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Upcoming Workshops: TBA
Upcoming Workshops: TBA
Past Workshops:
Past Workshops:
May 16-20, 2022
May 16-20, 2022
The overall theme of the workshop is…
How do I integrate social determinants into my work?
Target audience: Professional statisticians, data analysts, informaticians, university faculty members, and graduate students in statistics, informatics or data science in Kenya working at universities, research centers, government ministries, or NGOs that are focused on analysis of biomedical and health data. This workshop is geared toward those who already have training in statistics and active project(s). While experience in machine learning is not necessary, attendees must have strong working knowledge of basic statistical analysis methods, including regression analysis, and familiarity with the use of statistical software such as SAS, SPSS, or Stata, and experience in R is desirable.
Domain experts such as those in medicine may attend if they have working knowledge of basic statistical analysis methods, including regression analysis, and familiarity with the use of statistical software such as SAS, SPSS, Stata, and preferably R.
Dates:
Application deadline: extended to April 15, 2022
Invitations issued: April 29, 2022
Workshop: May 16-20, 2022
Cost and other requirements:
Tuition for the course is free. Selection is by application only. Given the online format, approximately 12 attendees will be selected. Participants must have access to a computer for the entire workshop, with Zoom and R installed.
Format:
This year the workshop will be held entirely online (hands on sessions: 10-12pm and didactic/activities: 3-5pm EAT) over the course of 4 days. Each day will consist of a combination of lectures, guest presentations/case studies and hands-on activities. Each topic will be underpinned by relevant case studies from Kenya that will also provide continuity across the week. The fifth day will be reserved for student consultations on projects and publishing.
Workshop Content (tentative):
Foundations of social determinants
Data for capturing social determinants (existing/secondary use, and collection of data)
Types of analytic questions (e.g. machine learning vs regression, prediction vs causal inference)
Representing social determinants via multi-level and causal models
Modern challenges in machine learning (e.g. algorithmic bias)
Funding: Financial support is provided by the Fogarty International Center of the US National Institutes of Health through grant U2RTW012121.
Instructors:
Rumi Chunara, PhD is an Associate Professor at New York University, jointly appointed in Computer Science (Tandon School of Engineering) and Biostatistic (School of Global Public Health). Broadly, her research is in developing computational and statistical approaches for acquiring, integrating and using data to improve population-level public health. She has taught data science and machine learning topics for a wide variety of students including those in biostatistics, computer science, nursing, medicine, and socio-behavioral sciences.
Ann Mwangi, PhD is an Associate Professor of Biostatistics Moi University and Adjunct Assistant Professor at Brown University. At Moi, she teaches biostatistics/ statistics and collaborates extensively with researchers in HIV. She received her PhD in Biostatistics from Brown in 2011. Drs Hogan and Mwangi co-Direct the AMPATH Data and Analysis team and the NAMBARI Biostatistics Program.
Rajesh Vedanthan, MD, MPH, is the Director of the Section for Global Health at the Institute for Excellence in Health Equity at the NYU Grossman School of Medicine. He is Associate Professor in the Departments of Population Health and Medicine/Cardiology. His areas of interest include implementation science, global health delivery, global cardiology, capacity building, and the intersection of health and development. He is the Principal Investigator or co-investigator of multiple global health-related NIH grants, and has mentored and supervised over 60 trainees, leading to several abstracts, publications, and research awards including several AHA Student Scholarships in Cardiovascular Disease, as well as multiple Fogarty Global Health and T32 Fellows.
Joseph Hogan, ScD is Professor of Biostatistics and Deputy Director of the Data Science Initiative at Brown University. He has carried out extensive research on the development and application of statistical methods for longitudinal data, missing data and causal inference, and has taught several university courses on these topics. He has published over 100 papers and is coauthor of the book Missing Data in Longitudinal Studies (Chapman & Hall, 2008).
Judith Mangeni, PhD, is a Senior Lecturer in the Department of Epidemiology and Medical Statistics within the School of Public Health at Moi University. Her research interests are malaria epidemiology, malaria infection during pregnancy, malaria behavior change communication strategies, and routine testing for HIV in clinical settings. She teaches research methods, epidemiology of infectious diseases and laboratory skills for field epidemiologists. She also supervises and mentors graduate students. Dr. Mangeni has experience in curriculum development at Moi; she was the lead faculty during the development of a new undergraduate curriculum in community health.
Further information:
Rumi Chunara (rumi.chunara@nyu.edu) or Ann Mwangi (annwsum@gmail.com)
Workshop materials:
Workshop materials:
Day 1 Slides:
Social determinants of health (theory and frameworks)
Social determinants of health (data)
Day 2 Slides:
Machine learning and statistics
Social determinants and machine learning
Day 3 Slides:
Social determinants and multi-level modeling
Day 4 Slides:
Other Resources:
Other Resources:
BIGPIC papers:
Ruchman SG, Delong AK, Kamano JH, Bloomfield GS, Chrysanthopoulou SA, Fuster V, Horowitz CR, Kiptoo P, Matelong W, Mugo R, Naanyu V, Orango V, Pastakia SD, Valente TW, Hogan JW, Vedanthan R. Egocentric social network characteristics and cardiovascular risk among patients with hypertension or diabetes in western Kenya: a cross-sectional analysis from the BIGPIC trial. BMJ Open. 2021 Sep 2;11(9):e049610. doi: 10.1136/bmjopen-2021-049610. PMID: 34475172
Vedanthan R, Kamano JH, Lee H, Andama B, Bloomfield GS, DeLong AK, Edelman D, Finkelstein EA, Hogan JW, Horowitz CR, Manyara S, Menya D, Naanyu V, Pastakia SD, Valente TW, Wanyonyi CC, Fuster V. Bridging Income Generation with Group Integrated Care for cardiovascular risk reduction: Rationale and design of the BIGPIC study. Am Heart J. 2017 Jun;188:175-185. doi: 10.1016/j.ahj.2017.03.012. Epub 2017 Mar 23. PMID: 28577673
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