2.4 Preventing Family Homelessness
-
Upload
national-alliance-to-end-homelessness -
Category
Documents
-
view
1.655 -
download
2
description
Transcript of 2.4 Preventing Family Homelessness
TARGETING HOMELESSNESS PREVENTION SERVICES MORE EFFECTIVELY: INTRODUCING A SCREENER FOR HOMEBASE
Andrew Greer and Marybeth ShinnVanderbilt University
Background & Rationale
Targeting services to prevent homelessness is difficult:
Numbers of shelter entrants are small and people with many risk factors for shelter entry avoid shelter
Prevention should be aimed at those most at-risk of becoming homeless
Study Questions
Question 1: What was the pattern of shelter entry over time among families who applied for Homebase services?
Question 2: What families were at highest risk of entering shelter?
Question 3: Is it possible to develop a short screening instrument to target services?
Question 4: If Homebase adopted better targeting, how much more effective might it be?
Data base
City provided a database of 11,105 families who applied for services between Oct 1, 2004 and June 30, 2008
Intake workers interviewed families about program eligibility and risk factors for homelessness
The City provided administrative data on shelter entry over the next 3 years
Risk Factor Domains
Demographics Human capital and poverty Housing Disability Interpersonal discord Childhood experiences Previous Shelter Dependent Variable: Time to Shelter Entry
Methods: Question 1
What was the pattern of shelter entry? Survival Analysis
Technique borrowed from medicine where “survival” is how long a patient lived after treatment
For us, the end point was not mortality, but shelter entry
Questions: “how long did people stay out of shelter?” (Survival Curve) “which periods of time were applicants at greatest risk of
shelter entry?” (Hazard Estimate)
Survival and Hazard Curves
Survival and Hazard Curves Used to illustrate survival and hazard rates
for subjects over time
Results: Question 1
What was the pattern of shelter entry over time among families who applied for Homebase services?
12.8% entered shelter within three years of applying
Most families who entered shelter did so shortly after applying for services
Methods: Question 2:
What families were at highest risk of entering shelter?
Survival Analysis Included predictors of shelter entry to
see which families were most at risk of entering shelter
Results: Questions 2Coefficient Haz Ratio Risk
directionConf Interval
Demographics
Female 1.28 + 1.01-1.63
Black 1.35 .90-2.04
Hispanic 1.07 .71-1.62
Age .98 - .98-.99
Child under 2 yrs old 1.14 + 1.01-1.29
# of Children 1.04 1.00-1.09
Pregnant 1.24 + 1.08-1.43
Married 1.09 .906-1.31
Veteran 1.119 .54-2.34
Results: Question 2Coefficient Haz Ratio Risk Direction Conf Interval
Human Capital/ Poverty
High School / GED .85 - .75-.96
Currently Employed .81 - .71-.93
Public Assistance History 1.30 + 1.13-1.49
Lost benefits in past year 1.14 .96-1.35
Housing
Name on lease .816 - .75-.96
Overcrowding or Discord 1.02 .87-1.20
Doubled up 1.14 .93-1.38
Threatened with eviction 1.20 + 1.04-1.38
Rent > 50% Income .93 .79-1.08
Arrears 1.00 1.00-1.00
Level of disrepair 1.02 .99-1.05
Number of times moved in past yr
1.16 + 1.08-1.24
Current subsidy .85 .68-1.07
Results: Question 2Coefficient Haz Ratio Direction Conf Interval
Disability
Chronic health probs or hosp
1.10 .96-1.26
Mental illness or hosp .82 .67-1.02
Substance abuse 1.22 .95-1.56
Criminal justice 1.11 .92-1.33
Interpersonal Discord
Domestic violence .87 .73-1.04
History with protective services
1.37 + 1.13-1.66
Legal involvement .98 .75-1.28
Av Discord with landlord/household
1.09 + 1.05-1.13
Results: Question 2Coefficient Haz
RatioRisk Direction
Conf Interval
Childhood Experiences
Teen mother .95 .81-1.10
Childhood Disruption index
1.15 + 1.08-1.22
Shelter
Shelter as an adult (self report)
1.43 + 1.22-1.66
Applied for shelter in last 3 mos
1.63 + 1.31-2.02
Seeking to reintegrate into community
1.29 + 1.06-1.59
Results: Question 2Coefficient Haz Ratio Risk Direction Conf Interval
Administrative Variables
Previous Shelter 1.15 .89-1.50
# Prior shelter applications
1.18 + 1.08-1.30
Previously found eligible
for shelter
1.10 .85-1.43
Exited shelter to a subsidy
.96 .73-1.24
How well does the model work?
Methods: Question 3
Is it possible to develop a short screening instrument? Eliminated location and administrative variables Eliminated racial categories Omitted variables that didn’t contribute reliably
to prediction of shelter entry Examined hazard ratios to assign 1-3 points for
each predictor For continuous variables like age, examined
patterns of shelter entry at different ages to decide on cut points
Results Question 3: Screener
1 point Pregnancy Child under 2 No high school/GED Not currently employed Not leaseholder Reintegrating into community
2 points Receiving public assistance (PA) Protective services Evicted or asked to leave by
landlord or leaseholder Applying for shelter in last 3
months
3 points Reports previous shelter as an
adult Age
1 pt: 23 - 28; 2 pts: ≤22
Moves last year 1 pt: 1-3 moves; 2 pts: 4+ moves
Disruptive experiences in childhood 1 pt: 1-2 experiences; 2 pts: 3+ experiences
Discord (landlord, leaseholder, or household) 1 pt: Moderate (4 – 5.59); 2 pts: Severe (5.6 – 9)
Methods: Question 4
If Homebase adopted better targeting, how much more effective might it be?
Compare decisions based on our screening model to:
1. Administrative data only2. Current Decisions3. Our full model Consider the percentage of shelter entrants
targeted at different levels of risk
Results: Question 4 Accurate TargetingModel Risk Criterion %
Applicants Served
% Shelter Entrants Targeted
Current Approach Judged eligible 62.4% 69.1%
• The intake worker assessment approach gives services to 62% of applicants and correctly targets 69% of shelter entrants.
Results: Question 4 Accurate TargetingModel Risk Criterion %
Applicants Served
% Shelter Entrants Targeted
Admin Data Any admin data 13.0% 25.7%
Current Approach Judged eligible 62.4% 69.1%
• People with past contact with the shelter system are at very high risk, but only 13% of HomeBase applicants have any past contact
• Giving services to them would reach only 26% of shelter entrants
Results: Question 4 Accurate TargetingModel Risk Criterion %
Applicants Served
% Shelter Entrants Targeted
Admin Data Any admin data 13.0% 25.7%
Current Approach Judged eligible 62.4% 69.1%
Full Model Cutoff based on % of Applicants
62.5% 89.6%
• If we use the full model to target the same proportion of HomeBase applicants who currently get services, we do a much better job of reaching those families who enter shelter
• We would reach almost 90% of shelter entrants, while the current system reaches 69%
Results: Question 4 Accurate TargetingModel Risk Criterion %
Applicants Served
% Shelter Entrants Targeted
Admin Data Any admin data 13.0% 25.7%
Current Approach Judged eligible 62.4% 69.1%
Full Model Cutoff based on % of Applicants
62.5% 89.6%
Screener 62.3% 88.9%
• A quick screener does almost as well as the full model
• Is this the right proportion? That’s a hard question that depends on lots of factors: How much do prevention or shelter stays cost? What are some of the other financial and moral costs of homelessness? How effective are services?
• Our data don’t answer these questions. But we can say what proportion of shelter entrants are reached at different proportions of applicants served.
Results: Question 4 Accurate TargetingModel Risk Criterion %
Applicants Served
% Shelter Entrants Targeted
Admin Data Any admin data 13.0% 25.7%
Current Approach Judged eligible 62.4% 69.1%
Full Model Cutoff based on % of Applicants
62.5% 89.6%
Screener 62.3% 88.9%
Screener 5 or more points 67.8% 91.9%
Screener 6 or more points 53.6% 84.4%
Screener 7 or more points 41.6% 73.8%
Screener 8 or more points 30.5% 61.0%
• The last lines show what happens when we target people by their risk scores
Conclusions
Our short screener can predict likelihood of shelter entry more accurately than current decisions
Prediction is hard: Even at the highest levels of risk, most families avoid shelter.
Determination of the proportion of families to serve is a question of available funds and costs, both to the homeless service systems and to society.
Recommendations
Workers should be able to override the recommendation of the model with written explanations
Although this exact screener may not work as well in other locations, the methods can be shared
Any model should be tested periodically to see if it misses recently vulnerable populations