Using provisional vital statistics for collaborative learning: A state-based learning model for improving sudden unexplained infant death

Background: Given that sudden unexplained infant death (SUID) is the leading cause of infant death in the US between 1 month and 1 year of life, improving safe sleep practices is among the strategies of the Infant Mortality Collaborative Innovation and Improvement Network (IM CoIIN)--a national effort to help states share and apply evidence-based and innovative strategies to improve infant mortality. The IM CoIIN utilizes quarterly, provisional vital statistics to track SUID for real-time feedback on programmatic efforts. However, many states experience challenges in reporting provisional statistics.

Methods: To identify effective strategies to improve use of provisional statistics, state epidemiologists representing between 27 and 29 states, were surveyed on their ability to report provisional statistics, challenges, and strategies to overcoming reported barriers.

Results: Four major challenges were identified by states, including lack of resources as well as data quality, data sharing, and technical issues. Each challenge was paired with strategies states had used to overcome these barriers.  For example, states with few resources used SSDI funding, shared personnel between departments of health and vital registrar offices, or built interdepartmental relationships to overcome this challenge. For data quality, such as pending SUID cases, states used combined approaches such as using SUID investigation forms from the CDC, looking at number of pending cases (R99), and setting minimum thresholds for completeness.

Conclusion: Despite challenges, states are sharing best practices to continue to improve their ability to use provisional vital statistics for real-time monitoring of efforts to promote safe sleep practices and reduce SUID.