Evaluation of the Corporation for Supportive Housing’s

Social Innovation Fund Initiative 

Final Report 

August 2017 

Prepared by the 

New York University Evaluation Team: 

Beth Weitzman, PhD 

Tod Mijanovich, PhD 

Carolyn Berry, PhD 

Maggie Giorgio, PhD MPH 

Maggie Paul, MS 
New York University CSH-SIF Evaluation Final Report 

Evaluation Team August 2017 

Page 2 of 34 

Acknowledgements 

This publication represents a collaborative effort by researchers at New York University 

Steinhardt School of Culture, Education and Human Development, College of Global Public 

Health, and School of Medicine. 

This work was funded by the Corporation for Supportive Housing, award #12-042-C 

Principal Authors: 

Beth C. Weitzman, PhD 

Tod Mijanovich, PhD 

Carolyn Berry, PhD 

Maggie Giorgio, PhD MPH 

Maggie Paul, MS 

The contents of this document are solely the responsibility of the NYU evaluation team and do 

not necessarily represent the official views of the Corporation for Supportive Housing or the 

Corporation for National Community Service. 

This document may be reproduced in whole or part without restriction as long as New York 

University is credited for the work. Upon request, the contents of this document will be made 

available in alternate formats to serve accessibility needs of persons with disability. 
New York University CSH-SIF Evaluation Final Report 

Evaluation Team August 2017 

Page 3 of 34 

Table of Contents 

Executive Summary 4

Introduction 5

 Program 5

 Evaluation 7

Methods and Analytic Approach 9

 Descriptive Statistics for Survey Sample 9

 Descriptive Statistics for Impact Sample 10

 Data Collection and Measurement 14

 Key Research Questions 16

 Counterfactual Condition 17

Findings 18

 Site Visits 18

 Participant Survey 19

 Impact Analysis 22

 Cost Effectiveness Analysis 29

Discussion 31

Conclusions 33
New York University CSH-SIF Evaluation Final Report 

Evaluation Team August 2017 

Page 4 of 34 

EXECUTIVE SUMMARY 

The Corporation for Supportive Housing (CSH) with support from the Corporation for National 

Community Service (CNCS) has funded a team of researchers at New York University (NYU) to 

test a multi-site program model that links a subset of ‘super-utilizers’ who experience

homelessness to affordable supportive housing with medically-focused case management 

services. This demonstration project is distinguished from the typical model of supportive 

housing in two ways: 1) the identification of high medical cost homeless individuals through the 

use of empirical data; and 2) the explicit integration of primary health care into the mix of 

services clients receive once housed. Once patients were targeted and enrolled, full service 

delivery thus required four key components: permanent housing; ongoing supportive case 

management; appropriate mix of mental health and substance abuse treatment services; and 

primary health care. The demonstration was implemented in four sites, including Connecticut 

(CT), Los Angeles (LA), Washtenaw County, Michigan (MI), and Francisco (SF). 

Implementation activities in each site were guided by the theory that when high cost, high risk 

homeless individuals are identified, housed and receive the case management they need to 

stabilize and access the other services they need, their physical and mental illnesses will be 

stabilized such that they will be healthier and incur less cost to the health care system. The 

evaluation of this demonstration project was also guided by this theory. 

While prior evaluations have described the effect of supportive housing on health care 

utilization, findings are mixed and the studies were often limited to assessing the impact of a 

program in a single site. This evaluation is distinguished from prior research in that it employs a 

randomized control trail (RCT) design across multiple, distinct study locations using a relatively 

large sample size. The evaluation included several key components used to assess program 

implementation and to build strong evidence of whether and to what extent the program 

produced impacts as whole and/or across all study sites in order to address gaps in existing 

research. The evaluation activities, staggered at appropriate intervals throughout the five-year 

grant period, included visits to all program sites, including interviews with a broad array of 

stakeholders, a pre/post participant survey to assess changes in self-reported measures (no 

comparison group), a cost effectiveness analysis, and an impact analysis. Information gathered 

on site visits was used to track program implementation, including site-specific facilitators and 

barriers to program implementation. An analysis of the participant survey data was used to assess 

statistically significant changes in participant responses from pre-test to post-test. The cost 

effectiveness analysis was performed using data gathered from sites to determine the extent to 

which cost-savings across multiple programs covered the cost of implementation. Finally, the 

impact analysis employed a RCT design in CT, MI, and SF1

, allowing for a rigorous assessment 

of program effects on health care utilization and cost and – to the extent that data were available 

– on shelter and jail utilization. LA, though included in the survey data analysis, was excluded 

from the impact analyses as there were no available data to support such analysis. Analyses were 

conducted using both intent-to-treat (ITT) and treatment-on-the-treated (TOT) approaches to 

assess changes in utilization as a result of participation in the program, which in this project is 

tenancy in supportive housing. The control group for the ITT approach was identified during 

random assignment (full control group used) whereas the comparison group for the TOT 

 

1 LA was not included in the impact analysis, as both the targeting strategy, which did not allow for randomization 

or the development of a comparison group, and the lack of administrative data precluded rigorous evaluation.
New York University CSH-SIF Evaluation Final Report 

Evaluation Team August 2017 

Page 5 of 34 

approach was identified using 1:1 pair matching of each housed individual with the control group 

member who was most similar in terms of their log of total cost, number of hospitalizations, and 

number of ED visits within a 12-month period prior to randomization.

INTRODUCTION 

Program 

The Corporation for Supportive Housing (CSH) with support from the Corporation for National 

Community Service (CNCS) has tested a multi-site program model that links a subset of ‘superutilizers’ who experience homelessness to affordable supportive housing with medically-focused 

case management services. This demonstration project is distinguished from the typical model of 

supportive housing in two ways: 1) the identification of high medical cost homeless individuals 

through the use of empirical data; and 2) the explicit integration of primary health care into the 

mix of services clients receive once housed. Full service delivery thus required four key 

components: permanent housing; ongoing supportive case management; appropriate mix of 

mental health and substance abuse treatment services; and access to primary health care. 

The grant period for program implementation was a five year period beginning in the summer of 

2012 and concluding in 2017. The four sites selected for program implementation included the 

state of Connecticut (CT), Los Angeles County (LA), Washtenaw County, Michigan (MI), and

San Francisco (SF). Each site was expected to enroll and place a minimum number of homeless 

individuals (SF = 502, MI = 100, CT = 150, LA=107) into supportive housing. Although the 

program in each site was based on the same overarching theory and core components, there were 

planned differences across sites, notably the type of housing model, the targeting strategy, and 

staffing model (Table 1). 

Table 1. Planned Cross-Site Differences in CSH-SIF Program Implementation 

Site Targeting 

Strategy

Service Providers Housing Model Staffing Model

CT Match list of 

highest 

utilizers 

Five agencies 

initially, down to 

four before end of 

first year

All agencies 

placed clients in 

scattered site 

housing

Patient navigators as main 

service providers, with some 

support from contracted case 

managers (eventually hired

in-house case managers in 

some sites)

 

2 While the SF site committed to house at least 50 individuals, the congregate facility in SF (the Kelly Cullen 

Community – or KCC) was used to house 172 homeless high utilizers. The first 50 individuals were 

randomized into housing and the remaining 122 initial slots and all slots resulting from turnover were identified 

and placed through the San Francisco Department of Public Health’s standard operating procedures for housing 

people on the waiting list for the Direct Access to Housing (DAH) program. Although only the first 50 individuals 

housed were randomized, all individuals housed in KCC during the study period were asked to take the 

participant survey. 
New York University CSH-SIF Evaluation Final Report 

Evaluation Team August 2017 

Page 6 of 34 

MI Match list of 

highest 

utilizers

Single agency Scattered site Case managers as main 

service providers

LA Point-ofcare using 

utilization 

triage tool 

Five agencies 

initially, down to 

four before end of 

first year

All agencies 

placed clients in 

scattered-site 

housing

Case managers as main 

service providers

SF Match list of 

highest 

utilizers

Single agency Single, 

congregate 

facility 

Social workers as main 

service providers, with inhouse, on-site support from a 

clinical team and a money 

manager

Three of the four sites employed some type of cross-system data matching procedure between 

the homeless and health care systems (Medicaid or health/hospital system), at least for part of 

their caseloads, to generate lists of people who meet some definition of “homeless” and some 

threshold of health care utilization costs. Program staff then received the “match lists” and 

attempted to locate and engage potential clients. MI identified high cost patients using data from 

two of the three hospital systems serving the county and matching those data with shelter use 

data from Washtenaw County. CT used statewide Medicaid data and the CT Homelessness 

Management Information System (HMIS) data. For 50 of its 172 slots, SF used data from the 

San Francisco Health Plan (SFHP), a Medicaid health care plan, and other sources of data on to 

target clients; however, once individuals were randomized into the 50 spots committed to CSHSIF, this procedure was no longer followed. LA utilized a point-of-care approach in which 

individuals were triaged for high utilization and homelessness and recruited into the program at 

their place of care. While this was a highly effective strategy for locating and engaging clients, it 

did not allow for a randomized study design. 

Two of the sites, LA and CT, used a regional approach to program implementation where they 

relied on multiple sub-contracted agencies across the state or county, while the programs in SF 

and MI were implemented by a single organization. CT, MI and LA used scattered-site housing 

in which clients were placed in available units throughout the catchment area. In SF, all clients 

were placed into the Kelly Cullen Community (KCC), a single building dedicated to housing 

homeless high utilizers that was co-located with a federally qualified health center on the ground 

floor. Staffing models also vary by site. As sites were required by CNCS to meet a one to one 

match for SIF funding, the SIF funds were not designed to fund the entire service and housing 

model. Given the diversity of sources of match funds across all sites there were differences in 

the model as well. The SIF funding supported case managers in LA and MI and these case 

managers are the core SIF program staff. In SF, on-site social workers from two different 

agencies worked together with public health nurses and a money manager in a team based 

approach. Case management services were not explicitly funded through the grant in CT, as the 

grantees anticipated that other service providers in the community would fill this role through 

partnership arrangements. The grant funded patient navigators in CT, who were designed to 

concentrate on health care and were not meant to provide ongoing supportive case management 

services. However, as the program matured, several sites in CT hired in-house case managers to 
New York University CSH-SIF Evaluation Final Report 

Evaluation Team August 2017 

Page 7 of 34 

support clients. As mentioned above, in all four sites, the program design required engagement 

of additional services and community partners, including hospitals and health care systems.

In addition to these planned differences, a number of other important and unplanned differences,

such as local context (e.g. local attitude and norms around supportive housing for substance 

users) and state policy (e.g. the extent to which Medicaid covers needs of target population 

especially in regard to substance abuse and mental health services) shaped program 

implementation to a high degree. While the implications of these factors are discussed in the 

findings section of this report, they are described in greater detail in the series of site visit reports 

produced by the evaluation team throughout the grant period. These reports are included as 

attachments. 

The overarching program theory guiding program implementation across all sites was that when 

high cost, high risk homeless individuals are identified, housed and receive the case management 

they need to stabilize and access the healthcare services they need, including integrated primary 

health care, their physical and mental illnesses will be stabilized and controlled such that they 

will lead healthier lives and incur less cost to the health care system. The evaluation of this 

demonstration project was also guided by this theory. 

Evaluation 

The evaluation timeline mirrored that of the program, developed around the original timeline, 

was a five-year funding period running through July 2017. While prior evaluations have 

described the effect of supportive housing on health care utilization, the findings from these 

studies are mixed and the designs are often limited to assessing the impact of a program in a 

single site. This evaluation is therefore distinguished from prior research in that it employs a 

randomized control trail (RCT) design across multiple, distinct study locations using a relatively 

large sample size. 

This study included several key components used to assess both program implementation and 

impacts across sites. These evaluation activities, which were staggered at appropriate intervals 

throughout the five-year study period, included a series of three site visits to all program sites, a 

pre/post participant survey, a cost effectiveness analysis, and an impact analysis. 

Site visits were divided into three rounds, with evaluators visiting all sites during each 

cycle. These visits occurred in the spring of 2013, 2015, and 2016, roughly corresponding 

to early/pre, intermediate and mature program phases. Site visits were conducted by two 

members of the evaluation team who interviewed key stakeholders in each site, typically 

speaking with between 75 and 90 individuals across all four sites in each round. These 

site visits were the primary source of information used to define the program in each site, 

and findings from the site visits were used to interpret cross-site differences in survey, 

cost-effectiveness, and impact analyses. Relevant attachments include: Round 1 site visit 

memo, round 2 site visit memo, round 3 site visit memo 

Participant surveys were the main source of data on key measures that were not 

available through secondary data. Survey topics included quality of life (including health-
New York University CSH-SIF Evaluation Final Report 

Evaluation Team August 2017 

Page 8 of 34 

related quality of life), substance use, health care access, mental health and wellbeing, 

history of incarceration, history of homelessness, and, at post-test, reflections on their 

experience with the CSH-SIF program. Both pre-and post-surveys were administered to 

clients across all sites in order to capture change in key metrics over time after client 

engagement with the program. Survey administration was only attempted among 

individuals receiving services and therefore there is no comparison group for this set of 

analyses. The baseline participant survey was launched among the program group shortly 

after the beginning of the study period, as SF was ready to begin enrolling clients at the 

start of the program period. The post survey was administered to clients who had been 

enrolled in the program for at least one year. Relevant attachments include: Final survey 

report 

Tracking sheet data were collected for program administrative purposes. The evaluation 

team used these data for the sole purpose of tracking entry and exit into the program and 

housing. 

Cost effectiveness analyses were used to compare the cost of implementing the program 

to the costs off-set from reductions in health care utilization as well as shelter and jail 

utilization. The NYU evaluation team worked with program teams in each site to 

determine the annual, per-capita cost of running the program, including all support 

services as well as rent for housing. As many of the sites experienced substantial and 

often unexpected start-up costs, all sites chose either year 3 or year 4 of the grant cycle to 

represent the stable, mature program. These costs were then compared to cost savings 

associated with identified program impacts in regard to healthcare, shelter use, and jail 

time. 

Impact analyses were used to assess whether and to what extent the demonstration had 

an effect on health care utilization as well as shelter stays and jail utilization (where data 

were available). It is worth noting up front that, of the evaluation components, the impact 

analyses presented the greatest challenges to the evaluation team due to the sites’ 

inability to independently provide the required administrative data. We therefore had to 

alter our scope of work to assist sites with data acquisition and never acquired appropriate 

data for LA (excluded from impact analysis). For the three sites in which data was 

available, we used two approaches – treatment on treated (TOT) and intent to treat (ITT) 

with propensity score matching – to assess impacts. Relevant attachments include: CSHSIF face-to-face meeting presentation 

Together, these components provided a rich description of the CSH-SIF demonstration that 

includes everything from a nuanced description of the differences in program implementation to 

a discussion of overarching findings from the impact analyses. In addition to the attachments 

referenced above, several reports, papers, and presentations have included findings from multiple 

components. Many of these are referenced in and attached to this report. While this report 

provides sufficient detail to understand major evaluation activities, please review all attachments 

for a full picture of the evaluation of the CSH-SIF demonstration. 
New York University CSH-SIF Evaluation Final Report 

Evaluation Team August 2017 

Page 9 of 34 

METHODS AND ANALYTIC APPROACH 

Descriptive Statistics for Survey Sample

The sample used in the analysis of survey data included 238 individuals spread across all four 

sites who took both a pre- and post-survey. Overall, the average survey response rate for both 

pre- and post-surveys among eligible participants was 47% with the lowest rate in LA (34%) and 

the highest in CT (55%). Descriptive statistics for demographics characteristics and housingsensitive indicators are provided in table 2. As shown in table 2, there were no statistically 

significant differences in key baseline characteristics between the group with pre and post 

surveys and those without. 

Table 2. Differences in demographic characteristics and housing-sensitive indicators by survey 

status among housed participants who completed surveys 

All surveys

(n=490)

Pre- or postsurvey only

(n=252)

Both surveys

(n=238)

p-value

Age

Mean(SD)

range

49.0 (10.7)

14-83

49.3 (11.3)

14-74

48.6 (10.0)

19-83 0.533

Gender

Male

Female

Trans/Queer

67.8% (329)

30.5% (148)

1.7% (8)

71.8% (178)

27.0% (67)

1.2% (3)

63.7% (151)

34.2% (81)

2.1% (5)

0.150

Race

Black

Hispanic

White

Other

40.1% (193)

12.5% (60)

41.2% (198)

6.2% (30)

37.7% (92)

13.9% (34)

39.8% (97)

8.6% (21)

42.6% (101)

11.0% (26)

42.6% (101)

3.8% (9)

0.099

Months on the street

Mean(SD)

range

59.6 (77.4)

0-432

61.2 (79.8)

0-420

58.1 (75.2)

0-432 0.678

Ever in foster care 13.2% (60) 14.5% (33) 11.9% (27) 0.406

Reported sleeping on the street/in a 

shelter most often during the 12 

months prior to being housed 

51.0% (237) 50.2% (115) 51.7% (122)

0.750

Number of lifetime arrests

Mean(SD)

range

11.4 (21.7)

0-200

12.1 (22.3)

0-200

10.7 (21.1)

0-200 0.529

Ever been treated for drug or alcohol 

use

60.9% (285) 60.9% (142) 60.8% (143) 0.984

LGBT 10.8% (46) 9.6% (21) 10.8% (25) 0.677

Site

CT

LA

MI

SF

30.6% (150)

20.0% (98)

17.1% (84)

32.2% (158)

25.8% (65)

26.2% (66)

15.9% (40)

32.1% (81)

35.7% (85)

13.5% (32)

18.5% (44)

32.3% (77)

0.002
New York University CSH-SIF Evaluation Final Report 

Evaluation Team August 2017 

Page 10 of 34 

Among those who were enrolled and housed, survey data indicates that participants experienced 

significant health care needs at baseline (Table 3). This finding is reinforced by the baseline data 

from the impact analysis presented in the following section. As illustrated by Table 3, among 

those who were surveyed in each site, well over half of the respondents indicated that they were 

in fair or poor health and had substantial difficulty with activities of daily living, such as dressing 

or bathing and walking or climbing stairs. Further, the proportion of respondents who indicated 

that they experienced medical problems every day in the past month ranged from 28% in CT to 

41% in LA, 36% in MI, and 45% in SF. Though not reported in this table, the survey also asked 

about clients’ usual source of care. While the vast majority of respondents across sites indicated 

that they had a usual source of care in the year prior to enrollment, only 15% of those in SF 

indicated the ED to be their usual source of care, as compared to 30% in CT, 52% in MI, and 

39% in LA. 

Table 3. Health related quality of life indicators at baseline, overall and by site 

Overall

(n=384)

CT

(n=131)

LA

(n=66)

MI

(n=62)

SF

(n=125)

Self-rated Health Status*

Excellent/very good

Good

Fair/poor

11% (41)

26% (99)

63% (237)

8% (11)

38% (50)

53% (69)

13% (8)

7% (4)

80% (48)

8% (5)

21% (13)

71% (44)

14% (17)

26% (32)

61% (76)

Any serious medical 

problems*

86% (319) 85% 

(109)

96% (55) 77% (48) 86% (107)

Days in the past month with a 

medical problem*

No days

Some days

Everyday

24% (87)

39% (140)

37% (134)

29% (36)

43% (53)

28% (34)

10% (6)

48% (28)

41% (24)

27% (16)

37% (22)

36% (21)

24% (29)

31% (37)

45% (55)

Difficulty walking or 

climbing stairs*

61% (230) 53% (69) 69% (42) 53% (33) 69% (86)

Difficulty dressing or 

bathing*

26% (98) 15% (19) 44% (25) 20% (12) 34% (42)

*Significant difference by site at p < 0.05 

Descriptive Statistics for Impact Analysis Sample 

Descriptive statistics for the randomized treatment and control groups are provided in Table 4.

Across the 3 sites included in the impact analysis, a total of 1,512 individuals were randomized 

to either the treatment or control groups (CT = 907, MI = 407, SF = 199). We randomized more 

individuals relative to program target numbers, as we expected that staff would not be able to 

find everybody on the list and, in fact, locating individuals was even more difficult than 
New York University CSH-SIF Evaluation Final Report 

Evaluation Team August 2017 

Page 11 of 34 

hypothesized. (Due to problems with identifying the full sample in subsequent Medicaid 

datasets, the analytic sample for CT=809). As seen in Table 4, the randomization process 

resulted in closely matched samples in each site. 

Table 4. Baseline characteristics for treatment and control groups by site1

Connecticut* Washtenaw County, MI** San Francisco

Treatment

(n=430)

Control

(n=379)

pvalueTreatment

(n=242)

Control

(n=156)

pvalueTreatment

(n=95)

Control

(n=94)

pvalue

Age

Mean (SD) 46.0 

(10.9)

46.8 

(11.5)

0.404 N/A N/A N/A 50.5 (9.9) 50.5 

(10.6)

0.951

Gender

Male 

Female

68%

32%

72%

28%

0.336 N/A N/A N/A 60%

40%

63%

37%

0.591

Race

White

Black

Hispanic

Other

53%

25%

21%

1%

54%

24%

21%

1%

0.978 N/A N/A N/A 41%

41%

5%

12%

40%

37%

11%

13%

0.691

Medical 

hospitalizations

Mean (SD)

3.4

(3.6)

3.0

(3.0)

0.112 2.0 (3.0) 1.4 (2.0) 0.025 2.1 (2.4) 2.0 (2.6) 0.684

Total hospital 

days

Mean (SD)

14.0

(26.4)

16.2

(35.1)

0.248 11.4 

(23.4)

6.1 

(10.8)

0.009 12.8 

(25.2)

11.4 

(24.9)

0.704

ED visits

Mean (SD) 8.9 (11.6) 8.4 

(10.9)

0.419 10.3 (9.9) 9.5 (8.1) 0.367 7.6 (8.2) 10.2 

(13.3)

0.100

Psychiatric 

hospitalizations

Mean (SD) 0.6 (1.1) 0.5 (0.9) 0.115 0.4 (1.0) 0.6 (1.3) 0.226 0.3 (0.7) 0.4 (1.0) 0.552

Outpatient visits

Mean (SD) 40.4 

(29.3)

39.7 

(34.8)

0.770 6.6 (10.6) 7.0 

(12.8)

0.749 8.6 (6.1) 6.6 (5.3) 0.152

Costs of care 

(dollars)

Mean (SD)

61,185

(38,599)

58,272

(54,789) 0.261

29,086

(45,012)

29,436

(104,006) 0.963

30,432

(n/a)

29,616

(n/a) n/a

**Data for age, gender, and race for CT is based on an earlier analysis with an N=526. We do not expect these 

numbers to change dramatically with the addition of the remaining cases. 

*Note that data on race was not available in the MI dataset 

With the exception of medical hospitalizations and hospital days in MI, there were no 

statistically significant differences in demographic characteristics or healthcare utilization in the 

year prior to randomization between the treatment and control groups for any of the sites. There 

were also no statistically significant differences between random assignment groups in average 

costs for CT or MI in the year prior to randomization between the treatment and control groups. 

There was, however, a large difference between average costs for each site; MI sample members 

accrued average costs of approximately $30,000 in the year prior to the intervention, SF about 

$30,000, and a little more than $60,000 in CT. We expect that this difference reflects the fact that 

our CT cost data captures all (paid) health system costs regardless of provider or category of 

utilization (which include all primary and specialty, medications, etc.), whereas our MI cost data 
New York University CSH-SIF Evaluation Final Report 

Evaluation Team August 2017 

Page 12 of 34 

captures utilization at two hospitals only. This difference may also reflect higher illness severity 

levels in the CT sample. In thinking about these numbers, it is important to remember that the 

size of the homeless population varies dramatically across these locales. The severity of illness 

was higher in CT which may be due to the fact that the program in MI was pulling from a 

smaller pool of eligible individuals, thereby representing a larger share of the homeless 

population more generally. In SF, where there is a strong safety net, it may be that individuals 

had a high level of access to care prior to engagement with the CSH-SIF program. 

Table 5 displays the burden of specific types of health conditions among the full randomized 

sample in CT, MI, and SF during the baseline period. The percentages in the table represent the 

proportion of those randomized in each site who had at least one visit in the baseline period with 

the selected outcome listed as the primary diagnosis. For example, 41% of randomized 

individuals in CT had at least 1 visit during the baseline period where alcohol use was listed as 

the primary diagnosis, compared to 36% in MI and 23% in SF. 

Table 5 reveals the deep medical needs of the CSH-SIF study population. The vast majority of 

those randomized had at minimum of 1 chronic disease diagnosis at baseline (81%-91%). 

Unsurprisingly, mental health and substance use issues were also common. Approximately half 

of the individuals targeted for randomization in MI and SF had a mental health visit in the 

baseline period, compared to 88% in CT. A similar trend was seen for any substance use (alcohol 

or illicit drug) across the 3 sites (72% in CT, 44% in MI, 42% in SF). While 6 in 10 randomized 

individuals in CT were “triple diagnosed” (substance use, mental health, and chronic disease), 

this percentage was lower in MI (18%) and SF (28%). 

Differences across the sites also emerged when looking at the burden of chronic disease as 

measured in this way. While approximately 60% of those randomized in CT and SF were 

diagnoses with 3 or more different types of chronic disease at baseline, this percentage drops to 

28% in MI. Further, we observe that MI has consistently lower rates of individual chronic 

conditions. These findings are consistent with the far lower average costs of health care at 

baseline in MI. 

Table 5. Alcohol, drug, mental health, and chronic disease diagnoses at baseline, by site 

and random assignment 

CT

(N=809)

MI

(N=405)

SF

(N=199)

Alcohol use 41% 36% 23%

Illicit drug use 59% 13% 27%

Any substance use (alcohol or drug) 72% 44% 42%

Mental health 88% 49% 53%

Any chronic disease 91% 81% 91%

Alcohol/drug and mental health 65% 22% 29%

Alcohol/drug, mental health, and any chronic 

disease

60% 18% 28%

Only 1 chronic disease 14% 27% 10%

2 chronic diseases 21% 26% 22%

3+ chronic diseases 57% 28% 60%

Cancer 6% 3% 3%

Liver 19% 6% 16%
New York University CSH-SIF Evaluation Final Report 

Evaluation Team August 2017 

Page 13 of 34 

Asthma 11% 7% 6%

Other lung 58% 34% 59%

Cardio 51% 33% 57%

Renal 6% 3% 6%

Diabetes 20% 12% 10%

HIV 10% 2% 16%

Arthritis 52% 33% 63%

Time to housing for those in the treatment group varied between sites. Less than half of the 

treatment group had been successfully located and engaged by program staff six months after 

randomization across the three sites (Table 6). A low “take-up” rate was expected. As previously 

mentioned, we anticipated that it would be difficult for teams to locate and engage homeless 

individuals off of a list and therefore the number of individuals randomized to the treatment 

group far exceeded program capacity. Indeed, this proved more difficult and took longer than 

anticipated. There was larger variation at the six month point in terms of actual housing, with 

50% (n=47) of the treatment group in SF housed, compared to 15% (n=71) in CT and 17% 

(n=41) in MI. Average time to housing from random assignment was 4.2 months for SF, 7.0 

months for CT, and 9.1 months for MI. By the end of the follow-up period, (18 months post 

randomization), approximately one third of clients randomized to the treatment group were 

located and housed in CT (31%, n=148) and MI (33%, n=79), compared to half of the treatment 

group in SF (50%, n=47). Of note, by the end of the program period, all program slots were filled 

though not necessarily in time to be included in the evaluation. 

Table 6. Treatment group’s identification and housing uptake at 6 months and 18 months after random 

assignment 

Connecticut

(n=481)

Washtenaw County, 

MI

(n=242)

San Francisco

(n=95)

6

months

18

months

6

Months

18

months

6

months

18

months

Percentage located and 

successfully engaged by program 

staff 

30.8%

(n=148)

35.6%

(n=171)

40.8%

(n=99)

66.1%

(n=160)

49.5%

(n=47)

49.5%

(n=47)

Percentage housed 14.8%

(n=71)

30.8%

(n=148)

16.9%

(n=41)

32.6%

(n=79)

49.5%

(n=47)

49.5%

(n=47)

Data Collection and Measurement 

Program Tracking Sheet 

Each site completed a site level client tracking sheet which they submitted to CSH on a quarterly 

basis. This was only used for the purpose of tracking entries and exits from housing. 
New York University CSH-SIF Evaluation Final Report 

Evaluation Team August 2017 

Page 14 of 34 

Participant Survey 

Survey data was collected from program participants only. Though we thoroughly explored the 

possibility, either the sites nor the evaluation team had the resources that would have been 

required to survey control group members. In CT, MI, and SF, this would have required locating, 

consenting, and surveying homeless individuals off of the match lists. Furthermore, because we 

were most interested in the pre/post analysis to assess statistically significant changes over time, 

we would have had to go through the process of locating and surveying this group of individuals 

– many of whom likely would not have had reliable contact information – again one year after 

the first survey. Ultimately, though it would have been useful, this did not seem feasible. At 

baseline, the survey was administered to program participants by front line program staff at the 

time of housing. We were not concerned about biased responses because, at this early stage, 

participants had not yet established a relationship with front line program staff. However, this 

was not the case with the follow up survey, which directly asked about participants’ views of the 

program including staff members. Therefore, at follow-up, sites utilized individuals outside of 

the program (e.g. SF used graduate student interns) to administer the survey. 

Survey measures included a broad range of variables intended to capture information on 

measures that we knew we could not get from the administrative data. Topic areas included 

socio-demographic variables, history of homelessness and engagement with the criminal justice 

system, health-related behaviors such as smoking and alcohol and drug use, quality of life, 

mental health, overall health status, and access to medical care. 

Cost Effectiveness 

During year 4 of the demonstration project, the evaluation team worked with sites to acquire and 

assess the detailed operating costs of the established program. We requested line item budgets 

that represented the actual operating costs, including but not restricted to the expenses covered 

through CSH-SIF grant funding, match sources, as well as other leveraged resources. Our team 

initially approached the directors of four service provider agencies (Avalon Housing, TNDC, 

Columbus House in CT and Housing Works in LA), who subsequently designated individuals - 

the CSH-SIF project manager(s) in SF, MI, and LA and the CFO in CT - to work directly with 

our team to fulfill the request. In both CT and LA, rather than collecting budgets on all the 

implementing sites, the NYU team requested budgets from the regional site that best reflected 

the services to be provided through the program model. The evaluation team reviewed initial 

budgets and then worked with the contacts at each site to further refine the information, asking 

clarifying questions and requesting missing information as needed. For example, many sites did 

not initially include an estimate of overhead costs or administrative staff time and therefore had 

to estimate these costs in subsequent drafts of the budget. Once a comprehensive budget was in 

hand, the evaluation team worked to identify common spending buckets across sites, ultimately 

including: personnel, fringe, client needs/activities, case manager travel expenses (this also 

includes patient navigator travel expenses where applicable), staff supplies, overhead, other 

direct costs, and housing related costs. These common categories emerged through careful 

review of the site-specific budgets and include all of the detailed line item expenses provided by 

sites. Once estimated, program costs were compared to estimated cost savings from changes in 

shelter and jail time by site to the extent that data were available. 
New York University CSH-SIF Evaluation Final Report 

Evaluation Team August 2017 

Page 15 of 34 

Impact Analysis 

The administrative data used for the impact analysis was collected on treatment and control 

groups, and data sources varied by site. In each of the three sites included in this study, 

administrative health care data was collected for all individuals in the treatment and control 

groups for the year prior to and after random assignment. Administrative data on days in jail 

were collected from the Washtenaw County Sheriff's Office (MI), and the Course Case 

Management System (CCMS) database in SF. Homeless Management Information System 

(HMIS) data on emergency shelter utilization were collected from the Washtenaw County Office 

of Community and Economic Development (MI), and Nutmeg Consulting (CT). The CT 

Department of Social Services provided Medicaid claims records of health care utilization. 

Administrative data from MI was obtained from the two major hospitals serving the area 

(University of MI and Saint Joseph’s), which comprised hospital, emergency department, and 

outpatient department billing records. Finally, health care utilization data for SF came from the 

San Francisco Health Plan, which is a Medi-Cal (California Medicaid) managed care provider. 

The main outcomes for the impact analyses were measured during the baseline period (the 12 

months prior to random assignment) and during the follow-up period (months 6 to 18 after 

random assignment). The six-month lag for the follow-up period allowed for a period of 

outreach, recruitment, housing placement, and for the intervention to begin to take effect (i.e. for 

clients to be housed and to begin receiving case management services). It also allowed the MI 

and CT sites to “catch up” in their housing participation rates to the much faster housing 

placements that occurred in SF. 

Outcomes included the following: 

 Number of medical hospitalizations.

 Number of hospitalizations related to psychological/psychiatric/behavioral factors. These 

include any hospitalizations with principal diagnoses of psychiatric disease, or drug or 

alcohol abuse.

 Number of days in hospital.

 Number of emergency department (ED) visits (not including ED visits on the day of a 

hospital admission, since these ED visits are inconsistently billed).

 Number of non-ED outpatient (OPD) visits. 

 Costs of care. In CT, “costs” were total payments by Medicaid. In MI, “costs” were 

imputed from charges using 0.3 as the cost-to-charge ratio (i.e., costs were considered to 

be 30% of charges). This ratio is slightly less than each hospital’s 2015 CMS-reported 

Medicare cost-to-charge ratio (which ranged from 0.33 to 0.36), in order to account for 

the somewhat less generous payments made by Medicaid. Since payment or charge data 

were unavailable in SF, for some analyses we imputed costs by assigning 2011-12 San 

Francisco Department of Public Health Medicare reimbursement rates to medical 

hospitalizations ($2072/day), psychiatric hospitalizations ($627/day), ED visits 

($482/visit), and outpatient visits ($199/visit).

 Days in jail 

 Nights in emergency shelter 
New York University CSH-SIF Evaluation Final Report 

Evaluation Team August 2017 

Page 16 of 34 

In order to limit the influence of outliers, outcome measures were top-coded at the 95th

percentile. 

We also used data on medical utilization to create subcategories of clients and care. Supportive 

housing is generally thought to reduce health care costs and utilization by reducing acute 

incidents of disease and by allowing chronic disease to be better managed through more stable 

living and improved access to primary care. Likewise the initial thought was also that some 

diseases and their care are much less likely to be affected by a change in housing status. Further, 

the first year of stability in supportive housing may bring about case finding of many serious 

conditions, previously neglected or undiagnosed. Therefore, using information gathered from our 

site visits and in consultation with two physicians directly involved in the care and oversight of 

services for homeless and other vulnerable populations, we identified categories of disease 

conditions that might be differently affected by supportive housing within our study’s time 

frame. These subgroups include: 

 Theorized “Housing-sensitive” conditions. The incidence of, and thus utilization for, 

these conditions was hypothesized to decrease with the receipt of housing. These 

conditions include dermatological conditions, physical trauma, injuries, poisonings, and 

TB.

 Theorized “Care management sensitive” conditions. These are conditions for which 

emergent utilization (inpatient hospitalization and ED) might be reduced due to the 

receipt of supportive housing and better care management, even as non-emergent (i.e., 

OPD) care might increase. Similar to “ambulatory care sensitive” conditions, these 

include diabetes, asthma, and mental and behavioral health conditions including alcohol 

and substance abuse. 

 “Silent” conditions. These are conditions that can remain relatively asymptomatic for 

extended periods of time, so we might expect to see that the support and improved care in 

a supportive housing setting would lead to new diagnoses of these conditions in a 

recently housed population. Treatment of many such conditions can be costly. These 

include cancer, renal failure and kidney disease, diabetes, hepatitis and liver disease, 

cardiovascular disease and hypertension. 

 Serious chronic conditions. These are serious conditions that, if already diagnosed in the 

homeless, are likely to continue driving significant utilization after individuals were 

housed. These include cancer, dialysis treatment, and neurological conditions like 

Multiple Sclerosis and Parkinson’s. Reduced utilization for such conditions can come 

only through changes in the pattern of utilization rather than reduced incidence or 

medical need. 

The above conditions were identified by categorizing the principal diagnoses from all utilization 

events using AHRQ Clinical Classification Software (CCS) categories. 

Key Research Questions 

While the data collected throughout the course of this evaluation are suitable to address many 

more questions, the focus of the evaluation was on answering a small number of key questions: 
New York University CSH-SIF Evaluation Final Report 

Evaluation Team August 2017 

Page 17 of 34 

 Is it possible to effectively target and engage the kinds of homeless high utilizers for 

whom this program was intended and provide them with the type of supportive housing 

that was thought likely to be effective? 

 How does the program vary by site, and what factors influence (both facilitate and 

impede) program implementation? 

 If housing incorporated supports oriented specifically to the needs of homeless high 

utilizers of the health care system, would we see impacts on health care utilization – that 

is reductions beyond what likely would have happened even without the program? 

 Would these impacts prove sufficient to cover the costs of the program? 

Although these questions are relatively straightforward, the answers are quite complex, as 

detailed in the findings and discussion sections.

Counterfactual Condition 

The evaluation team utilized comparison groups for the impact analyses. Those identified for 

randomization into treatment and control groups were to represent the most medically costly of 

homeless individuals in their locale. Although the specific data-driven approach of targeting 

clients differed by sites, each was highly effective in identifying individuals who were homeless 

and high cost users of health care. Individuals were systematically randomized into treatment and 

control groups by first sorting individuals in order of total costs or ED utilization in the prior 

year, then randomly selecting either even or odd rows in the sorted list to be assigned to each 

group. This method served to create comparable treatment and control groups. Of note, in SF, 

the individuals included in the RCT represent somewhat less than half of those served by the 

program. An additional 122 individuals considered medically vulnerable were also included in 

the program but not in the RCT. Impact analyses are restricted to those in the RCT but the 

survey data that are presented were obtained from both groups. An a priori power analysis for 

cost impacts was conducted early in the evaluation planning phase. Assuming program and 

control group n’s of 549 each, average costs of $25K in the baseline year with a standard 

deviation of $25K, a difference-in-differences design with a pre/post correlation of 0.7, a twosided test with alpha=0.05, and power of 85%, we estimated the minimum detectable effect as a 

$3,500 change in cost relative to the control group. 

Due to the expected difficulties of locating those who had been targeted for treatment and the 

strategic oversampling to address this challenge, more than half of the treatment group were 

never enrolled in the program and were unlikely to have received housing in this relatively short 

time period or with the same degree of medically-oriented support. This less-than-full 

participation rate limits the power of our analyses by attenuating intent-to-treat impacts toward 

zero, such that even large declines in utilization among those who received the treatment may not 

be sufficient to reach statistically significant impact. Therefore, in addition to our RCT analyses 

of impacts, average treatment effects on the treated (TOT) were also estimated, using 1:1 pair 

matching of each housed individual with the control group member who was most similar in 

terms of their log of total cost, number of hospitalizations, and number of ED visits within a 12-
New York University CSH-SIF Evaluation Final Report 

Evaluation Team August 2017 

Page 18 of 34 

month period prior to randomization.3 This alternative analytic strategy is intended to help 

address the limited participation rate. In these analyses, the follow-up period matches the first 12 

months housed, rather than months 6-18 after randomization.

FINDINGS 

Site Visits 

Each of the four participating sites successfully used a data-driven approach to engage a client 

population who had been homeless and had high levels of health care utilization in the year prior 

to being targeted for the program. That said, the three sites that used a match list approach for 

identifying potential clients found the approach to be slow, cumbersome, and resource intense. 

Using administrative data to identify a list of those at highest cost, assigning them to treatment or 

control groups, and then finding and engaging those on the treatment list was effective in both 

getting the “right” people into the program and in allowing for a robust evaluation. However, 

this approach appears difficult, and perhaps impossible, to sustain without dramatically increased 

capacity and resources at the site level and such an effort may not be justified and may not be a 

desirable strategy for all service providers.). However, attention should be made to ensure that 

those who are, indeed, both homeless and high utilizers are served; the use of clear guidelines is 

critical in making these determinations. 

While each of the sites were able to fill and place the allocated program slots this took far more 

time than originally anticipated in all sites but for San Francisco. The success of the program 

across all sites rests, in no small part, on the availability of appropriate and affordable housing 

units, whether in specially designed facilities (as in SF) or in the private scattered site housing 

markets. Further, without housing vouchers, placement in the private market is impossible. 

Breaks in the availability of vouchers (for example, due to the federal sequestration) resulted in 

huge delays in placement, especially in MI and LA.

It is important to remember that federal policies are further shaped by state policies and are 

implemented in a local context. Medicaid policies, which vary greatly from state to state, are of 

considerable importance to understanding variations in this program. For example, In 

Washtenaw County the overwhelmingly main source of mental health services is the county 

mental health system; private providers who take Medicaid are extremely rare in the county. But 

people with a primary diagnosis of substance abuse are excluded from the county mental health 

 

3 Matches were required to be within .2 standard deviations of each of these three quantities, and the selected match 

was the control group member with the smallest sum of differences in these three standard deviation scores. ZIP 

models were then estimated for the impacts of supportive housing on the number of hospitalizations and the number 

of ED visits within 12 months of the date of move-in. OLS models were fitted for impacts on total costs for that 

same period.
New York University CSH-SIF Evaluation Final Report 

Evaluation Team August 2017 

Page 19 of 34 

system and must receive services through one of the two substance abuse programs in the 

county. Respondents felt that there was, moreover, a bias towards making substance abuse the 

primary diagnosis when there were co-occurring diagnoses of mental illness and substance 

abuse. Further, the local context shaped the nature of the population served, the kinds of health 

problems they faced, the housing that was available to these clients and the health services 

available in the surrounding community. This was not the case in the other sites. Implementing 

this program was, at first, challenging to MI because they had no prior experience with housing 

first models. Again, other sites had a “leg up” in this regard. We should expect a good deal of 

variation across states and localities in adopting or, more accurately, adapting the SIF model. 

Participant Survey 

Without a comparison group, we don’t really know whether and to what degree the observed 

changes among participants would have occurred without supportive housing; however, the 

survey data that each site collected as part of the demonstration captured clients’ perspectives 

before housing and after a period of one year in the program. The differences in their responses 

from pre-test to post-test strongly suggest that providing such housing to a medically-frail 

population greatly improves the quality of their lives and their access to care, even as their 

significant health problems and conditions remain.

The data presented represents responses of only the subgroup of the clients for which both a 

baseline and follow-up survey were available. Yet, as previously discussed, we are reasonably 

confident that these respondents are a good reflection of all the clients served since those with 

baseline and follow up surveys look quite similar on a number of key characteristics to the entire 

client group and to those who only have baseline interviews. 

After a year of housing, many clients feel much better about their lives, with noticeable and 

statistically significant improvements among many of the variables capturing quality of life and 

mental health (Table 7).

Table 7. Overall pre/post changes in mental health and quality of life indicators (N=238) 

Baseline Follow-up Pre/post change

Bothered by medical problems in 

past 30 days*

Not at all

Slightly/moderately

Considerably/Extremely

21% (48)

31% (72)

48% (111)

36% (83)

29% (67)

35% (81)

Better 

Remained unbothered

Remained bothered

Worse

36% (83)

13% (31)

36% (83)

15% (35)

Feelings towards overall life*

Negative

Mixed

Positive

30% (70)

32% (74)

38% (89)

19% (44)

23% (50)

60% (139)

Better 

Remained positive

Remained 

mixed/negative

Worse

40% (93)

26% (60)

18% (41)

17% (39)
New York University CSH-SIF Evaluation Final Report 

Evaluation Team August 2017 

Page 20 of 34 

Frequency of feeling nervous, 

tense, worried, frustrated, or afraid 

in past month*

Not at all

Once/several times a month

Several times a week/every day

15% (35)

34% (78)

51% (119)

19% (44)

45% (105)

36% (83)

Better 

Remained infrequent

Remained 

weekly/daily

Worse

35% (80)

22% (52)

22% (52)

21% (48)

Frequency of feeling depressed or 

sad in the past month

Not at all

Once/several times a month

Several times a week/every day

13% (31)

42% (96)

45% (104)

23% (52)

42% (96)

36% (83)

Better 

Remained infrequent

Remained 

weekly/daily

Worse

30% (70)

30% (70)

24% (55)

16% (36)

Frequency of feeling lonely in the 

past month

Not at all

Once/several times a month

Several times a week/every day

22% (51)

35% (81)

44% (102)

30% (70)

30% (69)

41% (95)

Better 

Remained infrequent

Remained 

weekly/daily

Worse

31% (72)

23% (54)

24% (56)

22% (52)

Feels life is organized* 45% (100) 69% (154) Better 

Remained organized

Remained unorganized

Worse

30% (68)

38% (86)

25% (56)

6% (14)

Feels life is stable* 54% (123) 80% (184) Better 

Remained stable

Remained unstable

Worse

36% (83)

44% (101)

10% (24)

9% (22)

*Significant difference, baseline to follow-up, at p < 0.05 

After a year of housing, there were statistically significant improvements across all access to care 

measures (Table 8). 

Table 8. Overall pre/post changes in access to healthcare services (N=238) 

Baseline Follow-up Pre/post change

Usual source of care*

No usual source of care

ED usual source of care

Medical office is usual 

source of care

5% (12)

31% (73)

64% (149)

8% (18)

16% (37)

77% (179)

Better

Continued medical 

Continued ED/no usual 

Worse

24% (55)

53% (125)

10% (23)

13% (31)

Difficulty finding a doctor 

in past 12 months*

23% (52) 13% (31) New access

Kept access

Continued problems

Lost access

16% (36)

71% (163)

7% (16)

6% (15)

Needed a dentist in past 12 

months but unable to see 

one*

56% (129) 35% (81) New access

Kept access

Continued no access

Lost access

30% (68)

35% (81)

27% (61)

9% (20)

*Significant difference, baseline to follow-up, at p < 0.05 
New York University CSH-SIF Evaluation Final Report 

Evaluation Team August 2017 

Page 21 of 34 

From pre-test to post-test, the majority of clients held constant in terms of activities of daily 

living – that is, there were relatively small positive changes in general but, also, few reported 

being worse off (Table 9).

Table 9. Overall pre/post changes in health status and related conditions (N=238) 

Baseline Follow-up Pre/post change

Self-rated Health

Excellent/Very Good

Good

Fair/Poor

10% (24)

27% (65)

63% (149)

15% (35)

22% (53)

63% (149)

Better

Stayed excellent/v.g./good

Stayed fair/poor

Worse

21% (50)

14% (33)

48% (114)

17% (40)

Number of not good physical 

health days in past 30

0

1-14

15-29

30

Mean (SD)

25% (56)

28% (63)

11% (25)

36% (80)

14.0 (13.1)

31% (69)

29% (65)

7% (16)

33% (74)

12.1 (13.2)

Better

Remained less than every 

day

Remained every day

Worse

32% (72)

26% (59)

18% (41)

23% (52)

Any serious medical problems 85% (199) 81% (190) Better 

Continued no problems

Continued medical 

problems

Worse

10% (23)

9% (21)

75% (176)

6% (14)

Difficulty walking/climbing 

stairs

62% (144) 60% (138) Better 

Continued no difficulty

Continued difficulties

Worse

13% (30)

28% (65)

49% (114)

10% (24)

Difficulty dressing/bathing 25% (59) 24% (55) Better 

Continued no difficulty

Continued difficulties

Worse

12% (28)

65% (151)

13% (31)

10% (24)

*Significant differences, baseline to followup, at p < 0.05 

As with client health status and conditions, most people reported stable substance use over the 

course of their engagement with the program, with a relatively small proportion of clients 

stopping or starting during their time in the program (Table 10). The exception to this was for 

illicit, non-prescription drugs, for which use declined by nearly 10% over the course of the study 

period. 

Table 10. Overall pre/post changes in substance use (N=238) 

Baseline Follow-up Pre/post change

Used alcohol regularly in the 

past year

39% (91) 33% (77) Stopped

Remained non-drinker

Remained drinker

Started drinking

13% (29)

54% (126)

27% (62)

6% (15)

Used marijuana regularly in the 

past year

20% (47) 25% (58) Stopped

Remained non-user

6% (13)

69% (160)
New York University CSH-SIF Evaluation Final Report 

Evaluation Team August 2017 

Page 22 of 34 

Remained user

Started using

15% (34)

10% (24)

Used other illegal or nonprescribed drugs regularly in the 

past year*

23% (52) 14% (33) Stopped

Remained non-user

Remained user

Started using

15% (34)

71% (162)

8% (18)

7% (15)

Current smoker 67% (158) 70% 

(166)

Stopped

Remained non-user

Remained user

Started using

1% (3)

28% (67)

66% 

(155)

5% (11)

*Significant difference, baseline to followup, at p < 0.05 

Clients appreciated the program, reporting that the program largely met all or most of their needs 

and that their quality of life improved since entering the program (Table 11)

Table 11. Satisfaction with the SIF program overall 

Overall

(n=329)

CT

(n=102)

LA

(n=54)

MI

(n=62)

SF

(n=111)

SIF program has met all or 

most of my needs* 90% (287) 96% (98) 94% (50) 95% (55) 80% (84)

Would recommend the SIF 

program to a friend* 95% (294) 98% (95) 94% (50) 100% (58) 89% (91)

Would come back to the 

SIF program if you were 

seeking help again* 94% (297) 97% (99) 98% (52) 100% (58) 85% (89)

My quality of life has 

improved since entering 

the program* 89% (293) 96% (98) 93% (50) 93% (57) 79% (88)

*Significant difference across sites at p < 0.05 

While clients reported improvements in general wellbeing and access to care, and were highly 

satisfied with the CSH-SIF program, the majority of respondents reported the continued 

existence of serious medical conditions one year into the program. This is in line with the high 

number of chronic conditions present at baseline. So, while tenants may have better care their 

underlining chronic conditions persist. Moreover, habits around substance use improved for 

relatively few clients. 

Impact Analysis 

Program Impacts on Utilization Categories 

Table 12 presents changes in health care utilization among treatment and control groups in the 

year following random assignment. Substantial reductions are observed among both groups. This 

finding is not surprising given the cyclical nature of health care utilization and it highlights the 

need to evaluate such programs using a control or strong comparison group. In the pooled 

analyses, we observed negative point impacts in the number of medical hospitalizations, 
New York University CSH-SIF Evaluation Final Report 

Evaluation Team August 2017 

Page 23 of 34 

psychiatric hospitalizations, and outpatient visits for the treatment group. However, these 

differences were not statistically significant. 

Table 12. Program impacts for the period 6-18 months after random assignment 

Outcome Site

Predicted number of events in the 

post-period Program effect

Treatment

(95% CI)

Control

(95% CI)

Difference

(95% CI) p-value

Medical hospitalizations

Pooled 1.64 

(1.45, 1.82)

1.62 

(1.42, 1.81)

0.92 

(-0.24, 0.27) 0.890

CT 2.11 

(1.83, 2.40)

2.00 

(1.71, 2.29)

0.11 

(-0.28, 0.50) 0.571

MI 1.10 

(0.88, 1.32)

0.97 

(0.67, 1.26)

0.13 

(-0.21, 0.47) 0.452

SF 0.73 

(0.47, 0.99)

1.36 

(0.97, 1.74)

-0.62 

(-1.08, -0.17)

0.006

Total hospital days

Pooled 7.71 

(6.36, 9.06)

7.92 

(6.47, 9.37)

-0.21 

(-2.14, 1.73) 0.833

CT 10.00 

(7.80, 12.21)

9.11 

(7.00, 11.23)

0.89 

(-2.09, 3.87) 0.559

MI 4.71 

(3.65, 5.76)

4.76 

(2.63, 6.89)

-0.05 

(-2.36, 2.26) 0.966

SF 4.25 

(2.00, 6.49)

9.47 

(5.97, 12.97)

-5.22 

(-9.33, -1.12) 0.013

ED visits

Pooled 4.78 

(4.38, 5.19)

4.44 

(3.99, 4.88)

0.34 

(-0.22, 0.91) 0.232

CT 4.79

(4.27, 5.30)

4.23 

(3.72, 4.75)

0.55 

(-0.11, 1.22) 0.104

MI 4.88 

(4.16, 5.60)

4.53 

(3.57, 5.49)

0.35 

(-0.80, 1.50) 0.551

SF 4.57 (

3.18, 5.96)

5.13 

(3.79, 6.48)

-0.56 

(-2.38, 1.25) 0.418

Psychiatric 

hospitalizations

Pooled 0.27 

(0.22, 0.32)

0.29 

(0.23, 0.35)

-0.02 

(-0.09, 0.06) 0.667

CT 0.43 

(0.34, 0.53)

0.44 

(0.33, 0.55)

-0.01 

(-0.15, 0.13) 0.899

MI 0.12 

(0.08, 0.16)

0.12 

(0.07, 0.17)

0.00 

(-0.06, 0.06) 0.945

SF 0.13 

(0.03, 0.22)

0.20 

(0.09, 0.31)

0.08 

(-0.22, 0.07)

0.308

Outpatient visits

Pooled 20.82 (19.23, 

22.41)

21.11 

(19.26, 22.96)

-0.56 

(-4.02, 2.90) 0.782

CT 32.57 

(30.20, 34.95)

33.14 

(30.26, 36.01)

-3.78 

(-9.24, 1.69) 0.751

MI 4.45 

(3.73, 5.17)

3.67

(2.94, 4.39)

0.79 

(-0.11, 1.68) 0.087

SF 5.11 

(3.18, 7.04)

6.50 

(4.91, 8.10)

-1.39 

(-3.77, 0.99) 0.108
New York University CSH-SIF Evaluation Final Report 

Evaluation Team August 2017 

Page 24 of 34 

Costs

Pooled

$36,782

($32,775, 

$40,788)

$39,046 

($30,763, 

$47,329)

-$2,265

(-$11,465, 

$6,936)

0.630

CT

$44,752

($39,352, 

$50,153)

$45,610

($37,906, 

$53,313)

-$858

(-$10,265, 

$8,550)

0.858

MI

$20,620

($15,401, 

$25,839)

$25,738

($6,121, 

$45,356)

-$5,118

(-$25,418, 

$15,181)

0.621

SF $16,749 $28,405 -$11,656 n/a

At the site level, we observed statistically significant impacts for both the number of 

hospitalizations (-0.62, p=0.006) and total number of hospital days (-5.22 per person, p=0.013) 

for SF. In the other sites and in regard to other measures of utilization, the treatment group 

typically experienced greater reductions than those in the control group, but these differences are 

not statistically significant. It is worth recalling that SF was able to locate and house 

approximately 50% of those in the targeted treatment group, as compared to only one-third in CT 

and MI. We will return to this in our discussion of findings from the TOT analysis.

Program Impacts on Medical Costs 

As previously noted, average costs in the year before random assignment were about $60,000 in 

CT and about $30,000 in MI. (Our imputed estimate of baseline costs in SF was approximately 

$30,000). As can be seen in Table 13, costs for both the treatment and control groups in CT and 

MI fell in the follow-up period. Further, while the point estimate of the overall cost impact in 

each site was negative, representing cost savings, neither estimate in CT or MI was statistically 

significant (-$5,049, p=0.331 in CT, -$2,394, p=0.422 in MI). In SF, the program impact on 

imputed costs in the follow-up period was -$11,656. This larger cost decrease in SF reflects the 

larger estimated reduction in hospitalizations in that site. 

Table 13 presents findings regarding the relationship between program impacts and baseline 

costs in MI and CT, the only sites in which it was possible to do so. Cost impacts varied with the 

level of costs incurred prior to program enrollment, as evidenced by statistically significant 

interactions between pre-period costs and the treatment indicator in our models. Figures 1 and 2 

are visual representations of this relationship. In each site, lower costs prior to enrollment were 

associated with positive and statistically significant cost impacts. In other words, those high 

utilizers who were in the lower range of cost at program entry saw net increases in health care 

costs after engaging in the program. In contrast, those most expensive at baseline randomized to 

treatment saw net decreases in cost relative to the controls; this was not, however, statistically 

significant. Of course, the relatively small number of cases within the 90th percentile and the 

relatively high degree of variance for health care costs make it difficult to achieve statistical 

significance. We will also explore these findings by adjusting, in future analyses, for individuals 

who were no longer eligible for Medicaid and/or no longer lived in the region. Such adjustments 

were not possible within the grant period. 
New York University CSH-SIF Evaluation Final Report 

Evaluation Team August 2017 

Page 25 of 34 

Table 13. Program impacts on cost of care in CT and MI for the period 6-18 months after random 

assignment, at selected percentiles of pre-period costs 

Average costs Program effect

Site Program impact on costs at the ... Treatment

(95% CI)

Control

(95% CI)

Difference

(95% CI) p-value

CT

... first quartile of pre-period costs

$33,920

($29,746, 

$38,095)

$27,129

($23,548, 

$30,710)

$6,791

($1,291, 

$12,291)

0.016

... third quartile of pre-period costs

$49,176

($43,838, 

$54,514)

$49,171

$43,065, 

$55,277)

$5

(-$8,104, 

$8,115)

0.999

... 90th percentile of pre-period 

costs

$65,094

($53,808, 

$76,381)

$77,041

($60,853, 

$93,228)

-$11,946

(-$31,680, 

$7,788)

0.235

MI

... first quartile of pre-period costs

$10,154

($7,901, 

$12,407)

$5,768

($4,364 

$7,173)

$4,386

($1,730 

$7,041)

0.001

... third quartile of pre-period costs

$19,673

($15,532, 

$23,814)

$19,442

($13,673, 

$25,212)

-$231

(-$6,871, 

$7,332)

0.949

... 90th percentile of pre-period 

costs

$36,669

($26,002, 

$47,336)

$43,858

($28,697, 

$59,019)

-$7,188

(-$25,726, 

$11,349)

0.447

Results are predicted values derived from site-specific generalized linear models (GLMs) of post-period costs 

regressed on pre-period costs, a program group indicator, and their interaction. GLMs utilized a gamma 

distribution for each site, a log link for CT, and an identity link for MI. All terms in both site-specific regressions 

were statistically significant at the .01 level. 
New York University CSH-SIF Evaluation Final Report 

Evaluation Team August 2017 

Page 26 of 34 

Figure 1. 

Figure 2. 
New York University CSH-SIF Evaluation Final Report 

Evaluation Team August 2017 

Page 27 of 34 

Program Impacts by Disease Categories 

We found no statistically significant impacts in regard to the theorized “housing sensitive 

conditions” (e.g., trauma), which were anticipated to have lowered incidence among those 

housed. While the medical needs of those with serious and expensive chronic conditions, like 

renal failure, cannot be eliminated through the program and will remain costly regardless of 

housing status, better management of care might be expected to result in some shifts in 

utilization patterns. While only relatively small proportions of the study sample entered the 

program with these conditions – 7% in MI, 16% in CT, and 34% in SF -- we do find some 

statistically significant impacts for them. SF program group members with serious chronic 

conditions had reduced hospitalizations and outpatient visits (both statistically significant). CT 

program participants also had somewhat reduced costs (though not significantly so). MI program 

group members, however, had post-intervention period utilization and costs very similar to those 

of control group members. 

Program Impacts among the Treated 

Impacts of the treatment on the treated are presented in Table 14. Using the matching procedure 

previously described, 159 (99%) of 161 program group members who were housed early enough 

to observe at least 12 months of follow up were successfully matched. Very high post-matching 

balance was achieved: mean program group values were within .25% of the mean comparison 

group values for pre-period ED visits, within .36% for the log of total pre-period costs, and 

within .7% for the number of hospitalizations. In CT, program impacts for housed participants 

were a significant -.53 hospitalizations (p=0.041), a marginally significant -1.0 ED visit 

(p=0.071), and a non-significant -$3,539 in costs (p=0.4812) in the 12-month follow-up period. 

In SF, we observed a significant decrease of 0.56 medical hospitalizations (p=0.023), and a 

statistically significant reduction in ED visits (-2.47, p<0.001). We did not observe statistically 

significant changes in utilization or cost in MI. 

Table 14. Impacts of the treatment on the treated 

Outcome Site

Predicted number of events in the 

post-period Program effect

Housed

(95% CI)

Comparison

(95% CI)

Difference 

(95% CI) p-value

Medical 

hospitalizations

CT 1.49

(1.12, 1.86)

2.02

(1.60, 2.45)

-0.53

(-1.05, -0.02)

0.041

MI 0.92

(0.62, 1.21)

0.79

(0.52, 1.05)

0.13 

(-0.25, 0.51)

0.495

SF 0.55

(0.23, 0.86)

1.11

(0.75, 1.47)

-0.56 

(-1.04, -0.08)

0.023

Outpatient visits

CT 27.96

(24.56, 31.36)

31.79

(27.97, 35.60)

-3.83

(-8.36, 0.70)

0.098

MI 4.33

(3.33, 5.32)

3.84

(2.90, 4.78)

0.49

(-0.76, 1.74)

0.440

SF 4.75

(3.44, 6.07)

5.76

(4.31, 7.21)

-1.01

(-2.88, 0.87)

0.293
New York University CSH-SIF Evaluation Final Report 

Evaluation Team August 2017 

Page 28 of 34 

ED visits

CT 4.23

(3.37, 5.09)

5.25

(4.27, 6.23)

-1.02

(-2.14, 0.09)

0.071

MI 4.88 

(3.79, 5.97)

4.95

(3.84, 6.07)

-0.07

(-1.59, 1.45)

0.927

SF 2.41

(1.52, 3.30)

4.88

(3.55, 6.21)

-2.47

(-3.90, -1.04)

<0.001

Total Costs

CT $36,329

($29,555, $43,104)

$39,868

($32,722, $47,015)

-$3,539

(-$13,386, $6,308)

0.481

MI $14,499

($10,998 $18,001)

$12,184

($9,011, $15,356)

$2,315

(-$2,197, $6,829)

0.313

Impacts on other Systems 

Although a full cost-benefit analysis was beyond the scope of our study, one goal of the 

evaluation was to assess the degree to which impacts on health care utilization –

especially hospitalizations – as well as shelter and jail utilization offset the cost of 

running the CSH-SIF program. We were able to estimate the impact of the program on 

shelter and jail utilization in SF and shelter utilization in CT (jail data not available). Due 

to a lack of available data, we were not able to assess cost effectiveness of the program in 

LA and the MI analysis was limited to program and health care and shelter costs (no jail 

data available). Table 15 presents findings from this analysis. While there were no 

statistically significant impacts on jail time in SF, we found statistically significant 

impacts on shelter use in SF (ITT and TOT analyses) as well as in CT (TOT only). The 

program did not affect shelter use in MI though, notably, there was a low level of shelter 

use among the target population at baseline – 55% of the randomized sample had no 

recorded shelter use – leaving less room for improvement relative to the other sites. This 

is also why the ITT estimated average number of shelter days in the post period looks so 

low for MI. Data on jail use for MI and CT were not available in time for this report. 

Table 15. Program impacts on shelter days* 

Outcome Site

Predicted number of events 

in the post-period Program effect

Treatment 

(95% CI)

Comparison

(95% CI)

Difference 

(95% CI) p-value

ITT

SF 7.6

(2.3, 12.9)

18.3

(10.5, 26.2)

-10.7

(-19.9, -1.4) 0.023

CT 11.6

(8.4, 14.8)

15.7

(11.3, 20.2)

-4.2

(-9.6, 1.3) 0.136

MI 0.9

(0.6, 1.3)

0.7

(0.3, 1.1)

0.2

(-0.3, 0.8) 0.398

TOT SF 12.8

(-1.5, 27.1)

42.2

(18.6, 65.8)

-29.4

(-54.7, 4.1) 0.023

CT 2.4 9.4 -7.0 0.009
New York University CSH-SIF Evaluation Final Report 

Evaluation Team August 2017 

Page 29 of 34 

(-0.02, 4.8) (4.7, 14.1) (-12.3, -1.7)

MI 1.9

(-4.1, 7.8)

8.0

(1.3, 14.7)

-6.1

(-15.1, 2.8) 0.180

*Shelter days top-coded at 95th percentile within site and time period 

Cost Effectiveness 

The cost effectiveness analysis was performed to compare impacts on health care and shelter 

systems to the overall cost of running the program. Table 16 presents program costs by site. It is 

clear from this analysis that the per-capita costs of running the CSH-SIF program vary by site in 

each category of spending. Of course, cost of living varies substantially across these sites. The 

total cost of support services category captures spending on personnel, fringe, client needs and 

activities, staff supplies, overhead, and other direct costs. The per-capita cost of support services 

was quite similar overall in CT, LA, and SF, whereas this expense was substantially lower in MI. 

The lower cost of support services in MI was driven by low expenditures on personnel and client 

needs and activities compared to the other sites. Notably, MI spent only $18 per capita on other 

than core client needs and activities compared to several hundreds of dollars in LA and SF and 

more than a thousand dollars per capita in CT for needs outside of rent and core health care 

connection or navigation. Travel expenses for case managers also varied by site and, as expected, 

were quite low in SF where clients are housed in a single building. 

Table 16. Per-capita Program Costs by Site 

Site: Columbus 

House

Housing Works Avalon Housing Kelly Cullen 

Community

Location: New Haven, 

CT

Los Angeles, 

CA

Washtenaw 

County, MI

San Francisco, CA

Year: 2015 July 2015 - June 

2016

2016 2015

Enrollment: 53 39 85 172

Personnel $3,859 $4,923 $3,593 $5,212

Fringe $1,266 $1,329 $1,074 $1,407

Client Needs and 

Activities $1,278 $762 $18 $566

Case Manager Travel 

Expenses $418 $154 $232 $7

Staff Supplies $51 $38 $174 $66

Overhead $780 $154 $52 $69

Other Direct Costs $0 $0 $22 $60

Total Cost of Support 

Services $7,653 $7,360 $5,164 $7,387

Housing Related Costs $12,936 $14,556 $10,104 $5,205
New York University CSH-SIF Evaluation Final Report 

Evaluation Team August 2017 

Page 30 of 34 

Total Per-Capita 

Program Cost $20,589 $21,916 $15,268 $12,592

These cross-site differences in costs are also consistent with our findings from the site visits. 

Although the broad strokes of the program model were consistent across sites, implementation 

varied in terms of many critical pieces, including organization and management, as well as the 

type of housing and nature and intensity of support services. These differences are reflected in 

how monies were spent in each site. 

 In CT, Rental Assistance Program housing vouchers were obtained from the state and 

readily allowed for placement into scattered site units in the private housing market. 

Patient Navigators supported clients by engaging them in the program and connecting 

them to appropriate health and social services over time. The grant funding did not 

include other case management services and the Patient Navigators played this role, 

as well, in sites that were unable to contract such services. 

 The program in LA relied heavily on case managers, who were responsible for a 

broad range of services with respect to clients, from initial engagement in the 

program, to securing housing, and connecting clients to appropriate care. As in CT 

and MI, clients relied on housing vouchers and were largely housed in scattered site 

units maintained by private landlords. In contrast to the other sites, participant 

eligibility was determined at the point of care at any one of several participating 

hospitals whereas case managers/Patient Navigators were responsible for this timeconsuming task in the other sites. 

 In MI, the initiative was undertaken by a consortium of non-profit and government 

agencies representing housing, health, and social services. Section 8, Shelter Plus 

Care, and Housing Choice housing vouchers were used to place clients in scattered 

site units in the private housing market. Service coordination and case management 

was achieved through regular and ongoing meeting of a multidisciplinary care team 

that cut across participating agencies. Case management services were intended to 

provide social and mental health support, as well as assistance in getting linked to 

health insurance and primary care; case managers typically met with each client 

several times per week. 

 SF differed from the other sites in that clients were housed in a brand-new congregate 

housing facility with a federally qualified health center (FQHC) located on the ground 

floor built specifically for the purpose of providing supportive housing to homeless 

high utilizers. This facility, named the KCC supportive housing site, was a joint 

effort for SF’s Department of Public Health and the Tenderloin Neighborhood 

Development Corporation. The on-site services team included KCC property 

management staff, one SIF project coordinator, five social workers, one nurse, one 

health worker, and one money manager. 

While sites varied in terms of organizational experience with this kind of program prior to SIF, 

all sites felt that year 3 or 4 represented their established program. Prior to that, the sites were 
New York University CSH-SIF Evaluation Final Report 

Evaluation Team August 2017 

Page 31 of 34 

focused on creating the program and making adaptations to “on-the-ground” conditions. This 

supports the notion that it takes service providers, even those with deep experience doing this 

kind of work with this population, a relatively long time to become fully operational after the 

beginning of the program implementation period. In comparing the impact of the program on 

health care and shelter utilization to the cost of the program, we see that the program costs, while 

not fully covered, are substantially offset by the cost savings stemming from reduced utilization 

of health care and shelter systems (Table 17). The majority of these savings come from 

reductions in health care utilization. Note that the cost analysis does not include outpatient visit 

impacts, since outpatient visit payments are reimbursed at such low rates that these impacts 

represent very small dollar amounts.

Table 17. Per-capita cost-effectiveness estimates in CT and SF based on TOT analysis

CT MI SF

Support services $7,653 $5,164 $7,387

Housing $12,936 $10,104 $5,205

Total program cost $20,589 $15,268 $12,592

Health care (TOT)* -$5,487

(-.53 hosp.*$8,906)+

(-1.0 ED*$767)

No impact -$6,682 

(-.56 hosp*$8,906)+ 

(-2.47 ED*$767)

Shelter (TOT) - $385 

(-7.0 days x $55/night)

No impact -$1,000 

(-29.4 days x $34/night)

Total cost savings -$5,827 N/A -$7,682

Estimated net cost $14,762 $15,268 $4,910

*Note: health care cost data represents national averages – hospitalization cost calculated from 

2014 HCUP data ($8,906) and ED cost provided by PEW ($767) 

DISCUSSION 

The assumptions underlying the overarching program theory did not hold in all sites. While all 

sites were eventually able, some only with strong supports from CSH and the NYU evaluation 

team, to implement a data-based targeting strategy, many sites subsequently faced challenges 

early on in terms of finding and engaging clients from the match lists. All sites abandoned the 

match list approach after the end of the study period, either because they did not have the 

resources to continue with that approach or because they preferred an alternate approach. So, 

while it seems that match-list targeting is possible within the context of a relatively resource-rich 

demonstration project, it is unlikely that organizations would be able to sustain such a model 

independent of these resources and research requirements. That said, LA used a data-driven 

approach to identify those who matched target criteria; doing this identification at the point of 

service was far more readily accomplished than by using a match list based on administrative 

records and, yet, ensured enrollment of those intended. Further, while obvious, it is worth 

mentioning that housing needs to be available to the target population once they are located and 

engaged. While staff were able to find housing for the large majority of those engaged, the lack 

of affordable housing, even when a housing subsidy is available, was and remains problematic in 

all sites but SF. 
New York University CSH-SIF Evaluation Final Report 

Evaluation Team August 2017 

Page 32 of 34 

This study is one of the first to rigorously investigate whether supportive housing has a 

significant impact on healthcare utilization and cost among homeless adults who are medical 

high-flyers. In the strict ITT analysis, impacts were somewhat elusive. Impacts on utilization 

were primarily found in the SF site. Importantly, we also find significant heterogeneity among 

the “high flyer” homeless individuals in our sample in regard to cost. While impact on cost was 

found in the overall models, for those who use fewer healthcare services while homeless, the 

receipt of supportive housing appears to have increased utilization, suggesting significant unmet 

needs. 

Our inability to find more robust impacts using the ITT analysis may partially be a reflection of 

several factors that made implementing an RCT design to address this specific research question 

more difficult. First, in many multi-site initiatives where data are pooled, the evaluators put great 

emphasis on model fidelity and comparability across sites in regard to both the program and the 

clients served. Yet, as is the case with so much federal policy, the delivery of uniform service to 

a uniform population is unrealistic; federal policies are implemented within the context of 

underlying conditions and relevant state and local policies. Housing conditions, housing 

supports, community based services, health care providers, and, most especially, Medicaid 

policies, varied enormously across locations. This shaped the specific services that could and 

could not be readily provided and who was and was not eligible for program services. Tailoring 

a national model to local context, as was done in this initiative, is a necessity but it makes us 

more cautious in how we use and interpret our analyses using pooled data. 

Analyzing impact by site and subgroup significantly reduced our statistical power. While our 

samples may be small from the standpoint of research, however, the reality is that it was very 

difficult for sites to identify and serve large numbers of participants in the desired time frame. 

Locating and engaging those eligible was made even more difficult by the use of a data based 

approach to identify and target those eligible and by a data matching procedure that under girded 

the RCT approach. Further, in all sites but SF (which had a congregate facility), external factors 

such as the 2013 United States Budget Sequestration4

 made securing housing vouchers, finding 

safe and appropriate housing, and ensuring secure placements all made the “small” size of the 

intervention seem rather large and difficult. For these reasons, we believe it may be unrealistic 

to imagine an experiment for this population with substantially more individuals in any one 

setting. Finding ways to make the most of demonstrations of this size is essential if we are to 

bring evidence to bear on critical policy decisions. 

Additionally, while we know whether or not those assigned to treatment actually received 

treatment (that is, they were enrolled in the SIF program), we do not know whether those in our 

control group received other such housing. Although information from each site suggested that 

relatively little “treatment contamination” occurred via housing of control group members, to the 

degree that this did occur, our treatment contrast was reduced, and our findings represent 

conservative estimates of program impacts. To this point, our quasi-experimental analysis did 

reveal significant impacts in CT for medical hospitalizations, OPD use, and shelter stays, even as 

the ITT analysis did not. 

 

4 In 2013, the United States Budget Sequestration resulted in a decrease in the number of federally funded 

rental assistance vouchers available to the SIF projects in both Los Angeles and MI. 
New York University CSH-SIF Evaluation Final Report 

Evaluation Team August 2017 

Page 33 of 34 

This study also has limitations beyond the difficultly of investigating the impacts of supportive 

housing in heterogeneous settings using an RCT design. We were constrained, as have been 

other researchers, by available data. As already noted, our data in MI did not represent all health 

care utilization but, rather, data for the two largest healthcare systems in Washtenaw County. In 

SF, we had utilization but not cost data. These gaps are not peculiar to our evaluation. Rather, 

they represent the fact that administrative data, gathered locally for purposes other than research, 

is an imperfect match to research needs. 

Further, we believe that an 18-month post-random assignment time horizon (which, in fact, 

typically represented about 12 months in supportive housing) may be insufficient to test the 

impacts of supportive housing on health care utilization and costs. Case managers reported that a 

substantial part of their time with clients in the first year was spent finding and settling clients 

into housing and then stabilizing them. Stabilization often involved focused attention on 

furnishing the apartments, linking to substance use services, setting boundaries with visitors, and 

attending to various financial issues. Only once stabilized could they turn their attention to 

linking clients to needed and appropriate primary and specialty health care services. Indeed, this 

is one important way in which SF differed from the other two sites and might explain that site’s 

stronger impacts in the RCT; in SF, clients could more rapidly be settled into their new homes 

and case managers could focus on other client needs. However, preliminary analyses of our MI 

data (not presented here) suggest that we may not find impacts on costs even with a longer time 

horizon. 

Finally, in examining the cost of the program relative to statistically significant reductions in 

health care and shelter utilization, we observe that – in CT and SF – a substantial portion of the 

cost of running the CSH-SIF program was offset by cost savings across these two systems. The 

majority of these cost savings stem from reductions if the cost incurred by health care systems, 

indicating that the health care sector may indeed be an appropriate source of funding for 

medically-oriented supportive housing that serves a high-flier population. That the overall percapita program cost in SF is substantially lower than CT may reflect an economy of scale, as the 

SF program was able to serve more individuals than the other sites. 

CONCLUSIONS

All in all the SIF initiative and evaluation allowed for new and significant contributions to the 

field on the impact supportive housing can have on health care access, utilization and costs for a 

high-utilizer populations. Based on our analysis, the following key takeaways can be gleaned 

from the evaluation: 

 It is possible to develop and deliver a medically-oriented supportive housing program 

targeted at homeless individuals who are high utilizers of health care using a data-driven 

approach, but it is very difficult in practice. 

 Program implementation and capacity for impact are both heavily influenced by local 

context and state policy. 

 This program can reduce utilization of shelters and costly health care in some

populations, and these reductions can substantially offset program costs. 
New York University CSH-SIF Evaluation Final Report 

Evaluation Team August 2017 

Page 34 of 34 

 While, on average, the program was associated with reduced costs and utilization, in 

some sites, and improvements in self-reported quality of life and access to care across 

sites, many participants were still experiencing deep and complex health problems one 

year into the program. 

The relationships between housing, health, and health care utilization is shaped by both 

individual and community conditions such as different and changing medical needs and 

conditions and services available in the community, and placing individuals who are homeless 

and high utilizers of the health care system in supportive housing may not be accompanied by 

immediate or dramatic drops in costly utilization within a 12 month time period. Yet, we think 

the evidence we present gives reasons for some optimism and further exploration. 

While not necessarily recommended in practice, our findings suggest that it may not be possible 

to achieve reductions in costly utilization within a 12 month window among all groups in this 

very sick population. This is in line with suggestions in some prior work, that supportive housing 

may do little to remedy those at the extremes, where health care needs have become deep and 

complex and not likely remedied through housing and improved primary care access. 

(Weinberger, Oddone, & Henderson, 1996; Williams, 2015) Indeed, in our site visits, case 

managers and other staff have frequently expressed their belief many of the clients are too sick to 

fully benefit from the program model. 

Findings from our analysis of medical conditions across program sites provide further insight 

into cross-site differences in impacts. MI is a much smaller community compared to SF and the 

many sites in CT and, as such, has relatively fewer homeless people. The CSH-SIF initiative 

therefore took a deeper "bite" into the homeless population – that is, it appears that the MI target 

population included individuals who were relatively healthy compared to individuals in CT and 

SF given that there was a smaller number of very sick people. Therefore, many people included 

as "high users" were sick but, overall, less so than those in SF and CT. This is evidenced by the 

average cost at baseline and is reinforced by the percentage with 2 or more chronic conditions at 

baseline, as well as the presence of some of the most serious conditions. Impacts on health care 

costs may therefore be less likely under these circumstances. 

Our main impact findings – reductions in utilization among the SF population – suggest to us 

that, indeed, savings from supportive housing could be achieved under the right conditions for 

the right people. But our findings also suggest there is much still-needed care among those who 

are homeless and frequent users of health care, since our least costly program participants 

experienced higher costs relative to those who were untreated. The hope of cost savings should 

not obscure the fact that this type of intervention may help people with deep medical needs 

receive needed and more appropriate care to meet those needs.