1. MCH Epidemiology Conference ● Philadelphia, PA ● September 16, 2016
Linking Data to Increase Maltreated Children’s Access to Early Intervention in Alaska
LISA
Lisa: Alaska Office of Children’s Services, Dept. of Health and Social Services
Ken: DSS-IT Business Applications, Dept. of Health and Social Services
Taletha Derrington, DaSy Center
Lisa Balivet, Alaska Department of Health and Social Services
Kenneth Smith, Alaska Department of Health and Social Services
Haidee Bernstein, DaSy Center
MCH Epidemiology Conference ● Philadelphia, PA ● September 16, 2016

2. Background
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.
LISA
Talk about higher maltreatment rates among < 3 compared to older (CM 2014: 24.4/1000 children for age <1; 12.3/1000 children for age 1; 11.6/1000 children for age 2).

3. Alaska’s Response to Referral Mandate
Referral automation in 20123
Policies/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.
LISA
In 2006, the state enacted policies and procedures for child welfare workers to refer children with substantiations to EI. However, this took a lot of time for both child welfare to make the referral manually, and for EI to enter the data in the ILP DB, sometimes needing to get back in touch with the case worker to get missing info or to get alternative contact info if the address or phone # in the initial referral didn’t work.
AUTOMATED REFERRAL: The referral process doesn’t require any special action by child welfare or the EI program.  When child welfare enters substantiation information into their data system, the automated procedure finds that new record and creates the EI referral – if it meets the criteria for a valid referral.
AUTOMATION SCHEDULE: The automated process runs every night, looking for new substantiations.  If found, EI referral records are created in the ILP database.
SUBSTANTIATION LAG: We adjusted the criteria for automated referrals from child welfare several times, to fine-tune the process.  If the substantiation happened too many days after the report of harm, providers were not able to contact the families.  If we limited the referrals to children who had substantiation within 60 days, we got few referrals.  We finally settled on the criterion that the substantiation had to happen within 90 days of the report of harm, or it is considered an invalid referral.

4. Study 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?
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5. Methods
Data source/sample: Child Welfare-EI linked data from
Variables (count, yes/no):
Referral
Evaluation among those referred
Eligibility among those evaluated
Enrollment among those eligible
Retention among enrollees
TALETHA
Referral: presence of a record
Evaluation: presence of evaluation date
Eligibility: status as entered in system
Enrollment: presence of IFSP date
Retention: reason for discharge was “age out” at 3 years or reached age expectations/no longer eligible, as contrasted with family declining services directly or indirectly through repeated no shows (lost to follow up).

6. Methods Analysis: Limitation:
Percentages / percentage increases for the pre-automation ( ) and post-automation periods ( ) for Child-Welfare-referred and Other-referred children (comparison group)
Retention post-automation includes 2012 data only, as children referred could still be enrolled.
Prevalence ratio comparisons
Limitation:
Administrative data quality is unknown
TALETHA

7. Referrals
The average yearly number of Child Welfare referrals increased significantly from Pre-Automation ( ) to Post-Automation ( ). 2%
50%
TALETHA
Repeat referral def.
If you look at referrals in 2005, when they began working on policies and procedures, and compare that to the latest year of available data in 2015, there was a 214% increase in CW referrals and a 6% increase in other referrals. So, the automation achieved it’s primary goal of increasing referrals.
PUT IN PRs

8. Evaluation
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.
TALETHA
Repeat evaluation def.
Another goal for Alaska’s collaboration was to increase the percentage of maltreated children who received developmental screenings and/or evaluations, and a second goal of the automation was to increase the accuracy and timeliness of receipt of referral data, which would increase the likelihood that EI could contact the family to set up an eligibility evaluation. Here we see that goal achieved (read action title)
Prevalence Ratio: post to pre, CW
(1788/2795)/(1500/3261)= 1.4 (95% CI , P < .0001)
Prevalence Ratio: post to pre, Other
(5698/8484)/(9052/14534)= 1.08 (95% CI , P < .0001)
Prevalence Ratio: pre, CW to other source
(1500/3261)/(9052/14534)= 0.74 (95% CI, , P < .0001)
Prevalence Ratio: post, CW to other source
(1788/2795)/(5698/8484)= 0.95 (95% CI , P < .01)

9. Eligibility
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.
TALETHA.
Repeat eligibility def.
Although automation was not designed to or expected to impact eligibility, we show it here by pre- and post-automation for comparability to the other slides as well as to understand what might be happening in the system. Alaska’s eligibility criteria were tightened in May of 2013, just after the automation. The decrease in eligibility could potentially offset the shock to the system of handling an increased number of referrals and evaluations, although it is of course not something we want to see in terms of trying to increase access to services for maltreated children and their families.
We also show it because as a part of this collaboration, EI and Child Welfare began to regularly review and discuss the data to identify issues as they emerged. Reviewing these data indicating the eligibility drop, they investigated reasons for that beyond the change in eligibility criteria. EI found that evaluators were not applying the criteria correctly, which disproportionately impacted child welfare referrals because of their higher risk status.
Prevalence Ratio: post to pre, CW
(986/1788)/(1172/1500) = 0.71 (95% CI, , P < .0001)
Prevalence Ratio: post to pre, Other
(3925/5698)/(7187/9052) = 0.87 (95% CI, , P < .0001)
Prevalence Ratio: pre, CW to other source
(1172/1500)/(7187/9052)= 0.98 (95% CI, , P = )
Prevalence Ratio: post, CW to other source
(986/1788)/(3925/5698)= 0.80 (95% CI, , P < .0001)p

10. Enrollment
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.
TALETHA
Repeat enrollment def.
Automation was also expected to support enrollment for reasons similar to supporting evaluation in that it facilitates contact with the families and might reduce time between referral and the offer of enrollment if found eligible, which research on child maltreatment prevention and family support programs has indicated is related to the likelihood of enrollment.
Prevalence Ratio: post to pre, CW
(720/986) /(801/1172) = 1.07 (95% CI, , P < .05)
Prevalence Ratio: post to pre, Other
(3391/3925)/(5799/7187) = 1.07 (95% CI, , P < .0001)
Prevalence Ratio: post, CW to other source
(720/986)/(3391/3925)= 0.85 (95% CI, , P < .0001)

11. Retention
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.
TALETHA
Repeat retention def.
Prevalence Ratio: post to pre, CW
Post Pre
(98/(166-10) / (503/(801-76)) = 0.91 (95% CI, , P = )
63% / 69%
Prevalence Ratio: post to pre, Other
(545/(816-76) /(3793/( ))= 0.97 (95% CI, , P =0.2705)
74% / 76%
Prevalence Ratio: pre, CW to other source
Pre Pre
(503/(801-76)) / (3793/( ))= 0.92 (95% CI, , P < 0.01)
69% / 76%
Prevalence Ratio: post, CW to other source
Post Post 2012
(98/(166-10) / (545/(816-76))= 0.85 (95% CI, , P < 0.05)
70% / 74%

12. Among All Infants/Toddlers with Substantiated Maltreatment in Alaska…
ACCESS
Number of children under age 3 with substantiated maltreatment4,5
The % REFERRED increased from 63% to 100%
ENGAGEMENT
% EVALUATED increased (46% to 66%)
% ELIGIBLE stable (38% to 36%)
% ENROLLED increased (18% to 32%)
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Be sure to orient people to the change in this comparison (state data to federally reported data) and the years (year before automation, 2011, to most recent year that is available from federal data).

13. Summary 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.
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14. Conclusions and Lessons Learned
Automation
Shared Understanding
Cannot substitute for interagency coordination
Ongoing state and local interagency relationships are crucial
Examining automation data helped build partnerships to identify areas for improvement and solutions
Cross-agency communication needed to fully understand
which children should be referred and when
stakeholder experiences with implementation
each system’s roles and responsibilities ★  ✔
LISA
Be sure to talk about eligibility here (that not all kids with high risk were being identified – data examination helped uncover that and AK is now working on that). #1
Interagency Coordination #2
Stakeholder Feedback #3
Clarify Roles & Responsibilities

15. Public Health Implications
Data 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.
Child Welfare
Health
Legal
Early Childhood
Mental Health EI
LISA
This study provides evidence of the utility of referral automation using data linkages to increase access to developmental services for maltreated infants/toddlers.
Additional efforts are needed to increase EI service engagement among Child-Welfare-referred children and will require coordination among multiple systems. Further program and policy research is needed to identify state and federal practices and policy levers that could support EI programs in effectively engaging these vulnerable young children and their families.

16. Visit the DaSy website at: http://dasycenter.org/
Thank You!
Taletha Derrington,
Lisa Balivet,
Kenneth Smith,
Haidee Bernstein,
Visit the DaSy website at:
LISA

17. References
1 Child Abuse Prevention and Treatment Act (2003). 42 U.S.C et seq; 42 U.S.C et seq.
2 Individuals with Disabilities Education Improvement Act of (2004). 20 U.S.C 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 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 Washington, DC: Author.

18. The contents of this presentation were developed under a grant from the U.S. Department of Education, # H373Z 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.