TY - JOUR
AU - Rabi, Doreen
AB - BackgroundThe objective of this study was to identify patients with diabetes in a comprehensive primary care electronic medical records database using a number of different case definitions (clinical, pharmacy, laboratory definitions and a combination thereof) and understand the differences in patient populations being captured by each definition.MethodsData for this population-based retrospective cohort study was obtained from The Health Information Network (THIN). THIN is a longitudinal, primary care medical records database of over 9 million patients in UK. Primary outcome was a diagnosis of diabetes, defined by the presence of a diabetes read code, or an abnormal laboratory result, or a prescription for an Oral Anti-diabetic drug or insulin. A 2-year washout period was applied prior to the index of diabetes to avoid inclusion of prevalent cases for each case definition.ResultsThis study demonstrated that different case definitions of diabetes identify different sub-populations of patients. When the cohorts were observed based on any measure of central tendency, each of the cohorts were reasonably comparable to each other. However, the distribution of each of the cohorts when grouped by age categories and sex, reveal differences. For example, using pharmacy case definition results in a bimodal distribution among women, one between 1–19 year and 35–39 age categories, and then again between 60–64 and 85 years—however, the histogram becomes more normally distributed when metformin was removed from the case definition.ConclusionOur results suggest that clinical, pharmacy, laboratory case definitions identify different sub-populations and using multiple case definitions is likely required to optimally identify the entire diabetes population within THIN. Our study also suggests that age and sex of patients may affect the indexing of diabetes in THIN and is critical to better understand these variations.
TI - Exploring novel diabetes surveillance methods: a comparison of administrative, laboratory and pharmacy data case definitions using THIN
JF - Journal of Public Health
DO - 10.1093/pubmed/fdx096
DA - 2018-09-01
UR - https://www.deepdyve.com/lp/oxford-university-press/exploring-novel-diabetes-surveillance-methods-a-comparison-of-wO9T6huXIh
SP - 652
EP - 658
VL - 40
IS - 3
DP - DeepDyve
ER -