bluuebunny/crossref_metadata_2025 huggingface dataset:
Experience Feedback
Authors: Kjellén Urban
Published: September 2008

Experience feedback is the process by which information on the results of an activity is fed back to decision makers as new input to modify and improve subsequent activities. The main source of experience used in risk assessments is historic industrial systems. The experience emanates from incidents, failures, and normal operation, as well as from earlier risk assessments. Laboratory and field experiments provide complementary experience. Experience‐based knowledge from these activities is directed to the risk assessment team through different channels. The article distinguishes between channels for transfer of explicit and tacit knowledge. Experience databases and experience carriers play an important role in the first case, where experience carriers represent a higher degree of synthesis. If properly designed, the risk assessment team will constitute an arena for tacit knowledge exchange between risk analysts, personnel with operational experience, and systems designers. The article describes the processes for update of experience databases and experience carriers with new information. The need for documenting the experience basis in the risk assessment report is highlighted. An example from environmental risk assessments illustrates how experience feedback is achieved in practice.
Reliability Optimization
Authors: Coit David W.
Published: September 2008

Reliability optimization involves the simultaneous evaluation and selection of components and a system‐level design configuration that collectively meets all design constraints, and at the same time, optimizes some objective function, often system reliability or availability. Often these problems are very constrained because of physical or budgetary limitations. There are several variations of reliability optimization and there have been many attempts to determine optimal design configurations for different problem formulations. Applicable optimization methods include dynamic programming, integer programming, mixed integer and nonlinear programming, and metaheuristics, e.g., genetic algorithms (GA). Notable extensions to this problem include multistate reliability optimization and multiple‐objective system reliability optimization.
Persistent Organic Pollutants
Authors: Ross Peter S. , Grant Paul B.C.
Published: September 2008

Persistent organic pollutants (POPs) comprise a wide range of chemicals, which for the most part have either been intentionally manufactured or created as an unintended consequence of human activities. Some have been used in closed systems (e.g., polychlorinated biphenyls, PCBs, as dielectric fluids), while others are intentionally applied to ecosystem compartments (e.g., organochlorine pesticides). Many POPs are classified as being persistent, bioaccumulative, and toxic: properties that shape their movement and fate in the environment, and dictate their impact on human and animal health. Accidental exposures in humans or widespread ecological impacts in the past led to a variety of national regulations and international treaties, such that some of the more notorious chemical classes are no longer widely used. Such “legacy” POPs contrast the continued use of POPs for which the evidence is less clear and the emergence of new chemical products for which a limited understanding exists (e.g., polybrominated diphenylethers, PBDEs). The extent to which experience from past failures and subsequently developed risk assessment paradigms will protect the biosphere from impacts in the future remains open to debate. Unfortunately, the witnesses are often those far removed from the source of the chemical and represent constituent groups with little influence on regulations, such as the highly POP‐contaminated subsistence‐oriented Inuit people of Arctic Canada, or the killer whales of the NE Pacific Ocean.
Mobile Phones and Health Risks
Authors: Blettner Maria , Samkange‐Zeeb Florence
Published: September 2008

Epidemiological studies which have been conducted up to date have found little evidence linking short‐term mobile phone use to an increased risk of brain tumors. Some studies have shown slight increases in risk for long‐term mobile phone users (>10 years). These results are however based on small numbers and need confirmation.
Cumulative Risk Assessment for Environmental Hazards
Authors: Sexton Ken
Published: September 2008

Humans and ecosystems face combined threats from exposures to numerous biological, chemical, physical, and psychosocial stressors in the environment. Cumulative risk assessment is a tool for organizing and analyzing information about combined adverse effects from collective exposure to multiple environmental stressors across all relevant sources, pathways, and routes. The process involves three interrelated phases: planning, scoping, and problem formulation; data analysis; and interpretation and risk characterization. Although in its infancy, cumulative risk assessment promises, over time, to provide decision makers with more realistic answers to pressing environmental questions.
B
ayes' Theorem and Updating of Belief
Authors: Lee Peter M.
Published: September 2008

The conditional probability of an event
A
given an event
B
is defined as Pr(
A
 ∩ 
B
)/Pr(
B
). If you start with hypotheses
H

i

for which your initial assessments of probability (your
prior probabilities
) are Pr(
H

i

) and you then observe data, the probability of which is dependent on which of the hypotheses is true (supposing them to be exclusive and exhaustive, so that one and only one of them is true), then
Bayes' theorem
allows you to update your assessments of probability to
posterior probabilities

Sometimes we have an unknown parameter that can take any value in a continuous range and the observation we are thinking of can similarly take values from a continuous range. In this case, we get Bayes' theorem for random variables in the form

These formulae can be used sequentially, taking the posterior form after one or more observations as the prior for the next.
Decision Conferencing/Facilitated Workshops
Authors: Phillips Lawrence D.
Published: September 2008

Decision conferencing provides a structured group approach for assessing the risks associated with several options, with expert judgment providing some or all of the inputs and models based on decision theory used to combine the inputs into an overall assessment. A facilitated workshop brings together experts about a specific risk criterion, with an impartial facilitator taking the group through the process of discussing the meaning of each criterion, establishing measurement scales, and appraising options on the scales. At a subsequent decision conference attended by the key players, the facilitator guides participants through the process of making the final judgments about inputs and trade‐offs between criteria that allow the decision theory model to bring together all the inputs to provide an overall ordering of the options. This approach allows the collective experience of specialists and key players to be brought to bear on issues of risk assessment, and creates committed alignment backed by the authority of the entire group.
Decision Modeling
Authors: Smith Jim Q. , Thwaites Peter
Published: September 2008

Bayesian decision analysis provides a coherent and logical methodology for not only the expression and exploration of potential risks under uncertainty, but also a framework through which to discover an optimal course of action. Uncertainties are expressed probabilistically and optimal acts discovered through maximizing the decision maker's expected utility. The basic methodology is illustrated by a simple example of agricultural risk management.
Multiattribute Utility Functions
Authors: Stewart Theodor J.
Published: September 2008

The multiattribute utility function is a means of systematically analyzing preferences in the context of multiattribute or multicriteria decision making under conditions of uncertain or risky outcomes, and thus integrates concepts from multiattribute value measurement and expected utility theory. The process starts by eliciting utility functions for each individual attribute in turn, which then have to be aggregated in some sense in order to form the overall multiattribute utility function. A weighted additive aggregation is simple, but is demonstrated to force certain preference properties that may not be justifiable in certain decision contexts. For this reason, weaker sets of assumptions have been developed, which lead either to multiplicative or multilinear aggregation rules. Unfortunately, the weaker the sets of assumptions made, the more the decision maker is required to make cognitively challenging judgments between multivariate lotteries.
Societal Decision Making
Authors: French Simon
Published: September 2008

The context of decision making in the public sector is somewhat different from that in business and industry. Decision making may not be confined to a small management team or board of directors; but rather involves many politicians, officials, and different political institutions. There is a need to address many stakeholder concerns and be seen to do so, particularly when the decision involves major technological, health, environmental, or other risks. The issues addressed tend to be more complex and involve many factors and uncertainties. Here, we discuss how decision and risk analyses fit into such contexts and the differences between their approach and that of cost–benefit analysis.
Value Function
Authors: Sarin Rakesh K.
Published: September 2008

Value functions rank order a set of objects. A measurable value function orders the differences in strength of preference between pairs of objects.
Geographic Disease Risk
Authors: Wakefield Jon
Published: September 2008

The examination of disease risk summaries across space has a long history in epidemiology and may be performed for disease mapping, cluster detection, examination of clustering, or spatial regression. Often these exercises are carried out to gain clues to etiology with respect to putative environmental exposures. The spatial nature of the data has positive and negative aspects. For disease mapping, the spatial aspect can be exploited, since one expects near‐by areas to be more similar in terms of their risk. For spatial regression, the analysis is complicated by the spatial aspect because the usual independence assumption of conventional analysis methods is violated, and there may also be “confounding by location”. As in all epidemiological investigations, interpretation depends crucially on issues of data quality, in particular, errors in population and disease counts and exposure misclassification. Ecological bias, which arises when aggregate associations are assumed to apply at the level of the individual, is also an issue that requires serious consideration.
Assessment of Risk Association with Beryllium Exposure
Authors: Cavender Finis
Published: September 2008

Beryllium is the lightest metal and has an excellent strength to weight ratio. It is used as a hardening agent in nonsparking tools, ceramics, computers, and aerospace industries. Early reports of health effects of beryllium salts were thought to be due to the anion rather than beryllium. Numerous health effects have been noted in occupational settings and in the populace surrounding industrial plants. Acute beryllium disease consists of pulmonary edema and severe pneumonitis caused by short‐term, massive exposures to soluble salts. Usually, workers who survive the edema are disease free for 1 year, but may develop lung cancer in later years. When hygienic standards were implemented in 1949, no new cases of acute beryllium disease were seen except following industrial accidents. Chronic beryllium disease (CBD) is an immune‐mediated disease starting with sensitization and progressing to a debilitating granulomatous disease; however, CBD can be successfully treated. In 2006, the ACGIH recommended lowering the TLV to 0.05 µg m−3as an 8‐h time‐weighted average along with a sensitization and a skin notation. When this TLV is fully implemented, it will virtually eliminate CBD.
Absolute Risk Reduction
Authors: Everitt Brian S.
Published: September 2008

Perceptions of risk are influenced by the way the risks are presented.Relative risk is often used in medicine, but for the individual, the absolute risk and the absolute risk reduction are more important. The latter is often presented in an alternative form as
number needed to treat.
Relative Risk
Authors: Landau Sabine
Published: September 2008

This article explains the difference between absolute and relative risk and formally defines the relative risk ratio.
Linkage Analysis
Authors: Shao Yongzhao
Published: September 2008

Linkage analysis is an important initial step in localizing and identifying genes in the chromosomes that underlie many human diseases and other traits of interest. Frequently, linkage analysis proceeds by comparing the inheritance pattern of the trait and that of the genetic markers to establish linkage between the trait and marker loci. This article provides a brief overview of commonly used methods for linkage analysis, which include model‐free and model‐based methods for mapping qualitative‐ and quantitative‐trait loci. Issues on the design of linkage studies are also briefly discussed.
Microarray Analysis
Authors: Kafadar Karen , Bjork Kathe E.
Published: September 2008

Microarray‐based experiments querying tens of thousands of genes, peptides, and other cellular components of varying organisms have evolved over the past several years to become a cornerstone screening method for numerous diseases and conditions, especially those with known genetic etiologies. They are also rapidly becoming a standard tool among researchers investigating reactions within and between hosts and agents relative to shifting environments and environmental conditions, and for quantitatively assessing risk to the health of humans, animals, and plants. The most commonly used microarray technologies for investigating gene expression are described herein, as well as examples of current and future research areas in public health.
Risk Characterization
Authors: Rai Shesh N.
Published: September 2008

Probabilistic risk characterization is now commonly used to estimate and predict risk caused by exposure to unwanted substances. We characterize risk as a function of risk factors and discuss two examples: one is based on a multiplicative model of contaminated drinking water exposure and the other is based on a general model of radon exposure. The risks associated with contaminated drinking water are easy to characterize and understand; however, those associated with radon exposure are not as well understood as the former. Radon exposure is an established risk factor for human lung cancer, and these data are used to project radon‐associated lung cancer risks to the general population.
Cohort Studies
Authors: Pearce Neil , Checkoway Harvey
Published: September 2008

Cohort studies provide the most comprehensive approach for evaluating overall patterns of health and disease. Every epidemiologic study should be based on a particular population's experience over time. The cohort design has the advantage, relative to other study designs, that ideally it includes all of the relevant person‐time experience of the population under study, whereas other study designs, such as case–control and cross‐sectional studies, involve sampling from that experience. However, cohort studies, particularly prospective studies, may be relatively expensive in terms of time and resources, and typically involve the efforts of epidemiologists, environmental scientists, occupational hygienists, biostatisticians, computer programmers, and clerical staff. The specific features of cohort studies that we present in this chapter include cohort enumeration, follow‐up procedures, the choice of comparison populations, person‐time analysis, stratified analysis, subcohort analyses, analyses of cumulative exposure, and latency and induction time.
Attributable Fraction and Probability of Causation
Authors: Greenland Sander
Published: September 2008

This article discusses the concepts and definitions associated with terms such as attributable fraction, attributable risk, and probability of causation. It is emphasized that there are several incompatible definitions of attributable risk. Some of these definitions apply to concepts of excess caseload and excess rate, which are estimable from epidemiologic data; others apply to concepts of etiologic fraction and probability of causation, which are not estimable without strong assumptions about biologic mechanisms of disease.
Estimation of Mortality Rates From Insurance Data
Authors: Melnick Edward L. , Tenenbein Aaron
Published: September 2008

The problem of using insurance intercompany data to determine mortality rates has had a long history of development. The development of these mortality rates involves two steps, namely, the estimation of the rates from the data and the interpolation and smoothing of these rates to conform to known trends in the mortality rates by age, and time since first insured. This paper will discuss the different methods used to determine these rates and discuss the advantages and disadvantages of each approach.
Occupational Cohort Studies
Authors: Rockette Howard E.
Published: September 2008

Retrospective occupational mortality studies have been one of the most effective mechanisms for investigating the relationship of exposure to chronic disease. Occupational cohorts are well suited to investigate such exposure‐disease relationships because exposure is usually better characterized in the workplace than in most other settings. In this chapter, we address issues related to the design, conduct, analysis, and interpretation of retrospective occupational mortality studies. We summarize aspects of cohort definition and verification, determination of vital status, coding of death certificates, and incorporation of exposure measurements. Selected mortality summary indices are reviewed, followed by a discussion of regression models appropriate for the analysis. Finally, we briefly describe some of the designs that combine the advantages of case–control and cohort studies.
Randomized Controlled Trials
Authors: Goldberg Judith D. , Belitskaya‐Levy Ilana
Published: September 2008

In the medical sciences, the ideal experiment is the randomized controlled clinical trial (RCT) in which the subject or patient is randomized to treatment with an experimental agent or control (either placebo or other active treatment), in a double‐blind manner. The RCT is the “gold standard” in medical research to establish the efficacy of a new treatment or intervention. The benefits of such an experiment are that any observed differences between the two groups (or more groups) with respect to the outcome can be attributed to the treatment and are without bias. That is, confounding is controlled and selection bias is removed since neither the subject nor the investigator chooses the intervention.

In this entry, we review the issues in the design, conduct, analysis, and interpretation of a randomized clinical trial.
Compliance with Treatment Allocation
Authors: Dunn Graham
Published: September 2008

Estimation of the effects of treatments in a randomized clinical trial is usually based on the intention‐to‐treat (ITT) principle. This procedure leads to an estimate of the average causal effect of treatment allocation based on everyone in the trial. When there are departures from randomized allocation (noncompliance), the ITT effect does not measure the impact of the receipt of treatment. The ITT estimate is an attenuated estimate of the effect of the receipt of treatment. We obtain a valid estimate of the latter by estimating the average effect of random allocation (i.e., ITT effect) on outcomes in the subgroup of participants who receive treatment if and only if they are randomized to its receipt (i.e., the compliers)—this is known as thecomplier‐average causal effect(CACE) of treatment. Noncompliance is often associated with loss to follow‐up. We illustrate simple methods to calculate CACE estimates both with and without the complication of missing outcme data.
Repeated Measures Analyses
Authors: Liu Mengling , Shao Yongzhao
Published: September 2008

Repeated measures data arise when multiple measurements of response variables and/or covariates are obtained in sequence from each experimental unit and occur frequently in many disciplines. The repeated measures design provides opportunities for investigators to examine the pattern of response variable under different measurement occasions, to understand the relationship between repeated outcomes and covariates, and to discern the dependence pattern among the repeated measurements. A special feature of repeated measures data is that measurements from the same unit are usually positively correlated; thus, valid and effective analysis of repeated measures must deal with this within‐subject correlation. In this article, we review some of the analysis methods for repeated measures, which include univariate summary methods, multivariate analysis of variance, parametric random‐effects methods, marginal generalized estimation equation methods, and modern semiparametric and nonparametric approaches. Some simple methods for data exploration and graphical display are demonstrated using real data examples. We also briefly discuss related computational tools and statistical packages.
Clinical Dose–Response Assessment
Authors: Burman Carl‐Fredrik
Published: September 2008

Essentially all pharmacologically active drugs give adverse effects at high enough doses. On the other hand, the intended effect is generally increasing with dose. A critical question is, therefore, which dose should be chosen, that is, which dose minimizes the total risk associated with both insufficient therapeutic effect and side effects. This article briefly describes the estimation and weighting of different risk components, as well as the design of dose‐finding trials.
Inferiority and Superiority Trials
Authors: Mukhi Vandana , Shao Yongzhao
Published: September 2008

Superiority clinical trials are often conducted to establish superior treatment effect of an experimental treatment compared to the control treatment. Noninferiority clinical trials are frequently designed to establish that the treatment effect of an experimental treatment is not much worse than that of an active control treatment, while the experimental treatment may offer other advantages such as reduced toxicity and/or cost. This article provides a brief introduction to the design and analysis of superiority and noninferiority clinical trials. The main issues discussed include, randomization and blinding during treatment allocation, prespecification of the noninferiority margin, the requirement of effective active control in noninferiority trials, risk of type I and type II errors, sample size and power analysis, and assessment and comparison of treatment effects between different treatment groups.
Blinding
Authors: Belitskaya‐Levy Ilana
Published: September 2008

The objective of blinding is to contribute to the elimination of bias that may be introduced intentionally or unintentionally by the various individuals involved in a research study. Different types of bias (e.g., information bias, detection bias, etc.) can be prevented or reduced by blinding patients, health care providers, data collectors, outcome assessors, and/or data analysts. Even when patients and those administering treatment cannot be blinded (e.g., if the study involves a surgical procedure), blinding of outcome assessment can still be possible and helps reduce detection bias. Owing to ambiguity in blinding methodology (e.g., single‐blinded, double‐blinded, etc.), it is important to carefully report the blinding status of all individuals involved in a research study and the blinding mechanism. This helps the reader judge the validity of the study and ensures the credibility of its results. In order to ensure the success of blinding in preventing or reducing bias, it is important to assess its efficacy before the study begins. However, assessment of the efficacy of blinding at the end of the study might produce ambiguous results as failures in blinding are often confounded with treatment efficacy and adverse events.
Truncation
Authors: Rai Shesh N. , Sun Jianguo
Published: September 2008

In collection and analysis of real‐life data, we always encounter incomplete data. Censoring and truncation are types of incomplete data that arise owing to a type of sampling scheme. We describe the differences and similarities between censoring and truncation. We also describe types of truncation and the role of truncation in the analysis of real‐life data.
Competing Risks
Authors: Ray Rose M.
Published: September 2008

Competing‐risk models are used to examine the relationship between different risk factors to study different types of failures that may occur in the same population. What would happen to the remaining risks if one or more causes of failure could be eliminated is a question that may be addressed with these models under some conditions. Competing risks are generally evaluated by comparing the predicted time to failure or time to event for the risks being considered.

In this article, the mathematical formulation of the problem is described and statistical methods for estimating competing risk are discussed. Applications of competing risk in the fields of engineering, medicine, economics, and finance are examined with specific examples.
Toxicity (Adverse Events)
Authors: Levinson Benjamin , Goldberg Judith D.
Published: September 2008

In the setting of a clinical trial, an adverse event (AE) is any unfavorable change in the structure, function, or chemistry in a patient or subject that is temporally associated with the participation in a clinical trial, regardless of whether or not this change is considered related to the therapeutic intervention. An AE, which is treatment emergent, is classified by its relationship to the therapeutic intervention. In a randomized controlled trial, only increases in AEs in the intervention group compared with the control group are attributable to the intervention. A serious adverse event (SAE) is an AE that is at least possibly related to the therapeutic intervention that results in death, life‐threatening experience, inpatient hospitalization or prolongation of existing hospitalization, persistent and significant disability or incapacity, jeopardy to patient and requires intervention, or a congenital anomaly or birth defect. An unexpected AE is as any unanticipated AE that is possibly related to the therapeutic intervention. AEs are graded and classified using standardized coding dictionaries into broad categories based on anatomy and/or pathophysiology, and by specific terms within each category. In clinical trials, we perform routine laboratory tests prior to the start of the study, to screen patients for eligibility into the trials, and during the trials to ensure patient safety. AEs and SAEs are monitored during a study and are summarized to provide assessments of the safety of the treatment or intervention.
Safety
Authors: Goldberg Judith D. , Levinson Benjamin
Published: September 2008

Patient safety is of primary importance; safety must be ensured throughout the clinical trials that are conducted during the process of developing a new drug (intervention) and maintained after the drug is approved for use. Once a drug is approved for marketing, the safety data that are available provide patients and physicians with the ability to assess safety risks relative to efficacy benefits. Approaches for safety monitoring, during clinical trials, at the patient level, at the clinical trial level, and across clinical trials are described. Issues specific to the assessment of patient safety after marketing approval, particularly, active and passive postmarketing surveillance, are discussed.
Statistical Arbitrage
Authors: Lehoczky John P. , Schervish Mark J.
Published: September 2008

This article gives historical background on the emergence of statistical arbitrage, and then describes some of the advances and tools in the area. These include pairs trading, cointegration, value strategies, contrarian strategies, momentum strategies, volatility trading, evaluation of strategies, and technical analysis.
History of Epidemiologic Studies
Authors: Samet Jonathan M. , Apelberg Benjamin J.
Published: September 2008

Epidemiology can be defined as the scientific method used to assess health and its determinants in populations. Central to the method are measures of disease occurrence, and study designs for identifying causes of disease and quantifying their strength as causal agents. Because epidemiological studies directly address threats to human health, their findings, when available, have received emphasis in qualitative and quantitative risk assessments. Epidemiological data have the notable strength of being based on the “right” species, “real‐world” exposures, and often the full range of susceptibility. Observational data often have inherent limitations, but strategies are available to handle them. The development of radiation risk models from epidemiological studies and the use of the models in quantitative risk assessment have set a risk assessment paradigm grounded in epidemiological research.
Logistic Regression
Authors: Dash Chiranjeev , Kleinbaum David G.
Published: September 2008

Logistic regression is a popular statistical tool used to analyze epidemiologic data. It is typically used when the outcome or disease/event under study is dichotomous. Logistic regression involves postulating a model to explain the disease process (dependent variable) using exposure and control variables (independent variables), fitting the model to the data obtained from the study using maximum likelihood procedures, and finally, making appropriate statistical inferences from the results of the modeling approach. The measure of effect reported in a logistic regression analysis is known as the
odds ratio
(
OR
). This measure describes the strength of the relationship between the exposure of interest and the outcome. The
OR
obtained can be
adjusted
for other covariates, known as
confounding
or
control variables
, by including these variables in the logistic model. Statistical inference in logistic regression analysis involves hypothesis testing and confidence interval estimation. Wald test and the likelihood ratio test are commonly used methods for testing hypotheses in logistic regression. In this chapter, we provide a brief overview of logistic regression and illustrate the use of this popular procedure using data from an epidemiologic study as an example.
Adverse Selection
Authors: Kunreuther Howard
Published: September 2008

If an insurer sets a premium based on the average probability of a loss in an entire population, those at higher‐than‐average risk for a certain hazard will benefit most from coverage, and hence will be the most likely to purchase insurance for that hazard. In an extreme case, the poor risks will be the only purchasers of coverage, and the insurer can expect to lose money on each policy sold. This situation, referred to as
adverse selection
, occurs when the insurer cannot distinguish between members of good‐ and poor‐risk categories in setting premiums. This article discusses the implications of adverse selection for insurance.
Hazards Insurance: A Brief History
Authors: Kunreuther Howard
Published: September 2008

This article briefly surveys the historical development of insurance against natural hazards and acts of God, including marine, fire, and wind hazards, leading to the rise of modern property‐casualty insurance companies.
Pharmaceuticals in the Environment
Authors: Schlenker Gerd
Published: September 2008

Up to 95% of pharmaceuticals used in human and veterinary medicine are excreted and land in the aquatic or terrestrial environment. Most of these substances are degraded with difficulty in the environment, resulting in biological effects on bacteria, plants, and animals. Antibiotics and the development of antibiotic‐resistant bacteria are a special problem. At present, environmental damage due to pharmaceuticals is not yet an acute problem for people and animals. Particularly in the European Union, special measures have been taken to reduce the risk of environmental damage due to drugs.
Syndromic Surveillance
Authors: Frickerk Ronald D.
Published: September 2008

Syndromic surveillance is the regular collection, analysis, and interpretation of real‐time and near‐real‐time indicators of diseases and other outbreaks by public health organizations. Motivated by the threat of bioterrorism, syndromic surveillance systems are being developed and implemented around the world. In a 2004 systematic review of publicly available information, 115 surveillance systems were identified, of which 29 were found that were designed specifically for detecting bioterrorism. In spite of their development, it is unknown how effective these systems will be at quickly detecting a bioterrorism attack. However, under the rubric of electronic biosurveillance, the goal of some of these systems has recently been expanded to include both early event detection and situational awareness, so that the focus is not simply on detection, but also on response and consequence management. Regardless of their utility for detecting bioterrorism, there seems to be a consensus that these biosurveillance systems are likely to be useful for detecting and responding to natural disease outbreaks such as seasonal and pandemic flu, and thus they have the potential to significantly advance and modernize the practice of public health surveillance.
Coding: Statistical Data Masking Techniques
Authors: Slavkovic Aleksandra B.
Published: September 2008

Coding broadly refers to altering the data in some way. In computer science literature, it may refer to encryption, compression, or error correction. These are relevant for cryptography and cryptanalysis, and for data privacy and homeland security. In statistical confidentiality literature, coding encompasses various data‐masking techniques. These techniques generally introduce bias and variance to data. The question is how much and what kind of bias and variance is introduced and how this influences the utility of data‐mining algorithms and precision of identification, that is the false positive error rate. This article gives a brief overview of statistical disclosure limitation techniques for data masking, pointing out the likelihood of their use and potential impacts for data privacy.
Digital Governance, Hotspot Detection, and Homeland Security
Authors: Patil Ganapathi P. , Acharya Raj , Phoha Shashi
Published: September 2008

Effort is in progress for spatial and spatiotemporal hotspot detection, early warning, and security. A hotspot can mean an unusual phenomenon, anomaly, aberration, outbreak, elevated cluster, critical area, or object‐recognition and tracking. The declared need may be for monitoring, etiology, early warning, or management. The responsible factors may be natural, accidental, or intentional.

This article attempts to address concepts, methods, tools, and a variety of case studies and applications of interest to agencies, academia, and the private sector involving societal issues for homeland security, such as public health, infectious disease, invasive species, crop pathogens, biosecurity, hospital networks and syndromic surveillance, drinking water network systems, crisis management, and others.
R&DPlanning and Risk Management
Authors: Cox Louis A.
Published: September 2008

This article discusses quantitative concepts and methods for making risk‐management decisions about R&D projects. Such decisions include what projects to fund, the order in which to attempt different activities within an R&D project, when to abandon projects that are not working out as hoped, and how to structure the incentives of R&D project managers and investors to take best advantage of the opportunities represented by risky projects.
NoFaultFound
Authors: Jones Jeffrey A.
Published: September 2008

This article describes the No Fault Found (NFF) phenomenon that occurs when a system is tested after a failure is reported but no failure is found during the test. This basic phenomenon is known by a large number of terms, which are often abbreviated to three letter acronyms, and are described in this article. The level of occurrence of such false positive faults in various technical fields is described and a number of possible reasons for the incidence are discussed. Finally a discussion of the management of such faults within the context of quantitative risk assessment is included.
Environmental Risk Assessment of Water Pollution
Authors: Al‐Khalidi Abdul Sattar
Published: September 2008

Environmental risk due to water pollution is an ongoing every day problem. Municipal wastewater systems are one of the largest pollution sources to surface waters. Other major sources include residential and industrial discharges, and agricultural runoff, dumped in to rivers directly or by seepage from contaminated groundwater. Threats from chemicals may come from sources such as pesticide use and waste disposal. Air pollution and land pollution also affect water quality.

Statistical methodologies and probabilistic models play major roles in assessing environmental water pollution risk, since risk is a “probabilistic measure for degree of harm associated with pollutant levels”. The analyses presented in this article are designed to assess the risk of surface water pollution from bacterial and chemical sources. The probabilistic approach in the analysis is designed to test the significance of pollution measuring parameters in the probability distribution model. This article gives illustrations of two statistical techniques that can assess the risk of surface water pollution from bacterial and chemical sources, as applied to a sample from Tigris river water after it had passed through the Baghdad metropolitan area. The first is an adaptation of existing methods to lognormal data, while the second is an application of intervention analysis with time series data.
Causal Diagrams
Authors: Greenland Sander , Pearl Judea
Published: September 2008

From their inception, causal systems models (more commonly known asstructural‐equations models) have been accompanied by graphical representations or path diagrams that provide compact summaries of qualitative assumptions made by the models. These diagrams can be reinterpreted as probability models, enabling use of graph theory in probabilistic inference, and allowing easy deduction of independence conditions implied by the assumptions. They can also be used as a formal tool for causal inference, such as predicting the effects of external interventions. Given that the diagram is correct, one can see whether the causal effects of interest (target effects, or causal estimands) can be estimated from available data, or what additional observations are needed to validly estimate those effects. One can also see how to represent the effects as familiar standardized effect measures. The present article gives an overview of the following: (a) components of causal graph theory; (b) probability interpretations of graphical models; and (c) methodological implications of the causal and probability structures encoded in the graph, such as sources of bias and the data needed for their control.
Survival Analysis
Authors: Landau Sabine
Published: September 2008

Survival analysis is the study of the distribution of event times (death, relapse, and completion of an experimental task). A distinguishing feature of survival or time to event data is the presence of censoring, that is, all that is known sometimes is that an event occurred later than a given point in time. This article provides a brief description of the classical methods for nonparametric, semiparametric, and fully parametric analysis of survival times.
Temperature profile in a reverse flow reactor for catalytic partial oxidation of methane by fast IR imaging
Authors: Simeone M. , Salemme L. , Allouis C. , Volpicelli G.
Published: September 2008

Catalytic partial oxidation of methane with air was investigated in a reverse flow reactor with commercial Rh/Al2O3 catalyst in pellets. Temperature profile of the catalyst bed was measured by fast IR thermography and product composition was measured with a continuous gas analyzer. The effect of internal heat recovery on reactor performance and catalyst thermal stress is presented and compared with steady state operation. Feed direction switching time, total flow rate, and methane to oxygen ratio were investigated as process operating parameters. Data of catalyst bed temperature evolution during the flow cycle are presented and discussed. Comparison of dynamic heat integration with external feed preheating in terms of product composition and catalyst temperature profile is also presented. © 2008 American Institute of Chemical Engineers AIChE J, 2008
Geographic altitude, surnames, and height variation of Jujuy (Argentina) conscripts
Authors: Bejarano Ignacio F. , Dipierri José E. , Andrade Alberto , Alfaro Emma L.
Published: September 2008

The height records of 48,589 conscripts born in Jujuy between 1870 and 1960 were examined in order to study the variation in adult male height (AMH) in terms of geographical altitude and surnames. Data were clustered by origin of surnames (divided into native and foreign names), decades, and according to the four Jujenean geographical regions distributed along an altitudinal gradient (Puna, Quebrada, Valle, and Ramal). The variation of surnames, geographical altitude, and time on human height were examined by analysis of variance. Regardless of the drafting year, individuals in the four regions bearing foreign surnames proved significantly taller (P < 0.001) than those who had a native surname. Average height, regardless of ethnic group, presented a reverse relationship to geographical altitude. A higher AMH was found in individuals bearing foreign surnames in Jujuy and lower ones in the population located in the highlands and bearing native surnames. Interregional and intergroup AMH variations would be affected by the complex interaction between geographical altitude and factors associated to it and by the ethnic characteristics of these population as well. Am J Phys Anthropol 2009. © 2008 Wiley‐Liss, Inc.
The petrous bone—A new sampling site for identifying early dietary patterns in stable isotopic studies
Authors: Jørkov Marie Louise S. , Heinemeier Jan , Lynnerup Niels
Published: September 2008

Intraskeletal variation in the composition of carbon (δ13C) and nitrogen (δ15N) stable isotopes measured in collagen is tested from various human bones and dentine. Samples were taken from the femur, rib, and petrous part of the temporal bone from well‐preserved skeletons of both adults (n = 34) and subadults (n = 24). Additional samples of dentine from the root of 1st molars were taken from 16 individuals. The skeletal material is from a medieval cemetery (AD 1200–1573) in Holbæk, Denmark. Our results indicate that the petrous bone has an isotopic signal that differs significantly from that of femur and rib within the single skeleton (P < 0.001 and P < 0.001, respectively), with only minor variation seen between femur and rib. On the other hand, there was no significant difference between the petrous bone and the 1st molar. The intraskeletal variation may reflect differences in turnover rate among skeletal elements. The inner periosteal layer of the petrous bone is formed in uterus and does not undergo any further remodelling after the age of 2 years, whereas the rib and femur have a continuous turnover rate of ∼5 and 10–20 years, respectively. From the results of this study it is believed the petrous bone may be a new useful bone element and a supplement or a proxy for teeth in the analysis of early dietary patterns as it may reflect diet in fetal stages and early years of life. Am J Phys Anthropol 2009. © 2008 Wiley‐Liss, Inc.
The effects of total hip arthroplasty on the structural and biomechanical properties of adult bone
Authors: Peck Joshua J. , Stout Sam D.
Published: September 2008

The responsiveness of bone to mechanical stimuli changes throughout life, with adaptive potential generally declining after skeletal maturity is reached. This has led some to question the importance of bone functional adaptation in the determination of the structural and material properties of the adult skeleton. A better understanding of age‐specific differences in bone response to mechanical loads is essential to interpretations of long bone adaptation. The purpose of this study is to examine how the altered mechanical loading environment and cortical bone loss associated with total hip arthroplasty affects the structural and biomechanical properties of adult bone at the mid‐shaft femur. Femoral cross sections from seven individuals who had undergone unilateral total hip arthroplasty were analyzed, with intact, contralateral femora serving as an approximate internal control. A comparative sample of individuals without hip prostheses was also included in the analysis. Results showed a decrease in cortical area in femora with prostheses, primarily through bone loss at the endosteal envelope; however, an increase in total cross‐sectional area and maintenance of the parameters of bone strength,Ix,Iy, andJ, were observed. No detectable differences were found between femora of individuals without prostheses. We interpret these findings as an adaptive response to increased strains caused by loading a bone previously diminished in mass due to insertion of femoral prosthesis. These results suggest that bone accrued through periosteal apposition may serve as an important means by which adult bone can functional adapt to changes in mechanical loading despite limitations associated with senescence. Am J Phys Anthropol 2009. © 2008 Wiley‐Liss, Inc.
Influence of methacryl polyhedral oligomeric silsesquioxane on the thermal and mechanical properties of methylsilicone resin
Authors: Liu Y. R. , Huang Y. D. , Liu L.
Published: September 2008

A methacryl polyhedral oligomeric silsesquioxane (POSS)‐reinforced methylsilicone resin was prepared in this work. The structures of the obtained products were confirmed with Fourier transform infrared and atomic force microscopy. The influence of methacryl‐POSS on the thermal behavior of the methylsilicone resin was studied by thermogravimetric analysis and isothermal thermogravimetric analysis. The results showed that the thermal stability of the methylsilicone resin was improved, and the degree of thermooxidative degradation was lowered; this was due to the retardation of polymer chain motion and the formation of a protective layer of SiO2. The interlaminar shear strength and flexural strength of quartz fiber/methylsilicone resin composites were tested to determine the effects of methacryl‐POSS on the mechanical properties of methylsilicone resin. The results revealed that the mechanical properties of the methylsilicone composites also increased obviously after the incorporation of methacryl‐POSS because of the increase in the cure degree and rigidity of the resin. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008