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Designing and Interpreting Cross-Sector Research
John A. Pandiani, Ph.D.Vermont Department of Mental Health
and The Bristol Observatory
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Data Are Everywhere
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Cross-Sector Research
Why ? How ?
and
Thinking About Findings
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Why Use Cross-Sector Research?
Enhance the Value of Your Data
Already Paid For
No New Data Collection
Improve Data Quality
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Two CMH Homeless Rates CMH MIS HMIS Overlap
0%
1%
2%
3%
4%
5%
Total SA AOP CRT
0%
1%
2%
3%
4%
5%
Total SA AOP CRT
0%
1%
2%
3%
4%
5%
Total SA AOP CRT
0%
1%
2%
3%
4%
5%
Total SA AOP CRT
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Why Use Cross-Sector Research?
ProvidesProspective, Retrospective,
andGeneral Population Comparisons
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How to do Cross-Sector Research
Direct Linkage
Probabilistic Linkage Case Management
Probabilistic Overlap EstimationService Systems Research
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jl
ijj ilP
1 365
365
This equation counts the number of birth dates to estimate the unduplicated number of people.
Probabilistic Population Estimation
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Birth Dates Number of People1 1.003 ± 0.10310 10.15 ± 0.776100 117 ± 9200 290 ± 22300 632 ± 64350 1,177 ± 171360 1,603 ± 325
For Specified Numbers of Birth Dates Within a Given Year
Probabilistic Population Estimation
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(A B) = A + B – (A B)
Caseload Overlap
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Two Critical Dimensions
Time and Space
Geographical SpaceOrganizational Space
Thinking About Findings
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Program Evaluation Paradigm
Access to Care
Treatment Outcomes
Practice Patterns
Thinking About Findings
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Thinking About Findings Discovery vs. Verification
My Approach
ObservationalIncrementalPragmatic
Follow the Flow of Analysis
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Widespread Sharing
Broad Community of Learners
Variety of Perspectives
Disseminating Findings
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Access to Care
Treatment Outcomes
Practice Patterns
Other Cross-Sector Analyses
Sixteen Examples of Cross-Sector Research Findings
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Access to Care
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0%
20%
40%
60%
Total Male Female 18-34 35-49 50+
Vermont New Hampshire
Homeless Adults’ Access to CMH Services
0%
20%
40%
60%
Total Male Female 18-34 35-49 50+
Vermont New Hampshire
Homeless Adults’ Access to CMH Services
0%
20%
40%
60%
Total Male Female 18-34 35-49 50+
Vermont New Hampshire
Homeless Adults’ Access to CMH Services
0%
20%
40%
60%
Total Male Female 18-34 35-49 50+
Vermont New Hampshire
Homeless Adults’ Access to CMH Services
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0%
10%
20%
30%
40%
Total Men Women 18-34 35-49 50+
Vermont Population Criminal Charge Homeless
Community MH Utilization
0%
10%
20%
30%
40%
Total Men Women 18-34 35-49 50+
Vermont Population Criminal Charge Homeless
Community MH Utilization
0%
10%
20%
30%
40%
Total Men Women 18-34 35-49 50+
Vermont Population Criminal Charge Homeless
Community MH Utilization
0%
10%
20%
30%
40%
Total Men Women 18-34 35-49 50+
Vermont Population Criminal Charge Homeless
Community MH Utilization
0%
10%
20%
30%
40%
Total Men Women 18-34 35-49 50+
Vermont Population Criminal Charge Homeless
Community MH Utilization
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Percent of SMI
<10% 10%-15%
15%-20% 20%+
SMI in Section 8 Housing in VT and CT
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0%
5%
10%
15%
20%
25%
Total Male Female 18-34 35-49 50-64 65+ White Other
Connecticut Vermont
SMI with Section 8 Housing
0%
5%
10%
15%
20%
25%
Total Male Female 18-34 35-49 50-64 65+ White Other
Connecticut Vermont
SMI with Section 8 Housing
0%
5%
10%
15%
20%
25%
Total Male Female 18-34 35-49 50-64 65+ White Other
Connecticut Vermont
SMI with Section 8 Housing
0%
5%
10%
15%
20%
25%
Total Male Female 18-34 35-49 50-64 65+ White Other
Connecticut Vermont
c
SMI with Section 8 Housing
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0%
10%
20%
30%
40%
50%
Medical MH SA
Homeless General Population
ER Utilization by Diagnosis, VT Adults
0%
10%
20%
30%
40%
50%
Medical MH SA
Homeless General Population
ER Utilization by Diagnosis, VT Adults
0%
10%
20%
30%
40%
50%
Medical MH SA
Homeless General Population
ER Utilization by Diagnosis, VT Adults
0%
10%
20%
30%
40%
50%
Medical MH SA
Homeless General Population
ER Utilization by Diagnosis, VT Adults
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Relative Rate of ER Utilization Homeless vs. Other VT Adults
0
5
10
15
20
25
30
Total Male Female 18-34 35+
Rela
tive
Rate
Medical Mental Health Substance Abuse
Relative Rate of ER Utilization Homeless vs. Other VT Adults
0
5
10
15
20
25
30
Total Male Female 18-34 35+
Rela
tive
Rate
Medical Mental Health Substance Abuse
1
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Treatment Outcomes
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Pe
rce
nt
Arr
es
ted
0%
10%
20%
30%
40%
50%
1 2 3 4 5 6 7 8 9 10Region
Before Treatment After Treatment
VT Substance Abuse Service Recipients Arrested Before and After Treatment
Pe
rce
nt
Arr
es
ted
0%
10%
20%
30%
40%
50%
1 2 3 4 5 6 7 8 9 10Region
Before Treatment After Treatment
Pe
rce
nt
Arr
es
ted
0%
10%
20%
30%
40%
50%
1 2 3 4 5 6 7 8 9 10Region
Before Treatment After Treatment
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VT SMI Service Recipients Incarcerated Before and After Treatment
0%
5%
10%
15%
20%
Total M F 18-34 35-49 50-64 M F M F M F
Gender Age 18-34 35-49 50-64
Per
cen
t In
carc
erat
ed
Before Treatment After Treatment
VT SMI Service Recipients Incarcerated Before and After Treatment
0%
5%
10%
15%
20%
Total M F 18-34 35-49 50-64 M F M F M F
Gender Age 18-34 35-49 50-64
Per
cen
t In
carc
erat
ed
Before Treatment After Treatment
VT SMI Service Recipients Incarcerated Before and After Treatment
0%
5%
10%
15%
20%
Total M F 18-34 35-49 50-64 M F M F M F
Gender Age 18-34 35-49 50-64
Per
cen
t In
carc
erat
ed
Before Treatment After Treatment
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Relative Risk of ArrestYoung Males (18 - 34) in Washington, DC
Post 9/11
Sep 2001 - Sep 2002
Post 9/11
Sep 2001 - Sep 2002
Pre 9/11
0
1
2
3
4
Rel
ativ
e R
isk
Oct 1999 - Aug 2001
Pre 9/11
0
1
2
3
4
Rel
ativ
e R
isk
Oct 1999 - Aug 2001
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Practice Patterns
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Residential Situation after State Hospital Discharge
0%
25%
50%
75%
Nursing
CMHC
Inpatient
Incarcerated
Section 8
Total M F M F M F M F
Gender 18-34 35-49 50+
Residential Situation after State Hospital Discharge
0%
25%
50%
75%
Nursing
CMHC
Inpatient
Incarcerated
Section 8
Total M F M F M F M F
Gender 18-34 35-49 50+
Residential Situation after State Hospital Discharge
0%
25%
50%
75%
Nursing
CMHC
Inpatient
Incarcerated
Section 8
Total M F M F M F M F
Gender 18-34 35-49 50+
Residential Situation after State Hospital Discharge
0%
25%
50%
75%
Nursing
CMHC
Inpatient
Incarcerated
Section 8
Total M F M F M F M F
Gender 18-34 35-49 50+
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Adult MH Outpatient Clients with Less than 4 Services
0%
20%
40%
60%
80%
100%
Homeless Other
Per
cen
t o
f C
lien
ts S
erve
d
0%
20%
40%
60%
80%
100%
Homeless Other
Per
cen
t o
f C
lien
ts S
erve
d
0%
20%
40%
60%
80%
100%
Homeless Other
Per
cen
t o
f C
lien
ts S
erve
d
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Other Cross-Sector Analyses
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Relative Rate of Domestic Violence Homeless Compared to General Population
0
5
10
15
20
25
Overall Men Women 18-34 35+
Rel
ativ
e R
ate
Offender Victim
0
5
10
15
20
25
Overall Men Women 18-34 35+
Rel
ativ
e R
ate
Offender Victim
0
5
10
15
20
25
Overall Men Women 18-34 35+
Rel
ativ
e R
ate
Offender Victim
0
5
10
15
20
25
Overall Men Women 18-34 35+
Rel
ativ
e R
ate
Offender Victim
1
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Homeless Adults with Services as Youth
0%
10%
20%
30%
40%
50%
Total Men Women
Perc
ent o
f Hom
eles
s Yo
ung
Adul
ts
Youth with Subsequent Adult Homelessness
0%
10%
20%
30%
40%
50%
Total Boys Girls
Perc
ent o
f You
th
CMH (SED) DCF (Delinquent) Special Education (EBD)
Children’s Services & Adult Homeless
Homeless Adults with Services as Youth
0%
10%
20%
30%
40%
50%
Total Men Women
Perc
ent o
f Hom
eles
s Yo
ung
Adul
ts
Youth with Subsequent Adult Homelessness
0%
10%
20%
30%
40%
50%
Total Boys Girls
Perc
ent o
f You
th
Homeless Adults with Services as Youth
0%
10%
20%
30%
40%
50%
Total Men Women
Perc
ent o
f Hom
eles
s Yo
ung
Adul
ts
Youth with Subsequent Adult Homelessness
0%
10%
20%
30%
40%
50%
Total Boys Girls
Perc
ent o
f You
th
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0
1
2
3
4
5
6
7
Total Men Women Men Women Men Women18-49 50+
Pe
rce
nt
Ho
me
les
s
Veterans Non-veterans
0
1
2
3
4
5
6
7
Total Men Women Men Women Men Women18-49 50+
Pe
rce
nt
Ho
me
les
s
Veterans Non-veterans
Homelessness among MH Service Recipients
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People with Homeless Services in Bordering Counties of Vermont & New Hampshire
Vermont (n =355)
New Hampshire
(n =706)
Both(n = 5)0.5%
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CMHC Overlapwith Eleven Caseloads in CT
0% 10% 20% 30% 40% 50%
Medicaid
Food Stamps
State Medical
Substance Abuse
Corrections
Probation
Cash Benefits
Temporary Family Assist.
Abuse/Neglect Offenders
Motor Vehicle Violators
Mental Retardation
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Thanks to
People who collected these dataPeople who manage the databases
Barbara Carroll, Brennan Martin, & Walter OchsVermont MH Performance Indicator Project
Karen DanyowThe Bristol Observatory
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USE THE
DATA
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