No abstract

No abstract

The results of an analysis of site-specific creel and angler information collected for the lower 6 miles of the Passaic River in Newark, NJ (Study Area), demonstrate that performing a site-specific creel/angler survey was essential to capture the unique characteristics of the anglers using the Study Area. The results presented were developed using a unique methodology for calculating site-specific, human exposure estimates from data collected in this unique urban/industrial setting. The site-specific human exposure factors calculated and presented include (1) size of angler population and fish-consuming population, (2) annual fish consumption rate, (3) duration of anglers' fishing careers, (4) cooking methods for the fish consumed, and (5) demographic information. Sensitivity and validation analyses were performed, and results were found to be useful for performing a site-specific, human health risk assessment. It was also concluded that site-specific exposure factor values are preferable to less representative "default values." The results of the analysis showed that the size of the angling population at the Study Area is estimated to range from 154 to 385 anglers, based on different methods of matching intercepts with anglers. Thirty-four anglers were estimated to have consumed fish; 37 people consumed fish from the river. The fish consumption rate for anglers using this area was best represented as 0.42 g/day for the central tendency and 1.8 g/day for the 95th percentile estimates. Anglers fishing at the river have relatively short fishing careers with a median of 0.9 yr, an average of 1.5 yr, and a 95th percentile of 4.8 yr. Consuming anglers tend to fry the fish they caught. The demographics of anglers who consume fish do not appear to differ substantially from those who do not, with no indication of a subsistence angling population.

The potential human health risks associated with consuming fish containing hazardous substances are related to the frequency, duration, and magnitude of exposure. Because these risk factors are often site specific, they require site-specific data. In anticipation of performing a risk assessment of the lower 6 miles of the Passaic River in New Jersey (Study Area), a year-long creel/angler survey collected such site-specific data. The lower Passaic River is urbanized and industrialized, and its site conditions present unique survey design and sampling challenges. For example, the combined population of the municipalities surrounding the Study Area is nearly 330,000, but because the Study Area is tidal, state law does not require fishing licenses for anglers to fish or crab in the Study Area. The sampling challenges posed by the lack of licensing are exacerbated by the industrialization and lack of public access in the lower half of the Study Area. This article presents a survey methodology designed to overcome these challenges to provide data for accurately estimating the Study Area's angling population and the fish and crabs they catch, keep, and eat. In addition to addressing the challenges posed by an urban and industrial setting, the survey methodology also addresses the issues of coverage, avidity, and deterrence, issues necessary for collecting a representative sample of the Study Area's anglers. This article is a companion to two other articles. The first companion article describes the analytical methodology designed to process the data collected during the survey. The second presents, validates, and interprets the survey results relating to human exposure factors for the lower Passaic River.

This paper presents a dynamic generalization of a model often used to aid marketing decisions relating to conventional products. The model uses stated-preference data in a random-utility framework to predict adoption rates for new pharmaceutical products. In addition, this paper employs a Markov model of patient learning in drug selection. While the simple learning rule presented here is only a rough approximation to reality, this model nevertheless systematically incorporates important features including learning and the influence of shifting preferences on market share. Despite its simplifications, the integrated framework of random-utility and product attribute updating presented here is capable of accommodating a variety of pharmaceutical marketing and development problems. This research demonstrates both the strengths of stated-preference market research and some of its shortcomings for pharmaceutical applications.