Youth smoking and anti-smoking policies in North Dakota: a system dynamics simulation study

Background: The current study utilizes system dynamics to model the determinants of youth smoking and simulate effects of anti-smoking policies in the context of North Dakota, a state with one of the lowest cigarette tax rates in the USA. Methods: An explanatory model was built to replicate historical trends in the youth smoking rate. Three different policies were simulated: 1) an increase in cigarette excise taxes; 2) increased funding for CDC-recommended comprehensive tobacco control programs; and 3) enforcement of increased retailer compliance with age restrictions on cigarette sales. Results: The explanatory model successfully replicated historical trends in adolescent smoking behavior in North Dakota from 1992 to 2014. The policy model showed that increasing taxes to $2.20 per pack starting in 2015 was the most effective of the three policies, producing a 32.6% reduction in youth smoking rate by 2032. Other policies reduced smoking by a much lesser degree (7.0 and 3.2% for comprehensive tobacco control program funding and retailer compliance, respectively). The effects of each policy were additive. Conclusions: System dynamics modeling suggests that increasing cigarette excise taxes are particularly effective at reducing adolescent smoking rates. More generally, system dynamics offers an important complement to conventional analysis of observational data.publishedVersio

A Practical Guide to Calculating Cohen’s f2, a Measure of Local Effect Size, from PROC MIXED

Reporting effect sizes in scientific articles is increasingly widespread and encouraged by journals; however, choosing an effect size for analyses such as mixed-effects regression modeling and hierarchical linear modeling can be difficult. One relatively uncommon, but very informative, standardized measure of effect size is Cohen’s f2, which allows an evaluation of local effect size, i.e., one variable’s effect size within the context of a multivariate regression model. Unfortunately, this measure is often not readily accessible from commonly used software for repeated-measures or hierarchical data analysis. In this guide, we illustrate how to extract Cohen’s f2 for two variables within a mixed-effects regression model using PROC MIXED in SAS® software. Two examples of calculating Cohen’s f2 for different research questions are shown, using data from a longitudinal cohort study of smoking development in adolescents. This tutorial is designed to facilitate the calculation and reporting of effect sizes for single variables within mixed-effects multiple regression models, and is relevant for analyses of repeated-measures or hierarchical/multilevel data that are common in experimental psychology, observational research, and clinical or intervention studies

A key indicator of nicotine dependence is associated with greater depression symptoms, after accounting for smoking behavior.

IntroductionDepression is a global burden that is exacerbated by smoking. The association between depression and chronic smoking is well-known; however, existing findings contain possible confounding between nicotine dependence (ND), a latent construct measuring addiction, and objective smoking behavior. The current study examines the possible unique role of ND in explaining depression, independently of smoking behavior.MethodsA nationally-representative sample of current adult daily smokers was drawn by pooling three independent, cross-sectional, biennial waves (spanning 2011-16) of the National Health and Nutrition Examination Survey (NHANES). The association between ND (operationally defined as time to first cigarette (TTFC) after waking) and the amount of depression symptoms was examined after adjusting for both current and lifetime smoking behaviors (cigarettes per day and years of smoking duration) and sociodemographic factors (gender, age, race, education and income to poverty ratio).ResultsEarlier TTFC was associated with more depression symptoms, such that those smoking within 5 minutes of waking had an approximately 1.6-fold higher depression score (PRR = 1.576, 95% CI = 1.324-1.687) relative to those who smoke more than 1 hour after waking. This relationship remained significant after adjusting for current and lifetime smoking behavior as well as sociodemographic factors (PRR = 1.370, 95% CI = 1.113, 1.687).ConclusionsThe latent construct of ND, as assessed by TTFC, may be associated with an additional risk for depression symptoms, beyond that conveyed by smoking behavior alone. This finding can be used for more refined risk prediction for depression among smokers

Time-Varying Effects of Parental Alcoholism on Depression.

INTRODUCTION: Children of alcoholic parents are at increased risk for lifetime depression. However, little is known about how this risk may change in magnitude across age, especially in mid-adulthood and beyond. METHODS: We used a nationally representative sample (N = 36,057) of US adults from the National Epidemiologic Survey on Alcohol and Related Conditions, wave III. After adjusting for demographic characteristics, we examined the relationship between parental alcoholism and outcomes of 1) major depressive disorder, Diagnostic and Statistical Manual of Mental Disorders-5th edition (DSM-5) and 2) DSM-5 persistent depressive disorder. To examine continuous moderation of this relationship across participants\u27 age, we used time-varying effect models. RESULTS: Parental alcoholism was associated in general with a higher risk for both major depressive disorder (odds ratio [OR], 1.98, 95% confidence interval [CI], 1.85-2.11; P \u3c .001) and persistent depressive disorder (OR, 2.28, 95% CI, 2.04-2.55; P \u3c .001). The association between parental alcoholism and major depressive disorder was stable and positive across age, but the association with persistent depressive disorder significantly declined among older adults; respondents older than 73 years old were not at increased risk for persistent depressive disorder. CONCLUSIONS: Findings from this study show that the risk of parental alcoholism on depression is significant and stable among individuals of a wide age range, with the exception of a decline in persistent depressive risk among older adults. These findings highlight the importance of screening for depression among adults with parental alcoholism