Current Report:
What is this report about ?
From June to October of 2021, Allegheny County Department of Human Services (DHS) provided a cash assistance program for transition-aged youth called Older Youth Pandemic Relief (OYPR). This report describes the methodology and results of a series of surveys that evaluated the impact of the cash assistance program.
What are the takeaways?
- 76% (n = 1,901) of the people who were eligible to receive the Older Youth Pandemic Relief (OYPR) payment applied for and received the money.
- The money went to young adults with a high level of need. 85% of recipients were enrolled in Medicaid, and 49% received Supplemental Nutrition Assistance Program (SNAP) benefits.
- Young adults planned to spend the money on meeting basic needs; top categories were bills, housing, car, food and clothing.
- The program re-engaged young adults with services. 587 of the people who applied for (and received) the OYPR payment qualified for other services available to transition-aged youth but were not using them.
- By filling out the OYPR application, they provided updated contact information and information about the types of assistance they need.
- The percentage of recipients who reported having enough money to meet their basic needs increased from 25% at baseline to 34% after receiving the money. This increase was larger for Black and female demographic groups, which reported lower ability to meet their basic needs at baseline.
How is this report being used?
Findings from this program and report are being used internally at DHS to advocate for new income assistance programs. These include both direct cash cash transfers and other forms of income support, such as subsidized transit.
What are these reports about?
Nationally and locally, policymakers and practitioners are interested in the people who frequently use publicly funded services, particularly crisis services. Most people who use crisis services do so infrequently during a year. A small number of people, however, use crisis services frequently, and sometimes they use more than one type of crisis service.
Allegheny County’s rich integrated data allows us to look at the people who use crisis services. This report summarizes key findings about the people who were involved with one or more of the following four crisis services in the years 2016 through 2017: hospital emergency departments, emergency homeless shelters, mental health crisis programs, and the criminal justice system. This summary report will be followed up by reports examining each of these four service areas in more detail.
What are the takeaways?
- Of the people who used at least one of the four crisis services examined, 6% (10,655) met the definition of frequent users in at least one system. They accounted for 26% of all service episodes during this period.
- There is little overlap between frequent utilizers of one type of crisis service and another. Just 9% of users were frequent in multiple systems. This does not mean they didn’t use other services, just that they were not frequent users of those systems.
- Nonetheless, 26% of frequent users of mental health crisis services were also frequent users of hospital emergency departments, indicating that the emergency room might be a point of intervention for people in mental health crisis.
- All frequent users of emergency shelter were connected to other human services prior to their first shelter stay during this period. This overlap suggests that although frequent utilizers of emergency shelters were connected to supports, the reasons behind people’s continued use of shelter were not adequately addressed through the services they were receiving.
Black residents are using crisis services at disproportionately high rates, and the disproportionality is more pronounced when looking at frequent utilizers. While 13% of the Allegheny County population is Black, 42% of people who used crisis systems (both frequent and non-frequent) were Black, and 49% of frequent utilizers were Black.
How is this report used?
This work is meant to be exploratory and descriptive in nature to help continue and expand the conversation about how we look at frequent utilizers and potential interventions going forward. By looking more closely at this population of frequent utilizers, we hope to gain insight into their needs, identify key intervention points, and find ways to encourage long-term wellness while reducing the need for repeat intense service usage.
Where can I go for more information?
For questions or suggestions, please reach out to DHS-Research@alleghenycounty.us
From June 2018 to December 2020, the Urban Institute conducted a systemwide assessment of the system response in Allegheny County, PA to intimate partner violence (IPV) to better understand the system as a whole and operations of some key agencies
What is this report about ?
Urban Institute presents the findings from their systemwide assessment. The goals of this assessment were to 1) examine how IPV cases enter the justice and child welfare systems in Allegheny county, 2) analyze agencies’ processes for responding to IPV and 3) recommend ways the county can improve responses to IPV.
What are the recommendations?
- Have county leaders prioritize IPV
- Shift the focus from case outcomes to people’s experiences, especially during early encounters with formal services.
- Reinstate and sustain IPV-focused fatality reviews and ensure they embrace a non-blaming culture.
- Establish a specialized IPV unit in the Allegheny County Public Defender office
- Differentiate IPV from DV throughout all systems.
- Record survivor information consistently and securely share it when possible.
- Prioritize and improve referrals to batterers’ intervention programs
- Create a mechanism to consistently track aggressors’ and survivors’ experiences at system entry points.
How is this report being used?
The county executive and Mayor of the City of Pittsburgh created an IPV Reform Leadership task force in May 2022 to actively work on addressing these recommendations and improving the system.
Independent Research Report
The National Center for Education Evaluation and Regional Assistance at the Institute of Education Sciences (US Department of Education) examined data from Allegheny County students to better understand predictors of near-term academic risks. The goal of this research to provide information for administrators, researchers, and student support staff in local education agencies who are interested in identifying students who are likely to have near-term academic problems such as absenteeism, suspensions, poor grades, and low performance on state tests.
What is this report about?
The report describes an approach for developing a predictive model and assesses how well the model identifies at-risk students using data from two local education agencies in Allegheny County, Pennsylvania: a large local education agency and a smaller charter school network. It also examines which types of predictors— in-school variables (performance, behavior, and consequences) and out-of-school variables (human services involvement and public benefit receipt)—are individually related to each type of near-term academic problem to better understand why the model might flag students as at risk and how best to support these students.
What are the takeaways?
The study finds that predictive models using machine learning algorithms identify at-risk students with moderate to high accuracy. In-school variables drawing on school data are the strongest predictors across all outcomes, and predictive performance is not reduced much when out-of-school variables drawing on human services data are excluded and only school data are used. However, some out-of-school events and services—including child welfare involvement, emergency homeless services, and juvenile justice system involvement —are individually related to near-term academic problems. The models are more accurate for the large local education agency than for the smaller charter school network. The models are better at predicting low grade point average, course failure, and scores below the basic level on state tests in grades 3–8 than at predicting chronic absenteeism, suspensions, and scores below the basic level on high school end-of-course standardized tests. The findings suggest that many local education agencies could apply machine learning algorithms to existing school data to identify students who are at risk of near-term academic problems that are known to be precursors to school dropout.