Verbal Autopsy: Reimagining Data & Automated Cause Assignment (using ALPHA Network data)

 描述(由申请人提供)：通过衡量有多少人因各种疾病而死亡或受到影响，来确定全球卫生优先事项并评估进展情况。大多数低收入和中等收入国家没有足够的重要统计数据，导致全球三分之二的死亡没有登记，也没有原因。当无法进行全面尸检和死亡认证时，口头尸检(VA)有可能成为一种可行的、负担得起的死因评估方法。现有的VA方法不能产生可重复的、可比较的死因分配，而且速度慢得令人无法接受。该项目旨在开发负担得起的、可靠的、经过校准的方法，通过完成以下四项活动来确定死亡原因的分布：(1)开发一种新的标准的、自动化的统计方法，以根据VA数据-InSilicoVA为死亡分配原因，(2)使用更多基于设施的数据和无偏见的医生分配的原因来创建新的、改进的症状-原因信息模块，由InsiloVA使用来通知原因的分配，(3)对现有的从VA数据分配原因的自动方法进行详尽、公平的比较，包括InsilicoVA，以及(4)通过在非洲多个长期实地工作地点开展实地工作，了解和规范退伍军人管理局面谈，然后测试新面谈设计和语言处理程序的有效性，从而减少退伍军人管理局数据中的测量误差。InSilicoVA将通过以下方式改进现有方法：(1)完全概率，(2)保持个人和总体水平的一致性，(3)报告两个水平的一致不确定性，(4)利用A采访的叙述部分的信息，(5)对测量误差进行核算，以及(6)采用分层结构，以便跨广泛地区进行汇总和比较。在本项目结束时，退伍军人管理局用户社区将拥有(1)对InSilicoVA的全面、开源的访问权限，(2)用于InSilicoVA和其他方法的有价值的新症状原因信息模块，(3)描述现有退伍军人管理局原因自动分配方法表现的迫切需要的客观信息，以及(4)创新的标准化退伍军人管理局面试设计和标准操作程序，用于在退伍军人管理局面试期间和之后处理语言和退伍军人管理局数据。总体效果将是增加使用可重复的方法以可量化的精确度确定具有可比原因的死亡人数。这反过来将提供一个机会，在世界某些地区改善国家、区域和全球人口健康指标，而不是 有足够的生命统计系统。有了关于人口健康的更好和更及时的信息，就可以更有效和更有效地分配资源和有针对性地采取干预措施。更多的生活将得到改善和延长。

 DESCRIPTION (provided by applicant): Global health priorities are set and progress assessed by measuring how many lives are ended or affected by various diseases. Most low and middle income countries do not have adequate vital statistics, with the result that two-thirds of global deaths are not registered and have no cause. Verbal autopsy (VA) has the potential to become a feasible, affordable method for assessing cause of death when full autopsy and death certification are not possible. Existing VA methods do not yield reproducible, comparable cause of death assignments and can be unacceptably slow. This project aims to develop affordable, robust, calibrated methods to assign causes to individual deaths and characterize the distribution of deaths by cause by completing the following four activities: (1) develop a new standard, automated, statistical method to assign causes to deaths from VA data - InSilicoVA, (2) use additional facility-based data and debiased physician-assigned causes to create new, improved symptom-cause information modules used by InSilicoVA to inform the assignment of causes, (3) conduct an exhaustive, fair comparison of existing automated methods for assigning causes from VA data, including InSilicoVA, and (4) reduce measurement error in VA data by conducting fieldwork at multiple long-term field sites in Africa to understand and standardize the VA interview and then test the effectiveness of a new interview design and language processing procedures informed by that understanding. InSilicoVA will improve on existing methods by (1) being fully probabilistic, (2) keeping individual and population levels consistent (3) reporting consistent uncertainty at both levels, (4) utilizing information from the narrative section of the A interview, (5) accounting for measurement error, and (6) incorporating a hierarchical structure to allow aggregation and comparison across broad regions. At the end of this project, the community of VA users will have (1) full, open-source access to InSilicoVA, (2) valuable, new symptom-cause information modules for use in InSilicoVA and other methods, (3) urgently needed, objective information describing the performance of existing automated VA cause-assignment methods, and (4) an innovative new standardized VA interview design and standard operating procedures for handling language and VA data during and after the VA interview. The overall effect will be to increase the number of deaths with comparable causes determined using a reproducible method with quantifiable accuracy. This, in turn, will provide an opportunity to improve national, regional and global population health indicators in parts of the world without adequate vital statistics systems. With better and more timely information on population health, resources can be allocated and interventions targeted more efficiently and with greater effect. More lives will be improved and extended.