Reducing the smoking-related health burden in the USA through diversion to electronic cigarettes: a system dynamics simulation study

Background Electronic cigarettes (“e-cigarettes”) have altered tobacco smoking trends, and their impacts are controversial. Given their lower risk relative to combustible tobacco, e-cigarettes have potential for harm reduction. This study presents a simulation-based analysis of an e-cigarette harm reduction policy set in the USA. Methods A system dynamics simulation model was constructed, with separate aging chains representing people in different stages of use (both of combustible cigarettes and e-cigarettes). These structures interact with a policy module to close the gap between actual (simulated) and goal numbers of individuals who smoke, chosen to reduce the tobacco-attributable death rate (i.e., mostly combustible cigarette-attributable, but conservatively allowing e-cigarette-attributable deaths) to that due to all accidents in the general population. The policy is two-fold, removing existing e-liquid flavor bans and providing an informational campaign promoting e-cigarettes as a lower-risk alternative. Realistic practical implementation challenges are modeled in the policy sector, including time delays, political resistance, and budgetary limitations. Effects of e-cigarettes on tobacco smoking occur through three mechanisms: (1) diversion from ever initiating smoking; (2) reducing progression to established smoking; and (3) increasing smoking cessation. An important unintended effect of possible death from e-cigarettes was conservatively included. Results The base-case model replicated the historical exponential decline in smoking and the exponential increase in e-cigarette use since 2010. Simulations suggest tobacco smoking could be reduced to the goal level approximately 40 years after implementation. Implementation obstacles (time delays, political resistance, and budgetary constraints) could delay and weaken the effect of the policy by up to 62% in the worst case, relative to the ideal-case scenario; however, these discrepancies substantially decreased over time in dampened oscillations as negative feedback loops stabilize the system after the one-time “shock” introduced by policy changes. Conclusions The simulation suggests that the promotion of e-cigarettes as a harm-reduction policy is a viable strategy, given current evidence that e-cigarettes offset or divert from smoking. Given the strong effects of implementation challenges on policy effectiveness in the short term, accurately modeling such obstacles can usefully inform policy design. Ongoing research is needed, given continuing changes in e-cigarette use prevalence, new policies being enacted for e-cigarettes, and emerging evidence for substitution effects between combustible cigarettes and e-cigarettes.publishedVersio

Predicting Unplanned Medical Visits Among Patients with Diabetes Using Machine Learning

Diabetes poses a variety of medical complications to patients, resulting in a high rate of unplanned medical visits, which are costly to patients and healthcare providers alike. However, unplanned medical visits by their nature are very difficult to predict. The current project draws upon electronic health records (EMR’s) of adult patients with diabetes who received care at Sanford Health between 2014 and 2017. Various machine learning methods were used to predict which patients have had an unplanned medical visit based on a variety of EMR variables (age, BMI, blood pressure, # of prescriptions, # of diagnoses on problem list, A1C, HDL, LDL, and a ranked variable for tobacco use severity). A radial-basis support vector machine (SVM) was the most accurate method, achieving a hit rate of 68.5% and a correct rejection rate of 62.9% during cross-validation testing. Follow-up testing of the trained SVM indicated that, of the modifiable prediction variables, high blood pressure and low levels of high-density lipoprotein (HDL) were most strongly predictive of unplanned medical visits. Future directions include refining and validating the predictive model, towards the ultimate goal of developing and implementing clinical recommendations for preventing unplanned medical visits among adult patients with diabetes

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

Engaging Diverse Students in Statistical Inquiry: A Comparison of Learning Experiences and Outcomes of Under-Represented and Non-Underrepresented Students Enrolled in a Multidisciplinary Project-Based Statistics Course

Introductory statistics needs innovative, evidence-based teaching practices that support and engage diverse students. To evaluate the success of a multidisciplinary, project-based course, we compared experiences of under-represented (URM) and non-underrepresented students in 4 years of the course. While URM students considered the material more difficult than non-URM students, URM students demonstrated similar levels of increased confidence in applied skills and interest in follow up courses as non-URM students. URM students were found to be twice as likely as non-URM students to report that their interest in conducting research increased. Increasing student confidence and interest gives all students a welcoming place at the table that will afford the best hope for achieving the kind of statistical literacy necessary for interdisciplinary research

The “Gateway” Hypothesis: Evaluation of Evidence and Alternate Explanations

Background: Electronic nicotine delivery systems (ENDS) offer a substantial harm reduction opportunity for adults who smoke and are unlikely to quit in the near term. However, a major concern about ENDS is their use by non-smoking youth, and particularly whether ENDS are acting as a “gateway” that leads youth to later start smoking cigarettes. However, evidence for the gateway hypothesis can be interpreted in alternate ways, e.g. that youth who have certain characteristics were already predisposed to use both ENDS and cigarettes (“common liability” explanation). Aims: This Perspective provides an evaluation of the gateway hypothesis that is accessible by a lay audience. This paper first reviews and evaluates the evidence interpreted as supporting the gateway hypothesis. Important alternate explanations (i.e., common liability) are discussed, as are different types of evidence (i.e., population-level trends) that can help differentiate between these competing explanations. Overview: The evidence provided in support of the gateway hypothesis is the finding that youth who use ENDS are more likely to also smoke cigarettes. However, this evidence suffers from an important flaw: these studies fail to fully account for some youths’ pre-existing tendency to use tobacco products, and inappropriately interpret the results as ENDS use causing some youth to smoke. Common liability studies suggest that ENDS use does not, in and of itself, directly cause youth to later smoke cigarettes, beyond their pre-existing tendency to use tobacco. Population-level trends show that youth cigarette smoking declined faster after ENDS use became common, which contradicts the central prediction of the gateway hypothesis (i.e. that youth smoking would be more common following ENDS uptake, than what would otherwise be expected). Conclusion: Evidence offered in support of the gateway hypothesis does not establish that ENDS use causes youth to also smoke cigarettes. Instead, this evidence is better interpreted as resulting from a common liability to use both ENDS and cigarettes. Population-level trends are inconsistent with the gateway hypothesis, and instead are consistent with ENDS possibly displacing cigarettes among youth who already had a tendency to use tobacco products, though more research is needed to support this diversion hypothesis

Session 2: Opportunities for Improving Health through Big Data and Data Science: \u3cem\u3eFloor Discussion\u3c/em\u3e

A floor discussion will be led by Arielle Selya of Sanford Research.1

Session 2 - Opportunities for Improving Health through Big Data and Data Science: \u3cem\u3eData-Drivin Healthcare: The Sanford Data Collaborative\u3c/em\u3e

The Sanford Data Collaborative was established by Sanford Research and Sanford Enterprise Data Analytics to help pave the way in data-sharing and collaborative access to real-life, timely health care data. This program, aimed at collaborating with regional and national institutional researchers, opens the door to exploring new and innovative methods to analyzing data, developing mutually-beneficial collaborations, and making an impact on population health and health services delivery. By partnering with external researchers who have unique skillsets and expertise, we enhance the opportunity to view health services delivery through a different lens, heightening Sanford’s awareness of available analytic trends, and develop valuable collaborations. Realizing the power of multidisciplinary teams, we can drive innovation behind improving health services delivery and patient outcomes. Previous years’ projects include innovations around: predictive algorithms and risk scores, enhancing patient engagement measurement, natural language processing of patient surveys, nursing turnover analysis, emergent care utilization, etc

Reducing the smoking-related health burden in the USA through diversion to electronic cigarettes: a system dynamics simulation study

Background Electronic cigarettes (“e-cigarettes”) have altered tobacco smoking trends, and their impacts are controversial. Given their lower risk relative to combustible tobacco, e-cigarettes have potential for harm reduction. This study presents a simulation-based analysis of an e-cigarette harm reduction policy set in the USA. Methods A system dynamics simulation model was constructed, with separate aging chains representing people in different stages of use (both of combustible cigarettes and e-cigarettes). These structures interact with a policy module to close the gap between actual (simulated) and goal numbers of individuals who smoke, chosen to reduce the tobacco-attributable death rate (i.e., mostly combustible cigarette-attributable, but conservatively allowing e-cigarette-attributable deaths) to that due to all accidents in the general population. The policy is two-fold, removing existing e-liquid flavor bans and providing an informational campaign promoting e-cigarettes as a lower-risk alternative. Realistic practical implementation challenges are modeled in the policy sector, including time delays, political resistance, and budgetary limitations. Effects of e-cigarettes on tobacco smoking occur through three mechanisms: (1) diversion from ever initiating smoking; (2) reducing progression to established smoking; and (3) increasing smoking cessation. An important unintended effect of possible death from e-cigarettes was conservatively included. Results The base-case model replicated the historical exponential decline in smoking and the exponential increase in e-cigarette use since 2010. Simulations suggest tobacco smoking could be reduced to the goal level approximately 40 years after implementation. Implementation obstacles (time delays, political resistance, and budgetary constraints) could delay and weaken the effect of the policy by up to 62% in the worst case, relative to the ideal-case scenario; however, these discrepancies substantially decreased over time in dampened oscillations as negative feedback loops stabilize the system after the one-time “shock” introduced by policy changes. Conclusions The simulation suggests that the promotion of e-cigarettes as a harm-reduction policy is a viable strategy, given current evidence that e-cigarettes offset or divert from smoking. Given the strong effects of implementation challenges on policy effectiveness in the short term, accurately modeling such obstacles can usefully inform policy design. Ongoing research is needed, given continuing changes in e-cigarette use prevalence, new policies being enacted for e-cigarettes, and emerging evidence for substitution effects between combustible cigarettes and e-cigarettes

Electronic Cigarettes and Nicotine Use Trends among US Adolescents

Poster presentation at the Society for Research on Nicotine and Tobacco (SRNT) 26th Annual Meeting, 11th-14th March, 2020, Hilton New Orleans Riverside, New Orleans, Louisian