moving from data on deaths to public health policy in agincourt, south africa approaches to analysing and understanding verbal autopsy findings从死亡的数据转移到公共卫生政策在阿金库尔战役中,南非的方法来分析和理解口头尸检结果.pdfVIP

Moving from Data on Deaths to Public Health Policy in Agincourt, South Africa: Approaches to Analysing and Understanding Verbal Autopsy Findings 1 1,2 1 1,2 1,2 Peter Byass *, Kathleen Kahn , Edward Fottrell , Mark A. Collinson , Stephen M. Tollman ˚ ˚ 1 Umea Centre for Global Health Research, Department of Public Health and Clinical Medicine, Umea University, Sweden, 2 MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa Abstract Background: Cause of death data are an essential source for public health planning, but their availability and quality are lacking in many parts of the world. Interviewing family and friends after a death has occurred (a procedure known as verbal autopsy) provides a source of data where deaths otherwise go unregistered; but sound methods for interpreting and analysing the ensuing data are essential. Two main approaches are commonly used: either physicians review individual interview material to arrive at probable cause of death, or probabilistic models process the data into likely cause(s). Here we compare and contrast these approaches as applied to a series of 6,153 deaths which occurred in a rural South African population from 1992 to 2005. We do not attempt to validate either approach in absolute terms. Methods and Findings: The InterVA probabilistic model was applied to a series of 6,153 deaths which had previously been reviewed by physicians. Physicians used a total of 250 cause-of-death codes, many of which