Device Characterization and Compact Modeling of the SiGe HBT in Extreme Temperature Environments

The silicon germanium heterojunction bipolar transistor, SiGe HBT, has very high frequency response but limited voltage range. Commercial communication applications in wireless and system integration have driven the development of the SiGe HBT. However, the device\u27s excellent electrical performance goes beyond the commercial environment. The SiGe HBT performs exceptionally at low temperatures. The device DC current gain and AC small-signal gain significantly increase in the cryogenic temperature range. Applications at low temperatures with expansive temperature range specifications need an HBT compact model to accurately represent the device\u27s performance. In this work, a compact model referenced at 300K was developed to accurately represent both DC and AC electrical performance of the SiGe HBT over an extended temperature range, down to 93K. This single expansive temperature, SET, model supports all functions of circuit simulation; DC quiescent operation and AC frequency response. The SET model was developed from the Mextram 504.7 bipolar model and accurately represents full transistor operation over an extreme temperature environment. The model correctly simulates SiGe HBT DC output performance from saturation, through quasi-saturation and the linear region including impact ionization effects. This model was developed through a combination of physical calculations based on doping profiles and optimization techniques for modeling fitting. The SET model of this dissertation added 32 parameters to the original Mextram 504.7 model\u27s 78 parameters. The device\u27s static and dynamic performance over the full temperature range down to 93K was fitted with a single group of SET model parameters. The model results show excellent correlation with measured data over the entire temperature range

Starting young? children’s experiences of trying smoking during pre-adolescence

Although the risks smoking poses to health are now well known, many young people continue to take up the habit. While numerous crosssectional studies of adolescents have identified correlates of smoking initiation, much less prospective, longitudinal research has been conducted with young children to gather their accounts of early experiences of smoking, and this study fills that significant gap. Quantitative and qualitative data, collected using questionnaires, interviews and focus groups, are presented from the pre-adolescent phase of the Liverpool Longitudinal Study of Smoking. By age 11, 27% of the cohort had tried smoking, 13% had smoked repeatedly and 3% were smoking regularly. Rates of experimentation increased over time. Qualitative data revealed that curiosity and the role of peers were central to children’s accounts of early smoking. By preadolescence, children are at different stages in their smoking careers, therefore interventionsmust be targeted to their varied experiences. Current prevention strategies often focus on restricting access to cigarettes, but a broad range of intervention measures is required which take account of the multifactorial nature of smoking onset. To be effective, policies that aim to prevent smoking must be grounded in children’s lived experiences

‘Arm-based’ parameterization for network meta-analysis

We present an alternative to the contrast‐based parameterization used in a number of publications for network meta‐analysis. This alternative “arm‐based” parameterization offers a number of advantages: it allows for a “long” normalized data structure that remains constant regardless of the number of comparators; it can be used to directly incorporate individual patient data into the analysis; the incorporation of multi‐arm trials is straightforward and avoids the need to generate a multivariate distribution describing treatment effects; there is a direct mapping between the parameterization and the analysis script in languages such as WinBUGS and finally, the arm‐based parameterization allows simple extension to treatment‐specific random treatment effect variances. We validated the parameterization using a published smoking cessation dataset. Network meta‐analysis using arm‐ and contrast‐based parameterizations produced comparable results (with means and standard deviations being within +/− 0.01) for both fixed and random effects models. We recommend that analysts consider using arm‐based parameterization when carrying out network meta‐analyses

Network meta-analysis on the log-hazard scale, combining count and hazard ratio statistics accounting for multi-arm trials: a tutorial.

BACKGROUND: Data on survival endpoints are usually summarised using either hazard ratio, cumulative number of events, or median survival statistics. Network meta-analysis, an extension of traditional pairwise meta-analysis, is typically based on a single statistic. In this case, studies which do not report the chosen statistic are excluded from the analysis which may introduce bias. METHODS: In this paper we present a tutorial illustrating how network meta-analyses of survival endpoints can combine count and hazard ratio statistics in a single analysis on the hazard ratio scale. We also describe methods for accounting for the correlations in relative treatment effects (such as hazard ratios) that arise in trials with more than two arms. Combination of count and hazard ratio data in a single analysis is achieved by estimating the cumulative hazard for each trial arm reporting count data. Correlation in relative treatment effects in multi-arm trials is preserved by converting the relative treatment effect estimates (the hazard ratios) to arm-specific outcomes (hazards). RESULTS: A worked example of an analysis of mortality data in chronic obstructive pulmonary disease (COPD) is used to illustrate the methods. The data set and WinBUGS code for fixed and random effects models are provided. CONCLUSIONS: By incorporating all data presentations in a single analysis, we avoid the potential selection bias associated with conducting an analysis for a single statistic and the potential difficulties of interpretation, misleading results and loss of available treatment comparisons associated with conducting separate analyses for different summary statistics

Vegetables and legumes in new Australasian food launches: how are they being used and are they a healthy choice?

Abstract Background Despite the health benefits of regular vegetable and legume consumption, on average Australians are consuming only half of the recommended daily intake. The reasons for this low consumption are complex, and are particularly driven by societal shifts towards convenient and ready-to-eat meal options. It is currently unknown how legumes and vegetables are being utilised in food products within the Australian context, and the nutritional value or level of processing of these products. Methods The Mintel Global New Food Database was used to identify all new products launched between May 2012 and May 2017 in Australasia which at least 0.5 serves of vegetables and/or legumes per recommended serving. Eligible products were coded using the NOVA food classification system and the Healthy Choices guidelines, and were categorized by the researchers based on the type and proportion of vegetable and legume ingredients used. Results Overall, 1313 products were identified, which contained a median of 55% vegetable and legume ingredients (IQR = 45%). This translated to approximately 1 (IQR = 1) serves of vegetables and legumes per recommended serving of the products. The product launches were most likely to be classified as an ‘amber’ choice, and be classified as either ‘processed’ or ‘ultra-processed’. Vegetables and legumes were mainly found in the form of new prepared meals, soups or whole vegetables products, however there were some more innovative uses of these ingredients, such as yoghurts and pastas. Conclusions Most of the new products currently released onto the Australian market which contain vegetable and legume ingredients do not provide meaningful amounts of these ingredients, and tend to be highly processed and unhealthier options. A multi-faceted approach is needed to improve vegetable and legume consumption, which includes improving the availability of products which help consumers to meet vegetable and legume consumption recommendations. Future research should consider the acceptability of these products to consumers, and the barriers for food manufacturers in creating products with a higher amount of vegetables and legumes

Practical metrics for establishing the health benefits of research to support research prioritisation

Introduction : We present practical metrics for estimating the expected health benefits of specific research proposals. These can be used by research funders, researchers, and health-care decision makers within low- and middle-income countries (LMICs) to support evidence-based research prioritisation. Methods : The methods require three key assessments: (1) the current level of uncertainty around the endpoints the proposed study will measure; (2) how uncertainty impacts on the health benefits and costs of health-care programmes; and (3) the health opportunity costs imposed by programme costs. Research is valuable because it can improve health by informing the choice of which programmes should be implemented. We provide a Microsoft Excel tool to allow readers to generate estimates of the health benefits of research studies based on these three assessments. The tool can be populated using clinical studies, existing cost-effectiveness models and expert opinion. Where such evidence is not available, the tool can quantify the value of research under different assumptions. Estimates of the health benefits of research can be considered alongside research costs, and the consequences of delaying implementation until research reports, to determine whether research is worthwhile. We illustrate the method using a case study of research on HIV self-testing programmes in Malawi. This analysis combines data from the literature with outputs from the HIV synthesis model. Results : For this case study we found a costing study that could be completed and inform decision making within one year offered the highest health benefits (67,000 DALYs averted). Research on outcomes improved population health to a lesser extent (12,000 DALYs averted) and only if carried out alongside programme implementation. Conclusion : Our work provides a method for estimating the health benefits of research in a practical and timely fashion. This can be used to support accountable use of research funds

Estimating the shares of the value of branded pharmaceuticals accruing to manufacturers and to patients served by health systems

Previous studies have estimated that patients served by health systems accrue 59-98% of the value generated by new pharmaceuticals. This has led to questions about whether sufficient returns accrue to manufacturers to incentivize socially optimal levels of R&D. These studies have not, however, fully reflected the health opportunity costs imposed by payments for branded pharmaceuticals. We present a framework for estimating how the value generated by new branded pharmaceuticals is shared. We quantify value in net health effects and account for benefits and health opportunity costs in the patent period and post-patent period when generic/biosimilar products become available. We apply the framework to 12 National Institute for Health and Care Excellence appraisals and show that realized net health effects range from losses of 160%, to gains of 94%, of the potential net health benefits available. In many cases, even in the long run, the benefits of new medicines are not sufficient to offset the opportunity costs of payments to manufacturers, and approval is expected to reduce population health. This cannot be dynamically efficient as it incentivizes future innovation at prices which will also reduce population health. Further work should consider how to reflect these findings in reimbursement policies