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

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.

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.

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.

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 birth. Using this approach to estimate the rates of unintended births and pregnancies across regions within Missouri revealed substantial within-state variation in the proportion and incidence of unintended pregnancy. States and other agencies could use this study's results or methods to better target interventions to reduce unintended pregnancy or address other public health needs.