Food environment trajectories: a sequence analysis from the CARTaGENE cohort
Researchers sought to categorize how people’s access to food changes over time based on their socioeconomic situations. Using data from 38,627 CARTaGENE participants from urban areas in Quebec, the findings revealed five patterns of food access, with those unable to work, living in larger households, and in low-income households having higher odds of experiencing limited access to food stores over time.
Molecular Genetic Characteristics of FANCI, a Proposed New Ovarian Cancer Predisposing Gene
Researchers investigated the genetic characteristics of the FANCI gene, which has been linked to an increased risk of ovarian cancer. Using data from 171 CARTaGENE participants and other sources, they confirmed that a specific FANCI variant is associated with ovarian cancer and discovered potential genetic links to other cancer types.
Associations between neighborhood walkability and walking following residential relocation: Findings from Alberta’s Tomorrow Project
This study aimed to estimate whether changes in neighbourhood walkability resulting from residential relocation were associated with leisure, transportation, and total walking levels. Using data from 5,977 urban adults (non-movers, movers to less walkability, and movers to more walkability), researchers found that time spent walking at follow-up was lower among those who moved to less walkable neighbourhoods, suggesting that relocating to less walkable neighbourhoods could negatively affect health.
Examining the influence of built environment on sleep disruption
Researchers sought to understand if modifying aspects of the built environment improved sleep. Using data from 28,385 BC Generations Project participants, they found that increased light-at-night, air pollution (SO2), and living <100 m from a main roadway were associated with insufficient sleep. Greenness had a positive effect on sleep.
A comparison of machine learning algorithms and traditional regression-based statistical modeling for predicting hypertension incidence in a Canadian population
This study evaluates different machine learning algorithms and compares their predictive performance with conventional models to predict hypertension incidence using data from 18,322 Alberta’s Tomorrow Project participants. The study found little difference in predictive performance between the machine learning algorithms and the conventional Cox PH model. The results suggest that conventional regression-based models can perform similarly to machine learning algorithms with good predictive accuracy in a moderate dataset with a reasonable number of features.
Agreement between self-report and administrative health data on occurrence of non-cancer chronic disease among participants of the BC generations project
Linked self-reported chronic disease history data to a Chronic Disease Registry (CDR) that applied algorithms to administrative health data to ascertain diagnoses of multiple chronic diseases in the Province of British Columbia.
Personalized breast cancer onset prediction from lifestyle and health history information
This article proposes a method for predicting when a woman will develop breast cancer (Bca) based on health and lifestyle history using data from 18,288 women in Alberta’s Tomorrow Project. Their approach produced seven actionable lifestyle features that a woman can modify to show how the model can predict the effects of such changes. This method can be used to identify interventions for those with a greater likelihood of developing BCa.
Dietary Intake and the Neighbourhood Environment in the BC Generations Project
This study examined how neighbourhood factors like access to amenities and social relationships, as well as greenness and walkability, can influence fruit and vegetable intake. ~28,000 participants from the BC Generations Project were involved. Those living in neighbourhoods with greater material and social deprivation were less likely to meet recommendations for fruit and vegetable consumption, while those living in neighbourhoods with higher greenness were more likely to meet recommendations. These findings highlight how multiple neighbourhood characteristics can impact dietary intake.
Patterns and predictors of adherence to breast cancer screening recommendations in Alberta’s Tomorrow Project
This study examined screening patterns in almost 5,000 women in Alberta’s Tomorrow Project. Most participants were up-to-date with screening at enrollment and follow-up, but 21.6% were not up-to-date at follow-up, and 3.2% had never participated. Having a family doctor was the strongest predictor of regular screening, while current smokers were less likely to be regular screeners. The study highlights the importance of promoting awareness of screening recommendations and the role of family doctors in encouraging screening.
The role of ultra-processed food consumption and depression on type 2 diabetes incidence: a prospective community study in Quebec, Canada
Researchers analyzed the association between depression and ultra-processed food (UPF) consumption as risk factors for developing type 2 diabetes (T2D) using baseline data (2009-2010) from 3,880 CARTaGENE participants. Participants with high depressive symptoms and high UPF consumption were at the highest risk for T2D. The study suggests that early management and monitoring of both risk factors could be essential for diabetes prevention.