Public health policy prospectively and retrospectively addresses the consequences of events ranging from the commonplace to the catastrophic. Informing policymakers and stakeholders by enhancing their understanding of complex causation to justify remedial or precautionary actions is a critical science-policy task. In this book, the key aspects of catastrophes (regardless of their nature) and routine events are identified through a common framework for their analyses, and the analyses of the consequences associated with the potential occurrence of these events also are discussed. The book is not about disaster planning; instead, it is focused on analysis and causation in the context of informing – rather than formulating – public health policy.
The author aggregates and fuses scientific information and knowledge in public health policy-science using alternative but complementary methods. The book first focuses on the analysis of catastrophes and commonplace events; the focus then shifts to causal models of multifactorial diseases, particularly at low doses or dose-rates, associated with these events. Topics explored among the chapters include:
- Policy and Legal Aspects of Precautionary Choices
- Catastrophes, Disasters, and Calamities: Concepts for Their Assessment
- Uncertainty: Probabilistic and Statistical Aspects
- Aggregating Judgments to Inform Precautionary Decision-making
The aim of the book is to show that the analyses of events are fundamentally similar, regardless of whether the concern is a global catastrophe or commonplace.
Analysis of Catastrophes and Their Public Health Consequences is a text that should engage students, instructors, and researchers in public health, science policy, and preparedness research, as well as serve as a useful resource for policy analysts, practitioners, and risk managers.
€53.49
Table of Content
Chapter 1: Introduction – This chapter introduces and develops the needs and means for obtaining an integrated analysis of information and knowledge about disease processes and their possible risk factors for consequences such as death and morbidity resulting from catastrophic events. The book has an epidemiological causation context but also includes key mechanistic (e.g., biological) aspects of causation as well as other risk factors (e.g., behavioral). It focuses on modeling using heterogeneous data and knowledge to inform policy about consequences that increase risks of toxic and carcinogenic effects from low exposures generated by the combination of natural (e.g., earthquakes) and industrial incidents (e.g., mishaps in chemical and other processes). The focus is causal because causation is essential to science-policy, by combining qualitative and quantitative descriptions that are legally defensible and non-arbitrary. An important aspect of this chapter is the use of surveillance data and their contribution to rapid preventive actions in response to catastrophic incidents – if actions were possible – even though this aspect is not central to the core interest of the book.
Chapter 2: Aggregation of Raw Data: Scales of Measurement, Averages, and Fuzzy Integrals – An important aspect of modeling disease processes requires summarizing data from different sources (their scales of measurement and dimensions) into a single representative number. This chapter exemplifies averaging and other formal means to combine raw information into single numbers; aggregation is the result of combining through different mathematical operators. These operators include the intersection (the min), various types of compensatory and non-compensatory averages, and the union (the max). This chapter includes a discussion and examples of scales of measurement (e.g., nominal, ordinal, and cardinal) as used in public health to describe the levels of risk factors and associated responses as well as the permissible operations using these scales.
Chapter 3: Probabilistic Causation – The chapter discusses and exemplifies probabilistic causation, in the context of the advantages and main limitations of probabilistic methods. The focus is on the predictive aspects of causation, which is the essential reason for modeling cause-and-effect.
Chapter 4: Policy and Representations of Incertitude – Policy is informed by judgments, hard numbers, models of various sorts, and vague propositions that are used routinely to describe events and their possible causes. This chapter exemplifies modeling causation through fuzzy controllers and fuzzy operations (e.g., union and intersection) that aggregate membership by using aspects of established causal models of cancer, respiratory conditions, and other diseases. The importance of these to inform policy-science, relative to what might be obtained from probabilistic methods, is discussed and exemplified.
Chapter 5: Network Analysis
– The chapter deals with Boolean, Bayesian, fuzzy (controllers), and other networks as means to develop graphical and mathematical aspects of causation, particularly when the disease process is deconstructed into different and heterogeneous sub-processes. Complex causation, modeled by Bayesian-probabilistic Directed Acyclic Graphs, is exemplified in the context of science-policy choices in public health. Examples from cancer and other multifactorial disease modeling are used to clarify the concepts developed in this chapter. This chapter also includes computing with fuzzy methods such as forward (data-driven search: begin with the data to produce the conclusion), and backward (goal-based search: begin with the conclusion to produce the antecedent) chaining. The advantages and disadvantages of the methods are exemplified and discussed in the context of informing science-policy rather than being extremely rigorous.
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Chapter 7: Multifactorial Disease
Cause-and-Effect –
The Evidence – The information developed in Chapters 5 and 6 are used to model the public health consequences of catastrophic events. The focus of the integration is the effects at low levels of exposure and response that are generally different from high exposure and multifactorial outcomes. Causal, disease-specific modeling should formally identify (conditional on the current state-of-knowledge) different aspects of the sets of risk factors. This chapter also discusses the effect of biases that affect epidemiological studies and the reliability of their results and describes how this book will help diminish their effect and result in more reliable knowledge. The chapter concludes with important aspects of epidemiological modeling, namely the specification error, for low exposures. This error is committed not by accounting for disease-specific risk factors and confounding variables, but rather by relying on a much simplified univariate dose-response model.
Chapter 8: Mechanistic and Probabilistic Modeling of Events Leading to Catastrophic Consequences – The mechanisms resulting in catastrophic incidents and the subsequent analysis of the consequences of rare, large-magnitude events are discussed in terms of their steady state, equilibria, chaotic, and other trajectories. Linkages to probability distributions such as power laws are included to exemplify the differences between non-routine failure of engineered systems and natural catastrophes. The implications of an emergent system, with self-organization as the ability to develop a novel structure wholly different from the observed one (e.g., the structure of bronchi does not allow for the prediction of the whole human being), are also discussed regarding prediction to inform policy. This chapter links the probabilistic aspects of these events to their consequences as distributions with fat tails such as the stretched Pareto distribution and others, as well as discusses the possibility of warnings by observing bubbling or other behaviors prior to the unexpected consequence in the data to which a fat tail distribution is suitable. The linkages between an event and its consequence are developed using the methods discussed in the earlier chapters, because an event – e.g., a magnitude 8.0 earthquake – can generate several possible catastrophic consequences depending on factors such as time of day, state of preparedness, and others. Hence, the ability to rebound after the catastrophe requires predictive modeling of the interactions between actors or agents as well as accounting for local logistics and other states of ex-ante preparedness and the unexpected situations that generally follow the aftermath of a catastrophe.
Chapter 9: Social Choice Theory – This chapter contains a discussion of voting and ranking of options based on the aggregation of individual expert scientific opinions. This chapter includes alternative sets of actors-stakeholders, such as would be found at a public meeting about the choice of alternative solutions to a public health problem. Aspects of decision analysis and game theory are useful to analyze these situations and are discussed, although the focus of the chapter is on how the aggregation of opinions and their criteria affect causal models adopted by individual experts and how conflicts in reaching an aggregate choice must be resolved.
Chapter 10: Integration and Fusion of Information and Knowledge in Public Health Policy-Science – The main objective of this concluding chapter is to discuss how modeling should inform public health policy-science options. An important consideration is the assessment of model quality, which can range from statistical considerations of lay individuals’ evaluations to models’ ex-ante prediction of a catastrophe. This aspect is examined in terms of attributes such as credibility, scientific merit, usefulness, and success stories (if available). For instance, credibility suggests aspects of scientific validity ranging from publication in high-impact journals to an official’s ability to explain convincingly her/his findings to a lay audience. This chapter reviews and completes the discussions of the issues raised in the book from the aggregation of raw data to knowledge propagation and fusion.
About the author
Paolo F. Ricci, Ph D,
LLM, is a professor at the EU’s Erasmus Mundus Programs in Italy, Spain, and Portugal. Previously he was a professor at the School of Public Health at the University of Massachusetts at Amherst; professor at Holy Names University in Oakland, California, USA; visiting professor at Xiamen University in China; and, professor at University of Bologna in Italy. For more than 30 years, Dr. Ricci — a senior Fulbright scholar (specialist, 2010-2015) and appointed peer reviewer for Fulbright Specialists selection (2013-2014) — has led qualitative and quantitative analyses in public health and epidemiology and conducted experimental work in, among other countries, the United States, Canada, Italy, Australia, France, Vietnam, China, the Ivory Coast, and the European Union. He was the head of the Environmental Technologies Clearinghouse of the IEA/OECD (with full diplomatic status) and has served as a peer reviewer of U.S. Department of Energy (DOE) activities regarding human health risks from past nuclear weapons tests at the Nevada Test Site. Until 2014, Dr. Ricci was an associate editor of the journal
Environment International for more than 15 years.
Language English ● Format PDF ● Pages 187 ● ISBN 9783030480660 ● File size 4.8 MB ● Publisher Springer International Publishing ● City Cham ● Country CH ● Published 2020 ● Downloadable 24 months ● Currency EUR ● ID 7561004 ● Copy protection Social DRM
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