Big Data for Reproductive Health (2019-2020)
Increasing access to family planning is a global health priority that improves health, economic and quality of life outcomes of women and families. Despite this, one-third of women in low-income countries wishing to prevent pregnancy with modern methods of contraception discontinue its usage within the first year, and half within the first two years. Ensuring women continue contraceptive use for pregnancy prevention requires strong health systems and informed advocates and policymakers.
Existing data on contraception discontinuation (quitting, switching and method failure) are obtained through household surveys administered every five years through a retrospective month contraceptive calendar. However, the data are difficult to analyze and are underutilized by advocates and policy makers. New and existing data must therefore be curated into user-friendly digital tools to provide a higher resolution picture of the pathways of contraceptive use.
This Bass Connections project aims to improve access to family planning by providing stakeholders with necessary data to inform their advocacy and policymaking efforts.
In 2019-2020, the specific goals of this project are to:
- Build and disseminate a web-based platform that curates freely available, raw data on contraceptive discontinuation from household surveys into a tool that makes higher resolution inferences possible by members of the family planning community.
- Apply big data analytic techniques to Demographic and Health Surveys (DHS) contraceptive calendar data, rendering the data more useful.
- Identify how big data can provide real-time surveillance around reproductive health, which can improve family planning policy.
This project will continue to engage key family planning and global health stakeholders in the Triangle, including the Carolina Population Center, FHI360, RTI, IntraHealth International, MEASURE Evaluation and Ipas.
Enhanced web platform to disseminate data visualization tool; enhanced user-interface for prediction tool built by previous teams; protocol to test usefulness and validity of prediction tool
Fall 2019 – Spring 2020
- Fall 2019: Begin weekly team meetings and split into sub-groups; develop and implement work plans, and develop timelines for completion; students working on programming begin working through Coursera course on machine learning in Python
- Spring 2020: Continue weekly meetings to track project progress; travel to conferences; begin field testing prediction tool
See related Data+ summer project, Big Data for Reproductive Health (2019).
- Amy Finnegan, IntraHealth International
- Megan Huchko, School of Medicine-Obstetrics and Gynecology
/graduate Team Members
Kelly Hunter, Public Policy Studies-PHD
Stephanie Skove, Biology (BS)
/undergraduate Team Members
Dennis Harrsch Jr., Computer Science (BS), Global Health (AB2)
Janel Ramkalawan, English (AB)
Saumya Sao, Gender Sexuality & Fem St(AB), Global Health (AB2)
Elizabeth Shulman, Computer Science (BS)
Amelia Steinbach, Political Science (AB)
Eugene Wang, Computer Science (BS)
/yfaculty/staff Team Members
Amy Herring, Arts & Sciences-Statistical Science
/zcommunity Team Members
Carolina Population Center