Biomedical subjects

B K Hope

Publications and source records attributed to B K Hope.

5 recordsLinked to original sources

PubMed2002

An overview of the Salmonella enteritidis risk assessment for shell eggs and egg products.

This article summarizes a quantitative microbial risk assessment designed to characterize the public health impact of consumption of shell eggs and egg products contaminated with Salmonella Enteritidis (SE). This risk assessment's objectives were to: (1) establish the baseline risk of foodborne illness from SE, (2) identify and evaluate potential risk mitigation strategies, and (3) identify data gaps related to future research efforts. The risk assessment model has five modules. The Egg Production module estimates the number of eggs produced that are SE-contaminated. Shell Egg Processing, Egg Products Processing, and Preparation & Consumption modules estimate the increase or decrease in the numbers of SE organisms in eggs or egg products as they pass through storage, transportation, processing, and preparation. A Public Health Outcomes module then calculates the incidence of illnesses and four clinical outcomes, as well as the cases of reactive arthritis associated with SE infection following consumption. The baseline model estimates an average production of 2.3 million SE-contaminated shell eggs/year of the estimated 69 billion produced annually and predicts an average of 661,633, human illnesses per year from consumption of these eggs. The model estimates approximately 94% of these cases recover without medical care, 5% visit a physician, an additional 0.5% are hospitalized, and 0.05% result in death. The contribution of SE from commercially pasteurized egg products was estimated to be negligible. Five mitigation scenarios were selected for comparison of their individual and combined effects on the number of human illnesses. Results suggest that mitigation in only one segment of the farm-to-table continuum will be less effective than several applied in different segments. Key data gaps and areas for future research include the epidemiology of SE on farms, the bacteriology of SE in eggs, human behavior in food handling and preparation, and human responses to SE exposure.

Animals↗

PubMed2001

A case study comparing static and spatially explicit ecological exposure analysis methods.

Exposure to chemical contaminants must be estimated when performing ecological risk assessments. A previous article proposed a habitat area and quality conditioned population exposure estimator, E[HQ]P, and described an individual-based, random walk, Monte Carlo model (SE3M) to facilitate calculation of E[HQ]P. In this article, E[HQ]P was compared with exposure estimates from a baseline risk assessment that evaluated mink and great blue heron exposure to fluoride at a federal Superfund site. Calculation of E[HQ]P took into consideration a receptor's forage area, movement behavior, population size, and the areal extent and quality of suitable habitat. The baseline assessment used four methods that did (total and unit Tier 2) and did not (total and unit Tier 1) consider habitat area or quality; where "total" included all exposure units on site and "unit" only a given exposure unit. Total Tier 1 estimates were consistently higher than E[HQ]P (e.g., 169.1 mg/kg x d versus 21.6 mg/kg x d). Risk managers using total Tier 1 results for decision making would be unlikely to underestimate exposure; however, implementability of correspondingly lower remedial objectives could be challenging. Unit Tier 1 estimates were higher (e.g., 96.5 mg/kg x d versus 61.6 mg/kg x d) or lower (e.g., 3.5 mg/kg x d versus 51.1 mg/kg x d) than E[HQ]P depending on variations in landscape features. Total Tier 2 and E[HQ]P estimates were similar (e.g., 20.7 mg/kg x d versus 21.6 mg/kg x d) when an ecologically questionable average exposure was assumed. Unit Tier 2 estimates were consistently well below E[HQ]P (e.g., 17.8 mg/kg x d versus 61.6 mg/kg x d) when an average exposure was not assumed. Risk managers using unit Tier 1 or 2 results could be basing their decisions on potentially large underestimates of exposure. By forgoing average exposure assumptions, and explicitly addressing landscape heterogeneity, SE3M appears capable of yielding exposure estimates that are not as potentially misleading to risk managers as those produced with traditional averaging methods.

Aluminum↗

PubMed2001

A spatially and bioenergetically explicit terrestrial ecological exposure model.

In typical exposure models, dose is a function of ingestion rate, which is a function of field metabolic rate and food energy availability. It is implicitly assumed that neither food energy nor ingestion rate is limited. This is unlikely to be true in the field. Poor habitat quality (expressed as limited or lacking food energy) or a physiologically limited maximum ingestion rate may collectively limit energy intake. A receptor may thus be as much at risk from lack of energy as from toxicant effects. To explore this possibility, an existing spatially explicit exposure model (SE3M) was enhanced to: 1) express 'habitat quality' in terms of gross energy available from a suite of habitat-specific food types, 2) track fulfillment of a receptor's daily energy needs as it traverses habitat patches with varying gross energy levels, 3) link intake of contaminants to food consumed to meet daily energy needs, and 4) track contaminant doses and resulting tissue residue levels as a receptor moves through habitat patches with differing levels of contamination. A feedback term through which chemical stressors affect a receptor's ability to intake and process energy was not considered at this time. The now spatially and energetically explicit exposure model, SE(4)M, provides a platform for exploring spatial and bioenergetic factors that may influence a receptor's acquisition of energy and contaminant tissue residues as it moves through space and time. An application of this model would be to provide predictions of tissue residue levels that are accessible to calibration or validation with empirical field data.

Animals↗

PubMed2000

Generating probabilistic spatially-explicit individual and population exposure estimates for ecological risk assessments.

Exposure to chemical contaminants in various media must be estimated when performing ecological risk assessments. Exposure estimates are often based on the 95th-percentile upper confidence limit on the mean concentration of all samples, calculated without regard to critical ecological and spatial information about the relative relationship of receptors, their habitats, and contaminants. This practice produces exposure estimates that are potentially unrepresentative of the ecology of the receptor. This article proposes a habitat area and quality-conditioned exposure estimator, E[HQ], that requires consideration of these relationships. It describes a spatially explicit ecological exposure model to facilitate calculation of E[HQ]. The model provides (1) a flexible platform for investigating the effect of changes in habitat area, habitat quality, foraging area, and population size on exposure estimates, and (2) a tool for calculating E[HQ] for use in actual risk assessments. The inner loop of a Visual Basic program randomly walks a receptor over a multicelled landscape--each cell of which contains values for cell area, habitat area, habitat quality, and concentration--accumulating an exposure estimate until the total area foraged is less than or equal to a given foraging area. An outer loop then steps through foraging areas of increasing size. This program is iterated by Monte Carlo software, with the number of iterations representing the population size. Results indicate that (1) any single estimator may over- or underestimate exposure, depending on foraging strategy and spatial relationships of habitat and contamination, and (2) changes in exposure estimates in response to changes in foraging and habitat area are not linear.

Animals↗

PubMed1994

A global biogeochemical budget for vanadium.

Evidence that environmental levels of vanadium are increasing has raised concern over the injection of vanadium into the atmosphere from anthropogenic sources. A simple global mass balance model was developed to demonstrate the influence of anthropogenic vanadium on the global distribution of this trace metal. Vanadium in particulate emissions owing to man's industrial activities were estimated to comprise approximately 53% of total atmosphere vanadium loading and exceeded natural continental or volcanogenic dust by only a narrow margin. Oceanic deposition of vanadium adhering to anthropogenic particles was estimated to comprise approximately 5% of total ocean vanadium loading. There is no suggestion that these inputs of anthropogenic vanadium pose a significant global environmental threat. It is entirely possible, however, that anthropogenic vanadium inputs could pose an environmental hazard given a more restricted area and a specific set of unfavorable circumstances.

Air Pollutants↗