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Bmc Pregnancy Childb · Jan 2017
Geographic information system for improving maternal and newborn health: recommendations for policy and programs.
- Yordanos B Molla, Barbara Rawlins, Prestige Tatenda Makanga, Marc Cunningham, Juan Eugenio Hernández Ávila, Corrine Warren Ruktanonchai, Kavita Singh, Sylvia Alford, Mira Thompson, Vikas Dwivedi, Allisyn C Moran, and Zoe Matthews.
- USAID's Maternal and Child Survival Program/Save the Children, Washington, DC, USA. ymolla@savechildren.org.
- Bmc Pregnancy Childb. 2017 Jan 11; 17 (1): 26.
AbstractThis correspondence argues and offers recommendations for how Geographic Information System (GIS) applied to maternal and newborn health data could potentially be used as part of the broader efforts for ending preventable maternal and newborn mortality. These recommendations were generated from a technical consultation on reporting and mapping maternal deaths that was held in Washington, DC from January 12 to 13, 2015 and hosted by the United States Agency for International Development's (USAID) global Maternal and Child Survival Program (MCSP). Approximately 72 participants from over 25 global health organizations, government agencies, donors, universities, and other groups participated in the meeting.The meeting placed emphases on how improved use of mapping could contribute to the post-2015 United Nation's Sustainable Development Goals (SDGs), agenda in general and to contribute to better maternal and neonatal health outcomes in particular. Researchers and policy makers have been calling for more equitable improvement in Maternal and Newborn Health (MNH), specifically addressing hard-to-reach populations at sub-national levels. Data visualization using mapping and geospatial analyses play a significant role in addressing the emerging need for improved spatial investigation at subnational scale. This correspondence identifies key challenges and recommendations so GIS may be better applied to maternal health programs in resource poor settings. The challenges and recommendations are broadly grouped into three categories: ancillary geospatial and MNH data sources, technical and human resources needs and community participation.
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