Katie Harron

I am a statistician with an interest in using data linkage to exploit existing data sources for improving maternal and child health. My research to date has focussed on the complexities of linking large datasets, evaluation of linkage quality, and the use of electronic healthcare data to support clinical trials.

My current research aims to identify and measure determinants of childhood health service and longer-term health and educational outcomes, focussing on maternal influences and children born preterm. I hold a Wellcome Trust postdoctoral fellowship and work with Professor Jan van der Meulen in the Department for Health Services Research and Policy and collaborate with researchers in the Farr Institute of Health Informatics Research London and UCL (Professor Ruth Gilbert and Professor Harvey Goldstein). My fellowship has involved spending time at the Institute for Clinical Evaluative Sciences in Ontario, comparing infant outcomes recorded in data from Canada and England. I also collaborate on a study investigating healthcare data quality with researchers at the Administrative Data Research Centre for England (ADRCE) and the Health and Social Care Information Centre. I am the course leader for the ADRCE Introduction to Data Linkage.

Contact details:  katie.harron@lshtm.ac.uk

Related Publications

[The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement].

Benchimol, E.I. ; Smeeth, L. ; Guttmann, A. ; Harron, K. ; Hemkens, L.G. ; Moher, D. ; Petersen, I. ; Sørensen, H.T. ; von Elm, E. ; Langan, S.M. ; RECORD Working Committee, . ;
[The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement].
Z Evid Fortbild Qual Gesundhwes, 2016; 115-116:33-48

Linking Data for Mothers and Babies in De-Identified Electronic Health Data.

Katie Harron, Ruth Gilbert, David Cromwell, Jan van der Meulen

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

Katie Harron, Angie Wade, Ruth Gilbert, Berit Muller-Pebody, Harvey Goldstein.

The analysis of record-linked data using multiple imputation with data value priors.

Goldstein H, Harron K, Wade A.

Opening the black box of record linkage.

Harron K, Wade A, Muller-Pebody B, Goldstein H, Gilbert R.

Linkage, Evaluation and Analysis of National Electronic Healthcare Data: Application to Providing Enhanced Blood-Stream Infection Surveillance in Paediatric Intensive Care

Harron K, Goldstein H, Wade A, Muller-Pebody B, Parslow R, Gilbert R.

Risk-adjusted monitoring of blood-stream infection in paediatric intensive care: a data linkage study.

Harron K, Wade A, Muller-Pebody B, Goldstein H, Parslow R, Gray J, Hartley JC, Mok Q, Gilbert R.

Identifying Possible False Matches in Anonymized Hospital Administrative Data without Patient Identifiers

Hagger-Johnson G, Harron K, Gonzalez-Izquierdo A, Cortina-Borja M, Dattani N, Muller-Pebody B, Parslow R, Gilbert R, Goldstein H.

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

Harron K, Parslow R, Mok Q, Tibby SM, Wade A, Muller-Pebody B, Gilbert R.

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

Hagger-Johnson G, Harron K, Fleming T, Gilbert R, Goldstein H, Landy R, Parslow RC.