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Abstract

Objective:

The goal of this paper was to analyze patient outcomes related to gout treatment including, serum uric acid (sUA) measures and treatment adherence across patients in metropolitan, micropolitan or rural counties.

Methods:

We conducted a drug-disease cohort study among patients with gout initiating urate lowering therapy. The proportion of patients with sUA < 6 mg/dL at 1 year of follow-up is compared over the cohort groups using a chi-square test and adjusted logistic regression. Adherence to urate lowering therapy was calculated using the proportion of days covered (PDC). A T-test was used to compare the average PDC and an adjusted logistic regression model was used to estimate the odds of a PDC greater than 80%.

Results:

A total of 9922 patients were included in the study. Most patients were in a metropolitan (77.4%) area, followed by micropolitan (11.8%) and finally, (10.8%) in a rural area. We found no statistically significant difference among the proportion of patients achieving target sUA of <6 mg/dL, 37.17% among metropolitan patients, 38.9% among micropolitan patients, and 37.7% for those in a rural area, P-value = .502. The proportion of patients achieving 80% treatment adherence was 49.92% in the metropolitan, 51.78% in the micropolitan, and 55.05% in the rural areas, P-value = .005. Adjusted regression models showed no statistically significant difference in proportions achieving target sUA levels or 80% adherence.

Conclusions:

Urban patients treated for gout did not have better gout outcomes compared to rural patients. Future research should consider provider-based interventions to improve outcomes.

Keywords

gout adherence rural health cohort study health outcomes

Introduction

Gout is an inflammatory rheumatoid condition resulting in inflammation, joint pain, and gait instability. Gout prevalence has increased yearly along with the primary risk factor for gout, hyperuricemia. Increased prevalence of gout is likely due to an aging population and increased comorbid conditions.¹ Treatment for gout involves lowering serum uric acid levels (sUA) to reduce the risk of subsequent flares.^2-4 The most common pharmacotherapies for urate lowering include xanthine oxidase inhibitors, notably allopurinol and febuxostat, as well as uricosuric agents such as probenecid. Despite gout being a relatively common condition, little is known about the differences, in treatment patterns and outcomes, between patients in rural and urban environments. Patients in rural area tend to have higher obesity rates, lower fruit consumption,⁵ and more comorbid conditions.⁶ Therefore, understanding the differences in outcome, specifically, sUA levels and treatment adherence by rurality could lead to improved therapy interventions for patients in both urban and rural environments. To evaluate the differences in adherence and sUA, we use a cohort of patients, treated by the US Department of Veterans Affairs, who initiate their first urate lowering therapy (ULT) after their initial gout diagnosis.

Materials and Methods

This drug-disease retrospective cohort study examining outcomes among gout patients treated with a urate lowering therapy used data from the United States Department of Veterans Affairs. Individual-level data on demographics, medical history, hospitalization, and outpatient medication dispensation was obtained using the Veterans Affairs Informatics and Computing Infrastructure (VINCI). The study was conducted in compliance with the Department of Veterans Affairs requirements and received Institutional Review Board and Research and Development approval.

The study cohort was created using patients eligible for VA care prior to 2020, with an outpatient diagnosis code indicating gout. All ICD-9-CM and ICD-10-CM codes used to extract the cohort are listed in Supplemental Table S1. The study index is defined as the first outpatient gout diagnosis and index dates range over the time period 1999 to 2021. Patients were required to have a serum uric acid laboratory measure within 30 days of index and their first prescription dispense for urate lowering therapy within 60 days after index. The urate lowering therapies for this study were defined as allopurinol (100, 300 mg), febuxostat (40, 80 mg), and probenecid (500 mg). To evaluate sUA changes 1 year post index, patients are required to have a follow-up serum uric acid result within 334 to 387 days post index. Patients were categorized into 3 different groups based on the urban influence code (UIC) of their county of residence. UIC values span a 12-point continuum, which is categorized into 3 categories based on rurality including metropolitan, micropolitan, and rural areas.⁷ Our grouping of the 12-point UIC scale included values 1, 2 coded as metropolitan, 3, 5, 8 coded as micropolitan, and values 4, 6 to 12 as rural. Further, as a secondary analysis we used a 4-category definition, splitting rural in to adjacent rural (UIC = 4, 6, 7) and remote rural (UIC = 9, 10, 11, 12). All analysis output for the 4-category definition appears in the Supplemental File.

The primary study outcome is sUA level at follow-up. Unadjusted analysis of sUA include 3 metrics, average sUA at follow-up, change from baseline to follow-up and the proportion of patients with a sUA < 6 mg/dL at follow-up. Average sUA levels and the average change from baseline are compared over the cohort groups using an ANOVA F-test. The proportion of patients with sUA < 6 mg/dL at follow-up are compared over the cohort groups using a chi-square test. Multivariate analysis of follow-up sUA is conducted using linear regression accounting for baseline sUA and all study covariates. A logistic regression model is used to examine the associations with study variables and achieving follow-up sUA < 6mg/dL. The secondary outcome is adherence to urate lowering therapy. Adherence is calculated as the proportion of days covered (PDC) during the study period. The average PDC for each cohort group is compared between cohort groups using a T-test. Multivariate analysis of PDC is conducted using a logistic regression model to estimate the odds of a PDC greater than 80%, a common threshold indicating adherence to medication. Results from the multivariate model are presented in a forest plot. P-values are 2-tailed at a 0.05 level of significance. All data analysis was conducted in SAS 9.4 (Cary, NC) and R (R core team).

Covariates such as demographics, including age, race, and sex are included in the multivariate analysis. Other comorbid conditions influencing sUA and gout such as body mass index (BMI) and smoking status are included as well. The Charlson comorbidity index is used as a measure of overall comorbidity burden at study baseline. Smoking status was ascertained through the VA Health Factors data. A current smoker was indicated if the screening closest to index indicated a current smoking status. All non-gout medications dispensed between 30 days prior to and 30 days post were summed for each patient to provide a measure of polypharmacy among the gout patients.

Results

Sample Characteristics

A total of 9922 patients were included in the study. Table 1 reports baseline characteristics. Most patients were in a metropolitan (7680, 77.4%) area, followed by micropolitan (1172, 11.8%) and lastly, in a rural area (1070, 10.8%). Metropolitan areas had the highest percentage of black patients (19%) and conversely the lowest percentage of white patients, 69%. Rural areas on the other hand had the lowest percentage of black patients (7%) and the highest percentage of white patients (82%). All cohorts were predominantly male (98%) and ranged, on average, from 62 (metropolitan) to 65 (rural) years of age. Baseline comorbidity status was similar between the groups, with average Charlson comorbidity scores of 1.6. Proportions of patients who smoked ranged from 5.7% in the metropolitan cohort to 6.8% in the micropolitan cohort. On average, patients in each cohort had approximately 6 other medications dispensed within 30 days ± of urate lowering therapy initiation.

Table 1.

Baseline Characteristics by Cohort.

Variable	Metropolitan, N = 7680	Micropolitan, N = 1172	Rural, N = 1070	P value
Age	62.95 (12.47)	64.66 (11.75)	65.31 (11.24)	<.001
Race
Black	1481 (19.28%)	110 (9.39%)	77 (7.20%)	<.001
Other/unknown	848 (11.04%)	124 (10.58%)	110 (10.28%)
White	5351 (69.67%)	938 (80.03%)	883 (82.52%)
Sex
Female	106 (1.38%)	15 (1.28%)	12 (1.12%)	.774
Male	7574 (98.62%)	1157 (98.72%)	1058 (98.88%)
Charlson comorbidity index	1.67 (2.16)	1.6 (2.1)	1.67 (2.147)	.65
BMI
<18.5	23 (0.3%)	# (#%)	# (#%)	.049
18.5-24.9	607 (7.90%)	97 (8.28%)	66 (6.168%)
25-29.9	2328 (30.31%)	345 (29.44%)	286 (26.73%)
30+	4722 (61.49%)	727 (62.03%)	714 (66.73%)
Current smoker	443 (5.77%)	80 (6.83%)	69 (6.45%)	.283
Number of other medications	6.27 (4.4)	5.91 (4.37)	6.08 (4.33)	.023
Year*	2012.15 (5.86)	2011.63 (6.08)	2011.12 (6.01)	<.001
Baseline serum uric acid	8.38 (2.402)	8.23 (2.354)	8.19 (2.133)	.012
Initial urate lower therapy
Allopurinol 100 mg	4941 (64.336%)	740 (63.14%)	639 (59.72%)	.007
Allopurinol 300 mg	2549 (33.19%)	404 (34.471%)	403 (37.664%)
Febuxostat 40 mg	77 (1.003%)	# (# %)	# (# %)
Febuxostat 80 mg	20 (0.26%)	# (# %)	# (# %)
Probenecid 500 mg	93 (1.211%)	20 (1.706%)	20 (1.869%)

#(# %): Less than 5 in sample size; *year of gout treatment initiation.

Baseline sUA ranged from 8.19 mg/dL, on average, in the rural cohort to 8.3 mg/dL in the metropolitan cohort. Using the 4-category classification of rurality revealed remote rural patients had an average baseline sUA of 8.09 mg/dL and those in adjacent rural areas had baseline sUA of 8.26 mg/dL (Supplemental Table S2). Most initiated allopurinol 100 mg dose, however, the rural cohort had the lowest percentage of patients, 59%, while the metropolitan cohort had the highest, 64%. Allopurinol 300 mg was the second most popular treatment. The rural cohort had the largest proportion of patients initiate 300 mg allopurinol, 37%, those in the metropolitan cohort had the lowest, 33%. Febuxostat was initiated by the fewest number of patients, at most 1% in the metropolitan cohort in the 40 mg dosage form. Too few patients were initiated on febuxostat in the rural and micropolitan cohort to report. Probenecid 500 mg was initiated in 1.2% of patients in the metropolitan cohort, up to 1.8% in the rural cohort.

Categorizing treatment regimens using the 4-category classification revealed that the remote rural patients had the lowest rate of 100 mg allopurinol initiation, 55.96% versus 62.4% in the adjacent rural areas (Supplemental Table S2). Patients in the remote rural areas had the largest proportion of patients receiving 300 mg allopurinol (42.25%). Among adjacent rural patients 34.4% received the 300 mg dose (Supplemental Table S2).

Serum Uric Acid

Table 2 presents sUA results, which were on average 6.7 mg/dL at follow-up across all cohorts. The metropolitan cohort, on average, had the largest reduction in sUA, −1.65 mg/dL, the rural cohort had the smallest reduction, −1.45 mg/dL. In analyzing the sUA reduction breaking out the remote and adjacent rural patients, we found that those in the remote rural areas had the smallest reduction in sUA (−1.25 mg/dL) while those in adjacent rural areas had a larger reduction (−1.6 mg/dL) (Supplemental Table S3). Patients in the rural and metropolitan areas had approximately 37% achieve sUA less than 6 mg/dL at follow-up, and those in micropolitan areas were slightly higher at 38.9% (Table 2). Figure 1 displays the forest plot for the OLS estimates modeling the effects of all the study variables on follow-up sUA levels. After accounting for covariates, patients in rural counties had a statistically significantly higher follow-up sUA level compared to those in metropolitan areas. Other factors significantly associated with higher follow-up sUA levels include, higher baseline sUA, male sex, a BMI of 30+, and a higher Charlson comorbidity index at baseline. Factors significantly associated with a lower follow-up sUA include the use of allopurinol 300 mg or febuxostat 80 mg, both having lower follow-up sUA on average compared to 100 mg allopurinol. Febuxostat 80 mg is associated with the greatest reduction of follow-up sUA, an estimated 1.26 mg/dL, on average, compared to 100 mg allopurinol, with all other variables held constant. Other factors associated with lower follow-up sUA include older age, and greater adherence (PDC) to treatment. Supplemental Figure S1 shows the adjusted model results for the 4-category classification. Patients in metropolitan areas had a statistically significantly lower follow-up sUA level compared to remote rural patients (Supplemental Figure S1). There was no statistically significant difference between remote rural and adjacent rural patients. Figure 2 displays the forest plot of odd ratios estimated from a logistic regression model estimating the odds of follow-up sUA < 6 mg/dL. No statistically significant difference was found between rural patients and metropolitan patients regarding the likelihood of achieving <6 mg/dL sUA levels. Variables associated with a lower likelihood of achieving a follow-up sUA < 6 mg/dL include higher baseline sUA, male sex and having a BMI greater than 30. Factors associated with a higher odd of achieving sUA < 6 mg/dL include treatment, with 300 mg allopurinol and both dosages of febuxostat having greater odds compared to 100 mg allopurinol. Older age and greater adherence (PDC) to treatment are also associated with a higher odds of achieving <6 mg/dL sUA level. Using the 4-category classification, we find no statistically significant difference in the likelihood of achieving target sUA level (<6 mg/dL) when comparing all areas to remote rural patients (Supplemental Figure S2).

Table 2.

Changes in Serum Uric Acid at Follow-Up.

Variable	Metropolitan, N = 7680	Micropolitan, N = 1172	Rural, N = 1070	P value*
Follow up serum uric acid (mg/dL)	6.73 (2.267)	6.72 (2.37)	6.74 (2.52)	.98
Change in uric acid from baseline	−1.65 (2.89)	−1.51 (2.85)	−1.45 (2.81)	.047
With uric acid <6 mg/dL at follow-up	2854 (37.17%)	456 (38.9%)	404 (37.7%)	.502

Unadjusted analysis.

Figure 1.

Adjusted OLS regression: Follow-up uric acid levels.

Figure 2.

Logistic regression: odds of follow-up serum uric acid <6 m mg/dL.

Treatment Adherence

In terms of adherence to treatment, patients in rural areas were the most adherent with an average PDC of 73%. In terms of patients achieving at least 80% or better adherence, the rural cohort had the highest proportion, 55%. The metropolitan cohort had the lowest PDC, on average 71%, and only 49% of patients achieving at least 80% adherence (Table 3). When using the 4-category classification adherence to ULT was 73.9% in the adjacent rural and 73.15% among the remote rural patients (Supplemental Table S4). Adjacent rural patients had 56.32% of patients achieve 80% or better adherence and remote rural patients had 53.26% (Supplemental Table S4). Figure 3 presents the forest plot for the odds ratios estimated from the logistic regression modeling achievement of at least 80% adherence. No statistically significant difference between rural patients and metropolitan patients was found (Figure 3). Both febuxostat dosages are associated with higher odds of achieving 80% adherence compared to the 100 mg allopurinol dosage. There was no statistically significant difference between the 300 mg allopurinol and 100 mg allopurinol dosage in terms of the odds of achieving 80% adherence. Compared to black patients, white patients and those in the other/unknown category have higher odds of achieving the 80% adherence level. Older age and more medications at baseline are also statistically significantly associated with higher odds of achieving 80% adherence. Using the 4-category classification, no significant difference in the odds of achieving 80% adherence was found between remote rural patients and any of the other groupings (Supplemental Figure 3).

Table 3.

Treatment Adherence.

Variable	Metropolitan, N = 7680	Micropolitan, N = 1172	Rural, N = 1070	P value*
Adherence to urate lowering therapy Mean (SD)	71.14 (26.68)	71.52 (27.12)	73.6 (25.89)	.019
PDC ≥ 80%, N (%)	3834 (49.92%)	607 (51.792%)	589( 55.05%)	.005

Unadjusted analysis.

Figure 3.

Logistic regression: odds of treatment adherence ≥80%.

Discussion

Hyperuricemia and gout prevalence has increased over time. An aging population as well as increased comorbidity burdens likely contribute to the increased prevalence of the disease. Medications targeting xanthine oxidase as well as uricosuric agents are the mainstays of treatment. The goal of these medications is to reduce sUA levels to 6 mg/dL or lower.^2-4,8 Despite gout pharmacotherapy being effective and generally available, a high percentage of patients fail to achieve target sUA levels.^9,10 Failure to achieve adequate sUA control has been associated with increased risk of kidney dysfunction,¹¹ diabetes¹² as well as cardiovascular decline.¹³ Therefore, understanding sUA treatment and outcomes are critical, not just for preventing gout reoccurrence, but also to prevent or slow the progression of comorbid conditions.

The goal of this study was to examine sUA outcomes and treatment adherence in patients categorized by rurality. Importantly, patients in rural areas tend to have higher comorbidity burdens and poorer health outcomes compared to patients in more urban areas. Understanding the differences in treatment or outcomes could better inform interventions to assist these patients.

Our study found that patients in rural areas tended to have higher sUA levels at follow-up even after adjusting for covariates. In the 4-category classifications we found that patients in metropolitan areas had lower follow-up sUA compared to remote rural areas. However, the results of our analysis also suggest that, rather than wide disparities in outcomes between more rural and urban patients, outcomes among gout patients are suboptimal across rural and urban divide. Importantly, across all cohorts the proportion of patients achieving the target sUA < 6 mg/dL is less than 40%. Moreover, multivariable model results suggest no difference between rural and non-rural patients in the likelihood of achieving this target sUA level. Our data are consistent with a study among Japanese men in which only 37.5% of patients achieved target sUA levels.¹⁴ Similarly, our multivariable model results suggest no statistically significant difference between rural patients and those in metropolitan areas in the likelihood of achieving 80% adherence or better. Overall, rates of achieving 80% adherence were low, ranging from 50% to 55%. Although the results from this sample of patients indicates poor adherence to treatment, non-adherence to ULT is a common finding among the gout patients.^15-19 A systematic review analysis found that less than 50% of patients were adherent to ULT.²⁰ Some studies report even poorer adherence, notably a study of Irish patients found only 35% of patients were adherent to ULT at 12 months.¹⁵ The results of this analysis suggest that the contribution of rurality to these poor outcomes is minimal further suggesting that access to care or distance-based barriers to treatment are not the culprit behind the poor outcomes.

Multivariate models reported in this analysis found several factors associated with better odds of both achieving target sUA and adherence levels. Consistent with prior work,²¹ we found that febuxostat has a statistically significantly higher odds of adherence compared to allopurinol (reference 100 mg dose). There was no difference in adherence between 100 and 300 mg dose of allopurinol. Further, our multivariate models revealed that both doses (40, 80 mg) of febuxostat have a statistically significantly higher odds of achieve achieving target sUA levels compared to the 100 mg dose of allopurinol. Similarly, 300 mg of allopurinol had a statistically significantly higher likelihood of achieving sUA < 6 mg/dL compared to 100 mg allopurinol. These results suggest that once the decision to treat gout using pharmacotherapy is made, febuxostat or 300 mg allopurinol may be a better option for achieving target sUA levels than initiating 100 mg allopurinol. However, this is based solely on sUA and does not include cost, side effects, and disease progression. Importantly, more research on optimizing urate lowering therapy is warranted. Consistent with prior studies,^22,23 the results of this analysis suggest older patients were more likely to be more adherent. Importantly, however, our results also reveal that older patients are less likely to achieve target sUA levels.

While gout is a common inflammatory condition, with generally effective treatment, adherence to therapy and consequently achieving urate targets is consistently low. A recent study revealed some potential reasons for non-adherence including lifestyle reasons, inconvenient dosing, and patients who felt they no longer needed the medications.²⁴ Our research sought to examine the possibility that patient locations could be an important predictor of gout outcomes. More urban patients are expected to have better access to medical providers, routine monitoring and as a result, better outcomes. Our results show that contrary to many disease states, that the rural-urban divide plays a less significant role in gout treatment adherence and outcomes. One possibility to explain this contrary finding is that providers do not treat gout as they would other chronic conditions and are less proactive in patient monitoring, even among patients with lower access barriers. As such the result of this research could help to focus future research on other aspects of gout care, in particular provider focused interventions.

Shifting provider thinking toward treating gout as a chronic condition subject to similar monitoring as patients on antihypertensives could improve outcomes.²⁵ In fact, studies show that with provider (nurse or pharmacist) led treatment programs, adherence to treatment significantly improves.^18,26 For example, a randomized control trial utilizing nurses trained in gout management and patient engagement were better than standard of care in achieving target sUA levels.²⁶ Further, a randomized control trial using pharmacist led intervention through automated phone messaging resulted in better adherence to medication and more patients achieving sUA levels.¹⁸ The evidence from these trials suggests that provider-based engagement with patients can improve gout outcomes. Methods to engage with patients via tele-consults or text messaging may improve gout related outcomes and future trials should assess interventions that are beneficial across the rurality spectrum. Within our data sample, we conducted an exploratory analysis comparing patients with a smoking cessation or preventive medicine screening prior to their follow-up visit (CPT codes 99407, G0375, G0376, 99078, 99381-99397). We found that patients with a smoking cessation or preventive medicine visit during the study period had a higher odds of achieving their serum uric acid target (Supplemental Figure S4). Although we cannot rule out a self-selection effect among these patients, the results suggest that preventive health and behavior modifying (e.g., smoking cessation) visits can positively impact gout outcomes. Further, medications that could also impact serum uric acid should be considered as well. For instance, as a supplemental analysis, we extracted patients with a prednisone prescription at study baseline. Our results suggest that patients who took prednisone, a steroid sometimes used to treat acute gout flares, have a higher odds of achieving target serum uric acid levels (Supplemental Figure S5). This result is consistent with other clinical studies showing prednisone lowers sUA through improved renal clearance.^27-29 These results suggest that optimizing urate lowering therapy, possibly utilizing medications not indicated for ULT could be critical in improving outcomes for gout patients.

Our study has many strengths, including the relatively large, national cohort of patients and multiple covariate adjustment. However, this study has limitations as well. Importantly, we cannot confirm whether patients were taking their medication only timing and number of dispenses for prescriptions. Our data are consistent with prior research; however, the results of this study may not be generalizable to non-veteran patients. Specifically, the VA population tends to be predominantly male and, as a result, may not be generalizable to other patient populations. Additionally, the notion of rurality is vague and there are various classifications of rurality including the Rural-Urban continuum codes (RUCC), index of relative rurality (IRR) as well as the Rural-Urban commuting areas (RUCA). Our analysis used urban influence codes (UIC), which is comprised of a 12-point scale of rurality. We used 2 different groupings of rurality, a main analysis coding categorizing remote and adjacent rural counties as 1 category and a secondary coding breaking out the 2. Although the UIC is a well-known and accepted measure, variability among important patient level characteristics such a transportation availability and income level could influence the results of this study as well.

Conclusion

Gout and hyperuricemia are associated with incidence and progression of comorbid conditions. Our study aimed to examine the differences in serum uric acid measures as well as treatment adherence by different levels of rurality. Consistent with prior studies, our results suggest that low proportions, <40%, of patients achieve target sUA of <6 mg/dL and adherence to treatment is generally poor. Importantly, we did not find consistent differences in outcomes across the spectrum of rurality, suggesting that the potential access to care advantage among more urban patients is not translated into better outcomes. Future research should be conducted into provider specific interventions for improving gout outcomes.

Supplemental Material

sj-docx-1-jpc-10.1177_21501319231167379 – Supplemental material for Odds of Achieving Target Serum Uric Acid Levels Among Gout Patients: The Role of Rurality in Outcomes and Treatment Adherence

Supplemental material, sj-docx-1-jpc-10.1177_21501319231167379 for Odds of Achieving Target Serum Uric Acid Levels Among Gout Patients: The Role of Rurality in Outcomes and Treatment Adherence by S. Scott Sutton, Joseph Magagnoli, Tammy H. Cummings and James W. Hardin in Journal of Primary Care & Community Health

Footnotes

Acknowledgements

No funding agency had a role in study design or conduct, data collection, analysis, interpretation, or manuscript writing. The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the US Department of Veterans Affairs, nor does mention of trade names, commercial products, or organizations imply endorsement by the US government. This paper represents original research conducted using data from the Department of Veterans Affairs and is the result of work supported with resources and the use of facilities at the Dorn Research Institute, Columbia VA Health Care System, Columbia, South Carolina.

Author Contributions

SS: Conceptualization, supervision, writing—reviewing and editing; JM: Conceptualization methodology, formal analysis, writing—original draft preparation; TC: Methodology, writing—reviewing and editing; JH: Reviewing and editing.

Data Availability

Analyses of the Veterans Health Administration Database were performed using data within the US Department of Veterans Affairs secure research environment, the VA Informatics and Computing Infrastructure (VINCI). All relevant data outputs are within the paper and its supplemental information.

Declaration of Conflicting Interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Sutton has received research grants from Boehringer Ingelheim, Coherus BioSciences, EMD Serono, and Alexion Pharmaceuticals, all for projects unrelated to study. The other authors declare no competing interests

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Sutton, Magagnoli, and Cummings were supported by NIH grant R01DA054992 and the South Carolina Center for Rural and Primary Healthcare unrelated to this study.

ORCID iD

Joseph Magagnoli

Supplemental Material

Supplemental material for this article is available online.

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