A statewide system for maternal-infant linked longitudinal surveillance: Indiana’s model for improving maternal and child health

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2024-11-11
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American English
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Swansea University
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Abstract

Indiana, located in the Midwest region of the United States, faces significant challenges with respect to health, especially maternal and child health (MCH). These challenges include high rates of stillbirth, neonatal abstinence syndrome (NAS) and congenital syphilis (CS). Not only are these often-fatal conditions underreported, but it can also be difficult to track them longitudinally, as mothers and infants are not routinely linked through electronic health records (EHRs). This paper describes the process, structure and planned outcomes of a partnership between Indiana University, Regenstrief Institute and public health partners in support of the U.S. Centers for Disease Control and Prevention’s Pregnant People-Infant Linked Longitudinal Surveillance (PILLARS) program. Together, academic, clinical and public health organisations are collaboratively developing an infrastructure and deploying novel methods to surveil stillbirth, CS and NAS longitudinally. The infrastructure includes: (a) deploying deterministic and probabilistic algorithms to link mothers and their infants using multiple, linked data sources; (b) creating and maintaining a registry of maternal-infant dyads; (c) using the registry to perform longitudinal surveillance in collaboration with Indiana public health authorities on stillbirth, NAS and CS and (d) translating information from surveillance activities into action by collaborating with public health and community-based organisations to improve and implement prevention activities in vulnerable Indiana communities. Our long-term goal is to improve outcomes for these conditions and other priority MCH outcomes by expanding our work to additional MCH use cases.

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Inderstrodt J, Riggins DP, Greenwell A, et al. A statewide system for maternal-infant linked longitudinal surveillance: Indiana's model for improving maternal and child health. Int J Popul Data Sci. 2024;9(2):2395. Published 2024 Nov 11. doi:10.23889/ijpds.v9i2.2395
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International Journal of Population Data Science
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PMC
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