Is Taking a Pill a Day Good for Health Expenditures? Evidence from a Cross Section Time Series Analysis of 19 OECD Countries from 1970 – 2000

This paper differs in two ways from previous comparative health system research. First, it focuses on the impact of pharmaceutical expenditures on total health expenditures as trends in pharmaceutical expenditures have been blamed of being a major driver of national health expenditures. In addition to pharmaceutical expenditures, other variables of interest are income, public financing, public delivery, ageing and urbanization. Second, the analysis includes a thorough sensitivity analysis on the proposed model using four samples (with and without the US, and imputed and not imputed data) to address the issue of robustness. Based on a typology of health care systems, trends of relevant explanatory variables are described using OECD Health Data 2003 data. Unlike any other of the variables, pharmaceutical expenditures show contradicting trends when measured as per capita pharmaceutical expenditures and pharmaceutical share of total health expenditures. Next, a regression analysis is performed on data from 1970 – 2000 for 19 OECD countries. Regression diagnostics indicated the absence of multicollinearity but the presence of heteroscedasticity and autocorrelation. Based on the Hausman test, a fixed effect model was chosen. As in all previous empirical research, per capita GDP turned out to be the most influential explanatory variable. While public financing of health care was always three out of four samples significantly inversely related to health expenditures, public delivery as a NHS dummy was always significantly positively related to the dependent variable. Unlike previous research, ageing is consistently and significantly related to higher total health expenditures and, so is urbanization. Finally, all samples show a highly negative relationship between share of pharmaceutical expenditures and health expenditures, suggesting support for the substitution theory.health care expenditure ; health care system ; health economics ; health policy ; comparative

Open loop control of a stepping motor with step loss detection and stall detection using back-EMF based load angle estimation

Stepping motors are the most used electrical machines for low power positioning. The drive controls the machine so that the rotor performs a fixed angular displacement after each step command pulse. Counting the step command pulses enables open-loop positioning. The vast majority of the stepping motor systems is driven in open-loop. When the rotor hits an obstacle stall occurs. Step loss due to overload is another typical problem with stepping motor driven systems. Both phenomena are not detected in open-loop which causes loss of synchronism. In this paper, a sensorless load angle estimator is used to detect step loss and stall. This algorithm is based on the typical stepping motor drive algorithms and does not depend on mechanical load parameters. The method therefore has a broad industrial relevance

Robust sensorless load angle control for stepping motors

In industry, the bulk of the stepping motors is driven in open loop full-step mode with maximum current to avoid step loss. This results in noisy operation due to torque ripples and a poor energy-efficiency. To tackle these problems the current current level at which the stepping motor is driven can be reduced to an optimal level. In this paper, a sensorless load angle controller is proposed and implemented to optimise the drive current level. However, reducing the current level results in a diminished torque margin for load disturbances. In this paper, a countermeasure to enhance the robustness of the sensorless load angle controller against torque disturbances is proposed and assessed trough measurements

Fast dynamic deployment adaptation for mobile devices

Mobile devices that are limited in terms of CPU power, memory or battery power are only capable of executing simple applications. To be able to run advanced applications we introduce a framework to split up the application and execute parts on a remote server. In order to dynamically adapt the deployment at runtime, techniques are presented to keep the migration time as low as possible and to prevent performance loss while migrating. Also methods are presented and evaluated to cope with applications generating a variable load, which can lead to an unstable system

Modelling Censored Losses Using Splicing: a Global Fit Strategy With Mixed Erlang and Extreme Value Distributions

In risk analysis, a global fit that appropriately captures the body and the tail of the distribution of losses is essential. Modelling the whole range of the losses using a standard distribution is usually very hard and often impossible due to the specific characteristics of the body and the tail of the loss distribution. A possible solution is to combine two distributions in a splicing model: a light-tailed distribution for the body which covers light and moderate losses, and a heavy-tailed distribution for the tail to capture large losses. We propose a splicing model with a mixed Erlang (ME) distribution for the body and a Pareto distribution for the tail. This combines the flexibility of the ME distribution with the ability of the Pareto distribution to model extreme values. We extend our splicing approach for censored and/or truncated data. Relevant examples of such data can be found in financial risk analysis. We illustrate the flexibility of this splicing model using practical examples from risk measurement

Sparse Regression with Multi-type Regularized Feature Modeling

Within the statistical and machine learning literature, regularization techniques are often used to construct sparse (predictive) models. Most regularization strategies only work for data where all predictors are treated identically, such as Lasso regression for (continuous) predictors treated as linear effects. However, many predictive problems involve different types of predictors and require a tailored regularization term. We propose a multi-type Lasso penalty that acts on the objective function as a sum of subpenalties, one for each type of predictor. As such, we allow for predictor selection and level fusion within a predictor in a data-driven way, simultaneous with the parameter estimation process. We develop a new estimation strategy for convex predictive models with this multi-type penalty. Using the theory of proximal operators, our estimation procedure is computationally efficient, partitioning the overall optimization problem into easier to solve subproblems, specific for each predictor type and its associated penalty. Earlier research applies approximations to non-differentiable penalties to solve the optimization problem. The proposed SMuRF algorithm removes the need for approximations and achieves a higher accuracy and computational efficiency. This is demonstrated with an extensive simulation study and the analysis of a case-study on insurance pricing analytics

Privacy Aware Offloading of Deep Neural Networks

Deep neural networks require large amounts of resources which makes them hard to use on resource constrained devices such as Internet-of-things devices. Offloading the computations to the cloud can circumvent these constraints but introduces a privacy risk since the operator of the cloud is not necessarily trustworthy. We propose a technique that obfuscates the data before sending it to the remote computation node. The obfuscated data is unintelligible for a human eavesdropper but can still be classified with a high accuracy by a neural network trained on unobfuscated images.Comment: ICML 2018 Privacy in Machine Learning and Artificial Intelligence worksho

A component-based approach towards mobile distributed and collaborative PTAM

Having numerous sensors on-board, smartphones have rapidly become a very attractive platform for augmented reality applications. Although the computational resources of mobile devices grow, they still cannot match commonly available desktop hardware, which results in downscaled versions of well known computer vision techniques that sacrifice accuracy for speed. We propose a component-based approach towards mobile augmented reality applications, where components can be configured and distributed at runtime, resulting in a performance increase by offloading CPU intensive tasks to a server in the network. By sharing distributed components between multiple users, collaborative AR applications can easily be developed. In this poster, we present a component-based implementation of the Parallel Tracking And Mapping (PTAM) algorithm, enabling to distribute components to achieve a mobile, distributed version of the original PTAM algorithm, as well as a collaborative scenario

Boosting insights in insurance tariff plans with tree-based machine learning methods

Pricing actuaries typically operate within the framework of generalized linear models (GLMs). With the upswing of data analytics, our study puts focus on machine learning methods to develop full tariff plans built from both the frequency and severity of claims. We adapt the loss functions used in the algorithms such that the specific characteristics of insurance data are carefully incorporated: highly unbalanced count data with excess zeros and varying exposure on the frequency side combined with scarce, but potentially long-tailed data on the severity side. A key requirement is the need for transparent and interpretable pricing models which are easily explainable to all stakeholders. We therefore focus on machine learning with decision trees: starting from simple regression trees, we work towards more advanced ensembles such as random forests and boosted trees. We show how to choose the optimal tuning parameters for these models in an elaborate cross-validation scheme, we present visualization tools to obtain insights from the resulting models and the economic value of these new modeling approaches is evaluated. Boosted trees outperform the classical GLMs, allowing the insurer to form profitable portfolios and to guard against potential adverse risk selection