A systematic review and meta-analysis of the criterion validity of nutrition assessment tools for diagnosing protein-energy malnutrition in the older community setting

Background: Accurate diagnosis is a key step in managing protein-energy malnutrition. This review seeks to determine the criterion (concurrent and predictive) validity and reliability of nutrition assessment tools in making a diagnosis of protein-energy malnutrition in community-living older adults. Methods: A systematic literature review was undertaken using six electronic databases in September 2016. Studies in any language were included which measured malnutrition via a nutrition assessment tool in adults ≥65 years living in their own homes. Data relating to the predictive validity of tools were analysed via meta-analyses. GRADE was used to evaluate the body of evidence. Results: There were 6,412 records identified, of which eight papers were included. Two studies evaluated the concurrent validity of the Mini Nutritional Assessment (MNA) and Subjective Global Assessment (SGA) and six evaluated the predictive validity of the MNA. The quality of the body of evidence for the concurrent validity of both the MNA andSGA was very low. The quality of the body of evidence for the predictive validity of the MNA in detecting risk of death was moderate (RR: 1.92 [95%CI: 1.55-2.39]; P Conclusions: Due to the small number of studies identified and no evaluation of the predictive validity of tools other than the MNA, there is insufficient evidence to recommend a particular nutrition assessment tool for diagnosing protein-energy malnutrition in older adults in the community setting. High quality diagnostic accuracy studies are needed for all nutrition assessment tools used in older community samples, including measuring of health outcomes subsequent to nutrition assessment by the SGA and PG-SGA

A systematic review and meta-analysis of the criterion validity of nutrition assessment tools for diagnosing protein-energy malnutrition in the older community setting (the MACRo Study)

Background & aims: Malnutrition is a significant barrier to healthy and independent ageing in older adults who live in their own homes, and accurate diagnosis is a key step in managing the condition. However, there has not been sufficient systematic review or pooling of existing data regarding malnutrition diagnosis in the geriatric community setting. The current paper was conducted as part of the MACRo (Malnutrition in the Ageing Community Review) Study and seeks to determine the criterion (concurrent and predictive) validity and reliability of nutrition assessment tools in making a diagnosis of protein-energy malnutrition in the general older adult community. Methods: A systematic literature review was undertaken using six electronic databases in September 2016. Studies in any language were included which measured malnutrition via a nutrition assessment tool in adults ≥65 years living in their own homes. Data relating to the predictive validity of tools were analysed via meta-analyses. GRADE was used to evaluate the body of evidence. Results: There were 6412 records identified, of which 104 potentially eligible records were screened via full text. Eight papers were included; two which evaluated the concurrent validity of the Mini Nutritional Assessment (MNA) and Subjective Global Assessment (SGA) and six which evaluated the predictive validity of the MNA. The quality of the body of evidence for the concurrent validity of both the MNA and SGA was very low. The quality of the body of evidence for the predictive validity of the MNA in detecting risk of death was moderate (RR: 1.92 [95% CI: 1.55–2.39]; P < 0.00001; n = 2013 participants; n = 4 studies; I2: 0%). The quality of the body of evidence for the predictive validity of the MNA in detecting risk of poor physical function was very low (SMD: 1.02 [95%CI: 0.24–1.80]; P = 0.01; n = 4046 participants; n = 3 studies; I2:89%). Conclusions: Due to the small number of studies identified and no evaluation of the predictive validity of tools other than the MNA, there is insufficient evidence to recommend a particular nutrition assessment tool for diagnosing PEM in older adults in the community. High quality diagnostic accuracy studies are needed for all nutrition assessment tools used in older community samples, including measuring of health outcomes subsequent to nutrition assessment by the SGA and PG-SGA

USING COPULAS TO MODEL DEPENDENCE IN SIMULATION RISK ASSESSMENT

Typical engineering systems in applications with high failure consequences such as nuclear reactor plants often employ redundancy and diversity of equipment in an effort to lower the probability of failure and therefore risk. However, it has long been recognized that dependencies exist in these redundant and diverse systems. Some dependencies, such as common sources of electrical power, are typically captured in the logic structure of the risk model. Others, usually referred to as intercomponent dependencies, are treated implicitly by introducing one or more statistical parameters into the model. Such common-cause failure models have limitations in a simulation environment. In addition, substantial subjectivity is associated with parameter estimation for these models. This paper describes an approach in which system performance is simulated by drawing samples from the joint distributions of dependent variables. The approach relies on the notion of a copula distribution, a notion which has been employed by the actuarial community for ten years or more, but which has seen only limited application in technological risk assessment. The paper also illustrates how equipment failure data can be used in a Bayesian framework to estimate the parameter values in the copula model. This approach avoids much of the subjectivity required to estimate parameters in traditional common-cause failure models. Simulation examples are presented for failures in time. The open-source software package R is used to perform the simulations. The open-source software package WinBUGS is used to perform the Bayesian inference via Markov chain Monte Carlo sampling

Risk Analysis of the Space Shuttle: Pre-Challenger Bayeisan Prediction of Failure

Dalal et al performed a statistical analysis of field and nozzle O-ring data collected prior to the ill-fated launch of the Challenger in January 1986. The purpose of their analysis was to show how statistical analysis could be used to provide information to decisionmakers prior to the launch, information that could have been expected to lead to a decision to abort the launch due to the low temperatures (~30o F.) present at the launch pad on the morning of the scheduled launch. Dalal et al. performed a frequentist analysis of the O-ring data, and found that a logistic regression model provided a relatively good fit to the past data. In the second portion of their paper, Dalal et al. propagated parameter uncertainties through the fitted logistic regression model in order to estimate the probability of shuttle failure due to O-ring failure at the estimated launch temperature of ~30o F. Because their analysis was frequentist in nature, probability distributions representing epistemic uncertainty in the input parameters were not available, and the authors had to resort to an approximate approach based on bootstrap confidence intervals. In this paper, we will re-evaluate the analyses of Dalal et al. from a Bayesian perspective. Markov chain Monte Carlo (MCMC) sampling will be used to sample from the joint posterior distribution of the model parameters, and to sample from the posterior predictive distributions at the estimated launch temperature, a temperature that had not been observed in prior launches of the space shuttle. Uncertainties, which are represented by probability distributions in the Bayesian approach, are propagated through the model to obtain a probability distribution for O-ring failure, and subsequently for shuttle failure as a result of O-ring failure. No approximations are required in the Bayesian approach and the resulting distributions can be input to a decision analysis to obtain expected utility for the decision to launch

Bayesian Modeling of Population Variability -- Practical Guidance and Pitfalls

With the advent of easy-to-use open-source software for Markov chain Monte Carlo (MCMC) simulation, hierarchical Bayesian analysis is gaining in popularity. This paper presents practical guidance for hierarchical Bayes analysis of typical problems in probabilistic safety assessment (PSA). The guidance is related to choosing parameterizations that accelerate convergence of the MCMC sampling and to illustrating the potential sensitivity of the results to the functional form chosen for the first-stage prior. This latter issue has significant ramifications because the mean of the average population variability curve (PVC) from hierarchical Bayes (or the mean of the point estimate distribution from empirical Bayes) can be very sensitive to this choice in cases where variability is large. Numerical examples are provided to illustrate the issues discussed

Common-Cause Failure Analysis in Event Assessment

This paper describes the approach taken by the U. S. Nuclear Regulatory Commission to the treatment of common-cause failure in probabilistic risk assessment of operational events. The approach is based upon the Basic Parameter Model for common-cause failure, and examples are illustrated using the alpha-factor parameterization, the approach adopted by the NRC in their Standardized Plant Analysis Risk (SPAR) models. The cases of a failed component (with and without shared common-cause failure potential) and a component being unavailable due to preventive maintenance or testing are addressed. The treatment of two related failure modes (e.g., failure to start and failure to run) is a new feature of this paper. These methods are being applied by the NRC in assessing the risk significance of operational events for the Significance Determination Process (SDP) and the Accident Sequence Precursor (ASP) program

A sugar phosphatase regulates the methylerythritol phosphate (MEP) pathway in malaria parasites

Isoprenoid biosynthesis through the methylerythritol phosphate (MEP) pathway generates commercially important products and is a target for antimicrobial drug development. MEP pathway regulation is poorly understood in microorganisms. We employ a forward genetics approach to understand MEP pathway regulation in the malaria parasite, Plasmodium falciparum. The antimalarial fosmidomycin inhibits the MEP pathway enzyme deoxyxylulose 5-phosphate reductoisomerase (DXR). Fosmidomycin-resistant P. falciparum are enriched for changes in the PF3D7_1033400 locus (hereafter referred to as PfHAD1), encoding a homologue of haloacid dehalogenase (HAD)-like sugar phosphatases. We describe the structural basis for loss-of-function PfHAD1 alleles and find that PfHAD1 dephosphorylates a variety of sugar phosphates, including glycolytic intermediates. Loss of PfHAD1 is required for fosmidomycin resistance. Parasites lacking PfHAD1 have increased MEP pathway metabolites, particularly the DXR substrate, deoxyxylulose 5-phosphate. PfHAD1 therefore controls substrate availability to the MEP pathway. Because PfHAD1 has homologs in plants and bacteria, other HAD proteins may be MEP pathway regulators

SPAR-H Step-by-Step Guidance

Step-by-step guidance was developed recently at Idaho National Laboratory for the US Nuclear Regulatory Commission on the use of the Standardized Plant Analysis Risk-Human Reliability Analysis (SPAR-H) method for quantifying Human Failure Events (HFEs). This work was done to address SPAR-H user needs, specifically requests for additional guidance on the proper application of various aspects of the methodology. This paper overviews the steps of the SPAR-H analysis process and highlights some of the most important insights gained during the development of the step-by-step directions. This supplemental guidance for analysts is applicable when plant-specific information is available, and goes beyond the general guidance provided in existing SPAR-H documentation. The steps highlighted in this paper are: Step-1, Categorizing the HFE as Diagnosis and/or Action; Step-2, Rate the Performance Shaping Factors; Step-3, Calculate PSF-Modified HEP; Step-4, Accounting for Dependence, and; Step-5, Minimum Value Cutoff