4.2 Article

Exposure measurement error when assessing current glucocorticoid use using UK primary care electronic prescription data

期刊

PHARMACOEPIDEMIOLOGY AND DRUG SAFETY
卷 28, 期 2, 页码 179-186

出版社

WILEY
DOI: 10.1002/pds.4649

关键词

electronic health records; glucocorticoids; pharmacoepidemiology; validity (epidemiology)

资金

  1. Medical Research Council [G0902272]
  2. Arthritis Research UK [20380]
  3. National Institute for Health Research
  4. MRC [G0902272, MR/K006665/1] Funding Source: UKRI

向作者/读者索取更多资源

Purpose To quantify misclassification in glucocorticoid (GC) exposure defined using UK primary care prescription data. Methods A cross-sectional study including patients with rheumatoid arthritis prescribed oral GCs in the past 2 years. Glucocorticoid exposure based on electronic prescription records was compared with participant-reported GC use captured using a paper diary. Prescription data (containing information about prescriptions issued but no dispensing information) was provided by the Clinical Practice Research Datalink. The following variables were defined: current use and dose of oral GCs and if (and when) participants had received a GC injection. For oral GCs, self-reported use was taken to represent true exposure. A dataset representing a hypothetical population was generated to assess the impact of the misclassification found for current use. Results A total of 67 of 78 study participants (86%) were correctly classified as currently on/off oral GCs; 32/38 (84.2%) participants reporting current GC use and 35/40 (87.5%) participants not reporting current use were correctly classified. Estimated values of current dose were imprecise (correlation coefficient 0.46). Concordance between reported and prescribed GC injections was poor (kappa statistic 0.14). Misclassification bias was demonstrated in the hypothetical population: For true relative risks of 1.5, 4, and 9, the observed relative risks were 1.33, 2.48, and 3.58, respectively. Conclusions Misclassification of current use of oral GCs was low but sufficient to lead to significant bias. Researchers should take care to assess the likely impact of exposure misclassification on their analyses.

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