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

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Listed:
  • Keith Kranker
  • Sarah Bardin
  • So O'Neil
  • Dara Lee Luca

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.

Suggested Citation

  • Keith Kranker & Sarah Bardin & So O'Neil & Dara Lee Luca, "undated". "Estimating the Incidence of Unintended Births and Pregnancies at the Sub-State Level to Inform Program Design," Mathematica Policy Research Reports e5c2e64372b84767b2d5a2242, Mathematica Policy Research.
    • Handle: RePEc:mpr:mprres:e5c2e64372b84767b2d5a2242e50f2ff
    DOI: 10.1371/journal.pone.0240407
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    References listed on IDEAS

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    1. Vidhura Tennekoon, 2017. "Counting unreported abortions: A binomial-thinned zero-inflated Poisson model," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 36(2), pages 41-72.
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    Cited by:

    1. Christie Sennott & Sadé L. Lindsay & Brian C. Kelly & Mike Vuolo, 2022. "The Liberalization of Cannabis Possession Laws and Birth Outcomes: A State-Level Fixed Effects Analysis, 2003–2019," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 41(4), pages 1809-1829, August.

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