Predicting Success in Mother-Child Interventions

Fellows: Sarah Abraham, Jeff Lockhart, Sarah Tan, Rafael Turner
Data Science Mentor(s): Young-Jin Kim
Project Partner: Nurse-Family Partnership

Young, low-income, first-time mothers and their babies often face dramatically increased risks to their health, education, and economic self-sufficiency. The Nurse-Family Partnership (NFP), a national nonprofit organization, intervenes by pairing these mothers with specially-trained, registered nurses. Expectant mothers receive regular home visits from pregnancy until the baby is two years old. The result: healthier pregnancies, more stable families, and better developmental outcomes for children.

NFP’s approach is based on decades of research, and in 2013 DSSG fellows helped NFP quantify its impact by combining its data with national demographic data to assess how nurse visits affect measures of early childhood development such as immunization and breastfeeding rates. Some local NFP agencies currently face greater demand than they can meet. With our models, we sought to identify mothers who will benefit the most from NFP’s programs and the most impactful timing of nurse visits. We hoped to help NFP better understand their target population and personalize their services based on each mother’s needs.

You can read more about this project here.