Effect of energy under-reporting on secular trends of dietary patterns in a mediterranean population

Diet is an important factor in the prevention of chronic diseases. Analysis of secular trends of dietary patterns can be biased by energy under-reporting. Therefore, the objective of the present study was to analyse the impact of energy under-reporting on dietary patterns and secular trends in dietary patterns defined by cluster analysis.

Two cross-sectional population-based surveys were conducted in Spain, in 2000 and 2005, with 3058 and 6352 participants, respectively, aged 25 to 74 years. Validated questionnaire was used to collect dietary data. Cluster analysis was run separately for all participants, plausible energy reporters (PER), and energy under-reporters (EUR) to define dietary patterns.

Three clusters, "healthy", "mixed" and "western", were identified for both surveys. The "mixed" cluster was the predominant cluster in both surveys. Excluding EUR reduced the proportion of the "mixed" cluster up to 6.40% in the 2000 survey; this caused secular trend increase in the prevalence of the "mixed" pattern. Cross-classification analysis of all participants and PER' data showed substantial agreement in cluster assignments: 68.7% in 2000 and 84.4% in 2005. Excluding EUR did not cause meaningful (≥ 15%) changes in the "healthy" pattern. It provoked changes in consumption of some food groups in the "mixed" and "western" patterns: mainly decreases of unhealthy foods within the 2000 and increases of unhealthy foods within the 2005 surveys. Secular trend effects of EUR were similar to those within the 2005 survey. Excluding EUR reversed the direction of secular trends in consumption of several food groups in PER in the "mixed" and "western" patterns.

EUR affected distribution of participants between dietary patterns within and between surveys, secular trends in food group consumption and amount of food consumed in all, but not in the "healthy" pattern. Our findings emphasize threats from energy under-reporting in dietary data analysis.