Big Data for Reproductive Health
An interactive data visualization and predictive analytics tool suite, intended to increase access to the Demographic and Health Survey contraceptive discontinuation data
As low- and middle-income countries transition from paper to digital systems, family planning programs can benefit from unprecedented opportunities to improve services. Investments in digital health tools have expanded exponentially, but information on what works—and what does not— remains limited and scattered. As investments have increased, digital applications and data fragmentation have proliferated, but stakeholders are moving towards more coordinated efforts to scale digital health solutions, support countries’ digital health infrastructure, and share evidence-based learnings.
This Digital Health Compendium enables users to explore case studies across a range of digital health technologies used to enhance family planning programs mainly in sub-Saharan Africa, but also in other regions of the world. Digital health applications in family planning programs can be broadly classified as those affecting demand generation, service delivery, supply chain management, and the policy and enabling environment. In many low- and middle-income countries, digital health innovations were adopted earlier in other health sectors, including HIV/AIDS, maternal and child health, and noncommunicable disease prevention and response. As a result, much of the impact evidence is likewise restricted to those sectors. To advance greater adoption of digital technology in family planning programs, more data and information on the challenges, opportunities, scalability, and results are needed. This compendium aims to consolidate emerging information and data on applications of digital technology in family planning programs to inform adoption and scale-up of successful approaches.
All of the case studies were submitted by the implementing organizations and include a description of the digital health intervention, program context, and, if available, important findings and lessons learned through rigorous evaluations or program data. The compendium facilitates a quick search for case studies based on the target user for digital health intervention, building block for the digital health enabling environment, family planning program classification, and country location. The case studies give policy and program decisionmakers insights on real-world applications of digital health, promising practices, challenges, and other lessons that can be applied to current and future programs.
An interactive data visualization and predictive analytics tool suite, intended to increase access to the Demographic and Health Survey contraceptive discontinuation data
Amy Finnegan
Senior Data Scientist, IntraHealth International
Adjunct Professor, Duke University
Email
Project leadership:
Amy Finnegan, PhD, IntraHealth International, co-PI
Megan Huchko, MD, Center for Global Reproductive Health at the Duke Global Health Institute, co-PI
Undergraduate, master’s, and doctoral students across Duke University have contributed to the development of this tool through the Bass Connections program.
Duke University Bass Connections program
Our project utilizes data from nearly all of the existing Demographic and Health Surveys contraceptive calendars (~50 LMICs).
May 2018 - present
Data Services Provider
Services and Applications
Policy and Enabling Environment
Approximately one-third of women living in low- and middle-income countries discontinue use of contraception within their first year of use, and nearly half discontinue use within their first two years even though they may wish to avoid pregnancy (Castle & Askew 2015). There is still a large need for improvement in ensuring that all women who want contraception have access to the family planning methods that work for them. Big Data for Reproductive Health (bd4rh) built an online, open access, interactive platform using R and R Shiny, an open source data analysis and visualization platform that can be utilized to aggregate and present data on contraceptive discontinuation from the Demographic and Health Survey (DHS) contraceptive calendar in novel ways to help advocates, researchers, and policymakers determine critical points for study or policy intervention.
The bd4rh team has developed three novel data visualization tools using R and R Shiny. See the Switch is a visualization of contraceptive switching and discontinuation that employs a chord diagram to show contraceptive use dynamics between two discrete time points. The online platform also includes a line graph that allows users to view contraceptive trends over time. In both tools, users can select to look at subsets of women by sociodemographic characteristics, including age and education. Users can also explore contraceptive switching and discontinuation with the reasons for doing so, as reported in the DHS, allowing users to design research questions or policy proposals to target these contraceptive use dynamics. The third tool, which is still under development, displays the results of unsupervised and supervised machine learning analysis applied to contraceptive use trajectories from the DHS contraceptive calendar. This tool allows users to explore the demographic composition of contraceptive use clusters in the data and compare the results of several machine learning algorithms to predict engagement and discontinuation with contraception.
A wide variety of stakeholders have been consulted in the development of the tools. The See the Switch and the line graph tools were launched at the 2018 International Conference on Family Planning in Kigali, Rwanda. Advocates and researchers at the DHS Program, the Population Reference Bureau, PMA2020, FP2020, IntraHealth International, FHI360, the Carolina Population Center at the University of North Carolina, and others have provided feedback throughout the development of the descriptive tools. A peer-reviewed version of See the Switch was published in Global Health Science & Practice in December 2019 (Finnegan, Sao, & Huchko 2019). The initial machine learning results upon which this tool is based were debuted at the 2019 Women Deliver conference in Vancouver, Canada and are still under development.
bd4rh’s innovative strategy to develop new visualizations and apply big data techniques to contraceptive calendar data can help unlock new insights about contraceptive dynamics that can help women have access to the contraceptive methods that support their family planning goals. Our team continues to engage with stakeholders and welcomes input on how to improve the functionality of the tools.