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Prevalence and Distribution of Prenatal Opioid Exposure by Identification Methods in the Cincinnati Tri-State Region

Duah, Henry
http://orcid.org/0000-0002-4842-6006
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1721230523225389

Abstract Details

2024, PhD, University of Cincinnati, Nursing: Nursing - Doctoral Program.
Background: Many children are directly and indirectly affected by the opioid epidemic and the consequences of opioid use during pregnancy through prenatal opioid exposure. Prenatal opioid exposure is associated with adverse neonatal and long-term outcomes and may develop into neonatal opioid withdrawal syndrome. Although recent reviews largely suggest negative outcomes after prenatal opioid exposure, they are limited by the heterogeneity of identification methods used to ascertain exposure. The impact of varying identification methods on the prevalence and outcomes of exposure is not clearly understood. The use of big data and larger data linkages in nursing science may help illuminate the impact of varying identification methods used to ascertain prenatal opioid exposure. Aims: This three-manuscript dissertation aimed to (1) discuss the use and potential of big data for nurse scientists, (2) conduct a scoping review of the varying identification methods in current literature, and (3) perform a secondary data analysis of a large integrated data to explore the prevalence of prenatal opioid exposure across identification methods to inform research, practice, and support children and families impacted by prenatal opioid exposure. Methods: The first manuscript was a discursive paper that provided an introductory guide for leveraging big data in nursing research. The second manuscript was a scoping review that synthesized the various identification methods used to ascertain opioid exposure in the United States over the last decade. Insights from the scoping review generated three identification methods leveraged in the third dissertation manuscript: (1) Maternal data (e.g., toxicology and diagnoses), (2) Infant data (e.g., toxicology and diagnoses), and (3) Combined method using maternal and infant data. The third manuscript was a secondary data analysis of a large perinatal linkage database in the Midwest to explore the prevalence of prenatal opioid exposure by varying identification methods in the Cincinnati Tri-State Region. The analytic sample comprised 22,292 mother-child dyads, with children born between 2015-2022. Statistical analysis included univariate and bivariate analysis, as well as data visualizations. Results: The first manuscript discussed the practical, social, ethical, and educational implications of using big data in nursing research and highlighted the need for pragmatic changes in nursing educational curricula to harness the potential of big data in nursing research. In the second manuscript, 42 articles published from 2012-2023 revealed heterogeneity in identification methods, with various maternal- and infant-level methods used to ascertain opioid exposure, demonstrating a lack of standardization in ascertaining prenatal opioid exposure. In the third manuscript, it was observed that the prevalence of prenatal opioid exposure varied across identification methods. The prevalence was consistently higher under the combined method, followed by maternal and infant data. Conclusion: This dissertation makes an important contribution to understanding the use of big data in nursing science and maternal and infant health research in the context of the opioid epidemic. Moreover, it provides data-driven insights into the impact of variations in identification methods on the prevalence of prenatal opioid exposure. The findings have implications for maternal and child health research, policy, and clinical practice
Joshua Lambert, Ph.D. (Committee Chair)
Sara Arter, Ph.D. R.N. (Committee Member)
Nichole Nidey, Ph.D. (Committee Member)
Samantha Boch, Ph.D. R.N. (Committee Member)
155 p.
Nursing
Prenatal Opioid Exposure; Neonatal Opioid Withdrawal Syndrome; Big data and Nursing; Nursing Research; Opioid Exposure Monitoring; Opioid use and Pregnancy

Recommended Citations

Citations

  • Duah, H. (2024). Prevalence and Distribution of Prenatal Opioid Exposure by Identification Methods in the Cincinnati Tri-State Region [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1721230523225389

    APA Style (7th edition)

  • Duah, Henry. Prevalence and Distribution of Prenatal Opioid Exposure by Identification Methods in the Cincinnati Tri-State Region. 2024. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1721230523225389.

    MLA Style (8th edition)

  • Duah, Henry. "Prevalence and Distribution of Prenatal Opioid Exposure by Identification Methods in the Cincinnati Tri-State Region." Doctoral dissertation, University of Cincinnati, 2024. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1721230523225389

    Chicago Manual of Style (17th edition)

ucin1721230523225389
© 2024, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.