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• W3010998579 abstract "Abstract Small-area studies offer a powerful epidemiological approach to study disease patterns at the population level and assess health risks posed by environmental pollutants. They involve a public health investigation on a geographical scale (e.g. neighbourhood) with overlay of health, environmental, demographic and potential confounder data. Recent methodological advances, including Bayesian approaches, combined with fast-growing computational capabilities, permit more informative analyses than previously possible, including the incorporation of data at different scales, from satellites to individual-level survey information. Better data availability has widened the scope and utility of small-area studies, but has also led to greater complexity, including choice of optimal study area size and extent, duration of study periods, range of covariates and confounders to be considered and dealing with uncertainty. The availability of data from large, well-phenotyped cohorts such as UK Biobank enables the use of mixed-level study designs and the triangulation of evidence on environmental risks from small-area and individual-level studies, therefore improving causal inference, including use of linked biomarker and -omics data. As a result, there are now improved opportunities to investigate the impacts of environmental risk factors on human health, particularly for the surveillance and prevention of non-communicable diseases." @default.
• W3010998579 created "2020-03-23" @default.
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• W3010998579 date "2020-03-17" @default.
• W3010998579 modified "2023-10-14" @default.
• W3010998579 title "Small-area methods for investigation of environment and health" @default.
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• W3010998579 doi "https://doi.org/10.1093/ije/dyaa006" @default.
• W3010998579 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7266556" @default.
• W3010998579 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32182344" @default.
• W3010998579 hasPublicationYear "2020" @default.
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