Child mortality is an important population health indicator. However, many countries lack high-quality vital registration to measure child mortality rates precisely and reliably over time. Research endeavors such as those by the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) and the Global Burden of Disease (GBD) study leverage statistical models and available data to estimate child survival summaries including neonatal, infant, and under-five mortality rates. UN IGME fits separate models for each age group and the GBD uses a multi-step modeling process. We propose a Bayesian survival framework to estimate temporal trends in the probability of survival as a function of age, up to the fifth birthday, with a single model. Our framework integrates all data types that are used by UN IGME: household surveys, vital registration, and other pre-processed mortality rates. We demonstrate that our framework is applicable to any country using log-logistic and piecewise-exponential survival functions, and discuss findings for four example countries with diverse data profiles: Kenya, Brazil, Estonia, and Syrian Arab Republic. Our model produces estimates of the three survival summaries that are in broad agreement with both the data and the UN IGME estimates, but in addition gives the complete survival curve.

翻译：儿童死亡率是重要的人口健康指标。然而，许多国家缺乏高质量的生命登记系统，无法精确可靠地长期监测儿童死亡率。联合国儿童死亡率估算机构间小组（UN IGME）和全球疾病负担（GBD）研究等机构通过统计模型和现有数据来估算儿童生存指标，包括新生儿死亡率、婴儿死亡率和五岁以下儿童死亡率。UN IGME为各年龄组分别建立模型，GBD则采用多步骤建模流程。本研究提出一种贝叶斯生存分析框架，通过单一模型将生存概率估计为年龄的函数（截至五周岁），从而评估其时间趋势。该框架整合了UN IGME使用的所有数据类型：家庭调查数据、生命登记数据及其他经预处理的死亡率数据。我们通过使用对数逻辑分布和分段指数生存函数证明，该框架适用于任何国家，并以肯尼亚、巴西、爱沙尼亚和阿拉伯叙利亚共和国四个具有不同数据特征的国家为例进行讨论。本模型生成的三项生存指标估计值与实际数据及UN IGME估计值基本一致，同时还能提供完整的生存曲线。