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

  • Xiaoxue Liu
  • , Fang Wang
  • , Chuanhua Yu*
  • , Maigeng Zhou
  • , Yong Yu
  • , Jinlei Qi
  • , Peng Yin
  • , Shicheng Yu
  • , Yuchang Zhou
  • , Lin Lin
  • , Yunning Liu
  • , Qiqi Wang
  • , Wenling Zhong
  • , Shaofen Huang
  • , Yanxia Li
  • , Li Liu
  • , Yuan Liu
  • , Fang Ma
  • , Yine Zhang
  • , Yuan Tian
  • Qiuli Yu, Jing Zeng, Jingju Pan, Mengge Zhou, Weiwei Kang, Jin Yi Zhou, Hao Yu, Yuehua Liu, Shaofang Li, Huiting Yu, Chunfang Wang, Tian Xia, Jinen Xi, Xiaolan Ren, Xiuya Xing, Qianyao Cheng, Fangrong Fei, Dezheng Wang, Shuang Zhang, Yuling He, Haoyu Wen, Yan Liu, Fang Shi, Yafeng Wang, Panglin Sun, Jianjun Bai, Xuyan Wang, Hui Shen, Yudiyang Ma, Donghui Yang, Sumaira Mubarik, Jinhong Cao, Runtang Meng, Yunquan Zhang, Yan Guo, Yaqiong Yan, Wei Zhang, Sisi Ke, Runhua Zhang, Dingyi Wang, Tingting Zhang, Shuhei Nomura, Simon I. Hay, Joshua A. Salomon, Juanita A. Haagsma, Christopher J.L. Murray, Theo Vos
*Corresponding author for this work
  • Wuhan University
  • Chinese Center for Disease Control and Prevention
  • Hubei University of Medicine
  • Shandong University
  • Fujian Provincial Center for Disease Control and Prevention
  • Liaoning Provincial Center for Disease Control and Prevention
  • Hunan Provincial Center for Disease Control and Prevention
  • Ningxia Center for Disease Control and Prevention
  • Yunnan Center for Disease Control and Prevention
  • Sichuan Center for Disease Control and Prevention
  • Inner Mongolia Integrative Center for Disease Control and Prevention
  • Jiangsu Provincial Center for Disease Control and Prevention
  • Heilongjiang Provincial Center for Disease Control and Prevention
  • Henan Provincial Center for Disease Control and Prevention
  • Shanghai Municipal Center for Disease Control and Prevention
  • Gansu Provincial Center for Disease Control and Prevention
  • Anhui Provincial Center for Disease Control and Prevention
  • Zhejiang Provincial Center for Disease Control and Prevention
  • Tianjin Centers for Disease Control and Prevention
  • Shanxi Center for Disease Control and Prevention
  • Huazhong University of Science and Technology
  • Hangzhou Normal University
  • Wuhan University of Science and Technology
  • Wuhan Centers for Disease Control and Prevention
  • Capital Medical University
  • China-Japan Friendship Hospital
  • Peking University
  • Keio University School of Medicine
  • Graduate School of Medicine
  • Institute for Health Metrics and Evaluation
  • Stanford University School of Medicine
  • Xuzhou Medical University

Research output: Contribution to journalArticleAcademicpeer-review

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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 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].

Original languageEnglish
Article number100520
JournalThe Lancet Regional Health - Western Pacific
Volume26
DOIs
Publication statusPublished - Sept 2022

Bibliographical note

Funding Information:
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].

Publisher Copyright:
© 2022 The Authors

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

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