Institute for Quantitative Social Science, Harvard University, Cambridge MA 02138, USA.
Popul Health Metr. 2010 Jun 23;8:19. doi: 10.1186/1478-7954-8-19.
Verbal autopsy analyses are widely used for estimating cause-specific mortality rates (CSMR) in the vast majority of the world without high-quality medical death registration. Verbal autopsies -- survey interviews with the caretakers of imminent decedents -- stand in for medical examinations or physical autopsies, which are infeasible or culturally prohibited.
We introduce methods, simulations, and interpretations that can improve the design of automated, data-derived estimates of CSMRs, building on a new approach by King and Lu (2008). Our results generate advice for choosing symptom questions and sample sizes that is easier to satisfy than existing practices. For example, most prior effort has been devoted to searching for symptoms with high sensitivity and specificity, which has rarely if ever succeeded with multiple causes of death. In contrast, our approach makes this search irrelevant because it can produce unbiased estimates even with symptoms that have very low sensitivity and specificity. In addition, the new method is optimized for survey questions caretakers can easily answer rather than questions physicians would ask themselves. We also offer an automated method of weeding out biased symptom questions and advice on how to choose the number of causes of death, symptom questions to ask, and observations to collect, among others.
With the advice offered here, researchers should be able to design verbal autopsy surveys and conduct analyses with greatly reduced statistical biases and research costs.
在世界上绝大多数没有高质量医学死亡登记的地方,死因推断分析被广泛用于估计特定死因死亡率(CSMR)。死因推断——对即将死亡者的照顾者进行的调查访谈——替代了医学检查或尸体解剖,而这些检查或解剖在实际操作中不可行或在文化上受到禁止。
我们介绍了一些方法、模拟和解释,可以改进自动数据衍生的 CSMR 估计的设计,这是基于 King 和 Lu(2008)的新方法。我们的结果为选择症状问题和样本量提供了建议,这些建议比现有实践更容易满足。例如,大多数先前的努力都致力于寻找具有高灵敏度和特异性的症状,但对于多种死因,这种方法很少成功。相比之下,我们的方法使这种搜索变得无关紧要,因为即使使用敏感性和特异性非常低的症状,也可以产生无偏估计。此外,新方法针对的是照顾者可以轻松回答的调查问题,而不是医生会问自己的问题。我们还提供了一种自动剔除有偏症状问题的方法,并提供了如何选择死因数量、要询问的症状问题以及要收集的观察结果等方面的建议。
有了这里提供的建议,研究人员应该能够设计死因推断调查并进行分析,大大减少统计偏差和研究成本。