{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T13:28:22Z","timestamp":1769779702468,"version":"3.49.0"},"reference-count":18,"publisher":"CSIRO Publishing","issue":"4","license":[{"start":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T00:00:00Z","timestamp":1763424000000},"content-version":"vor","delay-in-days":2960,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,10,11]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Evidence on the comparative validity of self-reported medication use in large-scale studies is limited. This study compared self-reported medication use of prescription-only medications to gold standard pharmaceutical claims (i.e. dispensing) data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>We selected a random sample of 500 participants from the 45 and Up Study, a large-scale Australian study, with complete ascertainment of Pharmaceutical Benefits Scheme dispensing records. Self-reported medication use was ascertained by questionnaire requesting data on medications used \u201cfor most of the last 4 weeks\u201d. In the dispensing data, we determined exposure to specific medications in the same 4-week window as the survey response if we observed a dispensing record \u226490 days before the start of the window. We calculated sensitivity and positive predictive values (PPVs) at the Anatomical Therapeutic Chemical (ATC) classification 3\u2011 and 7-digit code levels.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>PPVs were \u226575% for 79% of the medications examined at the 3-digit ATC level. The sensitivity\/PPV of self-reported versus claims data at the 3-digit level were highest for chronic medications, including cardiovascular medications: 94.4%\/96.9%, respectively, for lipid-lowering agents; 92.5%\/97.5% for angiotensin agents; 88.8%\/93.1% for beta-blockers; and 88.0%\/96.9% for calcium-channel blockers. PPVs were \u226565% and sensitivity of self-reported data was 78.9% for psychoanaleptics, 42.1% for analgesics, 26.0% for psycholeptics and 4.8% for antibacterial agents. PPVs for individual medications were \u226575% for 81% of the individual medications examined at the 7-digit level. The sensitivity\/PPV for self-reported versus claims data at the 7-digit level varied across individual medications, with highest values being 96.9%\/96.9% for warfarin, 94.5%\/92.0% for atorvastatin, 94.3%\/84.6% for pantoprazole and 93.3%\/95.5% for atenolol. The lowest sensitivity of self-reported versus claims data for individual medications was 16.7% for temazepam, 15.2% for perindopril, 11.5% for irbesartan, 11.1% for oxazepam and 3.3% for amoxicillin.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>Self-reported data of the type reported here are useful for identifying exposure to prescription medications, particularly those for chronic use. However, they are likely to be of lesser validity for ascertaining short-term and\/or intermittent medication exposure.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.17061\/phrp27341702","type":"journal-article","created":{"date-parts":[[2017,9,1]],"date-time":"2017-09-01T01:36:22Z","timestamp":1504229782000},"source":"Crossref","is-referenced-by-count":32,"title":["Ascertainment of self-reported prescription medication use compared with pharmaceutical claims data"],"prefix":"10.1071","volume":"27","author":[{"given":"Danijela","family":"Gnjidic","sequence":"first","affiliation":[{"name":"A Faculty of Pharmacy, University of Sydney, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Du","sequence":"additional","affiliation":[{"name":"B National Centre for Epidemiology and Population Health, Research School of Population Health, Australian National University, Canberra, ACT"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sallie-Anne","family":"Pearson","sequence":"additional","affiliation":[{"name":"A Faculty of Pharmacy, University of Sydney, NSW, Australia"},{"name":"C Centre for Big Data Research in Health, Faculty of Medicine, UNSW Sydney, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sarah","family":"Hilmer","sequence":"additional","affiliation":[{"name":"D Sydney Medical School, University of Sydney, NSW, Australia"},{"name":"E Kolling Institute, Sydney, NSW, Australia"},{"name":"F Royal North Shore Hospital, Sydney, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emily","family":"Banks","sequence":"additional","affiliation":[{"name":"B National Centre for Epidemiology and Population Health, Research School of Population Health, Australian National University, Canberra, ACT"},{"name":"G Sax Institute, Sydney, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"67","published-online":{"date-parts":[[2017,10,11]]},"reference":[{"key":"2026012917163972100_R1","doi-asserted-by":"crossref","unstructured":"Page E, Kemp-Casey A, Korda R, Banks E. 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