Pinellas Data Collaborative Preliminary Results Paul Stiles, J.D., Ph.D. Diane Haynes, M.A.

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@fmhi.usf.edu haynes@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 (813) 974-9327 [FAX] PDC Preliminary Results

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? PDC Preliminary Results

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 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. 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. 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. 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. PDC Preliminary Results

8. 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. 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. PDC Preliminary Results

10. 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 PDC Preliminary Results

11. Total PopulationC-SIR Ratings • MMH & IDS • MMH & DSS • MMH & CJIS • IDS & DSS • IDS & CJIS • DSS & CJIS • Cumulative Overlap between all Systems PDC Preliminary Results

12. 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% PDC Preliminary Results

13. 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% PDC Preliminary Results

14. 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% PDC Preliminary Results

15. 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% PDC Preliminary Results

16. 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% PDC Preliminary Results

17. 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% PDC Preliminary Results

18. 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 PDC Preliminary Results

19. Heavy UsersCost & Claims/Events/Activities • Identification of Heavy Users • C-SIR Ratings 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 $5,196.10 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. 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 = 1,776 168 392 901 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 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 $20,003.75 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 = 588 342 246 339 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. 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 $9,206.31 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. PDC Preliminary Results

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

25. Heavy Users C-SIR Rating by Cost PDC Preliminary Results

26. Non Heavy Users • Identification • C-SIR Ratings PDC Preliminary Results

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

28. Demographics • Gender • Age Group • Race PDC Preliminary Results

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

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

31. Total Population by Race PDC Preliminary Results

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

33. Cost Heavy Users by Race PDC Preliminary Results

34. Non Heavy Users by Race PDC Preliminary Results

35. Case Studies • Identifying the 92 individuals • Demographics • Identifying 3 case studies • Timelines • Service Breakdown PDC Preliminary Results

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

37. 92 –IDS Service Code PDC Preliminary Results

38. 92 – IDS Primary Diagnosis 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 PDC Preliminary Results

40. Individual diagnosis of Affective Psychosis PDC Preliminary Results

41. Individual diagnosis of Schizophrenic Psychosis PDC Preliminary Results

42. Individual diagnoses of both Schizophrenic andAffective Psychosis PDC Preliminary Results

43. 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. PDC Preliminary Results

44. 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. PDC Preliminary Results

45. 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 PDC Preliminary Results

46. 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 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), 268-277. PDC Preliminary Results