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Parametric estimation of P(X > Y) for normal distributions in the context of probabilistic environmental risk assessment.

ABSTRACT: Estimating the risk, P(X > Y), in probabilistic environmental risk assessment of nanoparticles is a problem when confronted by potentially small risks and small sample sizes of the exposure concentration X and/or the effect concentration Y. This is illustrated in the motivating case study of aquatic risk assessment of nano-Ag. A non-parametric estimator based on data alone is not sufficient as it is limited by sample size. In this paper, we investigate the maximum gain possible when making strong parametric assumptions as opposed to making no parametric assumptions at all. We compare maximum likelihood and Bayesian estimators with the non-parametric estimator and study the influence of sample size and risk on the (interval) estimators via simulation. We found that the parametric estimators enable us to estimate and bound the risk for smaller sample sizes and small risks. Also, the Bayesian estimator outperforms the maximum likelihood estimators in terms of coverage and interval lengths and is, therefore, preferred in our motivating case study.

SUBMITTER: Jacobs R

PROVIDER: S-EPMC4548477 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Parametric estimation of P(X > Y) for normal distributions in the context of probabilistic environmental risk assessment.

Jacobs Rianne R Bekker Andriëtte A AA van der Voet Hilko H Ter Braak Cajo J F CJ

PeerJ 20150818

Estimating the risk, P(X > Y), in probabilistic environmental risk assessment of nanoparticles is a problem when confronted by potentially small risks and small sample sizes of the exposure concentration X and/or the effect concentration Y. This is illustrated in the motivating case study of aquatic risk assessment of nano-Ag. A non-parametric estimator based on data alone is not sufficient as it is limited by sample size. In this paper, we investigate the maximum gain possible when making stron ...[more]

PMID: 26312175

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Project description:BackgroundIn Timor-Leste there have been intermittent and ineffective soil-transmitted helminth (STH) deworming programs since 2004. In a resource-constrained setting, having information on the geographic distribution of STH can aid in prioritising high risk communities for intervention. This study aimed to quantify the environmental risk factors for STH infection and to produce a risk map of STH in Manufahi district, Timor-Leste.Methodology/principal findingsGeoreferenced cross-sectional data and stool samples were obtained from 2,194 participants in 606 households in 24 villages in the Manufahi District as part of cross sectional surveys done in the context of the "WASH for Worms" randomised controlled trial. Infection status was determined for Ascaris lumbricoides and Necator americanus using real-time quantitative polymerase chain reaction. Baseline infection data were linked to environmental data obtained for each household. Univariable and multivariable multilevel mixed-effects logistic regression analysis with random effects at the village and household level were conducted, with all models adjusted for age and sex. For A. lumbricoides, being a school-aged child increased the odds of infection, whilst higher temperatures in the coolest quarter of the year, alkaline soils, clay loam/loam soils and woody savannas around households were associated with decreased infection odds. For N. americanus, greater precipitation in the driest month, higher average enhanced vegetation index, age and sandy loam soils increased infection odds, whereas being female and living at higher elevations decreased the odds of infection. Predictive risk maps generated for Manufahi based upon these final models highlight the high predicted risk of N. americanus infection across the district and the more focal nature of A. lumbricoides infection. The predicted risk of any STH infection is high across the entire district.Conclusions/significanceThe widespread predicted risk of any STH infection in 6 to 18 year olds provides strong evidence to support strategies for control across the entire geographical area. As few studies include soil texture and pH in their analysis, this study adds to a growing body of evidence suggesting these factors influence STH infection distribution. This study also further supports that A. lumbricoides prefers acidic soils, highlighting a potential relatively unexplored avenue for control.Trial registrationClinicalTrials.gov ACTRN12614000680662.

Dataset Information

Parametric estimation of P(X > Y) for normal distributions in the context of probabilistic environmental risk assessment.

Publications

Parametric estimation of P(X > Y) for normal distributions in the context of probabilistic environmental risk assessment.

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