Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2018 Jul 11;20(1):143.
doi: 10.1186/s13075-018-1633-9.

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

Huai Leng Pisaniello  1 , Susan Lester  2   3 , David Gonzalez-Chica  4 , Nigel Stocks  4 , Marie Longo  5 , Greg R Sharplin  6 , Eleonora Dal Grande  3 , Tiffany K Gill  3 , Samuel L Whittle  2   3 , Catherine L Hill  2   3
Affiliations

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

Huai Leng Pisaniello et al. Arthritis Res Ther. .

Abstract

Background: Gout 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.

Methods: Data 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.

Results: Self-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.

Conclusions: Despite 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.

Keywords: Gout; Population; Predictors; Prevalence; Urate-lowering therapy.

PubMed Disclaimer

Conflict of interest statement

Ethics approval and consent to participate

This study survey was approved by the Human Research Ethics Committees of the University of Adelaide (project H-097-2010) and the South Australian Department of Health. The study participation was voluntary with verbal informed consent obtained prior to the interview.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
The relative proportions of allopurinol never, previous, and current users among respondents with gout. The brackets enclose 95% confidence intervals
Fig. 2
Fig. 2
Predicted, population-averaged marginal probabilities of allopurinol use (classified as never, prior, current) estimated from the multinomial regression model, with five predictors as described in “Results”. Representative values were selected for the continuous covariates. a Sex. b Age. c Body mass index (BMI). d Index of Relative Social Advantage and Disadvantage (IRSAD) (socioeconomic status (SES)). e Cholesterol medication
See this image and copyright information in PMC

References

    1. Smith EU, Diaz-Torne C, Perez-Ruiz F, March LM. Epidemiology of gout: an update. Best Pract Res Clin Rheumatol. 2010;24(6):811–827. doi: 10.1016/j.berh.2010.10.004. - DOI - PubMed
    1. Hak AE, Curhan GC, Grodstein F, Choi HK. Menopause, postmenopausal hormone use and risk of incident gout. Ann Rheum Dis. 2010;69(7):1305–1309. doi: 10.1136/ard.2009.109884. - DOI - PMC - PubMed
    1. Kramer HM, Curhan G. The association between gout and nephrolithiasis: the National Health and nutrition examination survey III, 1988-1994. Am J Kidney Dis. 2002;40(1):37–42. doi: 10.1053/ajkd.2002.33911. - DOI - PubMed
    1. Kuo CF, Grainge MJ, Mallen C, Zhang W, Doherty M. Rising burden of gout in the UK but continuing suboptimal management: a nationwide population study. Ann Rheum Dis. 2015;74(4):661–667. doi: 10.1136/annrheumdis-2013-204463. - DOI - PMC - PubMed
    1. Annemans L, Spaepen E, Gaskin M, Bonnemaire M, Malier V, Gilbert T, Nuki G. Gout in the UK and Germany: prevalence, comorbidities and management in general practice 2000-2005. Ann Rheum Dis. 2008;67(7):960–966. doi: 10.1136/ard.2007.076232. - DOI - PMC - PubMed