摘要：Objectives: To conduct a comparative analysis of the distribution and effects of under-five mortality correlates using Demographic and Health Survey (DHS) and Demographic Surveillance System (DSS) data from Ethiopia, and to investigate the methodological bias in DHS-based childhood mortality rates due to the impossibility of including children whose mothers were deceased. Methods: Using all-cause under-5 mortality as an outcome variable, the distribution and effects of risk factors were modeled using survival analysis. All live births in rural Ethiopia in the 5-year period before the 2005 DSS+ survey and between 01/01/2000 and 31/12/2004 in the DSS in the Butajira Rural Health Program (in the Southern Nations, Nationalities, and People's (SNNP) region of Ethiopia) were included. Results: Overall, similar estimates of hazard rate ratios were derived from both DHS and DSS data and the child mortality risk profile is similar between each data source, with multiple births and living in less populous households being significant risk factors for under-five mortality. Nevertheless, some notable differences were observed. The DSS data was more sensitive to local variations in population composition and health status, whilst the more dispersed DHS approach tended to average out local variation across the country. Excluding children whose mothers were deceased from the DSS analysis had no important effect on risk profiles or estimates of survival functions at age 5 years. DHS survival functions were somewhat lower than DSS estimates (BRHP=0.87, DHS rural Ethiopia=0.67, DHS SNNP=0.66). Conclusion: Despite differing methodologies, cross-sectional DHS and longitudinal DSS data produce estimates of the distribution and effects of under-five mortality risk factors that are broadly similar. The differing methodological characteristics of DHS and DSS mean that when combined, these two data sources have the potential to provide a comprehensive picture of national population composition and health status as well as the extent of local variation – both of which are important for health monitoring and planning.
