Evidence map›Paper›PMID 29720237›Full record

ArticleArthritis research & therapy2018

Factors associated with physicians' prescriptions for rheumatoid arthritis drugs not filled by patients.

Hong J Kan, Kirill Dyagilev, Peter Schulam, Suchi Saria, Hadi Kharrazi, David Bodycombe, Charles T Molta, Jeffrey R Curtis

Abstract read
In one paragraph

Article in Arthritis research & therapy, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Health Disparities in Rheumatology in the United States.Open access rheumatology : research and reviews · 2025
    Review
  4. Article
  5. Article
  6. Not obtaining a medication the first time it is prescribed: primary non-adherence to cardiovascular pharmacotherapy.Clinical research in cardiology : official journal of the German Cardiac Society · 2024
    Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Hong J KanCenter for Population Health IT, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Hampton House HH502, 624 N. Broadway, Baltimore, MD, 21205, USA. hkan1@jhu.edu.
Kirill Dyagilev, Cortica US, 425 Broadway, New York, NY, 10013, USA.
Peter SchulamComputer Science Department, Johns Hopkins University, 3400 N Charles Sreett, Baltimore, MD, 21218, USA.
Suchi SariaComputer Science Department, Johns Hopkins University, 3400 N Charles Sreett, Baltimore, MD, 21218, USA.
Hadi KharraziCenter for Population Health IT, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Hampton House HH502, 624 N. Broadway, Baltimore, MD, 21205, USA.
David BodycombeCenter for Population Health IT, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Hampton House HH502, 624 N. Broadway, Baltimore, MD, 21205, USA.
Charles T MoltaMain Line Rheumatology, Lankenau Medical Center, 100 Lancaster Avenue, Wynnewood, PA, 19096, USA.
Jeffrey R CurtisDivision of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, 510 20th Street South, Birmingham, AL, 35294, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThis study estimated the extent and predictors of primary nonadherence (i.e., prescriptions made by physicians but not initiated by patients) to methotrexate and to biologics or tofacitinib in rheumatoid arthritis (RA) patients who were newly prescribed these medications.

methodsUsing administrative claims linked with electronic health records (EHRs) from multiple healthcare provider organizations in the USA, RA patients who received a new prescription for methotrexate or biologics/tofacitinib were identified from EHRs. Claims data were used to ascertain filling or administration status. A logistic regression model for predicting primary nonadherence was developed and tested in training and test samples. Predictors were selected based on clinical judgment and LASSO logistic regression.

resultsA total of 36.8% of patients newly prescribed methotrexate failed to initiate methotrexate within 2 months; 40.6% of patients newly prescribed biologics/tofacitinib failed to initiate within 3 months. Factors associated with methotrexate primary nonadherence included age, race, region, body mass index, count of active drug ingredients, and certain previously diagnosed and treated conditions at baseline. Factors associated with biologics/tofacitinib primary nonadherence included age, insurance, and certain previously treated conditions at baseline. The area under the receiver operating characteristic curve of the logistic regression model estimated in the training sample and applied to the independent test sample was 0.86 and 0.78 for predicting primary nonadherence to methotrexate and to biologics/tofacitinib, respectively.

conclusionsThis study confirmed that failure to initiate new prescriptions for methotrexate and biologics/tofacitinib was common in RA patients. It is feasible to predict patients at high risk of primary nonadherence to methotrexate and to biologics/tofacitinib and to target such patients for early interventions to promote adherence.

Indexed as

PhysiciansAdultAgedAged, 80 and overAntirheumatic AgentsArthritis, RheumatoidFemaleHumansLogistic ModelsMaleMethotrexateMiddle AgedPatient CompliancePiperidinesPrescriptionsPyrimidinesAntirheumatic AgentsMethotrexatePiperidinesPyrimidinesPyrrolestofacitinibBiologicsDisease-modifying anti-rheumatic drugsMethotrexatePredictive modelingPrimary nonadherenceRheumatoid arthritis

Identifiers

PMID29720237
PMCPMC5932861

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.