Background: Marijuana is the most widely used illicit substance in the United States. Public approval of marijuana has driven its legalization in twenty states and the District of Columbia for medical use; and, this year alone (2013), two states have legalized recreational use of the drug. Despite the nation’s growing trend towards marijuana acceptance, the evidence regarding the health effects of its use remains vague. This study was designed to evaluate the health profile of marijuana users by determining the association of marijuana use with quality of life, defined in terms of perceived overall health and as self-reported medical conditions. Methods: The 2007-2010 National Health and Nutrition Examination Survey data was used to evaluate the health profile and quality of life of marijuana smokers. Chi-square and one-way ANOVA analyses were respectively used to compare prevalence and mean differences of select characteristics across different categories grouped by marijuana use. Logistic regression analyses were then performed to determine the association between the reported number of unhealthy days or medical conditions and marijuana use in the past month. All analyses were performed with SAS 9.2 software using weighted data, while 95% confidence intervals were used to determine statistical significance. Results: In total, 7716 cases were included in the study analysis. The prevalence of lifetime marijuana use was 59% (N = 3632), while the prevalence of current (past month) marijuana use was 12.6% (N = 861). Current marijuana users differed significantly from never users with respect to age, gender, income-to-poverty ratio, cigarette smoking, and alcohol and drug use. Current marijuana users also reported more unhealthy days per month, but less frequently reported diagnosis of a medical condition. Results of logistic regression analysis demonstrated that after controlling for confounders, there was no significant association between unhealthy days and current marijuana use, but there was an inverse association with reporting 3+ medical conditions and current marijuana use. Conclusions: This study shows that marijuana users are more likely to engage in health risk behaviors, and report lower quality of life when compared to individuals who have never used marijuana. However, after controlling for confounders, marijuana use was not found to be associated with poor health outcomes.

Background: Women experience greater health consequences of alcohol compared to their male counterparts. In recent years, rates of drinking and heavy alcohol use have increased in women while remaining relatively steady in men. Thus, our aim was to newly examine associations between sex, AUD, and the presence of medical conditions in a large nationally representative, cross-sectional dataset. Methods: Using data from the U.S. National Epidemiologic Survey on Alcohol and Related Conditions (NESARC-III; n = 36,309), we evaluated relationships among sex and DSM-5 AUD, and their association with past year clinician-confirmed medical conditions. Results: Women were 1.5 to 2 times more likely to be diagnosed with a past year cancer, pain, respiratory, or other significant medical condition compared to men (odds ratio [OR] = 1.331–2.027). Individuals with an ongoing DSM-5 AUD were nearly 1.5 to 2 times more likely to report a confirmed past year liver, cardiovascular, cancer, or other significant medical condition compared to those without an AUD (OR = 1.437–2.073). Interactive effects demonstrated that women with an ongoing AUD were 2 to 3 times more likely to report a past year doctor- or health professional-confirmed medical condition compared to men; specifically, respiratory conditions and cancers (OR = 1.767–2.713). Conclusions: Results identify that AUD is a critical factor associated with disease that spans organ systems. Associations between AUD and respiratory conditions or cancers are particularly robust in women. Effective interventions for a broad spectrum of medical conditions should consider the role of problematic alcohol use, especially given that rates of drinking in women are increasing.

No description available.

National rates of chronic non-cancer pain (CNCP) are rising alongside increasing reports of prescription opioid abuse and mortality. Associations between the rise in CNCP and in opioid abuse seem logical, yet research on CNCP among individuals with opioid dependence is currently limited due to the complicated nature of comorbid conditions in research and treatment. This study aims to expand the CNCP knowledge base by responding to the question: Do individuals with CNCP participating in an opiate treatment program have better or worse treatment outcomes than individuals without CNCP? This study used a secondary dataset including 483 adults from Kentucky’s Opiate Recovery Treatment Outcome Study. Individuals in the sample met DSM-IV-TR criteria for opioid dependence and were in treatment at a licensed opiate treatment program (OTP). Analysis compared cases with and without CNCP on national treatment outcome measures including substance use, recovery support, education, employment, mental health symptoms, and criminal justice system involvement. Results indicated no differences at follow-up between the CNCP (n=163) and non-CNCP (n=320) individuals on substance abstinence, recovery supports, education level, or criminal justice system involvement. At baseline and follow-up there were more unemployed individuals and individuals receiving disability benefits in the CNCP group than the non-CNCP group. Reported anxiety and depression symptoms increased at follow-up, while use of prescription medicine for mental health symptoms declined for both groups (non-significant differences). The only predictors for CNCP cases in this sample were tobacco use and presence of a chronic medical condition. Recommendations include expansion of smoking cessation programs in substance abuse treatment settings. Future research might examine integrated treatment and medical home health models to better address biopsychosocial components of clients with comorbid conditions like opioid dependence and CNCP.

The purpose of the current study was to examine and compare the quality of the mother-adolescent relationship, the prevalence of bullying and cyberbullying perpetration and victimization, and the prevalence of externalizing behaviors, as well as the relationship among these constructs, in a clinical and a nonclinical sample of adolescents. It tested a series of hypotheses focused on group differences in the mother-adolescent relationship, peer victimization, and externalizing behaviors (i.e. deviant behaviors and bullying perpetration) for the clinical and nonclinical samples. It also tested the relationships between the mother-adolescent relationship and peer victimization, deviant behaviors, and bullying perpetration, and whether these links varied in the clinical versus non-clinical samples. Multiple regressions were used to test the first three hypotheses, while path analyses were used to test the latter hypotheses. Findings provide evidence that adolescents in the clinical group reported significantly closer relationships with their mothers and lower levels of externalizing behaviors; no differences were found in the likelihood of experiencing peer victimization. Maternal support was a negative predictor of peer victimization, and maternal support and monitoring were negative predictors of deviant behaviors and bullying perpetration. These links were invariant across clinical versus non-clinical samples.

No description available.

The primary goal of Machine learning (ML) models in the prediction of medical conditions is to accurately predict (classify) the occurrence of a disease, or therapy. Many ML models, traditional and deep, have been utilized for the prediction of disease diagnosis, or prediction of the most optimal therapeutic approach. Almost all categories of medical conditions were subject to ML analysis. When creating predictive ML algorithms in medicine, it is pivotal to consider what problems are intended to be solved and how much and what types of training data are available. For challenging prediction (classification) problems, the understanding of disease pathogenesis makes the selection of an adequate ML model and accurate prediction more likely. The hypothesis of the research was to demonstrate that the optimal and adequate selection of model inputs as well as the selection and design of adequate ML methods improves the prediction accuracy of occurrence of diseases and their outcomes. The effectiveness and accuracy of created deep learning and traditional methods have been analyzed and compared. The impact of different medical conditions and different medical domains on optimal selection and performance of ML models was also studied. The effectiveness of advanced ML models was tested on four different diseases: Alzheimer’s disease (AD), Diabetes Mellitus type 2 (DM2), Influenza, and Colorectal cancer (CRC). The objective of the first part of the thesis (AD study) was to determine could prediction of AD from Electronic medical records (EMR) data alone be significantly improved by applying domain knowledge in positive dataset selection rather than setting naïve filters. Selected Clinically Relevant Positive (SCRP) datasets were used as inputs to a Long-Short-Term Memory (LSTM) Recurrent Neural Network (RNN) deep learning model to predict will the patient develop AD. The LSTM RNN method performed significantly better when learning from the SCRP dataset than when datasets were selected naïvely. Accurate prediction of AD is significant in the identification of patients for clinical trials, and a better selection of patients who need imaging diagnostics. The objective of the DM2 research was to predict if patients with DM2 would develop any of ten selected complications. RNN LSTM and RNN Gated Recurrent Units (GRU) models were designed and compared to Random Forest and Multilayer Perceptron traditional models. The number of hospitalizations registered in the EMR data was an important factor for the prediction accuracy. The prediction accuracy of complications decreases over time. The RNN GRU model was the best choice for EMR type of data, followed by the RNN LSTM model. An accurate prediction of the occurrence of complications of DM2 is important in the planning of targeted measures aimed to slow down or prevent their development. The objective of the third part of the thesis was to improve the understanding of spatial spreading of complicated cases of influenza that required hospitalizations, by constructing social network models. A novel approach was designed, which included the construction of heatmaps for geographic regions in New York state and power-law networks, to analyze the distribution of hospitalized flu cases. The methodology constructed in the study allowed to identify critical hubs and routes of spreading of Influenza, in specific geographic locations. Obtained results could enable better prediction of the distribution of complicated flu cases in specific geographic regions and better prediction of required resources for prevention and treatment of hospitalized patients with Influenza. The fourth part of the thesis proposes approaches to discover risk factors (comorbidities and genes) associated with the development of CRC, which can be used for future ML models to predict the influence of risk factors on prognosis and outcomes of cancer and other chronic diseases. A novel social network and text mining model was developed to study specific risk factors of CRC. Identified associations between comorbidities, CRC, and shared genes can have important implications on early discovery, and prognosis of CRC, which can be subject to predictive ML models in the future. Prediction ML models could help physicians to select the most effective diagnostic, preventive and therapeutic choices available. These ML models can provide recommendations to select suitable patients for clinical trials, which is very important in searching for medical solutions in health emergencies. Successful ML models can make medicine more efficient, improve outcomes, and decreases medical errors. / Computer and Information Science

No description available.

No description available.

In this article, the authors present the Willingness to Foster Scale - Disabilities and Medical Conditions (WFS-DMC) and report results concerning its psychometric properties. The WFS-DMC is a new measure designed to accurately and efficiently assess the willingness of parents to foster children with special needs, in particular, disabilities and special medical conditions. The authors tested the WFS-DMC with a national sample of 298 foster mothers. Internal consistency reliability was excellent (α =. 90). With reference to construct validity, mothers with higher WFS-DMC scores fostered longer, fostered and adopted more children, and requested the removal of a smaller proportion of foster children. Furthermore, the mothers' WFS-DMC scores were unrelated to demographic characteristics. The WFS-DMC could help guide the decision-making process involved in matching children who have special needs with parents willing to care for them.