Last week, experts in maternal and reproductive health gathered at the Wilson Center in Washington, D.C. to discuss the latest data on maternal health in conflict and crises, identify policy and funding gaps and explore how to address maternal health challenges in these hard-to-reach areas. The dialogue, Closing the Gaps of Maternal Health in Conflict and Crises, was part of the Maternal Health Task Force’s Advancing Policy Dialogue on Maternal Health Series in partnership with the United Nations Populations Fund (UNFPA) and the Wilson Center. The panelists offered a range of perspectives and recommended the following key actions to improve maternal health in humanitarian settings.
Do not wait for perfect data
While maternal and newborn care should be grounded in high quality evidence, the humanitarian response must not be stalled as randomized control trials are conducted and studies are peer-reviewed. As Dr. Paul Spiegel, Director of the Center for Refugee and Disaster Response at the Johns Hopkins Bloomberg School of Public Health, articulated, “There is not a one-size-fits-all response… We need to have principles, but they need to be applied differently according to context.” Using implementation science and operational research methods allow researchers and program implementers to utilize data from development settings and apply lessons learned to humanitarian settings. Spiegel called upon the global maternal health community to maximize existing evidence: “If we were all using similar case definitions, similar ways of collecting data—even if they are not the same instruments—we could do a lot more with what we have.”
Support programmers in humanitarian settings
Echoing Spiegel’s urgent call to action, Massimo Diana, Representative for UNFPA in Syria, stressed that programmers in humanitarian settings need support operationalizing data to improve maternal and sexual and reproductive health care: “The scientific and expert community could help humanitarians find agile mechanisms for how data could be processed and fast assessment methodology.” While these methods may not be the strongest from a scientific perspective, they are “feasible, practical, adaptable and implementable on the ground.” According to Diana, the field must work together to explore how to leverage existing tools such as mobile technology to develop simple, effective strategies for collecting and interpreting data from various settings.
Expand maternal newborn health care
The global capacity to address reproductive health issues in humanitarian crises has grown over the last decade, as illustrated by increased funding and uptake of the Minimum Initial Service Package (MISP). However, according to the Inter-Agency Working Group (IAWG) on Reproductive Health in Crises’ 2012-2014 Global Evaluation, despite increased need, conflict-affected settings receive 57% less funding for reproductive health than non conflict-affected areas. Sandra Krause, Program Director of Reproductive Health at the Women’s Refugee Commission, explained that maternal newborn health funding is typically allocated to antenatal care, postnatal care and nutrition programs, while little is designated for emergency obstetric and newborn care, family planning and safe abortion care.
Engage the private sector
Joy Marini, Executive Director of Global Community Impact, Maternal, Newborn, Child Health and Women and Girls at Johnson & Johnson, highlighted the important role that the private sector has in improving maternal and child health in fragile and conflict-affected states. To engage the private sector, Marini advised the maternal health community to look through a different lens: Key approaches include reminding stakeholders in the private sector that humanitarian settings are markets and emphasizing corporate social responsibility. When working with the private sector, however, programmers must expand timelines to allow for quality baseline data collection and collaborate with key stakeholders to ensure sustainability. As Marini articulated, programs built with local ownership and quality data from the beginning are more likely to be successful at scale.
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