An integrated approach to processing WHO-2016 verbal autopsy data: the InterVA-5 model
An integrated approach to processing WHO-2016 verbal autopsy data: the InterVA-5 model
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DOI：
10.1186/s12916-019-1333-6
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发表时间：
2019-05-30
期刊：
影响因子：
9.3
通讯作者：
Petzold, Max
中科院分区：
文献类型：
--
作者：
Byass, Peter;Hussain-Alkhateeb, Laith;Petzold, Max
背景资料：口头尸检是一种越来越重要的方法，可以确定未经证实的死亡原因，这些死亡约占全球死亡率的50%，给卫生规划带来很大的不确定性。世界卫生组织为死因推断访谈的结构和可从死因推断数据合理得出的原因类别制定了国际标准。此外，需要计算机模型来有效处理大量的死因推断访谈，以标准化的方式确定死因。在这里，我们提出了InterVA-5模型，该模型是为符合WHO-2016死因推断标准而开发的。这是一个协调模型，可以处理来自WHO-2016以及更早的WHO-2012和Tariff-2格式的输入数据，以生成不同情况下的标准化死因特异性死亡率概况。软件开发涉及在早期的InterVA-4模型基础上进行构建，InterVA-5所需的扩展知识库是通过对来自人口健康研究协作组织死因推断参考数据集的训练数据集进行分析以及专家输入来提供信息的。新模型是根据来自人口健康研究合作组织的6130例病例和来自阿富汗国家死亡率调查的4009例病例的测试数据集进行评估的数据集。这两个来源都包含来自WHO-2016、WHO-2012和Tariff-2格式的大约四分之三的输入项。在参与的三级医院和测试数据集中的InterVA-5中分配的原因之间，比较了所有适用的WHO原因类别中的原因特异性死亡率分数，儿童的一致性相关系数为0.92，成人为0.86。在阿富汗数据集中评估了InterVA-5模型处理不同输入格式的能力，WHO-2016和WHO-2012格式之间儿童和成人的一致性相关系数分别为0.97和0.96，WHO-2016和Tariff-2格式之间的一致性相关系数分别为0.92和0.87。尽管在确定死因时确定“真相”存在固有的困难，但这些发现表明，InterVA-5模型表现良好，并成功地协调了一系列输入格式。随着在WHO-2016下收集的更多原始数据可用，InterVA-5可能会根据实际经验进行轻微的版本调整。该模型是衡量和评价全球死因别死亡率的重要资源。
Background: Verbal autopsy is an increasingly important methodology for assigning causes to otherwise uncertified deaths, which amount to around 50% of global mortality and cause much uncertainty for health planning. The World Health Organization sets international standards for the structure of verbal autopsy interviews and for cause categories that can reasonably be derived from verbal autopsy data. In addition, computer models are needed to efficiently process large quantities of verbal autopsy interviews to assign causes of death in a standardised manner. Here, we present the InterVA-5 model, developed to align with the WHO-2016 verbal autopsy standard. This is a harmonising model that can process input data from WHO-2016, as well as earlier WHO-2012 and Tariff-2 formats, to generate standardised cause-specific mortality profiles for diverse contexts. The software development involved building on the earlier InterVA-4 model, and the expanded knowledge base required for InterVA-5 was informed by analyses from a training dataset drawn from the Population Health Metrics Research Collaboration verbal autopsy reference dataset, as well as expert input.Results: The new model was evaluated against a test dataset of 6130 cases from the Population Health Metrics Research Collaboration and 4009 cases from the Afghanistan National Mortality Survey dataset. Both of these sources contained around three quarters of the input items from the WHO-2016, WHO-2012 and Tariff-2 formats. Cause-specific mortality fractions across all applicable WHO cause categories were compared between causes assigned in participating tertiary hospitals and InterVA-5 in the test dataset, with concordance correlation coefficients of 0.92 for children and 0.86 for adults. The InterVA-5 model's capacity to handle different input formats was evaluated in the Afghanistan dataset, with concordance correlation coefficients of 0.97 and 0.96 between the WHO-2016 and the WHO-2012 format for children and adults respectively, and 0.92 and 0.87 between the WHO-2016 and the Tariff-2 format respectively.Conclusions: Despite the inherent difficulties of determining "truth" in assigning cause of death, these findings suggest that the InterVA-5 model performs well and succeeds in harmonising across a range of input formats. As more primary data collected under WHO-2016 become available, it is likely that InterVA-5 will undergo minor re-versioning in the light of practical experience. The model is an important resource for measuring and evaluating cause-specific mortality globally.