Visualizing Public Health Data Anamaria Crisan, MSc PhD student - - PowerPoint PPT Presentation

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Visualizing Public Health Data Anamaria Crisan, MSc PhD student - - PowerPoint PPT Presentation

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Visualizing Public Health Data Anamaria Crisan, MSc PhD student with Drs. Jennifer Gardy & Tamara Munzner UBC School of Population and Public Health Why am I giving this talk? PhD Master of Science (Computational Public Health)

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Visualizing Public Health Data

Anamaria Crisan, MSc

PhD student with Drs. Jennifer Gardy & Tamara Munzner UBC School of Population and Public Health
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Why am I giving this talk?

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Master of Science

( Bioinformatics )

PhD

(“Computational Public Health”) GenomeDx Biosciences British Columbia Centre for Disease Control

2010 2013 2015 2008

@amcrisan http://cs.ubc.ca/~acrisan

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I’m not an artist. I’m a data analyst.

http://blog.framed.io/ Computer Science Skills + Data Visualization Skills!
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Disclaimer

I’ll be talking about a project I worked on while employed at GenomeDx Biosciences. Everything I am presenting is publically available, but this doesn’t mean that I endorse their products or the products of their competitors. Furthermore, I am relaying high level details of my own thought process during and after this project, not the thoughts of others at the organization.

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Eventually I had Explain my Work to Experts with Different Backgrounds

I often used data visualization to explain the results of data mining and statistical techniques But one day I got tasked with a rather challenging problem…

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The Question:

The task: We had developed a genomic biomarker panel to assess a man’s risk of metastatic prostate cancer following prostatectomy

How do we communicate “risk”?

XKCD Comic #881
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I wanted to take more ownership of the question “how do we communicate risk?”

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I wanted to take more ownership of the question “how do we communicate risk?” There wasn’t a simple answer

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http://bit.ly/1Knrj19

Just show a Number …

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SLIDE 11 http:/ /bit.ly/1FxtT2z

Is a Data Visualization really Necessary?

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60%

Probability Frequency Visualization

6 in 10

< <

(difficult to understand) (easier to understand)

Evidence from Risk Communication Literature

Whiting et. al (2015) “How well do health professionals interpret diagnostic information? A systematic review”

Numeracy : the ability to reason with numbers Individuals with low numeracy have a difficulty interpreting numbers and probabilities Visualizations can help people with low numeracy make sense of data, But, there is some evidence that low numeracy affects reasoning with graphs as well.

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Example : Data Visualization in Shared decision Making

Garcia-Retamero et. al (2013) “Visual representation of statistical information improves diagnostic inferences in doctors and their patients” R A N D O M I Z E Probability Frequency R N D Visual Aid No Visual Aid R N D Visual Aid No Visual Aid Patients + Doctors

STUDY DESIGN RESULTS

Visualization improved comprehension of both doctors and patients Visualization improved concordance between doctors and patients Quasi-randomized trial with four conditions Outcome : correctly calculating the risk (essentially a math test)
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Yes! Data visualization was more than a “nice to have”!

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Example Report: OncotypeDx DCIS report

Show a Number and a Picture

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Example Report: Myriad Prolaris Prostate Cancer Test Report

Show a Number and a Picture

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Example Report: Decipher Prostate Cancer Test Report

Primary population: Men, who are susceptible to red- green colour blindness

Show a Number and a Picture

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Example : Deciding upon an Intervention

Baseline Visualization Alternative 1 Alternative 2

Zikmund-Fisher (2013). A demonstration of ''less can be more'' in risk graphics. Zikmund-Fisher (2008). Improving understanding of adjuvant therapy

  • ptions by using simpler risk

graphics

Helping breast cancer patients decide between multiple treatment

  • ptions.
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SO… what is data visualization?

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Data visualization is not art

Beyond Building Pretty & Cool Visualizations

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Design Art

Ideas taken from @rachelbinx’s 2016 Open Vis talk And http:/ /featureguru.com/art-vs-design.html

Data Visualization

(I argue data visualization is much more about design)

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Defining Data Visualization

Beyond Building Pretty & Cool Visualizations

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There’s more to data visualization than simply communicating numerical data

BUT WAIT!

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Example : Hypothesis Generation

John Snow’s Visualization of the 1854 Cholera Outbreak

Allowed John Snow to form the hypothesis of what may be leading to the cholera

  • utbreak
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Example : Hypothesis Generation

John Snow’s Visualization of the 1854 Cholera Outbreak

Allowed John Snow to form the hypothesis of what may be leading to the cholera

  • utbreak
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Example : Checking Assumptions of Statistical Models

Anscombe’s quartet, four datasets that have near identical descriptive statistics but that look very different when visualized.

Anscombe, F. (1973) “Graphs in Statistical Analysis”

Data visualization has long complemented applied statistical

  • practices. Consider Tukey’s classic

“Exploratory Data Analysis”, which is rife with suggestions for how to visualization data.

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So what should be think about when designing data visualizations?

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Why?

Why do you need to visualize data?

What?

What kind of data is being visualized?

How?

How is data being visualized?

A Data visualization in 3 Questions:

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(Motivation) (Data) (Visual and Interaction Design)

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Why? A Data visualization in 3 Questions:

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What? How?

Design Evaluation

Does the visualization solve a relevant problem? Are you using the right data, or deriving the right data? Are the visual and interactive design choices appropriate?

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Why What How How

Steps to Design and Evaluate a Data Visualization

DESIGN EVALUATION

29 Munzner (2014) “Visualization Analysis and Design”
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Why What How How

Steps to Design and Evaluate a Data Visualization

Qualitative Methods, Domain Knowledge Qualitative & Quantitative Methods Design & Cognitive Science Computer Science

Methodology

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The “Design Space” metaphor

Sedlmair 2012 https://www.cs.ubc.ca/nest/imager/tr/2012/dsm/dsm-talk.pdf
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The “Design Space” metaphor

Sedlmair 2012 https://www.cs.ubc.ca/nest/imager/tr/2012/dsm/dsm-talk.pdf

OPTIMIZATION!

How Data Visualization is like Statistical Modelling

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SLIDE 33 33 Sedlmair 2012 https://www.cs.ubc.ca/nest/imager/tr/2012/dsm/dsm-talk.pdf

The “Design Space” metaphor

Progressively Identify the Right Visualization

Use “why, what, and how” framework to guide the selection

  • f the optimal design choice
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The Importance of Thinking Broadly

Munzner (2014) “Visualization Analysis and Design”

Use “why, what, and how” framework to guide the selection

  • f the optimal design choice
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Designs for Visualizing Health Data (http:/

/www.vizhealth.org/) 35
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A preview of some things I am working on

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How do we design good visualizations for public health?

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BUT…..

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To what extent and in what ways does the visualization of genomic, administrative, and contact network data support decision making for communicable disease prevention and control Primary Research Question

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To what extent and in what ways does the visualization of genomic, administrative, and contact network data support decision making for communicable disease prevention and control Primary Research Question

  • aka. “How is visualization of communicable disease (public

health) data useful? Can I quantify how useful it is?”

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What is the best way to visually represent data in an outbreak context to promote a rapid response? How can stakeholders explore their data more effectively to identify areas of needs and develop effective outreach programs? What is the most effective way to show genomic data over space and time? Some Example Sub Questions

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Example 1

Visualizing Tuberculosis data at the British Columbia Centre for Disease Control

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WHY

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Clinical Social Lab

Combining Data will Prepare us for the Pandemics of the Future

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But, that’s a lot of data….

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Can Visualizing TB data help Decision Support?

We wanted to create an interactive and visual tool that allowed

  • ur public health stakeholders to analyze the different data types

We want to understand how this tool can be used by different public health stakeholders

TB Nurses TB Clinicians Medical Health Officers 45 Researchers Epis / Biostats
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WHAT

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Treatment Genomic Contact Network Patient Data Outcomes Geography / Location

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Treatment Genomic Contact Network Patient Data Outcomes Geography / Location

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TB whole genome Genotyping

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Treatment Genomic Contact Network Patient Data Outcomes Geography / Location time

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HOW

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An Iterative Approach to Development

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An iterative approach to development allows us to get feedback before committing to ineffective design choices

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Introducing EpiCOGs

DEMO

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EpiCogs is a data viewer and currently a sandbox environment for developing data visualizations

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Technology Changes

Factors Influencing the Current Design

Support for data visualization tools in R improved greatly allowing for the creation of better data visualizations

Data Driven Interface and Analysis

Created a data driven interface that is responsive to the user’s data.

Policies and Procedures

Existing policies and procedures at the BCCDC inform the utility of such a tool and how it can integrate into existing workflows

Needs of individuals

Gathered through meetings, dialogue with individuals, and various iterations of EpiCOGs

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Much initial work was to understand the tool’s feasibility

Initial Work & Next Directions

Could it meet the needs of stakeholders? How could it integrate (security & workflow)? How could it be supported long term? (Choice of R) Could we build a useful tool in R?

Next phases will explore genotypes, genomics, and contact networks

Right now, users can filter based on assigned genotype clusters (which will show patients on map), but we’re working towards better visual and interactive design for these data

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TRY THE DEMO:

https:/ /amcrisan.shinyapps.io/EpiCOGSDEMO/

GET THE CODE

(& contribute to the project!) :

https:/ /github.com/amcrisan/EpiCOGS/

This is an Open Source Project

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Call for Guinea Pigs!

To make relevant tools I need feedback! If you want to be involved and get project updates let me know!

E-mail: anamaria.crisan@bccdc.ca Twitter: @amcrisan Web : cs.ubc.ca/~acrisan

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Example 2

Visualizing the Ebola Outbreak – An example of a design process

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This was what we started with

A very familiar layout, all the information is there, but you have to do some work to put it together

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This was what we started with

Bedford Lab – Next Strain

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Can we improve the Design of the Visualization?

  • How do different public health stakeholders use the data?
  • Epidemiologists want to know:

– Where is it spreading? – How is it spreading? – How many people are impacted?

  • Researchers want to know:

– What’s spreading? – How similar are the outbreak clusters? – How is changing over time?

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Step 1: Small multiples by time

Ag Aggregate case distribution over entire sampling period Ag Aggregate case distribution by month
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Step 2: Small multiples by time and genome cluster

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Step 3: Small multiples by time and genome cluster and with sequence similarity

White: dominant nucleotide Grey : less dominant nucleotide
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Highly populous capital is very difficult to see

By abstracting the geography, we can represent more data more easily

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Capital city gets a more prominent view

By abstracting the geography, we can represent more data more easily

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X-axis ordering

Alphabetically within high level administration regions Geographic distance
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Part 3:

Take home messages

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Beyond Building Pretty & Cool Visualizations

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Data visualization is not art It is a research process.

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Data Visualization is not an art or graphic design project

Take Home Messages

Relevance (utility) and usability trump aesthetics

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Data Visualization is not an art or graphic design project Deciding upon the most appropriate data visualization can be a research problem

Think about ”why, what, and how” framework Parallels to finding the right statistical model Relevance (utility) and usability trump aesthetics Design & Evaluation

Take Home Messages

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Data Visualization is not an art or graphic design project Deciding upon the most appropriate data visualization can be a research problem

Relevance (utility) and usability trump aesthetics

Think broadly, progressively find the right data visualization

The Design Space Concept Iterative development Think about ”why, what, and how” framework Parallels to finding the right statistical model Design & Evaluation

Take Home Messages

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