Evidence map›Paper›PMID 36071909›Full record

ArticleFrontiers in neurology2022

Spatiotemporal analysis of regional TIA trends.

Andrew Kawai, Samuel Hui, Richard Beare, Velandai K Srikanth, Vijaya Sundararajan, Henry Ma, Thanh G Phan

Open access · goldAbstract read
In one paragraph

Article in Frontiers in neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.6field-weighted citation impact, top 31% 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

1 citing paper in PubMed, 4 citations in OpenAlex.

  1. Trial
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

7 authors at 4 institutions in 1 country.

Andrew KawaiSchool of Clinical Sciences at Monash Health, Stroke and Aging Research Group, Clinical Trials, Imaging & Informatics Division, Melbourne, VIC, Australia.
Samuel HuiDepartment of Medicine, Monash Health, Melbourne, VIC, Australia.
Richard BeareAcademic Unit, Peninsula Clinical School, Central Clinical School, Monash University, Melbourne, VIC, Australia.
Velandai K SrikanthAcademic Unit, Peninsula Clinical School, Central Clinical School, Monash University, Melbourne, VIC, Australia.
Vijaya SundararajanDepartment of Public Health, La Trobe University, Melbourne, VIC, Australia.
Henry MaSchool of Clinical Sciences at Monash Health, Stroke and Aging Research Group, Clinical Trials, Imaging & Informatics Division, Melbourne, VIC, Australia.
Thanh G PhanSchool of Clinical Sciences at Monash Health, Stroke and Aging Research Group, Clinical Trials, Imaging & Informatics Division, Melbourne, VIC, Australia.
Monash Health · AULa Trobe University · AUMonash University · AUPeninsula Health · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: There has been a decline in the stroke incidence across high income countries but such knowledge exists at Country or State rather than areal unit level such local government area (LGA). In this disease mapping study, we evaluate if there are local hot spots or temporal trends in TIA rate. Such knowledge will be of help in planning healthcare service delivery across regions. Methods: Linked hospital discharge data (Victorian Admitted Episodes Dataset or VAED) was used to collect TIA (defined by ICD-10-AM codes G450-G459) cases from 2001 to 2011. The State of Victoria is the second most populous state in Australia, with a population of 6.7 million and can be divided into 79 administrative units or LGA. The data is anonymized and contains residence of the patient in terms of LGA but not exact location. The date of the TIA event when the patient is admitted to hospital is provided in the dataset. The number of TIAs per year was aggregated for each LGA. Standardized TIA ratios were calculated by dividing actual over expected cases for each LGA per year. We used Integrated Nested Laplace Approximation (INLA) to perform spatial and spatiotemporal regression, adjusting for hypertension, sex and population, age (≥60), and socio-economic status (SES) decile within the LGA. The final model was chosen based on the lowest the Deviance Information Criterion (DIC) and Watanabe-Akaike information criteria (WAIC). Results: Choropleth maps showed a higher standardized TIA ratios in North-West rural region. Compared to the baseline model (DIC 13,159, WAIC 13,261), adding in a spatial random effect significantly improved the model (DIC 6,463, WAIC 6,667). However, adding a temporal component did not lead to a significant improvement (DIC 6,483, WAIC 6,707). Conclusion: Our finding suggests a statically significant spatial component to TIA rate over regional areas but no temporal changes or yearly trends. We propose that such exploratory method should be followed by evaluation of reasons for regional variations and which in turn can identify opportunities in primary prevention of stroke, and stroke care.

Indexed as

areal unitdisease mappingspatial regressionspatiotemporal regressionTIA

Identifiers

PMID36071909
PMCPMC9441554
OpenAlexW4292566543

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.