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

04/19/23 PDC Preliminary Results 1

Pinellas Data Collaborative

Preliminary Results

Paul Stiles, J.D., Ph.D. Diane Haynes, M.A.

813) 974-9349 [voice] (813) 974-8209 [voice]

[email protected] [email protected]

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]


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?


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


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.


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.


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.


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.


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.


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.


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- 1

Duplicated Count

Largest Undup. Count- 1

* 100


Total PopulationC-SIR Ratings

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


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 7,104 7,127 56.06%

IDS 11,640 11,443 34.92%


System Integration/Segregation Between MMH & DSS

C-SIR Rating of 6

DSS

15,666

527

6,600 MMH


DSS 16,176 16,193 3.25%

MMH 7,104 7,127 7.39%


System Integration/Segregation between IDS & DSS

C-SIR Rating of 7

DSS

14,801

1,392

10,051

IDS


DSS 16,176 16,193 8.29%

IDS 11,640 11,443 12.16%


System Integration/Segregation between MMH & CJIS

C-SIR Rating of 8

MMH

6,433

694

33,476

CJIS


CJIS 35,351 34,170 2.03%

MMH 7,104 7,127 9.73%


System Integration/Segregation betweenIDS & CJIS

C-SIR Rating of 11

CJIS

32,499

1,671

9,772

IDS


CJIS 35,351 34,170 4.89%

IDS 11,640 11,443 14.60%


System Integration/Segregation betweenDSS & CJIS

C-SIR Rating of 14

CJIS

31,069

3,101

13,092DSS


CJIS 35,351 34,170 9.07%DSS 16,176 16,193 19.15%


System Integration/Segregation Cumulative of All Four Systems

C-SIR Rating of 16

CJIS

34,078 IDS

11,351

7,035

DSS 16,101

MMH


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


Heavy UsersCost & Claims/Events/Activities

Identification of Heavy Users

C-SIR Ratings


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.


Identification of Heavy Users in CJIS System1. 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 jail1,767 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


387


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

Costn = 588

342 246 339

Eventsn = 585

C-SIR Rate of 27



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

Claimsn = 352

174

178 174

Costn = 352

C-SIR Rate of 34NOTE: 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.


Heavy Users C-SIR Rating by Claims/Events/Activities

Rate PPE OverlappMMH & IDS 37 172

MMH & DSS 0 4

MMH & Court 0 4

IDS & DSS 0.27 4

IDS & Court 0 0

DSS & Court 0.21 4

Jail & IDS 0 3

Jail & MMH 0 4

Jail & DSS 0.21 5

Jail & Court 29.5 784

Cumulative Overlap 11 956

System Count PPE CountMMH 354 352 Under EstimatedIDS 586 588 Over EstimatedDSS 808 815 Over EstimatedCourt 1,767 1,766 Under EstimatedJail 1,767 1,767 Exactly

C-SIR Rating (0 - 100)

Activity - Actual and Estimated Count Comparison


Heavy Users C-SIR Rating by CostRate PPE Overlapp

MMH & IDS 21 105

MMH & DSS 0 1

MMH & Court 0 1

IDS & DSS 0.27 3

IDS & Court 0 7

DSS & Court 0 1

Jail & IDS 0 1

Jail & MMH 0 0

Jail & DSS 0 1

Jail & Court 0.9 567

Cumulative Overlap 15 683

System Count PPE CountMMH 354 352 Under EstimatedIDS 586 585 Under EstimatedDSS 808 812 Over EstimatedCourt 1,767 1,766 Under EstimatedJail 1,767 1,750 Under Estimated

Cost - Actual and Estimated Count Comparison



Non Heavy Users

Identification

C-SIR Ratings


Non Heavy Users C-SIR Ratings

Rate PPE Overlapp

MMH & IDS 40 3,395

MMH & DSS 6 545

MMH & CJIS 7 548

IDS & DSS 7 1,202

IDS & CJIS 11 1,455

DSS & CJIS 12 2,509

All Four Systems 15 8,420

System Count PPE CountMMH 6,575 6,591 Over EstimatedIDS 10,714 10,547 Under EstimatedDSS 15,087 15,069 Under EstimatedCJIS 32,095 31,155 Under Estimated

Actual and Estimated Count Comparison


People who use multiple systems are non –heavy hitters


Demographics

Gender

Age Group

Race


Total Population by Gender

0

5,000

10,000

15,000

20,000

25,000

30,000

Male Female Unknown

Gender

Num

ber

of P

erso

ns

CJIS

DSS

MMH

IDS

* Other population breakouts had similar patterns


Total Population by Age Group

* Other population breakouts had similar patterns

02,0004,0006,0008,000

10,00012,00014,00016,00018,000

Age Group

Nu

mb

er

of

Pe

rso

ns

CJIS

DSS

MMH

IDS


Total Population by Race

0

5,000

10,000

15,000

20,000

25,000

30,000

Black White Other

Race

Nu

mb

er o

f Per

son

s

CJIS

DSS

MMH

IDS


Claims/Events/Activities Heavy Users by Race

0

200

400

600

800

1000

1200

1400

Black White Other

Race

Nu

mb

er

of

Pers

on

s CJIS -Jail Events

CJIS - Court

DSS

MMH

IDS


Cost Heavy Users by Race

0

200

400

600

800

1000

1200

Black White Other

Race

Nu

mb

er

of

Pe

rso

ns CJIS -Jail Days

CJIS - Court

DSS

MMH

IDSMMH


Non Heavy Users by Race

0

5,000

10,000

15,000

20,000

25,000

Black White Other

Race

Num

ber o

f Per

sons CJIS

DSS

MMH

IDS


Case Studies Identifying the 92 individuals

Demographics

Identifying 3 case studies

Timelines

Service Breakdown


Demographics of 92

Count Frequency Count FrequencyMale 38 41.3 CJISFemale 54 58.7 CNH 82 89.1

CCTH 3 3.3CJDH 5 5.4

Count Frequency CJEH 7 7.60-4 0 05-19 2 2.2 DSS

20-34 38 41.3 DNH 90 97.835-49 39 42.4 DAH 2 2.250-64 12 13 DCH 2 2.265+ 1 1.1

Unknown IDSINH 86 93.5IAH 5 5.4

Count Frequency ICH 4 4.3Black 23 25White 68 73.9 MMMOther 1 1.1 MNH 85 92.4

MAH 0 0MCH 7 7.6

Race - 92 - in all four systems

Where in Systems - 92 - in all Four SystemsGender - 92 - in all four systems

Age Group - 92 - in all four systems

The majority of individuals had 1 to 10 claims


92 –IDS Service CodeService

CodeService

DescriptionRecord Count

Ind.Count Frequency

1 On Person Behalf 337 26 28.268 Assess/Functional 2 2 2.1712 Assess/Psychosocial 8 8 8.6916 Behaviorial Services 8 8 8.6922 Counseling/Family 1 1 1.0823 Counseling/Group 2 1 1.0824 Counseling/HIV/TB Screen 9 1 1.0826 Counseling/Individual 31 7 7.6029 Daycare/Adult 18-54 109 5 5.4331 Daycare/Adult CSU 67 35 38.0436 Daycare/Resid Detox 109 11 11.9543 Daycare/Subst Abuse 492 11 11.9547 Day Tx/Adult 18-54 254 6 6.5249 Day Tx/Substance Abuse 19 3 3.2650 Emergency Screen 30 12 13.0451 Evaluation/Forensic 1 1 1.0853 Evaluation/Police 11 10 10.8654 Evaluation/Professional 14 8 8.6955 Evaluation/Psychiatric 49 26 28.2657 Evaluation/Voluntary 13 10 10.8658 Face to Face 472 28 30.4364 Living Support 17 2 2.1766 Medic Admin Drugs 96 32 34.7867 Medic Admin Other 138 39 42.3968 Medic Admin subst Abuse 652 5 5.4371 Partial Hospital 1 1 1.0877 Psychiatric TX Individual 66 32 34.7881 Supp Employ Individual 31 1 1.0884 Telephone Contact 169 23 25.00


92 – IDS Primary DiagnosisDiagnosis

CodeDiagnosis

DescriptionRecord Count

Ind.Count Frequency

290 Senile/Organic Psychogic 0 0 0.00291 Alcoholic Psychosis 1 1 1.00292 Drug Psychosis 0 0 0.00293 Transient Organic Psychosis 2 1 1.00294 Other Organic Psychotic Conditions 0 0 0.00295 Schizophrenic Psychosis 788 24 26.00296 Affective Psychosis 698 42 45.65297 Paranoid States 3 1 1.00298 Other Non-Organic Psychosis 2 1 1.00299 Psychosis with Origin/Children 0 0 0.00300 Neurotic Disorders 103 2 2.00301 Personality Disorders 2 1 1.00302 Sexual Deviations and Disorders 0 0 0.00303 Alcohol Dependence 3 1 1.00304 Drug Dependence 62 4 4.00305 Non-Dependent Drug Abuse 7 3 3.00306 Physical condition from Mental Factors 0 0 0.00307 Special symptoms not Elsewhere Classified 0 0 0.00308 Acute Reaction to Stress 0 0 0.00309 Adjustment Reaction 4 1 1.00310 Specific Non-Psych Mental Diaorder 0 0 0.00311 Depressive Disorder nto Elsewhere Classified 32 8 8.69312 Conduct Disturbance not Elsewhere Classified 0 0 0.00313 Emotional Disturbance Specific to Adolescence 0 0 0.00314 Hyperkenetic Syndrome of Childhood 0 0 0.00315 Specific Delays in Development 0 0 0.00999 Unknown 1,501 43 46.73


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


Individual diagnosis of Affective Psychosis

CJIS-Jail

CJIS-Ct

DSS

MMH

IDS

Demographics: White female in the age group of 35-49 yoa


Individual diagnosis of Schizophrenic Psychosis

CJIS-Jail

CJIS-CT

DSS

MMH

IDS

Demographics: White female in the age group of 35-49 yoa


Individual diagnoses of both Schizophrenic andAffective Psychosis

CJIS-Jail

CJIS-Court

DSS

MMH

IDS

Demographics: Black female in the age group of 20-34 yoa

Arrested 12/11/97 Tampa Police Dept. Violation of Domestic Injunction


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.


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.


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


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.

Documents

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