Sanjeev Sridharan | October 2015
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Presentation at the Global Maternal Newborn Health Conference, October 19, 2015

Maternal health inequities have multiple levels of drivers: community or area level factors include community norms, social practices, area-level poverty, as well as individual level factors such as employment status, literacy, religion and caste. Given the multilevel nature of the maternal health inequities, it is important to explore if and how programs disrupt such multiple levels of factors and also explore the synergistic impacts of cross-level factors.  In this presentation we explore the multilevel drivers of maternal health inequities in Uttar Pradesh (UP), the most populated state of India with comparatively poor performance on several development indicators. The UP Technical Support Unit (TSU) provides a range of “techno-managerial” assistance to support the Government in addressing the inequities in the reproductive, maternal, newborn and child health (RMNCH) outcomes in 100 blocks of the state. For the most part, this intervention’s theory of change has a limited a priori understanding of multilevel contexts in which the program is likely to work.  Using the baseline data from this project, this paper explores the factors associated with existing health outcome inequities and the approach of the UP-TSU in addressing them. Multilevel models were developed to explore measures of access to health care and health outcomes through the continuum of care. Contextual factors that were explored included block-level measures on literacy, sex-ratios and employment rates.   Innovative features of our application are the use of multilevel models to identify blocks that “buck” the general trend, and the combination of the multilevel-model data with the qualitative data to validate and triangulate the results.  We discuss whether the intervention is responding to the knowledge of context. The findings emphasize the need to pay attention to local context and the need to have a heterogeneous package of program supports to address inequities in maternal health.