Tobacco Control Policies and Individual-Level Trajectories of Youth Cigarette Use

描述（申请人提供）：虽然对烟草控制政策和吸烟率的宏观层面和重复的跨部门研究以及对特定地理区域新政策的评价研究提供了政策有效性的一些证据，但很少有研究审查各级政策如何执行在关键的青少年和年轻成人时期，吸烟行为（例如，地方、县和州）影响个人内的吸烟轨迹（开始、流行、持续时间和停止）。如果没有一种方法来审查一段时间内全国范围内对同一个人同时实施的各种政策，决策者就无法充分评估特定烟草控制政策的效力，因此，就有可能投资于无效的法规。利用全国代表性的纵向数据，烟草控制条例的完整记录和创新的统计方法，该项目的目标是为政策制定者和健康研究人员提供工具，使他们能够在青少年吸烟预防和干预方面做出最明智的烟草控制政策决定，包括哪些个人或地区最不容易受到政策实施所导致的行为变化的影响。为了充分了解烟草控制政策如何随着时间的推移影响青少年吸烟模式，我们需要一个地理编码的，重复观察的青少年吸烟和预测风险因素的数据集，烟草控制法规的综合数据库，以及能够处理复杂的分层数据结构的统计方法。我们解决这些需求，每年收集的全国青年纵向调查1997年（NLSY97）与美国非吸烟者的权利（ANR）的烟草条例和法规的国家存储库合并的数据，并使用分层交叉分类模型的最新发展。为了确定烟草政策对青年卷烟使用轨迹的主要影响，我们将联合收割机结合两个地理编码数据集，并使用最近开发的分层交叉分类模型进行分析。在控制了众所周知的风险因素和地方结构条件之后，特定烟草政策的效力将表明， 应在更广泛的基础上采取政策，最终影响健康和死亡率，并降低保健和社会成本。为了评估政策效果是否因个人特征或环境而异，我们将分析个人水平特征和政策措施以及地方水平特征和政策措施之间的相互作用效果，这将揭示特定政策是否对某些类型的个人或居住在类似地区的人更有效。政策对某些人缺乏有效性将暴露 这些政策需要包含针对特定群体或地区的内容。该提案的创新之处在于，它审查了青年人吸烟行为随时间的变化，因为它涉及到以前审查和未经审查的烟草限制，在不同的执行水平，并在发展和促进创新的统计方法，数据集，包含重复的观察在多个层面上。

DESCRIPTION (provided by applicant): While macro-level and repeated cross-sectional studies of tobacco control policies and smoking rates, as well as evaluative studies of new policies in specific geographic areas, provide some evidence of policy effectiveness, little research has examined how policies at various levels implementation (e.g. local, county, and state) affect within-individual trajectories (initiation, prevalence, duration, and cessation) of smoking during the critical teenage and young adult years. Without an approach that examines the full range of policies simultaneously nationwide on the same individuals over time, policymakers cannot adequately assess the efficacy of particular tobacco control policies and, thus, risk investment in non-effective regulations. Using nationally representative longitudinal data, a complete record of tobacco control ordinances, and innovative statistical methods, the goal of this project is to provide policymakers and health researchers with the tools to make the most informed tobacco control policy decisions regarding youth smoking prevention and intervention, including which individuals or locales are least susceptible to changes in behavior resulting from policy implementation. To fully understand how tobacco control policy affects youth smoking patterns over time, we require a geocoded, repeated observations dataset of youth cigarette use and predictive risk factors, a comprehensive database of tobacco control regulations, and a statistical method capable of handling a complex hierarchical data structure. We address those needs by merging data from the annually collected National Longitudinal Survey of Youth 1997 (NLSY97) with the Americans for Nonsmokers' Rights (ANR) national repository of tobacco ordinances and regulations, and using recent developments in hierarchical cross-classification models. To identify the main effect of tobacco policies on trajectories of youth cigarette use, we will combine the two geocoded datasets and analyze them with recently developed hierarchical cross- classification models. After controlling for well-known risk factors and local structural conditions, the efficacy of particular tobacco policies will demonstrate which policies should be adopted on a wider basis, ultimately impacting health and mortality and reducing costs to health care and society. To assess whether the effect of policy varies by characteristics of the individual or context, we will analyze interaction effects between individua-level characteristics and policy measures and local-level characteristics and policy measures, which will reveal if a given policy is more effective for certain types of individuals or those residing in similar areas. The lack of effectiveness of policies on certain individuals will expose the need for those policies to incorporate a component that addresses particular groups or areas. This proposal's innovation lies in both its examination of within-youth changes in smoking behavior over time as it relates to both previously examined and unexamined tobacco restrictions at various levels of implementation and in the development and furthering of innovative statistical approaches necessary for datasets that contain repeated observations on multiple levels.