Rheumatoid Arthritis Disability and Absence Trends in the United States

Objectives: Employers increasingly focus on absence benefits and connections with employee health. United States absence benefits include Sick Leave (SL), Short- and Long-Term Disability (STD and LTD, respectively) for non-work-related injuries/illnesses, and Workers’ Compensation (WC) for work-related injuries/illnesses. This research explores all-cause absence (SL, STD, LTD, and WC) utilization and changes from baseline for eligible employees with rheumatoid arthritis to determine if the use a constant payment factor is appropriate for models. Study Design: Retrospective multi-year database analysis. Methods: The Workpartners database (1/1/2001-12/31/2019) was used to identify employees with rheumatoid arthritis with adjudicated medical claims. Annual prevalence, benefit utilization, mean days of leave, and median payments (as % of salary) were analyzed. Annual outcomes were calculated as a percent of baseline (2001). Results: Rheumatoid arthritis prevalence averaged 0.5% between 2001 and 2019. At baseline, the percent of eligible employees using STD = 15.5%, LTD = 0.7%, WC = 1.7%, SL = 61.7%. Mean absence days were 48.5, 367.5, 43.8 for STD, LTD, WC, respectively and median payments were 70.5%, 22.2%, 65.7% of salary for STD, LTD, WC, respectively. From 2002-2019: 11.7%-16.9% of eligible employees filed STD claims for 82.1%-995.9% of baseline days and 80.4%-125.9% median payments; 0.6%-2.9% of eligible employees filed LTD claims for 66.6%-114.7% of baseline days and 63.2%-254.8% median payments; 0.3%-1.6% of eligible employees filed WC claims for 44.0%-472.8% of baseline days and 70.4%-271.5% median payments. Median payments were highest in 2012, 2019, 2003 for STD, LTD, WC, respectively and the most absence days were used in 2017 for SL and LTD, 2008 for STD, and 2005 WC. Conclusion: Employees with rheumatoid arthritis used absence benefits at differing rates over time with varying leave-lengths and payments. Using a constant cost or salary replacement factor for absence costs over time and across benefits is not accurate.

Simultaneous Perturbation Stochastic Approximation of the Quantum Fisher Information

The Quantum Fisher Information matrix (QFIM) is a central metric in promising algorithms, such as Quantum Natural Gradient Descent and Variational Quantum Imaginary Time Evolution. Computing the full QFIM for a model with d parameters, however, is computationally expensive and generally requires O(d2) function evaluations. To remedy these increasing costs in high-dimensional parameter spaces, we propose using simultaneous perturbation stochastic approximation techniques to approximate the QFIM at a constant cost. We present the resulting algorithm and successfully apply it to prepare Hamiltonian ground states and train Variational Quantum Boltzmann Machines.

Disability and workers' compensation trends for employees with mental disorders and SUDs in the United States

Introduction US employee absence benefits may include workers' compensation (WC) for work-related injuries/illnesses, short- and long-term disability (STD and LTD, respectively) for non–work-related injuries/illnesses, and discretionary sick leave (SL). Absences can significantly impact business performance, and employers are intensifying efforts to manage benefits and connections with employee health. This research compares all-cause STD/LTD/WC/SL use and variation from baseline (2002) for eligible employees (EMPs) with mental disorders (MDs) and SUDs to determine if use/payments varied over time. Methods Employees incurring medical claims with Agency for Healthcare Research and Quality MD and SUD ICD-9/10 codes were identified in the WorkPartners database (January 1, 2002 to December 31, 2019). Retrospective analysis was performed on annual prevalence, benefit use, mean days of leave, and median payments as a percent of salary (including lump-sum distributions and potentially extending beyond initiation year). WC claims without work absences were excluded. For each benefit, annual outcomes were calculated as a percent of baseline to show variability. Results Use was 48.1% to 202.2% (median, 102.8%) of baseline rates for SL (SUD-EMPs), and 87.3% to 108.4% (median, 97.3%) for STD (MD-EMPs). Days of LTD leaves were 21.5% to 657.8% (median, 359.2%) of baseline days (MD-EMPs), and 122.7% to 1042.2% (median, 460.0%) of baseline days for (SUD-EMPs). Median payments for WC were 78.6% to 253.6% (median, 114.6%) of baseline (MD-EMPs) and 97.9% to 481.6% (median, 104.0%) for SUD-EMPs. Discussion Employees with MD/SUD used absence benefits at differing rates over time with varying days of leave and payments as a percent of salary. Using a constant cost or salary replacement factor over time for all benefits is not accurate or appropriate.

Hosting the Olympics: why are we always getting the cost wrong?

PurposeIn the last 50 years, every Olympics has experienced a major cost overrun. This paper explores the reasons for this cost underestimation and looks into the occurrence of the winner's curse. We also forecast the spending pattern of future host cities. Finally, we discuss the role of the International Olympic Committee in making the Olympic Games more efficient and sustainable.Design/methodology/approachThe review of literature on the topic of constant cost overrun in the Olympic Games is surprisingly very thin. We comprehensively review the existing literature to understand the scholarship in this area. This paper also produces future cost trends for the host cities.FindingsWe argue that cost underestimation is resulting from the outdated bid process which encourages spectacle over efficiency. A no-bid environment is more efficient and allows the host city to negotiate effectively with the International Olympic Committee. The Los Angeles Games of 1984 was profitable and has shown reusing the infrastructure can save a lot of money which could help make the Olympic Games economically sustainable.Originality/valueThis paper adds to the existing knowledge on the cost overrun aspect of Olympics financing. We also forecast the cost trends of hosting future Olympics.

A Provably Secure Distant Data Integrity Checking with Homomorphic Hash Function in Secure Cloud Storage

The cloud storage with user facilities like great data storage quality, higher computing, scalable and flexible has been one of the major application of cloud computing. Large number of data holders is subcontracting the files to the cloud. The cloud server being a public domain is not very reliable thus the data holders are required to find a reliable and trustworthy way to check the ownership of the data files that they subcontract on the cloud server which is present in a remote location. To tackle this drawback number of Distant Data Integrity Checking (DDIC) protocols is in the literature but these protocols have liabilities with respect to the data dynamics and the efficiency. This work recommends a novel DDIC protocol built across homomorphic-hash-function. This system gives a resistance against number of attacks like replay, replaces and falsifications. This work introduces the Operation-record-table(ORT) which is an optimized table resulting in keeping the constant cost for supporting the data variations and keep tracks of blocking the file operations.

Speed, Accuracy, and the Optimal Timing of Choices

We model the joint distribution of choice probabilities and decision times in binary decisions as the solution to a problem of optimal sequential sampling, where the agent is uncertain of the utility of each action and pays a constant cost per unit time for gathering information. We show that choices are more likely to be correct when the agent chooses to decide quickly, provided the agent’s prior beliefs are correct. This better matches the observed correlation between decision time and choice probability than does the classical drift-diffusion model (DDM), where the agent knows the utility difference between the choices. (JEL C41, D11, D12, D83)

Algorithm for Searching an Equilibrium in a Routing Game with Piecewise Constant Cost Functions

We consider nonatomic routing games which are used to model transportation systems with a large number of agents and suggest an algorithm to search for the user equilibrium in such games, which is a generalization of the notion of Nash equilibrium. In general, finding a user equilibrium in routing games is computationally a hard problem. We consider the following subclass of routing games: games with piecewise constant cost functions, and construct an algorithm finding equilibrium in such games. This algorithm is based on the potential function method and the method of piecewise linear (PWL) costs enumeration which finds min-cost flow in a network with PWL cost functions. If each cost function increases, then the complexity of our algorithm is polynomial in the parameters of the network. But if some cost functions have decreasing points, then the complexity is exponential by the number of such points.

Minimizing the Cost of Guessing Games

A two-player “guessing game” is a game in which the first participant, the “Responder,” picks a number from a certain range. Then, the second participant, the “Questioner,” asks only yes-or-no questions in order to guess the number. In this paper, we study guessing games with lies and costs. In particular, the Responder is allowed to lie in one answer, and the Questioner is charged a cost based on the content of each question. Guessing games with lies are closely linked to error correcting codes, which are mathematical objects that allow us to detect an error in received information and correct these errors. We will give basic definitions in coding theory and show how error correcting codes allow us to still guess the correct number even if one lie is involved. We will additionally seek to minimize the total cost of our games. We will provide explicit constructions, for any cost function, for games with the minimum possible cost and an unlimited number of questions. We also find minimum cost games for games with a restricted number of questions and a constant cost function. KEYWORDS: Ulam’s Game; Guessing Games With Lies; Error Correcting Codes; Pairwise Balanced Designs; Steiner Triple Systems

Dynamic Optimization of Combined Cooling, Heating, and Power Systems with Energy Storage Units

In this paper, a combined cooling, heating, and power (CCHP) system with thermal storage tanks is introduced. Considering the plants’ off-design performance, an efficient methodology is introduced to determine the most economical operation schedule. The complex CCHP system’s state transition equation is extracted by selecting the stored cooling and heating energy as the discretized state variables. Referring to the concept of variable cost and constant cost, repeated computations are saved in phase operating cost calculations. Therefore, the most economical operation schedule is obtained by employing a dynamic solving framework in an extremely short time. The simulation results indicated that the optimized operating cost is reduced by 40.8% compared to the traditional energy supply system.

Long-Term Ensemble Learning for Cross-Season Visual Place Classification

This paper addresses the problem of cross-season visual place classification (VPC) from the novel perspective of long-term map learning. Our goal is to enable transfer learning efficiently from one season to the next, at a small constant cost, and without wasting the robot’s available long-term-memory by memorizing very large amounts of training data. To achieve a good tradeoff between generalization and specialization abilities, we employ an ensemble of deep convolutional neural network (DCN) classifiers and consider the task of scheduling (when and which classifiers to retrain), given a previous season’s DCN classifiers as the sole prior knowledge. We present a unified framework for retraining scheduling and we discuss practical implementation strategies. Furthermore, we address the task of partitioning a robot’s workspace into places to define place classes in an unsupervised manner, as opposed to using uniform partitioning, so as to maximize VPC performance. Experiments using the publicly available NCLT dataset revealed that retraining scheduling of a DCN classifier ensemble is crucial in achieving a good balance between generalization and specialization. Additionally, it was found that the performance is significantly improved when using planned scheduling.