How do policy levers shape the quality of a national health system?

Poor quality of care may have a detrimental effect on access and take-up and can become a serious barrier to the universality of health services. This consideration is of particular interest in view of the fact that health systems in many countries must address a growing public-sector deficit and respond to increasing pressures due to COVID-19 and aging population, among other factors. In line with a rapidly emerging literature, we focus on patient satisfaction as a proxy for quality of health care. Drawing on rich longitudinal and cross-sectional data for Spain and multilevel estimation techniques, we show that in addition to individual level differences, policy levers (such as public health spending and the patient-doctor ratio, in particular) exert a considerable influence on the quality of a health care system. Our results suggest that policymakers seeking to enhance the quality of care should be cautious when compromising the level of health resources, and in particular, health personnel, as a response to economic downturns in a sector that traditionally had insufficient human resources in many countries, which have become even more evident in the light of the current health crisis. Additionally, we provide evidence that the increasing reliance on the private health sector may be indicative of inefficiencies in the public system and/or the existence of features of private insurance which are deemed important by patients.     

Translating cross-lagged effects into incidence rates and risk ratios: The case of psychosocial safety climate and depression

Longitudinal studies are the gold standard of empirical work and stress research whenever experiments are not plausible. Frequently, scales are used to assess risk factors and their consequences, and cross-lagged effects are estimated to determine possible risks. Methods to translate cross-lagged effects into risk ratios to facilitate risk assessment do not yet exist, which creates a divide between psychological and epidemiological work stress research. The aim of the present paper is to demonstrate how cross-lagged effects can be used to assess the risk ratio of different levels of psychosocial safety climate (PSC) in organisations, an important psychosocial risk for the development of depression. We used available longitudinal evidence from the Australian Workplace Barometer (N?=?1905) to estimate cross-lagged effects of PSC on depression. We applied continuous time modelling to obtain time-scalable cross effects. These were further investigated in a 4-year Monte Carlo simulation, which translated them into 4-year incident rates. Incident rates were determined by relying on clinically relevant 2-year periods of depression. We suggest a critical value of PSC?=?26 (corresponding to ?1.4 SD), which is indicative of more than 100% increased incidents of persistent depressive disorder in 4-year periods compared to average levels of PSC across 4 years.     

A dynamic business modelling approach to design and experiment new business venture strategies

Business Modelling has evolved as a key activity to reflect new business venture strategy by framing the way a firm will operate and how it will function in achieving its goals (e.g., profitability, growth, innovation, social impact). However, scholars and practitioners have criticized the adoption of a too static perspective in the design and use of conventional Business Model representations. Such a static perspective prevents nascent entrepreneurs experimenting with their Business Models and, as a result, identifying the most effective strategies, especially in terms of business sustainability and profitability. In this paper, we argue that combining conventional Business Model schemas with System Dynamics modelling results in a strategy design tool that may overcome several limitations related to a static view of Business Model representation. Mapping the different key elements underlying value creation processes into a system of causal interdependencies – through the use of simulation – allows strategy analysts and entrepreneurs to experiment and learn how the business reacts to strategic and organizational changes in terms of performance, innovation and value creation. As such, Dynamic Business Models provide useful insights to strategy formulation and business venturing by capturing how critical Business Model elements interact to produce enduring competitive advantages over time.     

Modelling daily multivariate pollutant data at multiple sites

Summary. This paper considers the spatiotemporal modelling of four pollutants measured daily at eight monitoring sites in London over a 4-year period. Such multiple-pollutant data sets measured over time at multiple sites within a region of interest are typical. Here, the modelling was carried out to provide the exposure for a study investigating the health effects of air pollution. Alternative objectives include the design problem of the positioning of a new monitoring site, or for regulatory purposes to determine whether environmental standards are being met. In general, analyses are hampered by missing data due, for example, to a particular pollutant not being measured at a site, a monitor being inactive by design (e.g. a 6-day monitoring schedule) or because of an unreliable or faulty monitor. Data of this type are modelled here within a dynamic linear modelling framework, in which the dependences across time, space and pollutants are exploited. Throughout the approach is Bayesian, with implementation via Markov chain Monte Carlo sampling.     

分形理论在计算机辅助图案造型中的应用研究

本文简单介绍了分形理论的概念和特征，着重阐述了分形理论在图案艺术设计方面的应用和方法。研究设计了计算机辅助图案艺术设计系统的功能和构成。     

开放式数控切割机神经网络误差补偿研究

分析了影响数控火焰切割机加工精度的主要因素,利用开放式数控系统的软件开放性,提出了采用IGCAQBP学习算法的神经网络方法来对包括金属热变形、机械传动误差等非线性因素在内的多种因素造成的加工误差进行误差补偿,设计了嵌入开放式数控系统中的神经网络误差补偿器,给出了实用的补偿器使用方法,并对误差补偿功能进行了扩展,仿真结果和实际应用表明该方法稳定有效。     

数值解法中的单元形状函数技巧

本文介绍从一组互不相关的函数中推导数值法中单元形状函数的简便而有效的算法,以特定的一维内插方法导出各种二维、三维的内插方法,它们对奇异性的模拟是有益的;为了适用于延伸到无限远的单元,假设形状函数类似于Lagrange多项式,但包含了一个按指数衰减的项。     

Discrete time modelling of disease incidence time series by using Markov chain Monte Carlo methods

Summary.  A stochastic discrete time version of the susceptible–infected–recovered model for infectious diseases is developed. Disease is transmitted within and between communities when infected and susceptible individuals interact. Markov chain Monte Carlo methods are used to make inference about these unobserved populations and the unknown parameters of interest. The algorithm is designed specifically for modelling time series of reported measles cases although it can be adapted for other infectious diseases with permanent immunity. The application to observed measles incidence series motivates extensions to incorporate age structure as well as spatial epidemic coupling between communities.     

ELICITING A DIRECTED ACYCLIC GRAPH FOR A MULTIVARIATE TIME SERIES OF VEHICLE COUNTS IN A TRAFFIC NETWORK

The problem of modelling multivariate time series of vehicle counts in traffic networks is considered. It is proposed to use a model called the linear multiregression dynamic model (LMDM). The LMDM is a multivariate Bayesian dynamic model which uses any conditional independence and causal structure across the time series to break down the complex multivariate model into simpler univariate dynamic linear models. The conditional independence and causal structure in the time series can be represented by a directed acyclic graph (DAG). The DAG not only gives a useful pictorial representation of the multivariate structure, but it is also used to build the LMDM. Therefore, eliciting a DAG which gives a realistic representation of the series is a crucial part of the modelling process. A DAG is elicited for the multivariate time series of hourly vehicle counts at the junction of three major roads in the UK. A flow diagram is introduced to give a pictorial representation of the possible vehicle routes through the network. It is shown how this flow diagram, together with a map of the network, can suggest a DAG for the time series suitable for use with an LMDM.     

Improved Monte Carlo inference for models with additive error

Some statistical models defined in terms of a generating stochastic mechanism have intractable distribution theory, which renders parameter estimation difficult. However, a Monte Carlo estimate of the log-likelihood surface for such a model can be obtained via computation of nonparametric density estimates from simulated realizations of the model. Unfortunately, the bias inherent in density estimation can cause bias in the resulting log-likelihood estimate that alters the location of its maximizer. In this paper a methodology for radically reducing this bias is developed for models with an additive error component. An illustrative example involving a stochastic model of molecular fragmentation and measurement is given.