Moderator. Rachel Yalowich, Project Director, National Academy for State Health Policy
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1 Moderator Rachel Yalowich, Project Director, National Academy for State Health Policy
2 Logis.cs for the Webinar If you are unable to listen to the webinar through your computer speakers, please use your phone: Dial in: (866) Access Code: Lines will not be open during this webinar. Comments are encouraged. Please use that chat box on the lower lem corner of your screen. The chat feature is available, but will not be visible in full screen mode
3 DATA VISUALIZATION JENNIFER LYONS
4 Why is data viz so important? Take Action Communicate Need Make Change Efficiency Illuminate Findings Funding Data Driven Decisions
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7 Access to space must be a naponal priority.
8 VISUALIZATION PROCESS
9 VISUALIZATION PROCESS 1. Build it 2. Break it down 3. Emphasize your story
10 VISUALIZATION PROCESS 1. Build it 2. Break it down 3. Emphasize your story
11 55% 45% 34% 34% 55% 45% 55% 45% 34%
12 55% 45% 34% 34% 55% 45% CHART FUNK? 45% 55% 34%
13 CHOOSING THE RIGHT CHART
14 Over the course of the year, sales decreased Jan Feb Mar April May June July Aug Sept Oct Nov Dec
15 Over the course of the year, sales decreased Jan Feb Mar April May June July Aug Sept Oct Nov Dec
16 Washington Missouri Kentucky Utah Kansas South Dakota Oklahoma New Hampshire Florida Oregon New Jersey Georgia North Carolina Michigan Illinois Louisiana Vermont Virginia California Texas Nevada Minnesota Delaware Maryland West Virginia New York Idaho Montana Colorado Alaska MassachuseZs Arkansas Wyoming Maine 0% 10% 20% 30% 40% 50% 60% 70% 80% 90%
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19 VISUALIZATION PROCESS 1. Build it 2. Break it down 3. Emphasize your story
20 REDUCE CLUTTER
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22 Protein Preference Beans Pork Tofu Fish Beef Extremely Dislike Dislike Slightly Dislike Neutral Slightly Like Like Extremely Like Chicken
23 Of all protein oppons, most people dislike tofu. Tofu Pork Beans Fish Beef Chicken Dislike Neutral Like
24 GESTALT
25 Proximity
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28 Example from Evergreen Data s blog Directly Labeling in Excel
29 Example from Evergreen Data s blog Directly Labeling in Excel
30 YES! NO Example from Stephanie Evergreen and Jennifer Lyons research on The Link Between Graphic Design and Actual Report Use
31 Focal Point
32 There is an average in-flow of 8 veterans coming into our homeless system every month Jan. Feb. Mar. April May June July Aug. Sept. Oct. Nov. Dec.
33 There is an average in-flow of 8 veterans coming into our homeless system every month Jan. Feb. Mar. April May June July Aug. Sept. Oct. Nov. Dec.
34 ConHnuity
35 There is an average in-flow of 8 veterans coming into our homeless system every month Jan. Feb. Mar. April May June July Aug. Sept. Oct. Nov. Dec.
36 There is an average in-flow of 8 veterans coming into our homeless system every month Jan. Feb. Mar. April May June July Aug. Sept. Oct. Nov. Dec.
37 VISUALIZATION PROCESS 1. Build it 2. Break it down 3. Emphasize your story
38 STRATEGIC TEXT
39 Example from Ann Emery s Blog
40 Example from Ann Emery s Blog
41 DescripHve Title AcHve Title
42 DescripHve Title AcHve Title Protein Preferences
43 DescripHve Title Protein Preferences AcHve Title Of all protein options, most people dislike tofu.
44 DescripHve Title Protein Preferences AcHve Title Of all protein options, most people dislike tofu vs Program Enrollment by Race
45 DescripHve Title Protein Preferences AcHve Title Of all protein options, most people dislike tofu vs Program Enrollment by Race 2016 enrollment for people of color has increased by 5%.
46 DescripHve Title Protein Preferences AcHve Title Of all protein options, most people dislike tofu vs Program Enrollment by Race 2016 enrollment for people of color has increased by 5%. Customer Satisfaction Survey Results
47 DescripHve Title Protein Preferences AcHve Title Of all protein options, most people dislike tofu vs 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 organization s customer service and follow-up.
48 COLOR
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50 I love learning about data visualization. It is so great to learn all of 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.
51 I love learning about data visualization. It is so great to learn all of 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.
52 2 out of 10 people receiving our services are women. VS. 2 out of 10 people receiving our services are women.
53 Regional sales for % 70% 60% 50% 40% 30% 20% Jan Feb Mar April May June July Aug Sept Oct Nov Dec Region 1 Region 2 Region 3 Region4 Region 5
54 Region three sustained the usual summer sales slump. 80% Region 5 70% Region 1 60% Region 2 Region 4 50% Region 3 40% 30% Jan Feb Mar April May June July Aug Sept Oct Nov Dec 20%
55 All sales increased significantly during the holiday season. 80% Region 5 70% Region 1 60% Region 2 Region 4 50% Region 3 40% 30% Jan Feb Mar April May June July Aug Sept Oct Nov Dec 20%
56 C O L O R Example from Evergreen Data s Blog
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58 SOCIAL MEDIA
59 P R E S S
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65 D AS H B O A R D Example from Natalya Wawrin s work with the VA in Ann Arbor
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67 DATA VISUALIZATION JENNIFER LYONS
68 MA DEPARTMENT OF PUBLIC HEALTH Monica Bharel, MD MPH Commissioner of Public Health
69 HIV/AIDS IN MASSACHUSETTS July 2017
70 People Diagnosed with HIV Infection by Exposure Mode by Exposure Mode: MSM/IDU Massachusetts, Other Heterosexual Sex 6% Injection Drug Use 6% 2% 1% Presumed Heterosexual Sex (Females) 13% Male-to-Male Sex 44% Undetermined 28% N=1,994 Data Source: MDPH HIV/AIDS Surveillance Program, Data as of 1/1/17
71 Individuals Diagnosed with HIV Infection by Exposure Mode and Year of Diagnosis: Massachusetts, MSM HIV Diagnoses IDU HTSX NIR Pres. HTSX 50 0 Other MSM/IDU Year of Diagnosis Data Source: MDPH HIV/AIDS Surveillance Program; Data as of 1/1/17
72 Percentage Distribution of Deaths among People Reported with HIV/ AIDS: Selected Exposure Modes & Year of Death: Percent 60% 50% Injection Drug Use 40% 30% Male-to-Male Sex 20% 10% HTSX Undetermined 0% Year of Diagnosis PRES. HTSX 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
73 Proportion of Individuals Diagnosed with HIV Infection Among PWID by Race and Year of Diagnosis: Massachusetts, Data Source: MDPH HIV/AIDS Surveillance Program; Data as of 1/1/17
74 OPIOIDS: USING DATA TO UNCOVER TRUTHS AND GUIDE POLICY July 2017
75 Opioid Related Deaths Figure 1. Opioid 1 -Related Deaths, All Intents Massachusetts Residents: January December 2016 Number of deaths 2,200 2,000 1,800 1,600 1,400 1,200 1, Confirmed Estimated 2,069 1,793 1,933 1,651 1, % INCREASE IN 16 YEARS 70% OF OPIOID DEATHS IN 2016 HAD THE PRESENCE OF FENTANYL
76 Opioid Related Deaths Percent Figure 4. Percent of Opioid Deaths with Specific Drugs Present MA: Fentanyl¹ Likely Heroin Prescription Opioid² Benzodiazepine Cocaine Year and Quarter
77 Opioid Related Deaths
78 Opioid Overdose Death Rates, All Intents Massachusetts: vs
79 Governor Baker s Opioid Working Group Prevention Intervention Treatment Recovery
80 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
81 Chapter 55 Data Mapping Chapter 55 Data Structure PDMP Toxicology Community Level Data BSAS Treatment Death Records OCME Intake NARCAN DistribuHon Town & Zip Census Data Needle Exchange MATRIS (EMS) State Police Opioid I.C.E. Measures Drug Seizure Data MDPHnet Depression Birth Records Cancer Registry APCD Spine MA Prisons MA Jails Service Indicator Flags Children & Families TransiHonal Assistance Medical Claims MassHealth Dept Dev Services Hospital and ED Veterans Services DMH DHCD Youth Services Commission for Blind
82 Chapter 55: Secure Data Access PSI #2 & AnalyPc PSI #3 & AnalyPc PSI #4 & AnalyPc PSI #5 & AnalyPc addihonal data PSI #N & AnalyPc PSI #1 & #2 PSI #1 & #3 PSI #1 & #4 PSI #1 & #5 addihonal data PSI #1 & #N PSI #1 APCD SPINE PSI #1 PSI = Project Specific Identifier Enterprise SAS or other software (Fixed or Cloud-based servers) 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. Machine 1 Machine 2 Machine 3 Machine 4 Machine 5 Machine 6 Machine 7 Machine 8 addihonal machines Machine N DRAFT - FOR POLICY DEVELOPMENT ONLY
83 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 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 State and Federal Government Agencies 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
84 Data Mapping: Key finding 2.5 Cumulative Incidence of Opioid-Related Death by Opioid Agonist Treatment Status Patients treated with methadone and/or buprenorphine (Opioid Agonist Treatment) following a nonfatal overdose were significantly less likely to die. Cumulative Incidence (%) Very few patients (~5%) receive Opioid Agonist Treatment following a nonfatal overdose Engaged in OAT Not Engaged in OAT
85 Data Mapping: Key finding The risk of opioid overdose death following incarceration is 56 times higher than for the general public Comparison of Opioid Death Rates Among Former Inmates to the Rest of State ( ) opioid deaths / 100, Former Inmates 15.4 opioid deaths/ 100,000 All Others
86 Data mapping Key Findings Does an abnormally high amount of prescribing physicians increase a patient s risk of fatal overdose? Individuals who obtain opioid 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 opioid overdose. ANALYTIC QUESTION PRELIMINARY FINDING Does the addition of benzodiazepines to opioids increase the risk of fatal opioid 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 overdose.
87 PMP activity trends
88 PMP activity trends 16.0 Figure 3. Rate 1 of Individuals with Activity of Concern 2 in MA Rate per 1,000 Individuals 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 opioid 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
89 Opioid Related Deaths 35 Figure 3. Rate of Opioid 1 -Related Deaths, All Intents Massachusetts Residents: Rate per 100,000 Residents % % %
90 Data visualization of findings from Chapter 55 Report Monica Bharel, MD, MPH Commissioner, Massachusetts Department of Public Health
91 Opioid map Chapter 55 Visualization Chapter 55 website allows for town-by-town analysis
92 Chapter 55 Visualization Adding interactive elements to help localize the epidemic
93 Connecting data with a story
94 THANK YOU & QUESTIONS
95 Ques.ons
96 HIV Health Improvement Affinity Group Thank you for parpcipapng in today s webinar! Please complete the evaluapon amer exipng the webinar.
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