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Health and Demographic Surveillance Systems as a Platform for More Precisely Identifying Causes of Death: Insights from the Champs Network

Solveig A Cunningham, Netherlands Interdisciplinary Demographic Institute and Emory University
Sunday Adedini, University of the Witwatersrand
Melkamu Mengesha, Haramaya University
Abu Mohd Naser Titu, Emory Global Health Institute, Emory University
Atique Chowdhury, Maternal and Child Health Division, icddr,b
Portia Mutevedzi, Respiratory and Meningeal Pathogens Research Unit,Chris Hani Baragwanath Hospital
David Obor, KEMRI/CDC Public health collaboration
Thomas Misore, Kenya Medical Research Institute
Uma Onwuchekwa, Centre Pour les Vaccins en Développement
Ariel Nhacolo, Manhica Health Research Center (CISM)
Nega Kassa, Haramaya University
Karen Kotloff, University of Maryland School of Medicine

The Child Health and Mortality Prevention Surveillance (CHAMPS) project aims to inform policy to prevent child deaths by adjudicating causes of death among children under-five years of age in high-mortality countries. As such, a key objective of CHAMPS is to define population-based rates for causes of death through diagnostic and laboratory methods (minimally-invasive tissue sampling) nested within population surveillance. CHAMPS includes a network of Health and Demographic Surveillance Systems (HDSS), which provide the population platform through demographic data. These data are necessary for estimating population-based mortality rates and contextual information for understanding factors associated with the childhood deaths. Six of the seven sites that constitute the CHAMPS network have active HDSS: Mozambique, Mali, Ethiopia, Kenya, Bangladesh, and South Africa. This presentation will describe the CHAMPS HDSS network, its role in the CHAMPS project, and early results from its efforts to more precisely identify causes of death among young children.

See paper.

  Presented in Session 48. Integrating Traditional and New Forms of Data Source including Accessibility of New and Innovative Data