Self-Reported Medication Use Validated Through Record Linkage to National Prescribing Data

Jonathan Hafferty, Archie Campbell, Lauren Navrady, M.j. Adams, D. Macintyre, Stephen Lawrie, K. Nicodemus, D.j. Porteous, Andrew McIntosh

Research output: Contribution to journalArticlepeer-review


Researchers need to be confident about the reliability of epidemiological studies that quantify medication use through self-report. Some evidence suggests that psychiatric medications are systemically under-reported. Modern record linkage enables validation of self-report with national prescribing data as gold standard. Here, we investigated the validity of medication self-report for multiple medication types.
Study Design and Setting
Participants in the Generation Scotland population-based cohort (N=10,244) recruited 2009-11 self-reported regular usage of several commonly prescribed medication classes. This was matched against Scottish NHS prescriptions data using three- and six-month fixed time windows. Potential predictors of discordant self-report, including general intelligence and psychological distress, were studied via multivariate logistic regression.
Antidepressants self-report showed very good agreement (κ=0.85, (95% Confidence Interval (CI) 0.84-0.87)), comparable to antihypertensives (κ=0.90, (0.89-0.91)). Self-report of mood stabilizers showed moderate-poor agreement (κ=0.42 CI 0.33-0.50). Relevant past medical history was the strongest predictor of self-report sensitivity, whereas general intelligence was not predictive.
In this large population-based study, we found self-report validity varied among medication classes, with no simple relationship between psychiatric medication and under-reporting. History of indicated illness predicted more accurate self-report, for both psychiatric and non-psychiatric medications. Although other patient-level factors influenced self-report for some medications, none predicted greater accuracy across all medications studied.
Original languageEnglish
JournalJournal of Clinical Epidemiology
Early online date30 Oct 2017
Publication statusE-pub ahead of print - 30 Oct 2017


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