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Historically, the expansion of soy plantations has been a major driver of land-use/cover change (LUCC) in Brazil. While a series of recent public actions and supply-chain commitments reportedly curbed the replacement of forests by soy, the expansion of the agricultural commodity still poses a considerable threat to the Amazonian and Cerrado biomes. Identification of areas under high risk of soy expansion is thus paramount to assist conservation efforts in the region. We mapped the areas suitable for undergoing transition to soy plantations in the Legal Amazon with a machine-learning approach adopted from the ecological modeling literature. Simulated soy expansion for the year 2014 exhibited favorable validation scores compared to other LUCC models. We then used our model to simulate how potential future infrastructure improvements would affect the 2014 probabilities of soy occurrence in the region. In addition to the 2.3 Mha of planted soy in the Legal Amazon in 2014, our model identified another 14.7 Mha with high probability of soy conversion in the region given the infrastructure conditions at that time. Out of those, pastures and forests represented 9.8 and 0.4 Mha, respectively. Under the new infrastructure scenarios simulated, the Legal Amazonian area under high risk of soy conversion increased by up to 2.1 Mha (14.6%). These changes led to up to 11.4 and 51.4% increases in the high-risk of conversion areas of pastures and forests, respectively. If conversion occurs in the identified high-risk areas, at least 4.8 Pg of CO2 could be released into the atmosphere, a value that represents 10 times the total CO2 emissions of Brazil in 2014. Our results highlight the importance of targeting conservation policies and enforcement actions, including the Soy Moratorium, to mitigate future forest cover loss associated with infrastructure improvements in the region.
Objective: This study was undertaken to calculate epilepsy-related direct, indirect, and total costs in adult patients with active epilepsy (ongoing unprovoked seizures) in Germany and to analyze cost components and dynamics compared to previous studies from 2003, 2008, and 2013. This analysis was part of the Epi2020 study.
Methods: Direct and indirect costs related to epilepsy were calculated with a multicenter survey using an established and validated questionnaire with a bottom-up design and human capital approach over a 3-month period in late 2020. Epilepsy-specific costs in the German health care sector from 2003, 2008, and 2013 were corrected for inflation to allow for a valid comparison.
Results: Data on the disease-specific costs for 253 patients in 2020 were analyzed. The mean total costs were calculated at €5551 (±€5805, median = €2611, range = €274–€21 667) per 3 months, comprising mean direct costs of €1861 (±€1905, median = €1276, range = €327–€13 158) and mean indirect costs of €3690 (±€5298, median = €0, range = €0–€11 925). The main direct cost components were hospitalization (42.4%), antiseizure medication (42.2%), and outpatient care (6.2%). Productivity losses due to early retirement (53.6%), part-time work or unemployment (30.8%), and seizure-related off-days (15.6%) were the main reasons for indirect costs. However, compared to 2013, there was no significant increase of direct costs (−10.0%), and indirect costs significantly increased (p < .028, +35.1%), resulting in a significant increase in total epilepsy-related costs (p < .047, +20.2%). Compared to the 2013 study population, a significant increase of cost of illness could be observed (p = .047).
Significance: The present study shows that disease-related costs in adult patients with active epilepsy increased from 2013 to 2020. As direct costs have remained constant, this increase is attributable to an increase in indirect costs. These findings highlight the impact of productivity loss caused by early retirement, unemployment, working time reduction, and seizure-related days off.
Objective: This study was undertaken to quantify epilepsy-related costs of illness (COI) in Germany and identify cost-driving factors.
Methods: COI were calculated among adults with epilepsy of different etiologies and severities. Multiple regression analysis was applied to determine any epilepsy-related and sociodemographic factors that serve as cost-driving factors.
Results: In total, 486 patients were included, with a mean age of 40.5 ± 15.5 years (range = 18–83 years, 58.2% women). Mean 3-month COI were estimated at €4911, €2782, and €2598 for focal, genetic generalized, and unclassified epilepsy, respectively. The mean COI for patients with drug-refractory epilepsy (DRE; €7850) were higher than those for patients with non-DRE (€4720), patients with occasional seizures (€3596), or patients with seizures in remission for >1 year (€2409). Identified cost-driving factors for total COI included relevant disability (unstandardized regression coefficient b = €2218), poorer education (b = €2114), living alone (b = €2612), DRE (b = €1831), and frequent seizures (b = €2385). Younger age groups of 18–24 years (b = −€2945) and 25–34 years (b = −€1418) were found to have lower overall expenditures. A relevant disability (b = €441), DRE (b = €1253), frequent seizures (b = €735), and the need for specialized daycare (b = €749) were associated with higher direct COI, and poorer education (b = €1969), living alone (b = €2612), the presence of a relevant disability (b = €1809), DRE (b = €1831), and frequent seizures (b = €2385) were associated with higher indirect COI.
Significance: This analysis provides up-to-date COI data for use in further health economics analyses, highlighting the high economic impacts associated with disease severity, disability, and disease-related loss of productivity among adult patients with epilepsy. The identified cost drivers could be used as therapeutic and socioeconomic targets for future cost-containment strategies.
