Utilizing tobacco discrete choice experiments to predict the population impacts of FDA regulatory policies

利用烟草离散选择实验来预测 FDA 监管政策对人群的影响

• 批准号: 10679412
• 负责人: Ce Shang
• 金额: $ 22.68万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-07 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10679412
• 关键词: Address; Adopted; Adult; Area; Authorization documentation; Behavior; Calibration; Characteristics; Communication; Consumer Preferences; Data; Development; Elasticity; Electronic cigarette; Experimental Designs; Future; Goals; Guidelines; Health; Literature; Low Prevalence; Marketing; Measures; Meta-Analysis; Methodology; Methods; Motion; Nicotine; Oral; Pattern; Persons; Policies; Population; Price; Protocols documentation; Public Health; Regulation; Reporting; Research Personnel; Risk; Shapes; Smoking; Surveys; Tobacco; Tobacco use; United States Food and Drug Administration; Variant; Visit; Youth; addiction; authority; behavior prediction; combustible tobacco; design; electronic cigarette user; experimental study; heated tobacco products; improved; method development; non-smoker; preference; response; risk perception; systematic review; tobacco advertising; tobacco products; tobacco regulation; tobacco smokers; tool; willingness to pay; young adult

PROJECT SUMMARY As the state-of-the-art method to assess consumers’ preferences for new products, discrete choice experiments (DCEs) have been increasingly applied to study the impacts of potential policies on tobacco use behaviors, particularly in the areas of product standards and messaging development (e.g., warnings and campaigns). Compared to observational survey data, DCEs have the advantage of providing nimble evidence on emerging products with low prevalence and predicting the impacts of potential policies on behaviors before they are implemented in the real world. However, their potential in guiding and defending FDA policies has not been fully realized due to several methodological challenges. The first challenge is the lack of consistency in tobacco DCE design and reporting. Researchers often adopt different design features (e.g., whether to include an opt-out option) and use different measures in reporting, such as willingness to pay (WTP) vs. relative importance scores. Therefore, it is important to review current practices, explore how to synthesize existing DCE evidence, and propose guidelines for future DCE design and reporting. The second challenge is hypothetical bias – the bias between stated preferences (SP) or “what people claim that they would do” and revealed preferences (RP) or “what people actually do in the real world.” Recent method development has combined DCEs and RP data (e.g., retail and survey data) to mitigate hypothetical bias, providing us a unique opportunity to revisit existing DCEs and improve their accuracy in policy impact prediction. The overarching goal of this project is to inform the impact of FDA regulatory policies on tobacco use behaviors using existing DCE evidence. We will conduct systematic reviews to synthesize evidence on product standards and messaging development by assessing the ranking of policy importance and WTP. Moreover, we will combine existing DCEs with Nielsen Retail Scanner data and nationally representative surveys to mitigate the hypothetical bias in DCEs, which improves the accuracy of using existing data to predict the impact of potential regulatory policies that have not been implemented yet. The specific aims are to 1) Conduct a systematic review of tobacco DCEs manipulating product attributes. 2) Conduct a systematic review of tobacco DCEs manipulating product messaging. 3) Combine stated-preference DCEs and revealed-preference real-world retail and survey data to predict the impacts of FDA regulatory policies on product market shares and tobacco use. This project will provide the first systematic review of tobacco DCEs in the context of informing FDA regulatory policies, further improving the accuracy of DCE in predicting policy impacts. In summary, this project uses existing data to address priority scientific domains of RFA-OD-21-003, including tobacco product characteristics, communication practices, and tobacco use patterns.

项目概要 作为评估消费者对新产品偏好的最先进方法，离散选择实验 （DCE）已越来越多地应用于研究潜在政策对烟草使用行为的影响， 特别是在产品标准和消息传递开发领域（例如警告和活动）。 与观察性调查数据相比，DCE 的优势在于能够为新兴市场提供灵活的证据。 流行率低的产品，并在潜在政策实施前预测其对行为的影响 然而，它们在指导和捍卫 FDA 政策方面的潜力尚未得到充分发挥。 由于一些方法学上的挑战，第一个挑战是烟草 DCE 缺乏一致性。 研究人员经常采用不同的设计特征（例如，是否包括选择退出） 选项）并在报告中使用不同的衡量标准，例如支付意愿（WTP）与相对重要性得分。 因此，有必要回顾当前的做法，探索如何综合现有的 DCE 证据，并 第二个挑战是假设性偏差——偏差。 陈述偏好（SP）或“人们声称他们会做什么”与显露偏好（RP）或 “人们在现实世界中实际上做了什么。”最近的方法开发结合了 DCE 和 RP 数据（例如， 零售和调查数据）以减少假设偏差，为我们提供了重新审视现有大商所的独特机会 并提高其政策影响预测的准确性 该项目的总体目标是向人们提供信息。 我们将利用现有的 DCE 证据来研究 FDA 监管政策对烟草使用行为的影响。 系统审查，通过评估产品标准和消息传递开发来综合证据 此外，我们将把现有的 DCE 与 Nielsen Retail Scanner 结合起来。 数据和全国代表性调查，以减轻 DCE 的假设偏差，从而改善 使用现有数据预测尚未实施的潜在监管政策影响的准确性 尚未实施的具体目标是 1) 对烟草 DCE 的操纵行为进行系统审查。 产品属性 2) 对操纵产品信息的烟草 DCE 进行系统审查。 结合陈述偏好的 DCE 和显示偏好的现实世界零售和调查数据来预测 FDA 监管政策对产品市场份额和烟草使用的影响该项目将提供第一个。 在为 FDA 监管政策提供信息的背景下对烟草 DCE 进行系统审查，进一步改进 DCE 在预测政策影响方面的准确性 总之，该项目使用现有数据来解决优先级问题。 RFA-OD-21-003 的科学领域，包括烟草产品特性、沟通实践和 烟草使用模式。