Browsing by Author "Solbak, Nathan M"

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    Patterns and predictors of adherence to colorectal cancer screening recommendations in Alberta’s Tomorrow Project participants stratified by risk
    (2018-01-25) Solbak, Nathan M; Xu, Jian-Yi; Vena, Jennifer E; Al Rajabi, Ala; Vaseghi, Sanaz; Whelan, Heather K; McGregor, S E
    Abstract Background Colorectal cancer (CRC) screening is an important modifiable behaviour for cancer control. Regular screening, following recommendations for the type, timing and frequency based on personal CRC risk, contributes to earlier detection and increases likelihood of successful treatment. Methods To determine adherence to screening recommendations in a large provincial cohort of adults, participants in Alberta’s Tomorrow Project (n = 9641) were stratified based on increasing level of CRC risk: age (Age-only), family history of CRC (FamilyHx), personal history of bowel conditions (PersonalHx), or both (Family/PersonalHx) using self-reported information from questionnaires. Provincial and national guidelines for timing and frequency of screening tests were used to determine if participants were up-to-date based on their CRC risk. Screening status was compared between enrollment (2000–2006) and follow-up (2008) to determine screening pattern over time. Results The majority of participants (77%) fell into the average risk Age-only strata. Only a third of this strata were up-to-date for screening at baseline, but the proportion increased across the higher risk strata, with > 90% of the highest risk Family/PersonalHx strata up-to-date at baseline. There was also a lower proportion (< 25%) of the Age-only group who were regular screeners over time compared to the higher risk strata, though age, higher income and uptake of other screening tests (e.g. mammography) were associated with a greater likelihood of regular screening in multinomial logistic regression. Conclusions The low (< 50%) adherence to regular CRC screening in average and moderate risk strata highlights the need to further explore barriers to uptake of screening across different risk profiles.
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    The effect of different methods to identify, and scenarios used to address energy intake misestimation on dietary patterns derived by cluster analysis
    (2021-05-08) Siou, Geraldine L; Akawung, Alianu K; Solbak, Nathan M; McDonald, Kathryn L; Rajabi, Ala A; Whelan, Heather K; Kirkpatrick, Sharon I
    Abstract Background All self-reported dietary intake data are characterized by measurement error, and validation studies indicate that the estimation of energy intake (EI) is particularly affected. Methods Using self-reported food frequency and physical activity data from Alberta’s Tomorrow Project participants (n = 9847 men 16,241 women), we compared the revised-Goldberg and the predicted total energy expenditure methods in their ability to identify misreporters of EI. We also compared dietary patterns derived by k-means clustering under different scenarios where misreporters are included in the cluster analysis (Inclusion); excluded prior to completing the cluster analysis (ExBefore); excluded after completing the cluster analysis (ExAfter); and finally, excluded before the cluster analysis but added to the ExBefore cluster solution using the nearest neighbor method (InclusionNN). Results The predicted total energy expenditure method identified a significantly higher proportion of participants as EI misreporters compared to the revised-Goldberg method (50% vs. 47%, p < 0.0001). k-means cluster analysis identified 3 dietary patterns: Healthy, Meats/Pizza and Sweets/Dairy. Among both men and women, participants assigned to dietary patterns changed substantially between ExBefore and ExAfter and also between the Inclusion and InclusionNN scenarios (Hubert and Arabie’s adjusted Rand Index, Kappa and Cramer’s V statistics < 0.8). Conclusions Different scenarios used to account for EI misreporters influenced cluster analysis and hence the composition of the dietary patterns. Continued efforts are needed to explore and validate methods and their ability to identify and mitigate the impact of EI misestimation in nutritional epidemiology.