Trends in Continuation of Long-Acting Reversible Contraception Among Adolescents Receiving Medicaid

Purpose: Despite increasing use of long-acting reversible contraception (LARC) among U.S. adolescents, there is limited literature on factors affecting intrauterine device (IUD) or subdermal implant use. This study aimed to describe statewide rates, and associated patient and provider factors of adolescent IUD or implant initiation and continuation. Methods: This retrospective cohort study used N.C. Medicaid claims data. 10,408 adolescents were eligible (i.e., 13-19 years, female sex, continuous Medicaid enrollment, had an IUD or implant insertion or removal code from January 1, 2013, to October 1, 2015). Bivariate analyses assessed differences in adolescents using IUD versus implant. Kaplan-Meier curves were created to assess IUD or implant discontinuation through December 31, 2018. Results: Adolescents initiated 8,592 implants and 3,369 IUDs (N = 11,961). There were significant differences in nearly all provider and patient factors for those who initiated implants versus IUDs. 16% of implants and 53% of IUDs were removed in the first year. Younger (i.e., age <18 years old), Hispanic, and Black adolescents had higher adjusted continuation of implants compared with older and White adolescents, respectively (both p < .001). Those whose IUD was inserted by an obstetrician/gynecologist provider had lower continuation of IUDs compared with non-obstetrician/gynecologist providers (p < .001). Discussion: We found that age-related, racial, and ethnic disparities exist in both implant and IUD continuation. Practice changes to support positive adolescent experiences with implant and IUD insertion and removals are needed, including patient-centered health care provider training in contraception counseling, LARC initiation and removal training for adolescent-facing providers, and broader clinic capacity for LARC services. (c) 2024 Society for Adolescent Health and Medicine. All rights are reserved, including those for text and data mining, AI training, and similar technologies.