Evidence map›Paper›PMID 40213285›Full record

ArticlePublic health in practice (Oxford, England)2025

Spatiotemporal patterns of influenza in Western Australia.

Kefyalew Addis Alene, Hannah C Moore, Archie C A Clements, Beth Gilmour, Dylan D Barth, Rebecca Pavlos, Ben Scalley, Christopher C Blyth

Abstract read
In one paragraph

Article in Public health in practice (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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.

Kefyalew Addis AleneSchool of Population Health, Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia.
Hannah C MooreSchool of Population Health, Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia.
Archie C A ClementsWesfarmers Centre of Vaccines and Infectious Diseases, The Kids Research Institute Australia, Perth, Western Australia, Australia.
Beth GilmourSchool of Population Health, Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia.
Dylan D BarthWesfarmers Centre of Vaccines and Infectious Diseases, The Kids Research Institute Australia, Perth, Western Australia, Australia.
Rebecca PavlosWesfarmers Centre of Vaccines and Infectious Diseases, The Kids Research Institute Australia, Perth, Western Australia, Australia.
Ben ScalleyNorth Metropolitan Health Service, Perth, WA, Australia.
Christopher C BlythWesfarmers Centre of Vaccines and Infectious Diseases, The Kids Research Institute Australia, Perth, Western Australia, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Understanding the geospatial distribution of influenza infection and the risk factors associated with infection clustering can inform targeted preventive interventions. We conducted a geospatial analysis to investigate the spatial patterns and identify drivers of medically attended influenza infection across all age groups in Western Australia (WA). Methods: Data for confirmed influenza cases were obtained from the WA Notifiable Infectious Diseases Database for the period 2017-2020. Data were also obtained for vaccination coverage, meteorological parameters, socioeconomic indicators, and healthcare access. Spatial clustering of influenza incidence was identified using Global Moran's I and Getis-Ord statistic. Bayesian spatial models were used to identify factors associated with spatial clustering of infection. Results: Of the 36,228 influenza cases reported, over half (18,773, 51·8 %) were in individuals aged between 15 and 64 years and more than three quarters (28,545, 78·9 %) in the Perth metropolitan region. The annual incidence rate ranged from 2·7 per 1000 population in individuals aged between 15 and 64 years to 5·2 per 1000 population in children <5 years of age. For all age groups, the lowest incidence (0·4 per 1000 population) and the highest incidence rate (8·8 per 1000 population) were reported during and pre-the COVID-19 pandemic respectively. The influenza incidence rate shows both seasonal and spatial variation. Spatial clustering was significantly associated with distance to the nearest health facility in minutes ( Conclusions: Spatial clustering of influenza incidence was significantly associated with climatic conditions and healthcare access.

Indexed as

EpidemiologyInfluenzaSpatialSpatiotemporalWestern Australia

Identifiers

PMID40213285
PMCPMC11982041

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.