Evidence map›Paper›PMID 42786600›Full record

ArticleAustralasian journal on ageing2026

Trends in Falls and Medicine Use in Australian Long-Term Care, 2014-2022: Evidence From 8298 Residents.

Ying Xu, Johanna I Westbrook, Isabelle Meulenbroeks, Maria C Inacio, Gillian E Caughey, Peter D Hibbert, Jeffrey Braithwaite, Nasir Wabe

Abstract read
In one paragraph

Article in Australasian journal on ageing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Ying XuAustralian Institute of Health Innovation, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0002-3793-7716
Johanna I WestbrookAustralian Institute of Health Innovation, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0003-1083-8192
Isabelle MeulenbroeksAustralian Institute of Health Innovation, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0002-9083-7845
Maria C InacioRegistry of Senior Australians (ROSA), South Australian Health & Medical Research Institute, Adelaide, South Australia, Australia.ORCID https://orcid.org/0000-0001-8261-2665
Gillian E CaugheyRegistry of Senior Australians (ROSA), South Australian Health & Medical Research Institute, Adelaide, South Australia, Australia.ORCID https://orcid.org/0000-0003-1192-4121
Peter D HibbertAustralian Institute of Health Innovation, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0001-7865-343X
Jeffrey BraithwaiteAustralian Institute of Health Innovation, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0003-0296-4957
Nasir WabeAustralian Institute of Health Innovation, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0002-9740-6319

Funding

Australian Government Medical Research Future Fund GNT 2015823Australian Government Medical Research Future Fund NCRI000109Australian Government Medical Research Future Fund PHRDI000009Department for Innovation and Skills, Government of South Australia 20172021National Health and Medical Research Council (NHMRC) 1174021National Health and Medical Research Council (NHMRC) GNT119378National Health and Medical Research Council (NHMRC) GNT2026400
6 · The paper itself

Abstract

OBJECTIVE(S): To evaluate changes in falls and medication-related quality indicators (QIs) including high sedative load, antipsychotic use and anti-anxiety or hypnotic medications in long-term care facilities (LTCFs), and to examine their alignment with major national reforms and COVID-19 in New South Wales, Australia.

methodsA repeated cross-sectional study was conducted using electronic health data from 27 LTCFs (8298 residents). From Jul 2014 to Jun 2022, quarterly prevalence estimates of four QIs were evaluated adjusting for potential confounders. Joinpoint regression analysis was used to identify QI trend changes. Average quarterly percentage changes for the study period were estimated.

resultsThe quarterly prevalence of residents who had any falls (28.5%-33.3%) and injurious falls (14.5%-18.6%) remained stable during the study period. The prevalence of residents who had falls requiring hospitalisation varied (3.8%-9.1%) and average quarterly percentage increase was 1.01% (95% confidence interval (CI) 0.30, 2.08). There were changes in quarterly prevalence of residents experiencing high sedative load (28.1%-32.1%), using antipsychotic(s) (8.9%-19.3%), and anti-anxiety and hypnotic medication(s) (11.4%-19.0%). Average quarterly percentage decreases were at 0.17% (95% CI 0.01, 0.35) in residents experiencing a high sedative load, 2.14% (95% CI 1.90, 2.41) for antipsychotics use and 1.63% (95% CI 1.45, 1.82) for anti-anxiety or hypnotic medication use. Regarding trend changes, medication-related QIs decreased from Q3 or Q4 2018, or Q1 2019, around when the Royal Commission was established. Medication-related QIs continued to decline when restrictions on risperidone were applied and COVID-19 started.

conclusionsMedication-related QIs decreased during the study period, especially after national reforms.

Indexed as

Accidental FallsLong-Term CareAgedAged, 80 and overAnti-Anxiety AgentsAntipsychotic AgentsCOVID-19Cross-Sectional StudiesFemaleHumansHypnotics and SedativesMaleNew South WalesNursing Home ResidentsPrevalenceQuality Indicators, Health CareAnti-Anxiety AgentsAntipsychotic AgentsHypnotics and Sedativesaccidental fallsanti‐anxiety agentsantipsychotic agentshypnotics and sedativeslong‐term care

Identifiers

PMID42786600
PMCPMC13612836

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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.