Evidence map›Paper›PMID 27747763›Full record

ArticleDrugs - real world outcomes2015

Associations of Potentially Inappropriate Medicine Use with Fall-Related Hospitalisations and Primary Care Visits in Older New Zealanders: A Population-Level Study Using the Updated 2012 Beers Criteria.

Sujita W Narayan, Prasad S Nishtala

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Drugs - real world outcomes, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 2 pooled it
2.1field-weighted citation impact, top 13% 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

14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 25 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

2 authors at 1 institution in 1 country.

Sujita W NarayanSchool of Pharmacy, University of Otago, PO Box 56, Dunedin, 9054, New Zealand.
Prasad S NishtalaSchool of Pharmacy, University of Otago, PO Box 56, Dunedin, 9054, New Zealand. prasad.nishtala@otago.ac.nz.
University of Otago · NZ

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIdentifying potentially inappropriate medicines (PIMs) leading to adverse drug events may reduce the risk of morbidity and mortality in older people.

objectiveThe aim of this study was to examine the relationship between exposure to PIMs and risk of Fall-related hospitalisations (FRH) and frequency of primary care visits in older New Zealanders.

methodsPharmaceutical collections (2011), diagnostic (2007-2011) and events (2012) information derived from the National Minimum Datasets were used to extract demographics, medication and diagnostic information for 537,387 individuals aged ≥65 years. Prescription and diagnostic information were matched through unique National Health Index numbers. The updated Beers 2012 criteria were used to identify PIMs. Polypharmacy was defined as five or more medicines dispensed concurrently for ≥90 days.

resultsIndividuals exposed to one or more PIMs had an increased risk of FRH with an incidence rate ratio (IRR) of 1.45 (95 % confidence interval [CI] 1.37-1.52) and a greater number of primary care visits (IRR 1.15; 95 % CI 1.15-1.16). Individuals exposed to polypharmacy had an IRR of 1.41 (95 % CI 1.33-1.50) for FRH and an IRR of 1.14 (95 % CI 1.13-1.15) for primary care visits.

conclusionPIMs identified by the 2012 Beers criteria showed an increased risk of FRH and a greater number of primary care visits. Age ≥85 years and female sex were identified as significant predictors of FRH and primary care visits.

Identifiers

PMID27747763
PMCPMC4883188
OpenAlexW2144250518

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.