Supplementing Community Public Health Surveillance
With Data From Electronic Healthcare Claims October 2002
Thomas Balzer, Ph.D.Chief Scientific Officer, Verispan
Who is Verispan?
• Scott-Levin• SMG Marketing Group• Synergy Healthcare• Amaxis
• Kelly-Waldron• data sources
Public Health Surveillance
“Surveillance: Continuous analysis, interpretation, and feedback of systematically collected data, generally using methods distinguished by their practicality, uniformity, and rapidity rather than by accuracy or completeness.”
John M. Last, A Dictionary of Epidemiology, 3rd Edition
Simple (in concept)
Stable (in operation)
Acceptable (to providers)
Standardized high-quality data
Timely (in reporting healthcare events)
Representative (of all areas)
Sensitive (to outbreaks & other changes over time by applying traditional & non-traditional approaches to surveillance)
Flexible (to changing surveillance needs)
4
Critical Attributes ofPublic Health Surveillance Systems
Verispan Data Warehouse:“Practicality, Uniformity, Rapidity”
The largest, broadest real-time and longitudinal sample of patient-
centric pharmacy and medical transactions in the world.
The Verispan Data Warehouse Began in 1998“A Simple & Stable System Already Working”
50,000+ Pharmacies
640,000+ Unique Subscribers
5+ Million Claims Loaded Daily
100+ Million Unique Patients
1.7 Billion Annual Rx or Mx Claims
5 Billion Claims - total in warehouse
Verispan PatientData Base
Verispan PatientData Base
Pharmacies &Prescription
Services
Commercial(PBM, HMO)
Government(Medicare/Medicaid)
Hospitals & Facilities
Physicians
BCBS
Daily Claims VolumeDaily Claims Volume
HXHX
MXMX
RXRX
Payors
Health Care Clearinghouse
Verispan Harnesses Routine Billing Practices“Acceptable to Providers”
De-Identification
Medical, Hospital, and Pharmacy Data are Available“Verispan Has Standardized, High-Quality Data”
Pharmacy Data Medical Data
Jan ‘98 - to date July ‘98 - to date
RX Pharmacy Data(NCPDP)
RX Pharmacy Data(NCPDP)
Patient ID
Patient Age & Gender
Date Written
Date Filled
NDC Code
Quantity Dispensed
Days Supply
Refill Flag
Prescribing Physician
Pharmacy
Payor Type
Patient ID
Patient Age & Gender
Date Written
Date Filled
NDC Code
Quantity Dispensed
Days Supply
Refill Flag
Prescribing Physician
Pharmacy
Payor Type
MX Provider Data (HCFA 1500)
MX Provider Data (HCFA 1500)
Patient ID
Patient Age & Gender
Diagnosis Codes (ICD9)
Procedure Codes (CPT)
Service Dates
Physician/Provider ID
Location of Care
Payor Type
Patient ID
Patient Age & Gender
Diagnosis Codes (ICD9)
Procedure Codes (CPT)
Service Dates
Physician/Provider ID
Location of Care
Payor Type
HX Facility Data (UB-92)
HX Facility Data (UB-92)
Patient ID
Patient Age & Gender
Diagnosis Codes (ICD9)
Procedure Codes (CPT)
DRG
Admit Date
Discharge Date
Physician/Provider ID
Location of Care
Payor Type
Patient ID
Patient Age & Gender
Diagnosis Codes (ICD9)
Procedure Codes (CPT)
DRG
Admit Date
Discharge Date
Physician/Provider ID
Location of Care
Payor Type
Providers are Motivated to File Timely Electronic Claims“The Verispan Data Warehouse Is Updated Daily
Lag Days for enteric illness among children, 2000 - 2001
0
50
100
150
200
250
000 007 014 021 028 035 042 049 056 063 070 077 084
Diarrhea Enteritis Infectious_Diarrhea
First_Claim Y Patient_State OH PROVIDER_COUNTY Hamilton AGE_GROUP_10YR 00 to 09 Place_of_Visit A_OFFICE
Count of PATIENT_ID
REPORTING_LAG_DAYS
DISEASE
Lag Days = Processing Date minus Service Date
Excellent Geographic Distribution“All Areas of the U.S. Are Represented”
Every StateEvery MSAEvery 3 Digit Zip Code
Examples of Outbreak Detection Using Non-traditional Approaches
To Surveillance (NTAS)
For Additional Information:[email protected], Fax 919-998-7263
CDC Outbreak Detection Challenge
CDC developed three case studies for evaluating supplementary data bases for their ability to identify outbreaks. CDC provided only limited information about these known 2001 outbreaks:
Case 1: Shigella sonnei gastroenteritis in Ohio Case 2: Neisseria meningitides meningitis in Ohio in
school-age children Case 3: Histoplasma capsulatum in multiple states
among travelers to Acapulco
Only Verispan rose to the challenge of identifying the outbreak footprints in existing data bases.
A Traditional, Diagnosis-based Approach to Detecting an Enteric Illness Outbreak in Children, 2001
SHIGELLOSIS
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SHIGELLOSIS
First_Claim Y Patient_State OH PROVIDER_COUNTYHamilton AGE_GROUP_10YR 00 to 09
Count of PATIENT_ID
SERVICE_EPI_WEEK
DISEASE
A Non-Traditional Approach to Outbreak DetectionUsing Surveillance of Enteric Syndromes
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Diarrhea Enteritis Infectious_Diarrhea
First_Claim Y Patient_State OH PROVIDER_COUNTYHamilton AGE_GROUP_10YR 00 to 09
Count of PATIENT_ID
SERVICE_EPI_WEEK
DISEASE
Two Previously Unknown Outbreaks The Outbreak
A Traditional, Diagnosis-based Approach to Detecting a Community Meningitis Outbreak, 2001
0
1
2
3
4
5
2000
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Cuyahoga - 00 to 09 Cuyahoga - 10 to 19 Cuyahoga - 20 to 29 Hancock - 20 to 29 Summit - 10 to 19
First_Claim Y Patient_State (All) DISEASE Meningococcal infection Place_of_Visit (All)
Count of PATIENT_ID
SERVICE_EPI_WEEK
PROVIDER_COUNTY
AGE_GROUP_10YR
The Outbreak
A Non-Traditional Approach to Outbreak DetectionUsing Surveillance of Vaccination Procedures
Meningococcal Vaccination
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10 to 19 - Cuyahoga 10 to 19 - Lake 10 to 19 - Mahoning 10 to 19 - Portage 10 to 19 - Stark 10 to 19 - Summit
20 to 29 - Cuyahoga 20 to 29 - Lake 20 to 29 - Mahoning 20 to 29 - Stark 20 to 29 - Summit
First_Claim Y Patient_State OH Proc Code 90733 Place_of_Visit (All)
Count of PATIENT_ID
PROCESS_EPI_WEEK
AGE_GROUP_10YR
PROVIDER_COUNTY
Unexpected Vaccination Pattern From the OutbreakExpected Vaccination
Pattern in Students Entering College
A Traditional, Diagnosis-based Approach to Detecting a National Histoplasmosis Outbreak, 2001 -
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(bla
nk)
AZ CT DE FL GA MA ME MI MN NC NE NJ NY OK OR PA SC SD TX VA WI WV
First_Claim Y Histo_Endemic (blank) DISEASE Histoplasmosis AGE_GROUP_5YR (All)
Count of PATIENT_ID
PROCESS_EPI_WEEK2
Patient_State
PA, NY, NJ, MI, TX, DE, MN, NE
A Non-Traditional Approach to Outbreak DetectionUsing Surveillance of Ketoconazole Prescriptions
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18 19 20 21 22 23 24
First RX Y HISTO_ENDEMIC(blank) NDC 51672402606 DRUGNAME KETOCONAZOLE PHARMACY_STATE (All)
Count of PATIENT_ID
SERVICE_EPI_WEEK2
AGE_IN_YEARS
Prescription “Footprint” of the Outbreak
Source: Outbreak of Escherichia coli O157:H7 and Campylobacter among attendees of the Washington
County Fair - New York, 1999 (MMWR 48(36); 803)
*Based on ICD•9•CM codes: 008.00, 008.04, 008.43, 009, 283.11, 787.91 [Values are raw and unadjusted]
6
1155
11
10
9
656
10
13
5
5
18
4
9
14
5
29
29
14
5
16
17
3
9
14
7
15
13
6
12
106
0
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20
30
40
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60
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80
Cas
es
(b
y E
PI-
wee
k o
f h
ea
lth
ca
re v
isit
)
30 31(Aug)
32 33 34 35(Sep)
36 37 38 39 40(Oct)
County Fair E. coli 0157:H7 Outbreak*, NY State, 1999 Using the Informatics Mx Data Base
Wash. Co. Saratoga Co. Rensselaer Co. Warren Co.
Combining Traditional and Non-Traditional Approaches to Detect Outbreaks”
19
Examples of Unique Daily Public Health Reports of Syndromes, Rxs, and Reportable and Non-
Reportable Conditions That Are Available From Verispan Through the Web
For Additional Information:[email protected], Fax 919-998-7263
Lyme Disease as Tracked by States & CDC in 2000 “Verispan Data Are Sensitive to Reportable Diseases”
Cumulative Cases Reported to CDC from State Health Departments: 2000
Cumulative Cases Reported to CDC from State Health Departments: 2000
Color Code Key:
# of Cases Color
3000+
100-2999
20-99
1-19
No Cases
Color Code Key:
# of Cases Color
3000+
100-2999
20-99
1-19
No Cases
2
7
104
0 0
0
0
0
0
0
0
3211
291393
89
13
28
34146
46
17
50
1,276
4,027
3984
1,098
34
45
2
4
17
4
4
1
36
1
11
9
3
4
9
15
4MD 559DC 11
DE 142NJ 1,467
CT 2,550RI 590
Lyme Disease:
ICD-9-CM: 088.81
Lyme Disease:
ICD-9-CM: 088.81
CDC preliminary case count: n = 13,309 (MMWR 49 [52])
28
Cumulative Cases Reported in Informatics Data: 2000Cumulative Cases Reported in Informatics Data: 2000
Lyme Disease:
ICD-9-CM: 088.81
Lyme Disease:
ICD-9-CM: 088.81
Color Code Key:
# of Cases Color
3000+
100-2999
20-99
1-19
No Cases
Color Code Key:
# of Cases Color
3000+
100-2999
20-99
1-19
No Cases
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171
1
38316
2
0
2
18
2
10
40
209
2
7
38
29
113
14
40
29
190
332122
79
131
214
82
29 15
205
12760
238
469
112761
8,527
3466
56
411
MD 1,376DC 30
DE 603NJ 5,554
CT 6,268RI 56
0
Quintiles case count: n = 27,184
CDC preliminary case count: n = 13,309 (MMWR 49 [52])
Lyme Disease as Tracked by Quintiles, 2000“Quintiles Data Are Sensitive to Reportable Diseases”
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Evaluate Community Responses to Emergencies“Verispan Data Are Flexible at the Local Level”
Cipro Daily Variation - WTC Area (60-Mile Radius)
-100%
0%
100%
200%
300%
400%
500%
08/0
1/20
01
08/0
8/20
01
08/1
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9/20
01
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01
10/1
7/20
01Va
ria
nc
e F
rom
Ex
pe
cte
d A
cti
vit
y
Departure from Expectation Lower Limit Upper Limit
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9/11
10/12 - Anthrax
Generate Hypotheses for Further Study“Verispan Data Are Flexible at the Local Level”
Source: Verispan Mx Database
Asthma Diagnoses Daily Variation - NYC
-100%
-80%
-60%
-40%
-20%
0%
20%
40%
60%
80%
100%
08/0
7/20
01
08/1
0/20
01
08/1
3/20
01
08/1
6/20
01
08/1
9/20
01
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2/20
01
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5/20
01
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01
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1/20
01
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3/20
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6/20
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2/20
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5/20
01
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01
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4/20
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09/2
7/20
01
Var
ian
ce F
rom
Exp
ecte
d A
ctiv
ity
Asthma Visits 95% Lower Conf 95% Upper Conf
Asthma Diagnoses Daily Variation - NYC
-100%
-80%
-60%
-40%
-20%
0%
20%
40%
60%
80%
100%08
/07/
2001
08/1
0/20
01
08/1
3/20
01
08/1
6/20
01
08/1
9/20
01
08/2
2/20
01
08/2
5/20
01
08/2
8/20
01
08/3
1/20
01
09/0
3/20
01
09/0
6/20
01
09/0
9/20
01
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2/20
01
09/1
5/20
01
09/1
8/20
01
09/2
1/20
01
09/2
4/20
01
09/2
7/20
01
Var
ian
ce F
rom
Exp
ecte
d A
ctiv
ity
Asthma Visits 95% Lower Conf 95% Upper Conf
9/11
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Oxycontin versus Control Set- Selected Counties5 Week Moving Average
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Week
Ind
ex v
ersu
s C
on
tro
l Set
Miami-Dade, Florida Cook, Illinois Middlesex, Massachusetts Queens, New York
Control Group: All Prilosec and Amaryl NDC’s
Other Surveillance Opportunities
Verispan’ Unique Factors
Proven technology and systems in use for several years
Extensive database of over 100M de-identified patients
Prescription / Medical data integration processes
Daily receipt of ~5 million health claims
Data modeling and statistical strengths
Access to neural networking technology for detection
Existing broadcast technology for alert messages
33