Anker M
Division of Emerging and other Communicable Diseases Surveillance and Control (EMC), World Health Organization, Geneva, Switzerland.
Int J Epidemiol. 1997 Oct;26(5):1090-6. doi: 10.1093/ije/26.5.1090.
Verbal autopsy (VA) studies are important for measuring cause-specific mortality in areas where medical certification of cause of death is uncommon. This paper explores the effects of misclassification errors on the results of verbal autopsy studies, and recommends ways to take misclassification errors into account in the interpretation of results.
Mathematical formulae are derived for determining the size and direction of the error in cause-specific mortality estimates based on VA studies caused by misclassification. The levels of sensitivity and specificity found in currently available validation studies for childhood VA are examined.
There can be substantial errors in the estimates of the cause-specific mortality fraction derived from VA studies. The cause-specific mortality fraction itself has an important influence on the size of the error for given levels of sensitivity and specificity, and when the cause-specific mortality fraction is small, the size of the error depends more on specificity than on sensitivity.
Despite its drawbacks VA seems to be the most promising way of establishing cause of death when most deaths take place at home without medical attention. However, more validation studies on standardized instruments are required in order to collect information about sensitivity and specificity and subsequently improve the design of the instrument. At the same time, analysts need to take misclassification errors into consideration in ways outlined in this paper.
在死亡原因医学认证不常见的地区,死因推断(VA)研究对于衡量特定病因死亡率很重要。本文探讨了错误分类误差对死因推断研究结果的影响,并推荐了在结果解释中考虑错误分类误差的方法。
推导了数学公式,以确定基于VA研究的特定病因死亡率估计中因错误分类导致的误差大小和方向。研究了目前可用的儿童VA验证研究中的灵敏度和特异度水平。
VA研究得出的特定病因死亡率比例估计可能存在重大误差。对于给定的灵敏度和特异度水平,特定病因死亡率比例本身对误差大小有重要影响,且当特定病因死亡率比例较小时,误差大小更多地取决于特异度而非灵敏度。
尽管存在缺陷,但当大多数死亡发生在家中且未得到医疗救治时,VA似乎是确定死亡原因最有前景的方法。然而,需要对标准化工具进行更多验证研究,以收集有关灵敏度和特异度的信息,进而改进工具设计。同时,分析人员需要按照本文所述方法考虑错误分类误差。