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Longitudinal Systems Analysis: Uncovering System Performance Trends

Explore in-depth system performance by populations and geography for nuanced insights. Learn about LSA dataset analysis, tools, and data quality. Discover Houston and Los Angeles real-world disparities and tools for data submission.

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Longitudinal Systems Analysis: Uncovering System Performance Trends

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  1. Longitudinal Systems Analysis: Digging Deeper into System Performance by Populations and Geography Julia Brown, Abt Associates ErolFetahagic andAna Rausch, Houston CoC Steven Rocha, Los Angeles CoC

  2. Discussion Goals • Understand the LSA Submission process • The role of your HMIS • The role of the HDX 2.0 • Understand how you can use your LSA data locally • How can you get your LSA dataset for analysis? • What can you do with your LSA dataset? • The Real World Houston: Looking at Race and Ethnicity Disparities in Demographics • The Real World Los Angeles: Looking at Geographic Differences in Performance • Understand the tools HUD is working on to help

  3. Submitting Your LSA HDX 2.0 Display Demographics by household type for people experiencing sheltered homelessness, using RRH, and using PSH System Use by household types and population groups HDX 2.0 Upload CoC-Level HMIS Data Demographics Length of Time Homeless and in the System Housing Outcomes Returns to Homelessness Built-in Data Visualization Tools (Future) Detailed Downloadable Analysis Tables Guided System Modeling Tools (Future)

  4. Warnings in the HDX 2.0 • Notably high missing data rate (>17%). • The number of people per household is [greater than 5/less than 2, depending on household type] • Children ages 5 and under that are unaccompanied by anyone older. • All of the [adults/children] fall into one gender category. • More people/households served in the last 1 year than in the last 3 years. • More enrollments/exits in the last 1 year than in the last 3 years.

  5. Data Quality Results in the HDX 2.0 • Count of invalid SSNs (e.g. consecutive zeros, non-numeric, shared SSNs, multiple SSNs) • Invalid values as a percentage of the total counts: • % of all clients with missing/invalid DoB, Gender, Race, Ethnicity • % of all adults with missing/invalid Veteran Status • % of all enrollments with missing/invalid Relationship to HoH, Disabling Condition • % of HoH and other adult enrollments with missing/invalid 3.917 data fields, Domestic Violence • % of all exits with missing/invalid Destinations • % of all household enrollments with no or more than 1 HoH

  6. Inventory Data in the HDX 2.0 Based on your PDDEs, the HDX 2.0 will calculate these summary data for each of the 9 reporting categories

  7. Submission Timeline

  8. Submission Timeline

  9. LSA Data by Population Group A Closer Look at the Houston CoC ErolFetahagic & Ana Rausch

  10. Cities that Fit into Houston TX-700 CoC = 3,739 sq. miles From: Knudson,LP www.knudsonlp.com

  11. TX-700 CoC – 3,739 sq. miles

  12. Houston HMIS Overview • The Coalition for the Homeless has been the HMIS Lead Agency for our CoC (TX-700) since 2004 • The TX-700 CoC includes Harris, Fort Bend, & Montgomery Counties in Texas (over 3700 sq miles) • Current system size: • 78 organizations • 241 projects • 778 end users • 24,888 annual homeless clients • 251,815 total client records

  13. Race & Ethnicity – U.S. Census

  14. Race & Ethnicity – HMIS Data

  15. Some Questions to Consider • Is Race/Ethnicity breakout different for general population vs. HMIS client data? • Do different HMIS project types serve different population groups? • Do all clients have equal access to permanent housing? • What are the contributing factors that cause over/underrepresentation of a certain racial/ethnic group in the homeless services system?

  16. LSA Raw Data • 10 CSV files exported from HMIS

  17. LSA in HDX 2.0 • Upload CSV files to HDX 2.0 • Export Summary Data

  18. Race & Ethnicity in LSA Summary Data

  19. LSA Race & Ethnicity by Project Type

  20. LSA Race & Ethnicity by Household Type

  21. LSA in HDX 2.0

  22. LSA Full Analysis Tables

  23. LSA Full Analysis Tables All Households Adult-Only Households age 18+ Child-Only Households <age 18 Households with Adults & Children Unaccompanied Children Unaccompanied Youth (18-21, 22-24, and 18-24) Parenting Children Parenting Youth (HoH age 18-24) Non-Veterans (all age 25+) Veteran Veteran Chronically Homeless Key All Demos; All Sys. Use Data All Demos; Ltd. Sys Use Ltd. Demos; Ltd. Sys Use Fleeing DV Fleeing DV Fleeing DV Disabled Disabled Disabled Large Families (3+ children) Older Adult (Age 55+) 1st Time/Returner 1st Time/Returner 1st Time/Returner Race/Ethnic Groups Race/Ethnic Groups Race/Ethnic Groups Quick-Returners Quick-Returners Quick-Returners Long-stayers Long-stayers Long-stayers

  24. LSA Data by Geography A Closer Look at the Los Angeles CoC Steven Rocha

  25. Los Angeles County HMIS Overview • The Los Angeles Homeless Services Authority has been the lead agency for the Los Angeles Continuum of Care (CA-600) since 1993. • LA County is comprised of four different CoCs: Los Angeles, Glendale, Pasadena, and Long Beach. Glendale and Pasadena are part of the LA HMIS collaborative, while Long Beach uses a separate HMIS. • Current system size for the LA HMIS Collaborative: • 138 organizations • 1209 projects • 3,000+ end users • 59,000+ homeless clients (HUD FY 2017) • 250,000+ total client records (since 10/1/2012)

  26. Geography of Los Angeles • Los Angeles is the largest urban county in the nation at 4,083 square miles. • LA is split into several geographic areas, each requiring unique reporting: • 4 Continuum of Cares • 8 Service Planning Areas • 82 Geo Codes • 5 Supervisorial Districts • 16 Senate Districts • 19 Assembly Districts • 18 Congressional Districts • 15 Council Districts • 2000+ Census Tracts • 86 Cities • 100+ Communities

  27. LSA Reporting by Geography • Per HUD specs, the LSA exports from HMIS can be generated at the agency and program level. • As part of the HIC process, LA compiles lists of agencies and projects by region, which can then be used to identify sets of regional projects to run LSA exports. • What metrics would be beneficial to see by geographic region?

  28. Length of Time Homeless and Other System Use • System path groups • Shows effectiveness (minimal length of stay) for different programs by type • Are there certain regions that have longer length of stay prior to RRH move in? Compare to other regions that have shorter length of stays, and see how programs in the longer length of stay region may need to be adjusted. • Population groups • Breaks down various demographics by length of time homeless for certain populations • Are there some program types within a region where certain races have longer lengths of stays than others? How would that compare to other regions, and how can that be addressed?

  29. PSH and RRH Utilization Detail • PSH Turnover rates • Shows stayers and leavers in PSH to get turnover rates. • Which regions have the highest turnover? What does returns to homelessness look like for those regions? • RRH move-ins • Identifies how long it’s taking to place participants into housing (if at all), and how long assistance is provided. • Are there regions where the time in RRH prior to move-in is significantly longer than others? Does population type have an affect on this?

  30. Successful Housing Placements and Retention • Exit destinations by system path groups • Helps to gauge which system path leads to the highest number of permanent placements. • Is there an outlying region that has a high number of PH system exits for a particular system path? What is that region doing right that can be applied to other regions? • Exit destinations by population groups • Breaks down various demographics by exit destination • Similar to length of time homeless, is there a region where certain races have lower PH exits than others? How would that compare to other regions, and how can that be addressed?

  31. Returns After a Previous System Exit • Timeframe for returns based on prior destination • Can gauge how successful certain destinations appear • Does a region have a high number of PH placements when compared to others, but also has a higher rate of returns from those PH exits? This can show a case where other regions may not benefit from this region’s example. • Timeframe for returns based on system path groups • Shows how effective each system path may be by showing number of returns. • Is there a particular system path that results in high returns? How does that relate to the regions resources, and can that model be applied to other regions?

  32. Data Visualization (Future)

  33. Data Visualization (Future)

  34. Back to the Present… • For now: • Focus on data quality, upload, reviewing your warnings, and clicking submit • Everything else is for the future… • Bookmark theHUD Exchange HDX page • Updated HDX 2.0 User Guide posted shortly • Beta guide available now • HUD will host two “Office Hours” style webinars to answer FAQs • 10/23 @ 2PM eastern • 11/6 @2 PM eastern

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