| Literature DB >> 33582938 |
Xin Liu1, Jian Sun2, Wei Fang1, Yanguo Xu3, Zizhao Zhu4, Yazhuo Liu5.
Abstract
The aim of this study was to comprehensively assess the prevalence of goiter and thyroid nodules (TNs) in relation to China's iodine nutrition level over the past 20 years and provide an effective reference for developing health policies. PubMed, EMBASE, Chinese National Knowledge Infrastructure, Chongqing VIP, and Chinese Wan Fang databases were searched for relevant studies from Jan 1996 to Feb 2020. Two reviewers extracted valid data from the eligible citations to determine the morbidity of TNs in different urinary iodine concentrations (UICs) and in patients of different genders, of different ages, who live in different geographic regions, and who live at different altitudes, as well as the P values of interactions between groups. There were 26 articles (34 studies) included in this analysis. The overall morbidity of TNs in mainland China was 23.4%. Morbidity was higher in urban areas (P < 0.001) than in rural and mixed areas. Coastal areas (P < 0.001), female patients (P < 0.001), high-altitude areas (P < 0.001), and residence in south China (P < 0.001) were all associated with higher morbidity of TNs. The lowest morbidity value of TNs, 16%, was in the more-than-adequate iodine subgroup. The highest morbidity, 27.2%, was in the adequate iodine subgroup. The morbidity of TNs increases with age, and women are more likely to have TNs. We also need to perform more epidemiological studies, and in the future, we should cultivate better understanding of the relationship between other thyroid diseases and provide more comprehensive and useful information for other researchers.Entities:
Keywords: China; Morbidity; Thyroid nodules; Urinary iodine concentration
Mesh:
Substances:
Year: 2021 PMID: 33582938 PMCID: PMC8516763 DOI: 10.1007/s12011-020-02565-2
Source DB: PubMed Journal: Biol Trace Elem Res ISSN: 0163-4984 Impact factor: 3.738
Fig. 1Flow diagram of the literature-search process
Characteristics of studies on the morbidity of TNs
| First author | Publication year | Location | Rural/urban | Inland/coastal | Study year | UIC (ug/L) | Sample size | Prevalence (%) | case |
|---|---|---|---|---|---|---|---|---|---|
| Yu XH [ | 2008 | Panshan, Liaoning | Rural | Inland | 1999 | 103.1 | 815 | 12.63 | 103 |
| Yu XH [ | 2008 | Huanghua, Hebei | Rural | Inland | 1999 | 614.6 | 1056 | 10.79 | 114 |
| Yu XH [ | 2008 | Zhangwu, Liaoning | Rural | Inland | 1999 | 374.5 | 1514 | 10.17 | 154 |
| Zhu WY [ | 2010 | Zhoushan, Zhejiang | Mixed | Coastal | 2006 | 320.7 | 3284 | 25.30 | 831 |
| Lou XM [ | 2011 | Xiangshan, etc., Zhejiang | Mixed | Coastal | 2009 | 275.6 | 280 | 21.07 | 59 |
| Lou XM [ | 2011 | Haining, etc., Zhejiang | Mixed | Coastal | 2009 | 256.1 | 321 | 14.95 | 48 |
| Lou XM [ | 2011 | Daishan, etc., Zhejiang | Mixed | Coastal | 2009 | 149.1 | 456 | 14.47 | 66 |
| Shao HJ [ | 2016 | Weihai, Shandong | Rural | Coastal | 2009 | 120.0 | 835 | 40.11 | 335 |
| Liu Y [ | 2012 | Chengdu, Sichuan | Urban | Inland | 2009 | 184 | 1500 | 17.00 | 255 |
| Yang YX [ | 2011 | Guiyang, Guizhou | Urban | Inland | 2009 | 228.73 | 1512 | 10.12 | 153 |
| Zou SR [ | 2012 | Shanghai | Mixed | Coastal | 2009 | 146.7 | 7369 | 27.29 | 2011 |
| Shen Y [ | 2013 | Shanghai | Mixed | Coastal | 2010 | 122.8 | 695 | 22.88 | 159 |
| Yang NZ [ | 2012 | Taizhou, Zhejiang | Mixed | Inland | 2010 | 178.25 | 793 | 22.95 | 182 |
| Chen ZX [ | 2013 | Hangzhou, Zhejiang | Mixed | Inland | 2010 | 172.6 | 9412 | 29.98 | 2822 |
| Zhao XF [ | 2015 | Ningbo, Zhejiang | Rural | Coastal | 2011 | 90.4 | 1177 | 19.88 | 234 |
| Du Y [ | 2014 | Shuozhou, Shanxi | Mixed | Inland | 2012 | 228.7 | 531 | 8.66 | 46 |
| Du Y [ | 2014 | Beihai, Guangxi | Mixed | Inland | 2012 | 62.0 | 636 | 22.17 | 141 |
| Du Y [ | 2014 | Taiyuan, Shanxi | Mixed | Inland | 2012 | 750.2 | 930 | 15.52 | 142 |
| Bao CH [ | 2014 | Xiangshan, Zhejiang | Mixed | Coastal | 2012 | 140.1 | 2463 | 43.80 | 1079 |
| Meng H [ | 2015 | Lishui, Zhejiang | Mixed | Inland | 2013 | 162.7 | 827 | 20.31 | 168 |
| Guo YY [ | 2016 | Urumqi, Xinjiang | Urban | Inland | 2013 | 133.4 | 1835 | 27.73 | 509 |
| Gu F[ | 2016 | Jiaxing, etc., Zhejiang | Mixed | Inland | 2013 | 180.0 | 7527 | 20.59 | 1550 |
| Gu F [ | 2016 | Hangzhou, etc., Zhejiang | Mixed | Coastal | 2013 | 152.0 | 7568 | 21.27 | 1610 |
| Xu FF [ | 2016 | Ningbo, Zhejiang | Mixed | Coastal | 2014 | 201.7 | 913 | 20.70 | 189 |
| Wu SB [ | 2018 | Huizhou, Guangdong | Mixed | Coastal | 2015 | 149. 25 | 896 | 41.96 | 376 |
| Jing GJ [ | 2020 | Longnan, Gansu | Rural | Inland | 2015 | 247.7 | 1289 | 16.66 | 214 |
| Yang WQ [ | 2018 | Yinchuan, Ningxia | Urban | Inland | 2015 | 347.6 | 1292 | 33.20 | 429 |
| Lian LX [ | 2018 | Harbin, Heilongjiang | Urban | Inland | 2015 | 159.8 | 2552 | 48.75 | 1244 |
| Hu YY [ | 2018 | Changde, etc., Hunan | Mixed | Inland | 2015 | 173.9 | 2650 | 13.77 | 365 |
| Song Jun [ | 2016 | Shanghai | Mixed | Coastal | 2015 | 132.5 | 5144 | 27.76 | 1428 |
| Cao C [ | 2018 | Lanzhou, Gansu | Urban | Inland | 2016 | 205.4 | 647 | 21.02 | 136 |
| Yi JP [ | 2018 | Zhoushan, Zhejiang | Mixed | Coastal | 2016 | 126.0 | 1382 | 22.72 | 314 |
| Nima YZ [ | 2018 | Lhasa, Tibet | Rural | Inland | 2017 | 140.4 | 383 | 38.64 | 148 |
| Nima YZ [ | 2018 | Lhasa, Tibet | Urban | Inland | 2017 | 158.0 | 1835 | 31.33 | 575 |
Fig. 2Forest plot of the pooled morbidity of TNs in mainland China
Morbidity of TNs in mainland China by different stratification factors
| Subgroups | Prevalence% (95% CI) | Number of studies | Heterogeneity | Case/total | |
|---|---|---|---|---|---|
| I2% | |||||
| Rural | 0.211 (0.142–0.279) | 7 | 98.5 | < 0.001 | 1302/7069 |
| Mixed | 0.229 (0.198–0.260) | 20 | 98.6 | < 0.001 | 13,586/54077 |
| Urban | 0.270 (0.165–0.376) | 7 | 99 | < 0.001 | 2893/8161 |
| Coastal | 0.261 (0.223–0.298) | 14 | 98.2 | < 0.001 | 4996/18366 |
| Inland | 0.215 (0.172–0.259) | 20 | 99.2 | < 0.001 | 9450/22934 |
| < 200 | 0.240 (0.205–0.276) | 24 | 99.1 | < 0.001 | 15,582/60565 |
| 200–500 | 0.17 (0.151–0.189) | 1 | / | / | 255/1500 |
| 500–1000 | 0.215 (0.093–0.337) | 2 | 98.8 | < 0.001 | 651/2765 |
| > 1000 | 0.227 (0.145–0.309) | 7 | 99 | < 0.001 | 1701/7489 |
| Insufficient | 0.207 (0.186–0.229) | 2 | 22.4 | 0.256 | 365/1844 |
| Adequate | 0.272 (0.235–0.310) | 20 | 99.1 | < 0.001 | 15,299/56937 |
| More than adequate | 0.160 (0.120–0.200) | 7 | 94.2 | < 0.001 | 845/5493 |
| Excess | 0.189 (0.107–0.272) | 5 | 99 | < 0.001 | 1670/8076 |
| North China | 0.177 (0.05–0.304) | 6 | 99.6 | < 0.001 | 1803/7398 |
| South China | 0.321 (0.127–0.515) | 2 | 98.6 | < 0.001 | 517/1532 |
| East China | 0.246 (0.217–0.274) | 17 | 98.1 | < 0.001 | 13,085/50446 |
| West China | 0.243 (0.177–0.310) | 8 | 98.6 | < 0.001 | 2419/10293 |
| Central China | 0.138 (0.127–0.515) | 1 | / | / | 365/2650 |
| Male | 0.180 (0.144–0.215) | 34 | 98.5 | < 0.001 | 6129/32502 |
| Female | 0.276 (0.237–0.315) | 34 | 98.4 | < 0.001 | 12,060/40867 |
| 0.234 (0.204–0.264) | 34 | 99 | < 0.001 | 18,189/72319 | |
Fig. 3Morbidity of TNs with different ages
Fig. 4Regional distribution of pooled morbidity of TNs in mainland China
Fig. 5Funnel plot with pseudo 95% confidence limits