Simulation sample sizes for Monte Carlo partial EVPI calculations

Partial expected value of perfect information (EVPI) quantifies the value of removing uncertainty about unknown parameters in a decision model. EVPIs can be computed via Monte Carlo methods. An outer loop samples values of the parameters of interest, and an inner loop samples the remaining parameters from their conditional distribution. This nested Monte Carlo approach can result in biased estimates if small numbers of inner samples are used and can require a large number of model runs for accurate partial EVPI estimates. We present a simple algorithm to estimate the EVPI bias and confidence interval width for a specified number of inner and outer samples. The algorithm uses a relatively small number of model runs (we suggest approximately 600), is quick to compute, and can help determine how many outer and inner iterations are needed for a desired level of accuracy. We test our algorithm using three case studies. (C) 2010 Elsevier B.V. All rights reserved

Exposure assessment of process-related contaminants in food by biomarker monitoring

Exposure assessment is a fundamental part of the risk assessment paradigm, but can often present a number of challenges and uncertainties. This is especially the case for process contaminants formed during the processing, e.g. heating of food, since they are in part highly reactive and/or volatile, thus making exposure assessment by analysing contents in food unreliable. New approaches are therefore required to accurately assess consumer exposure and thus better inform the risk assessment. Such novel approaches may include the use of biomarkers, physiologically based kinetic (PBK) modelling-facilitated reverse dosimetry, and/or duplicate diet studies. This review focuses on the state of the art with respect to the use of biomarkers of exposure for the process contaminants acrylamide, 3-MCPD esters, glycidyl esters, furan and acrolein. From the overview presented, it becomes clear that the field of assessing human exposure to process-related contaminants in food by biomarker monitoring is promising and strongly developing. The current state of the art as well as the existing data gaps and challenges for the future were defined. They include (1) using PBK modelling and duplicate diet studies to establish, preferably in humans, correlations between external exposure and biomarkers; (2) elucidation of the possible endogenous formation of the process-related contaminants and the resulting biomarker levels; (3) the influence of inter-individual variations and how to include that in the biomarker-based exposure predictions; (4) the correction for confounding factors; (5) the value of the different biomarkers in relation to exposure scenario’s and risk assessment, and (6) the possibilities of novel methodologies. In spite of these challenges it can be concluded that biomarker-based exposure assessment provides a unique opportunity to more accurately assess consumer exposure to process-related contaminants in food and thus to better inform risk assessment

Informing investment to reduce inequalities: a modelling approach

Background: Reducing health inequalities is an important policy objective but there is limited quantitative information about the impact of specific interventions. Objectives: To provide estimates of the impact of a range of interventions on health and health inequalities. Materials and methods: Literature reviews were conducted to identify the best evidence linking interventions to mortality and hospital admissions. We examined interventions across the determinants of health: a ‘living wage’; changes to benefits, taxation and employment; active travel; tobacco taxation; smoking cessation, alcohol brief interventions, and weight management services. A model was developed to estimate mortality and years of life lost (YLL) in intervention and comparison populations over a 20-year time period following interventions delivered only in the first year. We estimated changes in inequalities using the relative index of inequality (RII). Results: Introduction of a ‘living wage’ generated the largest beneficial health impact, with modest reductions in health inequalities. Benefits increases had modest positive impacts on health and health inequalities. Income tax increases had negative impacts on population health but reduced inequalities, while council tax increases worsened both health and health inequalities. Active travel increases had minimally positive effects on population health but widened health inequalities. Increases in employment reduced inequalities only when targeted to the most deprived groups. Tobacco taxation had modestly positive impacts on health but little impact on health inequalities. Alcohol brief interventions had modestly positive impacts on health and health inequalities only when strongly socially targeted, while smoking cessation and weight-reduction programmes had minimal impacts on health and health inequalities even when socially targeted. Conclusions: Interventions have markedly different effects on mortality, hospitalisations and inequalities. The most effective (and likely cost-effective) interventions for reducing inequalities were regulatory and tax options. Interventions focused on individual agency were much less likely to impact on inequalities, even when targeted at the most deprived communities

Eliciting Dirichlet and Gaussian copula prior distributions for multinomial models

In this paper, we propose novel methods of quantifying expert opinion about prior distributions for multinomial models. Two different multivariate priors are elicited using median and quartile assessments of the multinomial probabilities. First, we start by eliciting a univariate beta distribution for the probability of each category. Then we elicit the hyperparameters of the Dirichlet distribution, as a tractable conjugate prior, from those of the univariate betas through various forms of reconciliation using least-squares techniques. However, a multivariate copula function will give a more flexible correlation structure between multinomial parameters if it is used as their multivariate prior distribution. So, second, we use beta marginal distributions to construct a Gaussian copula as a multivariate normal distribution function that binds these marginals and expresses the dependence structure between them. The proposed method elicits a positive-definite correlation matrix of this Gaussian copula. The two proposed methods are designed to be used through interactive graphical software written in Java

QuantCrit: education, policy, ‘Big Data’ and principles for a critical race theory of statistics

Quantitative research enjoys heightened esteem among policy-makers, media and the general public. Whereas qualitative research is frequently dismissed as subjective and impressionistic, statistics are often assumed to be objective and factual. We argue that these distinctions are wholly false; quantitative data is no less socially constructed than any other form of research material. The first part of the paper presents a conceptual critique of the field with empirical examples that expose and challenge hidden assumptions that frequently encode racist perspectives beneath the façade of supposed quantitative objectivity. The second part of the paper draws on the tenets of Critical Race Theory (CRT) to set out some principles to guide the future use and analysis of quantitative data. These ‘QuantCrit’ ideas concern (1) the centrality of racism as a complex and deeply-rooted aspect of society that is not readily amenable to quantification; (2) numbers are not neutral and should be interrogated for their role in promoting deficit analyses that serve White racial interests; (3) categories are neither ‘natural’ nor given and so the units and forms of analysis must be critically evaluated; (4) voice and insight are vital: data cannot ‘speak for itself’ and critical analyses should be informed by the experiential knowledge of marginalized groups; (5) statistical analyses have no inherent value but can play a role in struggles for social justice

Sympatho-renal axis in chronic disease

Essential hypertension, insulin resistance, heart failure, congestion, diuretic resistance, and functional renal disease are all characterized by excessive central sympathetic drive. The contribution of the kidney’s somatic afferent nerves, as an underlying cause of elevated central sympathetic drive, and the consequences of excessive efferent sympathetic signals to the kidney itself, as well as other organs, identify the renal sympathetic nerves as a uniquely logical therapeutic target for diseases linked by excessive central sympathetic drive. Clinical studies of renal denervation in patients with resistant hypertension using an endovascular radiofrequency ablation methodology have exposed the sympathetic link between these conditions. Renal denervation could be expected to simultaneously affect blood pressure, insulin resistance, sleep disorders, congestion in heart failure, cardiorenal syndrome and diuretic resistance. The striking epidemiologic evidence for coexistence of these disorders suggests common causal pathways. Chronic activation of the sympathetic nervous system has been associated with components of the metabolic syndrome, such as blood pressure elevation, obesity, dyslipidemia, and impaired fasting glucose with hyperinsulinemia. Over 50% of patients with essential hypertension are hyperinsulinemic, regardless of whether they are untreated or in a stable program of treatment. Insulin resistance is related to sympathetic drive via a bidirectional mechanism. In this manuscript, we review the data that suggests that selective impairment of renal somatic afferent and sympathetic efferent nerves in patients with resistant hypertension both reduces markers of central sympathetic drive and favorably impacts diseases linked through central sympathetics—insulin resistance, heart failure, congestion, diuretic resistance, and cardiorenal disorders

Understanding How Microplastics Affect Marine Biota on the Cellular Level Is Important for Assessing Ecosystem Function: A Review

Plastic has become indispensable for human life. When plastic debris is discarded into waterways, these items can interact with organisms. Of particular concern are microscopic plastic particles (microplastics) which are subject to ingestion by several taxa. This review summarizes the results of cutting-edge research about the interactions between a range of aquatic species and microplastics, including effects on biota physiology and secondary ingestion. Uptake pathways via digestive or ventilatory systems are discussed, including (1) the physical penetration of microplastic particles into cellular structures, (2) leaching of chemical additives or adsorbed persistent organic pollutants (POPs), and (3) consequences of bacterial or viral microbiota contamination associated with microplastic ingestion. Following uptake, a number of individual-level effects have been observed, including reduction of feeding activities, reduced growth and reproduction through cellular modifications, and oxidative stress. Microplastic-associated effects on marine biota have become increasingly investigated with growing concerns regarding human health through trophic transfer. We argue that research on the cellular interactions with microplastics provide an understanding of their impact to the organisms’ fitness and, therefore, its ability to sustain their functional role in the ecosystem. The review summarizes information from 236 scientific publications. Of those, only 4.6% extrapolate their research of microplastic intake on individual species to the impact on ecosystem functioning. We emphasize the need for risk evaluation from organismal effects to an ecosystem level to effectively evaluate the effect of microplastic pollution on marine environments. Further studies are encouraged to investigate sublethal effects in the context of environmentally relevant microplastic pollution conditions