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Health indicator recording in UK primary care electronic health records: key implications for handling missing data

Authors Petersen I, Welch CA, Nazareth I, Walters K, Marston L, Morris RW, Carpenter JR, Morris TP, Pham TM

Received 20 October 2018

Accepted for publication 4 December 2018

Published 11 February 2019 Volume 2019:11 Pages 157—167


Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Professor Henrik Toft Sørensen

Irene Petersen,1,2 Catherine A Welch,3 Irwin Nazareth,1 Kate Walters,1 Louise Marston,1 Richard W Morris,4 James R Carpenter,5,6 Tim P Morris,5 Tra My Pham1

1Department of Primary Care and Population Health, University College London, London NW3 2PF, UK; 2Department of Clinical Epidemiology, Aarhus University, 8200 Aarhus N, Denmark; 3Department of Health Sciences, University of Leicester, Leicester LE1 7RH, UK; 4Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK; 5MRC Clinical Trials Unit at UCL, London WC1V 6LJ, UK; 6Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK

Background: Clinical databases are increasingly used for health research; many of them capture information on common health indicators including height, weight, blood pressure, cholesterol level, smoking status, and alcohol consumption. However, these are often not recorded on a regular basis; missing data are ubiquitous. We described the recording of health indicators in UK primary care and evaluated key implications for handling missing data.
Methods: We examined the recording of health indicators in The Health Improvement Network (THIN) UK primary care database over time, by demographic variables (age and sex) and chronic diseases (diabetes, myocardial infarction, and stroke). Using weight as an example, we fitted linear and logistic regression models to examine the associations of weight measurements and the probability of having weight recorded with individuals’ demographic characteristics and chronic diseases.
Results: In total, 6,345,851 individuals aged 18–99 years contributed data to THIN between 2000 and 2015. Women aged 18–65 years were more likely than men of the same age to have health indicators recorded; this gap narrowed after age 65. About 60–80% of individuals had their height, weight, blood pressure, smoking status, and alcohol consumption recorded during the first year of registration. In the years following registration, these proportions fell to 10%–40%. Individuals with chronic diseases were more likely to have health indicators recorded, particularly after the introduction of a General Practitioner incentive scheme. Individuals’ demographic characteristics and chronic diseases were associated with both observed weight measurements and missingness in weight.
Conclusion: Missing data in common health indicators will affect statistical analysis in health research studies. A single analysis of primary care data using the available information alone may be misleading. Multiple imputation of missing values accounting for demographic characteristics and disease status is recommended but should be considered and implemented carefully. Sensitivity analysis exploring alternative assumptions for missing data should also be evaluated.

Keywords: primary care, EHRs, recording, QOF, multiple imputation, statistics, epidemiology, research methods, data analysis

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