Combined-survey estimation and Markov-chain simulation of childhood obesity

儿童肥胖的联合调查估计和马尔可夫链模拟

• 批准号: 7906929
• 负责人: MICHAEL S. RENDALL
• 金额: $ 46.42万
• 依托单位: RAND CORPORATION
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-15 至 2013-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7906929
• 关键词: Address; Adolescence; Adolescent; Adolescent Development; Adopted; Adult; Age of Onset; Area; Behavior; Behavioral; Biological; Biology; Birth; Birth Weight; Child; Childhood; Computing Methodologies; Data; Data Analyses; Data Set; Demographer; Environment; Equation; Family; Fetal Development; Genetic; Goals; Health; Health behavior; Heritability; Individual; Intermediate Variables; Intervention; Investigation; Joints; Lead; Life; Life Cycle Stages; Life Expectancy; Link; Literature; Longevity; Longitudinal Surveys; Markov Chains; Measurement; Measures; Methodology; Methods; Modeling; Neighborhoods; Nutritional; Obesity; Other Genetics; Outcome; Overweight; Parents; Physical activity; Policies; Population; Population Policies; Premature Mortality; Process; Public Health; Relative (related person); Research; Research Personnel; Resources; Sample Size; Schools; Scientist; Simulate; Skin; Smoking; Solutions; Staging; Statistical Models; Surveys; Testing; Time; To specify; Weight; Work; base; cohort; design; effective intervention; experience; family genetics; innovation; meetings; models and simulation; multilevel analysis; obesity in children; population health; population survey; public health priorities; public health relevance; simulation; social; socioeconomics; transmission process

DESCRIPTION (provided by applicant): Two major research needs emerge from the literature on understanding childhood obesity and on designing effective interventions to address this major threat to public health. The first is a need for a joint understanding of biological and social determinants and their interactions. The second is a need to develop a better understanding of childhood obesity as a longitudinal trajectory of weight status. To meet those needs this study will develop methods that combine two longitudinal survey datasets to estimate and simulate trajectories of weight status from birth to adolescence. Modeling multiple (more than three) levels of biological and social influence simultaneously is beyond the feasibility of current methods and data. A single survey may have some, but not all of the variables need to estimate these effects, nor will a single survey have sufficient sample size for estimation of these multiple effects in a longitudinal model. Our solution to the problem of how to include more than three levels of influence involves: (1) the use of existing statistical modeling methods for up to two or three levels of influence in any one equation; (2) the use of multiple imputation from one survey to another to allow pooling of child observations across two surveys to greatly increase effective sample sizes in this model estimation; and (3) the use of Markov-chain simulation models to allow for more than three levels of influences to be accumulated across four separate equations describing the childhood weight-status trajectory. Estimates of the absolute and relative impacts on childhood obesity of biological, behavioral, and socio-economic variables, and of potential policy interventions, are then derived from simulated childhood life paths. PUBLIC HEALTH RELEVANCE: In this study, we develop statistical and computational methods that combine two longitudinal survey datasets to estimate and simulate trajectories of weight status from birth to adolescence. Estimates of the impacts on childhood obesity of biological, behavioral, and socio-economic variables, and of potential policy interventions, are derived from simulated childhood life paths.

描述（由申请人提供）：两个主要的研究需求出现在了解儿童肥胖和设计有效的干预措施，以解决这一重大威胁的公共健康的文献。第一，需要共同理解生物和社会决定因素及其相互作用。第二，需要更好地了解儿童肥胖症作为体重状况的纵向轨迹。为了满足这些需求，本研究将开发联合收割机结合两个纵向调查数据集的方法，以估计和模拟从出生到青春期的体重状况轨迹。同时对生物和社会影响的多个（三个以上）层面进行建模超出了当前方法和数据的可行性。一个单一的调查可能有一些，但不是所有的变量需要估计这些影响，也不会有足够的样本量估计这些多重效应在纵向模型。对于如何包含三个以上影响水平的问题，我们的解决方案包括：（1）使用现有的统计建模方法，在任何一个方程中最多可以包含两个或三个影响水平;（2）使用从一个调查到另一个调查的多重插补，允许合并两个调查中的子观察结果，以大大增加模型估计中的有效样本量;以及（3）使用马尔可夫链模拟模型，以允许在描述儿童体重状态轨迹的四个独立方程中累积三个以上的影响水平。然后，从模拟的童年生活路径中得出生物、行为和社会经济变量以及潜在政策干预措施对儿童肥胖的绝对和相对影响的估计。 公共卫生相关性：在这项研究中，我们开发了统计和计算方法，结合联合收割机两个纵向调查数据集，以估计和模拟从出生到青春期的体重状况的轨迹。对儿童肥胖的生物，行为和社会经济变量的影响，以及潜在的政策干预的估计，来自模拟的童年生活路径。