Developing an innovative statistical framework to integrate multiple verbal autopsy datasets to estimate cause-specific mortality
开发创新的统计框架来整合多个口头尸检数据集,以估计特定原因的死亡率
基本信息
- 批准号:10710402
- 负责人:
- 金额:$ 7.3万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-09-30 至 2024-08-31
- 项目状态:已结题
- 来源:
- 关键词:AddressAdoptedAlgorithmsArchivesAutopsyBayesian ModelingBiological MarkersCause of DeathCertificationCessation of lifeClassificationCollectionComplexComputer softwareCountryDataData ReportingData SetDependenceEffectivenessFailureFoundationsFutureHealthHeterogeneityIndividualInterventionMeasurementMedical HistoryMethodsModelingMonitorPhysiciansPopulationProbabilityQuestionnairesReproducibilityResearchSigns and SymptomsStandardizationStatistical AlgorithmStatistical ModelsStructureSurveysSymptomsSystemTarget PopulationsTissue SampleUncertaintyVital StatisticsWorkWorld Health Organizationanalysis pipelineanalytical methodburden of illnesscomputer frameworkdata qualitydesignflexibilityglobal healthhealth goalsheterogenous dataimprovedinnovationinsightlow and middle-income countriesmortalitynovelopen sourcepopulation basedpopulation healthpreventpublic health interventionstatisticstoolvalidation studiesverbal
项目摘要
Project Abstract
Cause of death data are essential for understanding the burden of disease, emerging health needs, and the
effectiveness of public health interventions. Few low- and middle-income countries (LMIC) have adequate vital
statistics systems that produce high quality statistics on causes of death. Verbal autopsy (VA) is a widely adopted
tool to collect information on causes of death when full autopsy and death certification are not possible. The
current analytical methods for VA are significantly limited by the lack of generalizability. Existing VA methods yield
inaccurate cause-of-death assignment and biased estimates of the distribution of deaths when they are deployed
to populations that are different than the populations based on which the models are developed. In this project,
we will develop robust, domain adaptive, and computationally feasible methods to assign causes to individual
deaths and estimate cause-specific mortality, by completing the following aims: (i) to develop statistical models
to characterize multiple heterogeneous VA datasets; (ii) to develop and evaluate domain adaptive algorithms
for cause-of-death assignment in new populations; and (iii) to extend the unified domain adaptation framework
to routine VA analysis pipeline. This new framework will improve on existing VA methods by utilizing the full
information available in reference deaths from multiple populations to achieve robustness to data shift across
populations. The framework will also incorporate the complex dependence relationship in the collected signs and
symptoms in an interpretable manner, and allow fast and streamlined implementation compatible with standard
VA questionnaires. We will develop the first unified framework for domain adaptive cause-of-death assignment
using VA data and offer critical insights into the relationship between the signs and symptoms collected by VA and
causes of death. The project will lay the groundwork for future research, such as integrating VAs with additional
covariates and biomarker information collected from medical history or tissue samples, and designing systematic
cause-of-death monitoring and surveillance using large-scale VA surveys.
项目摘要
死亡原因数据对于了解疾病负担、新出现的卫生需求以及
公共卫生干预措施的有效性。低收入和中等收入国家(LMIC)很少有足够的生命力。
提供高质量死因统计数据的统计系统。口头尸检(VA)是一种广泛采用的
在无法进行全面尸检和颁发死亡证明时收集死因信息的工具。的
目前VA的分析方法受到缺乏普遍性的严重限制。现有的VA方法产生
死亡原因分配不准确,部署时对死亡分布的估计有偏差
与开发模型所基于的群体不同的群体。在这个项目中,
我们将开发强大的,域自适应的,计算上可行的方法来分配个人的原因,
(一)建立统计模型,
表征多个异构VA数据集;(ii)开发和评估域自适应算法
(iii)扩展统一的艾德域适应框架
到常规VA分析管道。这个新的框架将通过利用完整的
多个人群的参考死亡中的可用信息,以实现对数据转移的稳健性
人口。该框架还将把复杂的依赖关系纳入所收集的迹象,
以可解释的方式描述症状,并允许快速和简化的实施,
退伍军人事务部问卷。我们将开发第一个统一的艾德框架,用于领域自适应死因分配
使用VA数据,并对VA收集的体征和症状之间的关系提供重要见解,
死因该项目将为未来的研究奠定基础,例如将VA与其他
从病史或组织样本中收集的协变量和生物标志物信息,并设计系统
使用大规模VA调查进行死因监测和监督。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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{{ truncateString('Zehang Li', 18)}}的其他基金
Developing an innovative statistical framework to integrate multiple verbal autopsy datasets to estimate cause-specific mortality
开发创新的统计框架来整合多个口头尸检数据集,以估计特定原因的死亡率
- 批准号:
10576014 - 财政年份:2022
- 资助金额:
$ 7.3万 - 项目类别:
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