Pinellas Data Collaborative Preliminary Results Paul Stiles, J.D., Ph.D.Diane Haynes, M.A.
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Transcript of 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]
04/19/23 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?
04/19/23 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
04/19/23 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.
04/19/23 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.
04/19/23 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.
04/19/23 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.
04/19/23 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.
04/19/23 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.
04/19/23 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- 1
Duplicated Count
Largest Undup. Count- 1
* 100
04/19/23 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
04/19/23 PDC Preliminary Results 12
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%
04/19/23 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 16,176 16,193 3.25%
MMH 7,104 7,127 7.39%
04/19/23 PDC Preliminary Results 14
System Integration/Segregation between IDS & DSS
C-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%
04/19/23 PDC Preliminary Results 15
System Integration/Segregation between MMH & CJIS
C-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%
04/19/23 PDC Preliminary Results 16
System Integration/Segregation betweenIDS & CJIS
C-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%
04/19/23 PDC Preliminary Results 17
System Integration/Segregation betweenDSS & CJIS
C-SIR Rating of 14
CJIS
31,069
3,101
13,092DSS
Unique ID Count PPE Count Population Cross
CJIS 35,351 34,170 9.07%DSS 16,176 16,193 19.15%
04/19/23 PDC Preliminary Results 18
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
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
04/19/23 PDC Preliminary Results 19
Heavy UsersCost & Claims/Events/Activities
Identification of Heavy Users
C-SIR Ratings
04/19/23 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.
04/19/23 PDC Preliminary Results 21
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
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
04/19/23 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
Costn = 588
342 246 339
Eventsn = 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.
04/19/23 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
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.
04/19/23 PDC Preliminary Results 24
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
04/19/23 PDC Preliminary Results 25
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
C-SIR Rating (0 - 100)
04/19/23 PDC Preliminary Results 26
Non Heavy Users
Identification
C-SIR Ratings
04/19/23 PDC Preliminary Results 27
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
C-SIR Rating (0 - 100)
People who use multiple systems are non –heavy hitters
04/19/23 PDC Preliminary Results 28
Demographics
Gender
Age Group
Race
04/19/23 PDC Preliminary Results 29
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
04/19/23 PDC Preliminary Results 30
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
04/19/23 PDC Preliminary Results 31
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
04/19/23 PDC Preliminary Results 32
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
04/19/23 PDC Preliminary Results 33
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
04/19/23 PDC Preliminary Results 34
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
04/19/23 PDC Preliminary Results 35
Case Studies Identifying the 92 individuals
Demographics
Identifying 3 case studies
Timelines
Service Breakdown
04/19/23 PDC Preliminary Results 36
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
04/19/23 PDC Preliminary Results 37
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
04/19/23 PDC Preliminary Results 38
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
04/19/23 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
04/19/23 PDC Preliminary Results 40
Individual diagnosis of Affective Psychosis
CJIS-Jail
CJIS-Ct
DSS
MMH
IDS
Demographics: White female in the age group of 35-49 yoa
04/19/23 PDC Preliminary Results 41
Individual diagnosis of Schizophrenic Psychosis
CJIS-Jail
CJIS-CT
DSS
MMH
IDS
Demographics: White female in the age group of 35-49 yoa
04/19/23 PDC Preliminary Results 42
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
04/19/23 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.
04/19/23 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.
04/19/23 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
04/19/23 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.