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

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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Publication Details

Journal
International Journal for Population Data Science
Published
2024-11-11
DOI
https://doi.org/10.23889/ijpds.v9i2.2395
Primary Topic
demographic modeling and climate adaptation
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Elizabeth Bowman, Allison Forkner, Brian E. Dixon, John Price et al.
International Journal for Population Data Science
demographic modeling and climate adaptation
article

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

Elizabeth Bowman, Allison Forkner, Brian E. Dixon, John Price, Daniel P. Riggins, Jill Inderstrodt, Sue Miller, Shaun J. Grannis, Titus Schleyer, Jennifer L. Williams, Acatia Greenwell, Eden Bezy
article en

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.

International Journal for Population Data ScienceVol. 9(2)
Regenstrief Institute (US), Indiana State Department of Health (US), Family and Social Services Administration (US), Eskenazi Health Foundation (US), Marion County Public Health Department, Eskenazi Health, Indiana Health Information Exchange, Indiana University School of Medicine, Indiana University – Purdue University Indianapolis (US)
Centers for Disease Control and Prevention
Partnerships for the goals
Openalex Percentile: Top 75%
demographic modeling and climate adaptation
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