Evidence map›Paper›PMID 27178197›Full record

ArticleBMC research notes2016

Application of a framework for determining number of drugs.

Amber M Goedken, Brian C Lund, Elizabeth A Cook, Mary C Schroeder, John M Brooks

Abstract read
In one paragraph

Article in BMC research notes, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Observational
  6. Article
  7. Article
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

5 authors.

Amber M GoedkenDivision of Health Services Research, University of Iowa College of Pharmacy, 115 S. Grand Ave, S557 PHAR, Iowa City, IA, 52242, USA. amber-goedken@uiowa.edu.
Brian C LundCenter for Comprehensive Access and Delivery Research and Evaluation, Iowa City Veterans Affairs Health Care System, Iowa City, IA, USA.
Elizabeth A CookClinical Trials Statistical and Data Management Center, University of Iowa, Iowa City, IA, USA.
Mary C SchroederDivision of Health Services Research, University of Iowa College of Pharmacy, 115 S. Grand Ave, S557 PHAR, Iowa City, IA, 52242, USA.
John M BrooksArnold School of Public Health, University of South Carolina, Columbia, SC, USA.

Funding

Comparative Effectiveness of Treatment Combinations Post MI in the ElderlyR01HS018381 · AHRQ · UNIVERSITY OF IOWA · PI SCHROEDER, MARY C. · 2010 to 2013
$1.6M
AHRQ HHS R01 HS018381
6 · The paper itself

Abstract

backgroundThere are many different methodologies used in the literature for determining the number of drugs used by a patient, and many are incompletely described. This may be attributable to the lack of a framework to help investigators choose and describe their methods and the lack of evidence on the implications of the choice. The purpose of the study was to propose a framework and illustrate how that framework can be used to create and succinctly describe various approaches to counting the number of drugs used by patients and to examine the impact of varying individual components of the framework on the resulting drug count.

methodsThe three component framework requires specification of scope, uniqueness, and timeframe. The framework was applied to Medicare beneficiaries admitted for acute myocardial infarction in 2008. Drug use was ascertained by Part D prescription drug event files. A default measure for drug count was established, and fourteen additional measures were created by separately altering individual components of the default to illustrate the application of the framework and understand how these changes impacted drug count. Median drug counts and the frequency distributions of beneficiaries experiencing a change in count from default were produced for each measure.

resultsThe median drug count for the default measure was 4. Alteration of the timeframe component had the largest impact on drug counts, with a look-back period of 180 days producing a median count of 8 and changing the count by at least two for 73 % of patients. Variations of the other components had less impact.

conclusionOur framework is intended to be used by investigators to select an approach to counting number of drugs in their studies. Extending the timeframe over which fills from a pharmacy refill database could be counted toward the drug count produced the greatest changes in the number of drugs.

Indexed as

HumansMedicare Part DMyocardial InfarctionPolypharmacyUnited StatesAcute myocardial infarctionDrug countFrameworkMedicarePharmacy refill database

Identifiers

PMID27178197
PMCPMC4865986

What OpenQuestion holds

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Registered trials

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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.