Estimating the incidence of unintended births and pregnancies at the sub-state level to inform program design

Abstract

Objectives Unintended (mistimed or unwanted) pregnancies occur frequently in the United States and have negative effects. When designing prevention programs and intervention strategies for the provision of comprehensive birth control methods, it is necessary to identify (1) populations at high risk of unintended pregnancy, and (2) geographic areas with a concentration of need. Methods 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. Each model was applied to Missouri birth certificate data from 2014 to 2016 to estimate the number of unintended births and pregnancies across regions in Missouri. Population sizes from the American Community Survey were incorporated to estimate the incidence of unintended births and pregnancies. Results About 24,500 (34.0%) of the live births in Missouri each year were estimated to have resulted from unintended pregnancies: about 25 per 1,000 women (ages 15 to 45) annually. Further, 40,000 pregnancies (39.7%) were unintended each year: about 41 per 1,000 women annually. Unintended pregnancy was concentrated in Missouri's largest urban areas, and annual incidence varied substantially across regions. Conclusions Our proposed methodology was feasible to implement. Random forest modeling identified factors in the data that best predicted unintended birth and pregnancy and outperformed other approaches. Maternal age, marital status, health insurance status, parity, and month that prenatal care began predict unintended pregnancy among women with a recent live

How accurately can we predict repeat teen pregnancy based on social ecological factors?

“…Correlations between the theoretically selected predictors and repeat pregnancy were weak, indicating there was limited signal in the data (results available from the first author by request). Related research that used predictive analytics to predict unintended births (Kranker et al, 2020) found area under the curve statistics similar to those in the current study, suggesting that it could be difficult to predict these types of outcomes. Nonetheless, this study makes an important contribution because few prior studies have examined how accurately repeat pregnancy could be predicted based on the significant predictors identified.…”

Performance Evaluation of Machine Learning Algorithm for Classification of Unintended Pregnancy among Married Women in Bangladesh

“…That is why ENR outperforms other current models for our datasets due to all of these properties [ 37 ]. However, in the study in Missouri, the researchers found that random forest performed better than other machine learning techniques in predicting unintended birth and pregnancy [ 38 ]. Furthermore, they did not apply the elastic net regression algorithm in their analysis.…”