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									Using the Birth Certificate to Implement
   the Pregnancy Risk Assessment
     Monitoring System (PRAMS)

                   Leslie Lipscomb, MPH
                    Chris Johnson, MS

        National Association for Public Health Statistics and
  Information Systems and the Vital Statistics Cooperative Program
                   Project Directors Joint Meeting
                           June 6-8, 2005
           What is PRAMS?
   Population-based surveillance system
    of woman and infants
   State-specific data collection within a
    standardized system
   Information on maternal attitudes,
    behaviors and experiences
   Action oriented
States Participating in PRAMS, 2005
                                                                            VT ME
     OR                                 MN

                                                       MI                          RI
                              NE                                                 NYC
                                                            OH                  NJ
               UT                                 IL
                         CO                                      WV             MD

                     NM            OK        AR                   SC
                                                  MS AL     GA
                              TX             LA

             PRAMS Objectives
   To promote collection of population-based
    data of high scientific quality

   To conduct comprehensive analyses

   To translate results into useable information

   To build state capacity for collecting,
    analyzing, and translating data
How the Birth Certificate is Used
     to Implement PRAMS
    Identifying the PRAMS Sample
    Data Collection
    Data Weighting
    Data Linkage
     PRAMS Population of Interest

   Mothers who are residents of state, who
    delivered a live-born infant within state
    during the calendar year.
         PRAMS Sampling Frame

   List representing the population eligible
    for inclusion in the sample.
   Operational sampling frame is list of all
    infants born alive within state to resident
    mothers during calendar year.
   Vital records birth certificate file serves
    as the source of the sampling frame.
      Exclusions to the Sampling
   Stillbirths, fetal deaths, & induced
   Out-of-state occurrences
   In-state births to nonresidents of the
   Records missing mother’s name
   Records processed too late (> 6 months
    from birth)
       Exclusions to the Sampling
           Frame (continued)
   Records processed too early (< 2 months
    from birth -- but these records are
    included in later batches)
   Multiple Gestation Infants
    – For twin and triplet sets, only one infant is
      selected to be included in the sampling
    – For multiple gestations of order 4 or more,
      all infants are excluded from the sampling
       Exclusions to the Sampling
           Frame (continued)
   Adopted Infants
    – If identified at the time the sample is drawn,
      adopted infants are excluded.
    – If not identified at the time the sample is
      • If birth mother is listed on certificate, she can be
      • If adoptive mother is listed on certificate, she
        should be dropped from further follow-up.
Inclusions to the Sampling Frame

   Infants who have died
   Records missing address information or
    other key birth certificate information
    (other than mother’s name)
              Stratified Sampling

   Allows precise estimates for subgroups
    – comparisons of greatest interest
   Alternative to proportional sample
    – groups not oversampled are represented
   Stratification Variable Choices
    Birthweight              Age
    Race and ethnicity       Area
    Education                Medicaid status
  Frame Construction
            Read in birth record.

           Out-of-state resident?

             Out-of-state birth?

          More than 6 mos. ago?

           Less than 2 mos. ago?

Hold for next frame.

               Multiple birth?

Select 1 from mom.

             Assign to stratum.
                     Sample Selection

                             Read frame record.

Extract info for stFRnnnn.DAT.

Demographic and medical info.

                   Apply systematic sampling within strata.


Extract info for stBCnnnn.DAT                  Extract info for BCENTRY.DAT

Demographic and medical info.            Contact information (names and addresses)
Data Collection
      Data Collection: Sequence of
   Monthly sample drawn from birth certificates
    (2-6 months after delivery)
   BCENTRY file created – contains personal
    identifiers to assist with contacting mothers
   Data collection period (up to 90 days)
    – Mailings
    – Search for telephone numbers
    – Telephone calls

   Data cleaning and quality control
   Data transmitted to CDC
PRAMS Weighting
Rationale for weighting

      x Coverage Weight
                                  x Sampling Weight

                                                               x Response Weight

 Population               Frame                       Sample                   Respondents
             Coverage weights

   Frame is constructed during the year.
   Some births don’t make it into the frame.
    – Home births
    – Remote/rural hospitals
   We account for missing births using the
    end-of-year “official” birth file and
    creating a coverage weight.
Coverage weight: Example

                 x Coverage Weight
                 = 100/95 = 1.053

                      95% of

         Population                Frame
             Sampling Weights
   To achieve better estimates within small
    groups, we oversample those groups.
    Examples are:
    – Minority race groups
    – Low birth-weights
    – Rural areas or counties
   Since we sample at different rates by
    group, we must “weight” each
    observation to represent the original
    sampling frame.
Sampling weights: Example

                 x Sampling Weight =
                     100/20 = 5

                     Sample 20%

         Frame                         Sample
              Response weights
   Survey data are analyzed using a CART
    analysis to determine which variables
    predict response. Examples:
    – Education
    – Marital status
    – Prenatal care
   Observations are assigned to response
    groups and weighted by their response
Response weights: Example

                  x Response Weight
                    = 100/80 = 1.25

                  Response Rate 80%

         Sample                       Respondents
   Twins on frame
   Duplicate records
   Plurality errors
Data Linkage
       PRAMS Weighting Linkage
   Must match twins and triplets to calculate
    proper sampling weight (internal linking)
   Algorithm
    – Loose linkage (plural births)
       • DOB + MDOB + Hospital + County of Birth
       • 01212000-03281968-792-125
    – Strict linkage (singletons)
       • LOOSE + Race + Education + County of Residence
       • 01212000-03281968-792-125 || 3-5-124
       PRAMS Weighting Linkage

   Matches are counted and ordered
   What goes wrong:
    – DOB is different – “midnight babies”
    – Data entry errors, etc.
   Find “near” matches, make decision,
    code by hand
        The Value of Data Linkage

   Reduces respondent burden
   Improves accuracy (better detection &
   Reduces follow-up costs
   The last PRAMS RFA invited data linkage
    activities as examples of enhanced
    projects (beyond the basics)
   Washington State’s “First Steps”
    – PRAMS, BC, Medicaid records
   DRH – Massachusetts DOH linkage of
    birth certificate records with records
    from Assisted Reproductive
    Technologies registry
   Utah
   Colorado
    Validation of Self-Report of
      Medicaid Utilization and
Differences Between Hispanic and
       Non-Hispanic Women

      Utah Department of Health
            Laurie Baksh, MPH
          Shaheen Hossain, PhD
           Lois Bloebaum, BSN
            Sharon Clark, MPH
            Brenda Ralls, PhD
             Gulzar Shah, PhD
               Validating Data
   To assess agreements between self-
    reported Medicaid coverage and actual
    Medicaid coverage, the Utah
    Department of Health linked the 2000
    PRAMS data set with a linked data set
    of birth certificates and Medicaid


   Phase I
    – Linking vital records birth data with
      Medicaid eligibility data.

   Phase II
    – Linking the 2000 PRAMS data set with the
      existing linked birth - eligibility file.
     Methodology - Linking VR and
           Medicaid data.
   The initial matching process was
    completed using Automatch software.
    – Both the birth file and Medicaid eligibility file
      were converted to an ASCII text format
    – Variables were re-coded to be consistent
      across data sets.
    – Probabilistic matching was conducted.

Linking Medicaid and Birth
   Records in Colorado

      Alyson Shupe, Ph.D.
    Section Chief, Health Statistics
 Colorado Department of Public Health
          and Environment

  PRAMS National Meeting w December 2002
   No access to Medicaid records
   No indication of payer source on BC
   Limited sense of SES of women giving
    birth in CO
   No idea whether programs aimed at
    reducing poor birth outcomes and
    costs work
   Unrestricted access to Medicaid
   Linked birth and Medicaid records from
    state FY 1998-2000
   Ability to analyze birth files by Medicaid
   Cost/benefit analysis of Prenatal Plus
   Obtained access to and training on using
    “STARS” database
   Matched on mother’s, father’s and infant’s
    names, and mother’s DOB & SSN
   Claims data were matched using mother’s
    Medicaid ID
   STARS searched for infant DOB, then period of
    service eligibility
   Infants matched on DOB, first and last name,
    and mother’s names
   Completeness of data set
    – Other DRGS
    – Claims not yet filed
    – HMO clients
    – Out of state births
   Reliability of claims data
   Inter-rater reliability on matching
    – Latino population
   More quality control
   Link Medicaid/birth data set with PRAMS data
    – Do PRAMS respondents’ report of Medicaid status
      match Medicaid claims data?
    – Add variable: “Medicaid at time of delivery” for
      analysis of birth record data
   Further exploration of claims data
   Revised birth certificate

   BC records are vital to PRAMS
   Completeness is reason for population-
    based results
   Linking data from various systems holds
    great promise

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