Valeria Butler, OPRE and Emily Madden, ASPE

September 21, 2020
BACKGROUND

January 21, 2016
Project Origins

 Patient Centered Outcomes Research Trust 

Fund (PCORTF)

– Competitive process

– Funded project FY2020

 Partnership between ASPE and OPRE, in ACF

 けLiミkiミg “tate Medicaid aミd Child Welfare Data 

for Outcomes Research on Opioid Use 

Disorders aミd other Beha┗ioral Health Issuesげ -

 Child and Caregiver Outcomes Using Linked 

Data (CCOULD)

3
Project Team  Federal Project Team – Valeria Butler, OPRE/ACF – Emily Madden, ASPE – Robin Ghertner, ASPE  RTI Project Team – Tami Mark, PI – Melissa Dolan, PM – Ben Allaire – Christina Bradley

4
Project Goals

 Support states in creating linked data from child 

welfare and Medicaid systems for research on 

parents who have substance use disorders and 

other behavioral health issues, and have children 

in the child welfare system

 Develop data infrastructure for ongoing data 

linking and analysis to inform policy decisions at 

local, state, and federal levels

 Develop a roadmap that provides for other states 

to link their own data

5
Key Objectives

 Objective 1-Develop linked datasets across 

State Medicaid and child welfare systems, in 2-

4 states, linking parent records as well as child 

records. 

 Objective 2-Prepare research-use datasets and 

develop a process for external researchers to 

access the data.

 Objective 3- Create a roadmap for other states 

to follow that documents the process and 

lessons learned.

 Objective 4- Design, conduct, encourage 

analyses on linked data

6
Key Strengths

 Paves the way for new measures of success 

related to treatment of Opioid Use Disorders 

(OUD)

 Leverages existing data sources and builds 

infrastructure to better support research for 

patient centered outcomes

 Has both research benefits as well as service 

benefits

 Scalable and sustainable

7
Site Selection Criteria

 Select states based on selection criteria, e.g.:

– State interest and buy-in from leadership

– Existing infrastructure and capacity

– Policies around data sharing

– Research partners

8
Example Analyses

 Identify trends in uptake and receipt of Medicaidfunded SUD treatment by parents involved in child 

welfare systems.

 Identify opportunities to connect parents involved 

in child welfare systems with treatment for SUD.

 Identifying treatment and service strategies that 

are most likely to be successful in treating SUDs 

among parents with children in child welfare 

systems

9
Example Analyses

 Examine how involvement in the child welfare system and 

services provided by child welfare are associated with

treatment uptake, adherence, and subsequent behavioral 

health outcomes

 How receipt of/adherence to treatment by parents with

children involved with child welfare relates to outcomes 

for their children; for example:

– Length of time in foster care

– Repeat maltreatment

– Re-entry to foster care

10
End users of the data

Primary end users of the data will be:

– Researchers within HHS

– PCOR researchers interested in exploring the 

social determinants affecting treatment 

effectiveness 

– Researchers in public health, substance use, 

and child welfare fields

– State Medicaid, child welfare, and SUD 

treatment agencies

11
End Users of the data

Secondary end users of the data will be:

– National and state health policy groups, 

governmental agencies, including HHS, as well 

as nongovernmental entities

– Child welfare advocacy groups, agencies and 

legislators

12
Family First Prevention Services Act 

 The Family First Prevention Services Act was signed into 

law as part of the Bipartisan Budget Act on February 9,

2018.

 Reforms the federal child welfare financing streams to 

provide services to families who are at risk of entering the 

child welfare system.

 FFPSA aims to prevent children from entering foster care 

by allowing federal reimbursement for mental health 

services, substance use treatment, and in-home parenting 

skill training

 Funds can be used to provide services to parents of 

childreミ ┘ho are さat riskざ of eミteriミg foster care

13
Benefits to Linked Data

 Support program evaluation, research on program 

effectiveness, and build an evidence-base for new 

interventions

 Provides infrastructure to support approaches to 

blend multiple funding sources (i.e., Medicaid, 

foster care funds) to provide a continuum of 

treatment and recovery supports. 

 Supports case management by state agencies and 

caseworkers to provide a more complete picture 

of all the services provided to a family, and 

identify gaps and opportunities for new services. 

January 21, 2016 14
PROJECT PLAN AND 

OBJECTIVES

Valeria Butler

September 21, 2020
Immediate Objectives

 1 - Longitudinal, person-level datasets linking state Medicaid records with 

child welfare records for parents and children involved in the child 

welfare system, available to the states for their use

 2- Archived Data files accessible with appropriate privacy protections to 

states, researchers, and evaluators for secondary analysis. 

 3 - A roadmap for other states to follow, documenting challenges and 

successes in sharing and linking data.

16
Data Linkage

 Up to 5 years of individual-level, longitudinal 

data

 Linked dataset comprised of state data from 

sources such as: 

– child welfare events, including services

– Medicaid or Childreミげs Health Iミsuraミce Prograマ 

(CHIP) beneficiary summary for children and their 

parents,

– Medicaid or CHIP claims for children and their 

parents

– Additional data as relevant/available

17
Common Data Model

 Develop a common data model that will define 

Medicaid and child welfare data elements 

consistently across participating states. 

– Identify variables that can be commonly defined 

across states to be included in a combined dataset.

– For variables that are not commonly defined 

across states, compare definitions and 

specifications across the states using their 

definitions, value sets, and frequencies and 

reconcile them.

18
Curating Data for Research Use

 Data quality analysis, missing data imputation

 State-level linked data with identifiers is 

available to the state agency and its research 

partner, if applicable

 Conduct statistical disclosure analysis and 

modify variables to reduce the likelihood of 

parent, child, or family identification while 

maintaining only the state abbreviation. 

19
Archived Data

 Develop research use datasets that combine statelevel data.

– General release/Public Use File that would 

contain no PHI or PII.

– Restricted release file that would include some 

PHI (e.g., dates of diagnosis and/or service 

receipt). 

 Develop dataset documentation and codebooks 

 Archive at the National Data Archive on Child 

Abuse and Neglect (NDACAN) 

www.ndacan.acf.hhs.gov

20
Roadmap

 Document lessons learned, including:

– Barriers to participation

– Motivating or contributing factors for 

participation

 Conduct interviews with key staff from the 

sites about the process and document 

lessons learned for a roadmap.

21
Sites

 Kentucky Cabinet for Health and Family 

Services

Other site(s) still in negotiation. 

Goal is to have 2-3 states participating.

22
Looking Forward

Though nascent, this project lays the 

groundwork to build partnerships, strengthen 

data sharing efforts, and show how 

administrative data can be leveraged to gain 

insights into better service provision and 

prevention of child abuse and neglect for 

children and families.

 Possibilities for future

– Analysis

– Additional states

– Additional datasets

23
Office of Planning Research and Evaluation

WE’D LOVE TO CONNECT

facebook.com/OPRE.ACF twitter.com/OPRE_ACF

Valeria Butler, OPRE valeria.butler@acf.hhs.gov

Emily Madden, ASPE Emily.madden@hhs.gov

24