Evidence map›Paper›PMID 29996922›Full record

ArticleArthritis research & therapy2018

Gout prevalence and predictors of urate-lowering therapy use: results from a population-based study.

Huai Leng Pisaniello, Susan Lester, David Gonzalez-Chica, Nigel Stocks, Marie Longo, Greg R Sharplin, Eleonora Dal Grande, Tiffany K Gill, Samuel L Whittle, Catherine L Hill

Open access · goldAbstract read
In one paragraph

Article in Arthritis research & therapy, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
4.4field-weighted citation impact, top 5% 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

17 citing papers in PubMed, 59 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Review
  5. The Evolving Landscape of Gout in the Female: A Narrative Review.Gout, urate, and crystal deposition disease · 2023
    Article
  6. Article
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  16. Aqueous and Methanol Extracts ofEvidence-based complementary and alternative medicine : eCAM · 2019
    Article
  17. Article
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

10 authors at 4 institutions in 1 country.

Huai Leng PisanielloDepartment of Rheumatology, The Queen Elizabeth Hospital, Woodville South, Australia. huaileng@gmail.com.ORCID 0000-0002-0425-1697
Susan LesterDepartment of Rheumatology, The Queen Elizabeth Hospital, Woodville South, Australia.
David Gonzalez-ChicaDiscipline of General Practice, Adelaide Medical School, The University of Adelaide, Adelaide, Australia.
Nigel StocksDiscipline of General Practice, Adelaide Medical School, The University of Adelaide, Adelaide, Australia.
Marie LongoDrug and Alcohol Services South Australia, Stepney, Australia.
Greg R SharplinBehavioural Research and Evaluation Unit, Cancer Council South Australia, Eastwood, Australia.
Eleonora Dal GrandeDiscipline of Medicine, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, Australia.
Tiffany K GillDiscipline of Medicine, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, Australia.
Samuel L WhittleDepartment of Rheumatology, The Queen Elizabeth Hospital, Woodville South, Australia.
Catherine L HillDepartment of Rheumatology, The Queen Elizabeth Hospital, Woodville South, Australia.
Queen Elizabeth Hospital · AUThe University of Adelaide · AUAlcohol and Drug Foundation · AUCancer Council SA · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGout has an increasing global prevalence. Underutilization of urate-lowering therapy (ULT) is thought to be common, via both suboptimal dosing and poor medication adherence. The aims of this study were to determine the prevalence of self-reported gout and the key predictors of ULT use in those with gout in a representative population survey in South Australia.

methodsData were obtained from the Spring 2015 South Australian Health Omnibus Survey, a multilevel, systematic, survey in a representative population sample involving face-to-face interviews (n = 3005). This study analyzed responses from respondents aged ≥ 25 years (n = 2531) about self-reported gout, ULT use, sociodemographic factors, lifestyle factors, and comorbidities, using survey weighting. Univariate and subsequent adjusted logistic regression analyses on self-reported gout were performed. ULT use was divided into three categories (never use, prior use, and current use) and these data were analyzed using a multinomial logistic regression model.

resultsSelf-reported gout prevalence was 6.8% (95% CI 5.8, 7.9). The mean age of respondents with gout was 64 years (standard deviation 16) and 82% were male. As expected, older age, male gender, lower socioeconomic status (SES), and higher body mass index (BMI) were associated with gout, as were high alcohol consumption, current smoking, other forms of arthritis, and hypertension or hypercholesterolemia medication, after adjustment for sociodemographic variables. Two thirds of respondents with gout reported ULT use (36% current; 29% previous) with only 55% continuing treatment. Predictors of ULT use included male gender, low SES, and concomitant cholesterol-lowering therapy. Respondents with gout with a higher BMI were more likely to remain on ULT.

conclusionsDespite gout being a common, potentially disabling joint disease, only 55% of respondents with gout in this study adhered to ULT. Identification of key predictors of ULT use will provide guidance on prescribing strategy in clinical practice and on the quality of gout care in the community.

Indexed as

AdultAgedAllopurinolAustraliaDrug MonitoringFemaleGoutGout SuppressantsHumansMaleMiddle AgedPrevalenceSurveys and QuestionnairesAllopurinolGout SuppressantsGoutPopulationPredictorsPrevalenceUrate-lowering therapy

Identifiers

PMID29996922
PMCPMC6042461
OpenAlexW2873840304

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.