Dietary pattern modeling and linkage with health outcomes is essential for development of evidence-based dietary guidelines to support reduction of chronic diseases. National nutrition surveys are not routinely linked with health administrative databases, resulting in a lack of evidence on the health impact of unhealthy diets at the population level.
Objectives and Approach
This study is the first to use a nationally-representative nutrition survey (i.e., Canadian Community Health Survey-Nutrition-2004) linked with health administrative databases to examine the association of 5 key dietary quality indices with mortality risk. In total, 16,212 adults \(\geq\)20 years were followed for an average of 7.3 years. Two 24-hour dietary recalls were used to estimate the usual dietary intakes using the National Cancer Institute’s method. Weighted regression calibration was performed to obtain a true parameter relating diet (continuous) to mortality. Population Attributable Fractions were calculated to estimate the burden of all-cause mortality attributable to poor dietary patterns in Canada.
The 5 dietary quality indices examined were Dietary Approaches to Stop Hypertension(DASH); Dietary Guidelines for Americans Adherence Index 2015(DGAI); Healthy Eating Index-2010(HEI); Alternative HEI-2010(AHEI); and Mediterranean Style Dietary Pattern Score(MSDPS). Having a better diet quality (90%ile vs. 10%ile of index score) was associated with a significant 31-51% reduction in all-cause mortality hazard ratio among adults 45 to 80 years and 10-35% reduction in those \(\geq\)20 years (in order of significance: DASH, DGAI, HEI, AHEI and MSDPS). Survival benefit was incrementally greater for higher diet quality scores; however, even the 90%ile scores (Reference) were notably lower than the recommended levels (45.99% of recommended score). On average, 26.42% of all mortality in Canada was attributable to poor dietary patterns (range: 19.01% for MSDPS to 31.39% for DGAI).
The diet-attributable burden of mortality was higher than those reported for other behavioural risks (e.g., smoking). This research informs future formulation of nutrition interventions and policies with a focus on dietary patterns. This project demonstrates the importance of leveraging linked data and analytical capacity to inform future evidence-based nutrition policies.