Presentation on theme: "1 Data Linkage Strategies Shihfen Tu, Ph.D. University of Maine"— Presentation transcript:
1 Data Linkage Strategies Shihfen Tu, Ph.D. University of Maine
2 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.
3 Acknowledgements University of Maine –Quansheng Song –Cecilia Cobo-Lewis Maine Bureau of Health –Kim Church –Pat Day –Ellie Mulcahy –Toni Wall
5 Data Linkage
8 Data Linkage - Probabilistic
10 Data Linkage - Probabilistic
11 Data Linkage - Probabilistic
12 Data Linkage - Inconsistency
13 Data Linkage - Inconsistency Inconsistency Detected Correcting…. Message
14 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=394 Brad A. Graham ID=4484 Brad A. Graham ID=7354 Brad Graham
15 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=4484 Brad A. Graham ID=7354 Brad Graham EHDI_ID=394 Brad A. Graham
16 Inconsistencies EHDI_ID=394 John A. Graham ID=4048 John A. Graham ID=4048 Jon A. Graham EHDI_ID=948 Jon A. Graham ID=9324 Jon Graham EHDI_ID=948 Jon Graham
17 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 Inconsistencies
18 Linkage Creep EHDI Database contributes an individual,Catherine A. Sampson
19 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 theyre the same person
20 Linkage Creep Link Birth Defects Registry Data –Name is Kathy A. Simpson –Are these the same person? –Perform probabilistic match (require.85) P Match is.90, we conclude theyre the same person
21 Linkage Creep If we compare to Catherine A. Sampson –P Match =.81 –Conclude they are NOT the same individual –Would not assign same ID Which is correct?
22 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
23 Linkage Creep When is this a problem? –Linkage creep will result in the two distinct individuals being erroneously combined under a single ID
24 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
25 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
26 Linkage Creep Forbidding creep will result in a single individual being divided into two IDs over time Further challengewhere to divide records into additional IDs?
27 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