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Michigan Newborn Screening & Live Births Records Linkage and Follow-Up of Potentially Un-Screened Infants Steven J. Korzeniewski, MA, MSc, Maternal & Child.

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Presentation on theme: "Michigan Newborn Screening & Live Births Records Linkage and Follow-Up of Potentially Un-Screened Infants Steven J. Korzeniewski, MA, MSc, Maternal & Child."— Presentation transcript:

1 Michigan Newborn Screening & Live Births Records Linkage and Follow-Up of Potentially Un-Screened Infants Steven J. Korzeniewski, MA, MSc, Maternal & Child Health Epidemiology Section Manager Violanda Grigorescu, MD, MSPH, Glenn E. Copeland, BS, William I. Young, Ph.D., Michigan Department of Community Health

2 Outline Background/Intent of linkage Methods (software, data management, algorithm) Results Discussion Public Health Implications

3 Background Linkages re-initiated to identify live births potentially unscreened. ▫Initial efforts were not sustainable Intended to be mutually beneficial to newborn screening follow-up program and vital records. ▫Means to assess data quality

4 Methods Data received from November 2007 through March 2008 were used for this study ▫2008 transitioned from DOS based to Web based electronic birth certificate system ▫Newborn screening card number included on birth record SAS v9.1 (Cary, N.C.) used to create text files Record linkage and follow-up conducted by NBS Follow- up Program ▫Newborn screening & Michigan Care Improvement Registry (MCIR) data used for follow-up

5 Methods Linkage via Link Plus ▫A probabilistic record linkage program ▫Developed for cancer registries at the Centers for Disease Control and Prevention’s (CDC) Division of Cancer Prevention and Control in support of CDC's National Program of Cancer Registries (NCPR). ▫Can be used with any type of data in fixed width or delimited format ▫Free $$

6 Methods Linkage score (probabilistic linkage) ▫based on the theoretical frame work developed by Fellegi and Sunter (1969) ▫sum of the logarithm of odds across all matching variables, based on the probability that a matching variable agrees given that a comparison pair is a match and the probability that a matching variable agrees given that a comparison pair is not a match

7 Methods Blocking variables - used to ‘block’ (or partition) the two files Matching variables are compared between records matching on the blocking variable.

8 Blocking VariablesMatching Variables Name Phonetic SystemNameMatching Method Mother's First Name Soundex 10 Mother's First Name First Name Mother's Last Name Infant ’ s First Name Mother's Last Name Last Name Infant ’ s Last Name Mother's Zip CodeZip-Code NBS Card Number*Value-Specific Birth Order Mother's Birth Date Infant ’ s Birth Date Mother's SSN Mother ’ s medical Record Number Methods

9 Follow-up ▫Unmatched records sent to follow-up staff ▫Staff search NBS data ▫Contact birth hospital ▫Send certified letter to parent requesting screen or signed refusal letter

10 Results Birth Year TotalExpired Infants MatchedUn-Matched Excluding Expired NNN%N% 2007123,981383122,51598.81,0830.9 200818,1975118,07599.3710.4 Total142,178434140,59098.91,1540.8

11 Results Follow-up ResultFrequency% NBS Already Completed28645.5 Parental Refusal of Screening Received Via Follow-up Staff487.6 N/A (Aged > 1 year)81.3 Out of State142.2 Parental Refusal at Time of Screening325.1 Expired60.95 Lost to Follow-Up/Pending18829.9

12 Discussion Probabilistic linkage is subjective………. & useful Linkage success is a function of ▫Cutoff selection ▫Data quality ▫Strategy ▫An ability to deal with discordance Match rates change over time and may require alternations in linkage algorithms ▫Manual checking of initial linkage results and follow-up results must be used to determine algorithm changes and avoid false matches. Live Births to NBS data matching is a “best case” scenario given data are collected at virtually the same time, same place, and often by the same person.

13 Discussion Significant investment of time for ▫Initial data management programming ▫Understanding how to select algorithm ▫Determination of borderline matches ▫Assessment of follow-up results Benefits beyond identification of potentially unscreened children include ▫Data quality check ▫Link to various datasets through vital records (i.e.- birth defects, EHDI, etc.)

14 Public Health Implications Link Plus is applicable to MCH databases Linkage facilitates data usage with minimal cost investments Linkage is successful at detecting unscreened infants (we have identified several) However, linkage should be handled with caution

15 Acknowledgements Co-investigators: Violanda Grigorescu, MD, MSPH, Glenn E. Copeland, BS, William I. Young, Ph.D., NBS Follow-up Staff

16 Contact KorzeniewskiS@Michigan.gov Thank You


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