A comparison of United States and United Kingdom EQ-5D health states valuations using a nonparametric Bayesian method

Few studies have compared preference values of health states obtained in different countries. This paper applies a nonparametric model to estimate and compare EQ-5D health state valuation data obtained from two countries using Bayesian methods. The data set is the US and UK EQ-5D valuation studies where a sample of 42 states defined by the EQ-5D was valued by representative samples of the general population from each country using the time trade-off technique. We estimate a function applicable across both countries which explicitly accounts for the differences between them, and is estimated using the data from both countries. The paper discusses the implications of these results for future applications of the EQ-5D and further work in this field.preference-based health measure; nonparametric methods; time trade-off; EQ-5D

Musculoskeletal Disorders and Association with Social Media Use Among University Students at the Quarantine Time Of COVID-19 Outbreak

Introduction: COVID-19 period was characterized by lockdown and quarantine, the aim of this cross-sectional analytical study is to investigate the effect of COVID-19 quarantine on social media use, and its association with musculoskeletal disorders (MSD) among university students. Methods: A cross sectional study was conducted among Al-Quds University students. 317 students (average age of 20.34 years) participated in this study. A self-designed questionnaire was used to collect data which was sent to students on social media using a simple random method in almost all academic year phases. Results: There was a statically significant increase in the following variables during quarantine compared to before (P0.05). There was no statistically significant difference in time spent on exercise before and during quarantine with average time before the quarantine of 0.80 hours to 0.7 hours during the quarantine (P>0.05). There was a statistically significant increase of severity of Musculoskeletal disorders (MSD) as measured by a scale of 0-10 during the quarantine (P<0.05) in terms of severity of headache (2 to 2, 78), neck pain (2.06 to 2.80), and back pain (2.17 to 3). This increase in the three dominant MSD was positively correlated with the hours of use of laptops, computers, and mobile phones, for communication and education (P<0.05). Statistically significant negative correlation was found in between night sleeping hours and severity of MSD reported by students (P<0.05). Age was correlated with less use of social media for leisure and with more exercise (P<0.05). StudentsConclusion: Quarantine increased the time of use of social media, and in turn increases the prevalence and severity of MSD among university

A comparison of United States and United Kingdom EQ-5D health states valuations using a nonparametric Bayesian method

Few studies have compared preference values of health states obtained in different countries. This paper applies a nonparametric model to estimate and compare EQ-5D health state valuation data obtained from two countries using Bayesian methods. The data set is the US and UK EQ-5D valuation studies where a sample of 42 states defined by the EQ-5D was valued by representative samples of the general population from each country using the time trade-off technique. We estimate a function applicable across both countries which explicitly accounts for the differences between them, and is estimated using the data from both countries. The paper discusses the implications of these results for future applications of the EQ-5D and further work in this field

Calculating partial expected value of perfect information via Monte Carlo sampling algorithms

Partial expected value of perfect information (EVPI) calculations can quantify the value of learning about particular subsets of uncertain parameters in decision models. Published case studies have used different computational approaches. This article examines the computation of partial EVPI estimates via Monte Carlo sampling algorithms. The mathematical definition shows 2 nested expectations, which must be evaluated separately because of the need to compute a maximum between them. A generalized Monte Carlo sampling algorithm uses nested simulation with an outer loop to sample parameters of interest and, conditional upon these, an inner loop to sample remaining uncertain parameters. Alternative computation methods and shortcut algorithms are discussed and mathematical conditions for their use considered. Maxima of Monte Carlo estimates of expectations are biased upward, and the authors show that the use of small samples results in biased EVPI estimates. Three case studies illustrate 1) the bias due to maximization and also the inaccuracy of shortcut algorithms 2) when correlated variables are present and 3) when there is nonlinearity in net benefit functions. If relatively small correlation or nonlinearity is present, then the shortcut algorithm can be substantially inaccurate. Empirical investigation of the numbers of Monte Carlo samples suggests that fewer samples on the outer level and more on the inner level could be efficient and that relatively small numbers of samples can sometimes be used. Several remaining areas for methodological development are set out. A wider application of partial EVPI is recommended both for greater understanding of decision uncertainty and for analyzing research priorities

Valuation of preference-based measures: Can existing preference data be used to select a smaller sample of health states?

Background Different countries have different preferences regarding health, and there are different value sets for popular preference-based measures across different countries. However, the cost of collecting data to generate country-specific value sets can be prohibitive for countries with smaller population size or low- and middle-income countries (LMIC). This paper explores whether existing preference weights could be modelled alongside a small own country valuation study to generate representative estimates. This is explored using a case study modelling UK data alongside smaller US samples to generate US estimates. Methods We analyse EQ-5D valuation data derived from representative samples of the US and UK populations using time trade-off to value 42 health states. A nonparametric Bayesian model was applied to estimate a US value set using the full UK dataset and subsets of the US dataset for 10, 15, 20 and 25 health states. Estimates are compared to a US value set estimated using US values alone using mean predictions and root mean square error. Results The results suggest that using US data elicited for 20 health states alongside the existing UK data produces similar predicted mean valuations and RMSE as the US value set, while 25 health states produce the exact features. Conclusions The promising results suggest that existing preference data could be combined with a small valuation study in a new country to generate preference weights, making own country value sets more achievable for LMIC. Further research is encouraged

Drift dependence of optimal trade execution strategies under transient price impact

We give a complete solution to the problem of minimizing the expected liquidity costs in presence of a general drift when the underlying market impact model has linear transient price impact with exponential resilience. It turns out that this problem is well-posed only if the drift is absolutely continuous. Optimal strategies often do not exist, and when they do, they depend strongly on the derivative of the drift. Our approach uses elements from singular stochastic control, even though the problem is essentially non-Markovian due to the transience of price impact and the lack in Markovian structure of the underlying price process. As a corollary, we give a complete solution to the minimization of a certain cost-risk criterion in our setting

Investigating aquifer vulnerability employing DRASTIC model and GIS techniques in Menzel Habib Shallow Aquifer, South-Eastern Tunisia

Groundwater vulnerability assessment shows an extreme sensitivity to in situ anthropogenic pollutants. A dichotomous assessment of geological and hydrological (inter alia) characteristics makes it possible to determine the vulnerability of an aquifer system. The natural vulnerability of an aquifer can be severely compromised by human activities. The physical structure and material composition of aquifers shows resistance to contaminants transport from surface to groundwater. Currently, numerous methods have been posited evaluating aquifer's vulnerability. Similarly, the DRASTIC and DRASTIC pesticides models utilize computer algorithms and hydro-geological data within a Geographical Information System (GIS) to compute spatial aquifer vulnerability. The DRASTIC and DRASTIC pesticides models are constructed using combined spatial datasets on Depth to groundwater (D), Aquifer Recharge (R), Aquifer media (A), Soil media (S), Topography (T), Impact of the Vadose Zone (I) and Hydraulic Conductivity (C) of the aquifer. The degree of vulnerability of the aquifer system can be evaluated by computing sensitivity analysis of DRASTIC index using GIS, showing the contribution of each parameter to vulnerability sensitivity. The GIS was used to develop a vulnerability map for Menzel Habib aquifer area. The obtained results indicated that moderately vulnerable areas are of 5%, while areas of no risk correspond to 95% using DRASTIC index. Otherwise, DRASTIC pesticide index indicated that 15% area of low vulnerability, 84% moderately vulnerable and 1% high vulnerability. The central area of Menzel Habib aquifer showed a low vulnerability due to dense human settlement and a deeper water level. However, agricultural areas recorded high vulnerability risk. Menzel Habib's environmental and socio-economic development is dependent on policy makers and planner's ability to use information effectively for decision making. The obtained groundwater vulnerability maps provide a basis for this aimed at protecting the aquifer from pollutants. Additionally, land use and development activities can be informed by mapping variables, showing that agriculture areas are highly vulnerable as compare to settlement areas

JunB Inhibits ER Stress and Apoptosis in Pancreatic Beta Cells

Cytokines contribute to pancreatic β-cell apoptosis in type 1 diabetes (T1D) by modulation of β-cell gene expression networks. The transcription factor Activator Protein-1 (AP-1) is a key regulator of inflammation and apoptosis. We presently evaluated the function of the AP-1 subunit JunB in cytokine-mediated β-cell dysfunction and death. The cytokines IL-1β+IFN-γ induced an early and transitory upregulation of JunB by NF-κB activation. Knockdown of JunB by RNA interference increased cytokine-mediated expression of inducible nitric oxide synthase (iNOS) and endoplasmic reticulum (ER) stress markers, leading to increased apoptosis in an insulin-producing cell line (INS-1E) and in purified rat primary β-cells. JunB knockdown β-cells and junB−/− fibroblasts were also more sensitive to the chemical ER stressor cyclopiazonic acid (CPA). Conversely, adenoviral-mediated overexpression of JunB diminished iNOS and ER markers expression and protected β-cells from cytokine-induced cell death. These findings demonstrate a novel and unexpected role for JunB as a regulator of defense mechanisms against cytokine- and ER stress-mediated apoptosis