33.5 ± 9.6 | 100% | 1327 (95.8%) | BREF | Shanxi | 6 |
Liu et al. (2017) [130] | Doctors | 21.0 ± 1.4, 17–34 | 68.1% | 276 (92.3%) | BREF | Hubei | 4 |
Zhang et al. (2017) [131] | Coal workers | 45.9 ± 11.1a | 63.7% | 881 (97.9%) | BREF | Shanxi | 7 |
Yi et al. (2018) [132] | Coal miners | 37.7 ± 8.5, 18–65 | Unknown | 263 (87.7%) | BREF | Henan | 4 |
Zeng et al. (2018) [133] | Military personnel | 38.7 ± 7.9 | 100% | 154 (96.3%) | BREF | Unknown | 4 |
Yang et al. (2018) [134] | Service workers | 24.9 ± 3.8 | 26.6% | 139 (Unknown) | BREF | Yunnan | 3 |
Lu et al. (2018) [135] | Migrant workers in Construction industry, catering industry, etc | 31.1 ± 9.7a, 16–56 | 55.4% | 267 (95.7%) | BREF | Tianjin | 4 |
Zhao et al. (2018) [136] | Nurses | 25.9 ± 4.7a, 18–36 | Unknown | 282 (95.6%) | BREF | Hebei | 4 |
Xue et al. (2018) [137] | Nurses | 36.8 ± 9.7a | 0% | 400 (87.0%) | BREF | Jiangsu | 6 |
Song et al. (2018) [138] | Medical staff | 32.8 ± 12.9a | 23.2% | 2274 (91.0%) | BREF | Beijing | 5 |
Yang et al. (2018) [139] | University teachers | 36.0, 20–70 | 47.0% | 25,066 (78.3%) | BREF | Unknown | 7 |
Yu et al. (2019) [140] | Nurses and other medical staffs | 37.2 ± 7.8a, 24–65 | 29.6% | 230 (Unknown) | BREF | Fujian | 3 |
He et al. (2019) [141] | Nurses and other medical staffs | 38.0 ± 3.2, 30–46 | 18.5% | 200 (Unknown) | BREF | Hebei | 3 |
Song et al. (2019) [142] | Nurses | 31.1 ± 3.4, 22–45 | 0% | 558 (93.0%) | BREF | Liaoning | 5 |
Ma et al. (2019) [143] | Coal workers | Unknown | 84.2% | 3090 (71.2%) | BREF | Shanxi | 6 |
Asante et al. (2019) [144] | Primary healthcare workers | 51.7 ± 12.6a, 20–65 | 50.9% | 873 (87.3%) | BREF | Guangdong | 6 |
Zhu et al. (2019) [145] | Nurses | 32.4 ± 6.9a | 100% | 315 (95.5%) | BREF | Shandong | 6 |
Wu et al. (2020) [146] | Fishermen | 27.9 ± 5.6a | 99.4% | 507 (Unknown) | BREF | Hainan | 5 |
Zeng et al. (2020) [147] | Nurses | 36.9 ± 11.3, 16–66 | 80.5% | 1449 (68.2%) | BREF | Unknown | 5 |
Liu et al. (2020) [148] | Nurses | 32.6 ± 8.8 | 9.3% | 75 (Unknown) | BREF | Tianjin | 3 |
Luo et al. (2020) [149] | White-collar workers | 29.1 ± 6.2, 21–40 | 28.0% | 410 (Unknown) | BREF | Zhejiang | 5 |
Wang et al. (2020) [150] | Military personnel | 34.3 ± 9.2 | 100% | 146 (97.3%) | BREF | Unknown | 4 |
Wei et al. (2020) [151] | Pediatricians and Pediatric Nurses | 24.3 ± 4.0 | 11.8% | 355 (93.4%) | BREF | Henan | 6 |
Chen et al. (2021) [152] | Radiation workers | 32.2 ± 8.3a | 69.9% | 449 (89.8%) | BREF | Guangdong | 5 |