| Title: | Curating a longitudinal research resource using linked primary care EHR data - a UK Biobank case study |
| Journal: | Journal of the American Medical Informatics Association |
| Published: | 13 Dec 2021 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/34897458/ |
| DOI: | https://doi.org/10.1093/jamia/ocab260 |
| URL: | https://academic.oup.com/jamia/article-pdf/29/3/546/42333190/ocab260.pdf |
Publication 10868
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Abstract
Primary care EHR data are often of clinical importance to cohort studies however they require careful handling. Challenges include determining the periods during which EHR data were collected. Participants are typically censored when they deregister from a medical practice, however, cohort studies wish to follow participants longitudinally including those that change practice. Using UK Biobank as an exemplar, we developed methodology to infer continuous periods of data collection and maximize follow-up in longitudinal studies. This resulted in longer follow-up for around 40% of participants with multiple registration records (mean increase of 3.8 years from the first study visit). The approach did not sacrifice phenotyping accuracy when comparing agreement between self-reported and EHR data. A diabetes mellitus case study illustrates how the algorithm supports longitudinal study design and provides further validation. We use UK Biobank data, however, the tools provided can be used for other conditions and studies with minimal alteration.</p>
6 Authors
- Philip Darke
- Sophie Cassidy
- Michael Catt
- Roy Taylor
- Paolo Missier
- Jaume Bacardit
1 Application
| Application ID | Title |
|---|---|
| 12184 | Type 2 diabetes, physical activity, sedentary behaviour and sleep. |
Enabling scientific discoveries that improve human health