Evaluating bias due to data linkage error in electronic healthcare records.

ABSTRACT:

Background

Linkage of electronic healthcare records is becoming increasingly important for research purposes. However, linkage error due to mis-recorded or missing identifiers can lead to biased results. We evaluated the impact of linkage error on estimated infection rates using two different methods for classifying links: highest-weight (HW) classification using probabilistic match weights and prior-informed imputation (PII) using match probabilities.

Methods

A gold-standard dataset was created through deterministic linkage of unique identifiers in admission data from two hospitals and infection data recorded at the hospital laboratories (original data). Unique identifiers were then removed and data were re-linked by date of birth, sex and Soundex using two classification m

SUBMITTER: Harron K 

PROVIDER: S-EPMC4015706 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature