Aarhus University Seal / Aarhus Universitets segl

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

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

DOI

  • Irene Petersen, UCL, University College London, University of London, Med Sch, Res Dept Primary Care & Populat Hlth
  • ,
  • Catherine A. Welch, Univ Leicester, University of Leicester, Dept Hlth Sci
  • ,
  • Irwin Nazareth, UCL, University College London, University of London, Med Sch, Res Dept Primary Care & Populat Hlth
  • ,
  • Kate Walters, UCL, University College London, University of London, Med Sch, Res Dept Primary Care & Populat Hlth
  • ,
  • Louise Marston, UCL, University College London, University of London, Med Sch, Res Dept Primary Care & Populat Hlth
  • ,
  • Richard W. Morris, Univ Bristol, University of Bristol, Bristol Med Sch, Dept Populat Hlth Sci
  • ,
  • James R. Carpenter, London Sch Hyg & Trop Med, University of London, London School of Hygiene & Tropical Medicine, Dept Med Stat
  • ,
  • Tim P. Morris, UCL, Medical Research Council Clinical Trials Unit, University of London, University College London, MRC Clin Trials Unit
  • ,
  • Tra My Pham, UCL, University College London, University of London, Med Sch, Res Dept Primary Care & Populat Hlth

Background: Clinical databases are increasingly used for health research; many of them capture in formation 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 'Hie Health !movement 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.

OriginalsprogEngelsk
TidsskriftClinical epidemiology
Vol/bind11
Sider (fra-til)157-167
Antal sider11
ISSN1179-1349
DOI
StatusUdgivet - 2019

Se relationer på Aarhus Universitet Citationsformater

ID: 189790447