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; 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.

项目摘要 在全球范围内，估计有590万儿童在2015年到达五岁生日之前死亡。 低收入和中等收入国家（LMICS），有关年龄和特定儿童死亡率的质量信息 （ACSCM）很少可用。最近，美国政府和国际社会已续签 他们致力于结束一代人的可预防儿童死亡。我们一直在出版以建模的国家 自2010年以来，LMIC的COD分布，我们估计了0-1和1-59个月的COD分布。 但是，人口统计学和流行病学证据等于结论儿童鳕鱼不统一 在1-59个月期间。在1-59个月以下的特异性水平上的国家经验数据通常不是 由于民用注册系统薄弱，可在LMIC中使用。这些数据和估计值相当科学 为特定年龄特定儿童干预措施的发展和影响评估的价值及 扩展。因此，了解在1-59个月期间较小的年龄组之间的鳕鱼分布是 有必要。以前的研究遭受了四个主要缺点：（i）使用自定义收集的数据来 了解单一原因的年龄动态； （ii）仅在广泛的年龄组中估算ACSCM； （iii）生产 每个年龄段的估计分别独立； （iv）在两个单独的 估计框架。这项研究的目标是系统地描述和公开可用 LMIC中儿童COD的经验年龄模式具有准确的不确定性间隔，并发展创新 理论驱动的，简约的贝叶斯分层建模框架，以得出国家鳕鱼的估计值 LMIC的分布与以前的研究相比，在年龄段之间具有部分数据。我们将实现 实现三个目标的目标：1）扩展和评估全年人口统计模型以估计年龄模式 带有VR数据的儿童死亡。 2）概念化，开发和评估新颖的同时ACSCM估计器 在高收入国家使用VR数据； 3）将统一的ACSCM估计框架推送到 LMICS。拟议的研究有两项重要的创新。首先，它提出了第一个统一框架 同时估计全年，全因，年龄和特定原因的儿童死亡率。如果成功，该研究 将为选定的LMIC提供系统估计的ACSCM，并为此提供有效的不确定性，并为 开发用于系统估算所有LMIC的ACSCM的方法，包括那些低质量，有限或 甚至没有数据。其次，该框架在尚未出版的年龄粒度时产生估计值 研究。这些附加信息对于实现五岁以下的儿童生存政策制定和 在颗粒状的年龄和成因上的计划评估，这将进一步促进可持续性 公平地降低5岁以下和新生儿死亡率的发展目标。这项研究 将为将来的研究奠定基础，例如将人类死亡数据库扩展到儿童 五个用于评估质量，调整偏置和系统组织的ACSCM估计值。