Abstract
Background/Objective:
Gout is the most common inflammatory arthritis, and its morbidity can be substantially reduced through urate-lowering therapy (ULT). However, adherence to allopurinol–the most common ULT–is notoriously poor. Prior studies have not fully elucidated factors associated with allopurinol adherence, particularly psychosocial factors.
Methods:
We used 2018–2021 data from the Medical Expenditure Panel Survey (MEPS), a national longitudinal survey on health care expenditures and utilization. We calculated the medication possession ratio (MPR) for allopurinol for participants with gout and categorized each as: no allopurinol fills; low-adherence (MPR ≤ 0.8); or high-adherence (MPR>0.8) to allopurinol. We used multivariable logistic regression to identify factors associated with high-adherence, using person-year as the unit of measure and accounting for clustering for participants who contributed more than one person-year.
Results:
The analyses included 919 respondents (1,453 person-years), representing a weighted total of 15,084,439 person-years. Across all years, 27.4% had no allopurinol fills, 37.4% had low adherence, and 35.2% had high adherence. In multivariable models for high adherence, Black race (OR 0.49, 95% CI 0.33–0.73 compared to White) and residence in the South US region (OR 0.54, 95% CI 0.35–0.82 compared to Northeast) were negatively associated with high adherence.
Conclusions:
Black race and residing in the Southern US were associated with lower allopurinol adherence among gout patients. Interventions to improve adherence, particularly among Black patients in the South, are needed to maximize the potential benefits of allopurinol.
Keywords: Gout, Adherence, Allopurinol, Medical Expenditure Panel Survey
Background
Gout is the most common inflammatory arthritis.1 Gout is caused by deposition of monosodium urate (MSU) crystals in the joints, which leads to acute flares. Over time, MSU crystals can form deposits called tophi, and recurrent episodes of acute gout flares can lead to joint damage, resulting in chronic arthritis. There is also evidence that MSU crystals can contribute to disease in other organ systems, such as cardiovascular disease and renal disease. While lifestyle and behavioral modification, including dietary changes, alcohol moderation, and weight loss, are important initial steps in reducing urate and managing gout, medications are often necessary. Both acute and chronic morbidity from gout can be substantially reduced by urate lowering therapy (ULT).2 Allopurinol is the most common ULT and is quite inexpensive,1 though adherence is notoriously poor.3 Previous studies3–6 have identified numerous factors that are associated with adherence to ULT, such as older age and more comorbidities associated with better adherence.3 However, prior studies have largely been conducted in limited geographical settings4 or with non-representative US populations, such as Veterans.5,6 Furthermore, many of these studies have not had access to psychosocial factors, such as education level and type of insurance (which is also not relevant for patients in the VA system), which have been shown to be important predictors of medication adherence in other studies.7 In this study, we sought to use a nationally representative dataset with a broad set of sociodemographic variables to identify factors associated with adherence to allopurinol among patients with gout.
Methods
We used 2018–2021 data from the Medical Expenditure Panel Survey (MEPS), a national survey on health care expenditures and utilization.8 MEPS collects information on health care use, diagnoses, and socioeconomic characteristics through direct interview, and validates information on medical care events by contacting providers and pharmacies identified by participants.8 Participants are typically followed for two years, though some participants were followed for longer than 2 years during the COVID pandemic,8 and we evaluated each year separately. We limited the sample to those with a diagnosis of gout recorded in a given year. The independent variable was allopurinol use/adherence. We calculated a medication possession ratio (MPR) for allopurinol and categorized each as no allopurinol use, low-adherence (MPR ≤ 0.8), or high-adherence (MPR>0.8).9 We compared these factors across the 3 levels of allopurinol use/adherence in bivariate analyses, applying sampling weights to make representative estimates. We then used multivariable logistic regression to identify factors associated with high adherence (compared to low adherence). The final models included variables for age, sex, race/ethnicity, education, marital status, geographic region, health insurance, poverty category, Charlson comorbidity score, and year. We did not include sampling weights in the multivariable analyses. In our primary analyses, we included only those with some allopurinol fills in the year as having low adherence. In a sensitivity analysis, we included those with no allopurinol fills as having low adherence.
Results
The analyses included 919 respondents (1,453 person-years), representing a weighted total of 15,084,439 person-years. Participant characteristics are shown in Table 1. The mean age was 65.6 years, 25.1% were female, 73.7% were White, and 13.6% were Black. Across all years, 27.4% had no allopurinol fills, 37.4% had low adherence, and 35.2% had high adherence.
Table 1.
Participant Characteristics
| Allopurinol Adherence | ||||
|---|---|---|---|---|
| Total | None | Low MPR ≤0.8 | High MPR >0.8 | |
| Weighted N, Millions | 15.08 | 4.14 | 5.64 | 5.31 |
| Percent of Total | 100 | 27.4 | 37.4 | 35.2 |
| Age, mean | 65.6 | 64.4 | 66.0 | 66.2 |
| 18–44, % | 8.5 | 7.7 | 9.5 | 8.0 |
| 45–64, % | 34.4 | 41.4 | 31.5 | 31.9 |
| 65–74, % | 29.5 | 23.8 | 29.7 | 33.7 |
| 75+, % | 27.7 | 27.2 | 29.3 | 26.4 |
| Female % | 25.1 | 29.0 | 25.1 | 22.0 |
| Race/ethnicity, % | ||||
| White | 73.7 | 68.8 | 71.9 | 79.5 |
| Black | 13.6 | 15.3 | 16.9 | 8.7 |
| Hispanic | 4.5 | 6.5 | 3.3 | 4.3 |
| Other | 8.2 | 9.3 | 8.0 | 7.5 |
| Education, % | ||||
| <HS | 9.3 | 9.6 | 11.0 | 7.3 |
| HS or GED | 48.2 | 46.0 | 49.3 | 48.8 |
| Some College+ | 42.4 | 44.4 | 39.7 | 43.8 |
| Marital Status | ||||
| Married | 58.8 | 62.2 | 56.8 | 58.3 |
| Not Married | 41.2 | 37.8 | 43.2 | 41.7 |
| Region, % | ||||
| Northeast | 14.7 | 11.7 | 11.8 | 20.0 |
| Midwest | 29.5 | 30.1 | 30.1 | 32.6 |
| South | 35.3 | 39.3 | 38.0 | 29.2 |
| West | 19.7 | 23.7 | 18.7 | 17.8 |
| Insurance, % | ||||
| Public only | 38.6 | 33.6 | 41.1 | 40.0 |
| Private only | 33.2 | 36.6 | 33.2 | 30.7 |
| Public & private | 27.3 | 28.1 | 25.2 | 28.9 |
| None | 0.8 | 1.6 | 0.6 | 0.5 |
| Poverty Category | ||||
| Poor | 10.1 | 10.0 | 11.2 | 9.1 |
| Near Poor | 4.4 | 3.0 | 4.4 | 5.5 |
| Low Income | 11.5 | 9.2 | 13.8 | 10.9 |
| Middle Income | 28.1 | 30.9 | 26.8 | 27.2 |
| High Income | 45.9 | 46.9 | 43.8 | 47.3 |
| Charlson, % | ||||
| 0 | 63.1 | 66.3 | 60.7 | 63.2 |
| 1 | 19.8 | 19.4 | 19.5 | 20.5 |
| ≥2 | 17.1 | 14.3 | 19.8 | 16.3 |
The results of the multivariable models of factors associated with high adherence to allopurinol are shown in Table 2. Black race (OR 0.49, 95% CI 0.33 – 0.73 compared to White) and South region (OR 0.54, 95% CI 0.35 – 0.82 compared to Northeast) were negatively associated with high-adherence to allopurinol. Age, sex, education, marital status, insurance type poverty category, Charlson comorbidity score, and year were not associated with adherence. A test for interaction between race and region was not significant.
Table 2.
Multivariable-adjusted Association of Key Variables with High Adherence to Allopurinol
| OR* | 95% CI | P value | |
|---|---|---|---|
| Age | |||
| 18–44 | 0.76 | 0.42 – 1.39 | 0.374 |
| 45–64 | Ref | ||
| 65–74 | 0.99 | 0.56 – 1.74 | 0.968 |
| 75+ | 0.73 | 0.40 – 1.31 | 0.294 |
| Female | 1.16 | 0.83 – 1.65 | 0.373 |
| Race/ethnicity | |||
| White | ref | ||
| Black | 0.49 | 0.33 – 0.73 | <.001 |
| Hispanic | 0.89 | 0.39 – 2.02 | 0.785 |
| Other | 0.91 | 0.54 –1.53 | 0.730 |
| Education | |||
| <HS | ref | ||
| HS or GED | 1.25 | 0.77 – 2.05 | 0.368 |
| Some College+ | 1.33 | 0.79 – 2.23 | 0.281 |
| Married | 0.92 | 0.67 – 1.25 | 0.584 |
| Region | |||
| Northeast | ref | ||
| Midwest | 0.66 | 0.44 – 1.03 | 0.070 |
| South | 0.54 | 0.35 – 0.82 | 0.004 |
| West | 0.72 | 0.45 – 1.16 | 0.179 |
| Insurance | |||
| Public only | ref | ||
| Private only | 0.87 | 0.49 – 1.54 | 0.633 |
| Public & private | 1.13 | 0.79 – 1.60 | 0.513 |
| None | 1.02 | 0.28 – 3.69 | 0.977 |
| Poverty Category | |||
| Poor | 1.20 | 0.71 – 2.02 | 0.495 |
| Near Poor | 1.27 | 0.69 – 2.35 | 0.442 |
| Low Income | Ref | ||
| Middle Income | 1.17 | 0.77 – 1.77 | 0.468 |
| High Income | 1.11 | 0.72 – 1.71 | 0.626 |
| Charlson Index | |||
| 0 | ref | ||
| 1 | 1.09 | 0.77 – 1.55 | 0.622 |
| ≥2 | 0.93 | 0.64 – 1.35 | 0.709 |
| Year | |||
| 2018 | ref | ||
| 2019 | 1.37 | 0.97 – 1.93 | 0.074 |
| 2020 | 1.00 | 0.70 – 1.42 | 1.00 |
| 2021 | 0.75 | 0.53 – 1.06 | 0.099 |
Multivariable-adjusted logistic regression model included the following variables: age, sex, race/ethnicity, education, marital status, region, insurance, poverty category, Charlson index, and the year
The sensitivity analysis where those with no allopurinol fills were categorized as having low adherence, we found the association to be largely similar to the main results, with only Black race (OR 0.52, 95% CI 0.35 – 0.75 compared to White) and South region (OR 0.53, 95% CI 0.35 – 0.79 compared to Northeast) being negatively associated with high-adherence to allopurinol (remaining estimates not shown).
Discussion
In this study of MEPS data from 2018–2021, we found that only about three-quarters of patients with gout received allopurinol in a given year, but that less than half of those had high adherence. We also found that Black race and being in the South US region were associated with lower likelihood of high adherence to allopurinol. We did not find that any financial factors, such as income or health insurance, were associated with adherence.
The reasons that patients choose not to regularly take allopurinol are multifactorial and complex. Previous studies, including qualitative research10 and surveys,11 have identified patient factors that relate to non-adherence. For example, the intermittent nature of gout leads patients to believe that medication is either ineffective after they experience a flare, or potentially unnecessary after a period of time without a flare. However, other studies have identified provider and health system factors that are associated with adherence to allopurinol. The most consistent findings in these studies are that older age and more comorbidities are associated with better adherence.3 Neither of these factors was associated with adherence in our study. Our finding that race and region are associated with adherence have also been observed in other studies,5 so our study bolsters the importance of these factors.
Our finding that financial factors such as income or insurance type were not associated with adherence might be explained by the low cost of allopurinol. Allopurinol has been available as a generic medication for decades, and the cost is very low, so cost is less likely to be a major obstacle for most patients. While education has been associated with adherence in other studies, such as diabetes, the association is not always consistent, and it has not been shown to be associated in the limited studies of gout patients,12 so it is not surprising that it was not present in our study. MEPS lacks data on uric acid levels, and therefore we could not perform analyses of effectiveness of continuous vs. intermittent vs. no allopurinol use.
Overall, our findings suggest that interventions to improve adherence of allopurinol should consider focusing specifically on Black patients in the South. A recent trial demonstrated that a nurse-led intervention that focused on frequent, individualized follow-up resulted in substantial increases in uptake and adherence with allopurinol.13 More studies are needed on interventions to increase adherence to ULT, with a recent systematic review identifying only 3 randomized controlled trials.14 Future efforts should consider tailoring interventions to this unique and particularly vulnerable population.
An interesting point to note is the low overall prevalence of gout identified in MEPS. The mean prevalence of gout in our study (when weighted to the US non-institutionalized population) was under 4 million, which is substantially less than recent estimates of over 12 million based on NHANES data.15 This discrepancy might be explained in part by the methodology of identifying comorbidities in MEPS. For certain priority conditions such as cancer and diabetes, all participants are queried about they have the condition. For other non-priority conditions, such as gout, the presence of that condition would only be captured file if there is an event (e.g., doctor visit or prescription etc.) associated with that condition. This means that some patients with gout who are not on any medication may not be captured, though it is unclear whether this factor alone would account for such a large discrepancy. Relevant to our main analyses, all patients who were prescribed any medication for gout should be captured as having gout.
This study is subject to limitations. First, each period of observation consisted of only 12 months, which could lead to misclassification. The nature of the data prevented a new-user design, so it is possible that those who only initiated treatment partway through the year or had discontinued the medication entirely could be misclassified as non-adherent. However, our finding that just under half of individuals who were prescribed allopurinol had high adherence is consistent with other studies, including a recent systematic review showing a mean of 46% of patients had high adherence across 13 studies,3 which provides some reassurance that any misclassification is likely low. Second, MEPS is only able to identify filled prescriptions, not prescriptions that go unfilled or declined recommendations that do not result in a prescription. Therefore, it is difficult to disentangle health system-system related factors vs. patient-related factors as reasons for low adherence or non-use. Third, we were unable to include potentially important factors, such as uric acid level and alcohol consumption, which are not available as variables in MEPS. There is also not a universal measure for adherence within MEPS, so while it is likely that low adherence to allopurinol is associated with low adherence to medications for other chronic conditions, we were not able to assess this association.
In conclusion, we found that Black race and being in the South region are associated with lower allopurinol adherence. Interventions to improve adherence, particularly among Black patients in the South, are needed to maximize the potential benefits of allopurinol.
Sources of Support:
The project was supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (K23 AR080224, Riggs). JAS is supported by research grants from the Patient Centered Outcomes Research Institute (PCORI; SDM-2017C2–8224; CER-2020C1–19193), National Institutes of Arthritis, Musculoskeletal and Skin Diseases (NIAMS; P50 AR060772), and the Department of Veterans Affairs (1 I01 RX002745; I01 BX005996–01A2). JAS is also supported by the resources and the use of facilities at the VA Medical Center at Birmingham, Alabama, USA.
Footnotes
Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the Department of Veterans Affairs or the United States government.
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