Community Perspective: Using Research and Technology to Identify Effective Solutions to Prevent and End Homelessness Michelle Hayes, The Cloudburst Group

Community Perspective: Using Research and Technology

to Identify Effective Solutions to Prevent and End Homelessness

Michelle Hayes, The Cloudburst GroupAdam Smith, Wisconsin Department of Commerce

2008 HMIS Training: Setting the Standard - U.S. Department of Housing and Urban Development2

What Can We Learn from HMIS?

• Analysis of universal data elements can generate: – Client characteristics of individuals and families- age,

gender, race, veterans, etc.– Prior living situation(s) – Length of stay

• Short term vs. long term homelessness• # of chronically homeless

– Cross tabulation of:• Age by gender• Prior living by individual (male/female) vs. family (# of

persons) • Length of stay by history of disabling condition (i.e.

physical, mental, emotional, developmental, HIV/AIDS)• And more…


What Can We Learn From HMIS?

• Analysis of Program-level data can generate: – Income and benefits

• Employment income• Mainstream benefit use (i.e. SSI, SSDI, TANF, etc)

– Disability data• # of persons with history of substance abuse, mental

health, HIV/AIDS, a physical disability, etc.

– Reason for leaving and destination – Cross tabulation of:

• Disability by reason for leaving/destination • Mainstream benefit use by disability • Employment income by destination • And more…


What We Learn From the HMIS Nationally?

• Analysis of HMIS and CoC data for the AHAR generates: – Number of sheltered and unsheltered persons on

a single night• Source: CoC point-in-time counts

– Nation’s capacity to house homeless persons• Source: CoC housing inventory data

– Number and characteristics of sheltered homeless individuals and families

• Source: CoC HMIS data


What Else Can We Learn From HMIS at the Local Level?

• Many communities are using HMIS to answer their own local research and policy questions to understand:– The effectiveness of various housing models;– The combination of homeless and mainstream

services that help homeless persons maintain permanent housing; and

– Where homeless congregate to inform local public health planning efforts.


Uses of Homeless Data at Local Level

• Many communities are now analyzing local HMIS data for more than required reporting. Other local uses include:– Washington, D.C. is rating and ranking projects for the CoC

NOFA through HMIS

– Cincinnati/Hamilton County CoC has made the HUD homeless certification electronic through their HMIS

– Kalamazoo, MI linked the homeless and healthcare information systems to better understand how clients access services across the CoC

• Source: Demonstrating the Uses of Homeless Data at the Local Level: Case Studies from Nine Communities, 2007 available at www.hmis.info


Identifying Best Practices in Uses of HMIS

• HUD published RFP to select CoCs able to demonstrate innovative uses of HMIS for local CoC planning and decision making

• 8 communities chosen from competitive process

• Presentations at 2nd Annual Homeless Data Users Meeting in Portland, OR in April 2008

• Case studies published in: “Community Perspectives: Using Research and Technology to Identify Effective Solutions to Prevent and End Homelessness.”– Disseminated at HMIS Grant Training – Available at www.hmis.info


Best Practices in CoC Uses of HMIS

• 7 CoCs in Minnesota are using HMIS to Evaluate Project Homeless Connect

• 11 CoCs are working together to understand regional movement and service utilization patterns of the homeless within the Bay Area of California

• The State of Michigan has merged homeless and human services data to generate data on service use patterns and provide reliable data on the true costs of homelessness on state systems of care


Use of Geographic Information Systems (GIS)

• Houston/ Harris County, TX CoC uses GIS data in the HMIS to identify at-risk populations during natural disasters or health outbreaks.

• Identification of encampments through documentation of street outreach encounters enables CoC to:– Expedite evacuations in the event of an impending hurricane – Identify and treat locations where mosquito born illnesses may

present a danger to homeless encampments

• Additional uses of GIS: – To identify correlations between zip code of last known address,

utility shut-offs, and homelessness


Evaluation of 10-Year Plans using HMIS

• Columbus/Franklin County, OH CoC found:– Clients exiting to stable housing were less likely to return to

shelter– Income at shelter exit increased exits to stable housing– “Churning” – moving from one shelter to another during

same episode of homelessness - decreased the likelihood of receiving a stable housing placement

• Quincy/Weymouth, MA CoC:– Documented discharge data by facility type (i.e. youth

services, mental health) to advocate changes in discharge policies from state system of care

• Received funding for new Housing First pilot program for young adults aging out of state system

– Found that Housing First was more cost-effective than housing clients in emergency shelter

State of Wisconsin

Analysis of Transitional Housing Program Outcomes

Using Individual and Program Level Factors


Network Analysis Project

• A Network Analysis is the study of the relations between social actors or specific entities.

• Network Analysis addressed clients who left transitional housing programs.


Research Questions

1. What factors are associated with successful client outcomes in transitional housing programs?

2. Do differences in the structure of transitional housing program networks affect client outcomes?


Foci of Network Analysis Project

• To determine if clients with significant barriers to achieving housing stability are being served in transitional housing programs.

• To give transitional housing providers a program model associated with successful client outcomes.


Analysis

• Client Risk (high risk versus low risk).

• Network Ties.

• Length of Stay.

• Volume/Intensity of Services.


Network Tie Example

SP

THP SP

THP

Program network 1

SP SP

SP

Program network 4

SP

THP

SPProgram network 2

SP

THP

SP

SP

Program network 3

Client


Preliminary Results

• 44% of Transitional Housing Programs in WI do not serve clients classified as high risk.

• High risk clients are almost twice as likely as low risk clients to have a shelter stay after participation in a transitional housing program.

• The longer clients stay in a transitional housing program, the less likely they are to have a shelter stay afterward.


Preliminary Results Continued

• More services provided during stay in transitional housing program decreases likelihood of post-program shelter stay.

• Clients who have ties to supportive service providers in the same program network as their transitional housing program are less likely to have a shelter stay afterward than those clients who do not.


Further Analysis

• 19% of all transitional housing program participants return to emergency shelter.

• Persons with either a primary diagnosis of AODA or Mental Illness have roughly the same recidivism rate.– 21.2% for AODA– 21.4% for Mental Illness


Impact and Next Steps

• An increased ability to award funding based on client risk.

• Further investigation into a model of extending program networks to aid client success.

• Detailed Network Analysis of:– Families vs. Singles– Unmet Needs– Employability and Income

Documents

Community Perspective: Using Research and Technology to Identify Effective Solutions to Prevent and End Homelessness Michelle Hayes, The Cloudburst Group