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Tobacco retail outlet (TRO) density has been associated with increased cotinine levels in pregnant persons and their children. As such, the higher densities of TROs may represent higher levels of active smoking during pregnancy. The purpose of this study is to simulate the reduction in cotinine (a biomarker of smoke exposure) and health care utilization that could occur in pregnant persons under enactment of several candidate TRO reduction policy recommendations. Using existing retail outlet data from the state of North Carolina and from the Newborn Epigenetic Study (NEST), the present study created hypothetical policy-informed datasets of TROs that a) limited the number of TROs to the same density as the 2014 San Francisco (SF) policy (Policy 1), b) set the minimum distance to 500 feet between TROs from a school and from other TROs (Policy 2), c) restricted the types of TROs to exclude pharmacies (Policy 3), and d) a combination of Policies 1-3 (Policy 4). We estimated the effects of each policy individually and in a separate model with their combined effects in terms of the reduction on cotinine levels and health care utilization, as measured by number of visits to the emergency department (ED). We found that the hypothetical policies were likely to be effective in reducing maternal cotinine and ED visits, with the majority of the mothers in the dataset demonstrating reductions in these outcomes after implementation of the policies. We found that Policy 1 led to moderate reductions in TRO exposure for the majority of the sample as well as stratified by race/ethnicity. Additionally, Policy 4 had slightly larger estimated effects than Policy 1, but could be more onerous to implement in practice. Overall, we identified evidence supporting the efficacy of TRO reduction strategies that could impact smoke exposure during pregnancy in our diverse sample in North Carolina., (© 2024. The Author(s).)

Importance: Environments associated with smoking increase a smoker's craving to smoke and may provoke lapses during a quit attempt. Identifying smoking risk environments from images of a smoker's daily life provides a basis for environment-based interventions., Objective: To apply a deep learning approach to the clinically relevant identification of smoking environments among settings that smokers encounter in daily life., Design, Setting, and Participants: In this cross-sectional study, 4902 images of smoking (n = 2457) and nonsmoking (n = 2445) locations were photographed by 169 smokers from Durham, North Carolina, and Pittsburgh, Pennsylvania, areas from 2010 to 2016. These images were used to develop a probabilistic classifier to predict the location type (smoking or nonsmoking location), thus relating objects and settings in daily environments to established smoking patterns. The classifier combines a deep convolutional neural network with an interpretable logistic regression model and was trained and evaluated via nested cross-validation with participant-wise partitions (ie, out-of-sample prediction). To contextualize model performance, images taken by 25 randomly selected participants were also classified by smoking cessation experts. As secondary validation, craving levels reported by participants when viewing unfamiliar environments were compared with the model's predictions. Data analysis was performed from September 2017 to May 2018., Main Outcomes and Measures: Classifier performance (accuracy and area under the receiver operating characteristic curve [AUC]), comparison with 4 smoking cessation experts, contribution of objects and settings to smoking environment status (standardized model coefficients), and correlation with participant-reported craving., Results: Of 169 participants, 106 (62.7%) were from Durham (53 [50.0%] female; mean [SD] age, 41.4 [12.0] years) and 63 (37.3%) were from Pittsburgh (31 [51.7%] female; mean [SD] age, 35.2 [13.8] years). A total of 4902 images were available for analysis, including 3386 from Durham (mean [SD], 31.9 [1.3] images per participant) and 1516 from Pittsburgh (mean [SD], 24.1 [0.5] images per participant). Images were evenly split between the 2 classes, with 2457 smoking images (50.1%) and 2445 nonsmoking images (49.9%). The final model discriminated smoking vs nonsmoking environments with a mean (SD) AUC of 0.840 (0.024) (accuracy [SD], 76.5% [1.6%]). A model trained only with images from Durham participants effectively classified images from Pittsburgh participants (AUC, 0.757; accuracy, 69.2%), and a model trained only with images from Pittsburgh participants effectively classified images from Durham participants (AUC, 0.821; accuracy, 75.0%), suggesting good generalizability between geographic areas. Only 1 expert's performance was a statistically significant improvement compared with the classifier (α = .05). Median self-reported craving was significantly correlated with model-predicted smoking environment status (ρ = 0.894; P = .003)., Conclusions and Relevance: In this study, features of daily environments predicted smoking vs nonsmoking status consistently across participants. The findings suggest that a deep learning approach can identify environments associated with smoking, can predict the probability that any image of daily life represents a smoking environment, and can potentially trigger environment-based interventions. This work demonstrates a framework for predicting how daily environments may influence target behaviors or symptoms that may have broad applications in mental and physical health.

Introduction: Smokers with attention deficit hyperactivity disorder (ADHD) have greater difficulty quitting than those without ADHD, but preliminary data (McClernon, Kollins, Lutz, Fitzgerald, Murray, Redman, et al., 2008) suggest equivalent severity of withdrawal symptoms following brief abstinence. The objective of this study was to characterize the differential effects of intermediate term smoking abstinence on self-reported withdrawal and ADHD symptoms in adult smokers with and without ADHD., Methods: Forty adult (50% female), nontreatment seeking moderate-to-heavy smokers with and without ADHD were enrolled in a 12-day quit study in which monetary incentives were provided for maintaining biologically verified abstinence. Self-reported withdrawal, mood, and ADHD symptoms were measured pre- and post-quitting., Results: ADHD and controls did not vary on smoking or demographic variables. Significant Group × Session interactions were observed across a broad range of withdrawal symptoms and were generally characterized by greater withdrawal severity among ADHD smokers, particularly during the first 5 days of abstinence. In addition, Group × Sex × Session interactions were observed for craving, somatic symptoms, negative affect, and habit withdrawal; these interactions were driven by greater withdrawal severity among females with ADHD. Group × Session interactions were not observed for ADHD symptom scales., Conclusions: The results of this study suggest that smokers with ADHD, and ADHD females in particular, experience greater withdrawal severity during early abstinence-independent of effects on ADHD symptoms. Whereas additional research is needed to pinpoint mechanisms, our findings suggest that smoking cessation interventions targeted at smokers with ADHD should address their more severe withdrawal symptoms following quitting.

Introduction: This study investigated the association between retrospectively reported attention-deficit/hyperactivity disorder (ADHD) symptoms experienced during childhood and five cigarette smoking-related outcomes in adulthood., Methods: A large sample (N = 1,117) of new mothers participating in an ongoing longitudinal study completed retrospective reports of their childhood ADHD symptomatology, as well as concurrent and retrospective reports of their smoking behavior. Linear regression models tested the association between ADHD symptomatology and smoking outcomes., Results: Childhood ADHD symptomatology was predictive of the number of cigarettes smoked per day currently and during pregnancy, as well as the age at onset of smoking. We found nonlinear associations between hyperactive-impulsive symptoms and the number of cigarettes smoked per day in pregnancy, as well as between inattentive symptoms and the number of cigarettes smoked per day currently. Women who retrospectively reported intermediate levels of ADHD symptoms during their childhood reported smoking more cigarettes per day than women who reported low or high levels of ADHD symptoms during childhood. We also found multiplicative relationship between inattentive and hyperactive-impulsive symptoms, such that inattentive symptoms were predictive of an earlier age at smoking onset only when hyperactive-impulsive symptoms were low; moreover, the magnitude of this association was stronger for Black relative to White women., Discussion: These findings demonstrate the importance of considering differential effects of ADHD symptoms and smoking outcomes as a function of sex and race. They also represent a potentially indirect means through which women who have even a moderate childhood history of ADHD symptomatology may create a set of circumstances that compromise the health and well-being of their own children.