Using HMIS to Identify and
Measure Chronic Homelessness
Ann Oliva, National HMIS Technical Assistance Initiative (Moderator)
Michael Ullman, University of Hawaii
Molly McEvilley, The Partnership Center
J. Stephen Cleghorn, Ph.D., The Community Partnership
September 13-14, 2005
St. Louis, Missouri
Sponsored by the U.S. Department of Housing and Urban Development
Chronic Homelessness
and Beyond
Michael D. Ullman
Honolulu, HI
September 13-14, 2005
St. Louis, Missouri
Sponsored by the U.S. Department of Housing and Urban Development
Short History of Chronic
Estimates
• Kuhn & Culhane, 1998 – 10% chronic and 10% episodic (NY/PA
administrative shelter data).
• HUD combines both definition – adding disabling condition –
issues chronic definition in 2003.
• 2003 CoC application has first request for chronic estimates
• Survey of 36 continuums (reported as % of point-in-time
homeless) shows wide variation (Ullman, 2004)
- All homeless - 3% to 66% (median 20%)
- Sheltered - 2% to 40% (median 17%)
- Unsheltered - 6% to 100% (median 34%)
• Recent studies – Riverside/Pasadena (Colletti & Hodge, 2005)
- Nearly 50% of point in time individuals
- 10% - 20% of annualized total individuals
September 13-14, 2005 St. Louis, Missouri
Sponsored by the U.S. Department of Housing and Urban Development
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Initial 2003 Continuum Estimate
(% of total point-in-time estimate reported to be chronic)
Longview, TX
66.2
Wake Cty, NC
25.3
DuPage Cty, IL
19.2
San Diego, CA
25.2
Bakersfield,CA
18.3
54.4
Miami-Dade, FL
24.7
South Dakota
15.2
48.1
Tarrant Cty, TX
23.2
Durham, NC
15.0
Pensacola, FL
21.8
Santa Clara, CA
15.0
Washington DC
20.3
Guam
11.1
Orange Cty, CA
20.3
Los Angeles, CA
10.0
Oahu
19.9
Sarasota, FL
9.8
Norfolk, VA
19.5
Duluth, MN
6.9
Provo
19.5
Philadelphia, PA
6.9
Hennipin Cty, MN 19.4
Richmond, VA
4.6
Oklahoma City
19.4
Erie, PA
3.0
New York City
19.2
San Bernadino, CA
Long Beach, CA
Neighbor Islands, HI
42.0
Salt Lake City, UT 37.4
Washentaw, MI
35.8
Norman, OK
35.8
Burlington, VT
32.3
Northeast MN
30.9
Abilene, TX
26.9
Utah Balance
26.1
September 13-14, 2005 St. Louis, Missouri
Sponsored by the U.S. Department of Housing and Urban Development
4
Operationalizing the Definition
•
You have two length of stay criteria that makes one “chronic”.
•
What does “continuously” mean?
- Every day homeless?
- One day per month in the HMIS? For 12, 11, 10, 9 mos?
•
What does “episode” mean? What defines NOT BEING
HOMELESS. Difficult to ascertain using just HMIS or shelter data.
•
Point-in-time chronic counts – missing episodically homeless.
•
Is chronic always a percentage of point-in-time? Can it be
annualized? Do you need more months (48) to calculate this?
PIT may underestimate because it can censor right and left.
•
Don’t wait for HUD – data always have problems – just state
your assumptions and modifications – sharing with others CoCs.
September 13-14, 2005 St. Louis, Missouri
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Variations on a Theme
• HUD’s “chronic homeless” definition only a starting point – not
ending point. In theory, indicative of the need for permanent
supportive housing NOT all housing and services needs.
• Be bold – create your own chronic definition variations and
understand why they might be important to you – don’t worry
about what the experts say – remember, you are the experts!
• No one – not even HUD - is not going to penalize you for having
MORE chronic estimates – you could influence future policy!
• Collect/analyze HMIS/survey information for other chronic
definitions including:
- Include families (single, two parent) - good info for grants
- Include non-disabled – chronicity is a disabling condition
- Other shelter patterns may be easier to analyze (180 day rule)
September 13-14, 2005 St. Louis, Missouri
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6
Variations on a Theme
•
Understand subpopulation of chronics – run simple
analysis by key demographic variables.
- % Chronic by Gender
- % Chronic among Military Veterans, Foster pops
- % Chronic among different races/ethnicities
•
Target your agency resources to this subpopulations (or
not) – ensure cultural competent services to ethnicities.
•
Finding the gateways to chronicity is as important as the
“back doors” to general homelessness.
•
Many homeless – the vast majority – find there out
without significant help from the services system – in
fact, sometimes service provision delays transition.
These people appear to be different.
September 13-14, 2005 St. Louis, Missouri
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Variations on a Theme
•
Continue to critically analyze the chronic definition.
•
Are there more salient divisions of homeless clients? Or
within chronic – are there subdivisions?
- Super chronic – perhaps minimum 3 or 5 years chronic
- Older chronics – over 50 and chronic
•
Chronic with serious mental illness vs. Chronic with other
disabling conditions vs. chronic alcoholics.
•
Chronics who won’t use service system – shelter chronics
versus street/park chronics.
•
Again, the key is – do these populations need different
service modalities, resources and show clinical
differences.
September 13-14, 2005 St. Louis, Missouri
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8
Significance of Being Chronic
•
Chronic definition came out from the initial development
of typologies of homelessness (Kuhn and Culhane, 1998)
from administrative shelter data from New York and
Pennsylvania– chronic, episodic, and transitional.
•
Chronic definition is useful because it is a theoretical
dividing line to understand what type of service delivery
may be necessary (permanent supportive housing,
intensive case mgmt, etc).
•
Recent research (Kertesz et al, 2005, Medical Care)
showed that chronicity was related to lower Healthrelated Quality of Life scores to reify the term clinically.
•
More research necessary to refine typologies – remember
original ones were northeastern urban shelter data –
other communities may be very different.
September 13-14, 2005 St. Louis, Missouri
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Significance of being Chronic
•
Chronic definition was also developed to pinpoint “heavy
users” of the system – i.e. most $$ resources.
•
Some chronics do, some don’t. Psychiatric in-patient
hospitalization costs were the key to Culhane’s analysis.
Some have minimum use of hospitalization.
- Some chronics are peaceful and never bother anyone
- Some non-chronics are a pain in the butt
- Which really use the most services?
•
Individuals in the criminal justice system are “expensive”
homeless clients – perhaps they deserve their own
typology – more data needed.
•
Perhaps we need “cost” typologies – that’s what
insurance does. Further linkage to other systems may
allow communities to know real costs.
September 13-14, 2005 St. Louis, Missouri
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10
How Case Workers Can Use
HMIS/Chronic Information
•
Case workers often under-estimate or are unwilling to
label/assess someone as mentally ill or chemically
dependent or other disabilities.
•
Shelter patterns often indicative of disabilities.
- Chronic shelter users (every day/multiple years) typically serious mentally ill (including schizophrenia,
personality disorder)
- Episodically homeless – more typically substance
abusers with milder mental illness.
•
Hawaii experience – nearly all individuals in shelters who
are continuously homeless were assessed as seriously
mentally ill by State Department of Adult Mental Health.
•
Sharing patterns can be helpful for diagnosis
assumptions, feasibility of goals in case plans.
September 13-14, 2005 St. Louis, Missouri
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11
How Case Workers Can Use
HMIS/Chronic Information
•
Social workers/case workers trained to help those “most
requesting service’’ not those who are the most drain on
the system.
•
Matching service intensity with type of chronic or nonchronic person.
•
Chronics with serious mental illness need intensive
services – typically through contracts with State/County
Mental Health services.
•
Case management “light” may be a waste of time and
resources – unless there is a crisis you must attend to.
•
For outreach providers, chronics may also need different
intensity levels – don’t treat them homogenously.
September 13-14, 2005 St. Louis, Missouri
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12
Last Thoughts
•
Everyone is experiencing a learning curve.
•
Staff takes time to understand – very analytical concept
plus it has negative “labeling” attributes. If you involve
staff in determining chronic counts – make sure they are
trained and do the job with methodological rigor.
•
Share other experiences and other prevalence numbers –
region variation should exist – but not too, too much
most likely – controlling for demographics (vets,males).
•
Just state your methods, assumptions and limitations.
•
Consistency over years is better than continuing to
change methods (unless something is radically wrong).
•
Tell me again – how much does the census of the US get
($16 per person; $4.5 billion total) – and how much do
Continuums receive to count the homeless?
September 13-14, 2005 St. Louis, Missouri
Sponsored by the U.S. Department of Housing and Urban Development
13
Identifying the Chronically
Homeless in Homeless
Management Information Systems
Molly McEvilley
The Partnership Center
Cincinnati, OH
September 13-14, 2005
St. Louis, Missouri
Sponsored by the U.S. Department of Housing and Urban Development
Chronic Homelessness
Definition
A person who is chronically homeless is:
“an unaccompanied homeless individual with a
disabling condition who has either been
continuously homeless for a year or more OR
has had at least four episodes of homelessness
in the past three years.”
September 13-14, 2005 St. Louis, Missouri
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Definition of Homeless
Homeless means “a person sleeping in a place not
meant for human habitation (e.g. living on the
streets) or in an emergency homeless shelter.”
September 13-14, 2005 St. Louis, Missouri
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Definition of Disabling Condition
A disabling condition is defined as “a diagnosable
substance use disorder, serious mental illness,
developmental disability, or chronic physical
illness or disability, including the co-occurrence of
two or more of these conditions.”
September 13-14, 2005 St. Louis, Missouri
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Unaccompanied
An unaccompanied homeless individual…
HMIS applications collect data about household
members served with the primary client. If a
client is served as a part of a household group,
then that client can not be considered chronically
homeless.
September 13-14, 2005 St. Louis, Missouri
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Homeless
An unaccompanied homeless individual…
HMIS applications collect data about where the
client slept on the night before program entry. If
the client was sleeping on the streets or in
another place not meant for human habitation, or
in an emergency shelter, the client meets the
homeless criterion in the chronically homeless
definition.
September 13-14, 2005 St. Louis, Missouri
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Disabling Condition
… homeless individual with a disabling condition…
HMIS applications collect information about a
client’s special needs, including Physical
disability, Developmental disability, HIV/AIDS,
Mental health problem, and Substance abuse
problem. A client with one or more of these
special needs meets the definition’s criterion for
disabling condition.
September 13-14, 2005 St. Louis, Missouri
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Disabling Condition (cont.)
HMIS applications also collect information about
income sources.
Supplemental Security Income (SSI) and Social
Security Disability Income (SSDI) are only
awarded to clients with a disability. A client who
is receiving SSI or SSDI may be assumed to have
a disabling condition, even when the condition
itself is not known or documented in HMIS.
September 13-14, 2005 St. Louis, Missouri
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Homelessness History
…who has either been continuously homeless for a
year or more OR has had at least four (4)
episodes of homelessness in the past three
(3) years.
Not all HMIS applications collect this data.
September 13-14, 2005 St. Louis, Missouri
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22
Using HMIS to Identify and
Measure Chronic Homelessness
J. Stephen Cleghorn, Ph.D.
Deputy Executive Director
The Community Partnership for the Prevention of Homelessness
Washington, D.C.
September 13-14, 2005
St. Louis, Missouri
Sponsored by the U.S. Department of Housing and Urban Development
Using HMIS to Identify and Measure
Chronic Homelessness
Philosophical Question
If chronically homeless persons live
on the streets or in our shelters, but
we cannot identify them in our HMIS,
do they really exist?
September 13-14, 2005 St. Louis, Missouri
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Using HMIS to Identify and Measure
Chronic Homelessness
Answer…
(escaping effortlessly the bonds of philosophy)
Of course they do!
It’s our job to create the means to
count them and help them get out
of the forest of homelessness.
September 13-14, 2005 St. Louis, Missouri
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Using HMIS to Identify and Measure
Chronic Homelessness
To do so, we must
use not only the
technology but our
people on the front
lines of services,
many of whom
know what we are
looking for but do
not record it
accurately.
September 13-14, 2005 St. Louis, Missouri
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Using HMIS to Identify and Measure
Chronic Homelessness
The Ideal:
• Length of Stay (LOS - continuous
& episodic) +
• Diagnosed Disability +
• Single Status =
• a Chronically Homeless Person
• Let the Count Begin!
September 13-14, 2005 St. Louis, Missouri
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27
Using HMIS to Identify and Measure
Chronic Homelessness
The Reality:
• Many CoCs do not have enough
longitudinal data to examine LOS.
• Many chronically homeless persons do
not have on record or will not reveal a
clinical diagnosis. (Imagine that!).
• Much of disability information is
provider-observed, often by people
without credentials to make a
diagnosis.
• LOS information is dependent in many
cases on quality data input.
September 13-14, 2005 St. Louis, Missouri
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28
Using HMIS to Identify and Measure
Chronic Homelessness
And there is still more to
cloud over our ideal count,
but let’s leave that out for
now because, as Jack
Nicholson once put it,
“You can’t
handle the
truth!”
September 13-14, 2005 St. Louis, Missouri
Sponsored by the U.S. Department of Housing and Urban Development
29
Using HMIS to Identify and Measure
Chronic Homelessness
No, just kidding…
We must remain calm, tell our
stories about counting the
chronically homeless, and learn
from one another. (Ommmm…)
The D.C. story is one of an
evolving methodology, part human
(on paper, no less!) and part
machine (HMIS) as of this year.
September 13-14, 2005 St. Louis, Missouri
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30
Using HMIS to Identify and Measure
Chronic Homelessness
• It begins with an annual regional enumeration of
the homeless with the Metropolitan Washington
Council of Governments (COG). The enumeration
covers nine Continuua of Care (CoCs).
• Three years ago, as the federal definition of
“chronically homeless” was emerging and HUD
began asking us in the 2002 SuperNOFA exhibit
to declare a strategy for ending chronic
homelessness, our regional COG homeless
committee decided in to add to its 2003 Point-inTime a survey a new data element.
September 13-14, 2005 St. Louis, Missouri
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31
Using HMIS to Identify and Measure
Chronic Homelessness
• The definition evolved:
• 2003 P-I-T: HUD’s working definition for “chronically
homeless” was: “An unaccompanied disabled
individual who has been continuously homeless for
over one year.”
• 2004 & 2005 P-I-T: HUD’s definition for “chronically
homeless” became: “An unaccompanied homeless
individual with a disabling condition who has either
been continuously homeless for a year or more OR
has had at least four (4) episodes of homelessness in
the past three (3) years.”
September 13-14, 2005 St. Louis, Missouri
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32
Using HMIS to Identify and Measure
Chronic Homelessness
Wow!
We’re so glad HUD
cleared that up.
But seriously…
September 13-14, 2005 St. Louis, Missouri
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33
Using HMIS to Identify and Measure
Chronic Homelessness
It was great to
have a final
definition. Now
we could really
get to it!
September 13-14, 2005 St. Louis, Missouri
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34
Using HMIS to Identify and Measure
Chronic Homelessness
So we began training
front line providers on
the finalized definition
and asking them to
include a count of the
chronically homeless
in the regional
enumeration.
September 13-14, 2005 St. Louis, Missouri
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35
Using HMIS to Identify and
Measure Chronic Homelessness
• Count based on
provider observations
of length of stay and
perceived disabilities.
• Mostly recorded the
old-fashioned way.
• (Hey, this was 2003!)
September 13-14, 2005 St. Louis, Missouri
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Using HMIS to Identify and Measure
Chronic Homelessness
• Which we then
laboriously counted up
by hand and entered
into a D.C. and regional
spreadsheet to end all
spreadsheets!
• Until 2005, when we
said: No more!
September 13-14, 2005 St. Louis, Missouri
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Using HMIS to Identify and Measure
Chronic Homelessness
• By 2004, our HMIS software solution had
added a nifty data element to our client
profile:
• “Is client chronically homeless?”
• Possible answers: “Yes” and “No”
September 13-14, 2005 St. Louis, Missouri
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38
Using HMIS to Identify and Measure
Chronic Homelessness
• So we said:
• Par-tay!
• Problem solved!
Now we can count
the chronically
homeless.
September 13-14, 2005 St. Louis, Missouri
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39
Using HMIS to Identify and Measure
Chronic Homelessness
• But soon that cloud of doubt returned and
rained down on our new found ideal as we
learned that those pesky providers were
not checking off the question and we were
stranded in “the Land of Null.”
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Using HMIS to Identify and Measure
Chronic Homelessness
The Land of Null
September 13-14, 2005 St. Louis, Missouri
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Using HMIS to Identify and Measure
Chronic Homelessness
• OK, SO THE QUESTION, CLEAR AS IT WAS,
UNDERPINNED BY TWO YEARS OF TRAINING…
WAS NOT BEING ANSWERED!
WHAT TO DO?
September 13-14, 2005 St. Louis, Missouri
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42
Using HMIS to Identify and Measure
Chronic Homelessness
• Get back to the point-in-time count, but this time
do it on the computer, at least in D.C., which
would serve as the guinea pig for the region.
• So we designed a point-in-time (P-I-T)
assessment for our HMIS which incorporated the
chronic homeless question based on provider
observation of LOS and disability, and we said to
our pesky and beloved providers that for the
P-I-T we would not take “NULL” for an answer!
September 13-14, 2005 St. Louis, Missouri
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43
Using HMIS to Identify and Measure
Chronic Homelessness
The
Washington,
D.C.
Point-in-time
assessment
within HMIS
September 13-14, 2005 St. Louis, Missouri
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Using HMIS to Identify and Measure
Chronic Homelessness
The Washington, D.C.
Point-in-time assessment within HMIS
(continued)
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Using HMIS to Identify and Measure
Chronic Homelessness
The idea was:
• We would identify all persons known to be
staying with providers on that day, and…
• Providers would identify any they believed to be
chronically homeless, and…
• We would take a month after that to “perfect”
(i.e., complete) data on all clients by answering
ALL questions on the P-I-T assessment.
September 13-14, 2005 St. Louis, Missouri
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46
Using HMIS to Identify and Measure
Chronic Homelessness
Thus emerging from
the gloom in the Land
of Null into the bright
sunlight of completed
data that finally
revealed how many
homeless people were
with us all the time.
September 13-14, 2005 St. Louis, Missouri
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47
Using HMIS to Identify and Measure
Chronic Homelessness
But not so fast!
There was still a
problem that left
us, shall we say…
September 13-14, 2005 St. Louis, Missouri
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48
Using HMIS to Identify and Measure
Chronic Homelessness
• Many providers did
not know how
many people, or
who, was sleeping
in their shelter
beds that night!
• Well, they did, but
their data told us
something else.
September 13-14, 2005 St. Louis, Missouri
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49
Using HMIS to Identify and Measure
Chronic Homelessness
• The problem was simple…
• Providers had not enumerated their beds so that
check-in, check-out could be done properly,
without which it was harder to confirm who was
in their shelter on a given day, and…
• They had people in their data who had already
left and they were missing people in their data
who were actually there because they were not
doing entry/exit sheets properly.
September 13-14, 2005 St. Louis, Missouri
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Using HMIS to Identify and Measure
Chronic Homelessness
• Recognizing a killer
problem when we saw
one, we went to work
immediately to
enumerate all our
shelter beds, which
was – as they say, as
simple as…
September 13-14, 2005 St. Louis, Missouri
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51
Using HMIS to Identify and Measure
Chronic Homelessness
We will now observe a
moment of silence in
order not to laugh
over the use of that
phrase “as simple as”
in any context having
to do with an HMIS.
September 13-14, 2005 St. Louis, Missouri
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52
Using HMIS to Identify and Measure
Chronic Homelessness
So by January 26, 2005 we had
made sure that all providers had
properly set up their bed lists and
on that day we took an HMIS
snapshot of who was in their
shelters, according to their data,
which looked something like the
following.
September 13-14, 2005 St. Louis, Missouri
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Using HMIS to Identify and Measure
Chronic Homelessness
September 13-14, 2005 St. Louis, Missouri
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Using HMIS to Identify and Measure
Chronic Homelessness
• We ran bed list reports for all emergency shelter
providers, including 990 seasonal beds on that
cold winter day, printed them and sent them back
to providers to verify the number of persons
there and the names. The accuracy of persons
recorded in their beds on that day varied.
September 13-14, 2005 St. Louis, Missouri
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Using HMIS to Identify and Measure
Chronic Homelessness
So we gave them a month to correct the HMIS bed lists
and entry/exit sheets with their more accurate paper
rosters on site and told them to send a corrected
count and list of names back to us on paper.
With that corrected count in hand, we expected them to
complete the same number of HMIS P-I-T
assessments with “Null” answers not acceptable.
After they did that, we ran reports to get the count of
persons, including how many were chronically
homeless on that day according to the providers’
observations.
September 13-14, 2005 St. Louis, Missouri
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Using HMIS to Identify and Measure
Chronic Homelessness
• This example shows
one shelter’s
response.
• Still some NULL
values, but a complete
response to the
question about chronic
homelessness.
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Using HMIS to Identify and Measure
Chronic Homelessness
Overall, using the P-I-T
survey and monitoring its
use closely gave us a
much more complete
picture of chronically
homeless in emergency
shelters.
We were emerging from
the Land of Null.
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Using HMIS to Identify and Measure
Chronic Homelessness
We were not exactly on top of it
yet, but we were beginning to see
what number was in the cloud. A
few more steps and we would
have our count for 2005.
September 13-14, 2005 St. Louis, Missouri
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Using HMIS to Identify and Measure
Chronic Homelessness
We had good HMIS P-I-T data from some outreach
providers that identified chronically homeless,
plus old-fashioned paper surveys from others.
We had emergency shelters and outreach agencies
not using the HMIS send us their paper surveys.
We tabulated the HMIS data and paper surveys in a
spreadsheet and came up with our count.
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Using HMIS to Identify and Measure
Chronic Homelessness
• 1,773!
• Exactamento!
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Using HMIS to Identify and Measure
Chronic Homelessness
Which satisfied us pretty much
since it showed a percentage of
chronically homeless in our shelters
at a P-I-T count (filling 46% of
available beds, making up 57% of
adults counted in shelter) as
predicted in the writings of the
esteemed professor and all-around
homeless data guru Dr. Dennis
Culhane (well, maybe a little high,
but close).
(and yes, we know Dennis does not have
a mustache & his hair is blonde, but
check out that boyish face underneath.)
September 13-14, 2005 St. Louis, Missouri
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Using HMIS to Identify and Measure
Chronic Homelessness
We still had a few problems to solve…
• Once the data are captured in P-I-T focus period,
making it part of the client’s permanent record.
• Design flaw in the P-I-T assessment that made
data on disabilities incomplete.
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Making P-I-T answer part of permanent record:
• While previously entered data was auto-entered into
the P-I-T assessment, including any previous notation
of chronic homelessness, the “date stamped” P-I-T
assessment data did not reflect back into the client
profile and HUD assessment, meaning we could not
run a P-I-T assessment on any given day later in the
year without re-entering the dreaded “Land of Null”
on the chronic homeless question. This is a design
issue we will fix next time.
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Disability data:
• We wanted disability data pulled into the
P-I-T assessment automatically, so we did
not ask providers to check off a pick-list
of disabilities according to their
observations, the result being that we got
mostly Null values on disabilities because
that assessment very seldom gets done
in the day-to-day. In the next version we
will put back the pick list on disabilities
that gathers provider observations.
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Lessons learned, going forward…
• The P-I-T focus using providers’ observations of
chronic homeless status works well and should be
repeated several times a year.
• Once identified, we can check over time to see
whether the chronically homeless are still using
the shelters or being reported by outreach, even
if they are entering permanent housing in HMIS.
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Lessons learned, going forward…
• Take what we have learned in P-I-T focus and
create a batch of identified chronically homeless
clients that we follow over time and prioritize for
permanent supportive housing.
• Examine the extent to which providers are
focusing services on these clients.
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Lessons learned, going forward…
• Persistence pays; if at first we don’t succeed, try
and try again.
• Learn from others, including those here today at
the HMIS national conference who tell us we did
it all wrong and why were we asked to present,
anyway?
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And keep trying, because it is so
important to know, until we get it right
and usher in that day when no
chronically homeless person goes
uncounted, the Land of Null is a distant
memory, we arrive at that great moment
when all our philosophical questions are
answered…
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and…
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of our D.C. story.
Respectfully submitted.
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