Data for Dollars Leveraging data to empower community coalitions

1. Data for DollarsLeveraging data to empower community coalitions Division of Behavioral Health Bureau of Policy and Quality Assurance Nathan Drashner, Research Analyst April 6th, 2011

2. Background • Idaho SEOW/PATR • Federally funded contract • Began in 2006 • Deliverables • State Profile • Community Profiles • Dissemination Tool • Prevention Subcommittee

3. Process

4. Risk Factor Selection Criteria • Communities That Care (CTC) Framework • Risk Factors had to be: • Collected on a county level • Collected annually • Aggregated to a standardized rate • FY 2008 • Potentially comparable nationally • Arizona, Utah, & Tennessee

5. Liquor Sales Adult Alcohol Arrests Adult Drug Arrests Impaired Driving Crashes Free/Reduced School Lunch Unemployment Risk Factors Community Domain School Domain Family Domain Individual Domain • Child Abuse/Neglect • Heavy Drinking • Illicit Drug Use • Juvenile Alcohol Arrests • Juvenile Drug Arrests • Adolescent Pregnancy • Truancies • Suspensions • Bullying

6. Project Partners

7. Dissemination Via The Web • Web-based software is well suited for: • Visualizing large, relational data sets • Extracting meaning from data • Discovering new relationships in data • Empowering decision making in public and private sectors

8. Idaho Prevention & Treatment Research Project Requirements Visualizations: Color Density (choropleth) Map Trend Charts (with motion) Data Importing: Built-in content management system that allows end-users to easily manage all site content Instant data import from excel spreadsheet Extensibility: House data for every county, for an unlimited # of years and unlimited # of risk factors

9. Extract More Meaning From Data When data for multiple risk factors is housed in a relational web-based application, it can be viewed in multiple dimensions: State-wide Dimension Compare each county to one another for a given risk factor Utilizing choropleth maps County-wide Dimension Compare risk factors to one another for a given county Utilizing trending charts

10. Discover New Relationships Fully interactive charts, graphs and maps create opportunities for learning that cannot be achieved with static images. When counties and risk factors are compared to each other in the same visual space, end-users are able to identify new, meaningful patterns that can have a profound impact on decision and policy making.

11. Case Study • BeTheParents.org • Awarded STP/DRK Grant • Limited to alcohol prevention • Youth Oriented • Needs Assessment

12. Needs Assessments • Past… • Often general (Statewide) • Occasionally purely anecdotal • Disparate literature review • With PATR… • Geo and issue specific • Quantitative + Qualitative = Causation • Central repository for multiple agencies products

13. Caveats • Disparate data sets are difficult to align • Reporting timeframes vary greatly • Correlation does not imply causality • Multiple local activities inform the data (DUI emphasis patrols, revised truancy policy) • Intended to inform discussion and further investigation at a local level

14. Live Implementation patr.idaho.gov Idaho Prevention & Treatment Research

15. Empower Decision Making Idaho has an opportunity to leverage our data by utilizing smart technology to transform it into tangible, actionable information. Web-based applications represent the next frontier in data dissemination tools as they empower the public and private to learn and make informed decisions.

16. Data Voids • Insufficient Data • Local • National • Protective Factors are not reflected (programs, services, community supports, etc…) • Database Cardinality

17. Next Steps • National standardization • Indexing • Protective Factors • Annual updates as data becomes available • Dissemination tool (website)