2.4 Preventing Family Homelessness

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2.4 Preventing Family Homelessness

Speaker: Andrew Greer and Marybeth Shinn

One of the keys to ending homelessness is to prevent it from happening in the first place. This workshop will examine the most effective strategies to prevent family homelessness, including using homelessness data to target interventions and partnering with providers serving high-risk families. Presenters will cover a wide array of services and solutions.

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2.4 Preventing Family Homelessness

  1. 1. TARGETINGHOMELESSNESSPREVENTION SERVICESMORE EFFECTIVELY:INTRODUCING ASCREENER FORHOMEBASEAndrew Greer and Marybeth ShinnVanderbilt University
  2. 2. 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
  3. 3. 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?
  4. 4. 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
  5. 5. Risk Factor Domains Demographics Human capital and poverty Housing Disability Interpersonal discord Childhood experiences Previous Shelter Dependent Variable: Time to Shelter Entry
  6. 6. 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  Forus, 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)
  7. 7. Survival and Hazard Curves Survival and Hazard Curves  Usedto illustrate survival and hazard rates for subjects over time
  8. 8. 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
  9. 9. 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
  10. 10. Results: Questions 2Coefficient Haz Ratio Risk Conf Interval directionDemographics 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
  11. 11. Results: Question 2Coefficient Haz Ratio Risk Direction Conf IntervalHuman 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 .96-1.35Housing 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 1.16 + 1.08-1.24 yr Current subsidy .85 .68-1.07
  12. 12. Results: Question 2Coefficient Haz Ratio Direction Conf IntervalDisability Chronic health probs or 1.10 .96-1.26 hosp Mental illness or hosp .82 .67-1.02 Substance abuse 1.22 .95-1.56 Criminal justice 1.11 .92-1.33Interpersonal Discord Domestic violence .87 .73-1.04 History with protective 1.37 + 1.13-1.66 services Legal involvement .98 .75-1.28 Av Discord with 1.09 + 1.05-1.13 landlord/household
  13. 13. Results: Question 2Coefficient Haz Ratio Risk Direction Conf IntervalChildhood Experiences Teen mother .95 .81-1.10Childhood Disruption index 1.15 + 1.08-1.22Shelter Shelter as an adult (self 1.43 + 1.22-1.66 report) Applied for shelter in last 3 1.63 + 1.31-2.02 mos Seeking to reintegrate into 1.29 + 1.06-1.59 community
  14. 14. Results: Question 2Coefficient Haz Ratio Risk Direction Conf IntervalAdministrativeVariables Previous Shelter 1.15 .89-1.50 # Prior shelter 1.18 + 1.08-1.30 applications Previously found 1.10 .85-1.43eligible for shelterExited shelter to a .96 .73-1.24 subsidy
  15. 15. How well does the model work?
  16. 16. 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
  17. 17. Results Question 3: Screener 1 point adult  Pregnancy  Age  Child under 2  1 pt: 23 - 28;  No high school/GED  2 pts: ≤22  Not currently employed  Moves last year  Not leaseholder  1 pt: 1-3 moves;  Reintegrating into community  2 pts: 4+ moves 2 points  Disruptive experiences in  Receiving public assistance (PA) childhood  Protective services  1 pt: 1-2 experiences;  Evicted or asked to leave by  2 pts: 3+ experiences landlord or leaseholder  Discord (landlord, leaseholder, or  Applying for shelter in last 3 household) months  1 pt: Moderate (4 – 5.59); 3 points  2 pts: Severe (5.6 – 9)  Reports previous shelter as an
  18. 18. 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
  19. 19. Results: Question 4 AccurateModel TargetingRisk Criterion % % Shelter Applicants Entrants Served TargetedCurrent 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.
  20. 20. Results: Question 4 AccurateModel TargetingRisk Criterion % % Shelter Applicants Entrants Served TargetedAdmin 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
  21. 21. Results: Question 4 AccurateModel TargetingRisk Criterion % % Shelter Applicants Entrants Served TargetedAdmin Data Any admin data 13.0% 25.7%Current Approach Judged eligible 62.4% 69.1%Full Model Cutoff based on % 62.5% 89.6% of Applicants• 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%
  22. 22. Results: Question 4 AccurateModel TargetingRisk Criterion % % Shelter Applicants Entrants Served TargetedAdmin Data Any admin data 13.0% 25.7%Current Approach Judged eligible 62.4% 69.1%Full Model Cutoff based on % 62.5% 89.6% of ApplicantsScreener 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
  23. 23. Results: Question 4 AccurateModel TargetingRisk Criterion % % Shelter Applicants Entrants Served TargetedAdmin Data Any admin data 13.0% 25.7%Current Approach Judged eligible 62.4% 69.1%Full Model Cutoff based on % 62.5% 89.6% of ApplicantsScreener 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
  24. 24. 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.
  25. 25. 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

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