Disability weight measurement for the severity of different diseases in Wuhan, China
Abstract Background Measurement of the Chinese burden of disease with disability-adjusted life-years (DALYs) requires disability weight (DW) that quantify health losses for all non-fatal consequences of disease and injury. The Global Burden of Disease (GBD) 2013 DW study indicates that it is limited...
| Main Authors: | , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2023-05-01
|
| Series: | Population Health Metrics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12963-023-00304-y |
| _version_ | 1797831970631188480 |
|---|---|
| author | Xiaoxue Liu Yan Guo Fang Wang Yong Yu Yaqiong Yan Haoyu Wen Fang Shi Yafeng Wang Xuyan Wang Hui Shen Shiyang Li Yanyun Gong Sisi Ke Wei Zhang Qiman Jin Gang Zhang Yu Wu Maigeng Zhou Chuanhua Yu |
| author_facet | Xiaoxue Liu Yan Guo Fang Wang Yong Yu Yaqiong Yan Haoyu Wen Fang Shi Yafeng Wang Xuyan Wang Hui Shen Shiyang Li Yanyun Gong Sisi Ke Wei Zhang Qiman Jin Gang Zhang Yu Wu Maigeng Zhou Chuanhua Yu |
| author_sort | Xiaoxue Liu |
| collection | DOAJ |
| description | Abstract Background Measurement of the Chinese burden of disease with disability-adjusted life-years (DALYs) requires disability weight (DW) that quantify health losses for all non-fatal consequences of disease and injury. The Global Burden of Disease (GBD) 2013 DW study indicates that it is limited by lack of geographic variation in DW data and by the current measurement methodology. We aim to estimate DW for a set of health states from major diseases in the Wuhan population. Methods We conducted the DW measurement study for 206 health states through a household survey with computer-assisted face-to-face interviews and a web-based survey. Based on GBD 2013 DW study, paired comparison (PC) and Population health equivalence (PHE) method was used and different PC/PHE questions were randomly assigned to each respondent. In statistical analysis, the PC data was analyzed by probit regression. The probit regression results will be anchored by results from the PHE data analyzed by interval regression on the DW scale units between 0 (no loss of health) and 1 (loss equivalent to death). Results A total of 2610 and 3140 individuals were included in the household and web-based survey, respectively. The results from the total pooled data showed health state “mild anemia” (DW = 0.005, 95% UI 0.000–0.027) or “allergic rhinitis (hay fever)” (0.005, 95% UI 0.000–0.029) had the lowest DW and “heroin and other opioid dependence, severe” had the highest DW (0.699, 95% UI 0.579–0.827). A high correlation coefficient (Pearson’s r = 0.876; P < 0.001) for DWs of same health states was observed between Wuhan’s survey and GBD 2013 DW survey. Health states referred to mental symptom, fatigue, and the residual category of other physical symptoms were statistically significantly associated with a lower Wuhan’s DWs than the GBD’s DWs. Health states with disfigurement and substance use symptom had a higher DW in Wuhan population than the GBD 2013 study. Conclusions This set of DWs could be used to calculate local diseases burden for health policy-decision in Wuhan population. The DW differences between the GBD’s survey and Wuhan’s survey suggest that there might be some contextual or culture factors influencing assessment on the severity of diseases. |
| first_indexed | 2024-04-09T14:00:13Z |
| format | Article |
| id | doaj.art-a08b2a64429440bc9ddb91cd1165884f |
| institution | Directory Open Access Journal |
| issn | 1478-7954 |
| language | English |
| last_indexed | 2024-04-09T14:00:13Z |
| publishDate | 2023-05-01 |
| publisher | BMC |
| record_format | Article |
| series | Population Health Metrics |
| spelling | doaj.art-a08b2a64429440bc9ddb91cd1165884f2023-05-07T11:20:04ZengBMCPopulation Health Metrics1478-79542023-05-0121111410.1186/s12963-023-00304-yDisability weight measurement for the severity of different diseases in Wuhan, ChinaXiaoxue Liu0Yan Guo1Fang Wang2Yong Yu3Yaqiong Yan4Haoyu Wen5Fang Shi6Yafeng Wang7Xuyan Wang8Hui Shen9Shiyang Li10Yanyun Gong11Sisi Ke12Wei Zhang13Qiman Jin14Gang Zhang15Yu Wu16Maigeng Zhou17Chuanhua Yu18Department of Epidemiology and Biostatistics, School of Pubic Health, Wuhan UniversityWuhan Centers for Disease Control and PreventionDepartment of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical UniversitySchool of Public Health and Management, Hubei University of MedicineWuhan Centers for Disease Control and PreventionDepartment of Epidemiology and Biostatistics, School of Pubic Health, Wuhan UniversityDepartment of Epidemiology and Biostatistics, School of Pubic Health, Wuhan UniversityGlobal Health Research Division, Public Health Research Center and Department of Public Health and Preventive Medicine, Wuxi School of Medicine, Jiangnan UniversityDepartment of Epidemiology and Biostatistics, School of Pubic Health, Wuhan UniversityDepartment of Epidemiology and Biostatistics, School of Pubic Health, Wuhan UniversityDepartment of Epidemiology and Biostatistics, School of Pubic Health, Wuhan UniversityDepartment of Epidemiology and Biostatistics, School of Pubic Health, Wuhan UniversityWuhan Centers for Disease Control and PreventionWuhan Centers for Disease Control and PreventionWuhan Centers for Disease Control and PreventionWuhan Centers for Disease Control and PreventionGlobal Health Research Division, Public Health Research Center and Department of Public Health and Preventive Medicine, Wuxi School of Medicine, Jiangnan UniversityNational Center for Chronic and Non-communicable Disease Control and Prevention, Chinese Center for Disease Control and PreventionDepartment of Epidemiology and Biostatistics, School of Pubic Health, Wuhan UniversityAbstract Background Measurement of the Chinese burden of disease with disability-adjusted life-years (DALYs) requires disability weight (DW) that quantify health losses for all non-fatal consequences of disease and injury. The Global Burden of Disease (GBD) 2013 DW study indicates that it is limited by lack of geographic variation in DW data and by the current measurement methodology. We aim to estimate DW for a set of health states from major diseases in the Wuhan population. Methods We conducted the DW measurement study for 206 health states through a household survey with computer-assisted face-to-face interviews and a web-based survey. Based on GBD 2013 DW study, paired comparison (PC) and Population health equivalence (PHE) method was used and different PC/PHE questions were randomly assigned to each respondent. In statistical analysis, the PC data was analyzed by probit regression. The probit regression results will be anchored by results from the PHE data analyzed by interval regression on the DW scale units between 0 (no loss of health) and 1 (loss equivalent to death). Results A total of 2610 and 3140 individuals were included in the household and web-based survey, respectively. The results from the total pooled data showed health state “mild anemia” (DW = 0.005, 95% UI 0.000–0.027) or “allergic rhinitis (hay fever)” (0.005, 95% UI 0.000–0.029) had the lowest DW and “heroin and other opioid dependence, severe” had the highest DW (0.699, 95% UI 0.579–0.827). A high correlation coefficient (Pearson’s r = 0.876; P < 0.001) for DWs of same health states was observed between Wuhan’s survey and GBD 2013 DW survey. Health states referred to mental symptom, fatigue, and the residual category of other physical symptoms were statistically significantly associated with a lower Wuhan’s DWs than the GBD’s DWs. Health states with disfigurement and substance use symptom had a higher DW in Wuhan population than the GBD 2013 study. Conclusions This set of DWs could be used to calculate local diseases burden for health policy-decision in Wuhan population. The DW differences between the GBD’s survey and Wuhan’s survey suggest that there might be some contextual or culture factors influencing assessment on the severity of diseases.https://doi.org/10.1186/s12963-023-00304-yDisability weightDisease burdenSequelaHealth statePaired comparison |
| spellingShingle | Xiaoxue Liu Yan Guo Fang Wang Yong Yu Yaqiong Yan Haoyu Wen Fang Shi Yafeng Wang Xuyan Wang Hui Shen Shiyang Li Yanyun Gong Sisi Ke Wei Zhang Qiman Jin Gang Zhang Yu Wu Maigeng Zhou Chuanhua Yu Disability weight measurement for the severity of different diseases in Wuhan, China Population Health Metrics Disability weight Disease burden Sequela Health state Paired comparison |
| title | Disability weight measurement for the severity of different diseases in Wuhan, China |
| title_full | Disability weight measurement for the severity of different diseases in Wuhan, China |
| title_fullStr | Disability weight measurement for the severity of different diseases in Wuhan, China |
| title_full_unstemmed | Disability weight measurement for the severity of different diseases in Wuhan, China |
| title_short | Disability weight measurement for the severity of different diseases in Wuhan, China |
| title_sort | disability weight measurement for the severity of different diseases in wuhan china |
| topic | Disability weight Disease burden Sequela Health state Paired comparison |
| url | https://doi.org/10.1186/s12963-023-00304-y |
| work_keys_str_mv | AT xiaoxueliu disabilityweightmeasurementfortheseverityofdifferentdiseasesinwuhanchina AT yanguo disabilityweightmeasurementfortheseverityofdifferentdiseasesinwuhanchina AT fangwang disabilityweightmeasurementfortheseverityofdifferentdiseasesinwuhanchina AT yongyu disabilityweightmeasurementfortheseverityofdifferentdiseasesinwuhanchina AT yaqiongyan disabilityweightmeasurementfortheseverityofdifferentdiseasesinwuhanchina AT haoyuwen disabilityweightmeasurementfortheseverityofdifferentdiseasesinwuhanchina AT fangshi disabilityweightmeasurementfortheseverityofdifferentdiseasesinwuhanchina AT yafengwang disabilityweightmeasurementfortheseverityofdifferentdiseasesinwuhanchina AT xuyanwang disabilityweightmeasurementfortheseverityofdifferentdiseasesinwuhanchina AT huishen disabilityweightmeasurementfortheseverityofdifferentdiseasesinwuhanchina AT shiyangli disabilityweightmeasurementfortheseverityofdifferentdiseasesinwuhanchina AT yanyungong disabilityweightmeasurementfortheseverityofdifferentdiseasesinwuhanchina AT sisike disabilityweightmeasurementfortheseverityofdifferentdiseasesinwuhanchina AT weizhang disabilityweightmeasurementfortheseverityofdifferentdiseasesinwuhanchina AT qimanjin disabilityweightmeasurementfortheseverityofdifferentdiseasesinwuhanchina AT gangzhang disabilityweightmeasurementfortheseverityofdifferentdiseasesinwuhanchina AT yuwu disabilityweightmeasurementfortheseverityofdifferentdiseasesinwuhanchina AT maigengzhou disabilityweightmeasurementfortheseverityofdifferentdiseasesinwuhanchina AT chuanhuayu disabilityweightmeasurementfortheseverityofdifferentdiseasesinwuhanchina |