How Much Uncertainty is Too Much and How Do We Know? A Case Example of the Assessment of Ozone Monitor Network Options

Limited time and resources usually characterize environmental decision making at policy organizations such as the U.S. Environmental Protection Agency. In these climates, addressing uncertainty, usually considered a flaw in scientific analyses, is often avoided. However, ignoring uncertainties can result in unpleasant policy surprises. Furthermore, it is important for decisionmakers to know how defensible a chosen policy option is over other options when the uncertainties of the data are considered. The purpose of this article is to suggest an approach that is unique from other approaches in that it considers uncertainty in two specific ways-the uncertainty of stakeholder values within a particular decision context and data uncertainty in the light of the decision-contextual data-values relationship. It is the premise of this article that the interaction between data and stakeholder values is critical to how the decision options are viewed and determines the effect of data uncertainty on the relative acceptability of the decision options, making the understanding of this interaction important to decisionmakers and other stakeholders. This approach utilizes the recently developed decision analysis framework and process, multi-criteria integrated resource assessment (MIRA). This article will specifically address how MIRA can be used to help decisionmakers better understand the importance of uncertainty on the specific (i.e., decision contextual) environmental policy options that they are deliberating.     

Robust multivariate change point analysis based on data depth

Modern methods for detecting changes in the scale or covariance of multivariate distributions rely primarily on testing for the constancy of the covariance matrix. These depend on higher-order moment conditions, and also do not work well when the dimension of the data is large or even moderate relative to the sample size. In this paper, we propose a nonparametric change point test for multivariate data using rankings obtained from data depth measures. As the data depth of an observation measures its centrality relative to the sample, changes in data depth may signify a change of scale of the underlying distribution, and the proposed test is particularly responsive to detecting such changes. We provide a full asymptotic theory for the proposed test statistic under the null hypothesis that the observations are stable, and natural conditions under which the test is consistent. The finite sample properties are investigated by means of a Monte Carlo simulation, and these along with the theoretical results confirm that the test is robust to heavy tails, skewness and high dimensionality. The proposed methods are demonstrated with an application to structural break detection in the rate of change of pollutants linked to acid rain measured in Turkey lake, a lake in central Ontario, Canada. Our test suggests a change in the rate of acid rain in the late 1980s/early 1990s, which coincides with clean air legislation in Canada and the US. The Canadian Journal of Statistics 48: 417–446; 2020 © 2020 Statistical Society of Canada     

Family and marital profiles of couples in which one partner has depression or anxiety

Twenty-nine couples in which one partner was depressed, 21 couples in which one partner had an anxiety disorder, and 26 nondistressed control couples were compared on measures of (1) quality of life, stress, and social support; (2) family functioning; (3) marital functioning; and (4) relationship attributions. The depressed group had significant difficulties in all four domains. In contrast, the control group showed minimal difficulties. The profile of the anxious group occupied an intermediate position between those of the other two groups, with some difficulties in all four domains, although these were less severe and pervasive than those of the depressed group.     

Marketing and Quality of Life: A Model for Improving Perinatal Health Status

Introduction: A marketing/business model using non-traditional Quality of Life measures was developed to assess perinatal health status on a micro-geographic level. This perinatal health status needs assessment study for Georgia South Central Region was conducted for the years 1994–1999. The model may be applied to any geographic unit in the U.S. – from a block group level to a state or a region. Methodology: An Infant Health Risk Score was created for each county and census tract by calculating the Z-scores of various Medical, Lifestyle, and Access variables so as to construct a Quality of Life Index. The scores identified the areas in the region that were at high risk for certain medical, lifestyle, and access variables (i.e., high risk for preterm births, low education levels, and poor access to perinatal services). A marketing tool, Claritas PRIZM Clusters, was used to identify a specific cluster and associated marketing information for each census tract within the region. Results: The Infant Health Quality of Life Risk Scores were linked with the PRIZM cluster marketing data to target areas in the region that exhibit high risk medical, lifestyle, and access scores. Health promotion and disease prevention strategies were developed using a marketing/business model. Specifically, media usage and consumer behavior purchasing patterns were identified and processed for every high risk area in the region. The categories for media usage were television, radio, and magazines and the categories for consumer behavior included restaurants, food items, and shopping locations. Discussion: The Perinatal Region is developing strategies to implement the media usage and consumer behavior marketing information to focus their prevention efforts to the high risk areas in the region based on the Quality of Life Measurements. Linking marketing business tools with a Quality of Life health status needs assessment has significant potential for improving the planning, the evaluation, and the focus of prevention efforts.     

Age, Health and the Willingness to Pay for Mortality Risk Reductions: A Contingent Valuation Survey of Ontario Residents

We present the results of a contingent valuation survey eliciting willingness to pay (WTP) for mortality risk reductions. The survey was self-administered using a computer by 930 persons in Hamilton Ontario aged 40 to 75. Visual and audio aides were used to enhance risk comprehension. Mean WTP figures for a contemporaneous risk reduction imply a value of a statistical life of approximately C$l.2 to C$3.8 million (1999 C$). Mean WTP is constant with age up to 70 years, and is about 30 percent lower for persons aged 70 and older. WTP is unaffected by physical health status, but is affected by mental health.     

Tools for Comparative Analysis of Alternatives: Competing or Complementary Perspectives?

A third generation of environmental policy making and risk management will increasingly impose environmental measures, which may give rise to analyzing countervailing risks. Therefore, a comprehensive analysis of all risks associated with the decision alternatives will aid decision-makers in prioritizing alternatives that effectively reduce both target and countervailing risks. Starting with the metaphor of the ripples caused by a stone that is thrown into a pond, we identify 10 types of ripples that symbolize, in our case, risks that deserve closer examination: direct, upstream, downstream, accidental risks, occupational risks, risks due to offsetting behavior, change in disposable income, macro-economic changes, depletion of natural resources, and risks to the manmade environment. Tools to analyze these risks were developed independently and recently have been applied to overlapping fields of application. This suggests that either the tools should be linked in a unified framework for comparative analysis or that the appropriate field of application for single tools should be better understood. The goals of this article are to create a better foundation for the understanding of the nature and coverage of available tools and to identify the remaining gaps. None of the tools is designed to deal with all 10 types of risk. Provided data suggest that, of the 10 types of identified risks, those associated with changes in disposable income may be particularly significant when decision alternatives differ with respect to their effects on disposable income. Finally, the present analysis was limited to analytical questions and did not capture the important role of the decision-making process itself.