Linking Data to Increase Maltreated Children’s Access to - PowerPoint Presentation

Slide1

Linking Data to Increase Maltreated Children’s Access to Early Intervention in Alaska

Taletha Derrington, DaSy Center Lisa Balivet, Alaska Department of Health and Social ServicesKenneth Smith, Alaska Department of Health and Social ServicesHaidee Bernstein, DaSy Center

MCH Epidemiology Conference ● Philadelphia, PA ● September 16, 2016Slide2

2Background

Maltreatment of infants and toddlers can have lifelong adverse developmental and health effects. Intervening early has the potential to reduce or reverse those effects.Beginning in 2003, federal laws1,2 require states to have policies and procedures for referral of children under age 3 years with substantiated maltreatment to state Part C Early Intervention (EI) programs.Data are limited on state policies and procedures and the extent to which they are facilitating access to and engagement in EI services. Slide3

3Alaska’s Response to Referral Mandate

Referral automation in 20123Policies/procedures enacted beginning in 2006 for child welfare caseworkers to refer children with substantiated maltreatment took considerable staff time and resulted in data errors.Child Welfare Data Manager suggested to EI Data Manager that they automate referrals using their data systems.Slide4

4Study Question

What were the trends in referral, eligibility evaluation, eligibility status, enrollment, and retention in EI services among Child-Welfare-referred children in comparison to children referred by all other sources in pre- and post-automation periods?Slide5

5Methods

Data source/sample: Child Welfare-EI linked data from 2005-2015Variables (count, yes/no): ReferralEvaluation among those referredEligibility among those evaluatedEnrollment among those eligibleRetention among enrolleesSlide6

6Methods

Analysis:Percentages / percentage increases for the pre-automation (2005-2011) and post-automation periods (2012-2015) for Child-Welfare-referred and Other-referred children (comparison group)Retention post-automation includes 2012 data only, as children referred 2013-2015 could still be enrolled.Prevalence ratio comparisonsLimitation: Administrative data quality is unknownSlide7

7

ReferralsThe average yearly number of Child Welfare referrals increased significantly from Pre-Automation (2005-2011) to Post-Automation (2012-2015).50%2%Slide8

8Evaluation

The percent of Child Welfare referrals receiving evaluations increased significantly from Pre-Automation to Post-Automation, becoming more comparable to the percent evaluated from other referral sources.Slide9

9Eligibility

Pre-Automation, the % of eligible children among those referred was comparable across the two groups but Post-Automation, Child-Welfare referrals were significantly less likely to be eligible compared with other referrals.Slide10

10Enrollment

The % of children who enrolled among those found eligible increased significantly from Pre- to Post-Automation for both groups, but enrollment was still lower among Child Welfare-referred children.Slide11

11Retention

The % of children who were retained in services among those who enrolled did not change significantly from Pre- to Post-Automation for either group, but Child Welfare-referred children were significantly less likely to be retained in both periods.Slide12

12

Among All Infants/Toddlers with Substantiated Maltreatment in Alaska…The % REFERRED increased from 63% to 100%2011 (N=855)2014 (N=647)ACCESS

Number of children under age 3 with substantiated maltreatment

4,5

ENGAGEMENT

% EVALUATED increased (46% to 66%)

% ELIGIBLE stable (38% to 36%)

% ENROLLED increased (18% to 32%)

Slide13

13Summary of Findings

Referral automation through data linkage improved maltreated infants’/toddlers’ access to EI services.Eligibility rates among Child Welfare referrals became significantly lower compared with other referrals.Enrollment increased but still represents less than one-third of maltreated infants/toddlers in AK.Retention among Child-Welfare-referred children remains significantly lower than for other-referred children.Slide14

14Conclusions and Lessons Learned

AutomationCannot substitute for interagency coordinationOngoing state and local interagency relationships are crucial Examining automation data helped build partnerships to identify areas for improvement and solutionsShared UnderstandingCross-agency communication needed to fully understandwhich children should be referred and whenstakeholder experiences with implementation each system’s roles and responsibilities#1Interagency Coordination#2Stakeholder Feedback

#3Clarify Roles & Responsibilities

★



✔Slide15

15

Public Health ImplicationsData linkages can support cross-agency coordination to increase access to developmental services.Engaging children and families involved with Child Welfare remains a challenge and will require coordination among multiple systems. Slide16

16Thank

You!Taletha Derrington, taletha.derrington@sri.comLisa Balivet, lisabalivet@gmail.com Kenneth Smith, EarlyIntervention@sundogsystems.comHaidee Bernstein, HaideeBernstein@westat.com Visit the DaSy website at: http://dasycenter.org/Slide17

17References

1 Child Abuse Prevention and Treatment Act 2003. (2003). 42 U.S.C. 5101 et seq; 42 U.S.C. 5116 et seq.2 Individuals with Disabilities Education Improvement Act of 2004. (2004). 20 U.S.C. 1400 et seq. 3 Derrington, T., Peters, M. L., Mauzy, D., & Ruggiero, R. (2015). State spotlight: Data sharing - Alaska: Improving referrals of victims of maltreatment to the IDEA Part C program. Menlo Park, CA: SRI International.4 U.S. Department of Health and Human Services, Administration for Children and Families, Administration on Children, Youth and Families, Children’s Bureau. (2012). Child maltreatment 2011. Washington, DC: Author.5 U.S. Department of Health and Human Services, Administration for Children and Families, Administration on Children, Youth and Families, Children’s Bureau. (2016). Child maltreatment 2014. Washington, DC: Author.Slide18

18

The contents of this presentation were developed under a grant from the U.S. Department of Education, # H373Z120002. However, those contents do not necessarily represent the policy of the U.S. Department of Education, and you should not assume endorsement by the Federal Government. Project Officers, Meredith Miceli and Richelle Davis.