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In Part 1 we introduced directed acyclic graphs (DAGs) as a better way to represent program theory. Program theory has three components: Theory of causation (causal model) Theory of change (selected strategy) Theory of action (selected intervention that activates change) Program theory is often called “theory of change.” I prefer “program theory,” but never forget its three components. Now we review the material with another example.


Introduction As a parent, sometimes I have given overly-cautious advice like: “The only way to guarantee not having a ‘drug problem’ is to not use drugs.” While the statement is true, it is totally useless advice for those who choose to experiment or consume a psychoactive drug with the potential for problem use.1 A better approach is to provide factual, evidence-based guidelines after the “overly-cautious advice.” Fortunately, Fischer et al. provide just that: evidence-based guidelines for lower-risk consumption of cannabis.


Every public health intervention has a program theory; however, very few can actually describe the program theory supporting their primary programmatic activity or research. Can you? If not, read on. I too could not describe the program theories supporting my own work until I read Funnell Rogers’ book Purposeful Program Theory [1]. It turns out that program evaluators not only live and breath program theory, but they call it by different names: logic model, program logic, theory of change, causal model, results chain, intervention logic, etc.


Our comforting conviction that the world makes sense rests on a secure foundation: our almost unlimited ability to ignore our ignorance. … Daniel Kahneman1 Table of Contents System 1 and System 2 (a.k.a. the “elephant” and the “rider”) Cognitive biases in decision making 1. Protection of mindset 2. Personality and habits 3. Faulty reasoning Faulty reasoning due to complexity: Faulty reasoning about uncertainty: 4.


Kudos to Cyndy Comerford and Max Gara from the San Francisco Department of Public Health, Health Impact Assessment (HIA) Program! Also, special acknowledgments to Michelle Kirian and Erik Dove for their important contributions. Excerpt from the Executive Summary On November 8, 2016, California voters passed Proposition 64, the “Adult Use of Marijuana Act.” This proposition made it legal for individuals age 21 and older to use, possess, and make non-medical cannabis available for retail sale.


Check out the video by Mehroz Baig from the Center for Learning and Innovation, Population Health Division, San Francisco Department of Public Health to hear how Bay Area social justice and public health experts think we can move this conversation forward. “Diseases don’t discriminate. Diseases also don’t operate in a vacuum. Public health professionals have seen disparities in health outcomes along racial and ethnic lines for decades. Data point to disparities in life expectancy, rates of new HIV diagnoses, rates of viral suppression for those who are HIV positive, rates of emergency room visits due to asthma or heart disease, among others.


Several years ago I (TJA) developed the ‘epitools’ R package for epidemiologic data and graphics. My goal was to have a practical package for practicing epidemiologists at local and state health departments. It was designed to conduct contingency table analyses for outbreak investigations, to construct epidemic curves, to improve graphical color selection, to implement basic methods from Chapter 4 of Rothman’s Modern Epidemiology, and more. Recently, ‘epitools’ maintenance was taken over by Adam Omidpanah from the Washington State University.


Leading transformation The Leading Population Health Framework (LPHF) is based on pursuing and acheiving essential population health goals: protecting and promoting health and equity, transforming people and place, ensuring a healthy planet, and achieving health equity. Population health continuous improvement requires leadership and transformation (Figure 1): a population health leadership philosophy,1 transforming self and interpersonal relationships, transforming teams and collaboratives, and transforming organizations and communities. The Leading Population Health Framework (LPHF) is based on a leadership philosophy and three necessary transformations.


Selected works

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View Dropbox curriculum vitae (PDF)

Key projects

Applied Epidemiology with R

Public Health 251D, University of California, Berkeley School of Public Health

Leading population health

Practical skills for transforming self, teams, organizations, and communities.

Population health data science with R

The art and science of transforming data in actionable knowledge to improve health.

Population health lean

A management system for continuous learning, improvement, and innovation based on lean thinking, leadership, and practice.

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