Multivariate Examination Of Risk Factors Associated With Chronic Hepatitis C Virus Infection In The United States Using National Health And Nutrition Examination Survey From 2003 to 2014

Introduction: Hepatitis C Virus (HCV) Infection is the most common blood-borne infection in the world with a global prevalence of ~3%. It also represents an underestimated and under-recognized viral infection because it is asymptomatic during the initial period of infection, which tends to span several decades. However, after establishing itself as a chronic state, HCV infection often leads to severe debilitating liver conditions such as cirrhosis, hepatocellular carcinoma to name a few resulting in poor quality of life, increased healthcare costs and mortality. Several earlier studies have examined risk factors associated with HCV. However, a comprehensive study that has simultaneously evaluated a wide range of addictive risk behaviors associated with HCV has not been conducted to date. This type of investigation will help to identify at-risk populations for HCV and provide valuable information regarding how one might efficiently link them to appropriate treatment and care. Aim: The primary aims of this study were 1). To estimate chronic HCV infection (CHI) prevalence in non-institutionalized U.S. adult population from 2003-2014 2). To perform a multivariate examination of all known behavioral risk factors significantly associated with CHI and 3). To identify less invasive questions regarding risk behaviors associated with CHI that could be used to predict state-level CHI prevalence using other state-specific data sources such as The Behavioral Risk Factor Surveillance System (BRFSS). Methods: The study utilized nationally representative data from National Health and Nutrition Examination Survey (NHANES) for the years 2003-2014. HCV RNA positive persons were CHI positive population. Bivariate analyses were performed to examine the frequency distributions of the study’s primary dependent variable (CHI) and all independent variables (demographical + behavioral risk factor variables). The analysis sample included 11,596 adults aged 20-59 years. Risk factors for CHI were examined using both bivariate and multivariate logistic regression analyses. We first conducted weighted bivariate logistic regression analyses to examine the relationships of dependent and independent variables without controlling for potential confounders. We then conducted weighted multivariate adjusted logistic regression analyses to examine the relationships between the dependent and independent variables while controlling for potential confounders. Odds ratios (OR), 95% confidence limits (CL) and p-values were calculated. A p-value \u3c 0.05 was considered statistically significant. SAS 9.4 was used for all statistical analyses. Results: The estimated number of CHI adults aged \u3e/= 20 years in 2014 was 1.93 million leading to an estimated CHI prevalence of 0.7%. Injection drug users (IDU) had the highest CHI prevalence of 30.24% by bivariate analyses. Multivariate logistic regression analysis in the chronic HCV full model indicated that age categories 40-49 y (OR: 7.9, 95% CL: 3.8-16.2) and 50-59 y (OR: 8.0, 95% CL: 3.5-18.2); non-Hispanic blacks (OR: 2.4, 95% CL: 1.3-4.1); less than high school education (OR: 2.6, 95% CL: 1.5-4.8); \u3c 2.0 times the poverty level (OR: 3.5, 95% CL: 1.9- 6.6); heroin consumers (OR: 2.3, 95% CL: 1.1-4.6); IDU (OR: 8.1, 95% CL: 3.1-21); blood transfusion recipients (OR: 2.9, 95% CL: 1.4-5.7) and \u3e/= 10 lifetime sex-partners (OR: 5.5, 95% CL: 1.5-19.7) were significantly associated with CHI. Multivariate logistic regression analysis in the chronic HCV risk factor model indicated that persons in moderate (OR: 2.5, 95% CL: 1.1-5.5) and high (OR: 30.3, 95% CL: 12.1-76) substance abuse risk factor categories were significantly associated with CHI. Multivariate logistic regression analysis in chronic HCV BRFSS model indicated that age categories 40-49 y (OR: 7.5, 95% CL: 3.5-15.9) 50-59 y (OR: 8.7, 95% CL: 4.2-18); males (OR: 3.1, 95% CL: 1.5-6.4); non-Hispanic black (OR: 1.8, 95% CL: 1.1-2.9); less than high school education (OR: 2.2, 95%CL: 1.3-3.8); \u3c 2.0 times the poverty level (OR: 3.7, 95% CL: 2.0-6.8); alcohol consumers (OR: 1.7; 95% CL: 1.0-2.9) and smokers (OR: 3.7, 95% CL: 1.8-7.6) were significantly associated with CHI. c-statistic of the three models were 0.94, 0.92 and 0.88 respectively thereby implying that all three models were strong models with a higher predictive accuracy of CHI. Conclusions: We conclude that the estimated prevalence of CHI in this analysis sample is 0.7%, however the true prevalence estimates of CHI are likely to be significantly higher if incarcerated, homeless and other population not presented in NHANES are included. IDU continues to be the strongest risk factor for CHI. Persons with two or more addictive behavioral risk factors have significant associations with CHI. Results from this study will enable identification of at-risk population for CHI and provide valuable information for linking them to appropriate treatment and care

Finding good enough coins under symmetric and asymmetric information

We study the problem of returning m coins with biases above 0:5. These good enough coins that are returned by the agent should be acceptable to the authority by meeting the authority's Family Wise Error Rate constraint. We design adaptive algorithms that invoke Sequential Probability Ratio Test to find these good enough coins. We consider scenarios that differ in terms of the information available about the underlying Bayesian setting. The symmetry or asymmetry of the underlying setup, i.e., the difference between what the agent and the authority know about the underlying prior and the support, presents different challenges. We also make notes on the algorithms' sample complexity.Electrical and Computer Engineerin

Overconfidence is a Dangerous Thing: Mitigating Membership Inference Attacks by Enforcing Less Confident Prediction

Machine learning (ML) models are vulnerable to membership inference attacks (MIAs), which determine whether a given input is used for training the target model. While there have been many efforts to mitigate MIAs, they often suffer from limited privacy protection, large accuracy drop, and/or requiring additional data that may be difficult to acquire. This work proposes a defense technique, HAMP that can achieve both strong membership privacy and high accuracy, without requiring extra data. To mitigate MIAs in different forms, we observe that they can be unified as they all exploit the ML model's overconfidence in predicting training samples through different proxies. This motivates our design to enforce less confident prediction by the model, hence forcing the model to behave similarly on the training and testing samples. HAMP consists of a novel training framework with high-entropy soft labels and an entropy-based regularizer to constrain the model's prediction while still achieving high accuracy. To further reduce privacy risk, HAMP uniformly modifies all the prediction outputs to become low-confidence outputs while preserving the accuracy, which effectively obscures the differences between the prediction on members and non-members. We conduct extensive evaluation on five benchmark datasets, and show that HAMP provides consistently high accuracy and strong membership privacy. Our comparison with seven state-of-the-art defenses shows that HAMP achieves a superior privacy-utility trade off than those techniques.Comment: To appear at NDSS'2

Bounds on hyper-status connectivity index of graphs

In this paper, we obtain the bounds for the hyper-status connectivity indices of a connected graph and its complement in terms of other graph invariants. In addition, the hyper-status connectivity indices of some composite graphs such as Cartesian product, join and composition of two connected graphs are obtained. We apply some of our results to compute the hyper-status connectivity indices of some important classes of graphs.Publisher's Versio

Evidence for particle-hole excitations in the triaxial strongly-deformed well of ^{163}Tm

Two interacting, strongly-deformed triaxial (TSD) bands have been identified in the Z = 69 nucleus ^{163}Tm. This is the first time that interacting TSD bands have been observed in an element other than the Z = 71 Lu nuclei, where wobbling bands have been previously identified. The observed TSD bands in ^{163}Tm appear to be associated with particle-hole excitations, rather than wobbling. Tilted-Axis Cranking (TAC) calculations reproduce all experimental observables of these bands reasonably well and also provide an explanation for the presence of wobbling bands in the Lu nuclei, and their absence in the Tm isotopes.Comment: 13 pages, 7 figure