ORCID Profile
0000-0001-8258-6890
Current Organisation
University of British Columbia
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Publisher: BMJ
Date: 12-2018
DOI: 10.1136/BMJOPEN-2018-026954
Abstract: Deficiencies in childhood development is a major global issue and inequalities are large. The influence of environmental exposures on childhood development is currently insufficiently explored. This project will analyse the impact of various modifiable early life environmental exposures on different dimensions of childhood development. Born to be Wise will study a Canadian cohort of approximately 34 000 children who have completed an early development test at the age of 5. Land use regression models of air pollution and spatially defined noise models will be linked to geocoded data on early development to analyse any harmful effects of these exposures. The potentially beneficial effect on early development of early life exposure to natural environments, as measured by fine-grained remote sensing data and various land use indexes, will also be explored. The project will use data linkages and analyse overall and age-specific impact, including variability depending on cumulative exposure by assigning time-weighted exposure estimates and by studying subs les who have changed residence and exposure. Potentially moderating effects of natural environments on air pollution or noise exposures will be studied by mediation analyses. A matched case–control design will be applied to study moderating effects of natural environments on the association between low socioeconomic status and early development. The main statistical approach will be mixed effects models, applying a specific software to deal with multilevel random effects of nested data. Extensive confounding control will be achieved by including data on a range of detailed health and sociodemographic variables. The study protocol has been ethically approved by the Behavioural Research Ethics Board at the University of British Columbia. The findings will be published in peer-reviewed journals and presented at scholarly conferences. Through stakeholder engagement, the results will also reach policy and a broader audience.
Publisher: Elsevier BV
Date: 11-2023
Publisher: Informa UK Limited
Date: 14-05-2021
Publisher: Oxford University Press (OUP)
Date: 14-12-2018
Abstract: Interpretation of exposure measurements has evolved into a framework based on the lognormal distribution. Most available practical tools are based on traditional frequentist statistical procedures that do not satisfactorily account for censored data and are not amenable to simple probabilistic risk statements. Bayesian methods offer promising solutions to these challenges. Such methods have been proposed in the literature but are not widely and freely available to practitioners. A set of computer applications were developed aimed at answering typical inferential questions that are important to occupational health practitioners: Is a group of workers compliant with an occupational exposure limit? Are some in iduals within this group likely to experience substantially higher exposure than its average member? How does an intervention influence the distribution of exposures? These questions were addressed using Bayesian models, simultaneously accounting for left, right, and interval-censored data with multiple censoring points. The models are estimated using the JAGS Gibbs s ler called through the R statistical package. The Expostats toolkit is freely available from www.expostats.ca as four tools accessible through a Web application, an offline standalone application or algorithms. The tools include a variety of calculations and graphical outputs useful according to current practices in analysis and interpretation of exposure measurements collected by occupational hygienists. Tool1 and its simplified version Tool1 Express focus on inferences from data from a similarly exposed group. Tool2 evaluates within- and between-worker components of variability, as well as the probability that an in idual worker might be overexposed. Tool3 compares exposure data across groups, e.g. evaluates the effect of an intervention. Uncertainty management includes the calculation of credible intervals and produces probabilistic statements about the exposure metrics (e.g. probability that over 5% of exposures are above a limit). Expostats is the first freely available toolkit that leverages the flexibility of Bayesian analysis to perform an extensive list of calculations recommended in several international guidelines on the practice of occupational hygiene.
No related grants have been discovered for Hugh Davies.