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Data Linkage Training Project for
Vital Statistics and Medicaid Claims Data:
Getting to Know Medicaid and CHIP Data
March 17, 2015
2:00-3:00 PM ET
Teleconference Instructions
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Technical Assistance
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•4
Discussion
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•5
Agenda
• Welcome and Introductions
• Medicaid Data 101
• Considerations for Linking Medicaid Data and Vital
Records
• Available Resources
• State Example: Iowa
• Closing Comments
6
Welcome and Introductions
• Ellen O’Brien and Stephanie Kennedy, AcademyHealth
• Danielle T. Barradas, Ph.D., Division of Reproductive
Health, NCCDPHP, CDC
• Lekisha Daniel-Robinson, M.S.P.H., Division of Quality,
Evaluation and Health Outcomes, CMCS, CMS
• Russell S. Kirby, Ph.D., University of South Florida
• Craig A. Mason, Ph.D., University of Maine
• Vivian Byrd, Keith Kranker, Michaela Vine, Researchers,
Mathematica Policy Research
• Debbie Kane, MCH Epidemiologist-CDC Assignee, Bureau
of Family Health, HPCDP, Iowa Department of Public
Health
7
Welcome and Introductions
Phase II States
• Connecticut
• Delaware
• Nebraska
• New Jersey
• Virginia
• Washington, DC
Phase I States
• Georgia
• Indiana
• Kentucky
• Maine
• Massachusetts
• Michigan
• Mississippi
• New Mexico
• Nevada
• West Virginia
• Wyoming
8
Medicaid Data 101
Vivian Byrd, MPP
Researcher
Mathematica Policy Research
9
Three Medicaid/CHIP Core Set Measures Can Be
Calculated Using Linked Data
Core Set Measure
Numerator Data Source Denominator Data Source
Low Birth Weight
Vital records
Vital records alone or linked
with Medicaid data to identify
payer source
Cesarean Delivery
Vital records alone or
linked with Medicaid data
to identify Cesarean
delivery and medical
record review
Vital records or linked with
Medicaid data to identify payer
source and medical record
review
PC-01: Elective
Delivery
Medicaid data and
medical record review
Medicaid data and medical
record review; vital records
may be used to measure
gestational age
PC-03: Antenatal
Steroids
Medicaid data and
medical record review
Medicaid data and medical
record review; vital records
may be used to measure
gestational age
Note: Measure-eligible population includes Medicaid and CHIP
enrollees.
10
10
Medicaid Data 101: Obtaining Data for Use
Medicaid Management
Information System
(MMIS)
•
•
•
•
Eligibility
Providers
Claims adjudication
Administrative costs
for Medicaid
• Data may be
transferred to an
easily accessible data
warehouse
11
Medicaid Statistical
Information System
(MSIS)
Medicaid Analytic
eXtract
(MAX)
• Federal reporting required of all
• Uses MSIS data
states
to create
• Many identifiers are removed
research-ready
(names, addresses, phone
data file
numbers, etc.)
• Person summary
• Every eligible individual for every
file for each state
month in fiscal year
by calendar year
• Every service rendered, reported
(based on date of
by adjudicated quarter in fiscal
service)
year
• 5 files: EL, IP, LT, OT, RX
• MSIS data dictionary specifies
formats
• “Validated” on a quarterly basis
Medicaid Data 101: Structure
Medicaid data systems typically contain two types of files
• Eligibility/Enrollment
• Eligibility determinations use different system than claims adjudication
• Point-in-time information
• Temporary ID numbers
• Claims/Encounters
•
•
•
•
12
Information required for a provider to receive payment
Fee for service (FFS) vs. Managed Care data
Data received and stored by states
Timeliness and quality vary
Medicaid Data Fields
• Eligibility – Enrollment
• Patient/beneficiary
information
•
•
•
•
Medicaid ID
Date of birth
Address
Pathway to Medicaid
Eligibility
• Claims - Payments
•
•
•
•
•
•
Diagnoses
Procedures
Provider IDs
Facility
Dates of Service
Billing
• Patient information
• Payment information
13
Fields for Medicaid-Vital Records Data Linkage
•
•
•
•
•
•
•
•
•
•
•
Mother’s Eligibility Record(s)
Social Security Number (SSN)
Medicaid/CHIP ID
Date of birth
Name
Address*
Phone
Race/Ethnicity
Mother’s “Delivery Claim”
Medicaid payment for the claim
(verify yes/no)
Date of delivery**
Hospital/facility of delivery
Other
•
•
•
•
•
•
•
•
Infant’s Eligibility Record(s)
Date of birth
Name
Address*
Phone
Gender
Infant’s “Birth Claim”
Medicaid payment for the claim
(verify yes/no)
Hospital/facility of birth
Other
* Street Address, City, State, ZIP code, and County ** Linked to infant’s date of birth in
vital records
14
Medicaid Data 101: Common Issues
•
•
•
•
•
15
Global billing for maternity care
Mother and infant IDs
Managed care encounter data
Timing/rollout and completeness/quality
Personnel needed to support extraction, manipulation,
and analysis
Major differences between
Medicaid and Vital Records data
Medicaid
Vital Records
Births/deliveries
included in file
Medicaid-covered births
(in-state or out-of-state)
All in-state births
Identifying Medicaidcovered
births/deliveries
Yes: accurate history of Medicaid
enrollment
Underreported
(especially in states with managed
care)
Variables for linkage
availability
Yes: when Medicaid data is
collected from state Medicaid
agencies (but not from CMS)
Yes: when Medicaid data is
collected from state Vital Records
agencies (but not from CDC)
Demographic/
characteristics
availablea
•
•
•
•
•
•
•
•
•
•
Data release
a Both
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Family income
Aged, blind, and/or disabled
eligibility
Fee-for-service or managed
care
SNAP and TANF participation
Rolling basis (claims may take up
to a year to be submitted)
Medicaid and Vital Records age, gender, race, ethnicity, and location
Pregnancy history
Pre-pregnancy weight
Education
Marital status
Foreign-born
WIC participation
Annually: usually the summer or
fall following each calendar year
Major differences in outcome measures between
Medicaid and Vital Records data
Medicaid
Vital Records
Prenatal care
Medicaid-covered prenatal
care only; issues with
bundled payments
Dates of first/last visit and
number of visits; may be
inaccurate
Gestation, birthweight,
smoking during pregnancy,
maternal weight gain, and
breastfeeding at discharge
Noa
Yes (some variables more
accurate than others)
Method of delivery,
maternal health complications, and
NICU admissions
Yes: diagnoses,
procedures, and other
fields in claims
Yes: checkboxes on birth
certificate
Mortality
Could be underreported
Yes, by linking birth
certificates to death
certificates
Medicaid-paid costsb and utilization
(office visits, days in hospital, etc.)
Yes
No
Outcomes after the birth /delivery
Yes
No
a Diagnosis
codes in claims only for gestation less than 36 weeks (765.2x) and
birthweight less than 2,500 grams (765.0x, 765.1x).
b Costs are typically available for fee-for-service beneficiaries only
17
Questions?
18
Considerations in Linking
Medicaid Data and Vital Records
19
Key Decisions
•
Are you …
1.
Linking mothers’ Medicaid data to Vital Records,
2.
Linking infants’ Medicaid data to Vital Records, or
3.
Both?
•
Should you link mothers’ and infants’ Medicaid data before
linking to Vital Records, or link the mothers and infants
separately?
•
Which data fields should be used?
•
How should the data files be prepared and structured?
•
How should the data be linked?
20
Fields for Medicaid-Vital Records Data Linkage
•
•
•
•
•
•
•
•
•
•
•
Mother’s Eligibility Record(s)
Social Security Number (SSN)
Medicaid/CHIP ID
Date of birth
Name
Address*
Phone
Race/Ethnicity
Mother’s “Delivery Claim”
Medicaid payment for the claim
(verify yes/no)
Date of delivery**
Hospital/facility of delivery
Other
•
•
•
•
•
•
•
•
Infant’s Eligibility Record(s)
Date of birth
Name
Address*
Phone
Gender
Infant’s “Birth Claim”
Medicaid payment for the claim
(verify yes/no)
Hospital/facility of birth
Other
* Street Address, City, State, ZIP code, and County ** Linked to infant’s date of birth in
vital records
21
Models for Medicaid-Vital Records Data Linkage:
(1) Using the Vital Records “as a Hub”
Mothers’
Medicaid/CHIP
Data
Vital
Records
Infants’
Medicaid/CHIP
Data
•
•
22
Linkage to Vital Records twice (not once)
Each linkage uses the mothers’
or infants’ information, but not both
Models for Medicaid-Vital Records Data Linkage:
(2) Linking Mothers’ to Infants’ Medicaid Data
Mothers’
Medicaid/CHIP
Data
Mothers
Linked to
Infants
Infants’
Medicaid/CHIP
Data
23
•
•
Vital
Records
Relies on household IDs
Linkage to Vital Records with
mothers’ and infants’ information
ID Numbers for Medicaid-Vital Records Data Linkage
• Social Security Numbers
• When available, SSN is a great, although imperfect, linking variable
• Mothers’ SSNs routinely collected in Vital Records
• Most Medicaid agencies collect most mothers’ SSNs
• Infants’ SSNs not collected in Vital Records, and not always collected by Medicaid
• Some agencies cannot release SSNs
• Medicaid/CHIP ID Numbers
• Collected in Vital Records in some states, but not all
• Some beneficiaries may have more than one ID
• Best to de-duplicate Medicaid IDs before matching with other datasets
• Medicaid Household ID Number (or Case ID Number)
• When available, can be used to link mothers’ and infants’ Medicaid data (for
some mother-infant dyads, but not all)
• Some Medicaid agencies do not routinely link mothers to their infants
24
Other Fields from the Medicaid Enrollment Records
• Dates of birth
• Usually complete in administrative data
• Not a unique identifier—multiple births occur each day—but important for
ruling out bad matches (e.g., mothers with multiple children)
• Names
• Usually available in administrative data
• Not a unique identifier; can be entered differently in separate data systems
• Phonetic algorithms and other techniques can address some challenges
• NYSIIS and SOUNDEX algorithms for last names and first names, respectively
• Address and phone number
• When these fields match, good indication of a match
• Fields often change: two records could match even if they do not have the
same address or phone number
• Other fields discussed in Technical Assistance Brief #4
• See list of Available Resources for link to brief
25
Fields from Medicaid Claims/Encounter Data
• Available fields for linking
• Type of provider (e.g., hospital or birth center)
• Hospital/provider names and ID numbers
• Date of delivery
• Estimate delivery date from mothers’ claims to within a few days
• Rare outcomes (e.g., multiple births or NICU admissions)
• Plan ahead to include additional fields for quality measures or
QI efforts
• Identifying the birth/delivery claim
• Identifying Cesarean deliveries
• Other fields related to diagnoses or procedures
• Summarize the claims data and merge onto the enrollment files
• One row per delivery (for mothers)
• One row per infant
26
Preparing and Cleaning the Data Before Linkage
• Assign unique numbers to the rows in each data set (before
doing anything)
• Restrict data to the time period and population of interest
• Use care to avoid inadvertently dropping records that should be
included
• Consider dropping multiple births
• Drop variables that will not be used for matching
• Some beneficiaries will have more than one enrollment record
• Remove completely redundant (identical) rows
• Consider keeping multiple rows per person
• Requires more care than data with one row per person
• Increases the likelihood of a match
27
Preparing and Cleaning the Data Before Linkage
(cont’d)
• Re-code variables to have the same structure in all files
• For example, dates stored in one field or three
• Use common coding schemes in all files
• For example, gender coded as 0/1 or M/F
• Give the same field the same name in all files
• Assess data quality, in particular the extent of missing
values or coding issues on variables used for matching
28
Additional Resources
• Kranker, Keith, So O’Neil, Vanessa Oddo, Miriam Drapkin,
and Margo Rosenbach. “Strategies for Using Vital Records to
Measure Quality of Care in Medicaid and CHIP Programs.”
Medicaid/CHIP Health Care Quality Measures, Technical
Assistance Brief no. 4. Cambridge, MA: Mathematica Policy
Research, January 2014.
Available at http://www.medicaid.gov/Medicaid-CHIPProgram-Information/By-Topics/Quality-ofCare/Downloads/Using-Vital-Records.pdf
29
Additional Resources (2)
• CDC/NCHS publications on LBW and C-section with technical
appendices and benchmark data
http://www.cdc.gov/nchs/data/nvsr/nvsr62/nvsr62_09.pdf
http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_06.pdf
http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_01.pdf
30
Additional Resources
• Mathematica prepared a TA Brief on strategies for accessing
vital records for quality measurement and improvement efforts
(http://www.medicaid.gov/Medicaid-CHIP-Program-Information/ByTopics/Quality-of-Care/Downloads/Using-Vital-Records.pdf)
• Medicaid partners can access a TA Mailbox with questions
about Core Set measures ([email protected])
• Information on Medicaid Analytic eXtract (MAX) available at
(http://www.medicaid.gov/Medicaid-CHIP-Program-Information/By-Topics/Dataand-Systems/MAX/MAX-General-Information.html)
• Information on Medicaid Statistical Information System (MSIS)
available at
(http://www.medicaid.gov/Medicaid-CHIP-Program-Information/ByTopics/Data-and-Systems/MSIS/Medicaid-Statistical-InformationSystem.html)
31
Questions?
32
State Example: Iowa
Debbie Kane, PhD
MCH Epidemiologist-CDC Assignee, Bureau of
Family Health, HPCDP
Iowa Department of Public Health
33
Iowa Project Overview
• Iowa has conducted linkage since 1989 legislative
mandate
– Senate File 538, 1989 General Assembly
• Collaboration between the Department of Health
Services and the Department of Public Health
• Linkage process transitioned from mainframe to
personal computer in 2010
–
–
–
–
Changes in claims data processing and where stored
More timely data
Upgrades in birth certificate (BC) data collection
Began using LinkPlus
34
Iowa’s Medicaid Data Request – Maternal
• Time frame
– 01/01/2014 through 12/31/2014
• Include the following fields:
– Date of service
– Maternal DRG codes
– All UB and HCFA 1500 with the above diagnoses (V22,
V23, V24, V27, V27.2, V27.3, V27.5.)
– The file layout (see excel spread sheet)
• Include institutional claims files
– Filter through DRG codes described above
35
Iowa’s Linkage Process
• Why LinkPlus?
– Can use CSV files/easy to move between SAS
– Ability to create output files and re-link files
– Ability to examine “uncertain” matches
• Data cleaning and import
– Import text files and clean data in SAS
• Deterministic match
– Requires common identifying fields
• BC – Mother’s last name, first name, date of birth (DOB), county of residence
• Medicaid claims – Mother’s last name, first name, DOB, county of residence
– Multiple passes
• BC – Mother’s maiden name, first name, DOB, county of residence
• Medicaid claims – Mother’s last name, first name, DOB, county of residence
36
Iowa’s Lessons Learned and Next Steps
Lessons
• Be careful what you ask for
– Be aware of global billing
– Consistent use of procedure codes
• Carve out time to prepare the data and do the actual linkage
– At least a week for the linkage (after data cleaning)
– Play with the linkage – try different blocking variables
Next Steps
• Document and develop protocol for how to handle “uncertain”
matches
• Need to examine how to handle multiple births
• Need to develop validation process
37
Questions?
38
Save the Date
• Tuesday, April 21 from 2-3 PM (ET)
– Web Training: “Preparing for the In-Person Training”
• Tuesday, April 28 from 2-3 PM (ET)
– Advanced Web Training: “Obtaining Data from MCOs and
Submitting Medicaid and CHIP Data to CARTS”
• In-Person Training
– May 4-5, 2015 in Washington, DC
Thank You!
• Please fill out the evaluation questions on
screen for today’s web training
• Contact Information:
– Ellen O’Brien, AcademyHealth
• [email protected]
– Stephanie Kennedy, AcademyHealth
• [email protected]