Do diabetic veterans use the Internet? Self-reported usage, skills, and interest in using My HealtheVet Web portal.

OBJECTIVE: The Veterans Health Administration has developed My HealtheVet (MHV), a Web-based portal that links veterans to their care in the veteran affairs (VA) system. The objective of this study was to measure diabetic veterans' access to and use of the Internet, and their interest in using MHV to help manage their diabetes. MATERIALS AND METHODS: Cross-sectional mailed survey of 201 patients with type 2 diabetes and hemoglobin A(1c) > 8.0% receiving primary care at any of five primary care clinic sites affiliated with a VA tertiary care facility. Main measures included Internet usage, access, and attitudes; computer skills; interest in using the Internet; awareness of and attitudes toward MHV; demographics; and socioeconomic status. RESULTS: A majority of respondents reported having access to the Internet at home. Nearly half of all respondents had searched online for information about diabetes, including some who did not have home Internet access. More than a third obtained "some" or "a lot" of their health-related information online. Forty-one percent reported being "very interested" in using MHV to help track their home blood glucose readings, a third of whom did not have home Internet access. Factors associated with being "very interested" were as follows: having access to the Internet at home (p < 0.001), "a lot/some" trust in the Internet as a source of health information (p = 0.002), lower age (p = 0.03), and some college (p = 0.04). Neither race (p = 0.44) nor income (p = 0.25) was significantly associated with interest in MHV. CONCLUSIONS: This study found that a diverse sample of older VA patients with sub-optimally controlled diabetes had a level of familiarity with and access to the Internet comparable to an age-matched national sample. In addition, there was a high degree of interest in using the Internet to help manage their diabetes.

Perceived cannabis use norms and cannabis use among adolescents in the United States.

Due to changes in cannabis policies, concerns about cannabis use (CU) in adolescents have increased. The population of nonwhite groups is growing quickly in the United States. We examined perceived CU norms and their association with CU and CU disorder (CUD) for White, Black, Hispanic, Native-American, Asian-American, Native Hawaiian/Pacific Islander (NH/PI), and mixed-race adolescents. Data were from adolescents (12-17 years) in the 2004-2012 National Surveys on Drug Use and Health (N = 163,837). Substance use and CUD were assessed by computer-assisted, self-interviewing methods. Blacks, Hispanics, Native-Americans, and mixed-race adolescents had greater odds of past-year CU and CUD than Whites. Among past-year cannabis users (CUs), Hispanics and Native-Americans had greater odds of having a CUD than Whites. Asian-Americans had the highest prevalence of perceived parental or close friends' CU disapproval. Native-Americans and mixed-race adolescents had lower odds than Whites of perceiving CU disapproval from parents or close friends. In adjusted analyses, adolescent's disapproval of CU, as well as perceived disapproval by parents or close friends, were associated with a decreased odds of CU in each racial/ethnic group, except for NHs/PIs. Adolescent's disapproval of CU was associated with a decreased odds of CUD among CUs for Whites (personal, parental, and close friends' disapproval), Hispanics (personal, parental, and close friends' disapproval), and mixed-race adolescents (personal, close friends' disapproval). Racial/ethnic differences in adolescent CU prevalence were somewhat consistent with adolescents' reports of CU norm patterns. Longitudinal research on CU health effects should oversample nonwhite adolescents to assure an adequate sample for analysis and reporting.

Recent national trends in Salvia divinorum use and substance-use disorders among recent and former Salvia divinorum users compared with nonusers.

CONTEXT: Media and scientific reports have indicated an increase in recreational use of Salvia divinorum. Epidemiological data are lacking on the trends, prevalence, and correlates of S. divinorum use in large representative samples, as well as the extent of substance use and mental health problems among S. divinorum users. OBJECTIVE: To examine the national trend in prevalence of S. divinorum use and to identify sociodemographic, behavioral, mental health, and substance-use profiles of recent (past-year) and former users of S. divinorum. DESIGN: Analyses of public-use data files from the 2006-2008 United States National Surveys on Drug Use and Health (N = 166,453). SETTING: Noninstitutionalized individuals aged 12 years or older were interviewed in their places of residence. MAIN MEASURES: Substance use, S. divinorum, self-reported substance use disorders, criminality, depression, and mental health treatment were assessed by standardized survey questions administered by the audio computer-assisted self-interviewing method. RESULTS: Among survey respondents, lifetime prevalence of S. divinorum use had increased from 0.7% in 2006 to 1.3% in 2008 (an 83% increase). S. divinorum use was associated with ages 18-25 years, male gender, white or multiple race, residence of large metropolitan areas, arrests for criminal activities, and depression. S. divinorum use was particularly common among recent drug users, including users of lysergic acid diethylamide (53.7%), ecstasy (30.1%), heroin (24.2%), phencyclidine (22.4%), and cocaine (17.5%). Adjusted multinomial logistic analyses indicated polydrug use as the strongest determinant for recent and former S. divinorum use. An estimated 43.0% of past-year S. divinorum users and 28.9% of former S. divinorum users had an illicit or nonmedical drug-use disorder compared with 2.5% of nonusers. Adjusted logistic regression analyses showed that recent and former S. divinorum users had greater odds of having past-year depression and a substance-use disorder (alcohol or drugs) than past-year alcohol or drug users who did not use S. divinorum. CONCLUSION: S. divinorum use is prevalent among recent or active drug users who have used other hallucinogens or stimulants. The high prevalence of substance use disorders among recent S. divinorum users emphasizes the need to study health risks of drug interactions.

Learning and Exploiting Camera Geometry for Computer Vision

This work explores the use of statistical methods in describing and estimating camera poses, as well as the information feedback loop between camera pose and object detection. Surging development in robotics and computer vision has pushed the need for algorithms that infer, understand, and utilize information about the position and orientation of the sensor platforms when observing and/or interacting with their environment.

The first contribution of this thesis is the development of a set of statistical tools for representing and estimating the uncertainty in object poses. A distribution for representing the joint uncertainty over multiple object positions and orientations is described, called the mirrored normal-Bingham distribution. This distribution generalizes both the normal distribution in Euclidean space, and the Bingham distribution on the unit hypersphere. It is shown to inherit many of the convenient properties of these special cases: it is the maximum-entropy distribution with fixed second moment, and there is a generalized Laplace approximation whose result is the mirrored normal-Bingham distribution. This distribution and approximation method are demonstrated by deriving the analytical approximation to the wrapped-normal distribution. Further, it is shown how these tools can be used to represent the uncertainty in the result of a bundle adjustment problem.

Another application of these methods is illustrated as part of a novel camera pose estimation algorithm based on object detections. The autocalibration task is formulated as a bundle adjustment problem using prior distributions over the 3D points to enforce the objects' structure and their relationship with the scene geometry. This framework is very flexible and enables the use of off-the-shelf computational tools to solve specialized autocalibration problems. Its performance is evaluated using a pedestrian detector to provide head and foot location observations, and it proves much faster and potentially more accurate than existing methods.

Finally, the information feedback loop between object detection and camera pose estimation is closed by utilizing camera pose information to improve object detection in scenarios with significant perspective warping. Methods are presented that allow the inverse perspective mapping traditionally applied to images to be applied instead to features computed from those images. For the special case of HOG-like features, which are used by many modern object detection systems, these methods are shown to provide substantial performance benefits over unadapted detectors while achieving real-time frame rates, orders of magnitude faster than comparable image warping methods.

The statistical tools and algorithms presented here are especially promising for mobile cameras, providing the ability to autocalibrate and adapt to the camera pose in real time. In addition, these methods have wide-ranging potential applications in diverse areas of computer vision, robotics, and imaging.

Robust Uncertainty Quantification and Scalable Computation for Computer Models with Massive Output

Uncertainty quantification (UQ) is both an old and new concept. The current novelty lies in the interactions and synthesis of mathematical models, computer experiments, statistics, field/real experiments, and probability theory, with a particular emphasize on the large-scale simulations by computer models. The challenges not only come from the complication of scientific questions, but also from the size of the information. It is the focus in this thesis to provide statistical models that are scalable to massive data produced in computer experiments and real experiments, through fast and robust statistical inference.

Chapter 2 provides a practical approach for simultaneously emulating/approximating massive number of functions, with the application on hazard quantification of Soufri\`{e}re Hills volcano in Montserrate island. Chapter 3 discusses another problem with massive data, in which the number of observations of a function is large. An exact algorithm that is linear in time is developed for the problem of interpolation of Methylation levels. Chapter 4 and Chapter 5 are both about the robust inference of the models. Chapter 4 provides a new criteria robustness parameter estimation criteria and several ways of inference have been shown to satisfy such criteria. Chapter 5 develops a new prior that satisfies some more criteria and is thus proposed to use in practice.