Estimating the Incidence of Unintended Births and Pregnancies at the Sub-State Level to Inform Program Design

Keith Kranker, Sarah Bardin, So O'Neil and Dara Lee Luca

Mathematica Policy Research Reports from Mathematica Policy Research

Abstract: To estimate the proportion and incidence of unintended births and pregnancies for regions in Missouri, two machine-learning prediction models were developed using data from the National Survey of Family Growth and the Missouri Pregnancy Risk Assessment Monitoring System.

Keywords: unintended births; pregnancy; Missouri; family growth; Data science; Geographical information system (GIS) (search for similar items in EconPapers)
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https://doi.org/10.1371/journal.pone.0240407 (text/html)

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Journal Article: Estimating the incidence of unintended births and pregnancies at the sub-state level to inform program design (2020)
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