Timothy S Anderson1,2, Edison Xu2,3, Evans Whitaker4, Michael A Steinman2,3. 1. Division of General Internal Medicine, University of California San Francisco, San Francisco, CA, USA. 2. San Francisco VA Medical Center, San Francisco, CA, USA. 3. Division of Geriatrics, University of California San Francisco, San Francisco, CA, USA. 4. Medical Library, University of California San Francisco, San Francisco, CA, USA.
Abstract
PURPOSE: Pharmacy dispensing databases are often used to identify patients' medications at a particular time point, for example to measure prescribing quality or the impact of medication use on clinical outcomes. We performed a systematic review of studies that examined methods to assess medications in use at a specific point in time. METHODS: Comprehensive literature search to identify studies that compared active medications identified using pharmacy databases to medications identified using nonautomated data sources. Two investigators independently reviewed abstracts and full-text material. RESULTS: Of 496 studies screened, 29 studies evaluating 50 comparisons met inclusion criteria. Twenty-nine comparisons evaluated fixed look-back period approaches, defining active medications as those filled in a specified period prior to the index date (range 84-730 days). Fourteen comparisons evaluated medication-on-hand approaches, defining active medications as those for which the most recent fill provided sufficient supply to last through the study index date. Sensitivity ranged from 48% to 93% for fixed look-back period approaches and 35% to 97% for medication-on-hand approaches. Interpretation of comparative performance of methods was limited by use of different reference sources, target medication classes, and databases across studies. In four studies with head-to-head comparisons of these methods, sensitivity of the medication-on-hand approach was a median of 7% lower than the corresponding fixed look-back approach. CONCLUSIONS: The reported accuracy of methods for identifying active medications using pharmacy databases differs greatly across studies. More direct comparisons of common approaches are needed to establish the accuracy of methods within and across populations, medication classes, and databases.
PURPOSE: Pharmacy dispensing databases are often used to identify patients' medications at a particular time point, for example to measure prescribing quality or the impact of medication use on clinical outcomes. We performed a systematic review of studies that examined methods to assess medications in use at a specific point in time. METHODS: Comprehensive literature search to identify studies that compared active medications identified using pharmacy databases to medications identified using nonautomated data sources. Two investigators independently reviewed abstracts and full-text material. RESULTS: Of 496 studies screened, 29 studies evaluating 50 comparisons met inclusion criteria. Twenty-nine comparisons evaluated fixed look-back period approaches, defining active medications as those filled in a specified period prior to the index date (range 84-730 days). Fourteen comparisons evaluated medication-on-hand approaches, defining active medications as those for which the most recent fill provided sufficient supply to last through the study index date. Sensitivity ranged from 48% to 93% for fixed look-back period approaches and 35% to 97% for medication-on-hand approaches. Interpretation of comparative performance of methods was limited by use of different reference sources, target medication classes, and databases across studies. In four studies with head-to-head comparisons of these methods, sensitivity of the medication-on-hand approach was a median of 7% lower than the corresponding fixed look-back approach. CONCLUSIONS: The reported accuracy of methods for identifying active medications using pharmacy databases differs greatly across studies. More direct comparisons of common approaches are needed to establish the accuracy of methods within and across populations, medication classes, and databases.
Authors: Taco B M Monster; Wilbert M T Janssen; Paul E de Jong; Lolkje T W de Jong-van den Berg Journal: Pharmacoepidemiol Drug Saf Date: 2002 Jul-Aug Impact factor: 2.890
Authors: Jeffrey R Curtis; Andrew O Westfall; Jeroan Allison; Allison Freeman; Stacey H Kovac; Kenneth G Saag Journal: Pharmacoepidemiol Drug Saf Date: 2006-10 Impact factor: 2.890
Authors: Rebecca L Drieling; Andrea Z LaCroix; Shirley A A Beresford; Denise M Boudreau; Charles Kooperberg; Susan R Heckbert Journal: Am J Epidemiol Date: 2016-07-07 Impact factor: 4.897
Authors: Terri L Warholak; Matthew McCulloch; Alysson Baumgart; Mindy Smith; William Fink; William Fritz Journal: J Manag Care Pharm Date: 2009 Nov-Dec
Authors: James M Bolton; Colleen Metge; Lisa Lix; Heather Prior; Jitender Sareen; William D Leslie Journal: J Clin Psychopharmacol Date: 2008-08 Impact factor: 3.153
Authors: Julie C Lauffenburger; Akhila Balasubramanian; Joel F Farley; Cathy W Critchlow; Cynthia D O'Malley; Mary T Roth; Virginia Pate; M Alan Brookhart Journal: Pharmacoepidemiol Drug Saf Date: 2013-05-21 Impact factor: 2.890
Authors: Timothy S Anderson; Bocheng Jing; Charlie M Wray; Sarah Ngo; Edison Xu; Kathy Fung; Michael A Steinman Journal: Med Care Date: 2019-10 Impact factor: 2.983
Authors: Constance P Fontanet; Niteesh K Choudhry; Thomas Isaac; Thomas D Sequist; Chandrasekar Gopalakrishnan; Joshua J Gagne; Cynthia A Jackevicius; Michael A Fischer; Daniel H Solomon; Julie C Lauffenburger Journal: Health Serv Res Date: 2021-08-13 Impact factor: 3.734
Authors: Timothy S Anderson; Alexandra K Lee; Bocheng Jing; Sei Lee; Shoshana J Herzig; W John Boscardin; Kathy Fung; Anael Rizzo; Michael A Steinman Journal: JAMA Netw Open Date: 2021-10-01
Authors: Timothy S Anderson; Sei Lee; Bocheng Jing; Kathy Fung; Sarah Ngo; Molly Silvestrini; Michael A Steinman Journal: JAMA Netw Open Date: 2020-03-02