Why do millions of infants continue to die needlessly each year when tools are available to save them? What is the best way to save lives through behavioral change? Two approaches have emerged recently to saving newborn lives using behavior change in communities with high rates of newborn death.
The first approach involves organizing groups of women who then participate in a facilitated process to prioritize local issues affecting maternal and neonatal health.
New analyses suggest that the presence and density of women’s groups, relative to the overall population in poor regions of the world, is a key determinant of whether or not the number of newborns who die decreases. How does it work? These groups of women design and implement solutions to problems they have identified. They then measure their progress and correct their approach in an action cycle of learning, measurement, and improvement. This method has been effective in several poor regions of the world: in Nepal (where newborn mortality has been reduced by 30 percent), India (which has seen a 45 percent reduction in newborn death) and Bangladesh (38 percent reduction).
The second approach for inducing behavior change emphasizes what communities can do to reduce the risk of newborns dying, and to identify specific improvements in maternal and newborn health care which can mitigate those risks.
This approach also looks at these risk factors in a sociocultural context. What does this do? It helps inform the development of powerful, impactful stories, interpersonal negotiations and demonstrations to help facilitate behavior change. Success is then measured as the proportion of pregnant women and newborns practicing certain life-saving behaviors.
This second pathway, termed behavior change management, was tested in Uttar Pradesh, India, where the interventions were preselected specifically for the region. In other words, the approach looked at causes of newborn death specific to that region—particularly serious infections—and risk factors such as hypothermia, poor hygiene, and lack of immediate and exclusive breastfeeding. When these issues were targeted, newborn deaths were greatly reduced—by 54 percent.
In contrast, in Mirzapur, Bangladesh, a newborn health intervention which involved home visits for mothers and newborns by health care providers during the antenatal and early postnatal period did not reduce the number of newborns who died. Why? On the surface, it seemed to address the issues. There were high levels of program coverage, demonstrably good quality of the interventions, and improved newborn care practices. Newborn death rates did not improve significantly, however, because the interventions failed to adequately address asphyxia and prematurity, key causes of mortality in that local community.
In other words, when an intervention or series of interventions do not take into account the local evidence for causes of newborn deaths and target the intervention approach to address the risk factors for those causes of death, the newborn mortality rate may not decrease in that community.
Ideally, we want to understand which of these two approaches best reduces the number of newborns dying in particular communities. Is it working with the women’s groups through action cycles of learning in the region? Or is it carefully crafted, socio-culturally relevant communications leading to changes in specific maternal and newborn care behaviors? The most efficient and effective ways to influence a population to change maternal and newborn care practices remain in question.
The Maternal Newborn and Child Health team at the Bill and Melinda Gates Foundation is testing (currently in Uttar Pradesh, India) whether a synthesis of the two approaches is best: empowering women in their community in women’s groups while simultaneously emphasizing evidence-based practices relevant to newborn mortality in a specific community, reinforced by early home visits by frontline health workers. The study should improve our understanding of how best to reduce newborn mortality through behavior change.