Mo Moder derator r Rachel Yalowich , Project Director, National - - PowerPoint PPT Presentation

Mo Moder derator r

Rachel Yalowich, Project Director, National Academy

for State Health Policy

Lo Logis.cs f gis.cs for the W r the Webinar ebinar

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DATA VISUALIZATION

JENNIFER LYONS

lyonsvisualiation@gmail.com

Why is data viz so important?

Take Action

Make Change

Communicate Need

Funding

Illuminate Findings

Data Driven Decisions

Efficiency

Access to space must be a naPonal priority.

VISUALIZATION PROCESS

• 1. Build it
• 2. Break it down
• 3. Emphasize

your story

VISUALIZATION PROCESS

VISUALIZATION PROCESS

• 1. Build it
• 2. Break it down
• 3. Emphasize

your story

55% 45% 34% 55% 45% 34%

34% 45% 55%

55% 45% 34% 55% 45% 34%

34% 45% 55%

CHART FUNK?

CHOOSING THE RIGHT CHART

100 80 75 80 50 60 55 50 40 42 30 20

Jan Feb Mar April May June July Aug Sept Oct Nov Dec

Over the course of the year, sales decreased.

100 80 75 80 50 60 55 50 40 42 30 20

Jan Feb Mar April May June July Aug Sept Oct Nov Dec

Over the course of the year, sales decreased.

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

Maine Wyoming Arkansas MassachuseZs Alaska Colorado Montana Idaho New York West Virginia Maryland Delaware Minnesota Nevada Texas California Virginia Vermont Louisiana Illinois Michigan North Carolina Georgia New Jersey Oregon Florida New Hampshire Oklahoma South Dakota Kansas Utah Kentucky Missouri Washington

• 1. Build it
• 2. Break it down
• 3. Emphasize

your story

VISUALIZATION PROCESS

REDUCE CLUTTER

50 100 150 200 250

Chicken Beef Fish Tofu Pork Beans

Protein Preference

Extremely Dislike Dislike Slightly Dislike Neutral Slightly Like Like Extremely Like

30 45 60 115 120 210 20 20 50 20 10 10 215 200 155 130 135 45

Chicken Beef Fish Beans Pork Tofu

Of all protein opPons, most people dislike tofu.

Dislike Neutral Like

GESTALT

Proximity

Example from Evergreen Data’s blog “Directly Labeling in Excel”

Example from Evergreen Data’s blog “Directly Labeling in Excel”

YES! NO

Example from Stephanie Evergreen and Jennifer Lyons research on “The Link Between Graphic Design and Actual Report Use”

Focal Point

13 11 6 11 11 4 10 4 5 2 12 4 8

Jan.

• Feb. Mar. April May June

July

• Aug. Sept. Oct. Nov. Dec.

There is an average in-flow of 8 veterans coming into our homeless system every month.

13 11 6 11 11 4 10 4 5 2 12 4 8

Jan.

• Feb. Mar. April May June

July

• Aug. Sept. Oct. Nov. Dec.

There is an average in-flow of 8 veterans coming into our homeless system every month.

ConHnuity

13 11 6 11 11 4 10 4 5 2 12 4 8

Jan.

• Feb. Mar. April May June

July

• Aug. Sept. Oct. Nov. Dec.

There is an average in-flow of 8 veterans coming into our homeless system every month.

13 11 6 11 11 4 10 4 5 2 12 4 8

Jan.

• Feb. Mar. April May June

July

• Aug. Sept. Oct. Nov. Dec.

There is an average in-flow of 8 veterans coming into our homeless system every month.

• 1. Build it
• 2. Break it down
• 3. Emphasize

your story

VISUALIZATION PROCESS

STRATEGIC TEXT

Example from Ann Emery’s Blog

Example from Ann Emery’s Blog

DescripHve Title AcHve Title

DescripHve Title AcHve Title

Protein Preferences

DescripHve Title AcHve Title

Protein Preferences Of all protein options, most people dislike tofu.

DescripHve Title AcHve Title

Protein Preferences Of all protein options, most people dislike tofu. 2015 vs. 2016 Program Enrollment by Race Protein Preferences Of all protein options, most people dislike tofu.

DescripHve Title AcHve Title

Protein Preferences Of all protein options, most people dislike tofu. 2015 vs. 2016 Program Enrollment by Race 2016 enrollment for people of color has increased by 5%.

DescripHve Title AcHve Title

Protein Preferences Of all protein options, most people dislike tofu. 2015 vs. 2016 Program Enrollment by Race 2016 enrollment for people of color has increased by 5%. Customer Satisfaction Survey Results

DescripHve Title AcHve Title

Protein Preferences Of all protein options, most people dislike tofu. 2015 vs. 2016 Program Enrollment by Race 2016 enrollment for people of color has increased by 5%. Customer Satisfaction Survey Results Overall, respondents were most satisfied by our

• rganization’s customer

service and follow-up.

COLOR

I love learning about data

• visualization. It is so great to learn all
• f these new data best practices I will

apply the things I have learned today to the data I use in my own work. Data visualization helps me better tell my story and communicate with my intended audience.

I love learning about data

• visualization. It is so great to learn all
• f these new data best practices I

will apply the things I have learned today to the data I use in my own

• work. Data visualization helps me

better tell my story and communicate with my intended audience.

2 out of 10 people receiving our services are women. 2 out of 10 people receiving

• ur services are women.

VS.

20% 30% 40% 50% 60% 70% 80% Jan Feb Mar April May June July Aug Sept Oct Nov Dec Region 1 Region 2 Region 3 Region4 Region 5

Regional sales for 2015

Jan Feb Mar April May June July Aug Sept Oct Nov Dec

Region three sustained the usual summer sales slump.

80% 20% 70% 60% 50% 40% 30%

Region 3 Region 4 Region 2 Region 1 Region 5

Jan Feb Mar April May June July Aug Sept Oct Nov Dec

All sales increased significantly during the holiday season.

80% 20% 70% 60% 50% 40% 30%

Region 3 Region 4 Region 2 Region 1 Region 5

C O L O R

Example from Evergreen Data’s Blog

SOCIAL MEDIA

P R E S S

D A S H B O A R D

Example from Natalya Wawrin’s work with the VA in Ann Arbor

DATA VISUALIZATION

JENNIFER LYONS

lyonsvisualiation@gmail.com

MA DEPARTMENT OF PUBLIC HEALTH

Monica Bharel, MD MPH Commissioner of Public Health

HIV/AIDS IN MASSACHUSETTS

July 2017

People Diagnosed with HIV Infection by Exposure Mode 2013 - 2015

by Exposure Mode: Massachusetts, 2013–2015

N=1,994

Undetermined 28% Heterosexual Sex 6% Injection Drug Use 6% MSM/IDU 2% Other 1% Presumed Heterosexual Sex (Females) 13% Male-to-Male Sex 44%

Data Source: MDPH HIV/AIDS Surveillance Program, Data as of 1/1/17

Individuals Diagnosed with HIV Infection by Exposure Mode and Year of Diagnosis: Massachusetts, 2005–2015

50 100 150 200 250 300 350 400

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Year of Diagnosis HIV Diagnoses

IDU

• Pres. HTSX

HTSX

Data Source: MDPH HIV/AIDS Surveillance Program; Data as of 1/1/17

MSM MSM/IDU NIR Other

Percentage Distribution of Deaths among People Reported with HIV/ AIDS: Selected Exposure Modes & Year of Death: 2005–2014

0% 10% 20% 30% 40% 50% 60% 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Percent Year of Diagnosis

N=2,732; HTSX = Heterosexual Sex; Pres. HTSX = Presumed Heterosexual Sex Data Source: MDPH HIV/AIDS Surveillance Program; Data are current as of 3/1/16 and may be subject to change

• PRES. HTSX

Undetermined Injection Drug Use Male-to-Male Sex HTSX

Proportion of Individuals Diagnosed with HIV Infection Among PWID by Race and Year of Diagnosis: Massachusetts, 2012–2015

Data Source: MDPH HIV/AIDS Surveillance Program; Data as of 1/1/17

OPIOIDS: USING DATA TO UNCOVER TRUTHS AND GUIDE POLICY

July 2017

Opioid Related Deaths

379 506 526 614 514 575 660 642 622 638 560 656 742 961 1,361 1,651 1,933

1,793 2,069 200 400 600 800 1,000 1,200 1,400 1,600 1,800 2,000 2,200

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

Number of deaths

Figure 1. Opioid1-Related Deaths, All Intents Massachusetts Residents: January 2000 - December 2016

Confirmed Estimated

70% OF OPIOID DEATHS IN 2016 HAD THE PRESENCE OF FENTANYL

4 4 6 % I N C R E A S E I N 1 6 Y E A R S

Opioid Related Deaths

10 20 30 40 50 60 70 80 90 1 2 3 4 1 2 3 4 1 2 3 4 2014 2015 2016 Percent Year and Quarter

Figure 4. Percent of Opioid Deaths with Specific Drugs Present MA: 2014-2016

Fentanyl¹ Likely Heroin Prescription Opioid² Benzodiazepine Cocaine

Opioid Related Deaths

Opioid Overdose Death Rates, All Intents Massachusetts: 2011-2013 vs. 2014 - 2016

Prevention Intervention Treatment Recovery

Governor Baker’s Opioid Working Group

Massachusetts Chapter 55 Legislation

• Signed into law in August 2015
• Requires a comprehensive report to the state legislature

and cross-agency collaboration to address 7 specific questions about opioid-related deaths

• Specifies major data sets across government
• Overcomes legal barriers for use of some data
• Work highlighted by Public Health Accreditation Board on

their site visit

Chapter 55 Data Mapping

PDMP

APCD Spine

Death Records BSAS Treatment Toxicology Medical Claims MATRIS (EMS) OCME Intake Hospital and ED MA Prisons MA Jails MassHealth DMH DHCD State Police Opioid Birth Records Veterans’ Services TransiHonal Assistance Youth Services Children & Families

Service Indicator Flags

Cancer Registry Dept Dev Services Commission for Blind

Chapter 55 Data Structure

Needle Exchange NARCAN DistribuHon Drug Seizure Data Town & Zip Census Data

Community Level Data

MDPHnet Depression I.C.E. Measures

PSI #1 & #2

PSI #1 APCD SPINE PSI #1

PSI #1 & #3 PSI #1 & #4 PSI #1 & #5 PSI #1 & #N

PSI = Project Specific Identifier

Enterprise SAS or other software (Fixed or Cloud-based servers)

Machine 1 Machine 2 Machine 5 Machine 4 Machine 3 Machine 6 Machine 7 Machine N Machine 8

… addiHonal data … … addiHonal machines … … addiHonal data … Chapter 55 Privacy Shield: Authorized users only, no write access, analysts cannot see data, automatic cell suppression, delete all temporary work files, full auditability of all data operations.

DRAFT - FOR POLICY DEVELOPMENT ONLY

PSI #2 & AnalyPc PSI #3 & AnalyPc PSI #4 & AnalyPc PSI #5 & AnalyPc PSI #N & AnalyPc

Chapter 55: Secure Data Access

Chapter 55: Partners Coming Together

Academic

• Brandeis University
• Boston University
• Brown University
• Harvard Medical School
• Harvard School of Public Health
• Massachuseas College of Pharmacy and Health Sciences
• Massachuseas InsHtute of Technology
• Northeastern University
• Tubs University
• University of Massachuseas Amherst
• University of Massachuseas Boston
• University of Massachuseas Medical School

State and Federal Government Agencies Hospitals & Private Industry

• Baystate Health
• Beth Israel Deaconess Medical Center
• Boston Medical Center
• Brigham & Women’s Hospital
• Children’s Hospital
• GE
• IBM
• Liberty Mutual
• Massachuseas General Hospital
• Massachuseas League of Community Health Centers
• McKinsey & Company
• The MITRE CorporaHon
• Partners Healthcare
• PwC
• Rand CorporaHon
• Boston Public Health Commission
• Center for Health InformaHon and Analysis
• Department of Housing and Community Development
• Department of Mental Health
• Department of CorrecHon
• Department of Public Health
• ExecuHve Office of Health and Human Services
• ExecuHve Office of Public Safety and Security
• Federal Bureau of InvesHgaHon
• High Intensity Drug Trafficking Area (NE)
• Health Policy Commission
• Massachuseas Sheriffs’ AssociaHon
• MassIT
• Office of the Chief Medical Examiner
• State Auditor’s Office

Data Mapping: Key finding

• Patients treated

with methadone and/or buprenorphine (Opioid Agonist Treatment) following a non- fatal overdose were significantly less likely to die.

• Very few patients

(~5%) receive Opioid Agonist Treatment following a non- fatal overdose.

0.5 1 1.5 2 2.5 Engaged in OAT Not Engaged in OAT Cumulative Incidence (%)

Cumulative Incidence of Opioid-Related Death by Opioid Agonist Treatment Status

Data Mapping: Key finding

The risk of opioid

• verdose death

following incarceration is 56 times higher than for the general public.

869.4 opioid deaths / 100,000 15.4 opioid deaths/ 100,000

100 200 300 400 500 600 700 800 900 1000 Former Inmates All Others

Comparison of Opioid Death Rates Among Former Inmates to the Rest of State (2013 - 2014)

Does an abnormally high amount of prescribing physicians increase a patient’s risk of fatal

• verdose?

Individuals who obtain

• pioid prescriptions from

more than 1 doctor may be at greater risk of death. Based on observed data, the use of 3 or more prescribers is associated with a 7-fold increase in risk of fatal

• pioid overdose.

Does the addition

• f benzodiazepines

to opioids increase the risk of fatal

• pioid overdose

relative to taking opioids alone? Preliminary findings support the hypothesis of increased risk of fatal overdose associated with concurrent use of opioids and benzodiazepines. Based on observed data, the use of benzodiazepines concurrent to opioids is associated with a 4-fold increase in risk of fatal opioid

• verdose.

ANALYTIC QUESTION PRELIMINARY FINDING

Data mapping– Key Findings

PMP activity trends

14.3 13.6 10.6 7.7 4.0 8.0 12.0 16.0 2013 2014 2015 2016 Rate per 1,000 Individuals

Figure 3. Rate1 of Individuals with Activity of Concern2 in MA3 2013–2016

Activity of Concern

1 Rates of individuals with activity of concern are based on the population of individuals who have received one or more Schedule II

• pioid prescriptions.

2 "Activity of Concern" is defined as an individual who received prescriptions for one or more Schedule II opioid drugs from four or

more different prescribers and had them filled at four or more pharmacies during the specified time period.

3 Activity of concern rates include only MA Residents

PMP activity trends

6.0 7.9 8.2 9.6 8.0 8.9 10.3 10.0 9.6 9.7 8.0 10.0 11.2 14.4 20.2 26.4 30.5 5 10 15 20 25 30 35

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Rate per 100,000 Residents

Figure 3. Rate of Opioid1-Related Deaths, All Intents Massachusetts Residents: 2000-2016

Opioid Related Deaths

40% 31% 16%

Data visualization of findings from Chapter 55 Report

Monica Bharel, MD, MPH Commissioner, Massachusetts Department of Public Health

http://www.mass.gov/chapter55/

Opioid map – Chapter 55 Visualization Chapter 55 website allows for town-by-town analysis

Chapter 55 Visualization Adding interactive elements to help localize the epidemic

Connecting data with a story…

THANK YOU & QUESTIONS

Qu Ques.ons

HIV Health Improvement Affinity Group

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