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

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 20...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9335373/
https://www.ncbi.nlm.nih.gov/pubmed/35910433
http://dx.doi.org/10.1016/j.lanwpc.2022.100520
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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
author_facet 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
author_sort Liu, Xiaoxue
collection PubMed
description 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 a little in absolute DW value but a lot in relative terms. FUNDING: This work was supported by the National Natural Science Foundation of China [grant number 82173626], the National Key Research and Development Program of China [grant numbers 2018YFC1315302], Wuhan Medical Research Program of Joint Fund of Hubei Health Committee [grant number WJ2019H304], and Ningxia Natural Science Foundation Project [grant number 2020AAC03436].
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spelling pubmed-93353732022-07-30 Eliciting national and subnational sets of disability weights in mainland China: Findings from the Chinese disability weight measurement study 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 Lancet Reg Health West Pac Articles 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 a little in absolute DW value but a lot in relative terms. FUNDING: This work was supported by the National Natural Science Foundation of China [grant number 82173626], the National Key Research and Development Program of China [grant numbers 2018YFC1315302], Wuhan Medical Research Program of Joint Fund of Hubei Health Committee [grant number WJ2019H304], and Ningxia Natural Science Foundation Project [grant number 2020AAC03436]. Elsevier 2022-07-26 /pmc/articles/PMC9335373/ /pubmed/35910433 http://dx.doi.org/10.1016/j.lanwpc.2022.100520 Text en © 2022 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Articles
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
Eliciting national and subnational sets of disability weights in mainland China: Findings from the Chinese disability weight measurement study
title Eliciting national and subnational sets of disability weights in mainland China: Findings from the Chinese disability weight measurement study
title_full Eliciting national and subnational sets of disability weights in mainland China: Findings from the Chinese disability weight measurement study
title_fullStr Eliciting national and subnational sets of disability weights in mainland China: Findings from the Chinese disability weight measurement study
title_full_unstemmed Eliciting national and subnational sets of disability weights in mainland China: Findings from the Chinese disability weight measurement study
title_short Eliciting national and subnational sets of disability weights in mainland China: Findings from the Chinese disability weight measurement study
title_sort eliciting national and subnational sets of disability weights in mainland china: findings from the chinese disability weight measurement study
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9335373/
https://www.ncbi.nlm.nih.gov/pubmed/35910433
http://dx.doi.org/10.1016/j.lanwpc.2022.100520
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