Motives for using electronic nicotine delivery systems (ENDS) as a cessation tool are associated with tobacco abstinence at 1-year follow-up: A prospective investigation among young adults in the United States Air Force

Introduction

Smokers use electronic nicotine delivery systems (ENDS), including e-cigarettes, as a harm reduction strategy even though the Food and Drug Administration (FDA) has not approved them for tobacco cessation. The limited literature about ENDS use for cigarette cessation is concerning for the U.S. military, which is largely comprised of young adults at increased risk for tobacco use. Thus, the current study aims to evaluate use of ENDS products as a cessation tool in relation to point-prevalence tobacco abstinence at one-year follow-up in a cohort of 8,901 U.S. Air Force personnel attending entry-level job training from March 2016 to April 2019.

Methods

A propensity-score adjusted multinomial logistic regression model was used to assess the association between the baseline motives for ENDS use (i.e., for cigarette cessation versus alternative reasons) and tobacco use at the one-year follow-up (cigarette use, non-cigarette tobacco product use, and tobacco abstinence) among those reporting history of cigarette use at baseline.

Results

Smokers reporting ENDS use for cigarette cessation were more likely to be abstinent at one-year follow-up (Odds Ratio[OR] = 1.62, 95% CI: 1.06-2.49, P =.03) as well as quit using non-cigarette tobacco products (OR = 2.11, 95% CI: 1.65-2.70, P <.001) than those reporting ENDS use for alternative reasons.

Conclusions

Current tobacco users are recommended to use FDA-approved products for smoking cessation, such as nicotine replacement therapy. However, given the high prevalence of cigarette use among military populations, ENDS may provide a useful alternative harm reduction strategy for this high-risk population.

A network approach to compute hypervolume under receiver operating characteristic manifold for multi-class biomarkers

Computation of hypervolume under ROC manifold (HUM) is necessary to evaluate biomarkers for their capability to discriminate among multiple disease types or diagnostic groups. However the original definition of HUM involves multiple integration and thus a medical investigation for multi-class receiver operating characteristic (ROC) analysis could suffer from huge computational cost when the formula is implemented naively. We introduce a novel graph-based approach to compute HUM efficiently in this article. The computational method avoids the time-consuming multiple summation when sample size or the number of categories is large. We conduct extensive simulation studies to demonstrate the improvement of our method over existing R packages. We apply our method to two real biomedical data sets to illustrate its application.

The polytomous discrimination index for prediction involving multistate processes under intermittent observation

With the increasing importance of predictive modeling in health research comes the need for methods to rigorously assess predictive accuracy. We consider the problem of evaluating the accuracy of predictive models for nominal outcomes when outcome data are coarsened at random. We first consider the problem in the context of a multinomial response modeled by polytomous logistic regression. Attention is then directed to the motivating setting in which class membership corresponds to the state occupied in a multistate disease process at a time horizon of interest. Here, class (state) membership may be unknown at the time horizon since disease processes are under intermittent observation. We propose a novel extension to the polytomous discrimination index to address this and evaluate the predictive accuracy of an intensity-based model in the context of a study involving patients with arthritis from a registry at the University of Toronto Centre for Prognosis Studies in Rheumatic Diseases.

Efficacy of a group-based brief tobacco intervention among young adults aged 18-20 years in the US Air Force

INTRODUCTION

Most smokers begin using tobacco before the age of 25 years, making it important to reduce tobacco use during adolescence and early adulthood. Rates of use are historically higher among military personnel. While 'Tobacco 21' made it illegal for US retailers to sell tobacco to those aged <21 years, the policy did not address cessation for current youth and young adult tobacco users. Additionally, there is limited research on cessation interventions among young adults under 21 years. The current study evaluated the efficacy of a group-based Brief Tobacco Intervention (BTI) among US Air Force trainees, who are predominantly aged 18-20 years and directly impacted by Tobacco 21 legislation.

METHODS

Participants were 2969 US Air Force Trainees from April 2017 through January 2018 cluster randomized to three conditions: 1) BTI + Airman's Guide to Remaining Tobacco Free (AG), 2) AG alone, and 3) the National Cancer Institute's Clearing the Air (CTA) pamphlet. To assess the efficacy of the interventions among people aged 18-20 years, a domain analysis (<21 years, n=2117; and ≥21 years, n=852) of a multinomial logistic regression model was run.

RESULTS

Mono tobacco users aged <21 years at baseline who received the BTI+AG had higher odds of quitting tobacco at 3 months (OR=2.13; 95% CI: 1.02-4.46). Dual and poly users aged <21 years at baseline who received the BTI+AG intervention had higher odds of reducing the number of tobacco products used at 3 months (OR=2.94; 95% CI: 1.03-8.37).

CONCLUSIONS

The BTI was effective for people aged 18-20 years. The current study offers insight into components of interventions that might be successful in helping this age group decrease tobacco use.

Evaluation of competing risks prediction models using polytomous discrimination index

For competing risks data, it is often important to predict a patient's outcome status at a clinically meaningful time point after incorporating the informative censoring due to competing risks. This can be done by adopting a regression model that relates the cumulative incidence probabilities to a set of covariates. To assess the performance of the resulting prediction tool, we propose an estimator of the polytomous discrimination index applicable to competing risks data, which can quantify a prognostic model's ability to discriminate among subjects from different outcome groups. The proposed estimator allows the prediction model to be subject to model misspecification and enjoys desirable asymptotic properties. We also develop an efficient computation algorithm that features a computational complexity of O(n log n). A perturbation resampling scheme is developed to achieve consistent variance estimation. Numerical results suggest that the estimator performs well under realistic sample sizes. We apply the proposed methods to a study of monoclonal gammopathy of undetermined significance.

Flexible diagnostic measures and new cut-point selection methods under multiple ordered classes

Medical diagnosis is essentially a classification problem and usually it is done with multiple ordered classes. For example, cancer diagnosis might be "non-malignant," "early stage," or "late stage." Therefore, appropriate measures are needed to assess the accuracy of diagnostic markers under multiple ordered classes. However, all existing measures fail to differentiate among some distinctly different biomarkers. This paper presents a multi-step procedure for evaluating biomarker accuracy under multiple ordered classes. This procedure leads to two new flexible overall measures as well as three new cut-point selection methods with great computational ease. The performance of proposed measures and cut-point selection methods are numerically explored via a simulation study. In the end, an ovarian cancer dataset from the Prostate, Lung, Colorectal, and Ovarian cancer study is analyzed. The proposed accuracy measures were estimated for markers CA125 and HE4, and cut-points were estimated for the risk of ovarian malignancy algorithm score.

Computing the polytomous discrimination index

Polytomous regression models generalize logistic models for the case of a categorical outcome variable with more than two distinct categories. These models are currently used in clinical research, and it is essential to measure their abilities to distinguish between the categories of the outcome. In 2012, van Calster et al proposed the polytomous discrimination index (PDI) as an extension of the binary discrimination c-statistic to unordered polytomous regression. The PDI is a summary of the simultaneous discrimination between all outcome categories. Previous implementations of the PDI are not capable of running on "big data." This article shows that the PDI formula can be manipulated to depend only on the distributions of the predicted probabilities evaluated for each outcome category and within each observed level of the outcome, which substantially improves the computation time. We present a SAS macro and R function that can rapidly evaluate the PDI and its components. The routines are evaluated on several simulated datasets after varying the number of categories of the outcome and size of the data and two real-world large administrative health datasets. We compare PDI with two other discrimination indices: M-index and hypervolume under the manifold (HUM) on simulated examples. We describe situations where the PDI and HUM, indices based on multiple comparisons, are superior to the M-index, an index based on pairwise comparisons, to detect predictions that are no different than random selection or erroneous due to incorrect ranking.

Statistical inference for decision curve analysis, with applications to cataract diagnosis

Statistical learning methods are widely used in medical literature for the purpose of diagnosis or prediction. Conventional accuracy assessment via sensitivity, specificity, and ROC curves does not fully account for clinical utility of a specific model. Decision curve analysis (DCA) becomes a novel complement as it incorporates a clinical judgment of the relative value of benefits (treating a true positive case) and harms (treating a false positive case) associated with prediction models. The preference of a patient or a policy-maker is formulated statistically as the underlying threshold probability, above which the patient would choose to be treated. Net benefit is then calculated for possible threshold probability, which places benefits and harms on the same scale. We consider the inference problems for DCA in this paper. Interval estimation procedure and inference methodology are provided after we derive the relevant asymptotic properties. Our formulation can accommodate the classification problems with multiple categories. We carry out numerical studies to assess the performance of the proposed methods. An eye disease dataset is analyzed to illustrate our proposals.

Influence of gender on initiation of tobacco and nicotine containing product use among U.S. Air Force trainees

Military personnel are a subgroup of young adults at risk for tobacco and nicotine containing product (TNCP) use. This study of US Air Force (USAF) trainees who were never users of TNCPs examined gender, peer tobacco use, and tobacco use intentions as predictors of TNCP initiation after Basic Military Training (BMT). We used a longitudinal cohort assessment study design with baseline and 1-year surveys completed (2011-2016) among 2393 USAF trainees: 73% men, 95% aged 18-25 years, 36% racial minorities. Overall, initiation of any TNCP use at 1-year was 23% (20% women, 24% men). From a multivariable multinomial logistic regression model predicting TNCP use at 1-year follow-up, significant 2-way interactions were detected between gender and number of close friends using tobacco before BMT (p = 0.015), and between gender and tobacco use intentions (p < 0.0001). Women reporting almost all or many close friends used tobacco were more likely to report TNCP use compared to women with none (Odds ratio [OR] = 5.8, 95% CI 2.5-13.5, Bonferroni corrected p < 0.0001). Having close friends using tobacco had little influence on TNCP use among men. Men with tobacco use intentions were more likely to report TNCP use compared to men having no intentions (OR = 8.0, 95% CI: 4.7-13.6, Bonferroni corrected p < 0.001), but tobacco use intentions had little influence among women. In this sample of USAF trainees, the study provides novel prospective findings on TNCP initiation, and how men and women are influenced differently by peer tobacco use and tobacco use intentions. Gender-specific prevention efforts focused on uptake of TNCPs appear warranted.

Use of the Activity Measure for Post-Acute Care "6 Clicks" Basic Mobility Inpatient Short Form and National Institutes of Health Stroke Scale to Predict Hospital Discharge Disposition After Stroke

OBJECTIVE

Therapists in the hospital are charged with making timely discharge recommendations to improve access to rehabilitation after stroke. The objective of this study was to identify the predictive ability of the Activity Measure for Post-Acute Care "6 Clicks" Basic Mobility Inpatient Short Form (6 Clicks mobility) score and the National Institutes of Health Stroke Scale (NIHSS) score for actual hospital discharge disposition after stroke.

METHODS

In this retrospective cohort study, data were collected from an academic hospital in the United States for 1543 patients with acute stroke and a 6 Clicks mobility score. Discharge to home, a skilled nursing facility (SNF), or an inpatient rehabilitation facility (IRF) was the primary outcome. Associations among these outcomes and 6 Clicks mobility and NIHSS scores, alone or together, were tested using multinomial logistic regression, and the predictive ability of these scores was calculated using concordance statistics.

RESULTS

A higher 6 Clicks mobility score alone was associated with a decreased odds of actual discharge to an IRF or an SNF. The 6 Clicks mobility score alone was a strong predictor of discharge to home versus an IRF or an SNF. However, predicting discharge to an IRF versus an SNF was stronger when the 6 Clicks mobility score was considered in combination with the NIHSS score, age, sex, and race.

CONCLUSION

The 6 Clicks mobility score alone can guide discharge decision making after stroke, particularly for discharge to home versus an SNF or an IRF. Determining discharge to an SNF versus an IRF could be improved by also considering the NIHSS score, age, sex, and race. Future studies should seek to identify which additional characteristics improve predictability for these separate discharge destinations.

IMPACT

The use of outcome measures can improve therapist confidence in making discharge recommendations for people with stroke, can enhance hospital throughput, and can expedite access to rehabilitation, ultimately affecting functional outcomes.

Non-monotone transformation of biomarkers to improve diagnostic and screening accuracy in a DNA methylation study with trichotomous phenotypes

We propose a non-monotone transformation to biomarkers in order to improve the diagnostic and screening accuracy. The proposed quadratic transformation only involves modeling the distribution means and variances of the biomarkers and is therefore easy to implement in practice. Mathematical justification was rigorously established to support the validity of the proposed transformation. We conducted extensive simulation studies to assess the performance of the proposed method and compared the new method with the traditional methods. Case studies on real biomedical and epigenetics data were provided to illustrate the proposed transformation. In particular, the proposed method improved the AUC values for a large number of markers in a DNA methylation study and consequently led to the identification of greater number of important biomarkers and biologically meaningful genetic pathways.