壮族的民间信仰与民俗医疗

壮族的传统医药文化非常丰富,民间至今依然盛行与宗教信仰密切相关的“神药两解”的民俗医疗方法。笔者在田野调查中发现,壮族乡村的病人在积极寻求药物治疗的同时,还借助麽公、囊妹、道士等神职人员施行各种仪式来治病,且往往能取得意想不到的效果。本文拟从田野资料出发,借鉴医学人类学的相关理论,以目前壮族农村盛行的巫术与祝由疗法为例,来探讨壮族的民间信仰与民俗医疗的关系。     

Program Evaluation and Causal Inference With High‐Dimensional Data

In this paper, we provide efficient estimators and honest confidence bands for a variety of treatment effects including local average (LATE) and local quantile treatment effects (LQTE) in data‐rich environments. We can handle very many control variables, endogenous receipt of treatment, heterogeneous treatment effects, and function‐valued outcomes. Our framework covers the special case of exogenous receipt of treatment, either conditional on controls or unconditionally as in randomized control trials. In the latter case, our approach produces efficient estimators and honest bands for (functional) average treatment effects (ATE) and quantile treatment effects (QTE). To make informative inference possible, we assume that key reduced‐form predictive relationships are approximately sparse. This assumption allows the use of regularization and selection methods to estimate those relations, and we provide methods for post‐regularization and post‐selection inference that are uniformly valid (honest) across a wide range of models. We show that a key ingredient enabling honest inference is the use of orthogonal or doubly robust moment conditions in estimating certain reduced‐form functional parameters. We illustrate the use of the proposed methods with an application to estimating the effect of 401(k) eligibility and participation on accumulated assets. The results on program evaluation are obtained as a consequence of more general results on honest inference in a general moment‐condition framework, which arises from structural equation models in econometrics. Here, too, the crucial ingredient is the use of orthogonal moment conditions, which can be constructed from the initial moment conditions. We provide results on honest inference for (function‐valued) parameters within this general framework where any high‐quality, machine learning methods (e.g., boosted trees, deep neural networks, random forest, and their aggregated and hybrid versions) can be used to learn the nonparametric/high‐dimensional components of the model. These include a number of supporting auxiliary results that are of major independent interest: namely, we (1) prove uniform validity of a multiplier bootstrap, (2) offer a uniformly valid functional delta method, and (3) provide results for sparsity‐based estimation of regression functions for function‐valued outcomes.     

Quantitative Risk Assessment of the New York State Operated West Valley Radioactive Waste Disposal Area

This article is based on a quantitative risk assessment (QRA) that was performed on a radioactive waste disposal area within the Western New York Nuclear Service Center in western New York State. The QRA results were instrumental in the decision by the New York State Energy Research and Development Authority to support a strategy of in‐place management of the disposal area for another decade. The QRA methodology adopted for this first of a kind application was a scenario‐based approach in the framework of the triplet definition of risk (scenarios, likelihoods, consequences). The measure of risk is the frequency of occurrence of different levels of radiation dose to humans at prescribed locations. The risk from each scenario is determined by (1) the frequency of disruptive events or natural processes that cause a release of radioactive materials from the disposal area; (2) the physical form, quantity, and radionuclide content of the material that is released during each scenario; (3) distribution, dilution, and deposition of the released materials throughout the environment surrounding the disposal area; and (4) public exposure to the distributed material and the accumulated radiation dose from that exposure. The risks of the individual scenarios are assembled into a representation of the risk from the disposal area. In addition to quantifying the total risk to the public, the analysis ranks the importance of each contributing scenario, which facilitates taking corrective actions and implementing effective risk management. Perhaps most importantly, quantification of the uncertainties is an intrinsic part of the risk results. This approach to safety analysis has demonstrated many advantages of applying QRA principles to assessing the risk of facilities involving hazardous materials.     

Linking Decision Theory and Quantitative Microbial Risk Assessment: Tradeoffs Between Compliance and Efficacy for Waterborne Disease Interventions

Achieving health gains from the U.N. Sustainable Development Goals of universal coverage for water and sanitation will require interventions that can be widely adopted and maintained. Effectiveness—how an intervention performs based on actual use—as opposed to efficacy will therefore be central to evaluations of new and existing interventions. Incomplete compliance—when people do not always use the intervention and are therefore exposed to contamination—is thought to be responsible for the lower‐than‐expected risk reductions observed from water, sanitation, and hygiene interventions based on their efficacy at removing pathogens. We explicitly incorporated decision theory into a quantitative microbial risk assessment model. Specifically, we assume that the usability of household water treatment (HWT) devices (filters and chlorine) decreases as they become more efficacious due to issues such as taste or flow rates. Simulations were run to examine the tradeoff between device efficacy and usability. For most situations, HWT interventions that trade lower efficacy (i.e., remove less pathogens) for higher compliance (i.e., better usability) contribute substantial reductions in diarrheal disease risk compared to devices meeting current World Health Organization efficacy guidelines. Recommendations that take into account both the behavioral and microbiological properties of treatment devices are likely to be more effective at reducing the burden of diarrheal disease than current standards that only consider efficacy.     

Voluntary Siting and Equity: The MRS Facility Experience in Native America

This article focuses on aspects of intragenerational and intergenerational equity in the context of a unique policy experiment: the effort of the U.S. government to site a monitored, retrievable storage (MRS) facility for high-level civilian nuclear waste. This process and its outcomes are examined from both normative and subjective perspectives. While the MRS siting process was designed to be equitable, its eventual focus on Native American communities raises profound questions about environmental justice, as well as procedural, outcome, and intergenerational equity in cross-cultural contexts. The diverse reactions among Native American tribes demonstrate that translating theoretical concepts of equity into practice is an extraordinarily complex exercise. The MRS siting process, instead of being a bold policy experiment that promoted equity, emerges substantially flawed after its implementation in the Native American context.     

Quantitative Methods for Environmental Justice Assessment of Transportation

Application of Executive Order 12898 to risk assessment of highway or rail transport of hazardous materials has proven difficult; in general, the location and conditions affecting the propagation of a plume of hazardous material released in a potential accident are unknown. Therefore, analyses have only been possible in a geographically broad or approximate manner. The advent of geographic information systems and development of software enhancements at Sandia National Laboratories have made kilometer-by-kilometer analysis of populations tallied by U.S. Census blocks along entire routes practicable. Tabulations of total or racially/ethnically distinct populations close to a route, its alternatives, or the broader surrounding area, can then be compared and differences evaluated statistically. This article presents methods of comparing populations and their racial/ethnic compositions using simple tabulations, histograms, and chi-square tests for statistical significance of differences found. Two examples of these methods are presented: comparison of two routes and comparison of a route with its surroundings.     

一类污水处理项目的运营与排污者行为动态分析

本文针对污水处理项目运营风险高、企业偷排行为控制难问题,首先依据排污信息和排污价格的波动与否,构建了4种计算实验情景;然后针对各种情景分别设计了污水处理价格形成机制、居民和企业社会属性与行为的变化规则,分析了经济压力、社会影响以及政府监管对排污价格形成、排污者行为变化的影响;最后运用计算实验方法模拟了污水处理项目运营与排污者行为在4种情景下的动态变化。结果表明:合理的静态定价策略要优于运营商动态定价策略;动态外部环境既给污水处理项目的运营带来风险,又给政府对企业的监管带来不便;单纯的市场价格机制或政府监管机制很难有效控制偷排现象,也不一定能实现居民的节水减排,企业和运营商的逐利行为可能会使得目标落空。     

Health Risk Assessment of a Modern Municipal Waste Incinerator

During the modernization of the municipal waste incinerator (MWI, maximum capacity of 180,000 tons per year) of Metropolitan Grenoble (405,000 inhabitants), in France, a risk assessment was conducted, based on four tracer pollutants: two volatile organic compounds (benzene and 1, 1, 1 trichloroethane) and two heavy metals (nickel and cadmium, measured in particles). A Gaussian plume dispersion model, applied to maximum emissions measured at the MWI stacks, was used to estimate the distribution of these pollutants in the atmosphere throughout the metropolitan area. A random sample telephone survey (570 subjects) gathered data on time-activity patterns, according to demographic characteristics of the population. Life-long exposure was assessed as a time-weighted average of ambient air concentrations. Inhalation alone was considered because, in the Grenoble urban setting, other routes of exposure are not likely. A Monte Carlo simulation was used to describe probability distributions of exposures and risks. The median of the life-long personal exposures distribution to MWI benzene was 3.2·10^–5 g/m³ (20th and 80th percentiles = 1.5·10^–5 and 6.5·10^–5 g/m³), yielding a 2.6·10^–10 carcinogenic risk (1.2·10^–10–5.4·10^–10). For nickel, the corresponding life-time exposure and cancer risk were 1.8·10^–4 g/m³ (0.9.10^–4 – 3.6·10^–4 g/m³) and 8.6·10^–8 (4.3·10^–8–17.3·10^–8); for cadmium they were respectively 8.3·10^–6 g/m³ (4.0·10^–6–17.6·10^–6) and 1.5·10^–8 (7.2·10^–9–3.1·10^–8). Inhalation exposure to cadmium emitted by the MWI represented less than 1% of the WHO Air Quality Guideline (5 ng/m³), while there was a margin of exposure of more than 10⁹ between the NOAEL (150 ppm) and exposure estimates to trichloroethane. Neither dioxins nor mercury, a volatile metal, were measured. This could lessen the attributable life-long risks estimated. The minute (VOCs and cadmium) to moderate (nickel) exposure and risk estimates are in accord with other studies on modern MWIs meeting recent emission regulations, however.     

Reliability Analysis for Hazardous Waste Treatment Processes

The reliability of a treatment process is addressed in terms of achieving a regulatory effluent concentration standard and the design safety factors associated with the treatment process. This methodology was then applied to two aqueous hazardous waste treatment processes: packed tower aeration and activated sludge (aerobic) biological treatment. The designs achieving 95 percent reliability were compared with those designs based on conventional practice to determine their patterns of conservatism. Scoping-level treatment costs were also related to reliability levels for these treatment processes. The results indicate that the reliability levels for the physical/chemical treatment process (packed tower aeration) based on the deterministic safety factors range from 80 percent to over 99 percent, whereas those for the biological treatment process range from near 0 percent to over 99 percent, depending on the compound evaluated. Increases in reliability per unit increase in treatment costs are most pronounced at lower reliability levels (less than about 80 percent) than at the higher reliability levels (greater than 90 percent, indicating a point of diminishing returns. Additional research focused on process parameters that presently contain large uncertainties may reduce those uncertainties, with attending increases in the reliability levels of the treatment processes.     

Sensitivity,uncertainty, and decision analyses in the prioritization of research

The evaluation of hazards from complex, large scale, technologically advanced systems often requires the construction of computer implemented mathematical models. These models are used to evaluate the safety of the systems and to evaluate the consequences of modifications to the systems. These evaluations, however, are normally surrounded by significant uncertainties related to the uncertainty inherent in natural phenomena such as the weather and those related to uncertainties in the parameters and models used in the evaluation.

Another use of these models is to evaluate strategies for improving information used in the modeling process itself. While sensitivity analysis is useful in defining variables in the model that are important, uncertainty analysis provides a tool for assessing the importance of uncertainty about these variables. A third complementary technique, is decision analysis. It provides a methodology for explicitly evaluating and ranking potential improvements to the model. Its use in the development of information gathering strategies for a nuclear waste repository are discussed in this paper.