Evidence map›Paper›PMID 30761662›Full record

SynthesisPharmacoepidemiology and drug safety2019

A systematic review of methods for determining cross-sectional active medications using pharmacy databases.

Timothy S Anderson, Edison Xu, Evans Whitaker, Michael A Steinman

Open access · greenAbstract readSystematic Review
In one paragraph

Synthesis in Pharmacoepidemiology and drug safety, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.4field-weighted citation impact, top 21% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 15 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 3 institutions in 1 country.

Timothy S AndersonDivision of General Internal Medicine, University of California San Francisco, San Francisco, CA, USA.ORCID 0000-0002-5384-275X
Edison XuSan Francisco VA Medical Center, San Francisco, CA, USA.
Evans WhitakerMedical Library, University of California San Francisco, San Francisco, CA, USA.
Michael A SteinmanSan Francisco VA Medical Center, San Francisco, CA, USA.
San Francisco VA Medical Center · USSan Francisco General Hospital · USUniversity of California, San Francisco · US

Funding

VARC CoreP30AG044281 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI REBECCA L SUDORE · 2013 to 2026
$19.9M
Prescribing cascades in older adults with and without dementiaK24AG049057 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI STEINMAN, MICHAEL A. · 2015 to 2025
$1.8M
Impact of Intensive Blood Pressure Treatment on Clinical Outcomes of Hospitalized Older AdultsR03AG064373 · NIA · BETH ISRAEL DEACONESS MEDICAL CENTER · PI ANDERSON, TIMOTHY S · 2019 to 2020
$274k
NIA NIH HHS K24 AG049057NIA NIH HHS K24AG049057NIA NIH HHS L30 AG060493NIA NIH HHS P30 AG044281NIA NIH HHS P30AG044281NIA NIH HHS R03 AG064373NIH HHS T32HP19025-14-00
6 · The paper itself

Abstract

purposePharmacy 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.

methodsComprehensive 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.

resultsOf 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.

conclusionsThe 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.

Indexed as

Data AccuracyDatabases, PharmaceuticalDrug PrescriptionsInformation Storage and RetrievalMedical Records Systems, Computerizeddatabasesdrug prescribingpharmaceuticPharmacoepidemiologyquality of careresearch methodologysystematic review

Identifiers

PMID30761662
PMCPMC7050409
OpenAlexW2913853231

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.