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Evidence suggests that combining tools that gather short- and long-term dietary data may be the optimal approach for the assessment of diet–disease associations in epidemiologic studies. Online technology can reduce the associated burdens for researchers and participants, but feasibility must be demonstrated in real-world settings before wide-scale implementation. The objective of this study was to determine the feasibility and acceptability of combining web-based tools (the Automated Self-Administered 24-hour Dietary Assessment Tool [ASA24-2016] and the past-year Diet History Questionnaire II [DHQ-II]) in a subset of participants in Alberta's Tomorrow Project, a prospective cohort. For this feasibility study, invitations were mailed to 550 randomly selected individuals enrolled in Alberta's Tomorrow Project. Consented participants (n = 331) were asked to complete a brief sociodemographic and health questionnaire, four ASA24-2016 recalls, the DHQ-II, and an evaluation survey. The study was conducted from March 2016 to December 2016 in Alberta, Canada. The majority of participants, mean age (SD) = 57.4 (9.8) years, were women (70.7%), urban residents (85.5%), and nonsmokers (95.7%). Primary outcomes were number of ASA24-2016 recalls completed, response rate of DHQ-II completion, and time to complete each assessment. The Wilcoxon signed rank sum test was used to assess differences in completion time. One-third (n = 102) of consenting participants did not complete any ASA24-2016 recalls. The primary reason to withdraw from the feasibility study was a lack of time. Among consenting participants, 51.9% (n = 172), 41.1% (n = 136), and 36.5% (n = 121) completed at least two ASA24-2016 recalls, the DHQ-II, and at least two ASA24-2016 recalls plus the DHQ-II, respectively. Median (25th to 75th percentile) completion times for participants who completed all recalls were 39 minutes (25 to 53 minutes) for the first ASA24-2016 recall and 60 minutes (40 to 90 minutes) for the DHQ-II. Findings indicate combining multiple ASA24-2016 recalls and the DHQ-II is feasible in this subset of Alberta's Tomorrow Project participants. However, optimal response rates may be contingent on providing participant support. Completion may also be sensitive to timing and frequency of recall administration. [ABSTRACT FROM AUTHOR]

The objective of this study was to determine the influence of strategies of handling misestimation of energy intake (EI) on observed associations between dietary patterns and cancer risk. Data from Alberta's Tomorrow Project participants ( n = 9,847 men and 16,241 women) were linked to the Alberta Cancer Registry. The revised-Goldberg method was used to characterize EI misestimation. Four strategies assessed the influence of EI misestimation: Retaining individuals with EI misestimation in the cluster analysis (Inclusion), excluding before (ExBefore) or after cluster analysis (ExAfter), or reassigning into ExBefore clusters using the nearest neighbor method (InclusionNN). Misestimation of EI affected approximately 50% of participants. Cluster analysis identified three patterns: Healthy, Meats/Pizza and Sweets/Dairy. Cox proportional hazard regression models assessed associations between the risk of cancer and dietary patterns. Among men, no significant associations (based on an often-used threshold of p < 0.05) between dietary patterns and cancer risk were observed. In women, significant associations were observed between the Sweets/Dairy and Meats/Pizza patterns and all cancer risk in the ExBefore (HR (95% CI): 1.28 (1.04-1.58)) and InclusionNN (HR (95% CI): 1.14 (1.00-1.30)), respectively. Thus, strategies to address misestimation of EI can influence associations between dietary patterns and disease outcomes. Identifying optimal approaches for addressing EI misestimation, for example, by leveraging biomarker-based studies could improve our ability to characterize diet-disease associations., Competing Interests: The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript and in the decision to publish the results.

The first annual report of the Chief Public Health Officer on the State of Public Health in Canada draws notable attention to health inequalities in Canada. This report provides a compelling presentation of our current health status and the uneven distribution of health across the population, noting persistent and sizeable gaps in life expectancy, infant mortality, self-reported health, prevalence of chronic diseases, and other health indicators between higher- and lower-income groups, as well as the extraordinary disadvantage experienced by Canada's Aboriginal peoples. However, the report falls short of offering a critical approach to addressing and reducing health inequalities. It fails to stimulate thinking about integrated strategies by profiling current responses that do little to address the underlying structural drivers of health inequalities and ignoring the population health framework's recognition of the complex interactions among the determinants of health. Despite its shortcomings, the report shines a light on major health inequalities in Canada, providing a foundation for further action. Public health workers in this country must build on this foundation, working together and with all sectors and levels of government to identify and implement integrated strategies to reduce health inequalities and inequities in Canada.