Evidence map›Paper›PMID 29460087›Full record

ArticleDrugs & aging2018

Prescription Changes During Geriatric Care Episodes: A Trend Analysis Conducted in Sweden.

Marianne Reimers, Maria Eriksdotter, Åke Seiger, Johan Fastbom

Abstract read
In one paragraph

Article in Drugs & aging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–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

6 citing papers in PubMed.

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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.

Marianne ReimersKarolinska Institutet, Stockholm, Sweden. marianne.reimers.wessberg@ki.se.ORCID 0000-0001-8112-116X
Maria EriksdotterKarolinska Institutet, Stockholm, Sweden.
Åke SeigerKarolinska Institutet, Stockholm, Sweden.
Johan FastbomKarolinska Institutet, Stockholm, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe number of drugs prescribed for old people has been rising in recent decades. With increasing age and multiple medications, the risk of complications and drug prescription complexity increases. Multiple changes to prescriptions could be a factor that improves treatment quality.

objectivesOur objective was to investigate trends in drug prescriptions and factors that contribute to prescription changes. Specific objectives were to find out whether high numbers of prescription changes are significantly correlated with age, sex, comorbidity, length of care episode, and number of drugs.

methodsData were extracted from geriatric clinic records in Stockholm in 2005, 2010, and 2015. Indicators for good drug therapy were used to assess the effects of prescription changes on quality, using an index of inappropriate drug use (IDU). Data were analyzed with Student's t-test, PR test, Wilcoxon's rank sum test, and linear regression.

resultsPatients had more comorbidities and used more drugs but had shorter hospital stays and significantly fewer prescription changes in 2015 than in 2005. Length of care episode was significantly associated with prevalence of prescription changes. Our model showed that, for each day by which the length of care episode decreased, the number of prescription changes decreased by 8%. The number of prescription changes was negatively correlated to the IDU index score.

conclusionsThe study showed that more prescription changes were associated with longer care episodes and improved drug prescribing quality as per the IDU index. Given prescription changes are regarded as a quality factor in geriatric care, quality may have decreased along with the length of care episodes between 2005 and 2015.

Indexed as

AgedAged, 80 and overComorbidityDrug PrescriptionsEpisode of CareFemaleGeriatricsHumansMalePractice Patterns, Physicians'PrevalenceSweden

Identifiers

PMID29460087
PMCPMC5884893

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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.