This study utilizes old and new Norovirus (NoV) human challenge data to model the dose-response relationship for human NoV infection. The combined data set is used to update estimates from a previously published beta-Poisson dose-response model that includes parameters for virus aggregation and for a beta-distribution that describes variable susceptibility among hosts. The quality of the beta-Poisson model is examined and a simpler model is proposed. The new model (fractional Poisson) characterizes hosts as either perfectly susceptible or perfectly immune, requiring a single parameter (the fraction of perfectly susceptible hosts) in place of the two-parameter beta-distribution. A second parameter is included to account for virus aggregation in the same fashion as it is added to the beta-Poisson model. Infection probability is simply the product of the probability of nonzero exposure (at least one virus or aggregate is ingested) and the fraction of susceptible hosts. The model is computationally simple and appears to be well suited to the data from the NoV human challenge studies. The model's deviance is similar to that of the beta-Poisson, but with one parameter, rather than two. As a result, the Akaike information criterion favors the fractional Poisson over the beta-Poisson model. At low, environmentally relevant exposure levels (<100), estimation error is small for the fractional Poisson model; however, caution is advised because no subjects were challenged at such a low dose. New low-dose data would be of great value to further clarify the NoV dose-response relationship and to support improved risk assessment for environmentally relevant exposures.
In this paper, the US Environmental Protection Agency (EPA) presents an approach and a national estimate of drinking water related endemic acute gastrointestinal illness (AGI) that uses information from epidemiologic studies. There have been a limited number of epidemiologic studies that have measured waterborne disease occurrence in the United States. For this analysis, we assume that certain unknown incidence of AGI in each public drinking water system is due to drinking water and that a statistical distribution of the different incidence rates for the population served by each system can be estimated to inform a mean national estimate of AGI illness due to drinking water. Data from public water systems suggest that the incidence rate of AGI due to drinking water may vary by several orders of magnitude. In addition, data from epidemiologic studies show AGI incidence due to drinking water ranging from essentially none (or less than the study detection level) to a rate of 0.26 cases per person-year. Considering these two perspectives collectively, and associated uncertainties, EPA has developed an analytical approach and model for generating a national estimate of annual AGI illness due to drinking water. EPA developed a national estimate of waterborne disease to address, in part, the 1996 Safe Drinking Water Act Amendments. The national estimate uses best available science, but also recognizes gaps in the data to support some of the model assumptions and uncertainties in the estimate. Based on the model presented, EPA estimates a mean incidence of AGI attributable to drinking water of 0.06 cases per year (with a 95% credible interval of 0.02-0.12). The mean estimate represents approximately 8.5% of cases of AGI illness due to all causes among the population served by community water systems. The estimated incidence translates to 16.4 million cases/year among the same population. The estimate illustrates the potential usefulness and challenges of the approach, and provides a focus for discussions of data needs and future study designs. Areas of major uncertainty that currently limit the usefulness of the approach are discussed in the context of the estimate analysis.Key words | attributable risk, Bayesian statistics, community water systems, drinking water, gastrointestinal illness, household-intervention, microbial risk, Monte Carlo analysis, national estimate, waterborne disease, water distribution systems OVERVIEW AND PURPOSE OF THE PAPERIn this paper, the US Environmental Protection Agency (EPA) presents a conceptual approach for developing a national estimate of endemic acute gastrointestinal illness This paper is in the public domain: verbatim copying and redistribution of this paper are permitted in all media for any purpose, provided this notice is preserved along with the paper's original DOI. Anyone using the paper is requested to properly cite and acknowledge the source as J. (AGI) due to drinking water and a national estimate analysis developed through a model application. We first present the app...
Public water systems are increasingly facing higher bromide levels in their source waters from anthropogenic contamination through coal-fired power plants, conventional oil and gas extraction, textile mills, and hydraulic fracturing. Climate change is likely to exacerbate this in coming years. We estimate bladder cancer risk from potential increased bromide levels in source waters of disinfecting public drinking water systems in the United States. Bladder cancer is the health end point used by the United States Environmental Protection Agency (EPA) in its benefits analysis for regulating disinfection byproducts in drinking water. We use estimated increases in the mass of the four regulated trihalomethanes (THM4) concentrations (due to increased bromide incorporation) as the surrogate disinfection byproduct (DBP) occurrence metric for informing potential bladder cancer risk. We estimate potential increased excess lifetime bladder cancer risk as a function of increased source water bromide levels. Results based on data from 201 drinking water treatment plants indicate that a bromide increase of 50 μg/L could result in a potential increase of between 10(-3) and 10(-4) excess lifetime bladder cancer risk in populations served by roughly 90% of these plants.
Cryptosporidium human dose-response data from seven species/isolates are used to investigate six models of varying complexity that estimate infection probability as a function of dose. Previous models attempt to explicitly account for virulence differences among C. parvum isolates, using three or six species/isolates. Four (two new) models assume species/isolate differences are insignificant and three of these (all but exponential) allow for variable human susceptibility. These three human-focused models (fractional Poisson, exponential with immunity and beta-Poisson) are relatively simple yet fit the data significantly better than the more complex isolate-focused models. Among these three, the one-parameter fractional Poisson model is the simplest but assumes that all Cryptosporidium oocysts used in the studies were capable of initiating infection. The exponential with immunity model does not require such an assumption and includes the fractional Poisson as a special case. The fractional Poisson model is an upper bound of the exponential with immunity model and applies when all oocysts are capable of initiating infection. The beta Poisson model does not allow an immune human subpopulation; thus infection probability approaches 100% as dose becomes huge. All three of these models predict significantly (>10x) greater risk at the low doses that consumers might receive if exposed through drinking water or other environmental exposure (e.g., 72% vs. 4% infection probability for a one oocyst dose) than previously predicted. This new insight into Cryptosporidium risk suggests additional inactivation and removal via treatment may be needed to meet any specified risk target, such as a suggested 10 annual risk of Cryptosporidium infection.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.