Evidence map›Paper›PMID 32948168›Full record

ArticleBMC health services research2020

Trends, determinants and differences in antibiotic use in 68 residential aged care homes in Australia, 2014-2017: a longitudinal analysis of electronic health record data.

M Z Raban, K E Lind, R O Day, L Gray, A Georgiou, J I Westbrook

Abstract read
In one paragraph

Article in BMC health services research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 2 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

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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

12 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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4 · The record

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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.

M Z RabanCentre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Level 6, 75 Talavera Rd, Macquarie Park, Sydney, NSW, 2109, Australia. magda.raban@mq.edu.au.ORCID http://orcid.org/0000-0002-6995-2849
K E LindCentre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Level 6, 75 Talavera Rd, Macquarie Park, Sydney, NSW, 2109, Australia.
R O DayDepartment of Clinical Pharmacology & Toxicology, St Vincent's Hospital, Therapeutics Centre, Level 2 Xavier Building, St Vincent's Hospital, Darlinghurst, Sydney, NSW, 2010, Australia.
L GrayCentre for Health Services Research, The University of Queensland, Level 2, Building 33, Princess Alexandra Hospital campus, Woolloongabba, Brisbane, 4102, QLD, Australia.
A GeorgiouCentre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Level 6, 75 Talavera Rd, Macquarie Park, Sydney, NSW, 2109, Australia.
J I WestbrookCentre for Health Systems and Safety Research, Australian Institute of Health Innovation, Macquarie University, Level 6, 75 Talavera Rd, Macquarie Park, Sydney, NSW, 2109, Australia.

Funding

Australian Research Council LP120200814National Health and Medical Research Council 9100002National Health and Medical Research Council APP1143941
6 · The paper itself

Abstract

backgroundInternationally, point prevalence surveys are the main source of antibiotic use data in residential aged care (RAC). Our objective was to describe temporal trends in antibiotic use and antibiotics flagged for restricted use, resident characteristics associated with use, and variation in use by RAC home, using electronic health record data.

methodsWe conducted a retrospective cohort study of 9793 unique residents aged ≥65 years in 68 RAC homes between September 2014 and September 2017, using electronic health records. We modelled the primary outcome of days of antibiotic therapy /1000 resident days (DOT/1000 days), and secondary outcomes of number of courses/1000 days and the annual prevalence of antibiotic use. Antibiotic use was examined for all antibiotics and antibiotics on the World Health Organization's (WHO) Watch List (i.e. antibiotics flagged for restricted use).

resultsIn 2017, there were 85 DOT/1000 days (99% CI: 79, 92), 8.0 courses/1000 days (99% CI: 7.6, 8.5), and 63.4% (99% CI: 61.9, 65.0) of residents received at least one course of antibiotics. There were 7.7 DOT/1000 days (99% CI: 6.69, 8.77) of antibiotics on the WHO Watch List administered in 2017. Antibiotic use increased annually by 4.09 DOT/1000 days (99% CI: 1.18, 6.99) before adjusting for resident factors, and 3.12 DOT/1000 days (99% CI: - 0.05, 6.29) after adjustment. Annual prevalence of antibiotic use decreased from 68.4% (99% CI: 66.9, 69.9) in 2015 to 63.4% (99% CI: 61.9, 65.0) in 2017, suggesting fewer residents were on antibiotics, but using them for longer. Resident factors associated with higher use were increasing age; chronic respiratory disease; a history of urinary tract infections, and skin and soft tissue infections; but dementia was associated with lower use. RAC home level antibiotic use ranged between 44.0 to 169.2 DOT/1000 days in 2016. Adjusting for resident factors marginally reduced this range (42.6 to 155.5 DOT/1000 days).

conclusionsAntibiotic course length and RAC homes with high use should be a focus of antimicrobial stewardship interventions. Practices in RAC homes with low use could inform interventions and warrant further investigation. This study provides a model for using electronic health records as a data source for antibiotic use surveillance in RAC.

Indexed as

Electronic Health RecordsAgedAged, 80 and overAnti-Bacterial AgentsAntimicrobial StewardshipAustraliaDrug UtilizationFemaleHomes for the AgedHumansMaleNursing HomesRetrospective StudiesUrinary Tract InfectionsAnti-Bacterial AgentsAnti-bacterial agentsElectronic health recordsHomes for the agedLong-term careNursing homesPharmacoepidemiologyResidential facilities

Identifiers

PMID32948168
PMCPMC7501612

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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.