Developing innovative analytics to estimate age-and cause-specific child mortality for low- and middle-income countries

开发创新分析来估计低收入和中等收入国家的年龄和特定原因儿童死亡率

• 批准号: 9766323
• 负责人: Li Liu
• 金额: $ 19.02万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-17 至 2021-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9766323
• 关键词: 5 year old; Age; Area; Autopsy; Bangladesh; Bayesian Modeling; Categories; Cause of Death; Cessation of life; Child; Child Mortality; Childhood; China; Communities; Country; Custom; Data; Data Quality; Databases; Decision Making; Dependence; Development; Disease; Effectiveness; Epidemiology; Evaluation; Foundations; Future; Generations; Goals; Government; Grouping; Heterogeneity; Impact evaluation; Income; International; Intervention; Investments; Knowledge; Life; Life Table Models; Logic; Longevity; Measurement; Methodology; Methods; Modeling; Neonatal Mortality; Pattern; Performance; Policies; Policy Developments; Policy Making; Program Evaluation; Publishing; Reproducibility; Research; Resolution; Resource Allocation; Sampling; Savings; Specificity; Sustainable Development; System; Uncertainty; Ursidae Family; Variant; Work; age group; base; human mortality; innovation; insight; low and middle-income countries; low income country; mortality; novel; prevent; programs; prototype; scale up; theories

Project Summary Globally, an estimated 5.9 million children died before reaching their fifth birthday in 2015. The majority died in low- and middle-income countries (LMICs), where quality information on age- and cause-specific child mortality (ACSCM) is rarely available. Recently, the US government and the international community have renewed their commitment to end preventable child deaths in a generation. We have been publishing modeled national COD distributions for LMICs since 2010, where we estimated COD distribution for 0-1 and 1-59-month olds. However, demographic and epidemiological evidence amounts to the conclusion that child COD is not uniform in the 1-59-month period. National empirical data at levels of specificity below 1-59 months are often not available in LMICs due to weak civil registration systems. Such data and estimates bear considerable scientific value to inform the development and impact evaluation of age-specific childhood interventions and their scale-up. Therefore, understanding the COD distribution among finer age groups in the 1-59-month period is warranted. Previous research has suffered from four main drawbacks: (i) using custom-collected data to understand age dynamics in a single cause; (ii) estimating ACSCM only in broad age groups; (iii) producing estimates in each age group separately and independently; and (iv) developing ACSCM in two separate estimation frameworks. The goals of this study are to systematically describe and make publicly available empirical age patterns of child COD in LMICs with accurate uncertainty intervals, and to develop the innovative theory-driven, parsimonious Bayesian hierarchical modeling framework to derive estimates of national COD distributions in LMICs with partial data among finer age groups than previous research. We will achieve the goals through three aims: 1) To extend and evaluate all-age demographic models to estimate age patterns in child deaths with VR data. 2) To conceptualize, develop and evaluate novel simultaneous ACSCM estimators using VR data in high-income countries; and 3) To extrapolate the unified ACSCM estimation framework to LMICs. The proposed study has two important innovations. First, it proposes the first unified framework for simultaneously estimating all-age, all-cause, age- and cause-specific child mortality. If successful, the study will offer systematically estimated ACSCM with valid uncertainty for selected LMICs, and lay the foundation for developing methods to systematically estimate ACSCM for all LMICs, including those low quality, limited, or even no data. Second, this framework produces estimates at an age granularity not yet seen in published research. This additional information is crucial to enable under-five child survival policy development and program evaluation at granular levels of ages and causes that would further contribute to the Sustainable Development Goals of equitably in reducing under-5 and neonatal mortality rates across countries. This study will lay the groundwork for future research, such as extending the Human Mortality Database to children under five to produce quality assessed, bias adjusted, and systematically organized ACSCM estimates.

项目概要 2015 年，全球估计有 590 万儿童在五岁生日前死亡。其中大多数死于 低收入和中等收入国家 (LMIC)，提供有关特定年龄和特定原因儿童死亡率的高质量信息 (ACSCM) 很少可用。近期，美国政府和国际社会再次重申 他们致力于在一代人的时间内消除可预防的儿童死亡。我们一直在出版国家模型 2010 年以来中低收入国家的 COD 分布，其中我们估计了 0-1 个月和 1-59 个月婴儿的 COD 分布。 然而，人口统计和流行病学证据得出的结论是，儿童 COD 并不统一 在 1-59 个月期间。低于 1-59 个月的特异性水平的国家经验数据通常不 由于民事登记系统薄弱，中低收入国家可用。这些数据和估计具有相当的科学性 为特定年龄的儿童干预措施及其影响的发展和影响评估提供信息的价值 扩大规模。因此，了解1-59个月期间更精细年龄组之间的COD分布是有必要的。 保证。先前的研究存在四个主要缺点：（i）使用定制收集的数据来 了解单一原因的年龄动态； (ii) 仅估计广泛年龄组的 ACSCM； (三) 生产 分别独立地对每个年龄组进行估计； (iv) 以两个独立的方式开发 ACSCM 估计框架。本研究的目标是系统地描述并向公众公开 中低收入国家儿童 COD 的经验年龄模式，具有准确的不确定性区间，并开发创新的 理论驱动的简约贝叶斯分层建模框架，用于推导出国家 COD 的估计值 中低收入国家的分布，部分数据比以前的研究更细化。我们将实现 通过三个目标来实现目标：1）扩展和评估全年龄人口模型，以估计年龄模式 通过 VR 数据了解儿童死亡情况。 2) 概念化、开发和评估新型同步 ACSCM 估计器 在高收入国家使用 VR 数据； 3) 将统一的 ACSCM 估计框架推断为 中低收入国家。拟议的研究有两个重要的创新。首先，它提出了第一个统一框架 同时估算全年龄、全原因、特定年龄和特定原因的儿童死亡率。如果成功的话，研究 将为选定的中低收入国家提供具有有效不确定性的系统估计 ACSCM，并为 开发方法来系统地评估所有中低收入国家的 ACSCM，包括那些低质量、有限或 甚至没有数据。其次，该框架以年龄粒度进行估计，在已发表的文章中尚未见过。 研究。这些额外信息对于制定五岁以下儿童生存政策和 对年龄和原因进行细粒度的计划评估，这将进一步促进可持续发展 公平降低各国 5 岁以下儿童和新生儿死亡率的发展目标。这项研究 将为未来的研究奠定基础，例如将人类死亡率数据库扩展到以下儿童 五是进行质量评估、偏差调整和系统组织的 ACSCM 估计。

项目成果

Methods for correcting inference based on outcomes predicted by machine learning.

• DOI: 10.1073/pnas.2001238117
• 发表时间: 2020-12-01
• 期刊: Proceedings of the National Academy of Sciences of the United States of America
• 影响因子: 11.1
• 作者: Wang S;McCormick TH;Leek JT
• 通讯作者: Leek JT