Evidence map›Paper›PMID 29110295›Full record

ArticleDrugs - real world outcomes2017

Prevalence and Variability in Medications Contributing to Polypharmacy in Long-Term Care Facilities.

Natali Jokanovic, Kris M Jamsen, Edwin C K Tan, Michael J Dooley, Carl M Kirkpatrick, J Simon Bell

Abstract read
In one paragraph

Article in Drugs - real world outcomes, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

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

  1. Pooled it
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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

6 authors.

Natali JokanovicFaculty of Pharmacy and Pharmaceutical Sciences, Centre for Medicine Use and Safety, Monash University (Parkville Campus), 407 Royal Parade, Parkville, VIC, 3052, Australia. Natali.Jokanovic@monash.edu.
Kris M JamsenFaculty of Pharmacy and Pharmaceutical Sciences, Centre for Medicine Use and Safety, Monash University (Parkville Campus), 407 Royal Parade, Parkville, VIC, 3052, Australia.
Edwin C K TanFaculty of Pharmacy and Pharmaceutical Sciences, Centre for Medicine Use and Safety, Monash University (Parkville Campus), 407 Royal Parade, Parkville, VIC, 3052, Australia.
Michael J DooleyFaculty of Pharmacy and Pharmaceutical Sciences, Centre for Medicine Use and Safety, Monash University (Parkville Campus), 407 Royal Parade, Parkville, VIC, 3052, Australia.
Carl M KirkpatrickFaculty of Pharmacy and Pharmaceutical Sciences, Centre for Medicine Use and Safety, Monash University (Parkville Campus), 407 Royal Parade, Parkville, VIC, 3052, Australia.
J Simon BellFaculty of Pharmacy and Pharmaceutical Sciences, Centre for Medicine Use and Safety, Monash University (Parkville Campus), 407 Royal Parade, Parkville, VIC, 3052, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundResearch into which medications contribute to polypharmacy and the variability in these medications across long-term care facilities (LTCFs) has been minimal.

objectiveOur objective was to investigate which medications were more prevalent among residents with polypharmacy and to determine the variability in prescribing of these medications across LTCFs.

methodsThis was a cross-sectional study of 27 LTCFs in regional and rural Victoria, Australia. An audit of the medication charts and medical records of 754 residents was performed in May 2015. Polypharmacy was defined as nine or more regular medications. Logistic regression was performed to determine the association between medications and resident characteristics with polypharmacy. Analyses were adjusted for age, sex and Charlson's comorbidity index. Variability in the use of the ten most prevalent medication classes was explored using funnel plots. Characteristics of LTCFs with low (< 30%), moderate (30-49%) and high (≥ 50%) polypharmacy prevalence were compared.

resultsPolypharmacy was observed in 272 (36%) residents. In adjusted analyses, each of the top ten most prevalent medication classes, with the exception of antipsychotics, were associated with polypharmacy. Between 7 and 23% of LTCFs fell outside the 95% control limits for each of the ten most prevalent medications. LTCFs with ≥ 50% polypharmacy prevalence were predominately smaller.

conclusionPolypharmacy was associated with nine of the ten most prevalent medication classes. There was greater than fourfold variability in nine of the ten most prevalent medications across LTCFs. Further studies are needed to investigate the clinical appropriateness of the variability in polypharmacy.

Identifiers

PMID29110295
PMCPMC5684050

What Socratic holds

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LicenceCC BY-NC
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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 Socratic graph.