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22 results on '"Zhou, Yuchang"'

6. Eliciting national and subnational sets of disability weights in mainland China: Findings from the Chinese disability weight measurement study

Author

Liu, Xiaoxue, Wang, Fang, Zhou, Maigeng, Yu, Yong, Qi, Jinlei, Yin, Peng, Yu, Shicheng, Zhou, Yuchang, Lin, Lin, Liu, Yunning, Wang, Qiqi, Zhong, Wenling, Huang, Shaofen, Li, Yanxia, Liu, Li, Liu, Yuan, Ma, Fang, Zhang, Yine, Tian, Yuan, Yu, Qiuli, Zeng, Jing, Pan, Jingju, Zhou, Mengge, Kang, Weiwei, Zhou, Jin-Yi, Yu, Hao, Liu, Yuehua, Li, Shaofang, Yu, Huiting, Wang, Chunfang, Xia, Tian, Xi, Jinen, Ren, Xiaolan, Xing, Xiuya, Cheng, Qianyao, Fei, Fangrong, Wang, Dezheng, Zhang, Shuang, He, Yuling, Wen, Haoyu, Liu, Yan, Shi, Fang, Wang, Yafeng, Sun, Panglin, Bai, Jianjun, Wang, Xuyan, Shen, Hui, Ma, Yudiyang, Yang, Donghui, Mubarik, Sumaira, Cao, Jinhong, Meng, Runtang, Zhang, Yunquan, Guo, Yan, Yan, Yaqiong, Zhang, Wei, Ke, Sisi, Zhang, Runhua, Wang, Dingyi, Zhang, Tingting, Nomura, Shuhei, Hay, Simon I., Salomon, Joshua A., Haagsma, Juanita A., Murray, Christopher J.L., Vos, Theo, and Yu, Chuanhua

Published
2022
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13. Can global or national disability weights represent provincial level?

Author

He, Qin, primary, Zhou, Maigeng, additional, Yin, Peng, additional, Jiang, Bo, additional, Qi, Jinlei, additional, Liu, Yunning, additional, Liu, Jiangmei, additional, You, Jinling, additional, Zhou, Yuchang, additional, Long, Zheng, additional, Xing, Xiuya, additional, Cheng, Qianyao, additional, Chen, Yeji, additional, Wang, Huadong, additional, and Liu, Zhirong, additional

Published
2022
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14. Eliciting national and subnational sets of disability weights in mainland China:Findings from the Chinese disability weight measurement study

Author

Liu, Xiaoxue, Wang, Fang, Yu, Chuanhua, Zhou, Maigeng, Yu, Yong, Qi, Jinlei, Yin, Peng, Yu, Shicheng, Zhou, Yuchang, Lin, Lin, Liu, Yunning, Wang, Qiqi, Zhong, Wenling, Huang, Shaofen, Li, Yanxia, Liu, Li, Liu, Yuan, Ma, Fang, Zhang, Yine, Tian, Yuan, Yu, Qiuli, Zeng, Jing, Pan, Jingju, Zhou, Mengge, Kang, Weiwei, Zhou, Jin Yi, Yu, Hao, Liu, Yuehua, Li, Shaofang, Yu, Huiting, Wang, Chunfang, Xia, Tian, Xi, Jinen, Ren, Xiaolan, Xing, Xiuya, Cheng, Qianyao, Fei, Fangrong, Wang, Dezheng, Zhang, Shuang, He, Yuling, Wen, Haoyu, Liu, Yan, Shi, Fang, Wang, Yafeng, Sun, Panglin, Bai, Jianjun, Wang, Xuyan, Shen, Hui, Ma, Yudiyang, Yang, Donghui, Mubarik, Sumaira, Cao, Jinhong, Meng, Runtang, Zhang, Yunquan, Guo, Yan, Yan, Yaqiong, Zhang, Wei, Ke, Sisi, Zhang, Runhua, Wang, Dingyi, Zhang, Tingting, Nomura, Shuhei, Hay, Simon I., Salomon, Joshua A., Haagsma, Juanita A., Murray, Christopher J.L., Vos, Theo, Liu, Xiaoxue, Wang, Fang, Yu, Chuanhua, Zhou, Maigeng, Yu, Yong, Qi, Jinlei, Yin, Peng, Yu, Shicheng, Zhou, Yuchang, Lin, Lin, Liu, Yunning, Wang, Qiqi, Zhong, Wenling, Huang, Shaofen, Li, Yanxia, Liu, Li, Liu, Yuan, Ma, Fang, Zhang, Yine, Tian, Yuan, Yu, Qiuli, Zeng, Jing, Pan, Jingju, Zhou, Mengge, Kang, Weiwei, Zhou, Jin Yi, Yu, Hao, Liu, Yuehua, Li, Shaofang, Yu, Huiting, Wang, Chunfang, Xia, Tian, Xi, Jinen, Ren, Xiaolan, Xing, Xiuya, Cheng, Qianyao, Fei, Fangrong, Wang, Dezheng, Zhang, Shuang, He, Yuling, Wen, Haoyu, Liu, Yan, Shi, Fang, Wang, Yafeng, Sun, Panglin, Bai, Jianjun, Wang, Xuyan, Shen, Hui, Ma, Yudiyang, Yang, Donghui, Mubarik, Sumaira, Cao, Jinhong, Meng, Runtang, Zhang, Yunquan, Guo, Yan, Yan, Yaqiong, Zhang, Wei, Ke, Sisi, Zhang, Runhua, Wang, Dingyi, Zhang, Tingting, Nomura, Shuhei, Hay, Simon I., Salomon, Joshua A., Haagsma, Juanita A., Murray, Christopher J.L., and Vos, Theo

Abstract

Background: The disability weight (DW) quantifies the severity of health states from disease sequela and is a pivotal parameter for disease burden calculation. We conducted a national and subnational DW measurement in China. Methods: In 2020–2021, we conducted a web-based survey to assess DWs for 206 health states in 31 Chinese provinces targeting health workers via professional networks. We fielded questions of paired comparison (PC) and population health equivalence (PHE). The PC data were analysed by probit regression analysis, and the regression results were anchored by results from the PHE responses on the DW scale between 0 (no loss of health) and 1 (health loss equivalent to death). Findings: We used PC responses from 468,541 respondents to estimate DWs of health states. Eight of 11 domains of health had significantly negative coefficients in the regression of the difference between Chinese and Global Burden of Disease (GBD) DWs, suggesting lower DW values for health states with mention of these domains in their lay description. We noted considerable heterogeneity within domains, however. After applying these Chinese DWs to the 2019 GBD estimates for China, total years lived with disability (YLDs) increased by 14·9% to 177 million despite lower estimates for musculoskeletal disorders, cardiovascular diseases, mental disorders, diabetes and chronic kidney disease. The lower estimates of YLDs for these conditions were more than offset by higher estimates of common, low-severity conditions. Interpretation: The differences between the GBD and Chinese DWs suggest that there might be some contextual factors influencing the valuation of health states. While the reduced estimates for mental disorders, alcohol use disorder, and dementia could hint at a culturally different valuation of these conditions in China, the much greater shifts in YLDs from low-severity conditions more likely reflects methodological difficulty to distinguish between health states that vary

Published
2022

15. Excess mortality in Wuhan city and other parts of China during the three months of the covid-19 outbreak: findings from nationwide mortality registries

Author

Liu, Jiangmei, primary, Zhang, Lan, additional, Yan, Yaqiong, additional, Zhou, Yuchang, additional, Yin, Peng, additional, Qi, Jinlei, additional, Wang, Lijun, additional, Pan, Jingju, additional, You, Jinling, additional, Yang, Jing, additional, Zhao, Zhenping, additional, Wang, Wei, additional, Liu, Yunning, additional, Lin, Lin, additional, Wu, Jing, additional, Li, Xinhua, additional, Chen, Zhengming, additional, and Zhou, Maigeng, additional

Published
2021
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16. Eliciting National and Subnational Sets of Disability Weights in Mainland China: Findings From the Chinese Disability Weight Measurement Study

Author

Liu, Xiaoxue, primary, Wang, Fang, additional, Yu, Chuanhua, additional, Zhou, Maigeng, additional, Yu, Yong, additional, Qi, Jinlei, additional, Yin, Peng, additional, Yu, Shicheng, additional, Zhou, Yuchang, additional, Lin, Lin, additional, Liu, Yunning, additional, Wang, Qiqi, additional, Zhong, Wenling, additional, Huang, Shaofen, additional, Li, Yanxia, additional, Liu, Li, additional, Liu, Yuan, additional, Ma, Fang, additional, Zhang, Yine, additional, Tian, Yuan, additional, Yu, Qiuli, additional, Zeng, Jing, additional, Pan, Jingju, additional, Zhou, Mengge, additional, Kang, Weiwei, additional, Zhou, Jinyi, additional, Yu, Hao, additional, Liu, Yuehua, additional, Li, Shaofang, additional, Yu, Huiting, additional, Wang, Chunfang, additional, Xia, Tian, additional, Xi, Jinen, additional, Ren, Xiaolan, additional, Xing, Xiuya, additional, Cheng, Qianyao, additional, Fei, Fangrong, additional, Wang, Dezheng, additional, Zhang, Shuang, additional, He, Yuling, additional, Meng, Runtang, additional, Cao, Jinhong, additional, Wen, Haoyu, additional, Shi, Fang, additional, Wang, Yafeng, additional, Sun, Panglin, additional, Liu, Yan, additional, Bai, Jianjun, additional, Wang, Xuyan, additional, Shen, Hui, additional, Zhang, Yunquan, additional, Guo, Yan, additional, Yan, Yaqiong, additional, Zhang, Wei, additional, Ke, Sisi, additional, Zhang, Runhua, additional, Wang, Dingyi, additional, Zhang, Tingting, additional, Nomura, Shuhei, additional, Salomon, Joshua A., additional, Haagsma, Juanita A., additional, and Vos, Theo, additional

Published
2021
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19. Short- and long-term prognosis after acute myocardial infarction in Chinese men and women

Author

Paul K. Whelton, Zhou Yuchang, Jiang He, Weng Xinzhi, and Michael J. Klag

Subjects
Adult, Male, medicine.medical_specialty, China, Epidemiology, Heart block, Myocardial Infarction, Killip Class III, Sex Factors, Internal medicine, Tachycardia, Heart rate, Prevalence, Medicine, Humans, cardiovascular diseases, Myocardial infarction, Survival rate, Stroke, Aged, Proportional Hazards Models, Aged, 80 and over, business.industry, Smoking, Middle Aged, medicine.disease, Prognosis, Surgery, Survival Rate, Cohort, Regression Analysis, Female, business, Follow-Up Studies
Abstract

Most, but not all, studies in Western countries have indicated that women have a higher mortality than men after an acute myocardial infarction, but this has not been well documented in the developing world. The authors studied 601 male and 294 female myocardial infarction patients, aged 26-94 years, admitted to a city hospital in Beijing, China, between 1974 and 1986. A total of 745 (98.8%) of the cohort of 754 who survived past their initial hospitalization were followed up in 1988. Compared with their male counterparts, female myocardial infarction patients were older (63.4 vs. 58.1 years) and had a higher prevalence of tachycardia (heart rate, > or = 110 beats/minute), rales, New York Heart Association class III or IV, and heart block, but a lower prevalence of smoking and white collar occupation (all p < 0.01). The mortality within 28 days of their myocardial infarction was higher in women than in men, mainly in the group whose age was less than 60 years (20.4% vs. 7.1%, p < 0.001). The greater risk of short-term mortality in women persisted even after adjustment for age, history of stroke and hypertension, tachycardia on admission, anterior infarction, Killip class III or IV, and arrhythmia (relative odds = 1.74, 95% confidence interval 1.17-2.60). The 10-year total cumulative mortality in persons who survived the first 28 days following their myocardial infarction was 51.1% for women and 35.9% for men (log rank p = 0.002). After adjustment for age, this difference was not statistically significant (p = 0.3). Our results suggest that, in Beijing, women have a higher short-term mortality after myocardial infarction compared with men.

Published
1994

20. The mortality and years of life lost for community-acquired pneumonia before and during COVID-19 pandemic in China

Author

Fan, Guohui, Zhou, Yuchang, zhou, Fei, Yu, Zhongguang, Gu, Xiaoying, Zhang, Xueyang, Liu, Zhengping, Zhou, Maigeng, and Cao, Bin

Abstract

Community-acquired pneumonia (CAP) is a leading cause of mortality worldwide, but disease burden of CAP is not clear so far. We aim to explore the spatial and temporal trends of mortality and years of life lost (YLL) due to CAP during 2013–2021 in mainland China, especially the mortality changes before and during COVID-19 pandemic due to COVID-19 related non-pharmaceutical interventions (NPIs).

Published
2023
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21. The Effects of Diabetes and Hypertension on the Severity of COVID-19 - Yichang, Hubei Province, 2020.

Author

Zhou Y, Yang J, Xu C, Hu C, Lu F, Xue F, and Zhang P

Abstract

What is already known on this topic?, COVID-19 has become a serious public health issue. A higher proportion of severe patients were senior patients with underlying diseases such as diabetes and hypertension and had a lack of statistical evidence so far., What is added by this report?, When severe illness was compared with non-severe illness, senior patients were at a greater risk (4.71) than young and middle-aged patients, as well as the odds ratio was about 2.99 patients with diabetes compared to patients without diabetes and hypertension. COVID-19-infectious senior patients with diabetes were inclined to suffer severe illness., What are the implications for public health practice?, Much more attention should be provided for the elderly and individuals with diabetes, for which a community-based education and surveillance program could be considered., (Copyright and License information: Editorial Office of CCDCW, Chinese Center for Disease Control and Prevention 2020.)

Published
2020
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22. [Evaluating the causal relationship between hip circumference and coronary heart disease: a Mendelian randomization study].

Author

Yuan T, Li H, Ma Y, Si S, Liu X, Yu Y, Li Y, Li W, Hou L, Liu L, Zhou Y, Xue F, Wang P, and Liu Y

Subjects
Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Polymorphism, Single Nucleotide, Coronary Disease, Mendelian Randomization Analysis
Abstract

Objective: To test the causal effect of hip circumference adjusted for body mass index(HCadjBMI) and coronary heart disease(CHD) using a Mendelian randomization analysis., Methods: Based on genome-wide association study, the associations between the genetic instruments(IVs) and HCadjBMI were obtained from the GIANT consortium(n=211 114, European), the associations between IVs and CHD were derided from CARDIoGRAM consortium(n=86 995, European). The inverse-variance weighted method was used to estimate a pooled OR for the effect of a 1 cm higher HCadjBMI on CHD. Evidence of directional pleiotropy averaged across all variants was sought using MR-Egger regression., Results: A total of 70 genetic variants that reached genome-wide significance and independent of each other were identified as IVs. A combined genetic variants expected to confer a lifetime exposure of per SD higher HCadjBMI was associated with a lower risk of CHD(OR=0. 831, 95%CI 0. 730-0. 946). MR-Egger regression intercept suggested that directional pleiotropy was unlikely to be biasing the result(intercept-0. 0012, P=0. 875). There was no specific single nucleotide polymorphism(SNP) detected by "leave one out" analysis., Conclusion: A genetic predisposition to higher HCadjBMI was associated with lower risk of CHD.

Published
2020
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