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Abstract Number: 117

Predictors of Gout Flares in a US Managed Care Setting

Robert Jackson1, Aki Shiozawa2, Erin Buysman3, Aylin Altan3, Stephanie Korrer3 and Hyon K. Choi4, 1Global Medical Office, Takeda Pharmaceuticals International, Inc, Deerfield, IL, 2One Takeda Parkway, Takeda Pharmaceuticals International, Inc, Deerfield, IL, 3Health Economics and Outcomes Research, Optum, Eden Prairie, MN, 4Boston University School of Medicine, Boston, MA

Meeting: 2014 ACR/ARHP Annual Meeting

Keywords: Administrative databases and gout

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Session Information

Title: Health Services Research

Session Type: Abstract Submissions (ACR)

Background/Purpose

Gout is the most common inflammatory arthritis in the US, and acute gout flares are among the most painful events experienced by humans. The goal of this study was to assess gout flares in a managed care setting to better understand the patient characteristics that are associated with frequent flares.

Methods

This was a retrospective cohort study using administrative claims data from a large US health plan of commercially insured and Medicare Advantage enrollees. Patients had evidence of gout based on medical and pharmacy claims indicating gout between January 1, 2009 and April 30, 2012. The 12 months prior to the index gout claim was used to assess baseline confounders including demographics, comorbid conditions, baseline health care resource utilization, and baseline serum uric acid (sUA) levels. Gout flares were assessed in the 12 months following the index gout claim based on diagnoses for gout or joint pain followed within 7 days by claims for NSAIDs, colchicine, corticosteroids, or joint aspiration/drainage. A negative binomial model was used to assess the relationship between patient characteristics and the count of gout flares in the 12-month follow-up period.

Results

Our study included 102,703 patients with gout; 56,611 patients (55%) had evidence of at least one gout flare in 12 months of follow-up, and 13,502 (13%)  had multiple flares. Patients had on average 0.73 flares per year, and the average time between flares in patients with multiple flares was 115 days. Characteristics associated with a higher count of flares included Black race, lower net worth, residing in the South census region, high levels of baseline ambulatory utilization, new initiation of urate lowering drugs (ULT) during follow-up, and higher baseline sUA (Table). Cardio-metabolic-renal comorbidities appeared to be associated with fewer flares; however, this may be due to our definition of flares in the claims based on medication use (e.g., NSAIDs) that is often contraindicated with these conditions. Age and gender were not significantly associated with flare frequency after controlling for other confounders.

Conclusion

This large contemporary study of gout patients in a managed care setting indicates over half of patients experiencing at least one flare in a 12-month period, and nearly a quarter of those patients experiencing multiple flares.  Black race, lower economic status, Southern region of residence, initiation of urate lowering therapy, and higher baseline sUA were associated with a higher risk for flares. These data may help identify patients at high risk for flares who could be targeted with a gout management plan aimed at preventing flares.

Table. Adjusted Incidence Rate Ratios for Gout Flares According to Patient Characteristics

Variable

Adjusted Incidence Rate Ratio

(95% Confidence Interval)

Gender x Age Interaction

 

Male, 18-44

reference

Male, 45-64

0.98 (0.93, 1.03)

Male, 65+

0.94 (0.87, 1.02)

Female, 18-44

0.94 (0.80, 1.09)

Female, 45-64

0.95 (0.87, 1.03)

Female, 65+

1.02 (0.93, 1.11)

Geographic Region

 

West/Other

reference

Northeast

1.07 (0.98, 1.16)

Midwest

1.09 (1.00, 1.18)

South

1.12 (1.05, 1.19)

Race

 

Black

reference

Other Race

0.86 (0.81, 0.90)

Net Worth

 

≥ $250,000

reference

Less than $250,000 / Unknown

1.06 (1.02, 1.11)

Baseline Comorbidities

 

Cardiovascular Conditions (not including Hypertension)

0.84 (0.80, 0.88)

Hypertension

0.96 (0.92, 1.01)

Diabetes

0.86 (0.81, 0.90)

Renal Impairment

0.89 (0.85, 0.93)

All-Cause Healthcare Resource Utilization

 

Inpatient Stay or ER Visit

1.04 (0.99, 1.09)

Ambulatory Visits (≥9)

1.14 (1.09, 1.20)

Baseline sUA Level (mg/dL)*

 

<5.0

reference

5.0 – <6.0

1.01 (0.91, 1.12)

6.0 – <7.0

1.34 (1.22, 1.47)

7.0 – <8.0

1.51 (1.38, 1.65)

8.0 – <9.0

1.59 (1.45, 1.74)

9.0+

1.79 (1.63, 1.95)

Follow-up ULT Medication Initiation

1.46 (1.40, 1.52)

* Based on 14,641 patients with baseline sUA levels in the database

 


Disclosure:

R. Jackson,

Takeda Pharmaceuticals International, Inc.,

3;

A. Shiozawa,

Takeda Pharmaceuticals International, Inc.,

3;

E. Buysman,

Takeda Pharmaceuticals International, Inc,

9;

A. Altan,

Takeda Pharmaceuticals International, Inc.,

9;

S. Korrer,

Takeda Pharmaceuticals International, Inc,

9;

H. K. Choi,

Takeda Pharmaceuticals International, Inc;,

5,

AstraZeneca,

5.

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