全国健康与营养检查调查
环境卫生
医学
数据收集
人口
公共卫生
膳食参考摄入量
老年学
营养物
生物
统计
数学
生态学
护理部
作者
Namanjeet Ahluwalia,Johanna T. Dwyer,Ana L Terry,Alanna Moshfegh,Clifford L. Johnson
出处
期刊:Advances in Nutrition
[Oxford University Press]
日期:2016-01-01
卷期号:7 (1): 121-134
被引量:692
标识
DOI:10.3945/an.115.009258
摘要
NHANES is the cornerstone for national nutrition monitoring to inform nutrition and health policy. Nutritional assessment in NHANES is described with a focus on dietary data collection, analysis, and uses in nutrition monitoring. NHANES has been collecting thorough data on diet, nutritional status, and chronic disease in cross-sectional surveys with nationally representative samples since the early 1970s. Continuous data collection began in 1999 with public data release in 2-y cycles on ∼10,000 participants. In 2002, the Continuing Survey of Food Intakes by Individuals and the NHANES dietary component were merged, forming a consolidated dietary data collection known as What We Eat in America; since then, 24-h recalls have been collected on 2 d using the USDA's Automated Multiple-Pass Method. Detailed and targeted food-frequency questionnaires have been collected in some NHANES cycles. Dietary supplement use data have been collected (in detail since 2007) so that total nutrient intakes can be described for the population. The continuous NHANES can adapt its content to address emerging public health needs and reflect federal priorities. Changes in data collection methods are made after expert input and validation/crossover studies. NHANES dietary data are used to describe intake of foods, nutrients, food groups, and dietary patterns by the US population and large sociodemographic groups to plan and evaluate nutrition programs and policies. Usual dietary intake distributions can be estimated after adjusting for day-to-day variation. NHANES remains open and flexible to incorporate improvements while maintaining data quality and providing timely data to track the nation's nutrition and health status. In summary, NHANES collects dietary data in the context of its broad, multipurpose goals; the strengths and limitations of these data are also discussed in this review.
科研通智能强力驱动
Strongly Powered by AbleSci AI