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Abstract Background Anticoagulant therapies are used to prevent atrial fibrillation-related strokes, with warfarin and direct oral anticoagulant (DOAC) the most common. In this study, we incorporate direct health care costs, drug costs, travel costs, and lost working and leisure time costs to estimate the total costs of the two therapies. Methods This retrospective study used individual-level patient data from 4000 atrial fibrillation (AF) patients from North Karelia, Finland. Real-world data on healthcare use was obtained from the regional patient information system and data on reimbursed travel costs from the database of the Social Insurance Institution of Finland. The costs of the therapies were estimated between June 2017 and May 2018. Using a Geographical Information System (GIS), we estimated travel time and costs for each journey related to anticoagulant therapies. We ultimately applied therapy and travel costs to a cost model to reflect real-world expenditures. Results The costs of anticoagulant therapies were calculated from the standpoint of patient and the healthcare service when considering all costs from AF-related healthcare visits, including major complications arising from atrial fibrillation. On average, the annual cost per patient for healthcare in the form of public expenditure was higher when using DOAC therapy than warfarin therapy (average cost = € 927 vs. € 805). Additionally, the average annual cost for patients was also higher with DOAC therapy (average cost = € 406.5 vs. € 296.7). In warfarin therapy, patients had considerable more travel and time costs due the different implementation practices of therapies. Conclusion The results indicated that DOAC therapy had higher costs over warfarin from the perspectives of the patient and healthcare service in the study area on average. Currently, the cost of the DOAC drug is the largest determinator of total therapy costs from both perspectives. Despite slightly higher costs, the patients on DOAC therapy experienced less AF-related complications during the study period.

Scholars are in agreement that the local food environment is shaped by a multitude of factors from socioeconomic characteristics to transportation options, as well as the availability and distance to various food establishments. Despite this, most place-based indicators of “food deserts”, including those identified as so by the US Department of Agriculture (USDA), only include a limited number of factors in their designation. In this article, we adopt a geodemographic approach to classifying the food access landscape that takes a multivariate approach to describing the food access landscape. Our method combines socioeconomic indicators, distance measurements to Supplemental Nutrition Assistance Program (SNAP) participating stores, and neighborhood walkability using a k-means clustering approach and North Carolina as a case study. We identified seven distinct food access types: three rural and four urban. These classes were subsequently prioritized based on their defining characteristics and specific policy recommendations were identified. Overall, compared to the USDA’s food desert calculation, our approach identified a broader swath of high-needs areas and highlights neighborhoods that may be overlooked for intervention when using simple distance-based methods.

This paper examines the impact of a new light rail system on single family housing values in Charlotte, North Carolina is evaluated from 1997 to 2008. We use a Hedonic Price Analysis (HPA) to estimate how proximity to light rail, housing characteristics and spatial components (at the neighborhood and block group level) affect single family housing values. The same method is applied to each of the four time periods (t1, t2, t3, t4) that coincide with the pre-planning, planning, construction and operation phase of the light rail system. We observe a trend suggesting a greater desirability to live closer to a light rail station as the transit system becomes operational.