HHS Administration for Community Living websites devoted to supporting Elder Justice
The APS TARC undertakes various research projects related to the maltreatment of older adults and adults with disabilities. Read more about these projects in the descriptions below.
The APS TARC is conducting the first-ever national evaluation of adult protective services programs. This evaluation will provide the foundation for ongoing and future technical assistance efforts.
Read more about this project on our Evaluation page.
The purpose of this study was to explore changes being implemented to APS programs across the country in response to the COVID-19 pandemic, how cases and workload were being affected, and how APS staff and their work were affected by these changes.
In consultation with the Administration for Community Living (ACL), the study team explored the following study objectives related to APS programs and COVID-19:
Read the report - Adult Protective Services Study on the Impact of COVID-19
The Administration for Community Living (ACL), in collaboration with the Centers for Medicare & Medicaid Services (CMS), have launched a project to explore the use of predictive analytics as one component of a broader strategy to predict and prevent adult and elder maltreatment. The project, Predicting Risk of Adult Maltreatment, or PRAM will leverage artificial intelligence, machine learning, and other “big data” tools to investigate patterns of risk and protective factors across multiple data sources to determine if there is an association with reported incidence of adult maltreatment. The goal of the project is to create and improve interventions to prevent, and effectively intervene in, adult and elder maltreatment, and as an outcome, improve disabled and older adults’ quality of life and health quality outcomes, and reduce health care expenses.
While used extensively in other fields, predictive analytics is a relatively new concept for home- and community-based services and for understanding interpersonal violence, particularly adult and elder maltreatment. Predictive analytics generally refers to the use of a range of statistical techniques such as predictive modeling and data mining that use current and historical data to make predictions about future events and uses powerful and flexible technology tools. In the human services field, child welfare programs have been early adopters of predictive analytic approaches. While still relatively new, several states and counties have begun to invest in predictive analytic approaches to help prevent child abuse and neglect, particularly deaths and serious injury, as well as to avoid other negative outcomes associated with long-term involvement in the child welfare system.
Initiatives by other federal agencies have presented new opportunities for CMS and ACL to move forward in this area. This work necessitates close collaboration among social science subject matter experts; data scientists; and cloud computing architects, developers, and integrators to develop and test algorithms that may allow risk factors to be identified and applied to data to determine the probability of abuse, exploitation, and maltreatment of older and disabled adults at the community and individual levels.
An infographic explanation of the two phases of the project is available here.
Phase 1 launched in September 2019 with the purpose of understanding the following:
The Adult Protective Service (APS) Guidelines Implementation and Technical Assistance (TA) Pilot project conducted by New Editions Consulting, Inc. in collaboration with the National Adult Protective Services Association (NAPSA) developed a survey to assess the extent in which states have tried to integrate the 2016 Guidelines for Adult Protective Services (APS) into state policies and practice, their overall experience integrating them. Some examples include, how state APS tried to integrate them, what challenges they encountered, what lessons they learned, successes achieved, and what changes they observed. They were also asked to share experiences addressing specific domains and elements in the 2016 Guidelines, effective practices for implementing the Guidelines, and what states consider effective APS practices.
Three states, (Louisiana, Nevada and South Carolina) helped create guiding principles for determining APS case findings, a consistency of findings matrix for APS workers to help ensure findings are determined in a consistent manner and providing guidance and training materials for APS staff.