Volume 50 | Number 4 | August 2015

Abstract List

Gareth Hagger‐Johnson Ph.D., Katie Harron Ph.D., Arturo Gonzalez‐Izquierdo Ph.D., Mario Cortina‐Borja Ph.D., Nirupa Dattani Ph.D., Berit Muller‐Pebody Ph.D., Roger Parslow Ph.D., Ruth Gilbert M.D., Harvey Goldstein Ph.D.


To identify data linkage errors in the form of possible false matches, where two patients appear to share the same unique identification number.

Data Source

Hospital Episode Statistics () in England, United Kingdom.

Study Design

Data on births and re‐admissions for infants (April 1, 2011 to March 31, 2012; age 0–1 year) and adolescents (April 1, 2004 to March 31, 2011; age 10–19 years).

Data Collection/Extraction Methods

Hospital records pseudo‐anonymized using an algorithm designed to link multiple records belonging to the same person. Six implausible clinical scenarios were considered possible false matches: multiple births sharing , re‐admission after death, two birth episodes sharing , simultaneous admission at different hospitals, infant episodes coded as deliveries, and adolescent episodes coded as births.

Principal Findings

Among 507,778 infants, possible false matches were relatively rare ( = 433, 0.1 percent). The most common scenario (simultaneous admission at two hospitals,  = 324) was more likely for infants with missing data, those born preterm, and for Asian infants. Among adolescents, this scenario ( = 320) was more common for males, younger patients, the Mixed ethnic group, and those re‐admitted more frequently.


Researchers can identify clinically implausible scenarios and patients affected, at the data cleaning stage, to mitigate the impact of possible linkage errors.