The Impact of Comorbid Sleep-Disordered Breathing on Hospitalization Risk Related to Diabetes and Atherosclerotic Disease: A Retrospective Cohort Analysis.

To determine the relationship between comorbid sleep-disordered breathing (SDB) and hospitalization rates related to diabetes mellitus (DM) and atherosclerotic disease (AD). This study used a retrospective cohort design from a large medical claims database with 5 years of data between 2018 and 2022. The presences of SDB, DM, and AD were identified using International Classification of Diseases (ICD-10) and relevant Current Procedural Terminology (CPT) codes.

Machine learning to identify chronic cough from administrative claims data.

Accurate identification of patient populations is an essential component of clinical research, especially for medical conditions such as chronic cough that are inconsistently defined and diagnosed. We aimed to develop and compare machine learning models to identify chronic cough from medical and pharmacy claims data. In this retrospective observational study, we compared 3 machine learning algorithms based on XG Boost, logistic regression, and neural network approaches using a large claims and electronic health record database.

A machine learning-based algorithm to identify U-500R insulin candidates among adults with type 2 diabetes mellitus in US retrospective databases.

Objective: To develop a machine learning-based predictive algorithm to identify patients with type 2 diabetes mellitus (T2DM) who are candidates for initiation of U-500R insulin (U-500R).

Methods: A retrospective cohort of patients with T2DM was used from a large US administrative claims and electronic health records (EHR) database affiliated with Optum. Predictor variables derived from the data were used to identify appropriate supervised machine learning models including least absolute shrinkage and selection operator (LASSO) and extreme gradient boosted (XGBoost) methods.

Development of Mobile Contingency Management for Cannabis Use Reduction.

Many interventions for cannabis use disorder (CUD) are associated with decreases in frequency and quantity of use but fail to increase overall rates of sustained abstinence. It is currently unknown whether reductions in use (in the absence of sustained abstinence) result in clinically significant improvements in functioning. The objective of this study was to refine a mobile contingency management approach to reduce cannabis use to ultimately evaluate whether reductions in frequency and quantity of cannabis are related to improvements in functional and mental health status.

Mobile health contingency management for smoking cessation among veterans experiencing homelessness: A comparative effectiveness trial.

Tobacco cessation is reduced in U.S. military veterans experiencing homelessness.

Development and refinement of the mobile anger reduction intervention for veterans with posttraumatic stress disorder.

Objective: Problematic anger is commonly reported among veterans with posttraumatic stress disorder (PTSD) and is associated with numerous psychosocial impairments. There is a clear need to develop innovative and effective anger interventions. One of the cognitive mechanisms associated with anger is the hostile interpretation bias, which is the tendency to interpret ambiguous interpersonal situations as hostile.

Predicting Response to Bolus Insulin Therapy in Patients With Type 2 Diabetes.

Background: The aim of this study was to develop a predictive model to classify people with type 2 diabetes (T2D) into expected levels of success upon bolus insulin initiation.

Methods: Machine learning methods were applied to a large nationally representative insurance claims database from the United States (dNHI database; data from 2007 to 2017). We trained boosted decision tree ensembles (XGBoost) to assign people into Class 0 (never meeting HbA1c goal), Class 1 (meeting but not maintaining HbA1c goal), or Class 2 (meeting and maintaining HbA1c goal) based on the demographic and clinical data available prior to initiating bolus insulin.

A Real-World Evaluation of a Smokeless Tobacco Cessation Text Message Program for Veterans: Outcomes and Comparison to Cigarette Smokers.

Introduction: Smokeless tobacco (ST) use and cessation rates have remained unchanged while cigarette smoking has declined, and cessation rates have increased. Text message programs have proved effective for cigarette smokers but have not been evaluated for ST users. The Veterans Health Administration (VHA) created a ST-specific arm of its SmokefreeVET automated text message program to help veteran ST users quit.

Real-World Utilization and Outcomes of the Veterans Health Administration's Smoking Cessation Text Message Program.

Introduction: Smoking cessation mobile health (mHealth) programs are effective and have been recommended for integration into health care services but have not been evaluated in real-world health care settings. The Veterans Health Administration, a safety net health care provider, provides health care for 9 million US military veterans. Veterans Health Administration implemented the SmokefreeVET text message program in 2013.

Mobile contingency management as an adjunctive treatment for co-morbid cannabis use disorder and cigarette smoking.

Introduction: Cannabis is the most widely used illicit drug in the U.S. with 19.

Validation of Veterans Affairs Electronic Medical Record Smoking Data Among Iraq- and Afghanistan-Era Veterans.

Background: Research using the Veterans Health Administration (VA) electronic medical records (EMR) has been limited by a lack of reliable smoking data.

Objective: To evaluate the validity of using VA EMR "Health Factors" data to determine smoking status among veterans with recent military service.

Design: Sensitivity, specificity, area under the receiver-operating curve (AUC), and kappa statistics were used to evaluate concordance between VA EMR smoking status and criterion smoking status.

Abstinence Reinforcement Therapy (ART) for rural veterans: Methodology for an mHealth smoking cessation intervention.

Introduction: Smoking is the most preventable cause of morbidity and mortality in U.S. veterans.

Engagement and abstinence among users of a smoking cessation text message program for veterans.

Background: SmokefreeVET is a text messaging smoking cessation program available to veterans enrolled in the Veterans Health Administration. SmokefreeVET was developed in collaboration with the National Cancer Institute as part of the SmokefreeTXT initiative.

Purpose: To evaluate the real world use of and effectiveness of the SmokefreeVET program for SmokefreeVET users who enrolled between 2013 and 2014.

Multicomponent smoking cessation treatment including mobile contingency management in homeless veterans.

Introduction: Smoking rates are 80% among persons who are homeless, and these smokers have decreased odds of quitting smoking. Little is known about relapse rates among homeless smokers. More information is needed regarding both quit rates and innovative methods to treat smoking cessation among homeless smokers.

Associations between smoking and psychiatric comorbidity in U.S. Iraq- and Afghanistan-era veterans.

The risk of smoking increases with specific psychiatric diagnoses (e.g., posttraumatic stress disorder); but the risk has also been shown to increase as a function of the number of psychiatric illnesses with which a person is diagnosed.

Mobile contingency management as an adjunctive smoking cessation treatment for smokers with posttraumatic stress disorder.

Introduction: Smokers with posttraumatic stress disorder (PTSD) smoke at higher prevalence rates and are more likely to relapse early in a quit attempt. Innovative methods are needed to enhance quit rates, particularly in the early quit period. Web-based contingency-management (CM) approaches have been found helpful in reducing smoking among other difficult-to-treat smoker populations but are limited by the need for computers.

The effect of draft DSM-V criteria on posttraumatic stress disorder prevalence.

Background: This study was designed to examine the concordance of proposed DSM-V posttraumatic stress disorder (PTSD) criteria with DSM-IV classification rules and examine the impact of the proposed DSM-V PTSD criteria on prevalence.

Method: The sample (N = 185) included participants who were recruited for studies focused on trauma and health conducted at an academic medical center and VA medical center in the southeastern United States. The prevalence and concordance between DSM-IV and the proposed DSM-V classifications were calculated based on results from structured clinical interviews.