IBS Speaker Series: Examining Contraceptive Access and Use among Youth with Child Welfare Involvement

In-person or via Zoom link: https://cuboulder.zoom.us/j/95248394614 – email ibs-contact@colorado.edu for password.

*Light lunch served at 1145, please RSVP.

Speaker:  Katie Massey Combs

Abstract:  Roughly half of females with child welfare involvement (i.e., child protective services) report a pregnancy by age 19, which is twice the rate of a national sample of youth of similar age and racial/ethnic composition. To date, little is known about sexual and reproductive health of youth with child welfare involvement beyond that they are at increased risk of unintended pregnancy. Recent studies have examined prevalence of pregnancy and identified multiple risk factors for pregnancy primarily in samples of older female adolescents transitioning from foster care. Even less is known about subgroups (i.e., males, younger adolescents, youth who are not removed from home) and few data exist on contraceptive access and use. Contraceptive access is critical, not only because of its implications for reducing unintended pregnancy, but because of its effect on reproductive agency, autonomy, and lifelong socioeconomic opportunities. This talk will describe a project seeking to fill these gaps by using secondary data from a multi-wave survey of youth ages 12 to 18 with child welfare involvement to provide basic information on contraceptive access and use across adolescence, identify modifiable factors that promote reproductive agency and autonomy, and clarify differences by biological sex and child welfare characteristics.

Bio:  Dr. Combs’ research focuses on the prevention of behavioral health problems and the promotion of well-being among vulnerable youth. Trained in social work and public health, she is keenly interested in interdisciplinary and applied research to understand what works, and in using research to inform practice and policy. In addition to a range of qualitative and quantitative methods, she utilizes advanced statistical techniques such as multi-level modeling, propensity score matching, and structural equation modeling to deepen existing knowledge about social problems and solutions.