Timely preterm-birth prediction among pregnant women in medicaid without preterm-birth history

Xiao Wang, Luyu Xie, Yolande M. Pengetnze, Weiwei Ouyang, Hannah K. Ligon, Donna I. Persaud, Sarah E. Messiah, Barry Lachman

Research output: Contribution to journalArticlepeer-review

Abstract

OBJECTIVES: To develop and prospectively validate a novel model incorporating claims and community-level socioeconomic data to predict preterm birth at scale among pregnant Medicaid women with no history of preterm birth (PTB). STUDY DESIGN: A longitudinal Texas Medicaid cohort study, with 2-year retrospective model building (October 2015-October 2017) and a 1-year prospective model validation phase (January 2018-December 2018). METHODS: Inclusion criteria were females aged 11 to 55 years with at least 1 live singleton birth and no history of PTB. The primary outcome was live singleton birth earlier than 35 weeks. Covariates were medical/mental/behavioral comorbidities, obstetric history, sociodemographic characteristics, and health services utilization. Of multiple models built, the most parsimonious was selected to classify pregnancies as very high, high, medium, and low risk. Model performance was evaluated using positive predictive value (PPV), sensitivity, case identification ratio (1/PPV), and timing of prediction. RESULTS: The model was built on 6689 pregnancies and validated on 7855 pregnancies. PTB rate earlier than 35 weeks was approximately 3.3%. Significant risk predictors included prenatal visit attendance, insurance gap days, and medical/obstetrical comorbidities. Model PPV was approximately 4-fold higher for very high-risk women (14.7%) vs cohort (3.3%) and so was the case identification ratio (1:7 vs 1:30, respectively). Sensitivity was good, with 57% of PTBs classified as medium risk or higher. Timing of prediction was clinically relevant, with more than 80% of PTBs risk stratified before 24 weeks. CONCLUSIONS: We report a novel PTB prediction model among pregnant Medicaid women without PTB history, which is timely, accurate, practical, and scalable. We leverage Medicaid and community data readily accessible by Medicaid plans to support population-level interventions to prevent PTBs.

Original languageEnglish (US)
Pages (from-to)E145-E151
JournalAmerican Journal of Managed Care
Volume27
Issue number5
DOIs
StatePublished - May 2021
Externally publishedYes

ASJC Scopus subject areas

  • Health Policy

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