Modeling patients’ time, travel, and monitoring costs in anticoagulation management: societal savings achievable with the shift from warfarin to direct oral anticoagulants

Abstract

BackgroundAnticoagulation therapy is used for atrial fibrillation (AF) patients for reducing the risk of cardioembolic complications such as stroke. The previously recommended anticoagulant, warfarin, has a narrow therapeutic window, and it requires regular laboratory monitoring, unlike direct oral anticoagulants (DOAC). From a societal perspective, it is important to measure time and travel costs associated with warfarin monitoring to better compare the total therapy costs of these two alternative forms of anticoagulation management. In this study we design a georeferenced cost model to investigate societal savings achievable with the shift from warfarin to DOACs in the study region of North Karelia in Eastern Finland.MethodsIndividual-level patient data of 6519 AF patients was obtained from the regional patient database. Patients’ geocoded home addresses and other GIS data were used to perform a network analysis for the optimal routes for warfarin monitoring visits. These measures of revealed accessibility were then used in the cost model to measure monetary time and travel costs in addition to direct healthcare costs of anticoagulation management.ResultsThe share of time and travel costs in warfarin monitoring is 26.6% of the total therapy costs in our study region. With current drug retail prices in Finland, the societal expense of anticoagulation management is only 2.6% higher with DOACs than in the baseline with warfarin. However, when 25% lower distributor’s prices are used, the total societal cost decreases by 13.6% with DOACs.ConclusionsOur results indicate that patients’ time and travel costs critically increase the societal cost of warfarin therapy; and despite the higher price of DOACs, they are already cost-efficient alternatives to warfarin in anticoagulation management. In the future, the cost of AF complications should be included in the cost comparison between warfarin and DOACs. Our modeling approach applies to different geographical regions and to different healthcare processes requiring patient monitoring.

Cost-Effectiveness of Apixaban versus Other Direct Oral Anticoagulants and Warfarin in the Prevention of Thromboembolic Complications Among Finnish Patients with Non-Valvular Atrial Fibrillation

“…However, the total costs associated with warfarin treatment may have been underestimated due to the exclusion of time and travelling costs which have been estimated to form 26.6% of total therapy costs for warfarin in Finland. 47 …”

“…Current evidence suggests that there may be inequalities in access to DOACs as DOAC-treated patients tend to have higher education and income levels. 45,46 In our NAXOS-study based analysis, the relative treatment effects were obtained from analyses where apixaban was separately compared to each of the comparators using propensity score matching. 7 Because of this, the current analysis should not be interpreted to provide evidence of the relative cost-effectiveness between the other three anticoagulants.…”

Patient-specific and healthcare real-world costs of atrial fibrillation in individuals treated with direct oral anticoagulant agents or warfarin

“…Studies have shown that multiple factors influence the choice of travel mode, such as age, gender, income, distance, physical functioning, weather, car ownership and place of residence [ 38 , 39 ]. Due to a lack of travel mode information by EHRs on non-reimbursed journeys, we applied prior study classifications to set the travel modes for journeys [ 31 , 40 ]. The selection criteria are shown in Table 1 .…”

“…Indirect costs have often been ignored or generalised based on economic evaluations, although the cost of INR monitoring represents a large proportion of the annual cost of warfarin therapy. A recent study has estimated that almost 25% of warfarin therapy costs consist of indirect costs when patients need frequent monitoring at laboratory units [ 31 ].…”

Fast travel-distance estimation using overhead graph

“…The selected data were originally used for other research purposes and includes mainly patients with alcohol-use disorder (Rautiainen, Ryynänen, and Laatikainen 2018), type 2 diabetes (Toivakka et al 2018), coronary heart disease (Repo et al 2018) and anticoagulation (Leminen et al 2019). The data is therefore not an exact representation of the entire population, but its geographic distribution is rather similar, and it is useful for our study.…”