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

描述(由申请人提供)：虽然对烟草控制政策和吸烟率的宏观和反复的横断面研究，以及对特定地理区域的新政策的评估研究，提供了一些政策有效性的证据，但很少有研究考察不同级别的政策实施(例如，地方、县和州)如何影响青少年和青年关键年龄段期间吸烟的个人内部轨迹(开始、流行、持续和戒烟)。如果不采取一种方法，随着时间的推移在全国范围内对同一个人同时审查各种政策，政策制定者就不能充分评估特定烟草控制政策的效力，从而无法对无效法规进行风险投资。利用具有全国代表性的纵向数据、控烟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卫生研究人员提供工具，以做出关于青少年吸烟预防和干预的最知情的控烟政策决策，包括哪些个人或地区最不容易受到政策实施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，我们需要一个关于青少年吸烟使用和预测风险因素的地理编码的重复观察数据集，一个全面的烟草控制法规数据库，以及一个能够处理复杂分层数据结构的统计方法。我们通过将每年收集的1997年全国青年纵向调查(NLSY97)的数据与非吸烟者权利美国人(ANR)全国烟草条例和法规储存库的数据合并，并使用分层交叉分类模型的最新发展来满足这些需求。为了确定烟草政策对青少年吸烟轨迹的主要影响，我们将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当地结构条件之后，特定烟草政策的效力将证明 应在更广泛的基础上采取政策，最终影响健康和死亡率，并降低医疗保健和社会成本。为了评估政策的效果是否因个人的特征或背景而不同，我们将分析个人层面的特征和政策措施与地方层面的特征和政策措施之间的互动效应，这将揭示特定政策对某些类型的个人或居住在类似地区的人更有效。针对某些个人的政策缺乏有效性将暴露出 这些政策需要纳入针对特定群体或领域的内容。这项提案的创新之处在于，它检查了青少年吸烟行为随时间的变化，因为它与以前检查过和未检查过的各级烟草限制措施有关，还在于为包含多个级别上重复观察的数据集开发和进一步发展了必要的创新统计方法。

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.