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队列效应是什么?比较三种统计方法建模肥胖流行率中的队列效应,美国,1971-2006 年。

What is a cohort effect? Comparison of three statistical methods for modeling cohort effects in obesity prevalence in the United States, 1971-2006.

机构信息

Department of Epidemiology, Columbia University, New York, NY 10032, USA.

出版信息

Soc Sci Med. 2010 Apr;70(7):1100-8. doi: 10.1016/j.socscimed.2009.12.018. Epub 2010 Feb 1.

Abstract

Analysts often use different conceptual definitions of a cohort effect, and therefore different statistical methods, which lead to differing empirical results. A definition often used in sociology assumes that cohorts have unique characteristics confounded by age and period effects, whereas epidemiologists often conceive that period and age effects interact to produce cohort effects. The present study aims to illustrate these differences by estimating age, period, and cohort (APC) effects on obesity prevalence in the U.S. from 1971 to 2006 using both conceptual approaches. Data were drawn from seven cross-sectional waves of the National Health and Nutrition Examination Survey. Obesity was defined as BMI >or=30 for adults and >or=95th percentile for children under the age of 20. APC effects were estimated using the classic constraint-based method (first-order effects estimated and interpreted), the Holford method (first-order effects estimated but second-order effects interpreted), and median polish method (second-order effects are estimated and interpreted). Results indicated that all methods report significant age and period effects, with lower obesity prevalence in early life as well as increasing prevalence in successive surveys. Positive cohort effects for more recently born cohorts emerged based on the constraint-based model; when cohort effects were considered second-order estimates, no significant effects emerged. First-order estimates of age-period-cohort effects are often criticized because of their reliance on arbitrary constraints, but may be conceptually meaningful for sociological research questions. Second-order estimates are statistically estimable and produce conceptually meaningful results for epidemiological research questions. Age-period-cohort analysts should explicitly state the definition of a cohort effect under consideration. Our analyses suggest that the prevalence of obesity in the U.S. in the latter part of the 20th century rose across all birth cohorts, in the manner expected based on estimated age and period effects. As such, the absence or presence of cohort effects depends on the conceptual definition and therefore statistical method used.

摘要

分析人员通常使用队列效应的不同概念定义,因此使用不同的统计方法,导致不同的实证结果。社会学中常用的定义假设队列具有独特的特征,这些特征与年龄和时期效应混杂在一起,而流行病学家通常认为时期和年龄效应相互作用产生队列效应。本研究旨在通过使用两种概念方法来估计年龄、时期和队列(APC)效应对美国 1971 年至 2006 年肥胖患病率的影响来说明这些差异。数据来自国家健康和营养检查调查的七个横断面波。肥胖定义为成年人 BMI≥30,20 岁以下儿童 BMI≥第 95 百分位数。使用经典约束基方法(估计和解释一阶效应)、霍尔福德方法(估计一阶效应但解释二阶效应)和中位数抛光方法(估计和解释二阶效应)估计 APC 效应。结果表明,所有方法都报告了显著的年龄和时期效应,生命早期肥胖率较低,随后的调查中肥胖率呈上升趋势。基于约束基模型,最近出生的队列出现了正的队列效应;当考虑二阶估计时,没有出现显著效应。一阶年龄-时期-队列效应估计经常因其对任意约束的依赖而受到批评,但对于社会学研究问题可能具有概念意义。二阶估计在统计学上是可估计的,并为流行病学研究问题产生概念上有意义的结果。年龄-时期-队列分析人员应明确说明所考虑的队列效应的定义。我们的分析表明,20 世纪后半叶美国肥胖症的患病率在所有出生队列中都有所上升,这与根据估计的年龄和时期效应预期的方式一致。因此,队列效应的存在或不存在取决于所使用的概念定义和统计方法。

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