clep-191437-health-indicator-recording-in-uk-primary-care-electronic-hea-020819.pdf (507.93 kB)
Health indicator recording in UK primary care electronic health records: key implications for handling missing data.
journal contributionposted on 2019-07-08, 16:39 authored by I Petersen, CA Welch, I Nazareth, K Walters, L Marston, RW Morris, JR Carpenter, TP Morris, TM Pham
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.
This study was initially carried out as part of the project “Missing data imputation in clinical databases: development of a longitudinal model for cardiovascular risk factor” led by Irene Petersen and funded by the Medical Research Council grant G0900701. James R Carpenter and Tim P Morris were supported by the Medical Research Council (grant numbers MC_UU_12023/21 and MC_UU_12023/29). Tra My Pham was supported by the National Institute for Health Research (NIHR) School for Primary Care Research (project number 379).
CitationClinical Epidemiology, 2019, 11, pp. 157-167
Author affiliation/Organisation/COLLEGE OF LIFE SCIENCES/School of Medicine/Department of Cardiovascular Sciences
- VoR (Version of Record)