A Comparison of Four Professional Groups\u27 Support for a Strengthened DUI Law

This study examined support patterns among criminal justice professionals for an enhanced DUI law. We surveyed North Dakota\u27s police, prosecutors, judges, and addiction counselors to measure their personal support and their perceptions of the support of others for the law. Respondents generally favored the strengthened law, but consistent with role theory, there were significant between group differences. There also were significant differences in personal versus perceived peer support and in perceived peer support versus the perceived support of other groups. Groups tended to agree in the differential levels of support they attributed to other groups. Implications for a coordinated system approach to combatting DUI are identified

A review of RCTs in four medical journals to assess the use of imputation to overcome missing data in quality of life outcomes

Background: Randomised controlled trials (RCTs) are perceived as the gold-standard method for evaluating healthcare interventions, and increasingly include quality of life (QoL) measures. The observed results are susceptible to bias if a substantial proportion of outcome data are missing. The review aimed to determine whether imputation was used to deal with missing QoL outcomes. Methods: A random selection of 285 RCTs published during 2005/6 in the British Medical Journal, Lancet, New England Journal of Medicine and Journal of American Medical Association were identified. Results: QoL outcomes were reported in 61 (21%) trials. Six (10%) reported having no missing data, 20 (33%) reported ≤ 10% missing, eleven (18%) 11%–20% missing, and eleven (18%) reported >20% missing. Missingness was unclear in 13 (21%). Missing data were imputed in 19 (31%) of the 61 trials. Imputation was part of the primary analysis in 13 trials, but a sensitivity analysis in six. Last value carried forward was used in 12 trials and multiple imputation in two. Following imputation, the most common analysis method was analysis of covariance (10 trials). Conclusion: The majority of studies did not impute missing data and carried out a complete-case analysis. For those studies that did impute missing data, researchers tended to prefer simpler methods of imputation, despite more sophisticated methods being available.The Health Services Research Unit is funded by the Chief Scientist Office of the Scottish Government Health Directorate. Shona Fielding is also currently funded by the Chief Scientist Office on a Research Training Fellowship (CZF/1/31)

A review of RCTs in four medical journals to assess the use of imputation to overcome missing data in quality of life outcomes

Peer reviewedPublisher PD

Johnson Noise Thermometry for Advanced Small Modular Reactors

Temperature is a key process variable at any nuclear power plant (NPP). The harsh reactor environment causes all sensor properties to drift over time. At the higher temperatures of advanced NPPs the drift occurs more rapidly. The allowable reactor operating temperature must be reduced by the amount of the potential measurement error to assure adequate margin to material damage. Johnson noise is a fundamental expression of temperature and as such is immune to drift in a sensor s physical condition. In and near core, only Johnson noise thermometry (JNT) and radiation pyrometry offer the possibility for long-term, high-accuracy temperature measurement due to their fundamental natures. Small, Modular Reactors (SMRs) place a higher value on long-term stability in their temperature measurements in that they produce less power per reactor core and thus cannot afford as much instrument recalibration labor as their larger brethren. The purpose of this project is to develop and demonstrate a drift free Johnson noise-based thermometer suitable for deployment near core in advanced SMR plants

Johnson Noise Thermometry for Advanced Small Modular Reactors

Temperature is a key process variable at any nuclear power plant (NPP). The harsh reactor environment causes all sensor properties to drift over time. At the higher temperatures of advanced NPPs the drift occurs more rapidly. The allowable reactor operating temperature must be reduced by the amount of the potential measurement error to assure adequate margin to material damage. Johnson noise is a fundamental expression of temperature and as such is immune to drift in a sensor s physical condition. In and near core, only Johnson noise thermometry (JNT) and radiation pyrometry offer the possibility for long-term, high-accuracy temperature measurement due to their fundamental natures. Small, Modular Reactors (SMRs) place a higher value on long-term stability in their temperature measurements in that they produce less power per reactor core and thus cannot afford as much instrument recalibration labor as their larger brethren. The purpose of this project is to develop and demonstrate a drift free Johnson noise-based thermometer suitable for deployment near core in advanced SMR plants

Independent test assessment using the extreme value distribution theory

The new generation of whole genome sequencing platforms offers great possibilities and challenges for dissecting the genetic basis of complex traits. With a very high number of sequence variants, a naïve multiple hypothesis threshold correction hinders the identification of reliable associations by the overreduction of statistical power. In this report, we examine 2 alternative approaches to improve the statistical power of a whole genome association study to detect reliable genetic associations. The approaches were tested using the Genetic Analysis Workshop 19 (GAW19) whole genome sequencing data. The first tested method estimates the real number of effective independent tests actually being performed in whole genome association project by the use of an extreme value distribution and a set of phenotype simulations. Given the familiar nature of the GAW19 data and the finite number of pedigree founders in the sample, the number of correlations between genotypes is greater than in a set of unrelated samples. Using our procedure, we estimate that the effective number represents only 15 % of the total number of independent tests performed. However, even using this corrected significance threshold, no genome-wide significant association could be detected for systolic and diastolic blood pressure traits. The second approach implements a biological relevance-driven hypothesis tested by exploiting prior computational predictions on the effect of nonsynonymous genetic variants detected in a whole genome sequencing association study. This guided testing approach was able to identify 2 promising single-nucleotide polymorphisms (SNPs), 1 for each trait, targeting biologically relevant genes that could help shed light on the genesis of the human hypertension. The first gene, PFH14, associated with systolic blood pressure, interacts directly with genes involved in calcium-channel formation and the second gene, MAP4, encodes a microtubule-associated protein and had already been detected by previous genome-wide association study experiments conducted in an Asian population. Our results highlight the necessity of the development of alternative approached to improve the efficiency on the detection of reasonable candidate associations in whole genome sequencing studies

A questionnaire to identify patellofemoral pain in the community: an exploration of measurement properties

Background Community-based studies of patellofemoral pain (PFP) need a questionnaire tool that discriminates between those with and those without the condition. To overcome these issues, we have designed a self-report questionnaire which aims to identify people with PFP in the community. Methods Study designs: comparative study and cross-sectional study. Study population: comparative study: PFP patients, soft-tissue injury patients and adults without knee problems. Cross-sectional study: adults attending a science festival. Intervention: comparative study participants completed the questionnaire at baseline and two weeks later. Cross-sectional study participants completed the questionnaire once. The optimal scoring system and threshold was explored using receiver operating characteristic curves, test-retest reliability using Cohen’s kappa and measurement error using Bland-Altman plots and standard error of measurement. Known-group validity was explored by comparing PFP prevalence between genders and age groups. Results Eighty-four participants were recruited to the comparative study. The receiver operating characteristic curves suggested limiting the questionnaire to the clinical features and knee pain map sections (AUC 0.97 95 % CI 0.94 to 1.00). This combination had high sensitivity and specificity (over 90 %). Measurement error was less than the mean difference between the groups. Test–retest reliability estimates suggest good agreement (N = 51, k = 0.74, 95 % CI 0.52–0.91). The cross-sectional study (N = 110) showed expected differences between genders and age groups but these were not statistically significant. Conclusion A shortened version of the questionnaire, based on clinical features and a knee pain map, has good measurement properties. Further work is needed to validate the questionnaire in community samples

Can Plan Recommendations Improve the Coverage Decisions of Vulnerable Populations in Health Insurance Marketplaces?

OBJECTIVE: The Affordable Care Act's marketplaces present an important opportunity for expanding coverage but consumers face enormous challenges in navigating through enrollment and re-enrollment. We tested the effectiveness of a behaviorally informed policy tool--plan recommendations--in improving marketplace decisions. STUDY SETTING: Data were gathered from a community sample of 656 lower-income, minority, rural residents of Virginia. STUDY DESIGN: We conducted an incentive-compatible, computer-based experiment using a hypothetical marketplace like the one consumers face in the federally-facilitated marketplaces, and examined their decision quality. Participants were randomly assigned to a control condition or three types of plan recommendations: social normative, physician, and government. For participants randomized to a plan recommendation condition, the plan that maximized expected earnings, and minimized total expected annual health care costs, was recommended. DATA COLLECTION: Primary data were gathered using an online choice experiment and questionnaire. PRINCIPAL FINDINGS: Plan recommendations resulted in a 21 percentage point increase in the probability of choosing the earnings maximizing plan, after controlling for participant characteristics. Two conditions, government or providers recommending the lowest cost plan, resulted in plan choices that lowered annual costs compared to marketplaces where no recommendations were made. CONCLUSIONS: As millions of adults grapple with choosing plans in marketplaces and whether to switch plans during open enrollment, it is time to consider marketplace redesigns and leverage insights from the behavioral sciences to facilitate consumers' decisions