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Abstract：
BACKGROUND: HALE is now a regular strategic planning indicator for all levels of the Chinese government. However, HALE measurements necessitate comprehensive data collection and intricate technology. Therefore, effectively converting numerous diseases into the years lived with disability (YLD) rate is a significant challenge for HALE measurements. Our study aimed to construct a simple YLD rate measurement model with high applicability based on the current situation of actual data resources within China to address challenges in measuring HALE target values during planning.
METHODS: First, based on the Chinese YLD rate in the Global Burden of Disease (GBD) 2019, Pearson correlation analysis, the global optimum method, etc., was utilized to screen the best predictor variables from the current Chinese data resources. Missing data for predictor variables were filled in via spline interpolation. Then, multiple linear regression models were fitted to construct the YLD rate measurement model. The Sullivan method was used to measure HALE. The Monte Carlo method was employed to generate 95% uncertainty intervals. Finally, model performances were assessed using the mean absolute error (MAE) and mean absolute percentage error (MAPE).
RESULTS: A three-input-parameter model was constructed to measure the age-specific YLD rates by sex in China, directly using the incidence of infectious diseases, the incidence of chronic diseases among persons aged 15 and older, and the addition of an under-five mortality rate covariate. The total MAE and MAPE for the combined YLD rate were 0.0007 and 0.5949%, respectively. The MAE and MAPE of the combined HALE in the 0-year-old group were 0.0341 and 0.0526%, respectively. There were slightly fewer males (0.0197, 0.0311%) than females (0.0501, 0.0755%).
CONCLUSIONS: We constructed a high-accuracy model to measure the YLD rate in China by using three monitoring indicators from the Chinese national routine as predictor variables. The model provides a realistic and feasible solution for measuring HALE at the national and especially regional levels, considering limited data.
METHODS: First, based on the Chinese YLD rate in the Global Burden of Disease (GBD) 2019, Pearson correlation analysis, the global optimum method, etc., was utilized to screen the best predictor variables from the current Chinese data resources. Missing data for predictor variables were filled in via spline interpolation. Then, multiple linear regression models were fitted to construct the YLD rate measurement model. The Sullivan method was used to measure HALE. The Monte Carlo method was employed to generate 95% uncertainty intervals. Finally, model performances were assessed using the mean absolute error (MAE) and mean absolute percentage error (MAPE).
RESULTS: A three-input-parameter model was constructed to measure the age-specific YLD rates by sex in China, directly using the incidence of infectious diseases, the incidence of chronic diseases among persons aged 15 and older, and the addition of an under-five mortality rate covariate. The total MAE and MAPE for the combined YLD rate were 0.0007 and 0.5949%, respectively. The MAE and MAPE of the combined HALE in the 0-year-old group were 0.0341 and 0.0526%, respectively. There were slightly fewer males (0.0197, 0.0311%) than females (0.0501, 0.0755%).
CONCLUSIONS: We constructed a high-accuracy model to measure the YLD rate in China by using three monitoring indicators from the Chinese national routine as predictor variables. The model provides a realistic and feasible solution for measuring HALE at the national and especially regional levels, considering limited data.
摘要：
背景：HALE现在是中国政府各级政府的常规战略规划指标。然而,HALE测量需要全面的数据收集和复杂的技术。因此,有效地将多种疾病转化为残疾年（YLD）率是HALE测量的重大挑战。我们的研究旨在基于中国境内实际数据资源的现状，构建一个简单的具有高适用性的YLD率测量模型，以解决在规划过程中测量HALE目标值的挑战。
方法：首先，基于2019年全球疾病负担(GBD)中中国人的YLD率，皮尔逊相关分析，全局最优方法，等。,用于从当前的中文数据资源中筛选最佳预测变量。预测变量的缺失数据通过样条插值填充。然后，多元线性回归模型构建YLD率测量模型。Sullivan方法用于测量HALE。蒙特卡罗方法用于产生95%的不确定度区间。最后,使用平均绝对误差(MAE)和平均绝对百分比误差(MAPE)评估模型性能.
结果：构建了一个三输入参数模型来衡量中国按性别划分的年龄YLD率，直接利用传染病的发病率，15岁及以上人群的慢性病发病率，以及增加5岁以下儿童死亡率协变量。合并YLD率的总MAE和MAPE分别为0.0007和0.5949％，分别。0岁组合并HALE的MAE和MAPE分别为0.0341和0.0526％，分别。男性（0.0197，0.0311％）略低于女性（0.0501，0.0755％）。
结论：我们使用中国国民常规的三个监测指标作为预测变量，构建了一个高精度模型来测量中国的YLD率。该模型为在国家尤其是区域层面测量HALE提供了现实可行的解决方案,考虑到有限的数据。
方法：首先，基于2019年全球疾病负担(GBD)中中国人的YLD率，皮尔逊相关分析，全局最优方法，等。,用于从当前的中文数据资源中筛选最佳预测变量。预测变量的缺失数据通过样条插值填充。然后，多元线性回归模型构建YLD率测量模型。Sullivan方法用于测量HALE。蒙特卡罗方法用于产生95%的不确定度区间。最后,使用平均绝对误差(MAE)和平均绝对百分比误差(MAPE)评估模型性能.
结果：构建了一个三输入参数模型来衡量中国按性别划分的年龄YLD率，直接利用传染病的发病率，15岁及以上人群的慢性病发病率，以及增加5岁以下儿童死亡率协变量。合并YLD率的总MAE和MAPE分别为0.0007和0.5949％，分别。0岁组合并HALE的MAE和MAPE分别为0.0341和0.0526％，分别。男性（0.0197，0.0311％）略低于女性（0.0501，0.0755％）。
结论：我们使用中国国民常规的三个监测指标作为预测变量，构建了一个高精度模型来测量中国的YLD率。该模型为在国家尤其是区域层面测量HALE提供了现实可行的解决方案,考虑到有限的数据。
