Data linkage errors in hospital administrative data when applying a pseudonymisation algorithm to paediatric intensive care records

BMJ Open

Volumn 5, Issue 8, 2015, Pages

  Hagger Johnson, Gareth ^a,b   Harron, Katie ^a   Fleming, Tom ^c   Gilbert, Ruth ^a   Goldstein, Harvey ^a,d   Landy, Rebecca ^e   Parslow, Roger C ^c  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

^b UNIVERSITY COLLEGE LONDON   (United Kingdom)

^c UNIVERSITY OF LEEDS   (United Kingdom)

^d UNIVERSITY OF BRISTOL   (United Kingdom)

^e QUEEN MARY UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ADOLESCENT; AFRICAN AMERICAN; ALGORITHM; ARTICLE; CHILD; CHINESE; ETHNIC GROUP; FEMALE; HOSPITAL ADMISSION; HOSPITAL READMISSION; HUMAN; INFANT; INTENSIVE CARE; INTENSIVE CARE UNIT; MALE; MEDICAL RECORD; NEWBORN; OUTCOME ASSESSMENT; PATIENT IDENTIFICATION; PEDIATRIC HOSPITAL; PREDICTOR VARIABLE; PSEUDOANONYMISATION ALGORITHM; REGISTER; SOCIAL CARE; UNITED KINGDOM; CLINICAL TRIAL; FACTUAL DATABASE; HOSPITALIZATION; MEASUREMENT ACCURACY; MULTICENTER STUDY; PEDIATRIC INTENSIVE CARE UNIT; PRESCHOOL CHILD; STANDARDS; STATISTICAL BIAS; STATISTICS AND NUMERICAL DATA;

ADOLESCENT; ALGORITHMS; BIAS (EPIDEMIOLOGY); CHILD; CHILD, PRESCHOOL; CRITICAL CARE; DATA ACCURACY; DATABASES, FACTUAL; FEMALE; HOSPITALIZATION; HUMANS; INFANT; INFANT, NEWBORN; INTENSIVE CARE UNITS, PEDIATRIC; MALE; MEDICAL RECORD LINKAGE; REGISTRIES; UNITED KINGDOM;

EID: 84941656791     PISSN: None     EISSN: 20446055     Source Type: Journal    
DOI: 10.1136/bmjopen-2015-008118     Document Type: Article

References (32)

1. Academy of Medical Royal Colleges. Hospital episode statistics (HES): improving the quality and value of hospital data. A discussion document. Leeds: Health and Social Care Information Centre, 2011. http://www.hscic.gov. uk/article/1946/Data-quality-resources-for-clinicians
2. Health and Social Care Information Centre. Replacement of the HES Patient ID (HESID). Leeds: Health and Social Care Information Centre, 2009. http://www.hscic.gov. uk/article/1825/The-processing-cycle-and-HES-data-quality
3. Health and Social Care Information Centre. A guide to linked mortality data from hospital episode statistics and the office for national statistics. Leeds: Health and Social Care Information Centre, 2013. http://www.hscic.gov. uk/onsmortality
4. Hagger-Johnson G. Identifying possible false matches in anonymised hospital administrative data without patient identifiers. Health Serv Res 2015;50:1162-78.
5. Fellegi I, Sunter A. A theory for record linkage. J Am Stat Association 1969;64:1183-210.
6. Jaro M. Probabilistic linkage of large public health data files. Stat Med 1995;14:491-8.
7. Chassin MR, Becher EC. The wrong patient. Ann Intern Med 2002;136:826-33.
8. Gray JE, Suresh G, Ursprung R, et al. Patient misidentification in the neonatal intensive care unit: quantification of risk. Pediatrics 2006;117:e43-7.
9. Suresh G, Horbar JD, Plsek P, et al. Voluntary anonymous reporting of medical errors for neonatal intensive care. Pediatrics 2004;113:1609-18.
10. Schmidlin K, Clough-Gorr K, Spoerri A, et al. Impact of unlinked deaths and coding changes on mortality trends in the Swiss National Cohort. BMC Med Inform Decis Mak 2013;13:1.
11. Lariscy JT. Differential record linkage by Hispanic ethnicity and age in linked mortality studies: implications for the epidemiologic paradox. J Aging Health 2011;23:1263-84.
12. Moore C, Amin J, Gidding H, et al. A new method for assessing how sensitivity and specificity of linkage studies affects estimation. PLoS ONE 2014;9:e103690.
13. Goldstein H, Harron K, Wade A. The analysis of record-linked data using multiple imputation with data value priors. Stat Med 2012;31:3481-93.
14. Goldstein B, Nadel S. The promise and potential of continuous improvement in the pediatric intensive care unit: the evolving story from the United Kingdom Paediatric Intensive Care Audit Network. J Pediatr 2013;163:935-7.
15. Paediatric Intensive Care Audit Network (PICANet). PICANet: a decade of data. Leeds: University of Leeds, 2014. http://www.picanet.org.uk/Audit/Annual-Reporting
16. Health and Social Care Information Centre. The HES processing cycle and HES data quality. Leeds: Health and Social Care Information Centre, 2014. http://www.hscic.gov. uk/article/1825/The-processing-cycle-and-HES-data-quality
17. Health and Social Care Information Centre. Methodology for creation of the HES Patient ID (HESID). Leeds: Health and Social Care Information Centre, 2014. http://www.hscic.gov. uk/article/1825/The-processing-cycle-and-HES-data-quality
18. Health and Social Care Information Centre. IQAP guidance on unknown, estimated and default birth dates. Leeds: Health and Social Care Information Centre, 2010. http://systems.hscic.gov. uk/data/dataquality/resources
19. Health and Social Care Information Centre. HES Data dictionary: Inpatients, 2010. http://www.hscic.gov. uk/hesdatadictionary
20. Department of Communities and Local Government. English indices of deprivation 2010. London: Stationery Office, 2011. https://www.gov. uk/government/statistics/english-indices-of-deprivation-2010
21. Dattani N, Datta-Nemdharry P, Macfarlane A. Linking maternity data for England 2007: methods and data quality (ONS Report). London: Office of National Statistics, 2007. http://www.ons.gov. uk/ons/rel/hsq/health-statistics-quarterly/no-53-spring-2012/linkage-of-maternityhospitalepisode-statistics-data.html
22. Royal College of Obstetricians and Gynaecologists. Patterns of maternity care in English NHS hospitals. London: RCOG, 2013. https://www.rcog.org.uk/en/guidelines-research-services/auditqualityimprovement/clinical-indicators-project
23. Fraser J, Maskrey C, Taylor H. Evaluation of the Paediatric Index of Mortality in children managed on adult intensive care units. Arch Dis Child 2004;89:974-6.
24. Harron K, Wade A, Gilbert R, et al. Calculating matching probabilities in record linkage. BMC Med Res Methodol 2014;14:36.
25. Connecting for Health. PDS tracing guidance for front line NHS staff. Leeds: Health and Social Care Information Centre, 2005. http://webarchive.nationalarchives.gov. uk/20130502102046/http://www.connectingforhealth.nhs.uk/elearning/pds/flash/supporting% 20documents/PDS%20Tracing%20Guidance%20v1.0.pdf
26. Barry S, Dinnett E, Kean S, et al. Are routinely collected NHS administrative records suitable for endpoint identification in clinical trials? Evidence from the West of Scotland Coronary Prevention Study. PLoS ONE 2013;8:e75379.
27. Health and Social Care Information Centre. IQAP guidance on ethnic naming conventions. Leeds: Health and Social Care Information Centre, 2009. http://systems.hscic.gov. uk/data/dataquality/resources/dqm002408.pdf
28. Health and Social Care Information Centre. How good is HES ethnic coding and where do the problems lie? Leeds: NHS Information Centre, 2011. http://www.hscic.gov. uk/hes
29. Secretary of State. Health and Social Care Act 2012. London: The Stationary Office, 2010. http://www.legislation. gov. uk/ukpga/2012/7/contents/enacted
30. Parry J. Pseudonymised data can protect patient confidentiality. Secondary pseudonymised data can protect patient confidentiality, 2014. http://www.hsj.co.uk/resource-centre/supplements/pseudonymised-data-can-protect-patient-confidentiality/5071244. article
31. Office of National Statistics. Beyond 2011: matching anonymous data. London: ONS, 2013. http://www.ons.gov. uk/ons/about-ons/who-ons-are/programmes-and-projects/beyond-2011/reports-and-publications/methods-and-policies-reports/index.html
32. Schnell R, Bachteler T, Reiher Jr. Privacy-preserving record linkage using Bloom filters. BMC Med Inform Decis Mak 2009;9:41.