The mission and vision of the Division, is built upon several premises:
- Population health research focuses on health and disease outcomes in populations rather than individuals.
- A life course approach, from gametes through adulthood, is important for studying health and disease.
- Hierarchical data are required for measuring environmental exposures affecting individuals, couples, or families.
- Trans-disciplinary research teams and partnerships improve population health.
- Timely translation of research findings is vital for maintaining and improving health.
To accomplish this mission, the Division designs and conducts innovative etiologic and interventional research from pre-pregnancy through adulthood, translates research findings into clinical practice or public policy to maximize health and eliminate health disparities, and builds capacity through mentoring and serving our professions.
The Division aims to be a leader in population health research by focusing on successful reproduction, the health and well-being of pregnant women and their infants, and the optimal growth and development of children and adolescents across the lifespan. With the population as its observational laboratory, the Division uses collaboration, discovery, ethics, innovation, interdisciplinary teamwork, and mentoring as core values in fulfilling its mission and vision.
Check Our Progress on NICHD Strategic Plan 2020 themes
The Biostatistics and Bioinformatics Branch invites candidates to apply for tenure-track investigator positions (PDF 196 KB) to establish independent research programs in modern areas of computational statistics and data science.
Congratulations to DIPHR scientists who recently received awards from the American Statistical Association:
- Rajeshwari (Raji) Sundaram received the 2020 Jeanne E. Griffith Mentoring Award .
- Katherine Laughton Grantz and Raji Sundaram received the 2020 Outstanding Statistical Application Award , along with colleagues from the Johns Hopkins School of Public Health, for their paper Joint Modeling of Competing Risks Data and Current Status Data: An Application to Spontaneous Labour Study, published in the Journal of the Royal Statistical Society, Series C (Applied Statistics) in 2019.
Division Annual Report 2019 (PDF 1 MB)