1 Data Linkage Strategies Shihfen Tu, Ph.D. University of Maine shihfen.tu@umit.maine.edu

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Data Linkage Strategies

Shihfen Tu, Ph.D.

University of Maine

shihfen.tu@umit.maine.edu

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Faculty Disclosure Information

In the past 12 months, I have not had a significant financial interest or other relationship with the manufacturer(s) of the product(s) or provider(s) of the service(s) that will be discussed in my presentation.

This presentation will not include discussion of pharmaceuticals or devices that have not been approved by the FDA.

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Acknowledgements

• University of Maine– Quansheng Song– Cecilia Cobo-Lewis

• Maine Bureau of Health– Kim Church– Pat Day– Ellie Mulcahy– Toni Wall

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Data Linkage

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Data Linkage

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Data Linkage

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Data Linkage - Probabilistic

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Data Linkage - Probabilistic

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Data Linkage - Probabilistic

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Data Linkage - Probabilistic

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Data Linkage - Inconsistency

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Data Linkage - Inconsistency

Inconsistency DetectedInconsistency DetectedCorrecting….Correcting….

Message

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Inconsistencies

• Record in EHDI links to two records in other database

• The other source indicates the records belong to different people

• How to address depends on processing of other database

EHDI_ID=394Brad A. Graham

ID=4484Brad A. Graham

ID=7354Brad Graham

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Inconsistencies

• Other source not de-duplicated ?• Other source de-duplicated, but insufficient evidence to conclude

ID=4484 and ID=7354 are the same person ?– BD may provide additional information so that these probabilities have changed

ID=4484Brad A. Graham

ID=7354Brad Graham

EHDI_ID=394Brad A. Graham

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Inconsistencies

EHDI_ID=394John A. Graham

ID=4048John A. Graham

ID=4048Jon A. Graham

EHDI_ID=948Jon A. Graham

ID=9324Jon Graham

EHDI_ID=948 Jon Graham

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• How this "cross-over" is resolved depends on whether one or neither file is given precedence

• Influenced by probabilistic de-duplication process performed after a linkage

EHDI_ID=394John A. GrahamEHDI_ID=394

John A. GrahamID=4048

John A. GrahamID=4048

John A. Graham

ID=4048Jon A. Graham

ID=4048Jon A. Graham

EHDI_ID=948Jon A. GrahamEHDI_ID=948Jon A. Graham

ID=9324Jon Graham

ID=9324Jon Graham

EHDI_ID=948Jon Graham

EHDI_ID=948Jon Graham

Inconsistencies

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Linkage Creep

• EHDI Database contributes an individual,Catherine A. Sampson

ID Source FirstName MiddleInitial LastName PMatch

113 EHDI Catherine A Sampson

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Linkage Creep

• Link the Electronic Birth Certificate– Name is Catherine A. Simpson– Are these the same person?– Perform probabilistic match

• Require .85 probability of a match to conclude two similar records are the same (Critical p = .85)

• Probability is .90, we conclude they’re the same person

ID Source FirstName MiddleInitial LastName PMatch

113 EHDI Catherine A Sampson113 EBC Catherine A Simpson 0.90

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Linkage Creep

• Link Birth Defects Registry Data– Name is Kathy A. Simpson– Are these the same person?– Perform probabilistic match (require .85)

• PMatch is .90, we conclude they’re the same person

ID Source FirstName MiddleInitial LastName PMatch

113 EHDI Catherine A Sampson113 EBC Catherine A Simpson113 BDR Kathy A Simpson 0.90

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Linkage Creep

• If we compare to Catherine A. Sampson– PMatch=.81

– Conclude they are NOT the same individual– Would not assign same ID

• Which is correct?

ID Source FirstName MiddleInitial LastName PMatch

113 EHDI Catherine A Sampson113 EBC Catherine A Simpson113 BDR Kathy A Simpson 0.81

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Linkage Creep• When is this a problem?

– Over time, two distinct individuals may project “tendrils” composed of combinations of identifiers that statistically overlap in probabilistic space

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Linkage Creep• When is this a problem?

– Linkage creep will result in the two distinct individuals being erroneously combined under a single ID

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Linkage Creep• When is this not problem?

– Over time, certain key identifiers for an individual are expected to change

– This phenomenon will increase as a historical database grows, and as additional sources are input into a centralized system

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Linkage Creep• Complexity of “creep” in longitudinal datasets

– Black records are related to all records– Yellow and Blue records are NOT related to White

record– Yellow record is also not related to Red record at

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Linkage Creep• Forbidding “creep” will result in a single

individual being divided into two IDs over time

• Further challenge—where to divide records into additional IDs?

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Tools for Evaluating Linkage• Inconsistencies can occur in deterministic linkage, but

are more common in probabilistic linkages• Probabilities that create potential for problems provide

a valuable tool for evaluating linkages– Instead of a “are two records the same person ?” Yes/No– Estimates or indices of how likely it is that two records are

the same person

• Should be able to estimate the number of erroneous linkages

• Possible to conduct a detailed examination of quality by ignoring very strong and very weak pairings, and only focusing on pairings that are ambiguous