Population Health *

Leadership – Lean – Data science

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I recently presented to our Board of Supervisors (BOS) about the public health issues related to street cleaning. In San Francisco there is increasing public concern about human defecation, discarded syringes and needles, and homeless encampments.1 2 Not too long ago we had an outbreak of shigellosis among our homeless population.3 In San Francisco we deploy an integrated model for controlling infectious diseases that we developed several years ago. We refined the model based on concepts from transmission dynamics (e.


Essential population health goals include protecting and promoting equity and health, transforming people and place, ensuring a healthy planet, and achieving health equity. Naturally, I am often asked to give talks on health inequities (see Presentations). My general approach is to connect structural trauma (poverty, racism, discrimination) and toxic (traumatic) stress (adverse childhood experiences), inter-generational transmission of biological and social risk to offspring, life course neurocognitive development affecting a child’s brain, learning, behavior, and health for life, and industry exploitation of our neuro-vulnerabilities to design and market products for addiction and overconsumption (tobacco, alcohol, prescription opioids, processed foods, gambling, gaming, mobile phones, etc.


In Part 1 we introduced directed acyclic graphs (DAGs) [1] as a better way to represent program theory [2]. A DAG is a Bayesian network where each directed arrow represents a causal link, not merely dependency [3]. 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.


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.


Selected works

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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.

We will be the best at getting better! An introduction to population health lean

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

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