Download - 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.


Applicants Served


Admin Data Any admin data 13.0% 25.7%


• 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


Applicants Served




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%


Applicants Served





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.


Applicants Served





62.5% 89.6%

Screener 62.3% 88.9%

Screener 5 or more points 67.8% 91.9%




• 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

Download - 2.4 Preventing Family Homelessness

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