About the Pilot + Feasibility program
The Carer Enhancement Core (CEC) aims to foster opportunities for junior investigators to direct research through a pilot project program, in turn supporting applications for extramural funding. Open to new and early stage investigators, this program supports projects which generate research which can guide the regulatory goals of the FDA’s Center for Tobacco Products, Office of Science. Applicants are encouraged to build networks across TCORS Centers and develop career development plans through this program.
To date, we have been able to fund 21 pilot projects led by graduate students, postdoctoral fellows, and early stage investigators. Details regarding current and past pilot projects can be found below.
We are no longer accepting applications for the current pilot funds cycle (2021-2022). Please check back for additional information regarding the next application cycle. For questions, please contact Molly Coeling (email@example.com).
2022 Funding Cycle Awardees
Please note: Listing describes appointments and affiliations at the time of award. Please check our Trainees page for current appointments and affiliations.
- Title: “The Effects of Tobacco 21 Adoption on Cigar Sales in the US”
- PI: Dr. Alex Liber, Assistant Professor at Georgetown University
- Abstract and accomplishments +
Dr. Alex LiberGeorgetown University
- Laws raising the minimum age of sale to 21 years of age for tobacco products (Tobacco 21 or T21 laws) proliferated across the US in the past decade, leading to the adoption of a federal T21 law in December 2019. By severing the social sources of tobacco products in US high schools, T21 appears to have helped further contain youth tobacco use. Prior research has confirmed that T21 policies have decreased both the use of tobacco products among young people and the sales of the cigarettes which young people smoke. However, little research has yet examined the effects of T21 passage on cigar use, the second-most used tobacco product among young people in the US. Glover-Kudon et al found that sales declines of large cigars and cigarillos declined faster than national trends after Hawaii and California passed the first state-level T21 laws in 2016. Little else has examined whether cigar smoking among young people has been affected by the proliferation of this important health policy. To better understand the effects of T21 on youth cigar use, we will examine state-level cigar sales trends using Nielsen Company data. To gauge the effects on the young, we will focus on sales of cigar brands consumed disproportionately by young people that are expected to be affected by T21 adoption. Prior work on the effects of T21 has not captured the effects of important policy implementation differences across states, namely the presence of provisions that ban the purchase, use, and possession of tobacco products by young people, or the effects of cannabis legalization. By running an adequately powered study, this proposed project tackles these issues through four specific aims. 1. Identify which cigars are disproportionately used by youths and young adults: Using data from the Population Assessment of Tobacco and Health, Current Population Survey-Tobacco Use Supplement, and the National Survey on Drug Use and Health, we will identify which cigar brands, flavors, price bands (premium v. economy) and styles, are consistently consumed by those under the age of 21 in a manner out of proportion with total youth and young adult consumption of all cigars. Those brands, flavors, price bands and styles that are consistently consumed by youth and young adults in disproportionately high proportions across the surveys will be deemed “disproportionately young” while those which are consistently consumed in low proportions will be deemed “disproportionately old.” 2. Describe pre- and post- T21 cigar sales trends: Using the Nielsen data from 30 states, we will test whether cigar sales are changed before and after T21 2 implementation using difference-in-difference techniques among total, disproportionately young, and disproportionately old brands. 3. Determine if key features of T21 laws including prohibitions on purchase, use, and possession (PUP) were associated with different sales trends: To control for state heterogeneity in tobacco youth access laws and to account for additional changes made beyond simply raising the minimum age of sale for tobacco products, additional analyses will consider PUP rules and changes to them which came into effect with T21. The variety of PUP law changes range from Kentucky maintaining all PUP prohibitions when T21 was passed in that state to Maryland dropping all prohibitions on youth purchase use and possession of tobacco products when T21 was passed there. Understanding the effect of these policy choices is essential to developing best practices and avoiding potential harms from inequitably enforced policies. 4. Determine if T21 affected sales of cigars used disproportionately by youth and young adults: Replicating the techniques used in Liber et al, we will determine whether the true date of T21 implementation best predicts sales patterns for disproportionately young (treatment) and old (control) cigars compared to randomly generated alternative “placebo” dates of T21 implementation in a modified exact permutation test. The analyses will control for other relevant state policy levels and changes, such as PUP laws, cannabis legalizations, and tobacco taxation.
- Coming Soon!
- Title: “Predicting Smoking Behaviors Using Machine Learning”
- PI: Mona Issabakhsh, Research Instructor at Georgetown University and Thuy Le, Postdoctoral Fellow at University of Michigan
- Abstract and accomplishments +
Thuy LeUniversity of MichiganMona IssabakhshGeorgetown University
- The United States’ smoking prevalence has significantly decreased over time (from 23.3% in 2000 to 13.7% in 2018). Cigarette smoking, however, is currently responsible for about 480,000 deaths annually and is still a major public health issue. Identification of factors and policies driving the transition of individuals between never smokers, current smokers, and former smokers is a critical need. Machine learning has been investigated widely in the last decades in various research studies and can recognize patterns and detect complicated relationships among data features, which humans are not able to do, to make accurate estimations, predictions, and decisions. Several studies have recently started to apply machine learning algorithms in tobacco research, such as smoker status classification from narrative clinical texts, and tobacco-related outcome prediction using administrative, survey, or clinical trial data. Current literature on smoking prevalence mainly employs mathematical and statistical models, accounting for few predictors (e.g., age, sex, and race). Complex multistate Markov models are established for smoking prevalence prediction, considering more predictors (e.g., age, sex, race, education, and income). These models focus only on a limited number of factors and policies to explain the transition of individuals between never, current, and former smokers. Another disadvantage of these models is that the state transition rates must be estimated, which increases the complexity of the model development. Machine learning algorithms make use of a flexible model structure with little or no parameter estimations, allowing for rapid updates and modifications. Machine learning also enables more efficient use of massive data for tobacco research by accounting for multiple predictors and policies and the discovery of complex patterns in large datasets to produce high-quality estimations and predictions. Given the enormous data on tobacco use, both cross-sectional and longitudinal, machine learning is a promising tool to leverage all the available data. For this study, we will apply binary machine learning classifiers to group individuals by smoking status (cigarette users and non-users). Classification is a supervised machine learning methodology that determines which class the dependent variable (response) belongs to, based on one or more independent variables (predictors). Classification algorithms involve predicting a qualitative response for an observation, or in other words, assigning the observation to a category or class. A classification algorithm learns from labeled data. After understanding the data, it determines how to best map input data to specific class labels by associating patterns to the unlabeled new data and learns how to assign labels to the new data. The dataset, therefore, must sufficiently represent the problem and have multiple examples of each class label. Many possible classification techniques or classifiers are available in the literature. The specific aims of this project are: Aim 1: Develop and train machine learning binary classifiers using the PATH data from waves 1-2 to find the key variables involving in the transition of individuals between never smokers, current smokers, and former smokers. Expected Outcomes. The model of each classifier will be developed with relevant features from the PATH survey, and the classifiers will be trained to detect the smoking status of individuals, using the trained dataset. Aim 2: Compare the performance of the trained classifiers and select the best one(s). Expected Outcomes. The best classifier(s) will be selected to predict the smoking behavior of individuals. Aim 3: Validate and test the performance of the selected classifiers using the PATH data from waves 3-5. Expected Outcomes. This study produces an exploratory model to understand how individuals’ transitions between never, current, and former smokers happen over time, and to provide insights into which attributes and policies are relevant to an individual's decision to initiate or quit smoking.
- Coming Soon!