Assessing the Use of Agent-Based Models for Tobacco Regulation (2015) / Chapter Skim
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6 Data and Implementation Needs for Computational Modeling for Tobacco Control
6 Data and Implementation Needs for Computational Modeling for Tobacco Control
Pages 143-160
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From page 143... ...
Empirical data, such as the results from cross-sectional studies, can be useful for indicating patterns of tobacco use in specific settings. Other types of model inputs, such as theoretical models and grounded theories that conceptualize social patterns and structures, qualitative data, and heuristics are also important to consider.
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From page 144... ...
.1 In 1994, Add Health collected nationally representative behavioral and network data on a baseline "core" sample of more than 90,000 students, including an "in-home" subsample drawn from the core who received more extensive interviews (n = 12,105) ; of these in-home respondents, 3,702 attended 1 of 16 "saturation schools" where a near-complete social network could be mapped out using answers to the questionnaires (Harris, 2013)
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Food and Drug Administration compliance checks National, State, and Local Policy Tracking ACTIVE Life Tobacco Free Worksite Survey American College Health Association College Campus Tobacco Cessation and Prevention Survey continued
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146 USE OF AGENT-BASED MODELS FOR TOBACCO REGULATION BOX 6-1 Continued American Lung Association's State Legislated Actions on Tobacco Issues American Nonsmokers' Rights Foundation: U.S. Tobacco Control Laws Database California Student Tobacco Survey California Tobacco Use Prevention Education Evaluation Teacher Survey CDC School Health Profiles Worksite and Restaurant Smoking Policy Questionnaires and Guide Media Tracking Adobe SiteCatalyst Arbitron Cision Clicktracks Optimizer DataSift Facebook Insights Gnip Google Analytics HootSuite Legacy Media Tracking Survey and Legacy Media Tracking Online LexisNexis Nielsen Pinterest Radian6 Sysomos Topsy Webalyzer YouTube Analytics Global Survey Tools Global Adult Tobacco Survey Global Health Professions Student Survey Global School Personnel Survey Global School-Based Student Health Survey Global Youth Tobacco Survey Tobacco Industry Monitoring Network of the National Cancer Institute New Product Watch, funded by Tobacco Surveillance, Epidemiology, and Evaluation Project SMART Money of California State Department of Public Health Retail Advertising Tobacco Survey University of California at San Francisco Tobacco Control Archives SOURCE: Adapted from CDC, 2014, which contains details on each of these sources.
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From page 147... ...
.2 Other studies using network and smoking data include the six-country European Smoking Prevention Framework studies by Mercken and colleagues (2009) , a study of online social networks supporting tobacco cessation (Cobb et al., 2010)
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After the enactment of the Tobacco Control Act -- and in response to emerging trends in tobacco use -- FDA and CDC b egan including detailed questions on nonconventional tobacco products in the 2012 National Youth Tobacco Survey (Apelberg et al., 2014)
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Such data could provide a better understanding of the influence of social networks and social context on tobacco use and on the behavior change process involved. Given the changing tobacco landscape, it is likely there will be an increasing need for detailed yet timely and accurate data for informing tobacco control efforts nationwide.
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From page 150... ...
It may also be possible to substitute existing or newly developed biomarkers of certain smoking behaviors for other forms of data collection, and in selected instances, information from other countries with similar populations may be of value. Network data, which are thought to require the elucidation of an entire social network, are particularly difficult to collect.
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Online platforms may offer yet another way to collect data. While tobacco companies are making extensive use of online social media to arket their products, the tobacco control community is using online m platforms to counter the marketing of tobacco products (Legacy, 2012)
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Social networks, particularly the ways in which information and resources are shared among stakeholder groups in the tobacco control regulatory landscape, have been described by Luke and colleagues (Harris et al., 2008; Luke et al., 2010)
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. Because addiction plays such a central role in tobacco use, modeling the process of addiction and the resulting difficult-to-change behavior could help strengthen ABMs.
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, and much of these data could help strengthen ABMs developed to guide tobacco control policy. These data could be used creatively to inform models, and more data could be collected from efforts that go beyond traditional survey methods, such as gathering information from online social media platforms.
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From page 155... ...
Regardless of where the models are developed, funders for policyrelevant models require access to expertise if they are to issue effective funding opportunity announcements or contracts; to make informed decisions about which modeling approaches are appropriate for the question at hand; to work effectively with the modeling team(s) throughout model development; to appropriately evaluate model inputs, processes, and outputs; and to interpret or translate model results appropriately to decision makers.
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From page 156... ...
,10 the Drug Policy Modelling Program,11 and the Energy Modeling Forum.12,13 Although individual models are a useful tool for informing policy decisions, having a range of modeling techniques will offer a fuller picture of the policy questions confronted by CTP -- for example, by creating various models to approach the same question or process (e.g., multiple ABMs or ABMs and aggregate models) , as is done by several modeling networks and forums.14 The documentation of model inputs, activities, and outputs by the model developers (as discussed in Chapter 4)
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2014. Enhancing youth tobacco surveillance to inform tobacco product regulation: Findings from the 2012 National Youth Tobacco Survey.
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Social Networks 25(4)
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2006. Effects of missing data in social networks.
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2013. Social networks and smoking: Exploring the effects of peer influence and smoker popularity through simulations.
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