A Bayesian Analysis of Abundance,Trend, and Population Viability for Harbor Seals in Iliamna Lake,Alaska

Harbor seals in Iliamna Lake, Alaska, are a small, isolated population, and one of only two freshwater populations of harbor seals in the world, yet little is known about their abundance or risk for extinction. Bayesian hierarchical models were used to estimate abundance and trend of this population. Observational models were developed from aerial survey and harvest data, and they included effects for time of year and time of day on survey counts. Underlying models of abundance and trend were based on a Leslie matrix model that used prior information on vital rates from the literature. We developed three scenarios for variability in the priors and used them as part of a sensitivity analysis. The models were fitted using Markov chain Monte Carlo methods. The population production rate implied by the vital rate estimates was about 5% per year, very similar to the average annual harvest rate. After a period of growth in the 1980s, the population appears to be relatively stable at around 400 individuals. A population viability analysis assessing the risk of quasi‐extinction, defined as any reduction to 50 animals or below in the next 100 years, ranged from 1% to 3%, depending on the prior scenario. Although this is moderately low risk, it does not include genetic or catastrophic environmental events, which may have occurred to the population in the past, so our results should be applied cautiously.     

An Empirical Analysis of Strike Durations in Ghana from 1980 to 2004

Abstract. This paper provides a seminal analysis of strike activity for an African country in investigating the determinants of strike durations in Ghana over the period 1980–2004. The empirical approach uses a set of well‐known parametric accelerated failure time strike duration models. There is a broad consensus among the different empirical models about the role exerted on average strike duration by strike size, the rate of inflation, enterprise ownership, and political governance. However, evidence on the relationship between strike durations and business cycle activity in Ghana is less clear‐cut.     

An Exposure Assessment for Methylmercury from Seafood for Consumers in the United States

An exposure model was developed to relate seafood consumption to levels of methylmercury (reported as mercury) in blood and hair in the U.S. population, and two subpopulations defined as children aged 2-5 and women aged 18-45. Seafood consumption was initially modeled using short-term (three-day) U.S.-consumption surveys that recorded the amount of fish eaten per meal. Since longer exposure periods include more eaters with a lower daily mean intake, the consumption distribution was adjusted by broadening the distribution to include more eaters and reducing the distribution mean to keep total population intake constant. The estimate for the total number of eaters was based on long-term purchase diaries. Levels of mercury in canned tuna, swordfish, and shark were based on FDA survey data. The distribution of mercury levels in other species was based on reported mean levels, with the frequency of consumption of each species based on market share. The shape distribution for the given mean was based on the range of variation encountered among shark, tuna, and swordfish. These distributions were integrated with a simulation that estimated average daily intake over a 360-day period, with 10,000 simulated individuals and 1,000 uncertainty iterations. The results of this simulation were then used as an input to a second simulation that modeled levels of mercury in blood and hair. The relationship between dietary intake and blood mercury in a population was modeled from data obtained from a 90-day study with controlled seafood intake. The relationship between blood and hair mercury in a population was modeled from data obtained from several sources. The biomarker simulation employed 2,000 simulated individuals and 1,000 uncertainty iterations. These results were then compared to the recent National Health and Nutrition Examination Survey (NHANES) that tabulated blood and hair mercury levels in a cross-section of the U.S. population. The output of the model and NHANES results were similar for both children and adult women, with predicted mercury biomarker concentrations within a factor of two or less of NHANES biomarker results. However, the model tended to underpredict blood levels for women and overpredict blood and hair levels for children.     

Ecological Information Needs for Environmental Justice

The concept that all peoples should have their voices heard on matters that affect their well‐being is at the core of environmental justice (EJ). The inability of some people of small towns, rural areas, minority, and low‐income communities, to become involved in environmental decisions is sometimes due to a lack of information. We provide a template for the ecological information that is essential to examine environmental risks to EJ populations within average communities, using case studies from South Carolina (Savannah River, a DOE site with minority impacts), Washington (Hanford, a DOE site with Native American impacts), and New Jersey (nonpoint, urbanized community pollution). While the basic ecological and public health information needs for risk evaluations and assessments are well described, less attention has been focused on standardizing information about EJ communities or EJ populations within larger communities. We suggest that information needed about EJ communities and populations includes demographics, consumptive and nonconsumptive uses of their regional environment (for example, maintenance and cosmetic, medicinal/religious/cultural uses), eco‐dependency webs, and eco‐cultural attributes. A purely demographics approach might not even identify EJ populations or neighborhoods, much less their spatial relation to the impact source or to each other. Using information from three case studies, we illustrate that some information is readily available (e.g., consumption rates for standard items such as fish), but there is less information about medicinal, cultural, religious, eco‐cultural dependency webs, and eco‐cultural attributes, all of which depend in some way on intact, functioning, and healthy ecosystems.     

Probability Distributions for Showering and Bathing Water-Use Behavior for Various U.S. Subpopulations

It has been shown that bathroom-type water uses dominate personal exposure to water-borne contaminants in the home. Therefore, in assessing exposure of specific population groups to the contaminants in the water, understanding population water-use behavior for bathroom activities as a function of demographic characteristics is vital to realistic exposure estimates. In this article, shower and bath frequencies and durations are analyzed, presented, and compared for various demographic groups derived from analyses of the National Human Activities Pattern Survey (NHAPS) database and the Residential End Uses of Water Study (REUWS) database as well as from a review of current literature. Analysis showed that age and level of education significantly influenced shower and bath frequency and duration. The frequency of showering and bathing reported in NHAPS agreed reasonably well with previous studies; however, durations of these events were found to be significantly longer. Showering frequency reported in REUWS was slightly less than that reported for NHAPS; however, durations of showers reported in REUWS are consistent with other studies. After considering the strengths and weaknesses of each data set and comparing their results to previous studies, it is concluded that NHAPS provides more reliable frequency data, while REUWS provides more reliable duration data. The shower- and bath-use behavior parameters recommended in this article can aid modelers in appropriately specifying water-use behavior as a function of demographic group in order to conduct reasonable assessments of exposure to contaminants that enter the home via the water supply.     

Analytical Predictive Bayesian Assessment of Occupational Injury Risk: Municipal Solid Waste Collectors

Unlike other waste streams, municipal solid waste (MSW) is collected manually, and MSW collection has recently been found to be among the highest-risk occupations in the United States. However, as for other occupational groups, actual total injury rates, including the great majority of injuries not compensated and those compensated by other insurance, are not known. In this article a predictive Bayesian method of assessing total injury rates from available information without computation is presented, and used to assess the actual numbers of musculoskeletal and dermal injuries requiring clinical care of MSW workers in Florida. Closed-form predictive Bayesian distributions that narrow progressively in response to information, representing both uncertainty and variability, are presented. Available information included workers' compensation (WC) data, worker population data, and safety records for one private and one public collection agency. Subjective input comprised epidemiological and medical judgment based on a review of 165 articles. The number of injuries was assessed at 3,146 annually in Florida, or 54 +/- 18 injuries per 100 workers per year with 95% confidence. Further, WC data indicate that the injury rate is 50% higher for garbage collectors specifically, indicating a rate of approximately 80 per 100 workers. Results, though subject to uncertainty in worker numbers and classification and reporting bias, agreed closely with a survey of 251 MSW collectors, of whom 75% reported being injured (and 70% reported illness) within the past 12 months. The approach is recommended for assessment of total injury rates and, where sufficient information exists, for the more difficult assessment of occupational disease rates.     

Utilization survey of a rural creek fishery in central Alabama

A one-year angler intercept survey was conducted on Choccolocco Creek, a rural, limited access tributary to the Coosa River in northeastern Alabama. The purpose of the survey was to collect data and information about the behaviors and fish consumption habits of the recreational anglers who fish there. Nine survey locations were included in the stratified sampling plan, and sampling occurred throughout daylight hours, on weekdays and weekends/holidays, during all four seasons of the year. Surveys were completed on a total of 101 survey days between June 28, 2008 and June 27, 2009.(6) Seventy-two anglers were observed fishing during the survey period, and 52 (72%) of those individuals agreed to participate in the survey. Based on the information collected by the survey clerks, the angler population fishes the Creek between 1 and 54 times per year, with an average frequency of seven trips per year. The average number of months fished was three months per year, with a range of one to nine months. Only 15% of the anglers who participated in the survey (eight individuals) had succeeded in catching fish by the end of their trips, and only four of those individuals (8%) had retained any of the fish they had caught for consumption. Reasons provided for not retaining fish were that they either only fished for sport, did not catch enough fish to eat, or the fish they caught were too small to keep. Because so few anglers used and harvested fish from the resource, fish consumption rates could not be determined with a high degree of confidence. However, from these limited data it was estimated that the three anglers for whom consumption rates could be estimated had annualized average daily fish consumption rates of 0.14, 0.44, and 7.9 grams per day (g/day). The majority of anglers traveled less than 10 miles to fish the Creek. It was estimated that a total population of 173 anglers use the Creek each year. The results of this survey indicated that Choccolocco Creek is a local fishery that is not heavily used by area residents.     

Empirical Analyses of Causal Relationships among Estimated Aggregate Unemployment Duration and Some Properly Chosen Economic Variables

ABSTRACT: The paper is concerned with estimating and analysing the duration of unemployment, that is the length of time people spend on average looking for work. The first issue of the paper is to estimate unemployment duration using data from the survey on labour force done quarterly in Italy by ISTAT, the National Institute of Statistics. The survey data on the duration of unemployment measure duration so far, that is the average length of unemployment spells in progress up to the date of the survey, but they don't provide any information about completed duration of unemployment experimented by people before finding a job or leaving the labour force. In order to estimate the average length of completed unemployment spells, we would have to use data on cohorts of people followed from the time of entry to the time of exit from the labour market. As longitudinal data is not available, the problem becomes rather complex. One way to get round this is to use data on flows to firstly estimate probabilities of leaving unemployment within a particular period. The available data refer to quarterly flows and yearly flows. This makes it possible to estimate short-term unemployment (less than six months) and long-term unemployment (more than twelve months). In another paper we have analysed the nature of the bias introduced by estimating short-term and long-term unemployment in the way we do, and we have come to the conclusion that the bias is approximately a constant, which can be very easily estimated and eliminated. The second issue of the paper is to analyse the estimated short-term and long-term unemployment in relation to cyclical changes in the economic system and with trend changes in the number of unemployed people seeking the first job. In particular, we want to establish the extent of causal relationships between the chosen explanatory variables and the dependent variable. These causal analyses are done separately for the male and female population, and cover the period from the first quarter of 1979 to the last quarter of 1986.     

Understanding the Positive Effects of Graphical Risk Information on Comprehension: Measuring Attention Directed to Written,Tabular, and Graphical Risk Information

Risk communications are an integral aspect of health education and promotion. However, the commonly used textual risk information is relatively difficult to understand for the average recipient. Consequently, researchers and health promoters have started to focus on so‐called decision aids, such as tables and graphs. Although tabular and graphical risk information more effectively communicate risks than textual risk information, the cognitive mechanisms responsible for this enhancement are unclear. This study aimed to examine two possible mechanisms (i.e., cognitive workload and attention). Cognitive workload (mean pupil size and peak pupil dilation) and attention directed to the risk information (viewing time, number of eye fixations, and eye fixation durations) were both measured in a between‐subjects experimental design. The results suggest that graphical risk information facilitates comprehension of that information because it attracts and holds attention for a longer period of time than textual risk information. Graphs are thus a valuable asset to risk communication practice for two reasons: first, they tend to attract attention and, second, when attended to, they elicit information extraction with relatively little cognitive effort, and finally result in better comprehension.     

Evacuation Risks: A Tentative Approach for Quantification

This study tries to assess the risk of deaths and injuries from motor vehicle accidents associated with an evacuation of population groups in case of nuclear plant accidents. The risk per person–km is evaluated using: (a) data from previous evacuation: information from Soufriere evacuation (Guadeloupe Island 1976) and Mississauga (1979), added to Hans and Sell's data: no road accident occurred for a sample of 1,500,000 persons; (b) national recording system for motor vehicle accident: the rates of 2.2 10 ^-8 deaths per person–km and 32 10^-8 injuries per person–km is calculated as an average. These last rates in France overestimate the number of casualties. A reasonable hypothesis is to assume that the probability of road accident occurrence follows a Poisson distribution, as these events are independent and unfrequent, as no accident was observed in a sample of 1,500,000 persons the probability is between 0 and an upper value of 0.24 10^-8 deaths per person-km and 3.29 10^-8 injuries per person–km. The average and maximum population involved within different radii around French and U.S. Nuclear power sites are taken as a sample size in order to study the total risk of deaths and injuries in the hypothesis of an evacuation being necessary to protect the populations.     

Effects of Uncertainties on Exposure Estimates to Methylmercury: A Monte Carlo Analysis of Exposure Biomarkers versus Dietary Recall Estimation

This article presents a general model for estimating population heterogeneity and "lack of knowledge" uncertainty in methylmercury (MeHg) exposure assessments using two-dimensional Monte Carlo analysis. Using data from fish-consuming populations in Bangladesh, Brazil, Sweden, and the United Kingdom, predictive model estimates of dietary MeHg exposures were compared against those derived from biomarkers (i.e., [Hg]hair and [Hg]blood). By disaggregating parameter uncertainty into components (i.e., population heterogeneity, measurement error, recall error, and sampling error) estimates were obtained of the contribution of each component to the overall uncertainty. Steady-state diet:hair and diet:blood MeHg exposure ratios were estimated for each population and were used to develop distributions useful for conducting biomarker-based probabilistic assessments of MeHg exposure. The 5th and 95th percentile modeled MeHg exposure estimates around mean population exposure from each of the four study populations are presented to demonstrate lack of knowledge uncertainty about a best estimate for a true mean. Results from a U.K. study population showed that a predictive dietary model resulted in a 74% lower lack of knowledge uncertainty around a central mean estimate relative to a hair biomarker model, and also in a 31% lower lack of knowledge uncertainty around central mean estimate relative to a blood biomarker model. Similar results were obtained for the Brazil and Bangladesh populations. Such analyses, used here to evaluate alternative models of dietary MeHg exposure, can be used to refine exposure instruments, improve information used in site management and remediation decision making, and identify sources of uncertainty in risk estimates.     

基于区间数证据推理方法的用户满意度调查

目前,加权平均法是一种比较常见的满意度调查结果的汇总方法,但是这种方法的前提条件是决策者的偏好结构满足加性独立条件,否则需要采用非线性综合方法。本文旨在考虑决策者偏好不满足加性独立条件下,将用户满意度抽样调查过程中产生的置信度和置信区间与调查问卷中的用户不确定的评价结果统一进行考虑,并采用mass函数值为区间数的证据推理方法分析基于抽样调查得到的以置信区间表示的用户满意度调查的结果综合问题。最后以某网络信息中心用户满意度调查为例展开实证分析。     

Assessment of Population Exposure and Carcinogenic Risk Posed by Volatile Organic Compounds in Indoor Air

A number of air pollutants are being found in low concentrations in indoor and outdoor air. Several studies have measured these concentrations for a large number of compounds in hundreds of residences. The concentrations range over several orders of magnitude over time and space. The log-normal distributions that are found make it possible to assess the impact on the health of populations, especially for genotoxic effects for which a threshold concentration is not expected to exist. Spreadsheets can be constructed which incorporate the unit risk of a substance for cancer, the distribution of concentrations of the substance in the population, and the size of the population into an assessment of the expected excess cases of cancer that can be attributed to the population exposure to such a toxicant. This approach allows for the evaluation of population risk for different substances as found in different environments, and for the evaluation of the relative efficacy of different strategies for population risk reduction.     

Quantifying the Distribution of Inhalation Exposure in Human Populations: 2. Distributions of Time Spent by Adults, Adolescents, and Children at Home, at Work, and at School

Using distributions of time spent at various ventilation levels, ranges of inhalation exposure in the population can be established. Distributions of exposure time were determined using results of a study by the California Air Resources Board (CARB) which focused on time spent by humans participating in various activities and the locations where the activities occurred. The daily at-home activities from the CARB study were assigned to one of three ventilation levels, generating aggregate time periods. Distinct age and gender populations were identified, and distributions for aggregate time were established for these populations at each of the ventilation levels. In addition to aggregate time spent at home, distributions for various ages and genders were established for aggregate time spent at school and work. By combining distributions of aggregate time with corresponding ventilation rates, the distribution of inhalation rates can be established for at home, at work, and at school exposures.     

Using Probabilistic Modeling to Evaluate Human Exposure to Organotin in Drinking Water Transported by Polyvinyl Chloride Pipe

The leaching of organotin (OT) heat stabilizers from polyvinyl chloride (PVC) pipes used in residential drinking water systems may affect the quality of drinking water. These OTs, principally mono- and di-substituted species of butyltins and methyltins, are a potential health concern because they belong to a broad class of compounds that may be immune, nervous, and reproductive system toxicants. In this article, we develop probability distributions of U.S. population exposures to mixtures of OTs encountered in drinking water transported by PVC pipes. We employed a family of mathematical models to estimate OT leaching rates from PVC pipe as a function of both surface area and time. We then integrated the distribution of estimated leaching rates into an exposure model that estimated the probability distribution of OT concentrations in tap waters and the resulting potential human OT exposures via tap water consumption. Our study results suggest that human OT exposures through tap water consumption are likely to be considerably lower than the World Health Organization (WHO) "safe" long-term concentration in drinking water (150 μg/L) for dibutyltin (DBT)—the most toxic of the OT considered in this article. The 90th percentile average daily dose (ADD) estimate of 0.034 ± 2.92 × 10⁻⁴μg/kg day is approximately 120 times lower than the WHO-based ADD for DBT (4.2 μg/kg day).     

Beyond Patient Classification: Using Individual Patient Characteristics in Appointment Scheduling

Scheduling patients involves a trade‐off between the productivity of the service provider and customer service. This study considers how outpatient medical facilities can improve their appointment scheduling by incorporating individual patient information in the scheduling process. Specifically, we obtain data on patient characteristics and examination durations from a health clinic, describe how that data can be used to predict patient examination durations in the clinic's appointment scheduling system, and evaluate the benefit of using individual patient characteristics over a conventional classification method. Computational results illustrate this method of patient scheduling reduces an overall cost function comprised of patient wait time, physician idle time, and over time by up to 24.2%, particularly when patients are sequenced with short duration patients being scheduled first. Several environmental characteristics are found to play critical roles in determining the magnitude of the benefit, including patient punctuality, no‐show probability, the clinic duration, the appointment rule used for scheduling, and the ratio of the physician's idle time cost to the patient wait cost. We also detail and evaluate a practical procedure for using heterogeneous scheduling under a fixed schedule.     

Assessing the Radiological Impact of Radiation Exposure Devices

Radiation exposure devices (REDs) are radiological weapons obtained by concealing a strong gamma‐emitting source in a place frequented by public to cause radiation injuries following the absorption of elevated radiation doses. The present work aims to assess the radiological impact of an RED by simulating its effects in both dynamical and static conditions of the covertly exposed population, with individual position and motion obtained through a Monte Carlo approach. The results indicate that in small enclosures the motion of people amplify the effects of radiation exposure with respect to the static case because it turns out in a larger number of individuals receiving doses above the threshold for the onset of deterministic effects. This behavior is mitigated in medium and large enclosures due to dose spreading over trajectories moving far away from the critical region close to the RED. The scaling laws obtained with a simple circular geometry were successfully applied to a more complex geometry like that of a stadium. The potentially large number of victims and the possibility to reiterate the attack raise the question of early detection. This can be achieved either by radiation survey or by indirectly inferring the presence of a strong radioactive source following the triage of patients with radiation sickness symptoms collected by the same hospital. In the former case careful design and operation of aerial or in situ monitoring is needed, while in the latter specific training should be given to healthcare personnel aimed to improve their discrimination and cooperation capabilities.