Hagger-Johnson, G., Harron, K., Gonzallez-Izquierdo, A., Cortina-Borja, M., Dattani, N., Muller-Pebody, B., Parslow, R., Gilbert, R. & Goldstein, H. (2015). Identifying possible false matches in anonymized hospital administrative data without patient identifiers. Health Services Research,50(4), 1162-1178. Retrieved from https://doi.org/10.1111/1475-6773.12272
Objective: 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 (HES) 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 HESID, re-admission after death, two birth episodes sharing HESID, 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 (n = 433, 0.1 percent). The most common scenario (simultaneous admission at two hospitals, n = 324) was more likely for infants with missing data, those born preterm, and for Asian infants. Among adolescents, this scenario (n = 320) was more common for males, younger patients, the Mixed ethnic group, and those re-admitted more frequently. Conclusions: Researchers can identify clinically implausible scenarios and patients affected, at the data cleaning stage, to mitigate the impact of possible linkage errors.
Learning Sciences Institute Australia
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