A longitudinal analysis of rural adolescents\u27 perceptions of success : a multicultural perspective.

Researchers have traditionally evaluated success and achievement by examining prescribed constructs and their relationship to specific behaviors, performance skills, or cognitive abilities. The adolescents that have been studied were primarily from urban or metropolitan environments, and these studies did not factor in the influence of cultural context on variations in adolescent success strivings. This longitudinal investigation attempted to expand upon existing studies by not only focusing on a rural, southern high school sample, but also by allowing the students to generate their own criteria for defining success. A 12 category questionnaire, which was developed for this research project, and the Piers-Harris Self-Concept Scale for Children were administered on two occasions to the same group of subjects. The first study (T1) was conducted at middle adolescence, when participants were ninth graders; the second study (T2) was conducted at late adolescence during the twelfth grade. At T1, 149 students, ages 14-16 participated. Eighty were African American (AA); 54 were Caucasian (C). At T2, 152 students, ages 16-19 completed the assessment. Fifty-nine in this study were C and 75 were AA. At both T1 and T2, participants consistently identified three criteria when they generated their own success definitions: accomplishments/recognition, personal attributes and work/career. This was true across the variable of race though some variation was noted in comparison of gender-specific responses. The most frequently cited component of success, accomplishment/recognition, was described as setting and achieving goals being their best, working hard and achieving fame. Comparison of self-concept scores between T1 and T2 showed a significant increase in self-esteem across variables of race and gender. These data provide a profile of rural, southern adolescents viewed from a cultural and longitudinal perspective. The definitions generated by these adolescents can be valuable in the assessment of their success oriented behaviors. Further investigations of adolescents\u27 perceptions of success can contribute to the development of plans and strategies for parents, educators and counselors to assist adolescents with their strivings for success

Managing at the Speed of Light: Improving Mission-Support Performance

The House and Senate Energy and Water Development Appropriations Subcommittees requested this study to help DOE's three major mission-support organizations improve their operations to better meet the current and future needs of the department. The passage of the Recovery Act only increased the importance of having DOE's mission-support offices working in the most effective, efficient, and timely manner as possible. While following rules and regulations is essential, the foremost task of the mission-support offices is to support the department's mission, i.e., the programs that DOE is implementing, whether in Washington D.C. or in the field. As a result, the Panel offered specific recommendations to strengthen the mission-focus and improve the management of each of the following support functions based on five "management mandates":- Strategic Vision- Leadership- Mission and Customer Service Orientation- Tactical Implementation- Agility/AdaptabilityKey FindingsThe Panel made several recommendations in each of the functional areas examined and some overarching recommendations for the corporate management of the mission-support offices that they believed would result in significant improvements to DOE's mission-support operations. The Panel believed that adopting these recommendations will not only make DOE a better functioning organization, but that most of them are essential if DOE is to put its very large allocation of Recovery Act funding to its intended uses as quickly as possible

A Review of Studies Examining Stated Preferences for Cancer Screening

INTRODUCTION: Stated preference studies for cancer screening programs are used to understand how the programs can be improved to maximize usage. Our objectives were to conduct a systematic review of stated preference studies for cancer screening, identify gaps in the literature, and determine which types of research should be conducted in the future. METHODS: We considered all studies in the PubMed database through May 2005 that measured utility-based stated preferences for cancer screening using contingent valuation or conjoint analysis. We abstracted data on 1) study characteristics and 2) study results and policy implications. RESULTS: We found eight (of 84 identified) preference studies for cancer screening. The most commonly studied cancer was breast cancer, and the most commonly used method was contingent valuation. We found no studies for prostate cancer or physician preferences. Studies demonstrated that although individuals are able to state their preferences for cancer screening, they do not weigh test benefits and harms, and a significant percentage would choose to have no screening at all. Several studies found that test accuracy and reduction in mortality risk were important for determining preferences. CONCLUSION: Few studies of cancer screening preferences exist. The available studies examine only a few types of cancer and do not explore practice and policy implications in depth. The results of this review will be useful in identifying the focus of future research, identifying which screening methods may be more preferred to increase use of the programs, and developing interventions and policies that could facilitate informed and shared decision making for screening

Applications of Big Data Analytics within a Dynamic Simulation Modeling Platform to Inform Osteoarthritis Care in Alberta

Introduction Osteoarthritis (OA) is a leading cause of chronic disability. There is need to leverage administrative data to support OA policy analysis. Our objective was to develop and apply a multidimensional data cube as an input parameter repository using health administrative data to populate an OA simulation model. Objectives and Approach Health administrative data including practitioner claims, inpatient and ambulatory visits from 1994 to 2013 were integrated into a multidimensional data cube. OA cases were identified using validated algorithms, and followed through stages of care (primary, specialist, acute and post-operative). The cube provided rate calculations, duration and average cost for each stage of care across the model dimensions (age categories, sex, comorbidity status and geographic zones). The rates were then linked to the model as input parameters to simulate patient flow across the continuum of care. We used the model to predict direct costs across all dimensions from 2010 to 2035. Results Using the model, total number of patients with OA in Alberta will increase from 312,000 in 2010 to 1.4 million in 2035. The average annual cost per OA patient also increases from $2,800 to$4,900, and the total cost increases from $450 million in 2010 to 2.2 billion in 2035. The majority of the patients were at earlier stages (non-surgical 78$3,300 vs. surgical $16,400) in 2010. As new administrative data are being provided routinely, the data cube is capable of providing real-time updates for the input parameters of the model, which will aid in validation of the model results and improving the precision of projections. Conclusion/Implications The data cube has significantly improved our ability to manage and analyze administrative data within a simulation model to project the burden of OA in Alberta. The integrated model can be used as a real time decision-support tool to inform osteoarthritis service planning and variations in resource utilization

Comparison of Ultrastructural Cytotoxic Effects of Carbon and Carbon/Iron Particulates on Human Monocyte-Derived Macrophages

In this study, we tested the hypothesis that the presence of iron in carbon particulates enhances ultrastructural perturbation in human monocyte-derived macrophages (MDMs) after phagocytosis. We used 1-μm synthetic carbon-based particulates, designed to simulate environmental particulates of mass median aerodynamic diameter ≤ 2.5 μm (PM(2.5)). Cultures of human MDMs or T-lymphocytes (as a nonphagocytic control) were exposed to carbon or carbon/iron particulates for various time periods and examined by transmission electron microscopy for ultrastructural changes. T-cells failed to internalize either of the particulates and showed no organelle or nuclear changes. Conversely, MDMs avidly phagocytized the particulates. MDMs treated with C particulates exhibited morphologic evidence of macrophage activation but no evidence of lysis of organelles. In contrast, MDMs treated with C/Fe particulates exhibited coalescence of particulate-containing lysosomes. This phenomenon was not observed in the case of C particulates. By 24 hr there was a tendency of the C/Fe particulates to agglomerate into loose or compact clusters. Surrounding the compact C/Fe agglomerates was a uniform zone of nearly total organelle lysis. The lytic changes diminished in proportion to the distance from the agglomerate. In such cells, the nucleus showed loss of chromatin. Although C particles induced no detectable oxidative burst on treated MDMs, C/Fe particles induced a nearly 5-fold increase in the extracellular oxidative burst by treated MDMs compared with untreated controls. Iron bound to C particles catalyzed the decomposition of hydrogen peroxide to generate hydroxyl radicals. Results of these studies suggest that, among particulates of similar size, biologic activity can vary profoundly as a function of particulate physicochemical properties

The Olympia anatomic polished cemented stem is associated with a high survivorship, excellent hip-specific functional outcome, and high satisfaction levels:follow-up of 239 consecutive patients beyond 15 years

Introduction: The Olympia femoral stem is a stainless steel, anatomically shaped, polished and three-dimensionally tapered implant designed for use in cemented total hip arthroplasty (THA). The primary aim of this study was to determine the long-term survivorship, radiographic outcome, and patient-reported outcome measures (PROMs) of the Olympia stem. Patients and methods: Between May 2003 and December 2005, 239 patients (264 THAs) underwent a THA with an Olympia stem in our institution. Patient-reported outcome measures were assessed using the Oxford Hip Score (OHS), EuroQol-5 dimensions (EQ-5D) score, and patient satisfaction at mean 10 years following THA. Patient records and radiographs were then reviewed at a mean of 16.5 years (SD 0.7, 15.3–17.8) following THA to identify occurrence of complications or revision surgery for any cause following surgery. Radiographs were assessed for lucent lines and lysis according to Gruen’s zones Results: Mean patient age at surgery was 68.0 years (SD 10.9, 31–93 years). There were 156 women (65%, 176 THAs). Osteoarthritis was the indication for THA in 204 patients (85%). All cause stem survivorship at 10 years was 99.2% (95% confidence interval [CI], 97.9%–100%) and at 15 years was 97.5% (94.6%–100%). The 15-year stem survival for aseptic loosening was 100%. Analysis of all-cause THA failure demonstrated a survivorship of 98.5% (96.3%–100%) at 10 years and 95.9% (92.4%–99.4%) at 15 years. There were 9 THAs with non-progressive lucent lines in a single Gruen zone and 3 had lines in two zones, and no patient demonstrated signs for lysis. At a mean of 10-year (SD 0.8, 8.7–11.3) follow-up, mean OHS was 39 (SD 10.3, range 7–48) and 94% of patients reported being very satisfied or satisfied with their THA. Conclusions: The Olympia stem demonstrated excellent 10-year PROMs and very high rates of stem survivorship at final follow-up beyond 15 years

A big data analytics platform to support simulation modeling for osteoarthritis care pathways

Introduction Technical solutions have been used in industry settings for many years to facilitate efficient management and analyses of big data sources. An initiative to apply a business solution to support development of simulation models for health systems research using nearly two decades of provincial administrative health data is described. Objectives and Approach Administrative data including practitioner claims, hospitalizations and ambulatory care visits for patients with a diagnosis of osteoarthritis were obtained from Alberta Health for the period 1994/95 to 2012/13. These data were incorporated into a multidimensional data cube using Microsoft SQL Server Analysis Services. Initial steps required dimensional modeling to restructure the data into a star schema format. This involved appending several data sets and defining additional reference tables to contain stratification variables and denominator data for rate calculations. The modeling expert worked closely with the information technology team throughout the process and assessed validity of the output. Results Development and validation of the multidimensional cube occurred in iterations over approximately 12 months. The final solution resulted in an analytics platform that compiled data from approximately 400 million records obtained from four different administrative data sources. Ten dimension tables containing 102 variables provided enhanced flexibility to conduct ad hoc stratified analyses in a fraction of the time that would be required using conventional methods. For example, some analyses that previously required a day of analyst time could be performed in less than 15 minutes. The efficiencies in analytic time were achieved by the pre-aggregated measures and slice and dice capability of the data cube, which negated many intermediary steps for data extraction and time consuming iterative analyses required for development of the simulation models. Conclusion/Implications This project demonstrated how a technical solution applied in industry can be utilized to address challenges encountered by researchers related to managing and analyzing large administrative health data sets. The methods could be applied in many other research settings to facilitate access to and analyses of information using big data