Evidence map›Paper›PMID 32728133›Full record

ArticleScientific reports2020

Geographic variation in cardiometabolic risk factor prevalence explained by area-level disadvantage in the Illawarra-Shoalhaven region of the NSW, Australia.

Renin Toms, Darren J Mayne, Xiaoqi Feng, Andrew Bonney

Abstract read
In one paragraph

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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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

2 citing papers in PubMed.

  1. Article
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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

4 authors.

Renin TomsSchool of Medicine, University of Wollongong, Wollongong, NSW, 2522, Australia. rmbst288@uowmail.edu.au.
Darren J MayneSchool of Medicine, University of Wollongong, Wollongong, NSW, 2522, Australia.ORCID http://orcid.org/0000-0002-9370-3463
Xiaoqi FengIllawarra Health and Medical Research Institute, Wollongong, NSW, 2522, Australia.
Andrew BonneySchool of Medicine, University of Wollongong, Wollongong, NSW, 2522, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cardiometabolic risk factors (CMRFs) demonstrate significant geographic variation in their distribution. The study aims to quantify the general contextual effect of the areas on CMRFs; and the geographic variation explained by area-level socioeconomic disadvantage. A cross sectional design and multilevel logistic regression methods were adopted. Data included objectively measured routine pathology test data between years 2012 and 2017 on: fasting blood sugar level; glycated haemoglobin; total cholesterol; high density lipoprotein; urinary albumin creatinine ratio; estimated glomerular filtration rate; and body mass index. The 2011 Australian census based Index of Relative Socioeconomic Disadvantage (IRSD) were the area-level study variables, analysed at its smallest geographic unit of reporting. A total of 1,132,029 CMRF test results from 256,525 individuals were analysed. After adjusting for individual-level covariates, all CMRFs significantly associated with IRSD and the probability of higher risk CMRFs increases with greater area-level disadvantage. Though the specific contribution of IRSD in the geographic variation of CMRF ranged between 57.8 and 14.71%, the general contextual effect of areas were found minimal (ICCs 0.6-3.4%). The results support universal interventions proportional to the need and disadvantage level of populations for the prevention and control of CMRFs, rather than any area specific interventions as the contextual effects were found minimal in the study region.

Indexed as

Health Status DisparitiesAdolescentAdultAgedAged, 80 and overBlood GlucoseBody Mass IndexCardiovascular DiseasesCholesterolCholesterol, HDLCreatinineCross-Sectional StudiesFemaleGeographyGlomerular Filtration RateHumansBlood GlucoseCholesterolCholesterol, HDLCreatinine

Identifiers

PMID32728133
PMCPMC7391748

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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.