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What is the DSSD Training Program?
What is the DSSD Training Program?
The overarching goal of the Data Science for Social Determinants (DSSD) pre- and post-doctoral training program is to develop future leaders in data science who are equipped to develop and analyze data to better leverage deep and rich survey as well as internet and other digitized data sources that can help us capture information on the social determinants of health. Such data can provide unique illumination into factors on where we live, work, and play which can be used to better understand and address health disparities across noncommunicable disease, infectious disease and injuries in the Kenyan context.
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First...
The program will provide long-term development of a cohort of PhD, postdoctoral and Faculty fellow trainees from Kenya who will benefit from interdisciplinary training at NYU that carefully layers a core set of competencies in data science across:
data management and wrangling
prediction and analytics and data communication and ethics,
didactic and experiential training in social determinants
best practices in responsible conduct of research, reproducibility and community-engaged, ethical data science
Second...
The program will leverage
international standard biostatistics and informatics faculty and programming at Moi with
trainees equipped in advanced data science coursework at NYU
To expand the base of local expertise
and capacity in data science by developing
MSc and PhD data science tracks
at the
Institute for Biomedical Informatics and Biostatistics programs
at Moi University
within
5 years
Finally...
Continuing education opportunities and networking will occur through workshops bringing together experts in health and data science in Kenya and surrounding areas. Programs will be evaluated annually by our Internal Advisory Board and Training Advisory Committee.
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Who are we?
Who are we?
The DSSD Training Program is a collaboration between New York University, The NYU Grossman School of Medicine, Moi University, and Brown University that is funded by the National Institutes of Health.
A broader network of academic, industrial and non-governmental organizations (IBM, Deep Learning Indaba, DataKind, AI.Kenya, Aga Khan University Nairobi and Karachi), opens a wide pool of potential, strong trainees from both data science and health backgrounds.
Program Leadership
Program Leadership
Rumi Chunara, PhD ScM
Program Co-Director (MPI) - New York University
Ann Mwangi, PhD
Program Co-Director (MPI) - Moi University
Rajesh Vedanthan, MD MPH
Program Co-Director (MPI) - NYU Grossman School of Medicine
Joseph Hogan, ScD MS
Program Co-Director (MPI) - Brown University
Judy Mangeni, PhD
Key Investigator - Moi University
Jessica Gjonaj, BA
Program Coordinator - NYU Grossman School of Medicine
Internal Advisory Board
Internal Advisory Board
Olugbenda G. Ogedegbe, MD MPH
Director - Institute for Excellence in Health Equity at NYU Langone Health
Rebecca Betensky, ScD
Professor and Chair - Department of Biostatistics at the NYU School of Global Public Health
Julia Kempe, PhD
Director - Center for Data Science at NYU
Winstone Nyandiko, MBChB MMED MPH
Associate Proessor - Child Health and Pediatrics at Moi Univeristy
Training Advisory Board
Training Advisory Board
Ana Mocumbi, MD PhD
Associate Professor - Universidade Eduardo Mondlane, Mozambique
Amos Laar, PhD
Associate Professor - Department of Population, Family and Reproductive Health at the University of Ghana
Boniface Akuku, PhD
Assistant Director -ICT at the Kenya Agricultural & Livestock Research Organization
Zulfiqar Bhutta, MBBS
Robert Harding Inagural Chair in Global Health - Hospital for Sick Children
DSSD Network
DSSD Network
Collaborating Organization Extended Network
Collaborating Organization Extended Network
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Why Data Science for Social Determinants?
Why Data Science for Social Determinants?
The types of research and studies in Kenya over the last few decades have clearly indicated the relevance of social determinants, though to-date capacity for these efforts have not been significant enough to leverage them.
Indeed, data science analytic approaches including machine learning are known to provide flexibility in modeling complex relationships between predictors, which can be particularly advantageous in addressing the multi-level interactions between different social determinants and outcomes.
Moreover, data science methods to leverage new data sources provide opportunity to uncover and measure novel risk factors that relate to health risk factors outside of the hospital in the types of studies prioritized in Kenya (e.g. services, environments, finances).
Overall, while such efforts and data science communities are burgeoning, we need to strengthen linkages between such communities and academic health centers to better address the health research questions pertinent in Kenya.
To learn more, please visit the resources section.
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