Improving Data, Improving Outcomes

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  • 1. Robert L. Fischer, Ph.D., Claudia J. Coulton, Ph.D., & Seok-Joo Kim, Ph.D. Center on Urban Poverty & Community Development Jack, Joseph and Morton Mandel School of Applied Social Sciences Case Western Reserve University Cleveland, Ohio September 16, 2013; Washington, DC “Improving Data, Improving Outcomes” How Can Partnerships with Higher Education Help Your State Agency Use Early Childhood Data for Decision-Making?
  • 2. Overview 2 • State-wide resource in Ohio (Ohio Educational Research Center) • Local data system in Cuyahoga County (Cleveland) • Leveraging existing data to answer new questions • Recommendations for pursuing this kind of work
  • 3. Overview Educational Data Projects from State to Local. State County Local OERC CHILD system Projects (examples) Ohio Cuyahoga Cleveland Area Project • Education projects • Collaboration with partners ImplementationLevel • Database for children • Geographic analyses I. Health care II. Homeless family III. 3rd Grade reading* *OERC project Researcher 3
  • 4. State: The OERC 4 The Ohio Education Research Center (OERC), is a network of Ohio-based researchers and research institutions, that develops and implements a statewide, preschool-through-workforce research agenda to address critical issues of education practice and policy. •Provide timely and high quality evaluation & research products •Maintain a research data base •Bridge needs, research, practice & policy •Bring together resources to improve access to knowledge
  • 5. Cleveland, OH Ohio Education Research Center State: The OERC 5 Current Projects Investigating the pathway to proficiency from Birth through 3rd grade Standards / Assess- ments State Success Factors Teachers & Leaders STEM Education Initiatives Future- Ready Students Early Childhood Education Improve- ment & Innovation Improving with Data Cleveland, Ohio
  • 6. County: CHILD system • Data helps inform our understanding of the early childhood system • Individuals and families interact with multiple systems and services, so integrated data offers a more complete view of reality [“Big Data”] • Understanding of how systems work and how to better meet existing needs can be informed by integrated data • Service models emphasize long term and collective impact, so data needed across services and over time The Need for Integrated Data. 6
  • 7. ID6 ID5 ID4 ID3 ID2 ID1 • Abuse/neglect reports • Involvement with ongoing services • Home visiting • Special needs child care • Early childhood mental health • Universal pre-k • Attendance • KRA-L • Proficiency test • Graduation test • Disability • Medicaid • Food Stamp • TANF • Child care voucher • Infant mortality • Elevated Blood Lead • Teen births • Low weight birth County: CHILD system Concept. Public Assists Public School Common ID ChildHood Integrated Longitudinal Data (CHILD) System 7
  • 8. County: CHILD system Structure. Geocode & Standardize Updated IDS Register-includes ID#’s, names, addresses, DOB, etc. IDS Register- includes ID#’s, names, addresses, DOB, etc. Outcomes E.g. Kindergarten Readiness Scores among children in UPK program Profiles E.g. Birth characteristics & service use for children entering kindergarten Geographic E.g. % LBW births receiving ongoing home visits by neighborhood Time Trends e.g. Total Children Served by birth cohort Data files-Births, Home Visiting, DCFS, UPK, KRA-L, Medicaid, etc. Longitudinal Master Files for Each Data Source REPORTS Match New Records to IDS Register 8
  • 9. Geographic Analyses 9 Indicators County District 2 (2008) County District 8 (2008) Cuyahoga County (2008) Births 1,443 1,877 16,246 # Teen Births, mother’s age 10-14 (per 1,000) 2 (1) 12 (2) 42 (1) # Teen Births, mother’s age 15-19 (per 1,000) 124 (39) 358 (79) 2,031 (41) % Mothers without High School diploma 14% 32% 19% % Low Birth Weight 9% 14% 10% % Premature Low Weight Births 6% 9% 7% % Mothers w/adequate prenatal care 52% 42% 53% % Mothers w/out prenatal care 1% 2% 1% % Healthy Births 53% 36% 49% # Infant Death (per 1,000 births) 10 (7) 29 (15) 164 (10)
  • 10. Cleveland Metropolitan School District Profile 10 Indicators Kindergarten 2008-9 Cleveland Cuyahoga County % Teen Births, mother’s age 10-14 <1 <1 <1 % Teen Births, mother’s age 15-19 22.4 16.7 9.8 % Mothers without High School diploma 41.7 30.2 15.9 % Low Birth Weight 12.6 11.6 9.4 % Premature Low Weight Births 8.7 8.2 6.7 % Mothers w/adequate prenatal care (Kessner Index) 63.1 69.4 81.3 % Mothers w/out prenatal care 1.9 1.9 .9 % Health Births 56.4 61.5 70.9 % Children with a substantiated or indicated report of abuse/neglect by age 4 12.1 9.6 5.1 % Children referred to ongoing services with Child & Family Services by age 4 19.8 14.7 7.6 % Children with any report of abuse/neglect by age 4, including substantiated and unsubstantiated 35.2 26.7 14.7 % Children in households receiving Food Stamps in 2008 76.9 51.1 28.8 % Children in households receiving Cash Assistance in 2008 19.0 11.3 6.1
  • 11. Data Influence Examples 11 1) More children have access to health care via public insurance, but are they using it? 2) How are homeless families involved with child welfare services? 3) What children will be most impacted by the State’s 3rd Grade reading Guarantee?
  • 12. Local Example I: Child Health • Dramatic increase in health insurance coverage for children ages 0-6 in the county: Hooray! • But only 43% of children get all the recommended well-child visits in the first year of life: Oh no! • Data show that 49% of these families were involved with supportive services close to birth, so we can use that connection to reach families: Hooray! • But wait, due to data lags and coordination issues, outreach would happen too late to have an effect: Oh, no! • A preventive approach could be adopted by having dedicated staff at clinics reach out to families… • Result o Medical Home Pilot launched at two health clinics; 86% of families completed scheduled well-child visits, double the rate for children born on Medicaid in Cuyahoga County; one clinic has integrated the model into care with 9 patient advocates serving the needs of families with infants Summary. 12
  • 13. Local Example II: Homeless Families • County undertaking social impact bond approach to social services o Fund preventive services that pay for themselves through lower use of later high-cost services • Focus on homeless families who are also involved with child welfare services o High-costs associated with of out-of-home placements and shelter stays • Found that 30% of women in shelter had children involved with welfare agency o 52% of these women had no children with them in shelter o 25% of their children were in a foster care placement • County developing strategies to intervene with mothers before they become homeless and to intervene when mothers enter shelters Summary. 13
  • 14. Example III: 3rd Grade Reading 14 Study Significance. • Importance of early childhood exposures o Early exposure to stressful circumstances, environmental hazards, and less than optimal early learning environments negatively and persistently affect early development. • Usefulness of longitudinal data • State adopted ‘3rd Grade reading Guarantee’ to ensure that students pass reading proficiency test before advancing beyond 3rd grade • Districts can project how many of their students will be held back when the policy is implemented • What is less understood is o What early childhood factors best predict the students who will be impacted by this policy? o What early childhood interventions appear to lessen the odds a child will not attain third grade reading proficiency?
  • 15. Example III: 3rd Grade Reading 15 Cohort Design. Cohort 1 Cohort 2 Cohort 3 Cohort 4 B 3rdK B 3rdK B 3rdK B 3rdK 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013Year Collected Recently collected Will be collected
  • 16. Birth Home Visits Medical Pre-K Child Care Nhood / Residence Family Economic 3rdK • Birth weight • Maternal risk • Housing distress • Abuse/Neglect • Out-of-home placement • Access to well-child care • Cash assist/ Poverty • Food insecurity • Newborn home visit • Help Me Grow • Mom’s First • Out-of-home child care • Public preschool • Universal Pre-K Pilot • Nhood condition • Housing distress • Residential instability Child Welfare • KRA-L • STAR • STAR Early Literacy • NWEA MAP • OAA • Benchmark Assessments K-3 Outcomes 1st Example III: 3rd Grade Reading Conceptual model 16
  • 17. Example III: 3rd Grade Reading • Sample (N=3,679): Children who took KRA-L in 2007 & 2008 and 3rd grade proficiency test in 2010 & 2011 in Cleveland Metropolitan School District, OH. • Sample and variables will be updated. 17 Current Process Educational Information % Demographic / Welfare / Neighborhood % Pass of 3rd grade readting test 55.7 Girl 49.7 KRA-L band 1 (Score 1-13) 38.1 Hispanic 10.6 KRA-L band 2 (Score 14-23) 44.6 African-American 69.3 KRA-L band 3 (Score 24-29) 17.3 Other race 4.3 White 15.8 Below 11% of attendance at Kindergarten 29.7 TANF + (Medicaid or SNAP) at Kindergarten 17.3 Reported disability before 3rd grade 14.5 Medicaid or SNAP at Kindergarten 67.4 No assistance at Kindergarten 15.3 Living a census tract with poverty rate above 30% at Kindergarten 49.4 (Substantiated or indicated) maltreatment before Kindergarten 17.5
  • 18. Example III: 3rd Grade Reading 19 Implications. • Collaboration with Cleveland Metropolitan School District o Data Sharing o Uses - Building profiles - Community collaborative planning - Risk factor reduction • Helpful to establish educational planning; especially schools with large numbers of disadvantaged students • Understand challenges for 3rd grade guarantee
  • 19. Discussion Observations… • Data don’t make policy… People with data make policy • Policy shapes research • Everyone wants outcomes… few want to pay for them (or pay very much) • Great divides need to be bridged in terms of institutional practice and philosophy Data into Practice 20
  • 20. Discussion • Data inclusion decisions o Relevance o Continuity o Correct geography 21 Ongoing Challenges for Integrated Data. • Data usage issues oData access oData quality oData linkage
  • 21. Discussion Recommendations. • Identify what data exist and in what form it exists; consider partnering with universities in this work • Become familiar with relevant federal and state laws and policies regarding data sharing/use • Convene interested parties – data holders and data users – to discuss the opportunities to learn from integrated data • Pilot data matching procedures to demonstrate how specific questions can be answered 22
  • 22. Discussion • Institute of Education Sciences has funding work to integrate data related to young children • US Department of Education Race to the Top funds can be used for longitudinal data systems using integrated data • Various federal funding opportunities exist for studies that could develop and draw on integrated data systems • MacArthur Foundation very interested in use of integrated data Funding Prospects. 23
  • 23. Thank you! Q / A 24 Contact Information: Robert Fischer, Ph.D. (fischer@case.edu) Resources • Ohio Education Research Center: http://oerc.osu.edu/ • Center on Urban Poverty & Community Development: http://povertycenter.case.edu/ • NEO CANDO: http://neocando.case.edu/ State Local County