Incremental Loss Research Articles

Archetypal Analysis of Kidney Allograft Biopsies Using Next-generation Sequencing Technology.

In kidney transplantation, molecular diagnostics may be a valuable approach to improve the precision of the diagnosis. Using next-generation sequencing (NGS), we aimed to identify clinically relevant archetypes. We conducted an Illumina bulk RNA sequencing on 770 kidney biopsies (540 kidney recipients) collected between 2006 and 2021 from 11 European centers. Differentially expressed genes were determined for 11 Banff lesions. An ElasticNet model was used for feature selection, and 4 machine learning classifiers were trained to predict the probability of presence of the lesions. NGS-based classifiers were used in an unsupervised archetypal analysis to different archetypes. The association of the archetypes with allograft survival was assessed using the iBox risk prediction score. The ElasticNet feature selection reduced the number of the genes from a range of 859-10 830 to a range of 52-867 genes. NGS-based classifiers demonstrated robust performances (precision-recall area under the curves 0.708-0.980) in predicting the Banff lesions. Archetypal analysis revealed 8 distinct phenotypes, each characterized by distinct clinical, immunological, and histological features. Although the archetypes confirmed the well-defined Banff rejection phenotypes for T cell-mediated rejection and antibody-mediated rejection, equivocal histologic antibody-mediated rejection, and borderline diagnoses were reclassified into different archetypes based on their molecular signatures. The 8 NGS-based archetypes displayed distinct allograft survival profiles with incremental graft loss rates between archetypes, ranging from 90% to 56% rates 7 y after evaluation (P < 0.0001). Using molecular phenotyping, 8 archetypes were identified. These NGS-based archetypes might improve disease characterization, reclassify ambiguous Banff diagnoses, and enable patient-specific risk stratification.

Misallocation under Trade Liberalization

This paper formalizes a classic idea that in second-best environments trade can induce welfare losses: incremental income losses from distortions can outweigh trade gains. In a Melitz model with distortionary taxes, we derive sufficient statistics for welfare gains/losses and show departures from the efficient case (Arkolakis, Costinot, and Rodríguez-Clare 2012) can be captured by the gap between an input and output share and domestic extensive margin elasticities. The loss reflects an endogenous selection of more subsidized firms into exporting. Using Chinese manufacturing data in 2005 and model-inferred firm-level distortions, we demonstrate that a sizable negative fiscal externality can potentially offset conventional gains. (JEL D22, F14, H25, L60, O19, P31, P33)

Overstaying our welcome: On the rise of women's seniority in the academy

AbstractIssues surrounding gender equality are—and should be—front and centre in the water resources community, and other science, technology, engineering and mathematics (STEM) fields. Very necessarily, the focus tends to be on recruitment, offering support for students and early career academics. The leaky pipeline concept used to describe the incremental loss of women from STEM fields with career duration describes a disproportionate loss of senior women, creating a parallel problem where highly qualified, top tier academics are lost from the system after significant financial and personnel investment by institutions. Ultimately, the leaky pipeline undermines the extensive investment of the hydrology and other STEM communities in equity, diversity, inclusion and accessibility (EDIA) recruitment and retention programmes by cutting short career ambitions and the trajectories of diverse top performing individuals, resulting in no net benefit of EDIA policy investments and a lack of diversity with seniority. Addressing this critical gender gap requires the attention and support of the hydrology community of practice with specific focus on generating opportunities for advancement, confronting systemic and structural biases and improving education around allyship. Institutions and professional organizations need to consciously grow diversity in leadership and recognize and outwardly manage the perception of academic excellence around slow research and education that attracts increased diversity. Supporting allyship, reducing competitiveness among community members and reinforcing collaboration will not only attract, but retain, a higher proportion of diversity in the hydrology community, academia and STEM professions in general. It is time for the water resources (and other STEM) communities to demand broader accountability and recognition of the barriers to women, implement and reward more diverse definitions of research excellence, and offer allyship training to the community of practice at large.

Optimizing Unit Scheduling with Fuzzy Logic: A Strategic Approach for Efficient Power Network Operations

This study delves into addressing the challenge of resolving the Unit Commitment (UC) problem, which focuses on enhancing the efficiency of production units and devising their operational schedules to accommodate fluctuations in consumption spanning from a day to a month. Given the intricate, combinatorial, and nonlinear constraints associated with each production unit, this study advocates an optimization approach rooted in fuzzy logic. A Langrangian function was established to simplify the UCP and to transform the different inequality into a linear unconstrained problem. The choice of fuzzy inputs was established using the partial derivatives of a Lagrangian function as a function of the powers injected into each node of the electrical network. This combination of the Lagrangian function and the input of the fuzzy regulator made it possible to control the different constraints in the total production cost function and to improve the operating efficiency of the different production units. This method was effectively applied to a 14-bus IEEE power network encompassing 5 generating units, to address the UC problem by optimizing generator load capacity (LCG) and minimizing Incremental Losses (IL). The numerical processing of the fuzzy linguistic variables was implemented using Mamdani-type fuzzy rules. This strategy stands out for its robust exploratory capability, facilitating the identification of optimal solutions to reduce production costs while ensuring optimal planning of production units.

Pairwise-Label-Based Deep Incremental Hashing with Simultaneous Code Expansion

Deep incremental hashing has become a subject of considerable interest due to its capability to learn hash codes in an incremental manner, eliminating the need to generate codes for classes that have already been learned. However, accommodating more classes requires longer hash codes, and regenerating database codes becomes inevitable when code expansion is required. In this paper, we present a unified deep hash framework that can simultaneously learn new classes and increase hash code capacity. Specifically, we design a triple-channel asymmetric framework to optimize a new CNN model with a target code length and a code projection matrix. This enables us to directly generate hash codes for new images, and efficiently generate expanded hash codes for original database images from the old ones with the learned projection matrix. Meanwhile, we propose a pairwise-label-based incremental similarity-preserving loss to optimize the new CNN model, which can incrementally preserve new similarities while maintaining the old ones. Additionally, we design a double-end quantization loss to reduce the quantization error from new and original query images. As a result, our method efficiently embeds both new and original similarities into the expanded hash codes, while keeping the original database codes unchanged. We conduct extensive experiments on three widely-used image retrieval benchmarks, demonstrating that our method can significantly reduce the time required to expand existing database codes, while maintaining state-of-the-art retrieval performance.

Context-Aware Iteration Policy Network for Efficient Optical Flow Estimation

Existing recurrent optical flow estimation networks are computationally expensive since they use a fixed large number of iterations to update the flow field for each sample. An efficient network should skip iterations when the flow improvement is limited. In this paper, we develop a Context-Aware Iteration Policy Network for efficient optical flow estimation, which determines the optimal number of iterations per sample. The policy network achieves this by learning contextual information to realize whether flow improvement is bottlenecked or minimal. On the one hand, we use iteration embedding and historical hidden cell, which include previous iterations information, to convey how flow has changed from previous iterations. On the other hand, we use the incremental loss to make the policy network implicitly perceive the magnitude of optical flow improvement in the subsequent iteration. Furthermore, the computational complexity in our dynamic network is controllable, allowing us to satisfy various resource preferences with a single trained model. Our policy network can be easily integrated into state-of-the-art optical flow networks. Extensive experiments show that our method maintains performance while reducing FLOPs by about 40%/20% for the Sintel/KITTI datasets.

Quantifying gaps in the tuberculosis care cascade in Brazil: A mathematical model study using national program data.

In Brazil, many individuals with tuberculosis (TB) do not receive appropriate care due to delayed or missed diagnosis, ineffective treatment regimens, or loss-to-follow-up. This study aimed to estimate the health losses and TB program costs attributable to each gap in the care cascade for TB disease in Brazil. We constructed a Markov model simulating the TB care cascade and lifetime health outcomes (e.g., death, cure, postinfectious sequelae) for individuals developing TB disease in Brazil. We stratified the model by age, human immunodeficiency virus (HIV) status, drug resistance, state of residence, and disease severity, and developed a parallel model for individuals without TB that receive a false-positive TB diagnosis. Models were fit to data (adult and pediatric) from Brazil's Notifiable Diseases Information System (SINAN) and Mortality Information System (SIM) for 2018. Using these models, we assessed current program performance and simulated hypothetical scenarios that eliminated specific gaps in the care cascade, in order to quantify incremental health losses and TB diagnosis and treatment costs along the care cascade. TB-attributable disability-adjusted life years (DALYs) were calculated by comparing changes in survival and nonfatal disability to a no-TB counterfactual scenario. We estimated that 90.0% (95% uncertainty interval [UI]: 85.2 to 93.4) of individuals with TB disease initiated treatment and 10.0% (95% UI: 7.6 to 12.5) died with TB. The average number of TB-attributable DALYs per incident TB case varied across Brazil, ranging from 2.9 (95% UI: 2.3 to 3.6) DALYs in Acre to 4.0 (95% UI: 3.3 to 4.7) DALYs in Rio Grande do Sul (national average 3.5 [95% UI: 2.8 to 4.1]). Delayed diagnosis contributed the largest health losses along the care cascade, followed by post-TB sequelae and loss to follow up from TB treatment, with TB DALYs reduced by 71% (95% UI: 65 to 76), 41% (95% UI: 36 to 49), and 10% (95% UI: 7 to 16), respectively, when these factors were eliminated. Total health system costs were largely unaffected by improvements in the care cascade, with elimination of treatment failure reducing attributable costs by 3.1% (95% UI: 1.5 to 5.4). TB diagnosis and treatment of false-positive individuals accounted for 10.2% (95% UI: 3.9 to 21.7) of total programmatic costs but contributed minimally to health losses. Several assumptions were required to interpret programmatic data for the analysis, and we were unable to estimate the contribution of social factors to care cascade outcomes. In this study, we observed that delays to diagnosis, post-disease sequelae and treatment loss to follow-up were primary contributors to the TB burden of disease in Brazil. Reducing delays to diagnosis, improving healthcare after TB cure, and reducing treatment loss to follow-up should be prioritized to improve the burden of TB disease in Brazil.

Global supply chains amplify economic costs of future extreme heat risk

Evidence shows a continuing increase in the frequency and severity of global heatwaves1,2, raising concerns about the future impacts of climate change and the associated socioeconomic costs3,4. Here we develop a disaster footprint analytical framework by integrating climate, epidemiological and hybrid input–output and computable general equilibrium global trade models to estimate the midcentury socioeconomic impacts of heat stress. We consider health costs related to heat exposure, the value of heat-induced labour productivity loss and indirect losses due to economic disruptions cascading through supply chains. Here we show that the global annual incremental gross domestic product loss increases exponentially from 0.03 ± 0.01 (SSP 245)–0.05 ± 0.03 (SSP 585) percentage points during 2030–2040 to 0.05 ± 0.01–0.15 ± 0.04 percentage points during 2050–2060. By 2060, the expected global economic losses reach a total of 0.6–4.6% with losses attributed to health loss (37–45%), labour productivity loss (18–37%) and indirect loss (12–43%) under different shared socioeconomic pathways. Small- and medium-sized developing countries suffer disproportionately from higher health loss in South-Central Africa (2.1 to 4.0 times above global average) and labour productivity loss in West Africa and Southeast Asia (2.0–3.3 times above global average). The supply-chain disruption effects are much more widespread with strong hit to those manufacturing-heavy countries such as China and the USA, leading to soaring economic losses of 2.7 ± 0.7% and 1.8 ± 0.5%, respectively.

Corporate governance matters: How do reduced state-owned shares lower firms’ market power in a developing country?

Corporate governance refers to certain approaches by which a company's stakeholders can receive a return on investment, one of which is to solve the agency problems that can arise from the separation of ownership and control. However, the basis for effective corporate governance is that the country has a mature modern corporate institution. The question of whether corporate governance can be effectively applied in firms in developing countries remains unclear. This study explores the influence of corporate governance in developing countries in the early stage of establishing a modern corporate institution. We use incremental job loss cost and agency cost reduction to represent the two consequences of corporate governance. Our estimates confirm a negative relationship between reduced state-owned shares and state-owned enterprises' (SOEs) market power and reveal the mediating effect of corporate governance on this negative relationship. Reduced state-owned shares can influence corporate governance and reduce agency costs. We also provide novel findings indicating that what really makes SOEs' market power decrease is the cost of job loss; however, we determine that the decline of market power is not necessarily a negative consequence as reduced state-owned shares can significantly promote the performance of SOEs by reducing market power. Our conclusions remain robust following a series of endogeneity tests. The study sheds new light on corporate governance by examining the relationship between corporate governance, SOE reform and employee welfare costs for the first time.

Cost-utility analysis of home blood pressure measurement for screening and diagnosis of hypertension through village health volunteer mechanism in Thailand.

This study aimed to evaluate the cost-effectiveness of blood pressure (BP) screening strategies, including 1) home blood pressure measurement (HBPM), (2) serial screening by CBPM followed by HBPM among individuals with high BP i.e., clinic BP ≥140/90 mmHg (Serial1), (3) serial screening by CBPM followed by HBPM among individuals without high BP i.e., clinic blood pressure <140/90 mmHg (Serial2) compared to CBPM alone. A Markov model was applied among Thai population aged 35 years who had not been previously diagnosed with hypertension (HT) during a lifetime horizon with one-year cycle length from a societal perspective. One-way and probabilistic sensitivity analyses using Monte Carlo simulation with 1,000 replications were performed. The total cost of Serial2 (118,283 baht) was the highest and followed by HBPM (110,767 baht), CBPM (110,588 baht) and Serial1 (78,310 baht). The total quality adjusted life years (QALYs) for the population undergoing BP screening with CBPM, HBPM, Serial1, and Serial2 were 22.1557, 22.1511, 22.1286, and 22.1564, respectively. Compared to CBPM, Serial1 was associated with an incremental cost saving of 32,278 and an incremental QALY loss of 0.0271, whereas HBPM was dominated by CBPM due to higher cost (179 baht) and fewer QALY (-0.0046). Additionally, the incremental cost-effectiveness ratio (ICER) of Serial2 was the highest (10,992,000 baht per QALY gained). Moreover, the incidence rate of HT among individuals at age 40-49 years was the most sensitive factor influencing the ICER of HBPM, Serial1 and Serial2. At the Thai societal willingness-to-pay (WTP) threshold of 160,000 baht per QALY gained, the cost saving associated with Serial1 outweighed the QALY loss. Therefore, it is recommended that Serial1 be implemented as a BP screening option in Thailand. This evidence informed policy information could be invaluable for policymakers in making decision regarding BP screening through village health volunteer mechanism in Thailand and similar settings.

Compression Characteristics of Liquid Ring Compressors With Fixed and Freely Rotating Casings

Abstract Liquid ring compressors (LRC) are used for a wide range of compression and vacuum applications, including corrosive or flammable gases for which other compression technologies may not be feasible. The presence of a surrounding liquid ring may offer the possibility of polytropic compression due to incremental heat loss to the liquid. This aspect may play a critical role in compression (and expansion) processes of heat engine cycles toward approaching the targeted Carnot efficiencies. To date, published research addressing the physical phenomena behind LRC is highly limited. Experimentally studying these machines will result in a demonstration of aggregate performance. In order to improve our understanding of LRC with and without freely rotating casings and to be able to analyze the complex inner workings, a numerical approach using computational fluid dynamics (CFD) tools, supported by available experimental data for validation purposes, has been established. Physical parameters such as water–air interface, temperature, pressure, entropy production, vorticity, and shear strain rate are presented for a baseline geometry taken from the open literature. Finally, temperature-entropy paths and isothermal and isentropic efficiencies are presented. The significant performance gain from the freely rotating casing is highlighted. Detailed results present insights into work addition processes of such machines.

Efficiency of distressed property sales in Lagos city, Nigeria

With recourse to data obtained from the online multiple listing services (MLS) of the Nigerian Property Centre, this study evaluated the efficiency of sale of distressed properties in Lagos city using a 6-month interval of observations protocol, comprising of listings of less than 6 months, 7 to 12 months, 13 to 18 months, and 19 to 24 months respectively. Time-on-the-market (TOM) in this study was perceived to be the duration (in months) from the listing date to the date of data collection. Results of selected parametric and non- parametric statistical tests indicated that there was an insignificant difference in the observation of distressed properties that have remained on the market for at most 6 months and those with marketing time beyond 6 months. Similarly, properties listed for distressed sales in Lagos city were found to have continuously attracted lower purchase rates in the same manner as the non-distressed properties. These results avowed the phenomenon of inefficient sale of distressed properties in Lagos city, Nigeria. It was recommended by the study that individuals and institutions in property brokerage business in Lagos city should conduct a risk assessment of the diminishing likelihood of distressed sales and the incremental losses associated with lengthy listing periods.

Forecasting Sea Level Rise-driven Inundation in Diked and Tidally Restricted Coastal Lowlands

Diked and drained coastal lowlands rely on hydraulic and protective infrastructure that may not function as designed in areas with relative sea-level rise. The slow and incremental loss of the hydraulic conditions required for a well-drained system make it difficult to identify if and when the flow structures no longer discharge enough water, especially in tidal settings where two-way flows occur through the dike. We developed and applied a hydraulic mass-balance model to quantify how water levels in the diked and tidally restricted coastal wetlands and water bodies dynamically respond to sea-level rise, specifically applied to the Herring River Estuary in MA, USA, from 2020 to 2100. Sensitivity testing of the model parameters indicated that primary outcomes were not sensitive to many of the chosen input values, though the terrestrial water input rate to the estuary and the flow coefficient for the hydraulic infrastructure were important. The relative importance of parameters, however, is expected to be site specific. We introduced a drainability metric that quantifies the net water volume drained over every tidal cycle to monitor and forecast how rising water levels on either side of the dike affected the net draining or impounding conditions of the system. Ensembles of model results across parameter and sea-level scenario uncertainties indicated that substantial impoundment of the Herring River Estuary was expected within ~ 20 years with the existing flow structures, a sluice and two flap gates. Simulations with up to three additional gates did not dampen this trend toward impoundment, suggesting that rising impounded water levels are likely even with major construction upgrades. Increasingly impounded diked coastal waterbodies present a hydrologic challenge with socioecological implications due to projected flooding and ecosystem impacts. Solutions to this challenge may be to allow coastal wetland restoration pathways or require substantial and recurring infrastructure improvement projects.

Harmonic-to-noise ratio as speech biomarker for fatigue: K-nearest neighbour machine learning algorithm

BackgroundVital information about a person's physical and emotional health can be perceived in their voice. After sleep loss, altered voice quality is noticed. The circadian rhythm controls the sleep cycle, and when it is askew, it results in fatigue, which is manifested in speech. Using MATLAB statistical techniques and the k-nearest neighbour (KNN) machine learning algorithm, this study assessed the efficacy of the harmonic-to-noise ratio (HNR) as a speech biomarker in differentiating fatigued and normal voice after sleep deprivation of one night. MethodsAfter one night of sleep deprivation, acoustic samples for sustained vowel/a/and visual reaction time were recorded from n = 32 healthy young Indian male volunteers (20–40 yrs). One-way ANOVA established significant changes in voice characteristics with progressive sleep deprivation. The effectiveness of speech HNR as a biomarker for the detection of healthy and fatigued voice was researched, using the KNN classifier in a machine learning algorithm. ResultsThe HNR voice feature was taken from an acoustic sample for three times: baseline (Time 1), 3 AM (Time 2), and 7 AM (Time 3) towards an incremental one-night sleep loss. At 3AM, the HNR changed significantly p<0.05. Utilizing an iterative signal extrapolation approach, the KNN classifier divided the submitted voice signal sample into normal and fatigued categories. ConclusionThe findings imply that the HNR can be used to link fatigue from sleep deprivation with vocal alterations by classifying voice samples in a KNN classifier. Along with the multimodal diagnostic features, this method may also offer an additional acoustic biomarker for the diagnosis of fatigue post sleep loss.

Multilevel Adaptive Knowledge Distillation Network for Incremental SAR Target Recognition

The existing synthetic aperture radar (SAR) automatic target recognition (ATR) methods have shown impressive results in static scenarios, yet the performance drops sharply as new categories of targets are continuously increased. In response to this problem, this letter proposes a novel ATR method named multi-level adaptive knowledge distillation network (MLAKDN) to achieve incremental SAR target recognition. To be specific, an adaptive weighted distillation strategy is first proposed, which can alleviate the model from forgetting the knowledge of old categories by distilling multi-stage soft label information of old categories at the classification level. Then, a feature distillation method based on gradient maximum criterion is developed to filter and distill discriminative features, so as to further recall more knowledge of old categories at the feature level. Meanwhile, a model rebalancing technique is designed to effectively strike the balance of the model on new categories and old categories. Finally, a weighted incremental classification loss is presented to train the whole model. Experiments on the moving and stationary target acquisition and recognition (MSTAR) dataset and the synthetic and measured paired labeled experiment (SAMPLE) dataset illustrate that the proposed method is superior to some state-of-the-arts for incremental SAR target recognition tasks.

Optimization of Orderly Charging/Discharging Strategy for Electric Vehicles Considering Incremental Cost of V2G

The integration of electric vehicles (EV) into demand-side response holds significant potential, which not only helps to reduce the operational expense of EVs, but also presents a viable strategy for the grid peak regulation. In this paper, the load simulation model of EVs is proposed based on the probability distribution of charging/discharging behaviours of different types of EV users from statistical data. On this basis, considering the incentive effect of hourly electricity price, an orderly charging/discharging optimization model is proposed to consider EV charging cost, discharge income and incremental battery loss cost caused by EV participating in the vehicle-to-grid (V2G) mode. This model provides a more accurate estimation on the incremental costs and market arbitrage benefits of EVs in V2G mode on the basis of conventional transportation utilization, so as to better optimize the operation strategy of the EVs for demand response. Numerical results show that in the context of tariff of usage (TOU), the orderly charge and discharge model reduces the operating cost of EV users, and can better exploit the potential of EV energy storage batteries in power grid peak regulation.

Use of nesting resources in an Australian arid-zone landbird community

We investigated the use of nesting resources in a landbird community in the arid zone near Peery Lake, northwestern New South Wales. Over a 3.5-year period (1990–1994), which encompassed pre-drought, drought and post-drought conditions, we recorded 504 active nests and 51 breeding species, including open- (45% of all breeding species), hollow- (27%), mud- (8%), ground- (8%) and tunnel-nesters (4%), and species that nested both in the open and in hollows (8%). Almost 90% of observed nests were located in run-on habitats – major creeklines and minor creeklines. Forty-two species nested in, and 24 species nested only in, run-on habitats. Only 23 species nested in the much more extensive surrounding run-off habitats, but the nests of six species were located only in run-off areas. Most nests (90%) were located in plants, all of which were perennials, with 408 nests in live plants and 47 nests in dead plants. The live plants included 20 tree and shrub species ≥0.5 m tall and 4 subshrub and grass species &lt;0.5 m tall. Most (88%) nests in perennial plants were in trees &gt;4 m tall but often low down in the tree (only 47% of all nests in perennial plants were at heights &gt;4 m). Different bird species favoured different plant species. Favoured trees included River Red Gum Eucalyptus camaldulensis for hollow-nesters, and Whitewood Atalaya hemiglauca and Black Box Eucalyptus largiflorens for open- and mud-nesters. Though fewer nests were located in plants &lt;4 m tall, they were in a wider variety of plant species (16 species) than the larger number of nests in plants &gt;4 m tall (14 plant species). Some nomadic species nested in low vegetation which, though perennial, died back and was unavailable in dry times. To support the nesting requirements of all birds, and to retain the diversity of arid-zone birds, the floristic and structural diversity of perennial plants needs to be maintained. This will be difficult to achieve if the current pattern of ongoing, incremental loss of trees and shrubs continues in the arid zone, particularly as the rate of loss is likely to be exacerbated by the impacts of climate change.

Distribution Network Reactive Power Optimization Method Based on Filter Coordinated Quantum State Transfer Algorithm

To solve the problem of mixed integer nonlinear programming in dynamic reactive power optimization of distribution networks, a two-stage dynamic reactive power optimization model is used to reduce the size of the reactive power optimization calculation.The first stage is nonlinear planning, with the objective of minimizing the combined system network loss and node voltage offset. The second stage is a mixed-integer planning, with the objective of minimizing the incremental power system network loss on the basis of satisfying the constraints such as the number of equipment regulation throughout the day. In view of the fact that the conventional penalty function method to deal with the constraints will result in improper selection of penalty factors, an improved quantum state transfer algorithm with mixed filter technique is proposed. The algorithm adopts a two-population parallel search approach, and the individual adaptation degree and constraint violation degree are constituted as filter pairs as evaluation metrics to replace the penalty functions. Finally, the proposed algorithm, the traditional state transfer algorithm and Colony Algorithm are solved separately for the model using the IEEE 14 system as examples, and the correctness of the proposed model and algorithm is verified by comparing and analyzing the simulation results.

Fiscal consequences of Alzheimer's disease and informal care provision in the UK: A “government perspective” microsimulation

• Alzheimer’s disease (AD) has a substantial impact on the UK government finances. • The fiscal loss to the UK government is £73,749 per person and carer affected by AD. • Welfare support and payments account for 60% of the estimated fiscal loss due to AD. • Lost earnings for informal carers are estimated at £27,189 over the AD continuum. • Total fiscal consequences of diseases should inform health policy decision-making. Estimating the fiscal consequences of Alzheimer’s disease (AD) on patients and informal carers using a UK public economic perspective. A simulated cohort of 1,000 pairs of people with AD and informal carers was compared with 1,000 demographically identical pairs in the general population. Both cohorts enter the model at the mean age of mild cognitive impairment onset. Time to AD onset was based on the literature and AD progression was modelled using published equations and a state-transition microsimulation. Labour participation, financial support, and paid taxes were linked to cognitive decline and caregiving needs using UK labour statistics and tax rates. Healthcare costs were based on published literature. Future costs and life-years were discounted at 3.5%. Results were reported as incremental differences in total tax revenue, financial support, and healthcare costs, over the AD continuum, between cohorts affected and unaffected by AD. Each AD-affected pair was associated with estimated incremental fiscal losses of £73,749 to the UK government. Financial support and healthcare costs were responsible for 59.3% and 22.2% of AD’s fiscal burden, respectively. Total lost tax revenue due to PwAD and carers’ reduced earnings represented 18.5% of total government losses. Sensitivity analyses confirmed the robustness of the results. Assuming mild cognitive impairment onset at age 60 let to incremental fiscal losses of £141,323 per AD-affected pair. Fiscal costs for entire UK population with AD were predicted to be £16 billion annually. Alzheimer’s disease strongly impacts UK’s public economy and should be considered to inform healthcare policymaking.

Live longer and better without aortic valve stenosis

Live longer and better without aortic valve stenosis