Department of Preventive Medicine, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, Korea.
Department of Preventive Medicine, Ewha Womans University School of Medicine, Seoul, Korea.
J Korean Med Sci. 2019 Feb 14;34(Suppl 1):e60. doi: 10.3346/jkms.2019.34.e60. eCollection 2019 Mar 26.
For the Korean Burden of Disease (KBD) 2015 study, we have amended disability weights for causes of disease adapting the methodology of the KBD disability weight 2012 study.
We conducted a self-administered web-based survey in Korea using ranking five causes of disease. A total of 605 physicians and medical college students who were attending in third or fourth grade of a regular course performed the survey. We converted the ranked data into paired comparison data and ran a probit regression. The predicted probabilities for each cause of disease were calculated from the coefficient estimates of the probit regression. 'Being dead (1)' and 'Full health (0)' were utilized as anchor points to rescale the predicted probability on a scale from 0 to 1.
As a result, disability weights for a total of 289 causes of disease were estimated. In particular, we calculated the disability weights of 60 causes of disease considering severity level. These results show that prejudice about the severity of cause of disease itself can affect the estimation of disability weight, when estimating the disability weight for causes of disease without consideration of severity. Furthermore, we have shown that disability weights can be estimated based on a ranking method which can maximize efficiency of data collection.
Disability weights from this study can be used to estimate disability adjusted life year and healthy life expectancy. Furthermore, we expected that the use of the ranking method will increase gradually in disability weight studies.
为了进行 2015 年韩国疾病负担研究，我们修订了疾病的失能权重，采用了 2012 年韩国失能权重研究的方法。
我们在韩国进行了一项基于网络的自我管理调查，使用排名前五位的疾病原因进行调查。共有 605 名正在上三年级或四年级正规课程的医生和医学生参与了调查。我们将排名数据转换为配对比较数据，并进行了概率回归分析。从概率回归分析的系数估计中计算出每种疾病原因的预测概率。“死亡（1）”和“完全健康（0）”被用作锚点，将预测概率重新调整为 0 到 1 的比例。
总共估计了 289 种疾病原因的失能权重。特别是，我们考虑了严重程度水平，计算了 60 种疾病原因的失能权重。这些结果表明，在不考虑严重程度的情况下，对疾病原因的严重程度本身的偏见会影响失能权重的估计。此外，我们还表明，可以基于排名方法来估计失能权重，这种方法可以最大限度地提高数据收集的效率。
本研究中的失能权重可用于估计伤残调整生命年和健康期望寿命。此外，我们预计排名方法的使用将在失能权重研究中逐渐增加。
