Data Fusion Methods for Human Health Risk Assessment: Review and Application

Zargar, Amin; Dyck, Roberta; Islam, Mohammad Shafiqul; Mohapatra, Asish; Sadiq, Rehan

doi:10.1080/10807039.2012.746145

Cited by 8 publications

(12 citation statements)

References 56 publications

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“…(), and Zargar et al. () explored the use of probabilistic methods to facilitate the integration of different data types and sources to improve risk assessment and quantify uncertainty. This study integrates risk perception but does not consider the other benefits associated with valuing perceptions in calculations of risk and future decision making.…”

Section: Discussionmentioning

confidence: 99%

“…A community-based participatory (CBP) study, using probabilistic Bayesian risk assessment methods (Serre et al, 2003;Zargar, Dyck, Islam, Mohapatra, & Sadiq, 2014), was applied to integrate drinking water risk perception in a quantitative holistic HHRA. Probabilistic Bayesian methods were used to describe variability of model parameters and quantify perception of drinking water safety.…”

Section: Research Approach and Community Partnershipsmentioning

confidence: 99%

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Risk Perception and Human Health Risk in Rural Communities Consuming Unregulated Well Water in Saskatchewan, Canada

et al. 2019

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Rural communities dependent on unregulated drinking water are potentially at increased health risk from exposure to contaminants. Perception of drinking water safety influences water consumption, exposure, and health risk. A community‐based participatory approach and probabilistic Bayesian methods were applied to integrate risk perception in a holistic human health risk assessment. Tap water arsenic concentrations and risk perception data were collected from two Saskatchewan communities. Drinking water health standards were exceeded in 67% (51/76) of households in Rural Municipality #184 (RM184) and 56% (25/45) in Beardy's and Okemasis First Nation (BOFN). There was no association between the presence of a health exceedance and risk perception. Households in RM184 or with an annual income >$50,000 were most likely to have in‐house water treatment. The probability of consuming tap water perceived as safe (92%) or not safe (0%) suggested that households in RM184 were unlikely to drink water perceived as not safe. The probability of drinking tap water perceived as safe (77%) or as not safe (11%) suggested households in BOFN contradicted their perception and consumed water perceived as unsafe. Integration of risk perception lowered the adult incremental lifetime cancer risk by 3% to 1.3 × 10−5 (95% CI 8.4 × 10−8 to 9.0 × 10−5) for RM184 and by 8.9 × 10−6 (95% CI 2.2 × 10−7 to 5.9 × 10−5) for BOFN. Probability of exposure to arsenic concentrations >1:100,000, negligible cancer risk, was 23% for RM184 and 22% for BOFN.

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Section: Discussionmentioning

confidence: 99%

Section: Research Approach and Community Partnershipsmentioning

confidence: 99%

Risk Perception and Human Health Risk in Rural Communities Consuming Unregulated Well Water in Saskatchewan, Canada

et al. 2019

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“…The data fusion techniques can be classified into three nonexclusive categories: (i) data association (identity level fusion), (ii) state estimation (feature level fusion), and (iii) decision level fusion [44,10]. The data association level fusion must determine the set of measurements that correspond to each target [18,24].…”

Section: Data Fusionmentioning

confidence: 99%

“…Data fusion can be defined as a combination of multiple sources to obtain improved information; in this context, improved information means less expensive, higher quality, or more relevant information [10,18]. Data fusion techniques can also be regarded as a mathematical techniques used to combine multiple values of a feature into single value [44]. The goal of using data fusion in this analysis is to obtain a lower prediction error probability and a higher reliability by using data from multiple distributed sources or models [24,4].…”

Section: Introductionmentioning

confidence: 99%