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
Critical Attributes of
Public Health Surveillance Systems
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
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 Harnesses Routine Billing Practices
“Acceptable to Providers”
Physicians
Hospitals
& Facilities
Pharmacies &
Prescription Services
MX
RX
HX
Daily Claims Volume
Health Care
Clearinghouse
De-Identification
Verispan Patient
Data Base
Payors
BCBS
Government
(Medicare/Medicaid)
Commercial
(PBM, HMO)
Medical, Hospital, and Pharmacy Data are Available
“Verispan Has Standardized, High-Quality Data”
Pharmacy Data
Medical Data
RX Pharmacy Data
(NCPDP)
HX Facility Data
(UB-92)
MX Provider Data
(HCFA 1500)

Patient ID

Patient ID

Patient ID

Patient Age & Gender


Patient Age & Gender

Patient Age & Gender
Date Written
Diagnosis Codes (ICD9)
Date Filled
Diagnosis Codes (ICD9)




Procedure Codes (CPT)

Procedure Codes (CPT)

DRG

Admit Date

Discharge Date

Physician/Provider ID

Location of Care

Payor Type

NDC Code

Quantity Dispensed

Service Dates

Days Supply

Physician/Provider ID

Refill Flag

Location of Care

Prescribing Physician

Payor Type

Pharmacy

Payor Type
Jan ‘98 - to date
July ‘98 - to date
Providers are Motivated to File Timely Electronic Claims
“The Verispan Data Warehouse Is Updated Daily
First_Claim Y
Patient_State OH
PROVIDER_COUNTY Hamilton
AGE_GROUP_10YR 00 to 09
Place_of_Visit A_OFFICE
DISEASE
Diarrhea
Enteritis
Infectious_Diarrhea
Count of PATIENT_ID
250
Lag Days = Processing Date minus Service Date
200
150
100
50
0
000
007
014
021
028
035
042
049
056
063
070
077
084
REPORTING_LAG_DAYS
Lag Days for enteric illness among children, 2000 - 2001
Excellent Geographic Distribution
“All Areas of the U.S. Are Represented”
Every State
Every MSA
Every 3 Digit
Zip Code
Examples of Outbreak Detection Using
Non-traditional Approaches
To Surveillance (NTAS)
For Additional Information:
Thomas.Balzer@Verispan.com
919-998-2547, 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.
2000_01
2000_03
2000_05
2000_07
2000_09
2000_11
2000_13
2000_15
2000_17
2000_19
2000_21
2000_23
2000_25
2000_27
2000_29
2000_31
2000_33
2000_35
2000_37
2000_39
2000_41
2000_43
2000_45
2000_47
2000_49
2000_51
2001_01
2001_03
2001_05
2001_07
2001_09
2001_11
2001_13
2001_15
2001_17
2001_19
2001_21
2001_23
2001_25
2001_27
2001_29
2001_31
2001_33
2001_35
2001_37
2001_39
2001_41
2001_43
2001_45
2001_47
2001_49
2001_51
A Traditional, Diagnosis-based Approach to Detecting an
Enteric Illness Outbreak in Children, 2001
First_Claim Y
Patient_State OH
PROVIDER_COUNTYHamilton
AGE_GROUP_10YR 00 to 09
SHIGELLOSIS
DISEASE
SHIGELLOSIS
10
Count of PATIENT_ID
5
0
SERVICE_EPI_WEEK
50
40
2000_01
2000_03
2000_05
2000_07
2000_09
2000_11
2000_13
2000_15
2000_17
2000_19
2000_21
2000_23
2000_25
2000_27
2000_29
2000_31
2000_33
2000_35
2000_37
2000_39
2000_41
2000_43
2000_45
2000_47
2000_49
2000_51
2001_01
2001_03
2001_05
2001_07
2001_09
2001_11
2001_13
2001_15
2001_17
2001_19
2001_21
2001_23
2001_25
2001_27
2001_29
2001_31
2001_33
2001_35
2001_37
2001_39
2001_41
2001_43
2001_45
2001_47
2001_49
2001_51
A Non-Traditional Approach to Outbreak Detection
Using Surveillance of Enteric Syndromes
First_Claim Y
Patient_State OH
PROVIDER_COUNTYHamilton
Diarrhea
AGE_GROUP_10YR 00 to 09
DISEASE
Enteritis
Infectious_Diarrhea
60
Count of PATIENT_ID
Two Previously
Unknown Outbreaks
SERVICE_EPI_WEEK
The Outbreak
30
20
10
0
2000_01
2000_03
2000_05
2000_07
2000_09
2000_11
2000_13
2000_15
2000_17
2000_19
2000_21
2000_23
2000_25
2000_27
2000_29
2000_31
2000_33
2000_35
2000_37
2000_39
2000_41
2000_43
2000_45
2000_47
2000_49
2000_51
2001_01
2001_03
2001_05
2001_07
2001_09
2001_11
2001_13
2001_15
2001_17
2001_19
2001_21
2001_23
2001_25
2001_27
2001_29
2001_31
2001_33
2001_35
2001_37
2001_39
2001_41
2001_43
2001_45
2001_47
2001_49
2001_51
A Traditional, Diagnosis-based Approach to Detecting
a Community Meningitis Outbreak, 2001
First_Claim Y
Patient_State (All)
DISEASE Meningococcal infection
Cuyahoga - 00 to 09
Cuyahoga - 10 to 19
Place_of_Visit (All)
PROVIDER_COUNTY
AGE_GROUP_10YR
Cuyahoga - 20 to 29
3
SERVICE_EPI_WEEK
Hancock - 20 to 29
Summit - 10 to 19
5
Count of PATIENT_ID
4
The Outbreak
2
1
0
A Non-Traditional Approach to Outbreak Detection
Using Surveillance of Vaccination Procedures
First_Claim Y
Patient_State OH
Proc Code 90733
Place_of_Visit (All)
Meningococcal Vaccination
AGE_GROUP_10YR
PROVIDER_COUNTY
10 to 19 - Cuyahoga
10 to 19 - Lake
10 to 19 - Mahoning
10 to 19 - Portage
10 to 19 - Stark
20 to 29 - Cuyahoga
20 to 29 - Lake
20 to 29 - Mahoning
20 to 29 - Stark
20 to 29 - Summit
10 to 19 - Summit
Count of PATIENT_ID
70
60
Expected Vaccination
Pattern in Students
Entering College
50
Unexpected Vaccination Pattern
From the Outbreak
40
30
20
10
2000_01
2000_03
2000_05
2000_07
2000_09
2000_11
2000_13
2000_15
2000_17
2000_19
2000_21
2000_23
2000_25
2000_27
2000_29
2000_31
2000_33
2000_35
2000_37
2000_39
2000_41
2000_43
2000_45
2000_47
2000_49
2000_51
2001_01
2001_04
2001_06
2001_08
2001_10
2001_12
2001_14
2001_17
2001_19
2001_21
2001_23
2001_25
2001_27
2001_29
2001_31
2001_33
2001_35
2001_37
2001_39
2001_41
2001_43
2001_45
2001_47
2001_49
2001_51
0
PROCESS_EPI_WEEK
10
2000_02
2000_04
2000_06
2000_08
2000_10
2000_12
2000_14
2000_16
2000_18
2000_20
2000_22
2000_24
2000_26
2000_28
2000_30
2000_32
2000_34
2000_36
2000_38
2000_40
2000_42
2000_44
2000_46
2000_48
2000_50
2000_52
2001_02
2001_04
2001_06
2001_08
2001_10
2001_12
2001_14
2001_16
2001_18
2001_20
2001_22
2001_24
2001_26
2001_28
2001_30
2001_32
2001_34
2001_36
2001_38
2001_40
2001_42
2001_44
2001_46
2001_48
2001_50
2001_52
(blank)
A Traditional, Diagnosis-based Approach to Detecting
a National Histoplasmosis Outbreak,
2001
First_Claim Y
Histo_Endemic (blank)
AZ
CT
DE
FL
DISEASE Histoplasmosis
GA
MA
ME
MI
MN
AGE_GROUP_5YR (All)
Patient_State
NC
NE
NJ
NY
PROCESS_EPI_WEEK2
OK
OR
PA
SC
SD
TX
VA
WI
WV
Count of PATIENT_ID
PA, NY, NJ, MI, TX, DE, MN, NE
8
6
4
2
0
A Non-Traditional Approach to Outbreak Detection
Using Surveillance of Ketoconazole Prescriptions
First RX Y
HISTO_ENDEMIC(blank)
NDC 51672402606
DRUGNAME KETOCONAZOLE
PHARMACY_STATE (All)
AGE_IN_YEARS
18
19
20
21
22
23
24
Prescription “Footprint” of the Outbreak
Count of PATIENT_ID
25
20
15
10
5
SERVICE_EPI_WEEK2
2001_50
2001_46
2001_42
2001_38
2001_34
2001_30
2001_26
2001_22
2001_18
2001_14
2001_10
2001_06
2001_02
2000_50
2000_46
2000_42
2000_38
2000_34
2000_30
2000_26
2000_22
2000_18
2000_14
2000_10
2000_06
2000_02
0
Combining Traditional and Non-Traditional Approaches to
Detect Outbreaks”
Cases (by EPI-week of healthcare visit)
County Fair E. coli 0157:H7 Outbreak*, NY State, 1999
Using the Informatics Mx Data Base
80
5
70
14
60
50
29
40
30
20
10
0
3
9
5
5
11
6
30
10
11
31
(Aug)
Wash. Co.
6
5
6
32
5
4
5
13
18
14
10
5
9
33
34
Saratoga Co.
35
(Sep)
17
6
13
6
10
15
12
14
29
16
36
7
37
9
38
Rensselaer Co.
39
40
(Oct)
Warren Co.
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]
19
Examples of Unique Daily Public Health Reports of
Syndromes, Rxs, and Reportable and NonReportable Conditions That Are Available From
Verispan Through the Web
For Additional Information:
Thomas.Balzer@Verispan.com
919-998-2547, Fax 919-998-7263
Syndromic Surveillance: Influenza-Like-Illness
Albany, NY, MSA, Jan 01 to Aug 02
Syndromic Surveillance: Septicemia
Fairfield County, CT, Jan 01 to Aug 02
Syndromic Surveillance: Septicemia
Geographic View
Syndromic Surveillance: Enteric Illness
Harrisburg, PA, MSA, Jan 01 to Aug 02
Prescription Surveillance: Anti-Influenza Drugs
New York City, Jan 01 to Aug 02
Surveillance of Non-Reportable Infectious Diseases:
Influenza, Pittsburgh, MSA, Jan 01 to Aug 02
Surveillance of Reportable Infectious Diseases:
Lyme Disease, CT, Jan 01 to Aug 02
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
9
0
0
Color Code Key:
2
39
393
15
4
291
0
4,027
0
9
# of Cases Color
34
4
3000+
1,276
4
3
100-2999
11
89
32
34
11
104
45
17
146
13
20-99
84
1,098
RI 590
CT 2,550
NJ 1,467
DE 142
MD 559
DC 11
46
1
1-19
0
28
4
17
0
No Cases
1
7
0
36
4
2
50
Lyme Disease:
ICD-9-CM: 088.81
0
CDC preliminary case count: n = 13,309 (MMWR 49 [52])
28
Lyme Disease as Tracked by Quintiles, 2000
“Quintiles Data Are Sensitive to Reportable Diseases”
Cumulative Cases Reported in Informatics Data: 2000
24
1
56
0
Color Code Key:
# of Cases Color
1
332
2
214
2
3000+
20-99
16
761
14
7
100-2999
34
66
411
8,527
122
17
190 131
2
112
40
383
38
113
469
79
1-19
238
82
18
No Cases
29
10
40
60
29
209
RI 56
CT 6,268
NJ 5,554
DE 603
MD 1,376
DC 30
15
127
29
2
205
Lyme Disease:
ICD-9-CM: 088.81
0
Quintiles case count: n = 27,184
CDC preliminary case count: n = 13,309 (MMWR 49 [52])
29
Variance From Expected Activity
Evaluate Community Responses to Emergencies
“Verispan Data Are Flexible at the Local Level”
Cipro Daily Variation - WTC Area (60-Mile Radius)
10/12 - Anthrax
500%
400%
9/11
300%
200%
100%
0%
-100%
Departure from Expectation
Lower Limit
Upper Limit
30
Generate Hypotheses for Further Study
“Verispan Data Are Flexible at the Local Level”
Asthma Diagnoses Daily Variation - NYC
80%
60%
9/11
40%
20%
0%
-20%
-40%
-60%
-80%
Asthma Visits
95% Lower Conf
09/27/2001
09/24/2001
09/21/2001
09/18/2001
09/15/2001
09/12/2001
09/09/2001
09/06/2001
09/03/2001
08/31/2001
08/28/2001
08/25/2001
08/22/2001
08/19/2001
08/16/2001
08/13/2001
08/10/2001
-100%
08/07/2001
Variance From Expected Activity
100%
95% Upper Conf
Source: Verispan Mx Database
31
Miami-Dade, Florida
Cook, Illinois
Middlesex, Massachusetts
Control Group: All Prilosec and Amaryl NDC’s
Week
Queens, New York
2001_51
2001_48
2001_45
2001_42
2001_39
2001_36
2001_33
2001_30
2001_27
2001_24
2001_21
2001_18
2001_15
2001_12
2001_09
2001_06
2001_03
2000_52
2000_49
2000_46
2000_43
2000_40
2000_37
2000_34
2000_31
2000_28
2000_25
2000_22
2000_19
2000_16
2000_13
2000_10
2000_07
2000_04
2000_01
Index versus Control Set
Other Surveillance Opportunities
Oxycontin versus Control Set- Selected Counties
5 Week Moving Average
0.10
0.09
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0.00
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