1 The Point in Time Enumeration Process in Washington, D.C. Darlene Mathews The Community Partnership for the Prevention of Homelessness www.community-partnership.org.

Slides:



Advertisements
Similar presentations
Overview and Introduction to HMIS Concepts: VA Community Contracts Part II: Data collection requirements, reporting requirements, AHAR, and Pulse.
Advertisements

The Annual Homeless Assessment Report (AHAR) January 1, 2006 – June 30, 2006.
2012 Point-In-Time Count (PIT), Housing Inventory Chart (HIC), and a Tool for Determining Unmet Need Utah State Community Services Office May 9, 2012.
Ohio Balance of State Continuum of Care Introduction to the 2014 HUD Data Standards July 17, 2014.
Impact of the HEARTH Act on Metro Denver Homeless Planning John Parvensky President Colorado Coalition for the Homeless.
Point-in-Time Count/Survey & Homeless Needs Assessment.
HUD Homeless Program Data
Point In Time Count: Focus, Feedback and Planning Cathy ten Broeke, Director, MN Office to Prevent and End Homelessness Amy Stetzel, Project Manager, MN.
2012 SuperNOFA Presented by: OC Partnership 1505 E 17 th Street Santa Ana, CA (714) HMIS Reports Training.
2014 H OUSING I NVENTORY C OUNT (HIC): W HAT YOU NEED TO KNOW ! J ANUARY 14, 2013.
The Gap Analysis and Homeless Populations Metro Detroit’s Community Summit on Ending Homelessness.
HMIS Homeless Management Information System. MISSION To provide standardized and timely information to improve access to housing and services, and strengthen.
HOMELESSNESS TASK FORCE PRESENTATION August 15, 2013.
2015 Point In Time Count: Broward County CoC Plan to End Homelessness
HOW TO MAKE HOMELESS POINT-IN-TIME (PIT) COUNT MORE SUCCESSFUL The Second Annual Nebraska-Western Iowa Symposium on Homelessness Homeless in the Heartland.
Annual Update on the Homeless Continuum of Care
2013 Point-in-Time Homeless Count Data Entry Volunteers.
Homeless Management Information System Donna Curley – HMIS Project Manager.
VICTIMS OF DOMESTIC VIOLENCE WORKGROUP Reallocate $ for more community based housing Need rapid rehousing dollars Adjust current grant to allow for more.
COSCDA Conference 2012 Washington, DC Karen DeBlasio, HUD March 13, 2012 Homeless Management Information Systems (HMIS)
Supportive Services for Veteran Families (SSVF) Data Bigger Picture Updated 5/22/14.
OCTOBER 24, 2012 PRESENTED BY RENEE LAMBERJACK, RESEARCH & EVALUATION ASSISTANT Annual Homeless Assessment Report Presentation to Safe Harbors Partners.
Supportive Services for Veteran Families (SSVF) Data
Safe Harbors Quarterly Partner’s Meeting November 17, Building.
Ending Family Homelessness The Basics National Alliance to End Homelessness Conference Seattle, Washington February 7, 2008 Sue Marshall The Community.
Nutmegit.com Provided by: P W HMIS DATA COORDINATOR MEETING February 2013.
1 Homeless Management Information System (HMIS) National Call Training Please Note – The audio portion of this training is available by dialing (800)
National Association for the Education of Homeless Children and Youth Conference 2014 “What you talking about Willis: The Different Strokes of data sharing.
Retooling the Crisis Response System Michelle Heritage Executive Director Community Shelter Board National Conference on Ending Homelessness.
Orientation to the Continuum of Care (CoC) July 29, 2014.
Think Change Be Change Lead Change CT PIT 2013 Program Staff Training January 2013 Training PowerPoint Provided by CCEH CT Coalition to End Homelessness.
COSCDA 2011 Annual Training Conference September 20, 2011 Susan Starrett (302)
2014 Homeless Management Information Systems (HMIS) Data Standards for ESG Presented by Melissa Mikel September
Supportive Services for Veteran Families (SSVF) Data Data Collection & Reporting: Basics Updated 5/22/14.
Conducting Better Point-in-Time Counts of Homeless Persons Erin Wilson Abt Associates Inc. Washington, DC July 9, 2007.
2015 POINT IN TIME & HIC. Sheltered PIT Data 2015 People in Households with Children.
It’s Not Just Numbers: Implementing Point-in-Time Counts, Using HMIS, and Ensuring Data Accuracy Erin Wilson, Abt Associates Inc. Julie Eberbach, Iowa.
COORDINATED ENGAGEMENT FOR YOUNG ADULTS Hannah Fisk, NWYS Emily Harris-Shears & Erin Maguire, CCSWW Washington State Conference on Ending Homelessness.
1. 2 INTRODUCTIONS Kathleen Wing 3 Volunteer Coordinator Manette Magera (321)
Massachusetts’ Efforts to End Family Homelessness
Think Change Be Change Lead Change CT PIT 2014 Permanent Housing Project Training January 2014 Training PowerPoint Provided by CCEH CT Coalition to End.
Counting the Homeless in Alaska Kris Duncan MSW Alaska Housing Finance Corp
Supportive Services for Veteran Families (SSVF) Data Data Collection & Reporting Basics.
HEADING HOME: Kitsap Homeless Housing Plan 2008 Update Kitsap Regional Coordinating Council.
Think Change Be Change Lead Change CT PIT 2014 Emergency Shelter Project Staff Training January 2014 Training PowerPoint Provided by CCEH CT Coalition.
Thursday, September 3, Agenda Status of Post Count Process Common Issues/Errors HMIS data Non-WISP data HIC Deduplication Impact of Service Based.
The 2007 Annual Homeless Assessment Report: A Report to Congress on Homelessness in America Paul Dornan, Office of PD&R, HUD Jill Khadduri, Abt Associates.
MOVING FROM DATA TO ACTION ADDRESSING HOMELESSNESS THROUGH A RBA FRAMEWORK POINT-IN-TIME COUNTS.
Think Change Be Change Lead Change CT PIT 2014 Transitional Housing Project Training January 2014 Training PowerPoint Provided by CCEH CT Coalition to.
HMIS for Point In Time Data Collection September 13-14, 2005 St. Louis, Missouri Sponsored by the U.S. Department of Housing and Urban Development Lea.
2010 Florida HMIS Conference 1. Using HMIS to Inform Performance Measurement Outcomes Objective: –Enhance awareness and understanding on using HMIS to.
2012 Summer Enhanced PIT Count Revised 06/21/ Summer PIT Count Who are we? WVCEH – WV Coalition to End Homelessness.
 Award of $923,339  Substantial Amendment › $300,000Homelessness Prevention › $480,000 Rapid Re-housing › $80,000 Housing Relocation and Stabilization.
Point-in-Time Count January What Does It Mean to Count Homeless People? A “count” = collecting information about the sheltered and unsheltered homeless.
2016 St. Johns County Point In Time Count When: Thursday, January 28, 2016.
2016 Point-in-Time Count of UNSHELTERED Persons Experiencing Homelessness PIT Volunteer Training by Diana T. Myers & Associates, Inc. (DMA) on behalf of.
September 18-19, 2006 – Denver, Colorado Sponsored by the U.S. Department of Housing and Urban Development HUD’s Goals, Objectives and Performance Measures.
2015 POINT IN TIME & HIC. Homeless Survey Sectors.
Homeless Management Information Systems The Calgary HMIS - A joint initiative between the CHF and the Homeless Serving Sector in Calgary Date: April 21,
HMIS Data Quality Training 211 Orange County. Learning Objective This training is scheduled for 2 hours. Objective 1.Teach users how to find deficiencies.
Thursday, March 1, Agenda Status of Post Count Process Common Issues/Errors HMIS data Non-WISP data HIC Deduplication Impact of Service Based Counting.
Tuesday, November 18, 2008 Robert Pulster, Executive Director of the Governor’s Interagency Council on Housing and Homelessness & Matthew D. Simmonds,
2017 Housing Inventory Count Webinar
2017 Housing Inventory Count Webinar
2017 Housing Inventory & Point-in-Time Night January 25, 2017
Detroit Continuum of Care (CoC) 2017 HIC and PIT Count
2017 HIC & PIT January 26, 2017.
Restructure & Repurpose 2017
Minnesota’s Homeless Management Information System (HMIS)
Shelter Count Training
Presentation transcript:

1 The Point in Time Enumeration Process in Washington, D.C. Darlene Mathews The Community Partnership for the Prevention of Homelessness National Alliance to End Homelessness Conference July 17, 2006

2 Point in Time in Washington, D.C. The District of Columbia has conducted Point in Time enumeration for six years. Our numbers are aggregated with regional numbers to produce the Metropolitan Washington Point in Time Report on behalf of the Council of Governments

3 Creating a Roadmap to Successful Data Collection Street Outreach Emergency Shelters Transitional Shelters Permanently Housed And categorized by Individuals or Persons in Families Determine what you seek to track. In D.C. we looked at demographic information on the homeless population broken down by:

4 Additional Categories & Subpopulations Tracked Housing Needs Employment Status Gender Chronic Homeless Status Physical Disability Mental Disability Veteran Status HIV/AIDS Status Domestic Violence Youth Chronic Health Problem Language Minority Housing needed today (for gaps analysis)

5 Develop Goals for Analysis Accurate distribution of homeless population across the region Changes and trends in the population over the time Meaningful Gaps Analysis Provide the government and public with good information about the homeless population in the D.C. area. Counteract the public image that the homeless are primarily street people.

6 The Ultimate Goal Over time, as we track trends in distribution of beds, housing needs and other variables, we are trying to illustrate the “problem” by identifying the amount of people on the streets in emergency and transitional shelters, but also the “solution” as we add to the numbers of people inside the Continuum residing in permanent supportive housing.

7 Point in Time Configuration In 2006, all programs within the Continuum of Care reporting to the Partnership were contractually required to use HMIS to complete the PIT survey. All private organizations operating programs in the D.C. Continuum, but not contracted with the Partnership were asked to complete paper forms and spreadsheets with the necessary information.

8 The Community Partnership’s Point in Time Process for D.C. Step 1 Our System Administrator created a custom Point in Time survey assessment that is user friendly and accurately captures all the information we sought to collect.

9

10 Step 2- Training and Communication The actual count was derived from bed lists so refresher trainings were held on how to enumerate bedlists & use the Survey Assessment The process was also reinforced through s, quick reference guides and ongoing technical assistance Contractual obligation to participate in the process

11 Step 3 Data Quality Control On January 26, D.C. the Partnership ran bed list reports for all Providers including hypothermia sites. Each Provider was sent an with the bedlist occupancy number. Providers were then given two weeks to clean their bed lists and complete a survey assessment on each client in their program.

12 Data Quality Control Cont’d After bed lists were cleaned up, the Partnership ran the Point in Time assessment for each residential program to make certain that the count of assessments per site was equal to the verified count from the bed list for that day. If the number of assessments were greater or less than the bed list count, Providers were required to go back to their data and correct the data so that the # of assessments = # reflected in the bedlist count for that day.

13 Null Value Report

14 Step 4 Filtering for Families In order to get accurate information on subpopulations, we had to filter for children. An additional query was run to filter for clients 18 and over to separate children from adults. To ensure our numbers were accurate, the total count for children and adults had to equal the sum reported in the bed list. If the numbers didn’t match up, we had to search for the answer.

15 Step 5-Findings After we were confident in our numbers, we exported data from HMIS into Excel for more in depth data analysis and compiled it with data submitted by private agencies.

16 Outreach Agencies In 2005 the Partnership created an Outreach Assessment for our providers It tracks service transactions for clients For Point in Time, we asked Outreach Providers to complete a Point in Time Assessment on every client they have served within the last 90 days that they believed to be sleeping on the street on January 25

17 Outreach Agencies We exported all of the outreach agencies’ information and de duplicated clients using MS Excel We then compared our outreach results to our emergency shelter results and were able to de duplicate which clients that were believed to be on the streets actually entered Emergency Shelter

18 Difficulties Utilizing HMIS for Point in Time Bed enumeration and basic data entry must be completed properly and monitored often otherwise Point in Time can be extremely difficult. Providers were not completing the entire assessment and leaving a lot questions blank. We had to create a null value report to send to the Provider to show them all the questions that needed to be answered.

19 Benefits of Using HMIS for Point in Time Ultimately it should make the process easier Great process to reconcile what we know on on a micro level within our programs and what is in HMIS Forces Providers to make sure their data is timely and correct Process highlights structural problems with the way HMIS and queries are set up Identifies Provider specific problems

20 Tips for Conducting a Successful Point in Time Have a solid data collection system in place Create a framework and timeline for completing tasks Have data quality control measures in place to ensure your data is accurate

21 Our Results The Problems We Face There are 6,157 homeless individuals & persons in families in the District. Today in the District of Columbia, 11 of every 1,000 persons are homeless.

22 The Progress We are Making The District’s Permanent Housing inventory is steadily increasing as indicated in the trend line in the chart below. Permanent Housing is seen as the SOLUTION to homelessness. The District’s 10 year plan goals calls for a continued increase in the development of Permanent Housing units.