|Marijuana and Our Health: What We Do and Don’t Know (Dialogue4Health)||Nov 14, 2017||10:30-12:00||Online||Webinar|
|Introduction to Design Thinking, by Dr. Rita Nguyen (SFDPH/PHD/DPC) (For info: contact Buffy.Bunting@sfdph.org)||Nov 16, 2017||12:00-14:00||25 Van Ness Ave, Rm 330A, SF||In person|
|Exploring Tax Policy to Advance Population Health, Health Equity, and Economic Prosperity (California Endowment)||Dec 07, 2017||full-day||Oakland CA||In person or webcast|
Professional development / training opportunities
|Leadership for Equity and Opportunity: Session 1/3 (Rise Together Bay Area)||Jan 24-26, 2018||full-day||Oakland, CA||In person|
|Leadership for Equity and Opportunity: Session 2/3 (Rise Together Bay Area)||Mar 21-23, 2018||full-day||Oakland, CA||In person|
|Leadership for Equity and Opportunity: Session 3/3 (Rise Together Bay Area)||May 15-17, 2018||full-day||Oakland, CA||In person|
|Public Health Law Academy (Change Lab Solutions)||Ongoing||n/a||n/a||Online|
Applied Epidemiology Using R, Fall, 2017
Public Health 251D
UC Berkeley School of Public Health
Division of Epidemiology
Mondays 4pm–6pm, Barrows 104
Berkeley Academic Calendar: http://registrar.berkeley.edu/calendar
This is an intensive one-semester introduction to the R programming language for applied epidemiology. This year we will be experimenting a population health data science perspective. Population health is “a systems framework for studying and improving the health of populations through collective action and learning.” Data science is the art and science of transforming data into actionable knowledge. Population health data science (PHDS) is “the art and science of transforming data into actionable knowledge to improve health.” The key words are actionable knowledge.
PHDS has five analytic domains (Figure 1 on the following page): (1) description: measuring the burden of risk factors and outcomes; (2) prediction: early targeting of prevention and response strategies; (3) discovery: testing causal pathways for designing prevention strategies, and discovering and testing new causal pathways; (4) simulation: modeling processes for epidemiologic and decision insights; and (5) optimization: optimizing decision-making, priority-setting, and resource allocation. Discovery, simulation, and optimization support causal and evidential reasoning that guide decisions, design, deployment, learning, and continuous improvement.
The core of population health data science is the timely analysis and synthesis of data using programming and computing power. Fortunately for us we have R! R is a freely available, multi-platform (Linux, Mac OS, Windows, etc.), versatile, and powerful program for statistical computing and graphics (http://www.r-project.org). This course will focus on core basics of organizing, managing, and manipulating population health data; basic population health applications; introduction to R programming; and basic R graphics. Students will complete and present a project in their field of interest.
Here are notes from Dr. Michael Samuel: TBD
Population Health Data Science
This book is early in its development and feedback is welcome and appreciated.