1. Pinellas Data Collaborative
Preliminary Results
Paul Stiles, J.D., Ph.D. Diane Haynes, M.A.
813) [voice] (813) [voice]
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
(813) [FAX]
2/22/2019
PDC Preliminary Results

2. PDC Preliminary Results
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?
2/22/2019
PDC Preliminary Results

3. PDC Preliminary Results
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
2/22/2019
PDC Preliminary Results

4. 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.
2/22/2019
PDC Preliminary Results

5. 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.
2/22/2019
PDC Preliminary Results

6. 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.
2/22/2019
PDC Preliminary Results

7. 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.
2/22/2019
PDC Preliminary Results

8. PDC Preliminary Results
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.
2/22/2019
PDC Preliminary Results

9. 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.
2/22/2019
PDC Preliminary Results

10. Caseload Segregation/Integration Ratio (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
2/22/2019
PDC Preliminary Results

11. Total Population C-SIR Ratings
MMH & IDS
MMH & DSS
MMH & CJIS
IDS & DSS
IDS & CJIS
DSS & CJIS
Cumulative Overlap between all Systems
2/22/2019
PDC Preliminary Results

12. System Integration/Segregation between MMH & IDS C-SIR Rating of 44
System Integration/Segregation between MMH & IDS C-SIR Rating of 44
IDS
MMH
7,447
  3,996
3,131
Unique ID Count PPE Count Population Cross
MMH , , %
IDS , , %
2/22/2019
PDC Preliminary Results

13. System Integration/Segregation Between MMH & DSS C-SIR Rating of 6
DSS
15,666
527
6,600 MMH
Unique ID Count PPE Count Population Cross
DSS , , %
MMH , , %
2/22/2019
PDC Preliminary Results

14. System Integration/Segregation between IDS & DSS C-SIR Rating of 7
14,801
1,392
10,051
IDS
Unique ID Count PPE Count Population Cross
DSS , , %
IDS , , %
2/22/2019
PDC Preliminary Results

15. System Integration/Segregation between MMH & CJIS C-SIR Rating of 8
6,433
694
33,476
CJIS
Unique ID Count PPE Count Population Cross
CJIS , , %
MMH , , %
2/22/2019
PDC Preliminary Results

16. System Integration/Segregation between IDS & CJIS C-SIR Rating of 11
32,499
1,671
9,772
IDS
Unique ID Count PPE Count Population Cross
CJIS , , %
IDS , , %
2/22/2019
PDC Preliminary Results

17. System Integration/Segregation between DSS & CJIS C-SIR Rating of 14
31,069
3,101
13,092
DSS
Unique ID Count PPE Count Population Cross
CJIS , , %
DSS 16, , %
2/22/2019
PDC Preliminary Results

18. PDC Preliminary Results
System Integration/Segregation Cumulative of All Four Systems C-SIR Rating of 16
CJIS
34, IDS
11,351
7,035
DSS 16,101
MMH
Unique ID Count PPE Count Population Cross
CJIS , , %
DSS , , %
IDS , , %
MMH , , % *
* Overlap between all systems is estimated at 92 people
2/22/2019
PDC Preliminary Results

19. Heavy Users Cost & Claims/Events/Activities
Identification of Heavy Users
C-SIR Ratings
2/22/2019
PDC Preliminary Results

20. 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, 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
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.
2/22/2019
PDC Preliminary Results

21. 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,
392
901
CJ Jail Jail Days
n = 1, 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
2/22/2019
PDC Preliminary Results

22. 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, or more during the FY 98/99
2. Top 5% of the population by the total number of claims/events/activities.
586individuals, who had 178 claims/events/activities or more during the FY 98/99
Cost
n = 588
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.
2/22/2019
PDC Preliminary Results

23. 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, 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
Cost
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.
2/22/2019
PDC Preliminary Results

24. Heavy Users C-SIR Rating by Claims/Events/Activities
2/22/2019
PDC Preliminary Results

25. Heavy Users C-SIR Rating by Cost
2/22/2019
PDC Preliminary Results

26. PDC Preliminary Results
Non Heavy Users
Identification
C-SIR Ratings
2/22/2019
PDC Preliminary Results

27. Non Heavy Users C-SIR Ratings
People who use multiple systems are non –heavy hitters
2/22/2019
PDC Preliminary Results

28. PDC Preliminary Results
Demographics
Gender
Age Group
Race
2/22/2019
PDC Preliminary Results

29. Total Population by Gender
* Other population breakouts had similar patterns
2/22/2019
PDC Preliminary Results

30. Total Population by Age Group
* Other population breakouts had similar patterns
2/22/2019
PDC Preliminary Results

31. Total Population by Race
2/22/2019
PDC Preliminary Results

32. Claims/Events/Activities Heavy Users by Race
2/22/2019
PDC Preliminary Results

33. Cost Heavy Users by Race
2/22/2019
PDC Preliminary Results

34. PDC Preliminary Results
Non Heavy Users by Race
2/22/2019
PDC Preliminary Results

35. PDC Preliminary Results
Case Studies
Identifying the 92 individuals
Demographics
Identifying 3 case studies
Timelines
Service Breakdown
2/22/2019
PDC Preliminary Results

36. Demographics of 92 The majority of individuals had 1 to 10 claims
2/22/2019
PDC Preliminary Results

37. PDC Preliminary Results
92 –IDS Service Code
2/22/2019
PDC Preliminary Results

38. 92 – IDS Primary Diagnosis
2/22/2019
PDC Preliminary Results

39. 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
2/22/2019
PDC Preliminary Results

40. Individual diagnosis of Affective Psychosis
2/22/2019
PDC Preliminary Results

41. Individual diagnosis of Schizophrenic Psychosis
2/22/2019
PDC Preliminary Results

42. Individual diagnoses of both Schizophrenic and Affective Psychosis
2/22/2019
PDC Preliminary Results

43. PDC Preliminary Results
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.
2/22/2019
PDC Preliminary Results

44. PDC Preliminary Results
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
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.
2/22/2019
PDC Preliminary Results

45. PDC Preliminary Results
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
2/22/2019
PDC Preliminary Results

46. PDC Preliminary Results
Reference
Banks, S. & Pandiani, J. (1998). The use of state and general hospitals for inpatient psychiatric care. American Journal of Public Health, 99(3),
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),
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
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 Children’s 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),
2/22/2019
PDC Preliminary Results