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Abstract Number: 2032

Psychological Factors Predict Adherence to Methotrexate (MTX) in Rheumatoid Arthritis (RA); Findings from a Systematic Review of Rates, Predictors and Associations with Patient Outcomes

Holly Hope1, James Bluett1,2, Kimme Hyrich3, Lis Cordingley4 and Suzanne M. Verstappen3, 1NIHR Manchester Musculoskeletal Biomedical Research Unit, Central Manchester University Hospitals NHS Foundation Trust, Manchester, United Kingdom, 2Arthritis Research UK Centre for Genetics and Genomics, The University of Manchester, Manchester, United Kingdom, 3Arthritis Research UK Centre for Epidemiology, The University of Manchester, Manchester, United Kingdom, 4Centre for Musculoskeletal Research, Institute of Inflammation and Repair, The University of Manchester, Manchester, United Kingdom

Meeting: 2014 ACR/ARHP Annual Meeting

Keywords: Compliance, methotrexate (MTX) and rheumatoid arthritis (RA)

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Session Information

Title: Epidemiology and Public Health (ACR): Rheumatoid Arthritis Pathogenesis and Treatment

Session Type: Abstract Submissions (ACR)

Background/Purpose:  Methotrexate (MTX) is a first line therapy for Rheumatoid Arthritis (RA). Treatment response to MTX is not universal, and nonadherence may partially explain poor treatment response to MTX, therefore it is imperative to investigate adherence to MTX specifically.  Previous systematic reviews have evaluated adherence to all DMARDs. The aims of this systematic review were:  1) to summarise existing rates of adherence to MTX, 2) to identify predictors of adherence and 3) assess the association between non-adherence with patient outcomes.

Methods: A systematic search of papers published between January 1980 to 2014 was conducted in PubMed, PsycINFO, EMbase and CINAHL databases. Studies were eligible for inclusion if; 1) MTX was used as monotherapy or in combination with other therapies, 2) MTX was used in an RA or inflammatory polyarthritis population, 3) adherence was defined and measured as the extent to which patients followed their MTX regimen during the period of prescription, and 4) it was an original piece of research. Papers were reviewed by two researchers and consensus agreed with a third. A quality assessment (QA) tool formally assessed each included article.

Results:  In total, ten studies met the inclusion criteria and eight were evaluated high quality using the QA tool. Rates of adherence ranged widely from 59% to 107%, and exposed differences in definitions of adherence, study methodologies and sample heterogeneity. The methods used to assess adherence included; Medication Event Monitoring Systems (n=2), Pharmacy Refill (n=5), validated self-report questionnaire (n=2) and 7 day diary (n=1) (Table 1). Twenty-one potential predictors of MTX adherence were identified; the strongest evidence occurred for beliefs in the necessity and efficacy of MTX, absence of low mood, mild disease and MTX as a monotherapy. Disease activity was the only patient outcome where the effect of nonadherence was assessed. Three studies tested this association and each found nonadherence associated with poor treatment response.

Conclusion:  Psychological factors were the strongest predictors of adherence rates in this first systematic review specific to MTX. It was the first to include synthesis of evidence on patient outcomes and show nonadherence to MTX affects treatment response in RA.   

Table 1. Comparison of MTX rates of adherence across studies

Author

QA score

High/ low quality

Adherence method used

Definition of MTX adherence

n

MTX adherence

95% CI/SD

MEMS

Waimann et al. (2013) [19]

15

High

MEMS

%  of correctly taken doses

76

63%

20%

de Klerk et al. (2003) [22]

12

Low

MEMS

%  of correctly taken doses

23

107%

98-117

Pharmacy refill

Cannon et al.  (2010) [18]

15

High

MPR

=>80% prescriptions filled

85

80%

NP

15

High

MPR

=> 80% prescriptions filled

370

85%

NP

de Thurah et al. (2010) [20]

14

High

CMG

% of treatment gaps (reversed)

941

87.7%

86.8-88.5

Grijalva et al. (2010) [17]

9

High

MPR

%  of  prescription filled

NP

59%

31-82

Harley, Frytak & Tandon (2003) [16]

8

High

MPR

≥ 80% prescriptions filled

1668

63.70%

23.8 – 102

Grijalva et al. (2007) [15]

8

High

MPR

% of prescriptions filled excluding the last prescription

2933

80%

NP

Self report

de Thurah et al (2010)a [21]

14

High

CQ-R

CQ-R score >25th percentile

85

BL 23.5%

NP

65

9 mo. 23.1%

NP

Contreras-Yanezet et al. (2010) [23]

11

Low

7 day DRR

% of correct doses taken across 3 time points

10

78%

NP

Salt & Frazier (2011) [24]

9

Low

MARS

MARS score >39

77

92.10%

NP

(CQ-R = Compliance Questionnaire-Rheumatology, DRR = Drug Record Registry, MARS = Medication Adherence Revised Scale, MPR = Medication Possession Ratio, CMG = Continuous Medication Gap, NP= information not presented) 

Table 2. Associations with patient outcomes

Author

Sample size

Predictor

Outcome

Statistical test

Unadjusted Strength of effect (95% CI)

P

Adjusted Strength of effect (95% CI)

P

Cannon et al. (2011) [17]:

 

 

 

 

 

 

 

 

Full cohort

455 (71 non –adherent, 384 adherent)

MPR ≥ 80%

 

Mean difference in DAS28

Linear regression

-0.34 (-0.68, -0.06)

 

< 0.05

 

-0.37 (-0.67, -0.07)

 

< 0.05

 

First time user cohort

85 (17 non adherent 68 adherent)

MPR ≥ 80%

Mean difference in DAS28

Linear regression

-0.54 (-1.18, 0.11)

NS

-0.40( -1.11, 0.30)

NS 

Established cohort

370 (54 non adherent 316 adherent)

MPR ≥ 80%

Mean difference in DAS28

Linear regression

-0.38 (-0.67, -0.05)

< 0.05

-0.37 (-0.72, -0.02)

< 0.05

 

Waimann,C.A.   et al. (2013) [19]

102

% of prescribed DMARD doses takena

DAS28 at 2 years

Linear regression

-0.02 (-0.03, -0.001)

NP

-0.2

0.03

Contreras-Yanez,I. Et al. (2010) [23]

68 (54 maintained remission 14 disease flare)

MPR ≥ 80%b

Maintained remission (DAS28 < 2.4)

Logistic regression

NP

NP

NP

< 0.001

a includes sDMARDS and bDMARDS. b includes other sDMARDS. NP = not presented. RF = rheumatoid factor. CCP = cyclic citrullinated peptide


Disclosure:

H. Hope,
None;

J. Bluett,
None;

K. Hyrich,
None;

L. Cordingley,
None;

S. M. Verstappen,
None.

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