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A randomized trial of permanent supportive housing for chronically homeless persons with high use of publicly funded services



Objective: To examine whether randomization to permanent supportive housing (PSH) versus usual care reduces the use of acute health care and other services among chronically homeless high users of county-funded services. Data sources: Between 2015 and 2019, we assessed service use from Santa Clara County, CA, administrative claims data for all county-funded health care, jail and shelter, and mortality. Study design: We conducted a randomized controlled trial among chronically homeless high users of multiple systems. We compared postrandomization outcomes from county-funded systems using multivariate regression analysis. Data collection: We extracted encounter data from an integrated database capturing health care at county-funded facilities, shelter and jails, county housing placement, and death certificates. Principal findings: We enrolled 423 participants (199 intervention; 224 control). Eighty-six percent of those randomized to PSH received housing compared with 36 percent in usual care. On average, the 169 individuals housed by the PSH intervention have remained housed for 28.8 months (92.9 percent of the study follow-up period). Intervention group members had lower rates of psychiatric ED visits IRR 0.62; 95% CI [0.43, 0.91] and shelter days IRR 0.30; 95% CI [0.17, 0.53], and higher rates of ambulatory mental health services use IRR 1.84; 95% CI [1.43, 2.37] compared to controls. We found no differences in total ED or inpatient use, or jail. Seventy (37 treatment; 33 control) participants died. Conclusions: The intervention placed and retained frequent user, chronically homeless individuals in housing. It decreased psychiatric ED visits and shelter use, and increased outpatient mental health care, but not medical ED visits or hospitalizations. Limitations included more than one-third of usual care participants received another form of subsidized housing, potentially biasing results to the null, and loss of power due to high death rates. PSH can house high-risk individuals and reduce emergent psychiatric services and shelter use. Reductions in hospitalizations may be more difficult to realize.
Health Serv Res. 2020;55(Suppl. 2):797–806.
Health Services Research
DOI : 10.1111 /1475-67 73.13 553
A randomized trial of permanent supportive housing for
chronically homeless persons with high use of publicly funded
Maria C. Raven MD, MPH, MSc1,2| Matthew J. Niedzwiecki PhD1,2,3 |
Margot Kushel MD4,5
This is an op en access article u nder the terms of the Creative Commons Att ribution License, which per mits use, distrib ution and reproduction in any medium,
provide d the origi nal work is properly cited.
© 2020 The Authors. Health Services Research published by Wiley Peri odicals LLC on behalf of Health Research and Educational Trust
1Department of Emergency Medicine,
University of California , San Francisco, San
Francisco, CA, USA
2Philip R . Lee Inst itute for Health Policy
Studies , University of California, San
Francisco, San Franc isco, CA , USA
3Mathematica Policy Research, Oakland,
4Center for Vulnerable Populations,
University of California , San Francisco, San
Francisco, CA, USA
5Department of Medicine, University of
Califo rnia, San Francisco, San Francisco,
Maria C. Raven , MD, MPH , MSc,
Department of Emergency Medicine,
University of California , San Francisco, 505
Parnassus Ave, Suite U -575, San Francis co,
CA 94143, USA .
Funding information
Arnold Ventures
Objective: To examine whether randomization to permanent supportive housing
(PSH) versus usual care reduces the use of acute health care and other services
among chronically homeless high users of county-funded services.
Data Sources: Between 2015 and 2019, we assessed service use from Santa Clara
County, CA, administrative claims data for all county-funded health care, jail and
shelter, and mortality.
Study Design: We conducted a randomized controlled trial among chronically home-
less high users of multiple systems. We compared postrandomization outcomes from
county-funded systems using multivariate regression analysis.
Data Collection: We extracted encounter data from an integrated database captur-
ing health care at county-funded facilities, shelter and jails, county housing place-
ment, and death certificates.
Principal Findings: We enrolled 423 participants (199 intervention; 224 control).
Eighty-six percent of those randomized to PSH received housing compared with 36
percent in usual care. On average, the 169 individuals housed by the PSH interven-
tion have remained housed for 28.8 months (92.9 percent of the study follow-up pe-
riod). Intervention group members had lower rates of psychiatric ED visits IRR 0.62;
95% CI [0.43, 0.91] and shelter days IRR 0.30; 95% CI [0.17, 0.53], and higher rates
of ambulatory mental health services use IRR 1.84; 95% CI [1.43, 2.37] compared to
controls. We found no differences in total ED or inpatient use, or jail. Seventy (37
treatment; 33 control) participants died.
Conclusions: The intervention placed and retained frequent user, chronically home-
less individuals in housing. It decreased psychiatric ED visits and shelter use, and
increased outpatient mental health care, but not medical ED visits or hospitalizations.
Limitations included more than one-third of usual care participants received another
form of subsidized housing, potentially biasing results to the null, and loss of power
due to high death rates. PSH can house high-risk individuals and reduce emergent
Health Services Research
Homelessness is associated with high use of acute health care ser-
vices, including emergency department (ED)1,2 and inpatient care.3
Among homeless individuals, a small group (referred to as “frequent
users”) account for a large proportion of all acute service use.4 Most
frequent users are chronically homeless individuals with tri-morbid
chronic physical and mental health conditions and substance use dis-
orders.5-7 In addition to high rates of use of ED and inpatient care,
many have high use of other publicly funded services (jail, home-
less shelters)8,9 and low use of longitudinal, outpatient health care.
Interventions to maintain housing and reduce acute care service use
in this population are a key policy interest for payers and providers.
Permanent supportive housing (PSH), defined as subsidized
housing with closely linked, voluntary supportive services (eg, case
management, physical and mental health services, substance use
treatment services) provides permanent housing for people with
chronic homelessness and behavioral health conditions.10 PSH is of-
fered on a “housing first”11 basis, meaning clients are not required to
be sober or engage in treatment. Most of the literature evaluating
the effect of PSH on health care and other service utilization has
used pre–post, noncontrolled, study designs.12,13 While these have
suggested large reductions in service use, they face threats to inter-
nal validity. By including only people who have enrolled successfully
in PSH, these studies do not provide insights into reach.14, 15 Because
they focus on change in utilization of a group selected on the basis of
high use, they are susceptible to regression to the mean.
Santa Clara, CA created 112 units of PSH earmarked for high
users of multiple public systems of care; over time, the project in-
creased to 130 units. As the population who met criteria greatly
exceeded PSH supply, we utilized lotter y-conditions to conduc t a
randomized controlled trial to examine the reach and effect on ser-
vice use of PSH comparing those randomized to PSH versus usual
We evaluated differences in use of county health, shelter and
criminal justice services, housing placement and maintenance, and
mortality, comparing individuals randomized to PSH to usual care.
We used an intention-to-treat framework. Members of the control
group were eligible for PSH provided through other county-funded
We evaluated Project Welcome Home, a “Pay for Success” based
project to create PSH for the highest users of county public systems
(ED, inpatient services, and jail) in Santa Clara, CA. Approximately
28 percent of units were scattered site and 72 percent were con-
gregated. Most of the congregate units are set-aside units in private
or nonprofit affordable housing buildings. A limited number of other
units are located in converted hotels owned by local housing pro-
viders. Housing and case management services were provided by
Abode Services. Between July 2015 and September 2019, we as-
sessed service use from the Santa Clara County (SCC) administrative
claims data for all county-funded health care, criminal justice, and
shelter services and assessed deaths from county death certificates
for all study participants. The projec t is ongoing.
2.1 | Study screening and enrollment
The screening process includes administrative data screening to de-
termine eligibility by usage criteria, followed by an in-person screen-
ing to determine other eligibility criteria and ability to consent.
Randomization occured after consent. A proprietar y platform inte-
grates study data with real-time data feeds from multiple sources.
Staff screened potential participants based on their use of coun-
ty-funded services over the prior 1-2 years. Our research team
developed an electronic triage tool that uses administrative data
to predict the likelihood of future high use of county-funded ser-
vices. To meet criteria, potential participants must have used various
psychiatric services and shelter use. Reductions in hospitalizations may be more dif-
ficult to realize.
criminal justice, frequent users, homelessness, integrated data, permanent supportive housing
What This Study Adds
We found that the PSH program intervention was able
to house 86 percent of chronically homeless adults ran-
domized to the treatment group based on their high use
of multiple systems who were randomized to the treat-
ment group.
On average, it took 2.5 months for par ticipants rand-
omized to housing to become housed and 70 percent
moved at least once, demonstrating that PSH can be
successful with high-risk participants but requires time
and flexibility.
• By using a randomized controlled trial design, we found
that those randomized to housing (versus usual care)
had lower use of psychiatric emergency departments
and shelters, but did not have large reductions in service
use described in previous uncontrolled studies.
Health Services Research
combinations of the ED and psychiatric ED, medical and psychiatric
inpatient stays in the County-funded public hospital, and/or jail over
the past 1-2 years, at high enough levels to meet a threshold score.
We embedded the triage tool into the study database and generated
a list of potentially eligible participants with the highest scores, re-
doing the calculation throughout the enrollment period. All county
agencies or service providers could refer individuals they suspected
met eligibility criteria, but study staf f always used the list generated
by the triage tool to confirm initial eligibility. County staf f used this
list to outreach to the highest using individuals.
In addition to meeting threshold use levels, participants had to:
(a) meet the Federal definition of chronic homelessness (homeless
for more than a year or 4 or more episodes in the prior three years
that last for more than a year total, with a disabling condition); (b) live
in SCC; (c) not be incarcerated; (d) not engage in another intensive
case management program or other permanent supportive housing
program; (e) not require nursing home level care; and (f) not have
metast atic cancer or qualif y for hospice care.
After they identified that a prospective participant met eligi-
bility requirements, the staff conducted informed consent, using a
teach-back method to ensure understanding. Then, staff random-
ized participants using a random number generator. Staff referred
individuals randomized to the intervention group to Abode for en-
gagement in the permanent supportive housing. They informed par-
ticipant s randomized to usual care that they remained eligible for
all standard services, including other permanent supportive housing
programs provided by the County. We continued enrollment until all
the units filled and then enrolled additional participants whenever a
unit opened, through participants’ leaving housing, requiring higher
level of care, or death.
2.2 | Intervention
If an individual agreed to engage with Abode, they began to deliver
case management ser vices, even if a housing unit was not yet avail-
able. If the individual did not agree to engage immediately, staff con-
tinued to reach out to build rappor t at least one time per week for six
to nine months (depending on program capacity). If the staff could
not engage the participant, the staff ceased outreach attempts.
Abode's case management services use an Intensive Case
Management16 model. This includes community-based services, pro-
vided by master's level social behavioral health providers, bachelor's
level case managers, and staff with lived experience (peers). Abode
integrated these ser vices with a flexible array of housing options
delivered through a Housing First approach, to provide temporary
housing (if no permanent unit available immediately), permanent
supportive housing, and rehousing (locating new housing units if
the participant was evicted or otherwise lost a unit). Participants re-
ceived a rental subsidy to pay for the housing unit. Caseloads ranged
from 1:10 to 1:15. Abode did not employ nurses or physicians.
Abode offers a range of additional supportive services to par-
ticipants. These include mental health and substance use services;
medication support, community living skills, educational and voca-
tional support, money management, leisure and spiritual opportuni-
ties, and connection to primar y care. Those in the intervention group
who were not lost to follow-up continued to receive case manage-
ment services as part of the PSH intervention throughout the inter-
vention, whether or not they remain housed.
2.3 | Usual care
At the time of enrollment, staff provided all participants randomized
to usual care referrals to shelters and other homeless services.
These participants remained eligible to receive all services pro-
vided for individuals experiencing homelessness in SCC, including
any form of shelter, and temporary or permanent housing, includ-
ing PSH not designated for Project Welcome Home. Staff conduc ted
a Vulnerability Index-Service Prioritization Decision A ssistance
Too l ( V I-SPDAT) 17 assessment, in order to place clients on a list for
County housing interventions. During the intervention period, SCC
created other programs to provide PSH to chronically homeless indi-
viduals, and participants in the control group were eligible to receive
case management services through other county programs.
2.4 | Data
We extrac ted encounter data from the SCC integrated database
that combines county-funded health care utilization data (ED and
inpatient stays for medical or psychiatric causes, outpatient mental
health and substance use treatment, outpatient medical treatment)
with data from the County jail (all jail utilization) and shelter data
from the Homeless Management Information System.
We linked data using participants’ social security numbers,
names, and dates of birth. An out side entit y linked data via an en-
tity resolution process that used name, date of birth, social security
number, and unique identifiers within each system (such as medical
record numbers) coupled with a process to review and update any
matches across systems.
2.5 | Participant characteristics
We defined age at the date of enrollment. We included self-reported
sex, Hispanic ethnicity, race (White, Black, other), smoking status
(current versus former/never), insurance status (Medicaid, Medicare,
or both). We report on service use characteristics for county-funded
services in the two years prior to enrollment.
2.5.1 | Health services utilization
Santa Clara Valley Medical Center (SCVMC), a public safety net
hospital is the main acute care ser vice provider for homeless
Health Services Research
patients in SCC. SCVMC was the source of data for primary care
use, ED visits (including psychiatric ED), and inpatient hospitaliza-
tions (including psychiatric admissions). Hospitals that contract
with SCVMC to provide psychiatric inpatient care provided data.
We did not have access to data on physical health visits from other
SCVMC provided data on outpatient mental health care and
substance use treatment, including initial and ongoing, group and
individual treatment, including mental health outpatient services
provided by Abode.
We examined whether participants identified a regular source
of non-ED outpatient care. We examined the number of primary
care physician (PCP) visits each year, as recorded in County admin-
istrative data. We defined a PCP visit as a visit to a physician, nurse
practitioner, or physicians’ assistant at a primary care clinic. We cat-
egorized physical health ED visits in three ways: visits that result in
discharge, visits that result in hospital admission, and total visits. We
defined inpatient hospital care as the number of hospital admissions
a participant had at SCVMC. We examined the number of acute bed
days (length of stay). To examine mental health services use, we ex-
amined a participant's number of outpatient mental health appoint-
ments at county facilities, number of visits to the count y psychiatric
ED that resulted in discharge, and number of psychiatric hospitaliza-
tions. Regarding substance use treatment services use, we examined
participants’ number of days in inpatient and outpatient detoxifica-
tion and rehabilitation facilities, as well as other outpatient clinical
substance use services.
2.5.2 | Criminal justice
The Count y provided jail data through the Criminal Justice
Information System for study participants that included the timing
of arrest s and the length of stay in SCC jails.
2.5.3 | Housing and shelter outcomes
For all participants, we repor t on whether they received housing
at any point during the study. For participants in the intervention
group, Abode reported whether and when they obtained, left, and
regained housing. For descriptive purposes, we examined how long
(after study enrollment) it took for Abode to house each par ticipant
and how many times participants needed to be rehoused. To assess
housing retention for those in the intervention group housed by
Abode, we examined the ratio bet ween total days each par ticipant
remained housed and the total possible housing days (the par tici-
pant's first move-in date until the end of the study follow-up period).
We converted our result to months.
For participants in the intervention group who did not receive
housing by Abode and all participants in the usual care group, we ob-
tained data from SCC that identified whether or not the par ticipant
had received housing through other County housing programs. If so,
these dat a included the last recorded date of housing placement and
whether or not the par ticipant remained in housing or exited. For
those who had been housed but had exited, the data included where
they exited to (eg, another housing placement outside of the county;
with family; to homelessness).
For all participants, we checked for any use of the emergency
shelters in SCC through data from the SCC Homeless Management
Information System. We calculated amount of time in shelter. We did
not have data on privately funded shelters.
2.5.4 | Mortality
Abode provided data on death for all participants who died while
living in Abode housing. We queried County death certificate data
on all participants who did not appear in any source of study data for
6 or more months.
2.6 | Data analysis
To assess outcomes, we grouped data into one-year spans of time
for each individual in the treatment and control group. For ex-
ample, if an individual was enrolled for 4 years, they would have
four separate one-year spans in the regression analysis. The use
of spans allows us to include the most available data for each indi-
vidual in the study.
For participants who had potential spans that lasted ≥6 but
<12 months, we prorated utilization counts. To account for outliers
in the data, we top coded all span-level counts to the 99th percen-
tile. We included indicators in the regression analysis to signify the
year in the program in order to account for patterns of use that may
decrease or increase over time.
We censored spans at the time of death. To account for the pos-
sibility that participants moved out of County, we censored data
6 months after the last point of contact and constructed spans with
the data that remained.
We used negative binomial regression analysis on count data
outcomes using an intention-to-treat framework based on assign-
ment to the treatment group. Since the treatment and control groups
were balanced on baseline characteristics, we did not include covari-
ates in the negative binomial regressions. We controlled for the time
since enrollment (span indicators), to account for the differences in
enrollment period. We present results as incidence rate ratios (IRR).
We clustered standard errors at the individual level to account for
individuals with multiple spans.
In sensitivity analyses, we recoded outcome variables to a bi-
nary indicator for whether an individual used any of a given ser-
vice within the one-year span. We conducted sensitivity analyses
(Table S1). We explored allowing the treatment effect to var y by
how long the individual was enrolled in the program by including
interaction terms for treatment status and year indicators (results
not reported).
Health Services Research
3.1 | Sample characteristics
After identification by the triage tool, county or health services
staff approached 426 potential participants. Two refused further
outreach. Study staff approached 424 participants, one of whom
refused consent. We enrolled 423 participants: 199 in the PSH in-
tervention group and 224 in usual care (Figure 1). We repor t on de-
mographics’ and county-funded services’ use in the two years prior
to enrollment in Table 1. We found no meaningful differences in
demographic characteristics between the groups. The participants’
mean age was 51.8 years for treatment, 51.2 years control. Most
were male (72 percent inter vention, 71 percent control). A quarter
identified as Hispanic (24 percent inter vention, 25 percent control).
Two-thirds identified as White (64 versus 66 percent) while a small
propor tion identified as Black (13 percent versus 15 percent). In the
two years prior to enrollment, those in the treatment group aver-
aged 5.1 inpatient admissions, 19.0 ED visits, 3.7 jail stays, and 36.7
shelter days. They had a mean of 6.5 outpatient substance use treat-
ment visits and 26.0 outpatient mental health visits. The control
group utilization was not statistically different from the treatment
group. However, participants in the control group had a higher prev-
alence of reporting a regular source of care in the two years prior to
enrollment (mean 83 percent vs. 70 percent).
3.2 | Descriptive statistics—outcomes
During the follow-up period, 86 percent of those randomized to the
PSH inter vention received housing compared with 36 percent of
those in the control group (Table 2). Of the 199 people randomized
to intervention, 169 received housing through this program; three
received housing through another program. The average time from
enrollment to housing placement was 74.2 days. Of the 169 partic-
ipants housed by Abode, 119 (70.4 percent) moved at least once.
Three-quarters (72.0 percent) of the participants who required
rehousing had no housing gap between placements. On average,
housed intervention group par ticipants moved an average of 2.06
times during the follow-up period (range 1-10 times). The 169 par-
ticipant s housed by Abode have remained housed for an average of
28.8 months and have been retained in housing (without gaps) for
92.9 percent of the possible study follow-up period. When examin-
ing one-year spans over the course of the study, the intervention
group was housed in 84.4 percent of a given span compared to 20.1
percent of those in the control group. (P < .01). Individuals in the
treatment group had 6.6 shelter days per year versus 16.8 in the
control group (P < .01).
Individuals in the treatment group received outpatient mental
health treatment 37.3 times per year versus 19.7 times per year in
the control (P < .01). Those in the treatment group had fewer psy-
chiatric emergency visits per year as compared to the control group:
FIGURE 1 Study Enrollment with
housing and mortality outcomes
comparing PSH intervention group to
usual care. Abbreviation: PSH, permanent
supportive housing
Located aer
triage tool
Refused further
Located for in-
person screen
Refused consent
n=33 (19.2%)
Not housed
n=27 (13.6%)
Ever housed
n=172 (86.4%)
n=9 (11.1%)
Not housed
n=143 (63.8%)
Ever housed
n=81 (36.2%)
n=199 (47.0%)
n=4 (14.8%)
Usual Care
n=224 (53.0%)
n=24 (16.8%)
Health Services Research
1.3 visits per year in the treatment group versus 1.9 per year in the
control group (P < .01).
Intervention and control groups had similar levels of ED visits,
inpatient admissions, psychiatric inpatient admissions, jail stays, and
outpatient substance abuse treatment services. We present mean
utilization rates during the study period for outcome variables in-
cluded in the regression analysis in Figure 2.
TABLE 1 Study sample demographic characteristics and health
services use in the t wo years prior to enrollment, treatment versus
usual care
Control Difference
Follow-up duration
Months 35.8 36.8 1.1
Male 72% 71% −2%
Hispanic ethnicity 24% 25% 1%
White race 64% 66% 2%
Black race 13% 15% 3%
Other race 23% 19 % −4%
Age in years 51. 8 51. 2 −0.595
Currently Smoking 65% 66% 1%
Medicaid insurance 65% 66% 1%
Medicare insurance 73% 73% −1%
Health services use
Regular source of care
(not ED)
70% 83% 14%
Ambulatory care visits 7.3 8.8 1.5
Inpatient psychiatr y stays 0.2 0.3 0.1
Total inpatient stays 5.1 4.8 1.5
Total bed days 14.5 15.1 0.6
ED visits (total) 19.0 20.1 1.1
ED visits discharged
16.7 18.0 1.4
ED visits admitted 2.3 2.1 −0.3
Emergency psychiatry
4.7 5.4 0.6
Jail stays 3.7 2.8 −0.9
Jail days 56.0 61.9 5 .9
Shelter use
Shelter stays 30.8 37. 5 6.6
Shelter days 36.7 42.0 5.2
Outpatient behavioral health
Outpatient subst ance use
treatment visits
6.5 5.5 −1.0
Outpatient mental health
26.0 28.9 2.9
Abbreviation: ED, emergency department.
TABLE 2 Logistic and negative binomial regression analysis of treatment status on Medical, criminal justice, and housing outcomes
Ever housed ED visits
psych visits
Total inpatient
psych stays Jail stays Shelter days
Outpatient subst ance use
treatment visits
Outpatient mental
health visits
Treatment Group 22.34** 0.85 0.62* 0.97 0.73 1.01 0.30** 0.76 1.84**
[11.69,42.68] [0.67,1.08] [0.43,0.91] [0 .70,1. 35] [0.3 6,1.45] [0.73 ,1.40] [0.17,0.53] [0.4 6,1. 24] [1. 43 ,2 .3 7]
Span 1 (reference) 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0
— —
Span 2 1.11 0.81** 0.79* 0.80* 0.66 0.89 0.51* * 0.65* 0.95
[0.95 ,1. 29] [0.71,0.93] [0.64,0.97] [0.66,0.96] [0. 31,1.4 4] [0.76,1.04] [0.33,0.78] [0.45,0.95] [0.82,1.09]
Span 3 1.13 0.74** 0.80 0.70* 0.78 0.83 0.34** 0.18** 0.86
[0.91,1.40] [0.62,0.88] [0.63,1.01] [0.53,0.92] [0. 35,1. 74] [0. 67,1.02] [0.21,0.55] [0.13,0.27] [0.71,1.05]
Span 4 1. 51* 0.63** 0.69* 0.51* * 0.61 0.81 0.32** 0.16** 0.85
[0. 84,2 .11] [0.49,0.81] [0.49,0.97] [0.35,0.75] [0.21 ,1.8 2] [0.61,1.08] [0.14,0.73] [0.09,0.27] [0.67,1.09]
N1070 1070 1070 1070 1070 1070 1070 1070 1070
Note: All re sults presented as an odds ratio for the binary outcome “ever housed” from a logistic regre ssion and as incidence r ate ratios for all other outcomes from negative binomial regressions. 95% confidence
intervals also presented. Covariates include treatment status as well as span indicators to control for the time since enrollment. No other covariates were included as the treatment was randomly assigned.
Abbreviation: ED, emergency department.
*P < .05, **P < .01.
Health Services Research
A total of 37 (18.6 percent) in the intervention group and 33 (14.7
percent) in the control group died during the study follow-up period.
Of those who died, 89.2 percent of those in the intervention group
had ever received housing during the study period, compared with
29.0 percent of those in the control group.
3.3 | Regression analysis
The treatment group was more likely to be ever housed during
the study period (odds ratio [OR]: 22.34, 95% CI: [11.69,42.68]).
The inter vention group had nearly two-thirds fewer days in shel-
ter compared to the control group (IRR: 0.30, 95% CI: [0.17, 0.53]).
Individuals in the treatment group had nearly twice as many outpa-
tient mental health visits as those in the control group (IRR: 1.84,
95% CI: [1.43,2.37]). Assignment to the treatment group was associ-
ated with a 38 percent reduction in psychiatric ED visits (IRR: 0.62,
95% CI: [0.43,0.91]).
No other differences were statistically significant. Those in the
treatment group had 15 percent fewer ED visits (IRR: 0.85, 95% CI:
[0.67,1.08]) and 27 percent fewer psychiatric inpatient admissions
(IRR: 0.73, 95% CI: [0.36,1.45]) but the difference did not reach
statistical significance. Both groups had similar rates of inpatient
admissions (IRR: 0.97, 95% CI: [0.70,1.35]) and jail stays (IRR: 1.01,
95% CI: [0.73,1.40]). Those in the treatment group received 24 per-
cent fewer outpatient substance use treatment visits (IRR: 0.76,
95% CI: [0.46,1.24]), but the result was not statistically significant.
When we interacted treatment status with year of enrollment, we
found no statistically significant dif ferences in the treatment effect
for any of the outcomes studied, although we were underpowered
to do so. We examined differences in the number of hospital days
and jail days as secondar y outcomes and found no differences.
(Appendix S1).
In a randomized control trial comparing chronically homeless indi-
viduals who were the highest users of multiple systems of care in
Santa Clara, CA randomized to receive permanent supportive hous-
ing versus usual care, we found that participants randomized to
PSH experienced reductions in psychiatric ED and shelter use but
no reductions in use of medical EDs, hospitals, or jail. Despite the
social complexity of the study participants, 86 percent of those ran-
domized to PSH entered housing and remained in housing for the
vast majority (92.9 percent) of the study follow-up period.
We found a significant reduction in use of psychiatric emergency
services and a concomitant increase in scheduled mental health vis-
its. Project Welcome Home included Intensive Case Management
with a low client-staff ratio led by licensed staff with behavioral
health training. Research has shown that experiencing homeless-
ness is one factor that leads to ED visits among psychiatric patients,
suggesting an unmet need for ment al health care.5,1 8 Our findings
suggest that these visits are amenable to prevention by providing
housing with associated low-barriers mental health ser vices. We did
not find a significant reduction in other acute medical care visits, al-
though the point estimates for both ED visits and psychiatric admis-
sions were less than one. These results differ from those reported
in studies of PSH that used uncontrolled designs. These found large
reductions in service use.12,19, 20 Without controls, these findings are
susceptible to regression to the mean.20, 21 Our finding of decreased
use in later span years independent of group assignment suggests
regression to the mean. Two related RCTs found statistically signifi-
cant reductions in ED visits, nonstatistically significant reductions in
inpatient medical hospitalizations and increases in psychiatric hos-
pitalizations.11,22 People who are high users of services likely have
unmet health needs that become apparent once housed. Our results
may have underestimated improvements due to misclassification:
FIGURE 2 Outcome variables, PSH intervention versus usual care. Abbreviations: ED, emergency department; PSH, permanent
supportive housing [Color figure can be viewed at]
40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0
inpaent psych stays
total inpaent stays
emergency psych visits
ED visits
mental health outpaent visits
outpaent substance use treatment
shelter days
jail stays
total bed days
Health Services Research
14 percent of participant s in our study did not engage in housing,
although most of these participants did receive case management
services. At the time of the study, SCC increased it s provision of
PSH.23 Thirty-six percent of those in the control group received PSH
or other forms of subsidized housing through other programs. This
would bias our results toward the null and could have obscured our
ability to see subtle differences.
Despite selection criteria that identified those at highest risk
for frequent utilization, and thus, most likely to experience mental
health and substance use disabilities, we found the 86 percent of in-
dividuals randomized to PSH entered housing and remained housed,
on average, for 93 percent of the time in the study. Engagement and
retention in housing is an important priority for policy makers.24
Our selection criteria aimed to identify those who were the high-
est users of services. Similar to Coordinated Entry, a Housing and
Urban Development policy that requires Counties to prioritize those
with the most significant barriers to housing to receive housing and
homeless assistance services, we designed the triage tool to iden-
tify those who, due to their high use of acute care services and jail,
likely faced the biggest barriers. Thus, our study has implications for
jurisdic tions who are using coordinated entr y to provide PSH only
to those at highest risk. After our initial screening using administra-
tive data, only two individuals refused additional outreach and, after
screening in, only one refused to participate in randomization, sug-
gesting that this population is interested in receiving services. We
found that by providing housing with appropriate services, the vast
majority of high-risk individuals could be housed successfully. Prior
pre–postliterature has suggested that upwards of 85 percent of peo-
ple engaged in PSH remain housed. We found that 86 percent of
high-risk individuals randomized to the intervention entered hous-
ing and these individuals remained housed for the vast majority of
the time. This finding extends the finding of pre–poststudies in two
ways. While pre–poststudies cannot address the issue of engage-
ment, we found that, even among the highest risk population, the
intervention was able to engage 86 percent in housing. Our study's
use of a targeting tool include people whose usage patterns sug-
gest that they will have the highest ongoing acute care use provides
additional reassurance that even the most high-risk individuals can
be successfully housed using a Housing First approach with inten-
sive case management. The housing patterns we found, however,
suggest the need for flexibilit y. Consistent with the experience of
many Housing First programs, over t wo-thirds of the housed inter-
vention participants required rehousing after their first placement
did not succeed. The ability to offer a new housing placement is a
key component of successful Housing First strategies when work-
ing with high complexit y populations. With the widespread use of
Coordinated Entry that will require that counties place individuals
with similar risk profiles into PSH, our findings provide support for
the need for flexibility, including the ability to rehouse individuals,
in order to serve those at highest risk. Our results offer a measured
sense of expected changes in their use of other services.
We found a similar high mortality rate in both treatment and con-
trol groups. Individuals experiencing homelessness have a greater
age-adjusted mortality rate than housed counterparts. 25 Among
those who died, 89 percent of those in the intervention group had
been housed compared with 28 percent in the control group. After
longst anding homelessness, housing may not be sufficient to pre-
vent or delay death. However, avoiding deaths while people are
homeless has value. The study excluded those with metastatic can-
cer or those who health c are providers deemed eligible for hospice.
The high death rate despite these exclusions suggests the vulnera-
bility of the population and the challenge of predicting mortality. It
is possible that some of the participants would have benefited from
referral to a higher level of c are instead of PSH. This requires further
We found no differences in criminal justice system encounters
betwee n participant s in the intervention and contro l groups, which is
consistent with prior research. 28 Individuals experiencing homeless-
ness are more likely to be arrested for offenses that can be directly
attributed to the state of being homeless, 29 including trespassing,
sleeping in vehicles, panhandling, and public use of illicit drugs and
alcohol. City-wide bans on public camping and panhandling have in-
creased by 69 and 43 percent, respectively, over the past decade.30
The lack of a difference may be attributed to the fact that some of
the jail st ays experienced by individuals who received housing were
caused by outstanding warrants that the criminal justice system
served once the individual received housing. For this high-risk pop-
ulation, programs to help detect and mitigate risk of criminal justice
involvement, as well as policies that support alternatives to incarcer-
ation, may need to be better integrated into PSH programs. This will
require future study.
4.1 | Limitations
Our study has impor tant limitations. We used a randomized, inten-
tion-to-treat framework so that all individuals who enrolled in the
study were included when evaluating outcomes. Sixteen percent of
individuals in the treatment group never received housing, and 36
percent of those in the control group received PSH through other
programs during a time of expansion of PSH in Santa Clara.23 This
could bias our findings toward the null. In addition, our higher-than-
expected mortality rate among participants limited follow- up peri-
ods for par ticipants who died. It is possible that we missed deaths
among the control group. This would ar tificially reduce service uti-
lization in this population and bias results toward the null. Only a
minority of individuals had a history of criminal justice system inter-
actions in the 2 years prior to enrollment. This may have limited our
power to detect differences, although our findings are consistent
with prior research. We had access to an integrated database that
allowed us to examine use of multiple county services. However,
we were unable to detect service use that may have occurred either
outside of the county or that occurred in other health care facilities
within SCC , with the exception of psychiatric inpatient services. This
may have led to underestimation of ser vice use in the study popu-
lation. If enrollment in the PSH intervention resulted in increased
Health Services Research
likelihood of referral for medical care to the County hospital (as com-
pared to other hospitals in the County), this may have differentially
impacted our abilit y to detect service use in the inter vention group.
Alternatively if, due to their housing, participants in the intervention
group preferentially increased their use of other hospitals, this could
have led us to deflate use in the intervention group.
We found PSH delivered in a Housing First method delivering ser-
vices through an Intensive Case Management model with a low
client to staff ratio successfully housed chronically homeless individ-
uals who were high users of multiple public systems of care. While
the inter vention reduced use of the psychiatric ED and shelters
and increased housing, it did not reduce ED use for physical health
care or hospitalizations. We found high death rates for participants
in both groups, emphasizing the medical frailty of the population.
While early, uncontrolled, studies of PSH may have overstated ex-
pected reductions in inpatient and ED care, these reductions may be
harder to realize in high need populations who experience underuse
of services. However, the intervention's ability to house, success-
fully, a high proportion of the most high-risk chronically homeless
population who were the highest user of multiple systems of care
demonstrates the potential of Housing First to house the highest
risk individuals.
Joint Acknowledgment/Disclosure Statement: This publication was
supported by a grant from Arnold Ventures with assistance from
Santa Clara County and Abode Services. Its contents are solely the
responsibility of the authors and do not necessarily represent the
official views of Arnold Ventures, Santa Clara County, or Abode
Services. All authors conceived of and designed the study, inter-
preted the data, and contributed substantially to the article's revi-
sion. M. N. analyzed the data and M. C. R., M. K., and M. N. assisted
with data interpretation. M. C . R. and M. K. drafted the article. M.
C. R. had full access to all of the data in the study and takes respon-
sibility for the paper as a whole. The authors would also like to ac-
knowledge Vivian Way and St acey Murphy of Abode Ser vices, Greta
Hansen of Santa Clara County, and Alice Yu of Palantir for their as-
sistance with data extraction, quality assurance and technical sup-
port, as well as Virginia Chan for her administrative support.
Matthew J. Niedzwiecki https://orcid.
1. Kushel MB, Perry S, Bangsberg D, Clark R, Moss AR. Emergency
department use among the homeless and marginally housed:
results from a community-based study. Am J Public Health.
2. Raven MC, Tieu L, Lee C T, Ponath C, Guzman D, Kushel M.
Emergency department use in a cohort of older homeless adults:
result s from the HOPE HOME Study. Acad Emer g Med Off J Soc Acad
Emerg Med. 2017;24(1):63-74.
3. Lin W-C, Bharel M, Zhang J, O’Connell E, Clark RE. Frequent emer-
gency department visits and hospitalizations among homeless peo-
ple with Medicaid: implications for Medicaid expansion. Am J Public
Health. 2015;105(Suppl 5):S716-722.
4. Mandelberg JH, Kuhn RE, Kohn MA . Epidemiologic analysis of an
urban, public emergency department’s frequent users. Acad Emerg
Med. 2000;7(6):637-646 .
5. Moulin A, Evans EJ, Xing G, Melnikow J. Substance use, home-
lessness, mental illness and Medicaid coverage: a set-up for
high emergency department utilization. West J Emerg Med.
2018 ;19(6) :90 2-90 6.
6. Bharel M, Lin W-C, Zhang J, O’Connell E, Taube R , Clark RE . Health
care utilization patterns of homeless individuals in Boston: prepar-
ing for Medicaid expansion under the Affordable C are Act. Am J
Public Health. 2013;103(Suppl 2):S311-317.
7. Zhang L, Norena M, Gadermann A , et al. Concurrent disorders and
health care utilization among homeless and vulnerably housed per-
sons in Canada. J Dual Diagn. 2018;14(1):21-31.
8. Kanzaria HK, Niedz wiecki M, Cawley CL, et al. Frequent emergency
department users: focusing solely on Medical utilization misses the
whole person. Health Aff. 2019;38(11):1866-1875.
9. Cusack M, Montgomery AE. Examining the bidirectional associ-
ation between veteran homelessness and incarceration within
the contex t of permanent supportive housing. Psychol Serv.
10. Rog DJ, Marshall T, Doughert y RH, et al. Permanent sup-
portive housing: assessing the evidence. Psychiatr Serv.
11. Stergiopoulos V, Mejia-L ancheros C, Nisenbaum R, et al. Long-term
effec ts of rent supplements and ment al health suppor t services
on housing and health outcomes of homeless adults with mental
illness: extension study of the At Home/Chez Soi randomised con-
trolled trial. Lancet Psychiatry. 2019;6(11):915-925.
12. Mackelprang JL, Collins SE, Clifasefi SL. Housing First is associated
with reduced use of emergency medical services. Prehospital Emerg
Care. 2014;18(4):476-482.
13. Kessell ER, Bhatia R, Bamberger JD, Kushel MB. Public health care
utilization in a cohort of homeless adult applicants to a supportive
housing program. J Urban Health. 2006;83(5):860-873.
14. Larimer ME, Malone DK , Garner MD, et al. Health care and pub-
lic service use and costs before and after provision of housing for
chronically homeless persons with severe alcohol problems. JAMA.
2009;301(13):13 49-1357.
15. Hwang SW, Gogosis E, Chambers C, Dunn JR, Hoch JS, Aubry T.
Health s tatus, qualit y of life, residential stability, subst ance use, and
health c are utilization among adults applying to a suppor tive hous-
ing progr am. J Urban Health. 2011;88 (6):1076-1090 .
16. New York State Office of Ment al Health. Asser tive Community
Treatment (ACT). b/act/. Accessed
November 5, 2019
17. CT Homeless Management Information Systems. VI-SPDAT. https:// l/vi-spdat/ 13. Accessed Novemb er 14,
18. Blonigen DM, Macia KS, Bi X, Suarez P, Manfredi L, Wagner TH.
Factors associated with emergency department use among veteran
psychiatric patients. Psychiatr Q. 2017;88(4):721-732.
19. Chhabra M, Spector E, Demuynck S, Wiest D, Buckley L, Shea JA .
Assessing the relationship between housing and health among
medically complex, chronically homeless individuals experienc-
ing frequent hospital use in the United States. Health Soc Care
Community. 2020;28(1):91-99.
Health Services Research
20. Ly A, L atimer E. Housing First impact on cost s and associ-
ated cost of fsets: a review of the literature. Can J Psychiatry.
2015;60 (11):475-4 87.
21. Kertesz SG, Baggett TP, O’Connell JJ, Buck DS, Kushel MB.
Permanent suppor tive housing for homeless people - reframing the
debate. N Engl J Med. 2016;375( 22):2115 -2117.
22. Latimer EA , Rabouin D, Cao Z, et al. Cost-effectiveness of Housing
First intervention with intensive case management compared with
treatment as usual for homeless adults with mental illness: sec-
ondar y analysis of a randomized clinical trial. JAMA Netw Open.
23. Destination: Home. Ending Chronic Homelessness through
Collective Impact. https://desti natio nhome Accessed
November 14, 2019
24. National Alliance to End Homelessness. Housing First. https://
endho meles rce/housi ng-first/. Accessed
November 5, 2019
25. Center for Disease Control and Prevention. National homeless per-
son’s memorial day. Centers for Disease Control and Prevention. res/homel essne ss/. Accessed
November 14, 2019
26. Roncarati JS , Bagget t TP, O’Connell JJ, et al. Mortality among un-
sheltered homeless adults in Boston, Massachusetts, 200 0-2009.
JAMA Intern Med. 2018;178(9):1242-1248.
27. Hwang SW. Mortality among men using homeless shelters in
Toronto, Ontario. JAMA. 2000;283(16):2152-2157.
28. Leclair MC, D eveaux F, Roy L, Goulet M-H , Latimer EA , Crocker AG.
The impact of Housing First on criminal justice outcomes among
homeless people with mental illness: a systematic review. Can J
Psychiatry. 2019;64(8):525-530.
29. Kouyoumdjian FG , Wang R, Mejia-Lancheros C, et al. Interactions
between police and persons who experience homelessness and
mental illness in Toronto, C anada: findings from a prospective
study. Can J Psychiatry. 2019;64(10):718-725.
30. National Low Income Housing Coalition. Criminalization of
Homelessness Increases in U.S. Cities.
rce/crimi naliz ation-homel essne ss-incre ases-us-cities. Accessed
November 5, 2019
Additional suppor ting information may be found online in the
Supporting Information section.
How to cite this article: Raven MC , Niedzwiecki MJ, Kushel M.
A randomized trial of permanent supportive housing for
chronically homeless persons with high use of publicly funded
services. Health Ser v Res. 2020;55(Suppl. 2):797–806. h t t ps : //
doi .or g/10 .1111/1475 -6773.13553
... Similar to other research in this domain (Gilmer et al., 2015;Raven et al., 2020;Wright et al., 2016), it has been found that supported housing interventions offered residents increased personalization in their health care access, while not contributing to increased levels of preventable or incidental system use (e.g., increased emergency F I G U R E 1 Forest plot of health system use. ...
... Further, the residents examined in this study self-reported statistically significant increases in having a designated primary care provider in the year following entering the supported housing arrangement, in comparison to the previous year (i.e., 89% vs. 73%, respectively).Other work exploring similar resident populations has found that outpatient and related health services increased, post-housing intervention implementation. For instance,Raven, Niedzwiecki, and Kushel (2020) reported statistically significant increases in ambulatory mental health services use after receiving a permanent supportive housing intervention in their randomized controlled trial study of 423 chronically homeless individuals in Santa Clara County, California (USA).Gilmer, Stefancic, Henwood, and Ettner (2015) also reported that all 5067 participants in their housing first intervention used higher levels of outpatient mental health visits in the 1 year post-implementation of a supported housing intervention. ...
What is known on the subject: Supported housing approaches that include case management and increased opportunities for independence and personal autonomy for people who are living with severe and persistent mental illness (SPMI) have been found to help reduce hospitalizations and use of the emergency department. What is not fully clear is if these types of supported housing arrangements also influence the use of primary health care and other specialist services. What the paper adds to existing knowledge: This study uncovered that individuals experiencing SPMI who lived in supported housing used more primary health care and specialist physician services, in the year following transition to this housing arrangement. What are the implications for practice: The findings of this study suggest that supported housing arrangements for people experiencing SPMI may help in improving the personalization of health services for individual residents, including increasing access to both primary health care and specialist services. This is important for nursing practice, as the findings of the study show that supported housing arrangements for people experiencing SPMI may assist in better supporting their complex health care needs. Abstract: INTRODUCTION: Supported housing for people who are living with severe and persistent mental illness (SPMI) has been found to help reduce hospitalizations and use of the emergency department. What is not fully clear is if these types of supported housing arrangements also influence the use of primary health care and other specialist services. Aim/question: The aim of this study was to compare the use of health services use of individuals with SPMI, before and after transition to the new supported housing program. Method: Using health care administrative databases, a pre-post cohort study was conducted examining the health system use of residents who transitioned from custodial to supported housing arrangements between 2017 and 2019. Results: Individuals with SPMI used more primary health care and specialist physician services after transition to the supported housing model. Discussion: The results suggest that a supported housing model may be associated with increased usage of outpatient person-centred health services in people experiencing SPMI. Implications for practice: The findings of this study suggest that supported housing arrangements for people experiencing SPMI may help in improving the personalization of health services for individual. This is important for nursing practice, as the findings of the study show that supported housing arrangements may assist in better supporting complex health care needs of individuals.
... Many of Western countries view PSH as an initial solution for decreasing chronic homelessness (Aubry et al., 2020;Rollings et al., 2021), as PSH aims to provide affordable housing and supports such as life skills training, mental health counseling, SUD treatment, support groups, and job-seeking assistance. PSH programs reportedly improve housing stability and decrease acute care use (Aubry et al., 2020;Raven et al., 2020;Rollings et al., 2021). Studies identify two major models of PSH: a scattered-site or private PSH model originating in Housing-First models, established mainly in North America (Tsemberis et al., 2004), and a single-site or congregate PSH model mainly implemented in Australia (Montgomery et al., 2019). ...
Full-text available
This study identified individual sociodemographic and clinical characteristics and service use patterns associated with quality of life (QoL) among 308 individuals living in permanent supportive housing (PSH) in Québec (Canada). Data were collected between 2020 and 2022, and linear multivariate analyses produced. Results demonstrated that better individual psychosocial conditions were positively associated with higher QoL. As well, living in PSH located in good neighborhoods for at least 5 years, higher self-esteem and community integration were positively associated with greater QoL. Met needs, satisfaction with housing support services, and no use of acute care were also linked with positive QoL. Comprehensive efforts to improve treatment for mental health disabilities responsive to the needs of PSH residents, and sustained long-term housing may reinforce QoL. Encouraging active participation in community-based activities, incorporating biophilic design into the neighborhoods around PSH, and promoting satisfaction with care may also enhance QoL.
... PSH is generally targeted to people who have been chronically homeless and have significant health conditions, including substance use disorders and mental illness. Decades of evidence, including multiple RCTs, have proven that PSH is highly effective in durably resolving an individual's homelessness, including for people with serious mental illness and people who use drugs [10][11][12][13][14]. There are currently 387,305 units of PSH across the USA, a number that has grown consistently in the past 15 years and that continues to grow [9,15]. ...
Full-text available
Background Permanent supportive housing (PSH)—subsidized housing paired with support services such as case management—is a key part of national strategic plans to end homelessness. PSH tenants face high overdose risk due to a confluence of individual and environmental risk factors, yet little research has examined overdose prevention in PSH. Methods We describe the protocol for a hybrid type 3 stepped-wedge cluster randomized controlled trial (RCT) of overdose prevention practice implementation in PSH. We adapted evidence-based overdose prevention practices and implementation strategies for PSH using input from stakeholder focus groups. The trial will include 20 PSH buildings (with building size ranging from 20 to over 150 tenants) across New York City and New York’s Capital Region. Buildings will be randomized to one of four 6-month intervention waves during which they will receive a package of implementation support including training in using a PSH Overdose Prevention (POP) Toolkit, time-limited practice facilitation, and learning collaboratives delivered to staff and tenant implementation champions appointed by each building. The primary outcome is building-level fidelity to a defined list of overdose prevention practices. Secondary and exploratory implementation and effectiveness outcomes will be examined using PSH staff and tenant survey questionnaires, and analysis of tenant Medicaid data. We will explore factors related to implementation success, including barriers and facilitators, using qualitative interviews with key stakeholders. The project is being conducted through an academic-community partnership, and an Advisory Board including PSH tenants and other key stakeholders will be engaged in all stages of the project. Discussion We describe the protocol for a hybrid type 3 stepped-wedge cluster RCT of overdose prevention practice implementation in PSH. This study will be the first controlled trial of overdose prevention implementation in PSH settings. The research will make a significant impact by testing and informing future implementation strategies to prevent overdose for a population at particularly high risk for overdose mortality. Findings from this PSH-focused research are expected to be broadly applicable to other housing settings and settings serving people experiencing homelessness. Trial registration, NCT05786222, registered 27 March 2023.
This chapter provides an overview Housing First, which is a housing-led solution to homelessness, which prioritises access to stable accommodation over the requirement for an individual to first engage with services or treatments, and then offers wrap around support. Housing First was introduced in England in 2010 following evidence of benefits in North America, but with mixed levels of adoption and often limited sustainability. Over the last decade, evidence has accumulated showing that Housing First results in sustained tenancies for most of those referred and can confer wider health and social wellbeing benefits.
Experts from the top hospitals in America's largest cities provide their insights into the disease states, injuries, patient populations, practice barriers, and societal conditions which present disproportionality in urban emergency departments. Distilling the authors' special expertise and skills in a clear and user-friendly way, this book enables the reader to recognize the impact of healthcare disparities on patient well-being and identify and manage the needs of special patient populations, including victims of substance abuse and intimate partner violence. Clinical chapters define conditions through case studies, discussing their prevalence in the urban setting, and offer expert advice for immediate and effective management. In addition, the book helpfully provides context and valuable tips for best practice and introduces new ways of thinking about the diseases and the problems discussed. Essential reading for clinicians looking to improve their knowledge of urban emergency medicine, from students through to senior attending practitioners.
This study assessed whether permanent supportive housing (PSH) participation is associated with health service use among a population of adults with disabilities, including people transitioning into PSH from community and institutional settings. Our primary data sources were 2014 to 2018 secondary data from a PSH program in North Carolina linked to Medicaid claims. We used propensity score weighting to estimate the average treatment effect on the treated of PSH participation. All models were stratified by whether individuals were in institutional or community settings prior to PSH. In weighted analyses, among individuals who were institutionalized prior to PSH, PSH participation was associated with greater hospitalizations and emergency department (ED) visits and fewer primary care visits during the follow-up period, compared with similar individuals who largely remained institutionalized. Individuals who entered PSH from community settings did not have significantly different health service use from similar comparison group members during the 12-month follow-up period.
Importance: Health-related social needs are increasingly being screened for in primary care practices, but it remains unclear how much additional financing is required to address those needs to improve health outcomes. Objective: To estimate the cost of implementing evidence-based interventions to address social needs identified in primary care practices. Design, setting, and participants: A decision analytical microsimulation of patients seen in primary care practices, using data on social needs from the National Center for Health Statistics from 2015 through 2018 (N = 19 225) was conducted. Primary care practices were categorized as federally qualified health centers (FQHCs), non-FQHC urban practices in high-poverty areas, non-FQHC rural practices in high-poverty areas, and practices in lower-poverty areas. Data analysis was performed from March 3 to December 16, 2022. Intervention: Simulated evidence-based interventions of primary care-based screening and referral protocols, food assistance, housing programs, nonemergency medical transportation, and community-based care coordination. Main outcomes and measures: The primary outcome was per-person per-month cost of interventions. Intervention costs that have existing federally funded financing mechanisms (eg, the Supplemental Nutrition Assistance Program) and costs without such an existing mechanism were tabulated. Results: Of the population included in the analysis, the mean (SD) age was 34.4 (25.9) years, and 54.3% were female. Among people with food and housing needs, most were program eligible for federally funded programs, but had low enrollment (eg, due to inadequate program capacity), with 78.0% of people with housing needs being program eligible vs 24.0% enrolled, and 95.6% of people with food needs being program eligible vs 70.2% enrolled. Among those with transportation insecurity and care coordination needs, eligibility criteria limited enrollment (26.3% of those in need being program eligible for transportation programs, and 5.7% of those in need being program eligible for care coordination programs). The cost of providing evidence-based interventions for these 4 domains averaged $60 (95% CI, $55-$65) per member per month (including approximately $5 for screening and referral management in clinics), of which $27 (95% CI, $24-$31) (45.8%) was federally funded. While disproportionate funding was available to populations seen at FQHCs, populations seen at non-FQHC practices in high-poverty areas had larger funding gaps (intervention costs not borne by existing federal funding mechanisms). Conclusions and relevance: In this decision analytical microsimulation study, food and housing interventions were limited by low enrollment among eligible people, whereas transportation and care coordination interventions were more limited by narrow eligibility criteria. Screening and referral management in primary care was a small expenditure relative to the cost of interventions to address social needs, and just under half of the costs of interventions were covered by existing federal funding mechanisms. These findings suggest that many resources are necessary to address social needs that fall largely outside of existing federal financing mechanisms.
Full-text available
Permanent supportive housing (PSH) for individuals experiencing homelessness and living with mental illness can reduce utilization of crisis care services and increase utilization of outpatient care, although the extent to which pre-housing utilization patterns influence post-housing utilization remains unclear. Therefore, pre- and post-housing health service utilization was examined in 80 individuals living with a chronic mental illness who were and were not utilizing health care services in the years pre- and post-housing. Overall, the proportion of tenants utilizing outpatient services, including outpatient behavioral health services, increased from pre- to post-housing. Tenants who did not use outpatient behavioral health services prior to housing were disproportionately less likely than their peers to use those services after being housed. Among tenants who utilized crisis care services prior to being housed, reductions were observed in the number of crisis care visits. Results suggest PSH leads to changes in health care utilization and associated costs.
Background: Older adults experiencing chronic homelessness (i.e., prolonged homelessness and a disabling condition) have low rates of advance care planning (ACP) despite high rates of morbidity and mortality. Rehousing of homeless-experienced individuals into permanent supportive housing (PSH) may present an opportunity to introduce ACP; but this is unknown. Therefore, we explored staff and resident perceptions of conducting ACP in PSH. Methods: We conducted semi-structured interviews with PSH staff (n = 13) and tenants (PSH residents) (n = 26) in San Francisco. We used the capability (C), opportunity (O), motivation (M), behavior (COM-B) framework within the Behavior Change Wheel model and the Theoretical Domains Framework (TDF) to inform interviews, categorize themes, and guide qualitative thematic analysis. Results: The mean age of PSH residents was 67 (SD = 6.1) years and 52% were women. Of staff, 69% were women. Important COM-B barriers included ACP complexity (C), complicated relationship dynamics (O), resource limitations (O), pessimism (M), variable staff confidence (M), and competing priorities (M). Facilitators included easy-to-use documents/videos, including the PREPARE for Your Care program (C), stability with housing (O), exposure to health crises (O), potential for strong relationships (O), and belief that ACP is impactful (M). Recommendations included adapting materials to the PSH setting, providing staff trainings/scripts, and using optional one-on-one or group sessions. Conclusions: We identified behavioral determinants related to ACP for formerly chronically homeless older adults in PSH. Future interventions should include using easy-to-use ACP materials and developing resources to educate PSH residents, train staff, and model ACP in groups or one-on-one sessions.
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Importance In the At Home/Chez Soi trial for homeless individuals with mental illness, the scattered-site Housing First (HF) with Intensive Case Management (ICM) intervention proved more effective than treatment as usual (TAU). Objective To evaluate the cost-effectiveness of the HF plus ICM intervention compared with TAU. Design, Setting, and Participants This is an economic evaluation study of data from the At Home/Chez Soi randomized clinical trial. From October 2009 through July 2011, 1198 individuals were randomized to the intervention (n = 689) or TAU (n = 509) and followed up for as long as 24 months. Participants were recruited in the Canadian cities of Vancouver, Winnipeg, Toronto, and Montreal. Participants with a current mental disorder who were homeless and had a moderate level of need were included. Data were analyzed from 2013 through 2019, per protocol. Interventions Scattered-site HF (using rent supplements) with off-site ICM services was compared with usual housing and support services in each city. Main Outcomes and Measures The analysis was performed from the perspective of society, with days of stable housing as the outcome. Service use was ascertained using questionnaires. Unit costs were estimated in 2016 Canadian dollars. Results Of 1198 randomized individuals, 795 (66.4%) were men and 696 (58.1%) were aged 30 to 49 years. Almost all (1160 participants, including 677 in the HF group and 483 in the TAU group) contributed data to the economic analysis. Days of stable housing were higher by 140.34 days (95% CI, 128.14-153.31 days) in the HF group. The intervention cost $14 496 per person per year; reductions in costs of other services brought the net cost down by 46% to $7868 (95% CI, $4409-$11 405). The incremental cost-effectiveness ratio was $56.08 (95% CI, $29.55-$84.78) per additional day of stable housing. In sensitivity analyses, adjusting for baseline differences using a regression-based method, without altering the discount rate, caused the largest change in the incremental cost-effectiveness ratio with an increase to $60.18 (95% CI, $35.27-$86.95). At $67 per day of stable housing, there was an 80% chance that HF was cost-effective compared with TAU. The cost-effectiveness of HF appeared to be similar for all participants, although possibly less for those with a higher number of previous psychiatric hospitalizations. Conclusions and Relevance In this study, the cost per additional day of stable housing was similar to that of many interventions for homeless individuals. Based on these results, expanding access to HF with ICM appears to be warranted from an economic standpoint. Trial Registration Identifier: ISRCTN42520374
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Objective: Housing First is increasingly put forward as an important component of a pragmatic plan to end homelessness. The literature evaluating the impact of Housing First on criminal justice involvement has not yet been systematically examined. The objective of this systematic review is to examine the impact of Housing First on criminal justice outcomes among homeless people with mental illness. Method: Five electronic databases (PsycINFO, MEDLINE, Embase, CINAHL, Web of Science) were searched up until July 2018 for randomised and nonrandomised studies of Housing First among homeless people with a serious mental disorder. Results: Five studies were included for a total of 7128 participants. Two studies from a randomised controlled trial found no effect of Housing First on arrests compared to treatment as usual. Other studies compared Housing First to other programs or compared configurations of HF and found reductions in criminal justice involvement among Housing First participants. Conclusions: This systematic review suggests that Housing First, on average, has little impact on criminal justice involvement. Community services such as Housing First are potentially an important setting to put in place strategies to reduce criminal justice involvement. However, forensic mental health approaches such as risk assessment and management strategies and interventions may need to be integrated into existing services to better address potential underlying individual criminogenic risk factors. Further outcome assessment studies would be necessary.
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Introduction: Frequent users of emergency departments (ED) account for 21-28% of all ED visits nationwide. The objective of our study was to identify characteristics unique to patients with psychiatric illness who are frequent ED users for mental health care. Understanding unique features of this population could lead to better care and lower healthcare costs. Methods: This retrospective analysis of adult ED visits for mental healthcare from all acute care hospitals in California from 2009-2014 used patient-level data from California's Office of Statewide Health Planning and Development. We calculated patient demographic and visit characteristics for patients with a primary diagnosis of a mental health disorder as a percentage of total adult ED visits. Frequent ED users were defined as patients with more than four visits in a 12-month period. We calculated adjusted rate ratios (aRR) to assess the association between classification as an ED frequent user and patient age, sex, payer, homelessness, and substance use disorder. Results: In the study period, 846,867 ED visits for mental healthcare occurred including 238,892 (28.2%) visits by frequent users. Patients with a primary mental health diagnosis and a co-occurring substance use diagnosis in the prior 12 months (77% vs. 37%, aRR [4.02], 95% confidence interval [CI] [3.92-4.12]), homelessness (2.9% vs 1.1%, odds ratio [1.35], 95% [CI] [1.27-1.43]) were more likely to be frequent users. Those covered by Medicare (aRR [3.37], 95% CI [3.20-3.55]) or the state's Medicaid program Medi-Cal (aRR [3.10], 95% CI [2.94-3.25]) were also more likely to be frequent users compared with those with private insurance coverage. Conclusion: Patients with substance use disorders, homelessness and public healthcare coverage are more likely to be frequent users of EDs for mental illness. Substance use and housing needs are important factors to address in this population.
Frequent emergency department (ED) users often have complex behavioral health and social needs. However, policy makers often focus on this population's medical system use without examining its use of behavioral health and social services systems. To illuminate the wide-ranging needs of frequent ED users, we compared medical, mental health, substance use, and social services use among nonelderly nonfrequent, frequent, and superfrequent ED users in San Francisco County, California. We linked administrative data for fiscal years 2013-15 for beneficiaries of the county's Medicaid managed care plan to a county-level integrated data system. Compared to nonfrequent users, frequent users were disproportionately female, white or African American/black, and homeless. They had more comorbidities and annual outpatient mental health visits (11.93 versus 4.16), psychiatric admissions (0.73 versus 0.07), and sobering center visits (0.17 versus <0.01), as well as disproportionate use of housing and jail health services. Our findings point to the need for shared knowledge across domains, at the patient and population levels. Integrated data can serve as a systems improvement tool and help identify patients who might benefit from coordinated care management. To deliver whole-person care, policy makers should prioritize improvements in data sharing and the development of integrated medical, behavioral, and social care systems.
Background: Housing First is increasingly implemented for homeless adults with mental illness in large urban centres, but little is known about its long-term effectiveness. The At Home/Chez Soi randomised controlled trial done in five cities in Canada showed that Housing First improved housing stability and other select health outcomes. We extended the At Home/Chez Soi trial at the Toronto site to evaluate the long-term effects of the Housing First intervention on housing and health outcomes of homeless adults with mental illness over 6 years. Methods: The At Home/Chez Soi Toronto study was a randomised, controlled trial done in Toronto (ON, Canada). Here, we present the results of an extension study done at the same site. Participants were homeless adults (aged ≥18 years) with a serious mental disorder with or without co-occurring substance use disorder. In phase 1, participants were stratified by level of need for mental health support services (high vs moderate), and randomly assigned (1:1) using adaptive randomisation procedures to Housing First with assertive community treatment (HF-ACT), Housing First with intensive case management (HF-ICM), or to treatment as usual (TAU). Participants with moderate support needs were further stratified by ethnoracial status. Considering the nature of the Housing First intervention, study participants and study personnel were not masked to group assignment. Phase 1 participants could choose to enrol in the extension study (phase 2). The primary outcome was the rate of days stably housed per year analysed in the modified intention-to-treat population, which included all randomly assigned participants who had at least one assessment for the primary outcome. Participants contributed data to the study up to the point of their last interview. Multilevel multiple imputation was used to handle missing data. The trial was registered with ISRCTN, ISRCTN42520374. Findings: Between Oct 1, 2009, and March 31, 2013, 575 individuals participated in phase 1 of the Toronto Site At Home/Chez Soi study (197 [34%] participants with high support needs and 378 [66%] with moderate support needs). Of the 378 participants with moderate support needs, 204 were randomly assigned to receive the HF intervention with ICM or with ethnoracial-specific ICM services (HF-ER-ICM; HF-ICM or HF-ER-ICM groups) and 174 were randomly assigned to TAU. Of the 197 participants with high support needs, 97 were randomly assigned to receive the HF intervention with ACT (HF-ACT treatment group) and 100 were randomly assigned to TAU group. Between Jan 1, 2014, and March 31, 2017, 414 (81%) of 575 phase 1 participants participated in the extended phase 2 study. The median duration of follow-up was 5·4 years (IQR 2·1-5·9). Among phase 2 participants, 141 had high support needs (79 participants in the HF-ACT group; 62 participants in the TAU group), and 273 had moderate support needs (160 participants in the HF-ICM or HF-ER-ICM group; 113 participants in the TAU group). 187 high support needs participants (93 participants in the HF-ACT group, 94 participants in the TAU group), and 361 moderate support needs participants (201 participants in the HF-ICM or HF-ER-ICM group, 160 participants in the TAU group) were included in the modified intention-to-treat analysis for the primary outcome. The number of days spent stably housed was significantly higher among participants in the HF-ACT and HR-ICM or HF-ER-ICM groups than participants in the TAU groups at all timepoints. For participants with moderate support needs, the rate ratio (RR) of days stably housed in the Housing First group, compared with TAU, was 2·40 (95% CI 2·03-2·83) in year 1, which decreased to 1·13 (1·01-1·26) in year 6. The RR of days stably housed for participants with high support needs, compared with TAU, was 3·02 (2·43-3·75) in year 1 and 1·42 (1·19-1·69) in year 6. In year 6, high support needs participants in the Housing First group spent 85·51% of days stably housed compared with 60·33% for the TAU group, and moderate needs participants in the Housing First group spent 88·16% of days stably housed compared with 78·22% for the TAU group. Interpretation: Rent supplements and mental health support services had an enduring positive effect on housing stability for homeless adults with mental illness in a large, resource-rich urban centre, with a larger impact on individuals with high support needs than moderate support needs. Funding: Mental Health Commission of Canada, Ontario Ministry of Health and Long-Term Care, and the Canadian Institute of Health Research.
In the United States and abroad, health systems have begun to address housing insecurity through programs that adhere to the Housing First model. The model provides permanent supportive housing without disqualification due to current mental health problems or substance use, along with optional case management services. This study used qualitative methods to explore how housing stability affected chronic disease management and social and community relationships among individuals with complex health and social needs and patterns of high hospital utilisation who were housed as part of a scattered‐site Housing First program in a mid‐size city in the northeastern United States. 26 individual, semi‐structured interviews were conducted with Housing First clients in their homes or day program sites between March and July 2017. Interviews were digitally recorded and transcripts were analysed using a qualitative descriptive methodology until thematic saturation was reached. Findings suggest that housing provided the physical location to manage the logistical aspects of care for these clients, and an environment where they were better able to focus on their health and wellness. Study participants reported less frequent use of emergency services and more regular interaction with primary care providers. Additionally, case managers' role in connecting clients to behavioural health services removed barriers to care that clients had previously faced. Housing also facilitated reconnection with family and friends whose relationships with participants had become strained or distant. Changes to physical and social communities sometimes resulted in experiences of stigmatisation and exclusion, especially for clients who moved to areas with less racial and socioeconomic diversity, but participation in the program promoted an increased sense of safety and security for many clients.
Objective We aimed to describe interactions between police and persons who experience homelessness and serious mental illness and explore whether housing status is associated with police interactions. Method We conducted a secondary analysis of 2008 to 2013 data from the Toronto, Canada, site of the At Home/Chez Soi study. Using police administrative data, we calculated the number and types of police interactions, the proportion of charges for acts of living and administration of justice, and the proportion of occurrences due to victimization, involuntary psychiatric assessment, and suicidal behavior. Using generalized estimating equations, we estimated the odds of police interaction by housing status. Results This study included 547 adults with mental illness who were homeless at baseline. In the year prior to randomization, 55.8% of participants interacted with police, while 51.7% and 43.0% interacted with police in Study Years 1 and 2, respectively. Of 2,228 charges against participants, 12.6% were due to acts of living and 21.2% were for administration of justice. Of 518 occurrences, 41.1% were for victimization, 45.6% were for mental health assessment, and 22.2% were for suicidal behavior. The odds of any police interaction during the past 90 days was 47% higher for those who were homeless compared to those who were stably housed (95% CI 1.26 to 1.73). Conclusions For people who experience homelessness and mental illness in Toronto, Canada, interactions with police are common. The provision of stable housing and changes in policy and practice could decrease harms and increase health benefits associated with police interactions for this population.
Importance Previous studies have shown high mortality rates among homeless people in general, but little is known about the patterns of mortality among “rough sleepers,” the subgroup of unsheltered urban homeless people who avoid emergency shelters and primarily sleep outside. Objectives To assess the mortality rates and causes of death for a cohort of unsheltered homeless adults from Boston, Massachusetts. Design, Setting, and Participants A 10-year prospective cohort study (2000-2009) of 445 unsheltered homeless adults in Boston, Massachusetts, who were seen during daytime street and overnight van clinical visits performed by the Boston Health Care for the Homeless Program’s Street Team during 2000. Data used to describe the unsheltered homeless cohort and to document causes of death were gathered from clinical encounters, medical records, the National Death Index, and the Massachusetts Department of Public Health death occurrence files. The study data set was linked to the death occurrence files by using a probabilistic record linkage program to confirm the deaths. Data analysis was performed from May 1, 2015, to September 6, 2016. Exposure Being unsheltered in an urban setting. Main Outcomes and Measures Age-standardized all-cause and cause-specific mortality rates and age-stratified incident rate ratios that were calculated for the unsheltered adult cohort using 2 comparison groups: the nonhomeless Massachusetts adult population and an adult homeless cohort from Boston who slept primarily in shelters. Results Of 445 unsheltered adults in the study cohort, the mean (SD) age at enrollment was 44 (11.4) years, 299 participants (67.2%) were non-Hispanic white, and 72.4% were men. Among the 134 individuals who died, the mean (SD) age at death was 53 (11.4) years. The all-cause mortality rate for the unsheltered cohort was almost 10 times higher than that of the Massachusetts population (standardized mortality rate, 9.8; 95% CI, 8.2-11.5) and nearly 3 times higher than that of the adult homeless cohort (standardized mortality rate, 2.7; 95% CI, 2.3-3.2). Non-Hispanic black individuals had more than half the rate of death compared with non-Hispanic white individuals, with a rate ratio of 0.4 (95% CI, 0.2-0.7; P < .001). The most common causes of death were noncommunicable diseases (eg, cancer and heart disease), alcohol use disorder, and chronic liver disease. Conclusions and Relevance Mortality rates for unsheltered homeless adults in this study were higher than those for the Massachusetts adult population and a sheltered adult homeless cohort with equivalent services. This study suggests that this distinct subpopulation of homeless people merits special attention to meet their unique clinical and psychosocial needs.
Objective Individuals who are homeless or vulnerably housed have a higher prevalence of concurrent disorder, defined as having a mental health diagnosis and problematic substance use, compared to the general housed population. The study objective was to investigate the effect of having a concurrent disorder on health care utilization among homeless or vulnerably housed individuals, using longitudinal data from the Health and Housing in Transition Study. Methods In 2009, 1190 homeless or vulnerably housed adults were recruited in Ottawa, Toronto and Vancouver, Canada. Participants completed baseline interviews and four annual follow-up interviews, providing data on socio-demographics, housing history, mental health diagnoses, problematic drug use with the Drug Abuse Screening Test (DAST-10 ≥ 6), problematic alcohol use with the Alcohol Use Disorders Identification Test (AUDIT ≥ 20), chronic health conditions, and utilization of the following health care services: emergency department (ED), hospitalization, and primary care. Concurrent disorder was defined as the participant having ever received a mental health diagnosis at baseline and having problematic substance use (i.e., DAST-10 ≥ 6 and/or AUDIT ≥ 20) at any time during the study period. Three generalized mixed effects logistic regression models were used to examine the independent association of having a concurrent disorder and reporting ED use, hospitalization, or primary care visit in the past 12 months. Results Among our sample of adults who were homeless or vulnerably housed, 22.6% (n = 261) reported having a concurrent disorder at baseline. Individuals with concurrent disorder had a significantly higher odds of ED use (AOR 1.71; 95% CI [1.4, 2.11]), hospitalization (AOR 1.45; 95% CI [1.16, 1.81]) and primary care visit (AOR 1.34; 95% CI [1.05, 1.71]) in the past 12 months over the four-year follow-up period, after adjusting for potential confounders. Conclusion Concurrent disorder was associated with higher rates of health care utilization, when compared to those without concurrent disorder among homeless and vulnerably housed individuals. Comprehensive programs that integrate mental health and addiction services with primary care as well as community-based outreach may better address the unmet health care needs of individuals living with concurrent disorder who are vulnerable to poor health outcomes.
Homelessness and incarceration share a bidirectional association: individuals experiencing homelessness are more likely to be incarcerated and former inmates are more likely to become homeless. Permanent supportive housing (PSH) programs have demonstrated positive outcomes for participants with criminal histories, yet participants continue to exit to jail or prison and experience subsequent homelessness. Using data on Veterans participating in a PSH program at 4 locations between 2011 and 2014 (N = 1,060), logistic regression was used to examine the risk factors for exiting PSH because of incarceration and returning to homelessness. Though exiting because of incarceration was uncommon, Veterans with a drug use disorder who decreased the frequency of related care over time had an increased risk for this outcome, and a history of incarceration increased Veterans' risk of experiencing ongoing homelessness. Findings can inform housing and reentry interventions which should account for participant risk factors and service needs in an effort to end the cycle of homelessness and incarceration.