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National Center on Homelessnessamong Veterans

New Research on Homelessness amongNew Research on Homelessness amongVeteransVeterans

July 13, 2011July 13, 2011

Delivering researchDelivering research--based solutions to end Veteran homelessnessbased solutions to end Veteran homelessness

Agenda

• Veteran Homelessness: A Supplemental Reportto the 2009 Annual Homeless AssessmentReport to Congress

• Modeling Inter-Continuum of Care Variation inRates of Veteran Homelessness

• Prevalence and Risk of Homelessness amongU.S. Veterans: A Multisite Investigation

• Aging among Homeless Populations andVeterans

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Veteran Homelessness: ASupplemental Report to the

2009 Annual HomelessAssessment Report to Congress

Ellen Munley

Annual Homeless AssessmentReport (AHAR)

• Required annual reportto Congress since 2005

• Estimate levels,describe homelesspopulation, location,patterns of shelter use

• Standard methods fordata collection acrossthe country

• PSH and HPRP

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Veteran Supplement

• New in 2009

• Uses similar methods asthe AHAR

• Adds:

– Differences betweenVeteran and non-Veterans

– Risk of homelessness forgroups of Veterans

Annual and Point in Time Estimates

Annual

• Full picture ofhomelessness throughentire year

• HMIS data fromproviders on whostayed in shelter/TH

• Unduplicated records

• 300 + communitiesreport

Point in Time (PIT)

• Answers the question,“How many Veteransare homeless on a givenday?”

• Includes sheltered andunsheltered counts

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2009 Estimates

Annual:

149,635

Winter

Fall

Summer

Spring

Point in Time:

January 29, 2009

• 75,609homelessVeterans

Characteristics of Veterans inShelter

Most are male(93%)

Almost halfare white

(49%)

Most are non-Hispanic (89%)

Largest agegroup is 31-50

(45%)

More than halfare disabled

(52%)

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Comparing Veterans and Non-Veterans in Shelter

Veterans are more likely tobe:

• male

• white

• non-Hispanic

• older

• disabled

than non-Veterans

Groups of Veterans at High Risk ofHomelessness

• AHAR Veteran Supplement compares risk ofhomelessness among groups of Veterans torisk for groups of non-Veterans.

• Certain groups are at high risk:

– Veterans under 30 years (OEF/OIF era)

– Female Veterans

– African American and Hispanic Veterans

– Risk is higher for poor members of these groups

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Risk of Homelessness Compared toNon-Veterans

Young Veterans(OEF/OIF)

2 times therisk

In poverty: 4times the risk

Female Veterans

2 times therisk

In poverty: 3times the risk

Risk of Homelessness Compared toNon-Veterans

Poor AfricanAmerican Veterans

AfricanAmerican

Veterans inpoverty: 2

times the risk

Poor Hispanic/LatinoVeterans

Hispanic/LatinoVeterans inpoverty: 3

times the risk

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Location of Veterans in Shelter

• More than half of homeless Veterans arelocated in just 4 states:

– California

– Florida

– New York

– Texas

• Majority are located in principal cities (72%)

– Compare to 37% of general population.

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Length of Stay in EmergencyShelter

Length of Stay Percent of Veterans inShelter

Less than one week 33%

Less than one month 61%

Less than 3 months 84%

Modeling Inter-Continuum ofCare Variation in Rates of

Veteran Homelessness

Ann Elizabeth Montgomery

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Background

• Both individual and structural factorscontribute to homelessness

• Previous research on the variation ofhomelessness rates across jurisdictions hassome limitations:

– Incomplete data on rates of homelessness

– Difficulty geographically matching predictors tooutcomes

Background

Although limited in geographic coverage, previous researchhas found a significant relationship between community-

level homelessness rates and a set of predictors, including:

– Vacancy rate (-)

– Percentage of renters (+)

– Price of rental housing (+)

– Low-rent housing stock (-)

– Population density (+)

– Funding for homelessprograms (-)

– Unemployment (+)

– Female-headed households (+)

– Mental health expenditures (+)

– Growth in employment (-)

– Single-person households (+)

– Temperature (+) andprecipitation (-)

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Objective

To identify the determinants of variation inhomelessness rates among Veterans across

CoCs as a function of community-levelvariables in six domains:

1. Housing

2. Economics

3. Safety Net

4. Veteran Affairs

5. Health

6. Criminal Justice

Dependent Variables

• Rates of homelessness are based on annual andPIT numbers in all reporting CoCs:

– Proportion of homeless Veterans who areunsheltered

– Ratio of unsheltered Veterans to sheltered Veterans

– Proportion of homeless adults who are Veterans

– Ratio of homeless Veterans to homeless non-Veterans

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Independent Variables

Housing • Fair market rents• 50th percentile rent estimates• Substandard/overcrowded housing units• Vacancy rate

Economics • Median household income• % population living below 50% poverty• Unemployment rate

Safety Net • Section 8 units• Veterans receiving VA compensation/pension• State supplements to federal spending

Independent Variables

VeteransAffairs

• Veterans by service era• VA homelessness resources• Veterans receiving homeless services

PublicHealth

• Mortality (life expectancy, suicide)• Chronic health conditions• Uninsured• Drug and alcohol misuse, smoking

CriminalJustice

• Homicide• Violent crime• Individuals on probation/parole

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Methods

• Match data sources using a common geography

– Most IVs are measured at the county, state, or VAfacility level; DVs are measured at the CoC level

• Conduct multilevel regression of each variable onselected outcome, controlling for clusters of CoCswithin states

• Develop multilevel model with most importantpredictors from all domains

Preliminary Findings

• Through a mapping application, matched eachCoC to one or more counties

• Modeled each predictor variable within eachdomain against selected outcome: % homelessVeterans who were unsheltered

• Variables across multiple domains weresignificantly associated with this outcome

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Preliminary Findings

*Variable measured at state level

Across CoCs, % homeless Veterans who were unshelteredwas significantly associated with:

Housing • % renter-occupied housing units (-)• % owner-occupied housing units (+)• % female-headed households (+)• Total properties with foreclosure filings (+)*• Doubled-up people (+)*

CriminalJustice

• Total individuals in prison (+)• Property crimes, burglary, theft (+)

Preliminary Findings

*Variable measured at state level

Across CoCs, % homeless Veterans who were unshelteredwas significantly associated with:

PublicHealth

• % adults with health insurance (-)• % adults in good health (-)• % adults in fair health (+)

SafetyNet

• State expenditures on public assistance (-)*• % Veterans receiving VA compensation or

pension (-)• State expenditures on Medicaid (-)*

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Preliminary Findings: Summary

RentersHealth insuranceGood healthState expendituresVA compensation/pension

OwnersFemale-headed householdsForeclosuresDoubled-upPrisonCrimes

Next Steps

• Determine significant predictors for each of theidentified dependent variables

• Develop multi-level, multi-domain model to explainthe variation in homelessness rates across CoCs

• Develop application allowing users to forecast howchanges in predictors (e.g., increasing healthcoverage or preventing foreclosures) could impactrates of homelessness among Veterans

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Prevalence and Risk ofHomelessness among U.S.

Veterans: A MultisiteInvestigation

Thomas Byrne

Background

• No definitive study on prevalence ofhomelessness among Veterans

• Great interest in understanding whether andwhy Veterans are at greater risk forhomelessness

– As risk factor for homelessness

– Government response to Veteran homelessness

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Prior Research

• Previous estimates of Veterans experiencing homelessness

– Rosenheck et al., 1987: % Veteran• 41% of homeless males were Veteran (Veterans in total male pop. = 34%)

– Urban Institute, 1996: % Veteran• 23% of homeless adults were Veteran (Veterans in total pop. = 14%)

• 33% of homeless males were Veteran (Veterans in total male pop. = 28%)

• 3-4% of homeless females were Veteran (Veterans in female pop. = 1%)

• White male Veterans at higher risk for homelessness than other race groups

– AHAR, 2009: % Veteran• 16% of homeless adults were Veteran (Veterans in total adult pop. = 10%)

• 0.6% Veterans experienced homelessness (homeless in total pop. = 0.5%)

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Limitations of Prior Research

• Many based on point-estimated prevalence rather thanannual counts

• Little information available on risk for homelessnessamong Veterans…– For various age by race by sex subgroups– For the general population as compared to the population

most at-risk for homelessness, the population in poverty

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Research Questions

• Are Veterans overrepresented in the homelesspopulation?– What is prevalence of Veterans among homeless?

– What proportion of Veterans are homeless?

• What demographic characteristics are risk factorsfor homelessness?– Is Veteran status a risk factor for homelessness?

– Does risk for homelessness vary among age, race, andsex subgroups?

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Methodology

• Data Sources

– Obtained individual-level 2008 HMIS data from 7geographically diverse Continuums of Care (CoC)

– Obtained 2006-2008 American Community Survey(US Census Bureau) estimates of populationcounts for same CoC geographies for age by raceby sex by Veteran by poverty subgroups

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Methodology

• Data Analysis– Separate sets of analyses for:

– 1) Total population and subset of population in poverty

– 2) Males and females

– Prevalence of homelessness among Veteranscomputed by taking ratios of HMIS over ACS datafor subgroups

– Risk for homelessness computed usingmultivariate statistical models with age, sex, race,and Veteran status assessed as risk factors

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Prevalence of Veterans InHomeless Population

Age Race Male Female

18–29 Black 3.8% 1.0%

Non-Black 2.7% 1.0%

30–44 Black 8.2% 3.2%

Non-Black 7.6% 1.3%

45–54 Black 21.0% 2.7%

Non-Black 19.6% 3.1%

55–64 Black 31.9% 1.8%

Non-Black 30.6% 3.1%

65+ Black 32.3% 1.4%

Non-Black 33.7% 2.4%

All Cases 13.6% 1.8%

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Prevalence of VeteranHomelessness: Total Population

% Vet in

Homeless

Population

% Vet in Overall

Population

Risk Ratio

Comparing %

Vet in Homeless

to % Vet in

General Pop.

Age Race Male Female Male Female Male Female

18–29 Black 3.8% 1.0% 1.9% 0.6% 2.0 1.7

Non-

Black 2.7% 1.0% 2.1% 0.5% 1.3 2.0

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Prevalence of VeteranHomelessness: Total Population

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Prevalence of Veteran Homelessness:Population in Poverty

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Rate of Homelessness Among Veterans% of Veterans Experiencing

Homelessness (TotalVeteran Population)

% of Veterans ExperiencingHomelessness ( VeteranPopulation in Poverty)

Age Race Male Female Male Female

18–29 Black 5.4% 7.9% 52.8% 36.3%

Non-Black 0.7% 1.6% 7.3% 11.9%

30–44 Black 4.7% 6.3% 33.8% 35.4%

Non-Black 1.0% 0.9% 17.2% 12.1%

45–54 Black 7.3% 3.2% 38.0% 29.1%

Non-Black 1.9% 1.1% 21.0% 12.3%

55–64 Black 3.8% 1.4% 24.2% 9.1%

Non-Black 0.6% 0.6% 10.5% 9.3%

65+ Black 0.6% 0.4% 4.8% 1.7%

Non-Black 0.1% 0.1% 2.1% 0.8%

All Cases 1.0% 1.6% 14.6% 15.0%

Prevalence Summary: TotalPopulation

• Overrepresentation of female (RR = 2.1) andmale (RR = 1.3) Veterans in homelesspopulation

– Young black male Veterans particularlyoverrepresented

• Prevalence of total Veterans who werehomeless: 1% for males and 1.6% for females

– Ranged from 1-8% depending on age and race(higher proportion among Black subpopulation)

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Prevalence Summary:Population in Poverty

• Overrepresentation of female (RR = 3.0) andmale (RR = 2.1) Veterans in homelesspopulation

– Young black male Veterans particularlyoverrepresented

• Roughly 15% of both male and femaleVeterans in poverty were homeless.

– Pronounced differences for Black (~27%) and non-Black (~11%) subpopulations

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Risk Factors for Homelessness

• Veteran status– Total population: 2-fold increased risk for females,

50% greater risk for males– Population in poverty: >3-fold increased risk for

females, 2-fold risk for males

• Race (for both men and women)– Total population: Blacks were >5 times more likely

than non-Blacks to be homeless– Population in poverty: Blacks were >3 times more

likely than non-Blacks to be homeless

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Risk Factors for Homelessness:Summary

• Age– As compared to 18-29 year olds…

• Males aged 30-54 were more likely, 55-64 wereequally likely, and 65+ were less likely to behomeless for both total and poverty populations

• Females, in total population, were less likely to behomeless as age increased

• Females, in poverty population, aged 30-54 wereequally likely and those older than 55 wereincreasingly less likely to be homeless

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46

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Conclusions

• Findings elucidate extent of homelessness amongVeteran population

• Veteran status as risk factor for homelessness

• Finding of increased risk among Veterans isparadoxical given benefits/services available toveterans

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Conclusions

• Findings suggest prevention efforts may beeffective to the extent that they can target the~1.4 million Veterans in poverty,

• Also, heightened awareness of specificsubpopulations at risk for homelessness– Young male Black Veterans– Female Veterans—and young female Veterans in

particular– Poverty

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Aging among HomelessPopulations and Veterans

Stephen Metraux

Hahn et al. 2006

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• The elderly population has historically beenunderrepresented among the homeless population

• research shows more rapid increases inhomelessness among adults ages 50 and older

• predicts that homelessness among elderly personswill increase substantially over the next decade dueto:– the overall growth in the elderly population.– elderly population consistently faces economic

vulnerability.

http://www.flickr.com/photos/sam_williams/3593754703/

Findings from New York City

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New York City Sheltered Homeless Population (singles and families) in 2005: Age Distribution

0%

2%

4%

6%

8%

10%

12%

14%

0-23-5

6-89-11

12-14

15-17

18-20

21-23

24-26

27-29

30-32

33-35

36-38

39-41

42-44

45-47

48-50

51-53

54-56

57-59

60-62

63-65

65+

Age Groups

Age distribution reflects 2005 prevalence population (29,326 single adults and 57,374 persons in 19,048 families)

Age of single adults, change over time (1988, 95, 2000 & 05), NYC (proportion by age in years).

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

18to

21

22to

24

25to

27

28to

30

31to

33

34to

36

37to

39

40to

42

43to

45

46to

48

49to

51

52to

54

55to

57

58an

d59

60an

d61

62to

64

65to

74

75an

dolde

r

1988

1995

2000

2005

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Age of family household heads only, change over time (1990 & 2000),

NYC (proportion by age in years).

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

18 to

20

21 to

23

24 to

26

27 to

29

30 to

32

33 to

35

36 to

38

39 to

41

42 to

44

45 to

47

48 to

50

51 to

53

54 and

older

1988

1995

2000

2005

Findings from US Census Data

http://media.namx.org/images/editorial/2010/04/0401/j_simas_homeless_census/j_simas_homeless_census_500x279.jpg

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Dis tr ibution, by Age, of Male Shelter Users in the US, 1990 and 2000 (US Census )

0.0

2.0

4.0

6.0

8.0

10.0

12.0

18to

21

22to

24

25to

27

28to

30

31to

33

34to

36

37to

39

40to

42

43to

45

46to

48

49to

51

52to

54

55to

57

58an

d59

60an

d61

62to

64

65to

74

75an

dolde

r

2000

1990

Relative Risk for Homelessness by Age & Sex for 1990 and 2000 US Census Adult Population

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

18to

21

22to

24

25to

27

28to

30

31to

33

34to

36

37to

39

40to

42

43to

45

46to

48

49to

51

52to

54

55to

57

58an

d59

60an

d61

62to

64

65to

74

75an

dol

der

1990 Male

1990 Female

2000 Male

2000 Female

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Homelessness & Aging Among Veterans

Aging Vet Population

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

4,000,000

< 20 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80-84 85-89 90+

2000

2005

2010

2015

2020

Source – VetPop 97 - http://www.va.gov/VETDATA/Demographics/Demographics.asp

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National Survey of Veterans (NSV)

0%

25%

50%

75%

100%

2002 2003 2005 2007 2008

65+

45-64

<45

Aging Among Homeless Vet Population

0%

25%

50%

75%

100%

2002 2003 2005 2007 2008

65+

45-64

<45

Source – NE Program and Evaluation Center Form X data

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Future Research

• More detailed description of aging trendsamong Veterans – homeless and non-homeless

• Examination of morbidity trends as homelessVeterans age

• Models forecasting how aging trends willimpact size and demographics of homelessveteran population

• Planning for services and accommodations