Background
Gout is the most common inflammatory arthritis. The reported prevalence of gout is highly variable across the world, ranging from 0.1% to approximately 10%, with prevalence estimates greater than 1% in most developed countries [
1]. The prevalence of gout in the UK was recently estimated as 2.5% [
2], 3.9% in the USA, [
3], and 5.2% in a recent Australian cohort study [
4]. High baseline serum urate level and subcutaneous tophi have been linked to increased mortality, mostly attributable to cardiovascular disease [
5,
6]. Furthermore, despite advances in understanding of the pathophysiology, risk factors, and therapy, gout remains a burden on the individual’s health-related quality of life (HRQoL) and on healthcare resources [
7‐
9].
Current guidelines from the American College of Rheumatology (ACR) and the European League Against Rheumatism (EULAR) advise that long-term urate-lowering therapy (ULT), with the aim of maintaining serum urate levels (generally below 6 mg/dL) [
10,
11], is key to effective control of gout and should be initiated in the presence of certain clinical features: for example, tophi, frequent gouty attacks (flares; two or more per year), and urate arthropathy [
12].
Nonetheless, studies suggest a poor adherence to guidelines [
13‐
16]. Reasons for this include inappropriate ULT dosing by prescribers or inadequate monitoring of serum urate levels [
16,
17] and low rates of continuation of therapy when prescribed [
18]. Gout flares are a clinical indicator of disease severity and the need for commencing or optimizing ULT and may continue to occur in up to one third of patients [
19‐
21].
There is a paucity of community-based data regarding gout flares. One internet-based case-crossover US-based study found that 53% of enrolled participants did not consult a health care physician during an acute gout flare [
22], suggesting most gout flares are self-managed in the community and that the “treatment gap” may be under-estimated.
The aim of this study was to identify the prevalence of self-reported gout flare and the frequency of allopurinol use in a representative community-based survey. In addition, sociodemographic and clinical covariates associated with flare frequency and associations with comorbidities and HRQoL were sought.
Methods
Study population
Data were obtained from the 2017 South Australian Health Omnibus Survey (HOS). The HOS is an annual, population survey, conducted by face-to-face interviews of approximately 3000 people aged 15 years and over, that obtains cross-sectional representative information on health, well-being, and related issues amongst the South Australian population living in metropolitan and rural areas. HOS has been designed to meet the highest standards of population survey methodology and is a clustered, multi-stage, systematic, self-weighting area sample [
23,
24].
The 2017 HOS survey consisted of data from 2977 interviews from 5300 selected households (participation rate 65.3%) and was conducted between September and December 2017.
Outcomes
Within the survey interview, three gout-related questions were asked, “Have you even been told by a doctor that you have gout?” with the response options of “Yes,” “No,” or “Do not know/refused.” To determine allopurinol use, the respondents were asked “Do you currently take/have you taken allopurinol for gout?” with the response options of “No, never taken” (never), “No, previously taken” (prior), or “Yes, still taking” (current), with a list of the current brand names of allopurinol available to the interviewees. Febuxostat was not included because it only became available on the Australian Pharmaceutical Benefits Scheme in 2015 as a second-line option if allopurinol was contraindicated or not tolerated [
25]
. A recent US study reported little change to gout therapy since the introduction of febuxostat to the market (prescribed to only 3% of gout study population) [
26].
To determine frequency of flares, respondents were asked, “If you have gout, how many gout attacks have you had over the last 12 months?” with the response options comprising of “None,” “One,” “Two,” “Three,” “Four,” or “Five or more.”
Covariates
Sociodemographic data collected included age, gender, and socioeconomic status (SES). SES was determined using the Index of Relative Socioeconomic Advantage and Disadvantage (IRSAD) [
27], which is normalized to a mean of 1000 and standard deviation of 100, and where a low index score suggests relative disadvantage, and a higher index score represents relative advantage.
Body mass index (BMI) was based on self-reported height and weight, calculated according to standard formula, and was classified according to World Health Organization (WHO) criteria [
28]. Information on comorbidities was obtained from the questions “Has a doctor ever told you that you have: (a) a heart attack/angina or did you undergo a heart procedure to unblock blocked vessels in your heart (called angioplasty or stenting), (b) Stroke, (c) High blood pressure, (d) Diabetes/high blood sugar, (e) High cholesterol levels”.
HRQoL was measured by the SF-12 v1 (US version) with Physical Component Scores (PCS) and Mental Component Scores (MCS) computed as norm-based
t-scores with a mean of 50 and a standard deviation of 10 [
29].
Statistical analyses
Analyses were performed using Stata v15.1 (StataCorp LLC, Texas, USA), and all tabulations, descriptive statistics, and regression models utilized appropriate survey weights. Only participants aged 25 and over were included in the analysis because gout is a disease of older adults. Prior to analysis, flares were grouped into three categories: none, 1, and ≥ 2 because, according to the ACR, ULT is indicated in patients with two or more gout flares/year [
12].
Data were weighted by the inverse of the individual’s probability of selection, as well as the response rate in metropolitan and country regions and then re-weighted to benchmarks derived from the Australian Bureau of Statistics 2016 Census data (age and sex). These person weights adjust the data to better align each case (individual) with the age, gender, and geographic location distribution in the total South Australian population. Survey-weighted logistic (gout prevalence), multinomial logistic (allopurinol use and frequency of gout flares per year), and linear (HRQoL) regression models were used to analyze relationships with relevant predictor variables. All models included the sociodemographic variables of age, gender, SES (IRSAD score), and BMI, with both linear and quadratic regression terms to allow for non-linearity in any relationship with the response variable. To enable interpretation of regression models, Stata post estimation commands were used to express results for each outcome as adjusted population-weighted marginal proportions/probabilities (for logistic or multinomial models), or predicted means (for linear regression), for different levels of each predictor variable, and to determine the effect size (the derivative or change in the marginal outcome with a change in the predictor variable) averaged over other covariates. For multinomial outcomes (flares and allopurinol use), Helmert contrasts of the outcomes were used to define meaningful comparisons, and joint p values were reported.
Discussion
This study is the first representative population-based study of gout flares. Nearly a quarter of all participants with gout reported two or more flares in the last 12 months, and, contrary to current guidelines, almost half of these participants were not on ULT. Frequent gout flares had a negative effect on physical HRQoL, comparable to that seen with a range of chronic health conditions [
30,
31]. The prevalence of ULT (37.1% current, 23% previous use) was consistent with previous studies reporting 28–51% current ULT [
7,
9,
22,
32]. Despite the established role of ULT in reducing flares [
21], participants on ULT were more likely to experience frequent gout flares, suggesting suboptimal use. Furthermore, the ULT discontinuation rate was nearly 40%. Collectively, these results are consistent with suboptimal management of gout, as has been identified in previous studies [
16,
22,
32].
This 2017 representative population-based study demonstrated a high prevalence of self-reported, medically diagnosed gout (6.5%, 95%CI 5.5%, 7.5%) in the South Australian population aged 25 and over. This prevalence is comparable to previous population-based estimates in the South Australian population [
4,
33], but greater than the 1.5% prevalence reported from an Australian primary care-based study [
34]. It is also higher than prevalence estimates from Europe and America, which range between 0.9 and 3.9% [
1,
2,
35]. There is, however, substantial heterogeneity between gout prevalence estimates [
36], with case definition identified as an important contributor to this heterogeneity [
36]. We used self-reported, medically diagnosed gout for case definition in this study, which has been validated against a hospital discharge diagnosis of gout or use of a gout-specific medication in two American population-based cohorts [
37], and shown to have high sensitivity and precision for case definition for gout genetic studies [
38]. As case ascertainment through medical records is contingent on both the patient seeking treatment and accurate recording of current and previous diagnoses, case definition by self-reported, medically diagnosed gout will capture a wider spectrum of patients.
There are known difficulties in optimizing ULT for the management of gout. Current rheumatology guidelines recommend a treat-to-target approach, requiring regular serum urate monitoring and slow up-titration of the dose until target serum urate levels are achieved [
10‐
12]. Flares can be precipitated by an initial sudden decrease in serum urate levels and may still occur until all tophi have resolved, which may be some time after the target serum urate level has been reached [
39]. While concomitant prophylaxis may prevent this, prescribing is not always appropriate or effective [
22]. Subsequently, patients may perceive therapy to be ineffective and continuation rates can be poor [
16,
32]. A lack of education for both medical practitioners and patients has been identified as a key barrier for success in establishing and maintaining ULT [
13].
We found that younger participants with gout had lower rates of allopurinol continuation and were more likely to have flares, findings that are comparable to those from two retrospective UK general practice database studies of people with incident gout [
40,
41]. Importantly, there was no evidence that low SES was a factor in either flares or ULT use; the predominantly public health care system in Australia may mean that this finding is not generalizable to countries with privatized health care systems. However, the roles of BMI and female gender in the management of gout warrant further consideration. In this study, higher BMI was associated with an increased prevalence of frequent flares, yet these patients were no more likely to receive ULT. Higher BMI has been causally linked to increased serum urate levels using a bidirectional Mendelian randomization approach [
42], and a predictive model for allopurinol maintenance dose necessary to achieve serum urate target was highly dependent on body weight [
43]. Interestingly, a prospective observational study from the US Multiple Risk Factor Intervention Trial database has demonstrated that, in individual patients with gout, there is a positive relationship between changes in BMI and the risk of recurrent gout flares [
44], and therefore, weight loss may potentially contribute to gout management.
Although gout predominantly affects men, women were less likely to commence or adhere to ULT and experienced a greater number of flares in our study. Other studies have identified that women with gout have more severe disease with a greater burden of comorbid conditions [
45] and poorer ULT adherence [
41]. Gender bias in the diagnosis, management, and treatment of chest pain and cardiovascular disease may contribute to poorer outcomes in women (reviewed in [
46]). Further research is required as there are limited data about the effect of gender on gout and its influence on management.
There are several limitations of this study. In addition to the use of self-reported, doctor-diagnosed gout, which has been validated for gout case definition [
37,
38], flares were also self-reported. Flares may be subject to recall bias, and lower functional health literacy, identified in self-reported medically diagnosed arthritis, including gout, may also affect the responses obtained [
47]. A tool for the definition of gout flare for clinical research, which utilizes patient reported flare as one of the criteria, has been validated and published following our data collection [
48], so was not used for this study. Dosage and duration of allopurinol use were not quantified, nor were serum urate levels; therefore, adherence and optimization of treatment were only indirectly assessed.
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