Pinellas Data Collaborative Preliminary Results Paul Stiles J

Pinellas Data Collaborative Preliminary Results Paul Stiles, J. D. , Ph. D. 813) 974 -9349 [voice] stiles@fmhi. usf. edu Diane Haynes, M. A. (813) 974 -8209 [voice] haynes@fmhi. usf. edu Department of Mental Health Law & 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] 9/30/2020 PDC Preliminary Results 1

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? 9/30/2020 PDC Preliminary Results 2

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 9/30/2020 PDC Preliminary Results 3

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. 9/30/2020 PDC Preliminary Results 4

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. 9/30/2020 PDC Preliminary Results 5

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. 9/30/2020 PDC Preliminary Results 6

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. 9/30/2020 PDC Preliminary Results 7

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. 9/30/2020 PDC Preliminary Results 8

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. 9/30/2020 PDC Preliminary Results 9

Caseload Segregation/Integration Ratio (C-SIR) C-SIR = Duplicated Count - 1 Unduplicated Count Duplicated Count Largest Undup. Count - 1 * 100 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. 9/30/2020 PDC Preliminary Results 10

Total Population C-SIR Ratings MMH & IDS MMH & DSS MMH & CJIS IDS & DSS IDS & CJIS DSS & CJIS Cumulative Overlap between all Systems 9/30/2020 PDC Preliminary Results 11

System Integration/Segregation between MMH & IDS C-SIR Rating of 44 IDS MMH 7, 447 3, 996 3, 131 Unique ID Count 9/30/2020 MMH 7, 104 IDS 11, 640 PPE Count Population Cross 7, 127 56. 06% 11, 443 PDC Preliminary Results 34. 92% 12

System Integration/Segregation Between MMH & DSS C-SIR Rating of 6 DSS 15, 666 527 6, 600 MMH 9/30/2020 Unique ID Count PPE Count Population Cross DSS 16, 176 16, 193 3. 25% MMH 7, 104 7, 127 7. 39% PDC Preliminary Results 13

System Integration/Segregation between IDS & DSS C-SIR Rating of 7 DSS 14, 801 1, 392 10, 051 IDS 9/30/2020 Unique ID Count PPE Count Population Cross DSS 16, 176 16, 193 8. 29% IDS 11, 640 11, 443 12. 16% PDC Preliminary Results 14

System Integration/Segregation between MMH & CJIS C-SIR Rating of 8 MMH 6, 433 694 33, 476 CJIS MMH 9/30/2020 Unique ID Count 35, 351 7, 104 PPE Count 34, 170 7, 127 PDC Preliminary Results Population Cross 2. 03% 9. 73% 15

System Integration/Segregation between IDS & CJIS C-SIR Rating of 11 CJIS 32, 499 1, 671 9, 772 IDS CJIS IDS 9/30/2020 Unique ID Count 35, 351 11, 640 PPE Count 34, 170 11, 443 PDC Preliminary Results Population Cross 4. 89% 14. 60% 16

System Integration/Segregation between DSS & CJIS C-SIR Rating of 14 CJIS 31, 069 3, 101 13, 092 DSS CJIS DSS 9/30/2020 Unique ID Count PPE Count Population Cross 35, 351 16, 176 34, 170 16, 193 9. 07% 19. 15% PDC Preliminary Results 17

System Integration/Segregation Cumulative of All Four Systems C-SIR Rating of 16 CJIS 34, 078 IDS 11, 351 * DSS 16, 101 7, 035 * Overlap between all systems is estimated at 92 people MMH 9/30/2020 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% PDC Preliminary Results 18

Heavy Users Cost & Claims/Events/Activities Identification of Heavy Users C-SIR Ratings 9/30/2020 PDC Preliminary Results 19

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. 9/30/2020 PDC Preliminary Results 20

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. Court Cases n = 1, 776 820 168 392 CJ Jail n = 1, 767 677 387 901 311 Jail Days n = 1, 750 C-SIR Rate of 23 NOTE: 9/30/2020 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 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. 586 individuals, 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. 9/30/2020 PDC Preliminary Results 22

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. 9/30/2020 PDC Preliminary Results 23

Heavy Users C-SIR Rating by Claims/Events/Activities 9/30/2020 PDC Preliminary Results 24

Heavy Users C-SIR Rating by Cost 9/30/2020 PDC Preliminary Results 25

Non Heavy Users Identification C-SIR Ratings 9/30/2020 PDC Preliminary Results 26

Non Heavy Users C-SIR Ratings People who use multiple systems are non –heavy hitters 9/30/2020 PDC Preliminary Results 27

Demographics 9/30/2020 Gender Age Group Race PDC Preliminary Results 28

Total Population by Gender * Other population breakouts had similar patterns 9/30/2020 PDC Preliminary Results 29

Total Population by Age Group * Other population breakouts had similar patterns 9/30/2020 PDC Preliminary Results 30

Total Population by Race 9/30/2020 PDC Preliminary Results 31

Claims/Events/Activities Heavy Users by Race 9/30/2020 PDC Preliminary Results 32

Cost Heavy Users by Race 9/30/2020 PDC Preliminary Results 33

Non Heavy Users by Race 9/30/2020 PDC Preliminary Results 34

Case Studies Identifying the 92 individuals Demographics Identifying 3 case studies Timelines Service Breakdown 9/30/2020 PDC Preliminary Results 35

Demographics of 92 The majority of individuals had 1 to 10 claims 9/30/2020 PDC Preliminary Results 36

92 –IDS Service Code 9/30/2020 PDC Preliminary Results 37

92 – IDS Primary Diagnosis 9/30/2020 PDC Preliminary Results 38

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 9/30/2020 PDC Preliminary Results 39

Individual diagnosis of Affective Psychosis 9/30/2020 PDC Preliminary Results 40

Individual diagnosis of Schizophrenic Psychosis 9/30/2020 PDC Preliminary Results 41

Individual diagnoses of both Schizophrenic and Affective Psychosis 9/30/2020 PDC Preliminary Results 42

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. 9/30/2020 PDC Preliminary Results 43

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. 9/30/2020 PDC Preliminary Results 44

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 9/30/2020 PDC Preliminary Results 45

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. 12 th 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. 9/30/2020 PDC Preliminary Results 46