Preventing stillbirths in high burden settings : Examining gaps and opportunities to strengthen routine perinatal data collection and to improve quality of care

Of the 2 million stillbirths globally every year, low- and middle-income
countries (LMIC) bear the largest burden with 75% taking place in
Sub-Saharan Africa and South Asia. Stillbirths are preventable with
quality pregnancy and childbirth care; however, stillbirth prevention is
impeded by the absence of quality perinatal data from high burden
settings that is critical to understanding the burden, risk factors and
causes to guide quality of care improvement and prevent these
deaths. There is insufficient knowledge and understanding of existing
perinatal data and data collection systems in LMIC. In this fellowship,
I will undertake an in-depth study of routine perinatal data and data
collection systems in health facilities in three countries (Afghanistan,
Benin and Cambodia) to identify data gaps and needs and how to
strengthen these systems. I will use quantitative methods and health
systems research to examine data quality attributes, understand the
flow of data at different health system levels, how this data is used
for decision-making and subsequently informs improvements in the
quality of care. I will explore barriers and facilitators to improving
these systems using qualitative interviews with healthcare providers,
health officials and data management practitioners. The findings will
inform strategies to improve the availability of reliable, quality,
perinatal data, produced locally by resilient systems to advance
stillbirth prevention and improve quality of care.