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Does prescribing apixaban or rivaroxaban versus warfarin for patients diagnosed with atrial fibrillation save health system costs? A multivalued treatment effects analysis

Author

Listed:
  • Michael Situ

    (Western University)

  • Ute I. Schwarz

    (Western University
    Western University)

  • Guangyong Zou

    (Western University
    Alimentiv Inc)

  • Eric McArthur

    (ICES (Formerly the Institute for Clinical Evaluative Sciences))

  • Richard B. Kim

    (Western University
    Western University
    Lawson Health Research Institute
    Western University)

  • Amit X. Garg

    (ICES (Formerly the Institute for Clinical Evaluative Sciences)
    Lawson Health Research Institute
    Western University)

  • Sisira Sarma

    (Western University
    ICES (Formerly the Institute for Clinical Evaluative Sciences)
    Lawson Health Research Institute)

Abstract

Background Non-valvular atrial fibrillation (AF) is a common heart arrhythmia in the elderly population. AF patients are at high-risk of ischemic strokes, but oral anticoagulant (OAC) therapy reduces such risks. Warfarin had been the standard OAC for AF patients, however its effectiveness is highly variable and dependent on close monitoring of the anticoagulant response. Newer OACs such as rivaroxaban and apixaban address these drawbacks but are more costly. It is uncertain which OAC therapy for AF is cost-saving from the healthcare system perspective. Methods We followed a cohort of patients in Ontario, Canada, aged ≥ 66 who were newly diagnosed with AF and prescribed OACs between 2012 and 2017. We used a two-stage estimation procedure. First, we account for the patient selection into OACs using a multinomial logit regression model and estimated propensity scores. Second, we used an inverse probability weighted regression adjustment approach to determine cost-saving OAC options. We also examined component-specific costs (i.e., drug, hospitalization, emergency department and physician) to understand the drivers of cost-saving OACs. Results We found that compared to warfarin, rivaroxaban and apixaban treatments were cost-saving options, with per-patient 1-year healthcare cost savings at $2436 and $1764, respectively. These savings were driven by cost-savings in hospitalization, emergency department visits, and physician visits, outweighing higher drug costs. These results were robust to alternative model specifications and estimation procedures. Conclusions Treating AF patients with rivaroxaban and apixaban than warfarin reduces healthcare costs. OAC reimbursement policies for AF patients should consider rivaroxaban or apixaban over warfarin as the first-line treatment.

Suggested Citation

  • Michael Situ & Ute I. Schwarz & Guangyong Zou & Eric McArthur & Richard B. Kim & Amit X. Garg & Sisira Sarma, 2024. "Does prescribing apixaban or rivaroxaban versus warfarin for patients diagnosed with atrial fibrillation save health system costs? A multivalued treatment effects analysis," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 25(3), pages 397-409, April.
  • Handle: RePEc:spr:eujhec:v:25:y:2024:i:3:d:10.1007_s10198-023-01594-7
    DOI: 10.1007/s10198-023-01594-7
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    More about this item

    Keywords

    Atrial fibrillation; Anticoagulant treatments; Health care costs; Multivalued treatment effect; Canada;
    All these keywords.

    JEL classification:

    • I10 - Health, Education, and Welfare - - Health - - - General
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • I19 - Health, Education, and Welfare - - Health - - - Other
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models

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