Child Maltreatment Incidence (CMI) Data Linkages

2017 - 2022

The Child Maltreatment Incidence Data Linkages (CMI Data Linkages) project aimed to explore how innovative administrative data linkages can improve our understanding of child maltreatment incidence and related risk and protective factors. CMI Data Linkages identified 5 sites using linked administrative data to examine child maltreatment incidence and related risk and protective factors. The project supported these sites as they enhanced their existing linked administrative data by:

  • Using innovative methods to link/analyze administrative data;
  • Linking novel administrative data sources; or
  • Scaling or replicating an existing data linkage or analysis approach in a new geographic area or jurisdiction.

The project provided sites with access to experts to help address challenges and supported a collaborative cross-site learning network that facilitated communication and information sharing. The project simultaneously conducted a cross-site feasibility study to: (1) examine the factors (including state and local context, resources, organizational capacity, peer support, training, and existing infrastructure) that promote or impede the enhancement or scaling of existing administrative data linkage and analysis practices related to child maltreatment; and  (2) determine the availability and quality of information on child maltreatment incidence and associated risk and protective factors that can be gleaned from linked administrative data. Findings aim to inform the accurate and ongoing surveillance of the incidence of child abuse and neglect and future prevention and treatment efforts.

This project was conducted through a contract to Mathematica.

Point(s) of contact: Jenessa Malin and Christine Fortunato.

Related Resources

This brief describes the development, validation, and cross jurisdiction replication of a risk prediction model designed to predict foster care placement.

This brief highlights promising practices for sharing and accessing data and discusses lessons learned related to four key activities essential to sharing and accessing data: (1) developing agreements for data sharing and use; (2) protecting the data’s security, confidentiality, and privacy; (3) securing institutional review board (IRB) and other approvals; and (4) accessing the data.

This brief highlights promising practices for preparing and linking data and discusses lessons learned related to (1) processing and cleaning data, (2) completing linkages, and (3) collaborating with partners to execute linkages.

This report presents an overview of the University of Alabama School of Social Work (UA-SSW) project, conducted as part of the Child Maltreatment Incidence Data Linkages (CMI Data Linkages) project.

The purpose of this project was to examine the association between county-level rates of opioid overdose events and child maltreatment indicators.

This report is an overview of the Children’s Data Network/Rady project, conducted as part of the Child Maltreatment Incidence Data Linkages (CMI Data Linkages) project.

This report describes the project the Alaska Department of Health and Social Services (ADHSS) and the Oregon Health Sciences University (OHSU) conducted as part of the Child Maltreatment Incidence Data Linkages (CMI Data Linkages) project.

Summary: Learn about a study that identified promising methods or practices, within and across the sites, for linking administrative data to inform understanding of the incidence of child maltreatment and related risk; discovered organizational factors that influenced the feasibility of enhancing data linkages; and found evidence that enhancing linkages is a feasible approach to questions about child maltreatment incidences, related risk factors, and protective factors.  

This report describes the project the Children’s Data Network and the California Child Welfare Indicators Project (CDN/CCWIP) conducted as part of the Child Maltreatment Incidence Data Linkages (CMI Data Linkages) project.

Accurate and ongoing surveillance of the incidence of child maltreatment and related risk and protective factors can help to inform policy and programs as well as shape prevention and intervention efforts. One promising approach to capturing this information is by linking local, state, or federal administrative records, such as those from child welfare, health, social services, education, public safety, and other agencies.

The Cross Jurisdiction Model Replication (CJMR) project sought to understand the degree to which a risk prediction model built from population-level and anonymized birth records in one state could be used to differentiate the risk of foster care placement in other jurisdictions.

This webinar highlighted promising practices and contextual and organizational factors related to using linked administrative data to understand the incidence of and risk and protective factors associated with child maltreatment.