Department of Public Health and Clinical Medicine, Umeå Centre for Global Health Research, Umeå University, Umeå, Sweden.
PLoS One. 2011;6(11):e27200. doi: 10.1371/journal.pone.0027200. Epub 2011 Nov 3.
InterVA is a probabilistic method for interpreting verbal autopsy (VA) data. It uses a priori approximations of probabilities relating to diseases and symptoms to calculate the probability of specific causes of death given reported symptoms recorded in a VA interview. The extent to which InterVA's ability to characterise a population's mortality composition might be sensitive to variations in these a priori probabilities was investigated.
A priori InterVA probabilities were changed by 1, 2 or 3 steps on the logarithmic scale on which the original probabilities were based. These changes were made to a random selection of 25% and 50% of the original probabilities, giving six model variants. A random sample of 1,000 VAs from South Africa, were used as a basis for experimentation and were processed using the original InterVA model and 20 random instances of each of the six InterVA model variants. Rank order of cause of death and cause-specific mortality fractions (CSMFs) from the original InterVA model and the mean, maximum and minimum results from the 20 randomly modified InterVA models for each of the six variants were compared.
CSMFs were functionally similar between the original InterVA model and the models with modified a priori probabilities such that even the CSMFs based on the InterVA model with the greatest degree of variation in the a priori probabilities would not lead to substantially different public health conclusions. The rank order of causes were also similar between all versions of InterVA.
InterVA is a robust model for interpreting VA data and even relatively large variations in a priori probabilities do not affect InterVA-derived results to a great degree. The original physician-derived a priori probabilities are likely to be sufficient for the global application of InterVA in settings without routine death certification.
InterVA 是一种用于解释死因推断调查（VA）数据的概率方法。它使用与疾病和症状相关的先验概率近似值，根据 VA 访谈中记录的报告症状，计算特定死因的概率。本研究旨在探讨 InterVA 识别人群死亡构成的能力对这些先验概率变化的敏感程度。
通过对数尺度上的 1、2 或 3 个步骤改变先验 InterVA 概率，基于原始概率。对原始概率的随机选择的 25%和 50%进行这些更改，得到六个模型变体。从南非随机抽取 1000 例 VA，作为实验基础，并使用原始 InterVA 模型和六个 InterVA 模型变体中的每个变体的 20 个随机实例处理。对原始 InterVA 模型和 20 个随机修改的 InterVA 模型中的每个变体的 20 个随机实例的死因排序和死因特异性死亡率分数（CSMF）进行比较。
原始 InterVA 模型和具有修改先验概率的模型之间的 CSMF 具有相似的功能，即使是基于先验概率变化最大的 InterVA 模型的 CSMF 也不会导致公共卫生结论的实质性差异。所有版本的 InterVA 的死因排序也相似。
InterVA 是一种解释 VA 数据的稳健模型，即使是先验概率的相对较大变化也不会对 InterVA 衍生的结果产生很大影响。在没有常规死亡证明的情况下，全球应用 InterVA 时，原始医生推导的先验概率可能已经足够。
