Utilising identifier error variation in linkage of large administrative data sources.
Linkage of administrative data sources often relies on probabilistic methods using a set of common identifiers (e.g. sex, date of birth, postcode). Variation in data quality on an individual or organisational level (e.g. by hospital) can result in clustering of identifier errors, violating the assumption of independence between identifiers required for traditional probabilistic match weight estimation. This potentially introduces selection bias to the resulting linked dataset. We aimed to measure variation in identifier error rates in a large English administrative data source (Hospital Episode Statistics; HES) and to incorporate this information into match weight calculation.



We used 30,000 randomly selected HES hospital admissions records of patients aged 0-1, 5-6 and 18-19 years, for 2011/2012, linked via NHS number with data from the Personal Demographic Service (PDS; our gold-standard). We calculated identifier error rates for sex, date of birth and postcode and used multi-level logistic regression to investigate associations with individual-level attributes (age, ethnicity, and gender) and organisational variation. We then derived: i) weights incorporating dependence between identifiers; ii) attribute-specific weights (varying by age, ethnicity and gender); and iii) organisation-specific weights (by hospital). Results were compared with traditional match weights using a simulation study.



Identifier errors (where values disagreed in linked HES-PDS records) or missing values were found in 0.11% of records for sex and date of birth and in 53% of records for postcode. Identifier error rates differed significantly by age, ethnicity and sex (p < 0.0005). Errors were less frequent in males, in 5-6 year olds and 18-19 year olds compared with infants, and were lowest for the Asian ethic group. A simulation study demonstrated that substantial bias was introduced into estimated readmission rates in the presence of identifier errors. Attribute- and organisational-specific weights reduced this bias compared with weights estimated using traditional probabilistic matching algorithms.



We provide empirical evidence on variation in rates of identifier error in a widely-used administrative data source and propose a new method for deriving match weights that incorporates additional data attributes. Our results demonstrate that incorporating information on variation by individual-level characteristics can help to reduce bias due to linkage error.
London School of Hygiene and Tropical Medicine, 15-17 Tavistock Place, London, WC1 H 9SH, UK. Katie.harron@lshtm.ac.uk.
Katie
0000-0002-3418-2856
K
Harron
Administrative Data Research Centre for England, UCL, 222 Euston Road, London, NW1 2DA, UK.
Gareth
G
Hagger-Johnson
Administrative Data Research Centre for England and UCL Great Ormond Street Institute of Child Health, 30 Guilford Street, London, WC1 N 1EH, UK.
Ruth
R
Gilbert
University of Bristol, Administrative Data Research Centre for England and UCL Great Ormond Street Institute of Child Health, 30 Guilford Street, London, WC1 N 1EH, UK.
Harvey
H
Goldstein
D000293:Adolescent; D000465:Algorithms; D015982:Bias; D002648:Child; D002675:Child, Preschool; D003198:Computer Simulation; D005260:Female; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D016247:Information Storage and Retrieval / Q000379:methods / Q000706:statistics & numerical data*; D008297:Male; D008498:Medical Record Linkage / Q000379:methods*; D009313:National Health Programs / Q000706:statistics & numerical data; D012042:Registries / Q000706:statistics & numerical data*; D006113:United Kingdom; D055815:Young Adult
Administrative data; Data linkage; Hospital admission; Linkage error; Linkage evaluation; Record linkage