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Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. REFERENCES
  9. Supporting Information

Objective

Disease-modifying antirheumatic drugs (DMARDs) are the standard of care for rheumatoid arthritis (RA); however, studies have found that many patients do not receive them. We examined predictors of starting and stopping DMARDs among a longitudinal cohort of patients with RA.

Methods

Study participants came from a cohort of RA patients recruited from a random sample of rheumatologists' practices in Northern California. We examined patterns and predictors of stopping and starting nonbiologic and biologic DMARDs during 1982–2009 based on annual questionnaires. Stopping was defined as stopping all DMARDs and starting was defined as transitioning from no DMARDs to any DMARDs across 2 consecutive years.

Results

The analysis of starting DMARDs included 471 subjects with 1,974 pairs of years with no DMARD use in the first of 2 consecutive years. From this population, subjects started DMARD use by year 2 in 313 (15.9%) of the pairs. The analysis of stopping DMARDs included 1,026 subjects with 7,595 pairs of years with DMARD use in the first of 2 consecutive years; in 423 pairs (5.6%), subjects stopped DMARD use by year 2. In models that adjusted for RA-related factors, sociodemographics, and comorbidities, significant predictors of starting DMARDs included younger age, Hispanic ethnicity, shorter disease duration, and the use of oral glucocorticoids. In separate adjusted models, predictors of stopping DMARDs included Hispanic ethnicity and low income, while younger age was associated with a reduced risk of stopping.

Conclusion

Efforts to improve DMARD use should focus on patient age, ethnicity, and income and RA-related factors.


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. REFERENCES
  9. Supporting Information

Since 2002, the American College of Rheumatology has recommended the use of disease-modifying antirheumatic drugs (DMARDs) for all patients with rheumatoid arthritis (RA) ([1]). However, many RA patients appear not to be using DMARDs ([2-4]). Some of this variation is likely due to whether RA is defined using standard classification criteria versus other methods. Many prior studies investigating predictors of DMARD use have relied on insurance databases that use diagnostic codes ([2, 3, 5, 6]) or population-based surveys ([4]). The positive predictive value associated with defining an RA cohort solely on diagnosis codes is lower than with definitions that include use of DMARDs ([7]), but including DMARD dispensing in the cohort definition will bias toward oversampling subjects more likely to be current DMARD users. Therefore, for the purposes of studying predictors of DMARD use, there is value to studying a cohort of patients with definite RA as diagnosed by a rheumatologist in which some patients consistently use DMARDs and others do not.

Prior studies have found that DMARD use is associated with RA-related and -unrelated factors. Older age consistently has been related to reduced DMARD use ([2-4]). An increased number of comorbidities and lower annual incomes also have been associated with reduced DMARD use ([2, 4]). We recently published data from the National Ambulatory Medical Care Survey showing that African American race was associated with a reduced probability of receipt of DMARDs ([4]). The strongest and most consistent correlate of DMARD use is a rheumatology visit ([2, 4, 8]).

The University of California, San Francisco (UCSF) RA Panel is a longitudinal cohort of patients with RA recruited from rheumatologists' offices in Northern California ([9]). Participants were surveyed annually since 1982, with additional recruitment during the last 30 years. Because of the long-term nature of this cohort diagnosed with RA by rheumatologists, the panel provides a distinctive opportunity to study DMARD use patterns and predictors in a sample with more clinically rich data than what might be found in an insurance claims cohort or a national survey. Specifically, we focused on patterns of DMARD stopping and starting, examining 3 types of predictors: RA related, sociodemographic, and comorbid conditions. We hypothesized that RA-related as well as other types of predictors would each predict use of DMARDs.

Box 1. Significance & Innovations

  • Patients with rheumatoid arthritis (RA) frequently start and stop disease-modifying antirheumatic drugs (DMARDs).
  • DMARD stopping has become less common over the period 1985–2005.
  • DMARD starting and stopping is related to specific patient characteristics, some disease related, but others not.
  • These results will inform current efforts to improve the consistency of DMARD use among patients with RA.

MATERIALS AND METHODS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. REFERENCES
  9. Supporting Information

Study design and cohort

This study is based on data (1982–2009) from the UCSF RA Panel study, which includes 1,507 people with RA from the practices of 50 randomly sampled rheumatologists in Northern California. The initial panel included 882 subjects enrolled between 1982 and 1983; further recruitment occurred in 1989, 1995, 1999, and 2003. Practices represented community-based rheumatologists, some of whom were in solo or group fee for service, while others were in health maintenance organizations. Other details about the structure of the panel and the validity of its measures are summarized elsewhere ([9-11]). Patients provided written informed consent at the time of entering the panel.

The principal data collection method for the RA Panel study is an annual, structured telephone interview conducted by a trained survey worker. These surveys collect basic demographic information, signs and symptoms of RA, number of comorbid conditions, RA treatment, physical and psychological health status, functional status, health care utilization information, and characteristics of health insurance plans. The RA treatment question (the outcome of interest for the current analyses) asked patients to answer the following item for each RA treatment separately: “Have you taken [DMARD] for at least 1 month during the past year?” Answering yes to any of the DMARDs qualified a subject as using any DMARD. Subjects answering no to all of the DMARD use questions were classified as nonusers.

To assess the predictors of starting or stopping a DMARD, we created 2 separate cohorts of eligible subjects from the UCSF RA Panel study. For the analysis of DMARD starters, subjects were identified who were not using any DMARD at a given annual questionnaire. Similarly, for the analysis of DMARD stoppers, subjects were identified who reported using at least 1 DMARD during a given annual questionnaire. Therefore, the same subject could contribute more than 1 observation; he or she could contribute to both the DMARD starter and stopper analyses during different years of followup.

We examined pairs of years, assessing predictors in the first of the 2-year pair and DMARD outcomes in the second year (Figure 1); DMARD starting was examined among patients taking no DMARDs in the first year and DMARD stopping was examined among those taking at least 1 DMARD in the first year. We required subjects to have data for both years in a given pair; therefore, missing data issues were avoided. The study was approved by the UCSF and Brigham and Women's Hospital Institutional Review Boards.

image

Figure 1. The study design in which we examined pairs of years during the several decades of followup is shown. Therefore, a given patient could be included as multiple observations in either the starting DMARD (A) or stopping DMARD analysis (B). All predictor variables were examined in year 1 and outcomes were assessed in year 2. DMARD = disease-modifying antirheumatic drug.

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DMARD use outcomes

DMARD stopping was only assessed among subjects who reported use of a DMARD in the first of 2 consecutive years. The goal of these analyses was examining patterns and predictors of starting and/or stopping DMARDs, not switching DMARDs. A subject qualified as stopping DMARDs if all such agents were discontinued, not if he or she stopped 1 of several DMARDs being used. Therefore, stopping was determined based on subjects being users of at least 1 DMARD during year 1 and users of no DMARDs in year 2. In a parallel fashion, a subject must have been using no DMARDs in the first of the pair of years to be assessed for the DMARD starting analysis. We then examined if any DMARD was begun in the second of 2 years.

This study covered several decades and many possible DMARDs, including the following nonbiologic DMARDs: azathioprine, cyclophosphamide, cyclosporine, D-penicillamine, oral or injectable gold compounds, hydroxychloroquine, leflunomide, methotrexate, and sulfasalazine. For biologic DMARDs, we assessed use of abatacept, adalimumab, anakinra, etanercept, infliximab, rituximab, and tocilizumab; however, no participants reported use of rituximab or tocilizumab.

Predictors of starting and stopping

We examined potential predictors of stopping and/or starting DMARDs during the first of the 2 years. One focus of this research was to assess the contribution of different types of variables. The variable types chosen follow a commonly used model of health care services utilization proposed by Aday and Andersen that considers enabling, predisposing, and patient need ([12]). Therefore, predictors were categorized into several groups: sociodemographic variables (age, sex, race, ethnicity, health insurance status and type, educational attainment, annual income, and marital status), RA-related variables (disease duration, Health Assessment Questionnaire [HAQ] score [13], self-assessed number of tender joints, self-assessed number of swollen joints, and use of oral glucocorticoids), and comorbid conditions (a current comorbidity count, a depression scale score [14], the use of non-DMARD medications, and acute care hospitalizations). Finally, visits to a rheumatologist were considered as a covariate. This variable was not included in any of the above categories. We grouped the number of rheumatology visits over the prior year into none, 1–6 visits, and >6 visits based on the distribution of the responses.

Statistical analyses

After forming the starter and stopper cohorts, we defined the characteristics of subjects in each group. These were defined for each potential starting and stopping period (allowing one subject to contribute to multiple periods), as well as by subject. Trends in starting and stopping DMARDs were described over the study period, calculating percentages on an annual basis and identifying which DMARDs were started and stopped. We examined predictors of starting and stopping DMARDs separately in generalized linear mixed models that account for the nonindependence of records from multiple pairs of years for an individual subject; the covariance matrix utilized a compound symmetry correlation structure according to Akaike's information criterion and the Bayesian information criterion. Models were fit for each category of potential predictors (sociodemographic, RA related, and comorbidities) separately and then including all potential predictors in fully adjusted models. We assessed the model fit using the C statistic calculated in logistic regression models. The C statistics were reported across the categories of potential predictors: sociodemographics, RA related, and comorbidities.

The above models were fit without inclusion of rheumatology visits as a potential predictor. Since this variable has been a strong and consistent predictor of DMARD use patterns ([2, 4]), we examined other types of variables in the primary analysis. In a secondary analysis, rheumatology visits were added to the final multivariable models. All analyses were conducted using SAS statistical software, version 9.2.

RESULTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. REFERENCES
  9. Supporting Information

From the UCSF RA Panel, we identified 1,687 subjects with 1,974 eligible periods without any DMARD use in the first year; these periods were examined as the potential DMARD starters. Similarly, we identified 1,874 subjects with 7,595 eligible periods with DMARD use in the first year; these periods were examined as the potential DMARD stoppers.

The characteristics of the UCSF RA Panel participants at each of the starting and stopping periods are shown in Table 1. (Baseline characteristics of participants without including every period, but only the first one, are shown in Supplementary Table 1, available in the online version of this article at http://onlinelibrary.wiley.com/doi/10.1002/acr.22286/abstract.) The mean age of the panel at the time of the starting and stopping periods was 60 years, with 81% women. The majority of subjects were non-Hispanic white and most had greater than a high school education. Few subjects lacked insurance and most were in fee-for-service insurance programs. The mean RA disease duration was 18 years and the mean HAQ score was 1.1. Approximately half of the subjects had no comorbid conditions reported in the current questionnaire. Approximately half of all subjects reported using oral glucocorticoids.

Table 1. Baseline characteristics of the total University of California, San Francisco RA Panel and the stopping and starting periods*
 Total periods (n = 9,569)Stopper periods (n = 7,595)Starter periods (n = 1,974)
  1. Values are the number (percentage) unless indicated otherwise. The same subject can contribute to both cohorts and multiple times based on how many eligible stopping and starting periods he or she experienced. RA = rheumatoid arthritis; HMO = health maintenance organization; PPO = preferred provider organization; HAQ = Health Assessment Questionnaire; GDS = Geriatric Depression Scale; DMARD = disease-modifying antirheumatic drug.

Sociodemographics   
Age, mean ± SD years60 ± 1360 ± 1363 ± 14
Women7,706 (81)6,156 (81)1,550 (79)
Ethnicity   
Hispanic821 (9)661 (9)160 (8)
African American253 (3)147 (2)106 (5)
Asian and Pacific Islander485 (5)363 (5)122 (6)
Other181 (2)150 (2)31 (2)
Non-Hispanic white7,829 (82)6,274 (83)1,555 (79)
Marital status   
Unmarried/divorced3,368 (35)2,571 (34)797 (40)
Married/partner6,201 (65)5,024 (66)1,177 (60)
Educational attainment   
Less than high school1,158 (12)835 (11)323 (16)
High school degree3,009 (31)2,397 (32)612 (31)
Beyond high school5,402 (56)4,363 (57)1,039 (53)
Annual household income   
Lowest category1,164 (12)826 (11)338 (17)
Low middle category2,642 (28)2,027 (27)615 (31)
High middle category4,482 (47)3,688 (49)794 (40)
Highest category1,281 (13)1,054 (14)227 (12)
Insurance status   
Primary health insurance   
Private/Medicaid/Medicare9,426 (99)7,482 (99)1,944 (98)
No insurance143 (1)113 (1)30 (2)
Managed care   
Fee for service4,031 (42)3,083 (41)948 (48)
HMO/PPO5,538 (58)4,512 (59)1,026 (52)
First year of observation, median (range)1988 (1986–2006)1989 (1986–2007)1993 (1986–2008)
Paired years of observation after 19993,964 (41)3,405 (45)559 (28)
RA-related factors, mean ± SD   
Disease duration, years18 ± 1118 ± 1120 ± 11
HAQ score1.1 ± 0.71.1 ± 0.71.2 ± 0.8
Number of painful joints6.1 ± 4.56.0 ± 4.56.3 ± 4.8
Number of swollen joints3.6 ± 3.23.6 ± 3.23.6 ± 3.3
Comorbidities   
GDS score >71,045 (11)788 (10)257 (13)
No. of comorbidities   
05,277 (55)4,190 (55)1,087 (55)
12,937 (31)2,370 (31)567 (29)
≥21,355 (14)1,035 (14)320 (16)
Oral glucocorticoid use4,892 (51)4,044 (53)848 (43)
Use of non-DMARD medications for RA4,483 (47)3,923 (52)560 (28)
Hospitalized in prior 12 months2,245 (23)1,709 (23)536 (27)

The analysis of starting DMARDs included 471 subjects with 1,974 pairs of years with no DMARD use in the first of 2 consecutive years. From this population, subjects started DMARD use by year 2 in 313 (15.9%) of the pairs. The percentage of potential DMARD starters who started these agents varied across years, with no significant temporal trend (P = 0.50 for trend) (Figure 2). The analysis of stopping DMARDs included 1,026 subjects with 7,595 pairs of years with DMARD use in the first of 2 consecutive years; in 423 pairs (5.6%), subjects stopped DMARD use by year 2. The percentage of potential DMARD stoppers who did stop decreased steadily from 9% to 3% (P < 0.001 for trend). Drugs that were started and stopped reflect typical DMARDs in use over the study period. The most commonly started and stopped DMARDs were methotrexate (28% of all started medications and 20% of all those stopped) and hydroxychloroquine (18% of all both started and stopped medications) (see Supplementary Table 2, available in the online version of this article at http://onlinelibrary.wiley.com/doi/10.1002/acr.22286/abstract).

image

Figure 2. The percentage of subjects starting and stopping disease-modifying antirheumatic drugs (DMARDs) in a given year during followup is shown. Below the figure, the potential starters and stoppers (denominators) for given selected years are indicated.

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We examined predictors of starting and stopping DMARDs in separate adjusted regression models (Table 2). The fully adjusted model demonstrated the following significant predictors of starting DMARDs: younger age (odds ratio [OR] 1.30, 95% confidence interval [95% CI] 1.13–1.50 per decade decrease), Hispanic ethnicity (OR 1.88, 95% CI 1.06–3.33), shorter disease duration (OR 1.11, 95% CI 1.01–1.22 per 5-year decrease), and the use of oral glucocorticoids (OR 1.91, 95% CI 1.36–2.67). In a separate fully adjusted model, significant predictors of stopping DMARDs included younger age (OR 0.88, 95% CI 0.80–0.98 per decade decrease), Hispanic ethnicity (OR 1.52, 95% CI 1.02–2.30), and lowest annual income compared with the highest (OR 1.83, 95% CI 1.13–2.96).

Table 2. Fully adjusted generalized linear mixed regression models fit for starting and stopping DMARDs*
 Models without adjustment for rheumatology visitModel adjusted for rheumatology visit
Starting DMARDsStopping DMARDsStarting DMARDsStopping DMARDs
  1. Values are the adjusted odds ratio (95% confidence interval). All models also include year of observation. DMARD = disease-modifying antirheumatic drug; HAQ = Health Assessment Questionnaire; TJC = tender joint count; SJC = swollen joint count; GDS = Geriatric Depression Scale.

  2. a

    Statistically significant.

  3. b

    Includes Pacific Islanders.

Sociodemographics    
Age, per decade decrease1.30 (1.13–1.50)a0.88 (0.80–0.98)a1.28 (1.12–1.48)a0.89 (0.80–0.99)a
Female sex1.37 (0.86–2.17)0.99 (0.72–1.35)1.35 (0.85–2.13)0.99 (0.72–1.35)
Race/ethnicity    
Hispanic1.88 (1.06–3.33)a1.52 (1.02–2.30)a1.65 (0.93–2.92)1.59 (1.06–2.38)a
African American, non-Hispanic0.42 (0.17–1.04)1.39 (0.66–2.95)0.40 (0.16–0.99)a1.32 (0.62–2.82)
Asian, non-Hispanicb0.71 (0.35–1.45)1.22 (0.69–2.95)0.69 (0.34–1.39)1.14 (0.65–2.01)
Other, non-Hispanic1.31 (0.33–5.29)0.56 (0.18–1.72)1.23 (0.31–4.85)0.57 (0.19–1.74)
Non-Hispanic white1.001.001.001.00
Educational attainment    
Less than high school0.85 (0.50–1.45)0.94 (0.64–1.40)0.89 (0.52–1.51)0.91 (0.62–1.37)
High school only0.90 (0.60–1.34)1.03 (0.78–1.36)0.90 (0.61–1.34)1.04 (0.79–1.37)
Beyond high school1.001.001.001.00
Annual household income    
Quartile 1 (lowest)0.80 (0.45–1.44)1.83 (1.13–2.96)a0.83 (0.46–1.49)1.61 (1.04–2.49)a
Quartile 20.80 (0.51–1.26)1.23 (0.81–1.85)0.83 (0.53–1.30)1.02 (0.75–1.45)
Quartile 30.67 (0.44–1.01)1.23 (0.87–1.75)0.69 (0.45–1.03)1.06 (0.78–1.42)
Quartile 41.001.001.001.00
Health insurance, none0.39 (0.10–1.50)1.22 (0.59–2.51)0.38 (0.10–1.45)1.25 (0.61–2.58)
Married (versus other)1.10 (0.76–1.59)0.99 (0.75–1.30)1.08 (0.75–1.57)0.99 (0.75–1.30)
Rheumatoid arthritis    
Disease duration, per 5-year decrease1.11 (1.01–1.22)a0.96 (0.91–1.02)1.11 (1.01–1.21)a0.97 (0.91–1.02)
HAQ, per unit increase1.17 (0.89–1.53)0.98 (0.79–1.21)1.12 (0.86–1.48)1.01 (0.82–1.25)
TJC, per increase1.03 (0.98–1.09)1.03 (1.00–1.07)1.03 (0.98–1.08)1.03 (0.99–1.07)
SJC, per increase1.06 (0.99–1.13)1.01 (0.97–1.06)1.06 (0.99–1.13)1.01 (0.97–1.06)
Use of oral steroids1.91 (1.36–2.67)a1.18 (0.93–1.49)1.66 (1.18–2.35)a1.24 (0.93–1.57)
Comorbidities    
GDS score >71.00 (0.64–1.56)1.19 (0.85–1.65)1.01 (0.65–1.58)1.18 (0.84–1.65)
No. of comorbidities    
01.21 (0.76–1.91)0.96 (0.91–1.02)1.22 (0.77–1.92)0.95 (0.67–1.34)
11.07 (0.68–1.70)0.91 (0.65–1.29)1.06 (0.67–1.68)0.93 (0.66–1.31)
≥21.001.001.001.00
Hospitalized in the past year0.87 (0.63–1.20)1.24 (0.98–1.57)0.84 (0.61–1.16)1.24 (0.98–1.58)
Rheumatology visit   
None1.001.00
1–61.70 (1.16–2.48)a0.46 (0.30–0.71)a
>62.15 (1.29–3.57)a0.34 (0.22–0.52)a

We examined different domains of potential predictors, including RA-related factors, socioeconomic factors, and comorbidities (see Supplementary Tables 3 and 4, available in the online version of this article at http://onlinelibrary.wiley.com/doi/10.1002/acr.22286/abstract). The C statistics for RA-related factors were 0.62 for starting a DMARD and 0.60 for stopping a DMARD. Including sociodemographic factors and comorbidities in the fully adjusted models improved the model fit for both sets of models, with C statistics of 0.69 for starting a DMARD and 0.68 for stopping a DMARD.

After including rheumatology visits in the prior year as a covariate in the full multivariable models, the predictors and their ORs changed slightly (Table 2). Several variables were associated with an increased probability of starting DMARDs: a greater number of rheumatology visits (OR 2.15, 95% CI 1.29–3.57 for >6 visits compared with 0 visits), younger age (OR 1.28, 95% CI 1.12–1.48 per decade), African American race (OR 0.40, 95% CI 0.16–0.99), and the use of oral glucocorticoids (OR 1.66, 95% CI 1.18–2.35). Predictors of stopping DMARDs in multivariable models included fewer rheumatology visits (OR 0.34, 95% CI 0.22–0.52 for 0 visits compared with >6 visits), older age (OR 0.89, 95% CI 0.80–0.99 per decade), Hispanic ethnicity (OR 1.59, 95% CI 1.06–2.38), and lowest annual income compared with the highest (OR 1.61, 95% CI 1.04–2.49).

DISCUSSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. REFERENCES
  9. Supporting Information

While clinical guidelines strongly support the use of DMARDs for all patients with RA, many studies have noted that their use is not ubiquitous ([2-6]). Some studies reporting low rates may include patients who have not been diagnosed with RA by a rheumatologist. We examined DMARD use patterns in a large, well-established longitudinal cohort from Northern California, the UCSF RA Panel, who all received an RA diagnosis by a rheumatologist. The focus of this study was assessing patterns and predictors of starting and stopping DMARDs, examining pairs of years to determine if patient factors in the first year predicted the starting or stopping of these agents. Among patients not using DMARDs in a given year, approximately 15% started them in the next year; this rate varied during the followup period, but without a consistent trend. Year-to-year stopping of all DMARDs dropped significantly during the study period, from 9% to 3%. RA-related variables as well as sociodemographic variables were significant predictors of starting and stopping DMARDs.

A surprising aspect of our analysis was that some of the same variables, i.e., Hispanic ethnicity, use of oral glucocorticoids, and more tender joints (trend but not statistically significant), predicted both starting and stopping DMARDs. These factors may correlate with frequent treatment switches. It may also be the case that use of oral glucocorticoids and more tender joints may motivate some patients not using DMARDs to start them, with oral glucocorticoids used as a “bridge” therapy in such patients. Conversely, patients using DMARDs who also require oral glucocorticoids and report more tender joints may decide to discontinue DMARDs if they perceive inadequate therapeutic benefits. The association of Hispanic ethnicity with higher rates of starting and stopping DMARDs may relate to differences in the availability of new therapies ([15]) coupled with less stable insurance coverage for medications ([16]) or limited English proficiency that may impair communication with physicians about medication side effects ([17]). The higher rate of stopping DMARDs among lower-income patients suggests that drug or visit costs may contribute to socioeconomic disparities in DMARD use despite clinical guidelines supporting their use.

The trends in starting and stopping DMARDs over the 23 years of the study are noteworthy. The gradual reduction in subjects stopping DMARDs may correlate with the increasing range of treatment options since the latter half of the 1990s. It may also be that rheumatologists have become less concerned about slightly abnormal laboratory results that occur occasionally by chance, for example, liver function tests among methotrexate users. In contrast, we did not observe strong trends in DMARD starting. The apparent swings in the starting DMARD pattern observed around 1998–1999 and 2002–2003 (Figure 2) may be accounted for by the periodic launches of tumor necrosis factor antagonists during the study period: etanercept and infliximab in late 1998–1999 and adalimumab in 2002. Finally, visits to rheumatologists was the strongest predictor of DMARD starting and stopping, consistent with prior literature ([2, 4]).

It is notable that the models' discrimination (C statistic) was relatively poor. Even the fully adjusted models, with all types of variables, had C statistics of only 0.69 (starting) and 0.68 (stopping). We suspect that unmeasured factors, including patients' personal preferences, real or perceived adverse events, and informational needs, likely contribute to starting and stopping DMARDs ([18]). We did find that non–RA-related factors were as important as RA-related factors, consistent with prior literature suggesting that age, income, race, and ethnicity all correlate with DMARD use ([2-5]).

This study has important strengths. The subjects were all diagnosed with RA by rheumatologists. The UCSF RA Panel study collected a robust set of potential predictors of DMARD use, and they have been collected in a consistent manner annually over many years. Study limitations include the lack of a standardized set of classification criteria to diagnose RA. However, all patients had been diagnosed with RA by a rheumatologist. All subjects were recruited from a geographically concentrated area, thereby limiting the generalizability of the sample. The medication questionnaire used has not been previously validated in patients with RA, and it is possible that the annual self-report of medications might be inaccurate. This should not introduce systematic bias. In addition, we did not include the medication history as a potential predictor. Virtually all patients used a DMARD at some point and few patients had incident RA and were new DMARD starters, so this addition of such a variable would not influence the predictive models. It is possible that a given rheumatologist might influence starting or stopping DMARDs. Many patients changed rheumatologists throughout the course of the study period, so this could not be accounted for. In addition, the joint counts were self-reported by subjects, but prior research has validated such methods ([19]).

In conclusion, rates of stopping all DMARDs from one year to the next have decreased in frequency among patients with RA since 1982. Predictors of starting and stopping DMARDs include RA-related and sociodemographic variables, such as age, income, and ethnicity. While there are data regarding correlates of DMARD use in the literature, there are no published data that we could identify that describe predictors of starting or stopping DMARDs. Our results inform current efforts to improve the consistency of DMARD use among patients with RA. These findings suggest that efforts to improve DMARD use will likely require improved economic access to rheumatologic care and DMARDs, 2 potential sources of disparities that may be barriers to consistent DMARD use. As health systems currently evolve with Medicaid expansions and a greater emphasis on primary care, reducing these barriers to appropriate care for patients with rheumatic disease will be an important goal.

AUTHOR CONTRIBUTIONS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. REFERENCES
  9. Supporting Information

All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be published. Dr. Solomon had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study conception and design. Solomon, Tonner, Kim, Ayanian, Brookhart, Yelin.

Acquisition of data. Solomon, Yelin.

Analysis and interpretation of data. Solomon, Tonner, Lu, Kim, Ayanian, Brookhart, Katz, Yelin.

REFERENCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. REFERENCES
  9. Supporting Information

Supporting Information

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. REFERENCES
  9. Supporting Information

Additional Supporting Information may be found in the online version of this article.

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ACR_22286_sm_SupplTables.docx58KSupplementary Tables

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