Classification of sub-populations for quantitative risk assessment based on awareness and perception: A cross-sectional population study of private well users in Ontario

Lavallee, Sarah; Hynds, Paul; Brown, R. Stephen; Majury, Anna

doi:10.1016/j.scitotenv.2022.159677

Cited by 3 publications

(1 citation statement)

References 53 publications

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“…Chi-square tests were used to assess bivariate proportional associations between categorical variables, followed by post-hoc testing via standardised residuals. Additionally, dummy variables, odds ratio (OR) estimates, and post-hoc testing using adjusted standardised residuals above and below the threshold of 1.50 were used to determine the presence and magnitude of associations between categorical (dichotomous/nominal) variables [ 45 ]. Kruskal–Wallis tests were used to investigate relationships between continuous and categorical variables, followed by post-hoc pairwise comparisons, while Spearman’s Rho was used to assess nonparametric associations between continuous variables.…”

Section: Methodsmentioning

confidence: 99%

Identifying Novel Data-Driven Dietary Patterns via Dimensionality Reduction and Associations with Socioeconomic Profile and Health Outcomes in Ireland

et al. 2023

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Dietary patterns and body mass index (BMI) play a significant role in the development of noncommunicable diseases (NCDs), which are the leading cause of mortality worldwide, including Ireland. A cross-sectional survey was conducted across Ireland to collate respondents’ socioeconomic profiles, health status, and dietary patterns with a representative sample size of 957 adult respondents. Principal component analysis (PCA) and statistical analyses were subsequently employed. To the author’s knowledge, this is the first study to use recent (2021) nationally representative data to characterise dietary patterns in Ireland via dimensionality reduction. Five distinct dietary patterns (“meat-focused”, “dairy/ovo-focused”, “vegetable-focused”, “seafood-focused”, and “potato-focused”) were identified and statistically characterised. The “potato-focused” group exhibited the highest mean BMI (26.88 kg/m2), while the “vegetable-focused” group had the lowest (24.68 kg/m2). “Vegetable-focused” respondents were more likely to be associated with a categorically healthy BMI (OR = 1.90) and urban residency (OR = 2.03). Conversely, “meat-focused” respondents were more likely to have obesity (OR = 1.46) and rural residency (OR = 1.72) along with the “potato-focused” group (OR = 2.15). Results show that data-derived dietary patterns may better predict health outcomes than self-reported dietary patterns, and transitioning to diets focusing on vegetables, seafood, and lower meat consumption may improve health.

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Section: Methodsmentioning

confidence: 99%