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: 8332710
• 负责人: MICHAEL S. RENDALL
• 金额: $ 45.11万
• 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 2015-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8332710
• 关键词: Address; Adolescence; Adolescent; Adolescent Development; Adopted; Adult; Age of Onset; Area; Behavior; Behavioral; Biological; Biology; Birth; Birth Weight; Child; Child Development; 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 Methods; 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）：有关了解儿童肥胖症以及设计有效的干预措施以应对公共卫生的主要威胁的文献中，有两项重大研究需要。首先是对生物和社会决定因素及其相互作用的共同理解。第二个是需要更好地理解对儿童肥胖的理解，因为体重状况的纵向轨迹。为了满足这些需求，本研究将开发结合两个纵向调查数据集以估算和模拟从出生到青春期的体重状态轨迹的方法。同时对多个（三个以上）生物学和社会影响进行建模超出了当前方法和数据的可行性。一项调查可能具有一些但并非所有变量都需要估计这些效果，单个调查也没有足够的样本量来估算纵向模型中这些多重影响。我们解决如何包括三个以上影响的问题的解决方案涉及：（1）在任何一个方程式中最多使用现有的统计建模方法最多两到三个级别的影响； （2）从一项调查到另一项调查的多个插补，以允许在两个调查中汇集儿童观察结果，以在此模型估计中大大增加有效的样本量； （3）使用马尔可夫链仿真模型，允许在描述儿童体重状态轨迹的四个单独的方程式中积累三个以上的影响。估计对生物学，行为和社会经济变量以及潜在政策干预措施的儿童肥胖的绝对和相对影响估计，然后源自模拟的儿童生活道路。 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，这些方法结合了两个纵向调查数据集，以估算和模拟从出生到青春期的体重状况的轨迹。估计对生物学，行为和社会经济变量以及潜在政策干预措施的儿童肥胖的影响的估计是从模拟的童年生活路径得出的。