Premature Deaths,Statistical Lives,and Years of Life Lost: Identification,Quantification, and Valuation of Mortality Risks

Mortality effects of exposure to air pollution and other environmental hazards are often described by the estimated number of “premature” or “attributable” deaths and the economic value of a reduction in exposure as the product of an estimate of “statistical lives saved” and a “value per statistical life.” These terms can be misleading because the number of deaths advanced by exposure cannot be determined from mortality data alone, whether from epidemiology or randomized trials (it is not statistically identified). The fraction of deaths “attributed” to exposure is conventionally derived as the hazard fraction (R – 1)/R, where R is the relative risk of mortality between high and low exposure levels. The fraction of deaths advanced by exposure (the “etiologic” fraction) can be substantially larger or smaller: it can be as large as one and as small as 1/e (≈0.37) times the hazard fraction (if the association is causal and zero otherwise). Recent literature reveals misunderstanding about these concepts. Total life years lost in a population due to exposure can be estimated but cannot be disaggregated by age or cause of death. Economic valuation of a change in exposure-related mortality risk to a population is not affected by inability to know the fraction of deaths that are etiologic. When individuals facing larger or smaller changes in mortality risk cannot be identified, the mean change in population hazard is sufficient for valuation; otherwise, the economic value can depend on the distribution of risk reductions.     

An Evaluation of the Effects of Very Difficult Goals

In 1968 the first cohesive theory on goal setting proposed that difficult goals produce higher levels of performance than easy goals and that specific goals produce a higher level of performance than “do your best” goals. While over 40 years of research supports this theory, there has been some discrepancy regarding the use of very difficult goals. This study was designed to examine the effects on performance of different levels of performance improvement goals and two different types of feedback, as feedback is often used in conjunction with goal setting. A group design was used with participants receiving one of two goal levels, and two different types of feedback. While no significant effects were found between the two types of feedback, goal level produced significant results in terms of performance and accuracy.     

Response Generalization in Behavioral Safety

Abstract

This paper details concerns with the potential misuse of the term “response generalization” in the behavioral safety literature. Stokes and Baer's (1977) technologies of generalization and the basic literature on response induction are used to make the claim. Ludwig and Geller's (2000) Journal of Organizational Behavior Management special issue describing their work with pizza delivery drivers is used as an example. The potential problems of non-technical use of language in a technological behavioral science are briefly discussed. Finally, Baer, Wolf, and Risley's (1968, 1987) criteria of applied behavior analysis are re-visited in this context in hopes of calling researchers of organizational behavior management to more closely align their work with the tradition of applied behavior analysis.     

Has Successful Terror Gone to Ground?

This article considers all 87 attacks worldwide against air and rail transport systems that killed at least two passengers over the 30‐year period of 1982–2011. The data offer strong and statistically significant evidence that successful acts of terror have “gone to ground” in recent years: attacks against aviation were concentrated early in the three decades studied whereas those against rail were concentrated later. Recent data are used to make estimates of absolute and comparative risk for frequent flyers and subway/rail commuters. Point estimates in the “status quo” case imply that mortality risk from successful acts of terror was very low on both modes of transportation and that, whereas risk per trip is higher for air travelers than subway/rail commuters, the rail commuters experience greater risk per year than the frequent flyers.     

乡村农户贫困状态特征因子筛选与分析框架：兼论我国乡村振兴路径选择

本文构建了刻画乡村农户贫困状态的特征因子的提取算法和分析框架，旨在实现和帮助完善家庭农场的持续发展，尤其是由贫困农户组成或参与的家庭农场在发展需要的融资方面的乡村信用评估，推动有效地制订对应政策和落地方案，巩固脱贫攻坚成果和防止返贫。本文的最大亮点是以“分类与回归树”(CART)分析和“吉布斯抽样”(Gibbs Sampling)的人工智能算法为工具，对乡村农户贫困状态的特征因子提取建立了对应的框架和分析流程。基于国内某地区乡村建档立卡数据库的31,116个样本，实证研究筛选出12个刻画乡村农户贫困状态高度关联的特征因子，并进一步对特征因子的有效性进行了ROC曲线和AUC测试。结果表明以特征因子分析框架为基础，建设配套的乡村信用评估体系是支持乡村振兴的可持续性最佳解决途径之一，除了能够为乡村贫困户获得持续工作的基本技能或生产环境的改善提升上得到持续性的融资支持提供评估依据和数据支持，也能为乡村和城镇“传帮带”等生产和商务平台的建立提供可持续的基础性数据和信用分析的动态支持。     

Regression Versus Causation,Revisited

I recently discussed pitfalls in attempted causal inference based on reduced‐form regression models. I used as motivation a real‐world example from a paper by Dr. Sneeringer, which interpreted a reduced‐form regression analysis as implying the startling causal conclusion that “doubling of [livestock] production leads to a 7.4% increase in infant mortality.” This conclusion is based on: (A) fitting a reduced‐form regression model to aggregate (e.g., county‐level) data; and (B) (mis)interpreting a regression coefficient in this model as a causal coefficient, without performing any formal statistical tests for potential causation (such as conditional independence, Granger‐Sims, or path analysis tests). Dr. Sneeringer now adds comments that confirm and augment these deficiencies, while advocating methodological errors that, I believe, risk analysts should avoid if they want to reach logically sound, empirically valid, conclusions about cause and effect. She explains that, in addition to (A) and (B) above, she also performed other steps such as (C) manually selecting specific models and variables and (D) assuming (again, without testing) that hand‐picked surrogate variables are valid (e.g., that log‐transformed income is an adequate surrogate for poverty). In her view, these added steps imply that “critiques of A and B are not applicable” to her analysis and that therefore “a causal argument can be made” for “such a strong, robust correlation” as she believes her regression coefficient indicates. However, multiple wrongs do not create a right. Steps (C) and (D) exacerbate the problem of unjustified causal interpretation of regression coefficients, without rendering irrelevant the fact that (A) and (B) do not provide evidence of causality. This reply focuses on whether any statistical techniques can produce the silk purse of a valid causal inference from the sow's ear of a reduced‐form regression analysis of ecological data. We conclude that Dr. Sneeringer's analysis provides no valid indication that air pollution from livestock operations causes any increase in infant mortality rates. More generally, reduced‐form regression modeling of aggregate population data—no matter how it is augmented by fitting multiple models and hand‐selecting variables and transformations—is not adequate for valid causal inference about health effects caused by specific, but unmeasured, exposures.     

Effectiveness of FISK,an Invasiveness Screening Tool for Non‐Native Freshwater Fishes,to Perform Risk Identification Assessments in the Iberian Peninsula

Risk assessments are crucial for identifying and mitigating impacts from biological invasions. The Fish Invasiveness Scoring Kit (FISK) is a risk identification (screening) tool for freshwater fishes consisting of two subject areas: biogeography/history and biology/ecology. According to the outcomes, species can be classified under particular risk categories. The aim of this study was to apply FISK to the Iberian Peninsula, a Mediterranean climate region highly important for freshwater fish conservation due to a high level of endemism. In total, 89 fish species were assessed by three independent assessors. Results from receiver operating characteristic analysis showed that FISK can discriminate reliably between noninvasive and invasive fishes for Iberia, with a threshold of 20.25, similar to those obtained in several regions around the world. Based on mean scores, no species was categorized as “low risk,” 50 species as “medium risk,” 17 as “moderately high risk,” 11 as “high risk,” and 11 as “very high risk.” The highest scoring species was goldfish Carassius auratus. Mean certainty in response was above the category “mostly certain,” ranging from tinfoil barb Barbonymus schwanenfeldii with the lowest certainty to eastern mosquitofish Gambusia holbrooki with the highest level. Pair‐wise comparison showed significant differences between one assessor and the other two on mean certainty, with these two assessors showing a high coincidence rate for the species categorization. Overall, the results suggest that FISK is a useful and viable tool for assessing risks posed by non‐native fish in the Iberian Peninsula and contributes to a “watch list” in this region.     

Estimating Health Risks from Natural Hazards Using Risk Assessment and Epidemiology

Risk assessment is the process of estimating the likelihood that an adverse effect may result from exposure to a specific health hazard. The process traditionally involves hazard identification, dose-response assessment, exposure assessment, and risk characterization to answer “How many excess cases of disease A will occur in a population of size B due to exposure to agent C at dose level D?” For natural hazards, however, we modify the risk assessment paradigm to answer “How many excess cases of outcome Y will occur in a population of size B due to natural hazard event E of severity D?” Using a modified version involving hazard identification, risk factor characterization, exposure characterization, and risk characterization, we demonstrate that epidemiologic modeling and measures of risk can quantify the risks from natural hazard events. We further extend the paradigm to address mitigation, the equivalent of risk management, to answer “What is the risk for outcome Y in the presence of prevention intervention X relative to the risk for Y in the absence of X?” We use the preventable fraction to estimate the efficacy of mitigation, or reduction in adverse health outcomes as a result of a prevention strategy under ideal circumstances, and further estimate the effectiveness of mitigation, or reduction in adverse health outcomes under typical community-based settings. By relating socioeconomic costs of mitigation to measures of risk, we illustrate that prevention effectiveness is useful for developing cost-effective risk management options.     

Distributed Decisions in Networks: Laboratory Study of Routing Splittable Flow

We study network games in which users choose routes in computerized networks susceptible to congestion. In the “unsplittable” condition, route choices are completely unregulated, players are symmetric, each player controls a single unit of flow and chooses a single origin–destination (O–D) path. In the “splittable” condition, which is the main focus of this study, route choices are partly regulated, players are asymmetric, each player controls multiple units of flow and chooses multiple O–D paths to distribute her fleet. In each condition, users choose routes in two types of network: a basic network with three parallel routes and an augmented network with five routes sharing joint links. We construct and subsequently test equilibrium solutions for each combination of condition and network type, and then propose a Markov revision protocol to account for the dynamics of play. In both conditions, route choice behavior approaches equilibrium and the Braess Paradox is clearly manifested.     

Probability of Causation and the Attributable Proportion Risk

A uranium miner who smokes develops lung cancer: what is the probability that radiation, rather than tobacco, caused it? This paper briefly explains the principles and limits of probability models for which this question makes sense, and then shows how principles of risk accounting can be applied to obtain a solution to the general problem of attributing risk in the presence of joint, possibly interacting, causes. A procedure for calculating each factor's “share” in a jointly caused risk is proposed, and shown to be a generalization of the “probability of causation” concept. Problems of implementation and interpretation for the proposed attribution procedure are discussed, and illustrative error bounds are derived for a simple decision rule, in which probability of causation or attributable risk share calculations are made using aggregate data as a proxy for unknown individual data.     

Behavior in a Dynamic Decision Problem: An Analysis of Experimental Evidence Using a Bayesian Type Classification Algorithm

Different people may use different strategies, or decision rules, when solving complex decision problems. We provide a new Bayesian procedure for drawing inferences about the nature and number of decision rules present in a population, and use it to analyze the behaviors of laboratory subjects confronted with a difficult dynamic stochastic decision problem. Subjects practiced before playing for money. Based on money round decisions, our procedure classifies subjects into three types, which we label “Near Rational,”“Fatalist,” and “Confused.” There is clear evidence of continuity in subjects' behaviors between the practice and money rounds: types who performed best in practice also tended to perform best when playing for money. However, the agreement between practice and money play is far from perfect. The divergences appear to be well explained by a combination of type switching (due to learning and/or increased effort in money play) and errors in our probabilistic type assignments.     

A Service Quality Process Map for Credit Card Processing

Evaluating marketing (external) and operations (internal) service quality performance in most organizations is a data-oriented management task that involves many variables for many different types of services. These two diverse sets of service quality information seldom “match up well” because the marketing and operations functions define and evaluate service quality “their way.” Management's interpretation of what the data “mean” often contributes to extra meetings, unnecessary disagreements within and between functional areas, and poor decision making. This situation can affect the quality and timing of decisions, and ultimately, the organization's profitability and market share. This paper quantitatively relates the customer's evaluation (perception) of service quality directly to the activities and performance criteria of the service delivery process for a credit card processing center. Basic statistical analyses and a recursive path analysis model define the relationships between marketing- and operations-based service quality information. The idea of a “service quality process map” is introduced. A service quality process map relates a flowchart of the service delivery process to the customer's perception of service using the power of multivariate data analysis. Management insights gained from an analysis of marketing (external) and operations (internal) measures of service quality performance can be used to allocate resources wisely. The objective is to gain the most improvement in the customer's perception of service for the least cost. Service quality with this type of evaluation capability can be used to gain competitive advantage in the marketplace.     

Experimental Analysis of Neighborhood Effects

Families, primarily female‐headed minority households with children, living in high‐poverty public housing projects in five U.S. cities were offered housing vouchers by lottery in the Moving to Opportunity program. Four to seven years after random assignment, families offered vouchers lived in safer neighborhoods that had lower poverty rates than those of the control group not offered vouchers. We find no significant overall effects of this intervention on adult economic self‐sufficiency or physical health. Mental health benefits of the voucher offers for adults and for female youth were substantial. Beneficial effects for female youth on education, risky behavior, and physical health were offset by adverse effects for male youth. For outcomes that exhibit significant treatment effects, we find, using variation in treatment intensity across voucher types and cities, that the relationship between neighborhood poverty rate and outcomes is approximately linear.     

Some Limitations of Qualitative Risk Rating Systems

Qualitative systems for rating animal antimicrobial risks using ordered categorical labels such as “high,”“medium,” and “low” can potentially simplify risk assessment input requirements used to inform risk management decisions. But do they improve decisions? This article compares the results of qualitative and quantitative risk assessment systems and establishes some theoretical limitations on the extent to which they are compatible. In general, qualitative risk rating systems satisfying conditions found in real‐world rating systems and guidance documents and proposed as reasonable make two types of errors: (1) Reversed rankings, i.e., assigning higher qualitative risk ratings to situations that have lower quantitative risks; and (2) Uninformative ratings, e.g., frequently assigning the most severe qualitative risk label (such as “high”) to situations with arbitrarily small quantitative risks and assigning the same ratings to risks that differ by many orders of magnitude. Therefore, despite their appealing consensus‐building properties, flexibility, and appearance of thoughtful process in input requirements, qualitative rating systems as currently proposed often do not provide sufficient information to discriminate accurately between quantitatively small and quantitatively large risks. The value of information (VOI) that they provide for improving risk management decisions can be zero if most risks are small but a few are large, since qualitative ratings may then be unable to confidently distinguish the large risks from the small. These limitations suggest that it is important to continue to develop and apply practical quantitative risk assessment methods, since qualitative ones are often unreliable.     

An Analysis of Changes in the Risk of Mortality from Chronic Obstructive Lung Disease, 1968-1977

Nonlinear hazard models are used to examine temporal trends in the age-specific mortality risks of chronic obstructive lung diseases for the U.S. population. These hazard functions are fit to age-specific mortality rates for 1968 and 1977 for four race/sex groups. Changes in the parameters of these models are used to assess two types of differences in the age pattern of the rates between 1968 and 1977. The first measure of trend in the age-specific mortality rates is the temporal change in the proportionality constant in the function used to model their age variation. By allowing only this proportionality parameter to vary between 1968 and 1977, it is possible to determine an age-constant percentage increase or decrease. The second measure reflects the absolute displacement in terms of years of life of the fitted mortality curves for the two time points. This second index can be interpreted as the acceleration or deceleration of mortality risks over the life span, i.e., the number of years that is needed for mortality rates to achieve the same level as in the comparison group. The analysis showed that the age changes in chronic obstructive lung disease mortality rates differed by race/sex group and for both measures of change over the period. Adjustment of the fitted curves for the effects of individual variability in risk was significant for three of four groups.     

FIELD EXPERIMENTS WHICH COMPARED THE EFFECTIVENESS OF DESIGN METHODS*

The influence of “method” on the design of services was investigated in three organizational settings. Models of “Systems,”“Behavioral,” and “Heuristic” design approaches were used to plan several service programs. These plans were compared using measures of their “quality,”“acceptance,” and “innovation.” Service plans designed by the Systems approach were found to have high quality (p < .05), and service plans designed by the Behavioral approach were found to be innovative (p < .05). These findings are employed to provide guides to select a Design Method and to suggest some research issues.     

A Model of the Consumption Response to Fiscal Stimulus Payments

A wide body of empirical evidence finds that approximately 25 percent of fiscal stimulus payments (e.g., tax rebates) are spent on nondurable household consumption in the quarter that they are received. To interpret this fact, we develop a structural economic model where households can hold two assets: a low‐return liquid asset (e.g., cash, checking account) and a high‐return illiquid asset that carries a transaction cost (e.g., housing, retirement account). The optimal life‐cycle pattern of portfolio choice implies that many households in the model are “wealthy hand‐to‐mouth”: they hold little or no liquid wealth despite owning sizable quantities of illiquid assets. Therefore, they display large propensities to consume out of additional transitory income, and small propensities to consume out of news about future income. We document the existence of such households in data from the Survey of Consumer Finances. A version of the model parameterized to the 2001 tax rebate episode yields consumption responses to fiscal stimulus payments that are in line with the evidence, and an order of magnitude larger than in the standard “one‐asset” framework. The model's nonlinearities with respect to the rebate size and the prevailing aggregate economic conditions have implications for policy design.