Multivariate estimate of eating patterns: is the whole different from the parts?

ABSTRACT: Objective: To describe the correlations between eating patterns for the years 2007 to 2012, and for each year of the period from 2007 to 2012. Method: Cross-sectional study with data from the System of Surveillance of Risk and Protection Factors to Chronic Diseases by Telephone Survey with the selection of 167,761 individuals aged 18 to 44 years old. Eating patterns were identified with a Principal Component Analysis. To compare the effects of the extraction and the estimate of eating patterns among different surveys we conducted the following analyzes: in the first, we used the total data set for the years from 2007 to 2012; in the second, the patterns were estimated in each annual set of data for the period from 2007 to 2012. Steps 1 and 2 were performed with no rotation, with Varimax rotation and with Promax rotation. After extracting the patterns, standardized scores with zero mean were generated for each pattern. The association between the patterns generated in the analyzes was estimated by the Pearson correlation coefficient (r). Results: In the non-rotated analyzes, the components retained in the set presented correlations that were higher than 0.90, with the retained patterns in each year. In the rotated analyzes, only the first component had correlations that were higher than 0.90. Conclusion: Estimates of eating patterns either segmented - year by year - or in general - all of the years - showed high correlation and consistency between the patterns identified when in the same data pool.

摘要：
研究目的：阐明2007至2012年各年度饮食模式之间的相关性，以及该时段整体饮食模式的相关性。
研究方法：本研究为横断面研究，数据源自《电话调查慢性疾病风险与保护因素监测系统》，共纳入167761名18至44岁的研究对象。采用主成分分析（Principal Component Analysis）识别饮食模式。为比较不同调查中饮食模式提取与估计的效果，本研究开展两组分析：第一组使用2007至2012年的全部合并数据集；第二组针对2007至2012年的每年度独立数据集分别估计饮食模式。两组分析均分别采用无旋转、方差最大（Varimax）旋转及普洛马克斯（Promax）旋转三种方式进行。提取饮食模式后，为每种模式生成均值为0的标准化得分。采用皮尔逊相关系数（Pearson correlation coefficient, r）估算各分析所得饮食模式间的关联强度。
研究结果：无旋转分析中，全数据集保留的主成分与各年度保留的饮食模式间相关系数均高于0.90；旋转分析中，仅第一主成分的相关系数高于0.90。
研究结论：无论是按年度分段估计饮食模式，还是采用全时段合并数据进行估计，同一数据集下所识别的饮食模式均呈现出较高的相关性与一致性。