Scaling-up impact in perinatology through systems science: Bridging the collaboration and translational divides in cross-disciplinary research and public policy.

Despite progress over the past decade in reducing the global burden of newborn deaths, gaps in the knowledge base persist, and means of translating empirical findings into effective policies and programs that deliver life-saving interventions remain poorly understood. Articles in this issue highlight the relevance of transdisciplinary research in perinatology and calls for increased efforts to translate research into public policy and to integrate interventions into existing primary care delivery systems. Given the complexity and multi-causality of many of the remaining challenges in newborn health, and the effects that social and economic factors have over many newborn conditions, it has further been proposed that integrated, multi-sector public policies are also required. In this article, we discuss the application of systems science methods to advance transdisciplinary research and public policy-making in perinatology. Such approaches to research and public policy have been used to address various global challenges but have rarely been implemented in developing country settings. We propose that they hold great promise to improve not only our understanding of complex perinatology problems but can also help translate research-based insights into effective, multi-pronged solutions that deliver positive, intended effects. Examples of successful transdisciplinary science exist, but successes and failures are context specific, and there are no universal blueprints or formulae to reproduce what works in a specific context into different social system settings. Group model building is a tool, based in the field of System Dynamics, that we have used to facilitate transdisciplinary research and, to a lesser extent, policy formulation in a systematic and replicable way. In this article, we describe how group model building can be used and argue for scaling its use to further the translation of empirical evidence and insights into policy and action that increase maternal and neonatal survival and well-being.