May 4, 2016

Officer’s Mess - Fort Calgary, Alberta

Calgary Homeless Foundation

The University of Calgary

The School of Public Policy

FIRST ANNUAL CANADIAN HOMELESSNESS DATA SHARING INITIATIVE

CALGARY’S HMIS DATA

9:00 – 9:30 a.m.

2008 Calgary was the first city to develop a plan to end homelessness

System to analyze flow and patterns of longevity during stays within shelters,

and graduation rates among tenants; source of income is a positive correlation

(3 year timeframe, 1,542 observed individuals)

Survival analysis – Following tenants

Hazard models – Time, determinants of outcomes

After 1 year, 40% graduate

-QUESTIONS-

How did you define graduation? How clearly tracked within data sets?

Every 3 months, there is follow up assistance. Through chatting and

self-reports information is tracked. Success is determined on minimal

assistance.

Intake is income at the time of housing? Only consider characteristics

during the beginning of the study. Determine before housed if they

have a source of income. Do you consider scattered sites temporary

or graduator? Temporary. Also depends who owns the land.

What should the graduation rate be? Is anyone else collecting data for

a comparative analysis? How can you get a control case?

Did you also look at permanent housing and retention rate? Yes, there

is a mixture of different programs. Some programs are not intended

to graduate from.

CITY OF OTTAWA – A CITY

WIDE SYSTEMOF

INTEGRATED EMERGENCY

SHELTER DATA IN REAL-

TIME

9:30 – 10:00 a.m.

9 Emergency shelters in Ottawa, Homeless Individuals and Families

Information System [HIFIS] - Federally created system

Background: Issues with duplications and merging data, lack of

communication between shelters

2007 Ottawa amalgamated data to a single database, the community

initiated (HIFIS/Citrix Project)

Challenges with data sharing protocol: technical hurdles, HIFIS

software, legal department, privacy and sector visibility, consistent

data dictionary

Page 2

Results Improved communication, better technology, data

integration giving real time metrics and improved accuracy.

Moving forward with new revision of HIFIS, Ottawa used the

information to aid the 10 year plan, also giving the information to the

community and developing a progress report.

-QUESTIONS-

How would the community groups feel about their data being open to

the community? Ottawa has looked at and discussed, will be meeting

with the shelters. Issues with inconsistent data, when you start

releasing data it must be handled the same way among community

members.

How have you dealt with cleaning up duplication? A lot of it had to do

with the software. Now when someone books in a client, they see a

unique ID. Clients can be sorted on resemblance to stored

information. There are still some duplicates in the system. Every

month Ottawa data scrubs and removes duplicates.

How long have you had the data? Since 1997. For the last 10 years

there has been good quality data. What are the values and

characteristics? Full potential = 500. Ex: Mental health, case

management. Limited information on clients with short intake time.

Who is Ottawa Insights? Community knowledge organization, vehicle

to enable citizens to better understand and visualize.

TORONTO’S SHELTER

INFORMATION SYSTEM

10:30 - 11:00 a.m.

“Using data for evidence-based service planning” – The Journey

Coordinate funding, 44% from municipal investment

Key service areas: social housing, emergency shelter, streets to homes,

funding for community based groups

Housing Stability Servicing Plan

Transforming emergency shelters to ‘Housing First’ approach

Shelter Management information System [SMIS]: all 60 shelters are obliged

to use this system. Current feedback is for more sharing methods. Began in

2010, first full set of data was 2011. The original purpose was to track

occupancy within shelter beds real time. System only includes permanent

shelter beds, not rotational/seasonal.

Utility of the information is in ‘length of stay’. 151 days is average length of

stay, 54 days median. The longer the clients remain, the higher use of

available resources.

4 key ways of using the data: system analysis, evidence based decision

making, program design and implementation, evaluating impact and

outcomes.

Page 3

Data was cultivated to build a case for future funding to go to ‘Housing First’

programs instead of previously increasing beds. Goal is to have a better

permanent outcome for clients, and decipher the issues behind long-term

shelter use. Understanding the relationship between acuity and length of

stay.

Hostels to Homes components: coordinated team of housing workers,

standardized assessment of needs, housing allowances, ICM follow-up

supports, partnerships with health sector.

Approximately 2,000 surveys completed per year

Future directions: Create HSMIS, enhance case management capacities and

tracking, integrate our standardized housing support needs assessment

tool,

Challenges: complexity of large service system, ensuring data consistency,

privacy considerations and data sharing, resources for data management

and analysis.

-QUESTIONS-

What predictive analysis are you planning to implement, who will use it? No

current plans, open to suggestions. The tools itself (SMIS) is an operational

tool to help manage shelter occupancy and client information. Front-line

staff would like to see improvements.

How many analytic styles of use? Mostly data manages agencies. Just

starting to shift focus to other ways of use. How many for policy planning

purposes? Approximately 2. Looking to build partnerships!

PANEL ON PIT COUNTS

11:00 – 12:15 a.m.

Panel 1 Patrick Hunter

PiT Count approach: Enumeration, survey data, understanding the

population

U.S. have been mandating since 2003, Canada for over a decade

Different times, seasons, screening questions

7 Cities coordinated count Mid October 2014 (1st time done in Canada)

Motivation to coordinate a national level (61 designated communities

across Canada) Supports: funding, training, tools; Partnerships with COH

Approach Jan – April 2016: common core populations and core screening

and survey questions (31 communities signed on)

Populations: unsheltered, shelters, transitional (Transitional is most

difficult to define nationally). Optional populations include systems and

hidden homeless

Data source: surveyed, observed, emergency shelter and VAW, transitional

housing, systems

Page 4

Survey includes screening to filter, and demographics: aboriginal, military,

immigrants

Difficult to classify chronic homelessness

Uses with data: study changes in population and demographics, emerging

service needs, degrees in reducing homelessness. Also mobilizing in transit

needs, geographical connections, engaging the community and building

partnerships

Variations in weather and methodology

This is a tool to be complemented by other data, may highlight an issue for

a future study

Future steps: reviewing lessons learned, comparative community reports

-QUESTIONS-

Are there shifts and changes among different jurisdictions? Not enough

evidence

Panel 2 Montreal 1st PiT Count Eric Latimer

Methodology based off Alberta, funded by the Montreal mayor

Aimed to count hidden homeless as well and describe population

Count and administration of questionnaires evening of March 24 in outside

location and 9 shelters. Entered soup kitchens 2 days following. 537

volunteers and 18 street workers. Entered restaurants, crack-houses, etc.

Used 50 decoys. Also searched therapy centers, partnered with

complementary summer survey.

Results needed to be reclassified directed at where the individual was last

night

280 street questionnaires, 110 observed homeless. 221 may have been

missed. Adjustment for decoys not found. 3,016 total.

Although comprehensive in the count, Montreal has among the least

individuals. However, highest on the street. Serious health needs, significant

role of youth detention centers, high prevalence of LGBTQ, summer survey

gave insight on population dynamics

Limitations: hidden homeless was difficult, missing sectors

Panel 3 Homelessness Data Discussion Tracey Lauriault

Unpacking socio-technological data assemblage. How does data transform

our cities? How do spaces translate? How do we produce knowledge and

classification systems?

Datadoubles, doppelganger, ghosts, trails/traces shadows/footprints.

Page 5

Dataveillace to survey people (Issues with sensitivity, sharing, privacy)

Case Study in Ireland: How the intake system works, regulatory

environment, how is the computer system completed and employed, how

displacement is classified

Improving methodology for simultaneous homeless census

The Cross-Department Team: Health and children, social, community and

family affairs, justice, department of finance, equality and law reform,

educational institutions.

Developing ways to communicate date in a better way (infographics etc.)

ESPC - Pilot Atlas of the Risk of Homelessness

Searching for a bigger picture and policies behind homelessness. Looking

for prevention methods (Ex: Calgary’s affordable housing, Toronto’s again

social housing stock by neighborhood)

Risk of homelessness variables needs to be standardized across nation (ex.

How much is spent on bus passes). Need to recognize that each city records

differently and looks for different indicators.

Community Data Program co-purchases a variety of data sets. Orders the

information at a community level (neighborhood data) and dispersed

among groups

-QUESTIONS-

Big data issues? Access to HIFIS? Broader analysis of how we can use the

data to change policies?

Panel 4 – Homelessness as a Public Issue Aaron

Receives all HIFIS data (Over 200 emergency shelters in Canada)

Mostly assume mental illness and addiction which have led to the individual

being homeless. However, it is more about the patterns and types of people

affected by homelessness (ex: family homelessness is far different, often stay

in shelters longer, average 50 days, usually only appear once)

Majority of shelter users are one-time users (60-80%)

Increase among older (50-55 years old), and decrease in younger

individuals

Correlation between macro, structural, policies

Homelessness is an issues in both small communities and large cities

-QUESTIONS-

How are surveys related to the PiT Count? To gather demographic data and

meet the PiT count requirements and registry. Waterloo registry week

benefits reducing homelessness community wide with HIFIS

Page 6

“How long have you been in this City?” needs to be supplemented with

questioning if they were homeless before and where they are from? This

would help understand migration patterns between rural and small towns.

Those who are originally rural and homeless are not in the system. May have

been given a bus ticket out of town and were previously homeless but not

documented. Gateway communities such as Prince George and Northern

Ontario have migration patterns different as well.

Concerning the Montreal Summer count; had individuals come from other

areas for the summer? Did they plan on coming and staying from Northern

Aboriginal communities?

Concerning the Political Economy; different cities have very different

profiles. Need to develop a local analysis with unique characteristics (Ex:

Vancouver’s condo boom leads to no affordable housing progression)

Comparing vacancy, rent rates, and availability across the nation in regards

to unique variables within cities needs to be incorporated into the PiT count.

HEALTH AND HOUSING IN

TRANSITION

1:15 – 2:00 p.m.

Observational cohort study of 1,190 homeless and vulnerably housed

adults in 3 cities; Vancouver, Toronto, and Ottawa

Understanding the value of transitions: staying with friends/family,

own housing, hospitals/drug treatment, prisons/jails

Eligibility criteria: shelters/meal programs, vulnerably housed (18+

years old)

Study timeline: baseline housing history, recruitment,1-4 year annual

follow up interviews

Mental health, physical health, health care utilization, narcotics and

alcohol use, chronic health

Results: 1/3 women, large number on disability and pension incomes.

Lifetime duration of homelessness varied from city to city however

averaged 2-3 years. Around half or more had 3+ chronic health

conditions

Strong database for predicting where people go from homelessness,

(what leads to homeless being housed).

Commencing administrative databases

Data requests are circulated to the research team to: ensure

representation from all sites, avoid duplication of efforts, update the

core papers list

-QUESTIONS-

How did you keep track of these individuals for 4 years? Asked for

information in early stages of contact information, where assistance

and cheques were forwarded to, maintained close relationships with

shelters, attempted social networking sites.

Page 7

More information on health related needs across the country? Used

number measures from Stats Canada population health survey as

comparable.

Is there a way to make this database available to other researchers

in a controlled and authorized way for future use?

THE AT HOME/CHEZ SOI DEMONSTRATION PROJECT

Want to look at the effectiveness in different cities of different

variance (2 year study)

Mixed methods, there was research done continually throughout the

program

Currently researching the sustainability of these programs

Allowed to research multiple cities. Often the programs are present

but negotiation is involved.

Looked at fidelity in Housing First programs

Pragmatic, multi-scale, randomized, mixed methods field trial in 5

sites across Canada. Transitioned from eternal to self-resources.

Eligibility: legal adults, absolute homelessness

High needs: psychotic disorders or hospitalized history

Study substance and service use; how to set up a self-report to

capture services over the course of individuals in the study.

70% in Manitoba identified as Aboriginal Persons

Attrition was a reasonable amount. Followed most for the full 2 years.

10% of the total sample participated in consumer narrative

interviews. Currently over 100 publications and over 50 researches

excluding students

Toolkit available online, continuously added to. Aimed at helping

communities launch Housing First and use the learnings across the

country.

-QUESTIONS-

There should be one place of directory homelessness data, how do we

do that? Portals of research based data has been combined. Ottawa U

may have access and contacts. The original people involved tend to

address the research questions. This data now is opened to use of

other researchers (ex: collaborator via England). Research embargos

help create a trusted space of protected data in your possession and

accessibility to share, McGill may have contacts.

Page 8

MANAGED ALCOHOL

PRORAM IN 6 CITIES

2:15 – 2:45 p.m.

(MAPs) Implementation and Effectiveness. Study from the University

of Victoria

Aims to achieve to central issues concerning homelessness

population and alcohol use (Males 38-75%)

Shelters and housing programs differ in how they approach alcohol

use: abstinence-based, tolerant shelters/housing, managed alcohol

programs

What is MAP? Harm reduction approach by providing

accommodation or housing stability, providing help and social

support. 7 MAP sites, 6 cities (Vancouver, Thunder Bay, Toronto,

Hamilton, Ottawa, Sudbury)

3 Year project 2013-2016

Control site partners: targeted engagement and diversion program,

emergency shelters, transitional housing, drop-in and meal programs,

community outreach and senior clubs.

Purpose is to rigorously evaluate MAPs

Does it lead to improvements on health?

4 main types of datasets including quantitative surveys, secondary

administrative data, qualitative interviews, policy and protocol

analysis

364 Quantitative surveys, mapped on gender and age

Survey measures: demographics, homelessness, housing quality and

stability, mental health, social functioning, substance use and related

harms, service and access to care

Follow-up strategy up to 14 months with MAP email and cell phone,

appointment cards, 3 participant contact, sites frequented, access to

shelter records, program staff helped locate individuals as well

78% follow-up for MAP participants

Secondary administrative data sets 10 year period (Jan 2008-Dec

2017). Looking at hospital use, treatment history, liver health, and

death records. Also accessing police service records, corrections

records, MAP data, and shelter use.

Qualitative data includes 82 interviews. 42 MAP clients which were

asked about their life before MAP best/worst experiences in MAP,

impacts on social, family, housing, and health, lastly staff perspective

and relationships between MAP and other programs

Challenges: incredibly challenging to locate the data sources, data

sharing implications, coordinating ethics approvals, informed

consent and privacy, and scope of REB approvals (ex: provincial

corrections wanted to review the entire survey)

Page 9

-QUESTIONS-

How much demand is there for MAP in Edmonton? Demand is

different depending on the city. Not familiar with Edmonton, however

the MAP locations in Thunder Bay illustrated signs of a need, in

Toronto as soon as there is a space available it is filled. A lot of secrecy

around female alcohol use making it harder to refer females into the

MAP programs. Start with one, go from there.

What is your timeline to the ISIS data? Expecting in the fall, however

backlog has shifted the timeline. We are going to start with alcohol

and move forward with other datasets in the fall

Do you have a strategy for getting over the stigma and involving

families? No, but that is very interesting. Many people in the program

have left their families. There is currently no children in the program

but there are paired couples.

Are you studying the impacts of the program opening at the local level

(ex: Sudbury)? One shelter in Hamilton took a strong absence

approach, leads to more issues of how we can direct individuals to

MAP. Looking at MAPs relationships to other programs.

When does non-beverage alcohol consumption begin? Something not

meant for human consumption, hairspray, Listerine mouth wash,

some programs can exchange rubbing alcohol for other beverage.

Is the alcohol administered through the health system? Depends on

the program and the individual. Most it is not medicalized. There is

medical support on site but not administered.

HOMELESSNESS IN

NORTHERN ONTARIO:

QUANTITATIVE AND

QUALITATIVE DATABASES

2:45 – 3:15 p.m.

Background: needed to obtain information about the “State of Homelessness”

in North Eastern Ontario. 160,000 population in Sudbury

Prior 2010; 9 studies found 400-600 homeless individuals (0.3%)

Northern CURA: Poverty, Homelessness, and Migration (PHM)

Implemented a 6 year research project of the center for research in social

justice and policy at Laurentian University.

PHM is bilingual and tri-cultural

Qualitative data involved digital storytelling, photovoice (1297 photos),

interviews (342), focus groups (10), case studies/narrative studies (12),

mixed methods/structured interviews (97 participants)

Difficulties include higher death rate and higher institutionalized

Differs in PiT counts as it prevails 7 days. Objective of period prevalence

counts (PPC). Gathered many socio-demographics and linguistic/cultural

characteristics. Forms of homelessness include absolute, at risk, chronic,

episodic. Information was gained on migration as well. Realistically looking

more at hidden homelessness, in rural areas this is the primary form even

though it is difficult to study

Page 10

Several community surveys and PPCs

Collected data required to remove duplicates, Total unduplicated 10,768

-QUESTIONS-

Is there a lot of back and forth migration? Yes, we need to look further into it,

but yes a lot movement (churn).

Did you do any core interviews or questions? Yes, a lot of interviewing with

indigenous people and the reason behind why they leave and come back.

Primarily has to do with experiencing racism. They have issues renting

property (ex: Timmons close to half). Sudbury has 45% indigenous

homeless. Teams went door to door and observed large crowding of hidden

homelessness.

How do you researchers go home and not intervene from front-line? Do you

become a median resource? Homelessness is not an easy area to do research

in. We do need to know ethics. In Timmons had to ask a present people with

the limits of confidentiality. If you see risk to harm, you must report it.

Comment: In Ireland there are psychologists waiting after the PiT count

Can the photos be used for educational purposes? Yes, we have created

exhibition catalogues. Spend a lot of time gathering, could improve on post-

steps.

OPEN QUESTIONS

3:15 – 3:45 p.m.

Overall how is success measured in the projects and data?

In the context of Montreal’s study, it was a success because it positively

impacted current policy

Where do we go from here, what do we do after a day of data? Where do we

deposit all of this information?

There will be a 2nd day, this was a success, and we will start organizing it now.

With this being the first time we have done this, we brought people with data,

we also need those keen to do analysis, who else do we bring? How largely do

we invite? What is a good sized group that is not exclusive?

Would be beneficial to broaden scope. Need to think about how we can

engage in a process that will change policy.

What is the quantified classifications behind social determinants?

When does data do something past proving a paper? How do we implement

a better program and influence people? Ask yourself why are you doing this

and what will it achieve? Data Doing. How do we use the information to

help a community, agency, or program to help clients succeed?

How can we persuade a national conversation?

Publishing in academic journals is not always achieving the change needed.

Try to push in the eye of the media and policy makers. Government sponsors

a lot of research but cannot treat it as consulting reports.

Page 11

In terms of homelessness research in Alberta, can we direct researchers for

specific important data? Influence data requests?

Can we have one space for homeless data to be accessed?

Do reach out to the engineers.

OVERVIEW

A one-day discussion of datasets on homeless persons being used across Canada, and future collaboration.

Presenters will describe and discuss their data sets and show how the data has been used, or is planned to

be used, in ways to address issues of homelessness

ATTENDANCE

Kofi Ameh, Tim Aubry, Meaghan Bell, Robbie Brydon, Steven Bulgin, Xinje Cul, Herb Emery, Nick Falvo,

Kutuadu Franklin, Bart Gajderowicz, Jim Graham, Patrick Hunter, Ali Jadidzadeh, Carol Kauppi, Ron

Kneebone, Diana Krecsy, Mubasiru Lamidi, Eric Latimer, Tracey Lauriault, Christian Methot, Chidom Otogwu,

Gillian Petit, Shirley Purves, Laural Raine, Kaylee Ramage, Gayle Rees, Joanne Roberts, John Rook, John

Rowland, Jia Ruiting, Heather Schmidt, Aaron Segaert, Samantha Sexsmith, Madison Smith, Karen Tang,

Chayla Van Koughnett, Jeannette Waegemaker Schiff, Ashley Wettlaufer, Adrian Wolfleg. (Apologies if any

name was missed or spelled incorrectly).