Pinellas%20Data%20Collaborative

1

• Pinellas Data Collaborative
• Preliminary Results
• Paul Stiles, J.D., Ph.D. Diane Haynes, M.A.
• 813) 974-9349 voice (813)
  974-8209 voice
• stiles_at_fmhi.usf.edu
  haynes_at_fmhi.usf.edu
• Department of Mental Health Law Policy
• Policy Services Research Data Center
• Louis de la Parte Florida Mental Health Institute
• University of South Florida
• 13301 Bruce B. Downs Blvd.
• Tampa, FL 33612
• [phone] FAX

2
Initial Questions

• What is the measure/degree to which CJIS, DSS,
  MMH, IDS systems have caseload overlap for FY
  98/99?
• What is the measure/degree to which heavy users
  in CJIS, DSS, MMH, IDS systems have caseload
  overlap for FY 98/99?
• What does an individuals service usage look like
  if they access all four systems for FY 98/99?

3
Overview

• The Four Systems (CJIS, DSS, MMH, IDS)
• The Statistical Method used in this study
• Total Population Findings
• Heavy User Population Findings
• Non-Heavy Hitter Population Findings
• Demographics Findings
• Case Studies
• Conclusion

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CJIS Criminal Justice System Of Pinellas County

• An automated computer system that contains
  criminal court and law enforcement related
  activity from the initial arrest, including jail
  movement, court appearances, docketing,
  sentencing and disposition of a case. A System
  Person Number (SPN) is used to identify an
  individual within the CJIS system.

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DSS The Department of Social Services in
Pinellas County

• An automated computer system that contains
  information of services received by individuals
  within the county of Pinellas. This includes
  general assistance, case management, medical
  services, and other assistance. The Social
  Security Number is used to identify an individual
  within the DSS System.

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IDS Integrated Data Systems

• An automated data system of ADM, a division of
  Children and Families dealing with alcohol, drug
  abuse mental health. It contains information
  such as mental health and substance abuse
  services, and demographics. The Social Security
  Number is used to identify an individual within
  the IDS System.

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MMH Medicaid Mental Health

• A statewide database containing Medicaid mental
  health and substance abuse information including
  claims and demographics. The Medicaid Recipient
  ID is used to identify an individual within the
  Medicaid Mental Health System. However, the
  system also has recipient Social Security Numbers.

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Statistical Method

• Probabilistic Population Estimation (PPE)
• Caseload Segregation/Integration Ratio (C-SIR)
• This process relies on information in existing
  databases and the agencies do not have to share
  unique person identifiers. It avoids the expense
  of case-by-case matching and sensitive issues of
  client-patient confidentiality.

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Probabilistic Population Estimation (PPE)

• A statistical method for determining the number
  of people represented in a data set that does not
  contain a unique identifier. The estimation is
  based on a comparison of information on the
  distribution of Date of Birth and Gender in the
  general population with the distribution of Date
  of Birth and Gender observed in the data sets.
• The number of distinct birthday/gender
  combinations that occurred in each data subset
  are counted. The number of people necessary to
  produce the observed number of birthday/gender
  combinations are then calculated.

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Caseload Segregation/Integration Ratio (C-SIR)

• C-SIR
• C-SIR is a rating between 0 and 100 which
  indicates
• the amount of overlap of clients between
  agencies.
• Zero being no overlap at all and 100 being total
• overlap.

?
Duplicated Count Unduplicated Count
Duplicated Count Largest Undup. Count
- 1
- 1

100
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Total PopulationC-SIR Ratings

• MMH IDS
• MMH DSS
• MMH CJIS
• IDS DSS
• IDS CJIS
• DSS CJIS
• Cumulative Overlap between all Systems

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System Integration/Segregation between MMH
IDSC-SIR Rating of 44
         
 

IDS MMH 7,447  
3,996 3,131    
Unique ID Count PPE Count
Population Cross MMH 7,104 7,127

56.06 IDS 11,640 11,443
34.92
 
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System Integration/Segregation Between MMH
DSSC-SIR Rating of 6
 
DSS 15,666
527 6,600 MMH Unique ID Count PPE
Count Population Cross DSS 16,176
16,193 3.25 MMH 7,104
7,127 7.39
 
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System Integration/Segregation between IDS
DSSC-SIR Rating of 7

DSS 14,801
1,392
10,051 IDS Unique ID Count PPE
Count Population Cross DSS 16,176
16,193 8.29 IDS 11,640
11,443 12.16
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System Integration/Segregation between MMH
CJISC-SIR Rating of 8

• MMH
• 6,433
• 694
• 33,476
• CJIS
• Unique ID Count PPE Count Population Cross
• CJIS 35,351 34,170 2.03
• MMH 7,104 7,127 9.73

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System Integration/Segregation betweenIDS
CJISC-SIR Rating of 11

• CJIS
• 32,499
• 
  1,671
• 9,772
• IDS
• Unique ID Count PPE Count Population Cross
• CJIS 35,351 34,170 4.89
• IDS 11,640 11,443 14.60

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System Integration/Segregation betweenDSS
CJISC-SIR Rating of 14
CJIS 31,069
3,101 13,092 DSS Unique ID
Count PPE Count Population Cross CJIS
35,351 34,170 9.07 DSS 16,176
16,193 19.15
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System Integration/Segregation Cumulative of All
Four SystemsC-SIR Rating of 16
CJIS 34,078 IDS
11,351 7,035 DSS 16,101
MMH Unique ID Count PPE
Count Population Cross CJIS 35,351
34,170 .26 DSS 16,176 16,193
.56 IDS 11,640 11,443 .80 MMH
7,104 7,127 1.29

Overlap between all systems is estimated at 92
people
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Heavy UsersCost Claims/Events/Activities

• Identification of Heavy Users
• C-SIR Ratings

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Identification of Heavy Users in DSS System
1. Top 5 of the population by the total cost of
services. 808 individuals, who had services
cost of 5,196.10 or more during the FY
98/99   2. Top 5 of the population by the total
number of claims/events/activities. 808
individuals, who had 66 claims/events/activities
or more during the FY 98/99
Cost n 812
525
528 287
Claims/Events/Activities n
815 C-SIR Rate of 48 NOTE Each of the
groups are not exclusive, meaning the same person
could have met the criteria for more than one
definition of a heavy hitter.
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Identification of Heavy Users in CJIS System
1. Top 5 of the population by the total number
of court cases. 1,767 individuals, who had 5 or
more court cases during the FY 98/99     2. Top
5 of the population by the total number of days
in jail 1,767 individuals, who had spent an
aggregate total of 280 days or more in
jail.     3. Top 5 of the population by the
total number of claims/events/activities
including arrests. 1,767 individuals, who had 7
claims/events/activities or more.  
820 Court Cases n
1,776 168 392
901
CJ Jail 677 311 Jail
Days n 1,767 n
1,750 C-SIR Rate of 23 NOTE Each of the
groups are not exclusive, meaning the same person
could have met the criteria for more than one
definition of a heavy hitter.
387
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Identification of Heavy Users in IDS System
  1. Top 5 of the population by the total cost
of services. 58 individuals, who had services
costs of 20,003.75 or more during the FY
98/99   2. Top 5 of the population by the total
number of claims/events/activities. 586individua
ls, who had 178 claims/events/activities or more
during the FY 98/99 Cost n
588 342 246
339 Events n 585 C-SIR Rate
of 27 NOTE Each of the groups are not
exclusive, meaning the same person could have met
the criteria for more than one definition of a
heavy hitter.
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Identification of Heavy Users in MMH System
1. Top 5 of population by the total cost of
services. 354 individuals, who services cost
of 9,206.31 or more during the FY 98/99   2.
Top 5 of population by the total number of
claims/events/activities. 354 individuals, who
had 221 claims/events/activities or more during
the FY 98/99 Claims n
352 174 178
174 Cost n 352 C-SIR Rate of
34 NOTE Each of the groups are not exclusive,
meaning the same person could have met the
criteria for more than one definition of a
heavy hitter.
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Heavy Users C-SIR Rating by Claims/Events/Activiti
es
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Heavy Users C-SIR Rating by Cost
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Non Heavy Users

• Identification
• C-SIR Ratings

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Non Heavy Users C-SIR Ratings
People who use multiple systems are non heavy
hitters
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Demographics

• Gender
• Age Group
• Race

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Total Population by Gender
Other population breakouts had similar patterns
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Total Population by Age Group
Other population breakouts had similar patterns
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Total Population by Race
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Claims/Events/Activities Heavy Users by Race
33
Cost Heavy Users by Race
34
Non Heavy Users by Race
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Case Studies

• Identifying the 92 individuals
• Demographics
• Identifying 3 case studies
• Timelines
• Service Breakdown

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Demographics of 92
The majority of individuals had 1 to 10 claims
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92 IDS Service Code
38
92 IDS Primary Diagnosis
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Case Studies Criteria Selection

• From the 92 individuals who used serivces
• in all four of the systems
• Diagnosis of Schizophrenic or Affective Psychosis
• Average individual had 1 to 10 claims

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Individual diagnosis of Affective Psychosis
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Individual diagnosis of Schizophrenic Psychosis
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Individual diagnoses of both Schizophrenic
andAffective Psychosis
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Conclusions

• There is very little overlap in users between the
  systems that were looked at.
• The caseload integration/segregation rating in
  this study varied from 5 to 44 on a scale of 0 to
  100. The greatest overlap is between IDS and MMH,
  the mental health systems
• It is the non-heavy users that are more likely to
  cross multiple systems, not the heavy users. If
  an individual is a heavy user in one system, they
  probably are not in the other systems.

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Conclusion (cont.)

• Twenty-six percent of the individuals, of the 92
  who touch all four systems, during a years time
  had a primary diagnosis in IDS as Schizophrenic
  Psychosis.
• Forty-Five percent of the individuals, of the 92
  who touch all four systems, during a years time
  had a primary diagnosis in IDS as Affective
  Psychosis.
• A person who is more likely to touch all four
  systems is a white female between the ages of
  20-49.
• The race demographic shows a dramatic increased
  proportion of the number of Blacks in the heavy
  users of the CJIS System. They have a longer
  length of stay in jail and cost more.

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Next Step

• Gather and incorporate data from other Pinellas
  Data Collaborative Members
• (Child Welfare, DJJ, JWB, EMS, Baker Act)
• Add Future years data
• Continue data analysis

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Reference
  Banks, S. Pandiani, J. (1998). The use of
state and general hospitals for inpatient
psychiatric care. American Journal of Public
Health, 99(3), 448-451.     Banks, S., Pandiani,
Gauvin, L, Readon, M.E., Schacht, L.,
Zovistoski, A. (1998). Practice patterns and
hospitalization rates. Administration and Policy
in Mental Health, 26(1), 33-44.   Banks, S,
Pandiani, J. James, B (1999). Caseload
segregation/integration A measure of shared
responsibility for children adolescents.
Journal of Emotional Behavioral Disorders,
7(2), p 66-17.   Banks, S, Pandiani, J., Bagdon,
W., Schacht, L. (1999). Causes and Consequences
of Caseload Segregation/Integration. 12th
Annual Research Conference (1999) Proceedings,
Research and Training Center for Childrens
Mental Health.   Pandiani, J., Banks, S.,
Gauvin, L. (1997). A global measure of access to
mental health services for a managed care
environment. The Journal of Mental Health
Administration, 24(3), 268-277.