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    Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. 

    GBD 2017 Risk Factor Collaborators
    The Lancet 2018; 392(10159) p.1923-1994
    BACKGROUND: The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 comparative risk assessment (CRA) is a comprehensive approach to risk factor quantification that offers a useful tool for synthesising evidence on risks and risk-outcome associations. With each annual GBD study, we update the GBD CRA to incorporate improved methods, new risks and risk-outcome pairs, and new data on risk exposure levels and risk-outcome associations. METHODS: We used the CRA framework developed for previous iterations of GBD to estimate levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs), by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or groups of risks from 1990 to 2017. This study included 476 risk-outcome pairs that met the GBD study criteria for convincing or probable evidence of causation. We extracted relative risk and exposure estimates from 46 749 randomised controlled trials, cohort studies, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. Using the counterfactual scenario of theoretical minimum risk exposure level (TMREL), we estimated the portion of deaths and DALYs that could be attributed to a given risk. We explored the relationship between development and risk exposure by modelling the relationship between the Socio-demographic Index (SDI) and risk-weighted exposure prevalence and estimated expected levels of exposure and risk-attributable burden by SDI. Finally, we explored temporal changes in risk-attributable DALYs by decomposing those changes into six main component drivers of change as follows: (1) population growth; (2) changes in population age structures; (3) changes in exposure to environmental and occupational risks; (4) changes in exposure to behavioural risks; (5) changes in exposure to metabolic risks; and (6) changes due to all other factors, approximated as the risk-deleted death and DALY rates, where the risk-deleted rate is the rate that would be observed had we reduced the exposure levels to the TMREL for all risk factors included in GBD 2017. FINDINGS: In 2017, 34·1 million (95% uncertainty interval [UI] 33·3-35·0) deaths and 1·21 billion (1·14-1·28) DALYs were attributable to GBD risk factors. Globally, 61·0% (59·6-62·4) of deaths and 48·3% (46·3-50·2) of DALYs were attributed to the GBD 2017 risk factors. When ranked by risk-attributable DALYs, high systolic blood pressure (SBP) was the leading risk factor, accounting for 10·4 million (9·39-11·5) deaths and 218 million (198-237) DALYs, followed by smoking (7·10 million [6·83-7·37] deaths and 182 million [173-193] DALYs), high fasting plasma glucose (6·53 million [5·23-8·23] deaths and 171 million [144-201] DALYs), high body-mass index (BMI; 4·72 million [2·99-6·70] deaths and 148 million [98·6-202] DALYs), and short gestation for birthweight (1·43 million [1·36-1·51] deaths and 139 million [131-147] DALYs). In total, risk-attributable DALYs declined by 4·9% (3·3-6·5) between 2007 and 2017. In the absence of demographic changes (ie, population growth and ageing), changes in risk exposure and risk-deleted DALYs would have led to a 23·5% decline in DALYs during that period. Conversely, in the absence of changes in risk exposure and risk-deleted DALYs, demographic changes would have led to an 18·6% increase in DALYs during that period. The ratios of observed risk exposure levels to exposure levels expected based on SDI (O/E ratios) increased globally for unsafe drinking water and household air pollution between 1990 and 2017. This result suggests that development is occurring more rapidly than are changes in the underlying risk structure in a population. Conversely, nearly universal declines in O/E ratios for smoking and alcohol use indicate that, for a given SDI, exposure to these risks is declining. In 2017, the leading Level 4 risk factor for age-standardised DALY rates was high SBP in four super-regions: central Europe, eastern Europe, and central Asia; north Africa and Middle East; south Asia; and southeast Asia, east Asia, and Oceania. The leading risk factor in the high-income super-region was smoking, in Latin America and Caribbean was high BMI, and in sub-Saharan Africa was unsafe sex. O/E ratios for unsafe sex in sub-Saharan Africa were notably high, and those for alcohol use in north Africa and the Middle East were notably low. INTERPRETATION: By quantifying levels and trends in exposures to risk factors and the resulting disease burden, this assessment offers insight into where past policy and programme efforts might have been successful and highlights current priorities for public health action. Decreases in behavioural, environmental, and occupational risks have largely offset the effects of population growth and ageing, in relation to trends in absolute burden. Conversely, the combination of increasing metabolic risks and population ageing will probably continue to drive the increasing trends in non-communicable diseases at the global level, which presents both a public health challenge and opportunity. We see considerable spatiotemporal heterogeneity in levels of risk exposure and risk-attributable burden. Although levels of development underlie some of this heterogeneity, O/E ratios show risks for which countries are overperforming or underperforming relative to their level of development. As such, these ratios provide a benchmarking tool to help to focus local decision making. Our findings reinforce the importance of both risk exposure monitoring and epidemiological research to assess causal connections between risks and health outcomes, and they highlight the usefulness of the GBD study in synthesising data to draw comprehensive and robust conclusions that help to inform good policy and strategic health planning. FUNDING: Bill & Melinda Gates Foundation.
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    Laboratory Experiments of Tradable Development Rights: A Synthesis of Different Treatments 

    Proeger, Till; Meub, Lukas; Bizer, Kilian
    Sustainability 2018; 10(6): Art. 1972
    Tradable development rights (TDR) are considered by scholars and regulators in various countries as a means of reducing land consumption efficiently. Similar to the development of CO2-certificate trading schemes, the methodology of experimental economics can be used to derive empirical evidence on the core parameters and problems of TDR schemes, thus extending theoretical modelling and evidence from case studies. Building on a common laboratory experimental framework, we discuss results from five distinct experiments that consider mechanisms of allocation, resilience against external shocks, political business cycles, communication and collusion, and risk. These results provide initial empirical directions for the further study and introduction of TDR schemes for managing and reducing environmental issues related to land consumption for building projects.
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    Health care service provision in Europe and regional diversity: a stochastic metafrontier approach 

    Schley, Katharina
    2018; 8(1): Art. 11
    In the last decades, demographic change coupled with new and expensive medical innovations have put most health care systems in developed countries under financial pressure. Therefore, ensuring efficient service provision is essential for a sustainable health care system. This paper investigates the performance of regional health care services in six West European countries between 2005 and 2014. We apply a stochastic metafrontier model to capture the different conditions in the health care systems in the countries within the European Union. By means of this approach, it is possible to detect performance differences in the European health care systems subject to different conditions and technologies relative to the potential technology available. The results indicate that regional deprivation plays a key role for the efficiency of health care provision. Furthermore, a pooled model which assumes a similar technology for all countries cannot sufficiently account for differences between countries. Surprisingly, the Scandinavian regions lag behind other regions with respect to the metafrontier.
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    Model selection in semiparametric expectile regression 

    Spiegel, Elmar; Sobotka, Fabian; Kneib, Thomas
    Electronic Journal of Statistics 2017; 11(2) p.3008-3038
    Ordinary least squares regression focuses on the expected response and strongly depends on the assumption of normally distributed errors for inferences. An approach to overcome these restrictions is expectile regression, where no distributional assumption is made but rather the whole distribution of the response is described in terms of covariates. This is similar to quantile regression, but expectiles provide a convenient generalization of the arithmetic mean while quantiles are a generalization of the median. To analyze more complex data structures where purely linear predictors are no longer sufficient, semiparametric regression methods have been introduced for both ordinary least squares and expectile regression. However, with increasing complexity of the data and the regression structure, the selection of the true covariates and their effects becomes even more important than in standard regression models. Therefore we introduce several approaches depending on selection criteria and shrinkage methods to perform model selection in semiparametric expectile regression. Moreover, we propose a joint approach for model selection based on several asymmetries simultaneously to deal with the special feature that expectile regression estimates the complete distribution of the response. Furthermore, to distinguish between linear and smooth predictors, we split nonlinear effects into the purely linear trend and the deviation from this trend. All selection methods are compared with the benchmark of functional gradient descent boosting in a simulation study and applied to determine the relevant covariates when studying childhood malnutrition in Peru.
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    Updated Nomogram Incorporating Percentage of Positive Cores to Predict Probability of Lymph Node Invasion in Prostate Cancer Patients Undergoing Sentinel Lymph Node Dissection. 

    Winter, Alexander; Kneib, Thomas; Wasylow, Clara; Reinhardt, Lena; Henke, Rolf-Peter; Engels, Svenja; Gerullis, Holger; Wawroschek, Friedhelm
    Journal of Cancer 2017; 8(14) p.2692-2698
    Objectives: To update the first sentinel nomogram predicting the presence of lymph node invasion (LNI) in prostate cancer patients undergoing sentinel lymph node dissection (sPLND), taking into account the percentage of positive cores. Patients and Methods: Analysis included 1,870 prostate cancer patients who underwent radioisotope-guided sPLND and retropubic radical prostatectomy. Prostate-specific antigen (PSA), clinical T category, primary and secondary biopsy Gleason grade, and percentage of positive cores were included in univariate and multivariate logistic regression models predicting LNI, and constituted the basis for the regression coefficient-based nomogram. Bootstrapping was applied to generate 95% confidence intervals for predicted probabilities. The area under the receiver operator characteristic curve (AUC) was obtained to quantify accuracy. Results: Median PSA was 7.68 ng/ml (interquartile range (IQR) 5.5-12.3). The number of lymph nodes removed was 10 (IQR 7-13). Overall, 352 patients (18.8%) had LNI. All preoperative prostate cancer characteristics differed significantly between LNI-positive and LNI-negative patients (P<0.001). In univariate accuracy analyses, the proportion of positive cores was the foremost predictor of LNI (AUC, 77%) followed by PSA (71.1%), clinical T category (69.9%), and primary and secondary Gleason grade (66.6% and 61.3%, respectively). For multivariate logistic regression models, all parameters were independent predictors of LNI (P<0.001). The nomogram exhibited a high predictive accuracy (AUC, 83.5%). Conclusion: The first update of the only available sentinel nomogram predicting LNI in prostate cancer patients demonstrates even better predictive accuracy and improved calibration. As an additional factor, the percentage of positive cores represents the leading predictor of LNI. This updated sentinel model should be externally validated and compared with results of extended PLND-based nomograms.
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    Supermarket purchase contributes to nutrition-related non-communicable diseases in urban Kenya. 

    Demmler, Kathrin M.; Klasen, Stephan; Nzuma, Jonathan M.; Qaim, Matin
    PloS one 2017; 12(9): Art. e0185148
    BACKGROUND: While undernutrition and related infectious diseases are still pervasive in many developing countries, the prevalence of non-communicable diseases (NCD), typically associated with high body mass index (BMI), is rapidly rising. The fast spread of supermarkets and related shifts in diets were identified as possible factors contributing to overweight and obesity in developing countries. Potential effects of supermarkets on people's health have not been analyzed up till now. OBJECTIVE: This study investigates the effects of purchasing food in supermarkets on people's BMI, as well as on health indicators such as fasting blood glucose (FBG), blood pressure (BP), and the metabolic syndrome. DESIGN: This study uses cross-section observational data from urban Kenya. Demographic, anthropometric, and bio-medical data were collected from 550 randomly selected adults. Purchasing food in supermarkets is defined as a binary variable that takes a value of one if any food was purchased in supermarkets during the last 30 days. In a robustness check, the share of food purchased in supermarkets is defined as a continuous variable. Instrumental variable regressions are applied to control for confounding factors and establish causality. RESULTS: Purchasing food in supermarkets contributes to higher BMI (+ 1.8 kg/m2) (P<0.01) and an increased probability (+ 20 percentage points) of being overweight or obese (P<0.01). Purchasing food in supermarkets also contributes to higher levels of FBG (+ 0.3 mmol/L) (P<0.01) and a higher likelihood (+ 16 percentage points) of suffering from pre-diabetes (P<0.01) and the metabolic syndrome (+ 7 percentage points) (P<0.01). Effects on BP could not be observed. CONCLUSIONS: Supermarkets and their food sales strategies seem to have direct effects on people's health. In addition to increasing overweight and obesity, supermarkets contribute to nutrition-related NCDs. Effects of supermarkets on nutrition and health can mainly be ascribed to changes in the composition of people's food choices.
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    A Conceptual Framework for the Integration of Corporate Social Responsibility and Human Resource Development Based on Lifelong Learning 

    Ketschau, Thilo
    Sustainability 2017; 9(9): Art. 1545
    Companies often see themselves as actors in a process of sustainable development that takes place in society. With this self-conception comes the challenge to act in a socially responsible way. The following paper presents a framework to integrate the concepts of Corporate Social Responsibility and Human Resource Development to create an approach that can address this responsibility. The concepts of Corporate Social Responsibility and Human Resource Development are linked by the idea of lifelong learning, incorporating concepts and ideas from the field of education into the framework, which makes it possible to examine the issue of promotion and social advancement irrespective of an individual’s social background. The article lays a foundation for the framework by describing the concepts named above and later on conceptualizes a three-part framework that helps to analyse the development of entrepreneurial structures that enable social commitment through company education. With this framework, an innovative approach to link a corporation’s social and educational engagement for mutual benefit is given an applicable form, with the immanent potential for the development of social sustainability. The research presented in this paper is purely theoretical and its results offer a connection point for practical interventions.
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    One Size Fits All? The Validity of a Composite Poverty Index Across Urban and Rural Households in South Africa 

    Steinert, Janina Isabel; Cluver, Lucie Dale; Melendez-Torres, G. J.; Vollmer, Sebastian
    Social Indicators Research
    Composite indices have been prominently used in poverty research. However, validity of these indices remains subject to debate. This paper examines the validity of a common type of composite poverty indices using data from a cross-sectional survey of 2477 households in urban and rural KwaZulu-Natal, South Africa. Multiple-group comparisons in structural equation modelling were employed for testing differences in the measurement model across urban and rural groups. The analysis revealed substantial variations between urban and rural respondents both in the conceptualisation of poverty as well as in the weights and importance assigned to individual poverty indicators. The validity of a ‘one size fits all’ measurement model can therefore not be confirmed. In consequence, it becomes virtually impossible to determine a household’s poverty level relative to the full sample. Findings from our analysis have important practical implications in nuancing how we can sensitively use composite poverty indices to identify poor people.
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    Economic growth and child malnutrition – Authors' reply 

    Vollmer, Sebastian; Harttgen, Kenneth; Subramanyam, Malavika; Finlay, Jocelyn; Klasen, Stephan; Subramanian, S V
    The Lancet Global Health 2016; 4(12): Art. e903
    not available
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    A review of the ecosystem functions in oil palm plantations, using forests as a reference system 

    Dislich, Claudia; Keyel, Alexander C.; Salecker, Jan; Kisel, Yael; Meyer, Katrin M.; Auliya, Mark; Barnes, Andrew D.; Corre, Marife D.; Darras, Kevin; Faust, Heiko; et al.
    Hess, BastianKlasen, StephanKnohl, AlexanderKreft, HolgerMeijide, AnaNurdiansyah, FuadOtten, FennaPe'er, GuySteinebach, StefanieTarigan, SuriaTölle, Merja H.Tscharntke, TejaWiegand, Kerstin
    Biological Reviews
    Oil palm plantations have expanded rapidly in recent decades. This large-scale land-use change has had great ecological, economic, and social impacts on both the areas converted to oil palm and their surroundings. However, research on the impacts of oil palm cultivation is scattered and patchy, and no clear overview exists. We address this gap through a systematic and comprehensive literature review of all ecosystem functions in oil palm plantations, including several (genetic, medicinal and ornamental resources, information functions) not included in previous systematic reviews. We compare ecosystem functions in oil palm plantations to those in forests, as the conversion of forest to oil palm is prevalent in the tropics. We find that oil palm plantations generally have reduced ecosystem functioning compared to forests: 11 out of 14 ecosystem functions show a net decrease in level of function. Some functions show decreases with potentially irreversible global impacts (e.g. reductions in gas and climate regulation, habitat and nursery functions, genetic resources, medicinal resources, and information functions). The most serious impacts occur when forest is cleared to establish new plantations, and immediately afterwards, especially on peat soils. To variable degrees, specific plantation management measures can prevent or reduce losses of some ecosystem functions (e.g. avoid illegal land clearing via fire, avoid draining of peat, use of integrated pest management, use of cover crops, mulch, and compost) and we highlight synergistic mitigation measures that can improve multiple ecosystem functions simultaneously. The only ecosystem function which increases in oil palm plantations is, unsurprisingly, the production of marketable goods. Our review highlights numerous research gaps. In particular, there are significant gaps with respect to socio-cultural information functions. Further, there is a need for more empirical data on the importance of spatial and temporal scales, such as differences among plantations in different environments, of different sizes, and of different ages, as our review has identified examples where ecosystem functions vary spatially and temporally. Finally, more research is needed on developing management practices that can offset the losses of ecosystem functions. Our findings should stimulate research to address the identified gaps, and provide a foundation for more systematic research and discussion on ways to minimize the negative impacts and maximize the positive impacts of oil palm cultivation.
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    Smoothing Parameter and Model Selection for General Smooth Models 

    Wood, Simon N.; Pya, Natalya; Säfken, Benjamin
    Journal of the American Statistical Association 2017; 111(516) p.1548-1563
    This article discusses a general framework for smoothing parameter estimation for models with regular likelihoods constructed in terms of unknown smooth functions of covariates. Gaussian random effects and parametric terms may also be present. By construction the method is numerically stable and convergent, and enables smoothing parameter uncertainty to be quantified. The latter enables us to fix a well known problem with AIC for such models, thereby improving the range of model selection tools available. The smooth functions are represented by reduced rank spline like smoothers, with associated quadratic penalties measuring function smoothness. Model estimation is by penalized likelihood maximization, where the smoothing parameters controlling the extent of penalization are estimated by Laplace approximate marginal likelihood. The methods cover, for example, generalized additive models for nonexponential family responses (e.g., beta, ordered categorical, scaled t distribution, negative binomial and Tweedie distributions), generalized additive models for location scale and shape (e.g., two stage zero inflation models, and Gaussian location-scale models), Cox proportional hazards models and multivariate additive models. The framework reduces the implementation of new model classes to the coding of some standard derivatives of the log-likelihood. Supplementary materials for this article are available online.
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    Managing risks in the Indonesian seaweed supply chain 

    Mulyati, Heti; Geldermann, Jutta
    Clean Technologies and Environmental Policy 2016; 19(1) p.175-189
    Seaweed supply chains in Indonesia, especially carrageenan and agar products, are subject to risks arising both inside the participating companies and in their external networks. Uncertainties in yield, quality, price, and infrastructure in one part of the supply chain can affect the whole chain. To ensure a sustainable seaweed industry, an appropriate supply chain risk management (SCRM) is needed. There are four critical steps in SCRM: identifying seaweed supply chains, identifying and categorizing risks, assessing risks, and mitigating risks. To identify seaweed supply chains, we conducted field research, in-depth interviews, and literature studies. The field survey was conducted in the Provinces of South Sulawesi, West Java, East Java, Banten, and West Nusa Tenggara. The seaweed supply chains were modeled by the software Umberto to get a better understanding of material and energy flows between the key members. To identify and categorize the risks, we started with the risks mentioned in the existing literature works, and then applied the Delphi method to analyze the potential risk sources, their causes, and their impacts. To assess risks, we used a semiquantitative approach through the face-to-face interviews to generate a risk map showing the likelihood, and impact of adverse events. Afterward, the risk intensity was categorized based on the value of the responses and classified into five categories: negligible, marginal, critical, most critical, and catastrophic risks. The mitigation strategies considered sustainability (environment, economy, and social) and risks criteria. Multi-criteria decision analysis was used to evaluate these strategies.
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    Development of mathematical competency in different German pre-vocational training programmes of the transition system 

    Weißeno, Simon; Seeber, Susan; Kosanke, Janna; Stange, Constanze
    Empirical Research in Vocational Education and Training 2016; 8(1)
    Background: Mathematical competency is central to life in modern society, and it is particularly important for many occupations and professions. In Germany, young people with insufficient mathematical skills experience significant difficulties securing a training position within the dual system, and subsequently, they often enrol in prevocational programmes of the transition system. Thus, the various one-year pre-vocational training programmes aim to provide support for enhancing mathematical skills. Currently, there is a lack of information regarding whether fundamental competencies are effectively developed within the context of these pre-vocational training. Methods: Therefore, this paper examines how competencies develop and are enhanced over the course of 1 year, based on data (N = 1.258) from three different 1-year pre-vocational programmes. Growth was based on a multidimensional mathematical competency construct measured at two distinct points: at the beginning and at the end of the pre-vocational training. Results and discussion: Incorporating selected background variables, the results of the stable and valid measurement indicate that, on average, mathematical competencies did not change over the course of 1 year. However, when development was considered in greater depth, a second dimension became visible. Specifically, the mathematical competencies of one group of young people were lower after completing the prevocational programme than they were before, whereas another group achieved recognizable improvements in their competencies. Keywords: Prevocational education and training, Mathematical competency, Development of mathematical competency
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    Measuring the Biodegradability of Plastic Polymers in Olive-Mill Waste Compost with an Experimental Apparatus 

    Castellani, Francesco; Esposito, Alessandro; Stanzione, Vitale; Altieri, Roberto
    Advances in Materials Science and Engineering 2016; 2016 p.1-7: Art. 6909283
    The use of biodegradable polymers is spreading in agriculture to replace those materials derived from petroleum, thus reducing the environmental concerns. However, to issue a significant assessment, biodegradation rate must be measured in case-specific standardized conditions. In accordance with ISO 14855-1, we designed and used an experimental apparatus to evaluate the biodegradation rate of three biopolymers based on renewable resources, two poly(𝜀-caprolactone) (PCL) composites, and a compatibilized polylactic acid and polybutyrate (PLA/PBAT) blend. Biodegradation tests were carried out under composting condition using mature olive-mill waste (OMW) compost as inoculum. Carbon dioxide emissions were automatically recorded by infrared gas detectors and also trapped in saturated Ba(OH)2 solution and evaluated via a standard titration method to check the results. Some of the samples reached more than 80% biodegradation in less than 20 days. Both the experimental apparatus and the OMWcompost showed to be suitable for the cases studied.
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    Spline-based procedures for dose-finding studies with active control. 

    Helms, Hans-Joachim; Benda, Norbert; Zinserling, Jörg; Kneib, Thomas; Friede, Tim
    Statistics in medicine 2015-01-30; 34(2) p.232-248
    In a dose-finding study with an active control, several doses of a new drug are compared with an established drug (the so-called active control). One goal of such studies is to characterize the dose-response relationship and to find the smallest target dose concentration d(*), which leads to the same efficacy as the active control. For this purpose, the intersection point of the mean dose-response function with the expected efficacy of the active control has to be estimated. The focus of this paper is a cubic spline-based method for deriving an estimator of the target dose without assuming a specific dose-response function. Furthermore, the construction of a spline-based bootstrap CI is described. Estimator and CI are compared with other flexible and parametric methods such as linear spline interpolation as well as maximum likelihood regression in simulation studies motivated by a real clinical trial. Also, design considerations for the cubic spline approach with focus on bias minimization are presented. Although the spline-based point estimator can be biased, designs can be chosen to minimize and reasonably limit the maximum absolute bias. Furthermore, the coverage probability of the cubic spline approach is satisfactory, especially for bias minimal designs.
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    Epidemiological and Ecological Characterization of the EHEC O104:H4 Outbreak in Hamburg, Germany, 2011. 

    Tahden, Maike; Manitz, Juliane; Baumgardt, Klaus; Fell, Gerhard; Kneib, Thomas; Hegasy, Guido
    PloS one 2016; 11(10): Art. e0164508
    In 2011, a large outbreak of entero-hemorrhagic E. coli (EHEC) and hemolytic uremic syndrome (HUS) occurred in Germany. The City of Hamburg was the first focus of the epidemic and had the highest incidences among all 16 Federal States of Germany. In this article, we present epidemiological characteristics of the Hamburg notification data. Evaluating the epicurves retrospectively, we found that the first epidemiological signal of the outbreak, which was in form of a HUS case cluster, was received by local health authorities when already 99 EHEC and 48 HUS patients had experienced their first symptoms. However, only two EHEC and seven HUS patients had been notified. Middle-aged women had the highest risk for contracting the infection in Hamburg. Furthermore, we studied timeliness of case notification in the course of the outbreak. To analyze the spatial distribution of EHEC/HUS incidences in 100 districts of Hamburg, we mapped cases' residential addresses using geographic information software. We then conducted an ecological study in order to find a statistical model identifying associations between local socio-economic factors and EHEC/HUS incidences in the epidemic. We employed a Bayesian Poisson model with covariates characterizing the Hamburg districts as well as incorporating structured and unstructured spatial effects. The Deviance Information Criterion was used for stepwise variable selection. We applied different modeling approaches by using primary data, transformed data, and preselected subsets of transformed data in order to identify socio-economic factors characterizing districts where EHEC/HUS outbreak cases had their residence.
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    Applying Binary Structured Additive Regression (STAR) for Predicting Wildfire in Galicia, Spain 

    Laura, Ríos-Pena; Carmen, Cadarso-Suárez; Thomas, Kneib; Manuel, Marey-Pérez
    Procedia Environmental Sciences 2015; 27 p.123-126
    Studies on causes and dynamics of wildfires make an important contribution to environmental. In the north of Spain, Galicia is one of the areas in which wildfires are the main cause of forest destruction. The main aim of this work is to model geographical and environmental effects on the risk of wildfires in Galicia using flexible regression techniques based on Structured Additive Regression (STAR) models. This methodology represents a new contribution to the classical logistic Generalized Linear Models (GLM) and Generalized Additive Models (GAM), commonly used in this environmental context. Their advantage lies on the flexibility of including spatial and temporal covariates, jointly with the other continuous covariates information. Moreover, these models generate maps of both structured and the unstructured effects, and they plotted separately. Working at spatial scales with a voxel resolution level of 1Km x 1Km per day, with the possibility of mapping the predictions in a color range, the binary STAR model represents an important tool for planning and management for the prevention of wildfires. Also, this statistical tool can accelerate the progress of fire behavior models that can be very useful for developing plans of prevention and firefighting.
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    Markov-switching generalized additive models 

    Langrock, Roland; Kneib, Thomas; Glennie, Richard; Michelot, Théo
    Statistics and Computing
    We consider Markov-switching regression models, i.e. models for time series regression analyses where the functional relationship between covariates and response is subject to regime switching controlled by an unobservable Markov chain. Building on the powerful hidden Markov model machinery and the methods for penalized B-splines routinely used in regression analyses, we develop a framework for nonparametrically estimating the functional form of the effect of the covariates in such a regression model, assuming an additive structure of the predictor. The resulting class of Markov-switching generalized additive models is immensely flexible, and contains as special cases the common parametric Markov-switching regression models and also generalized additive and generalized linear models. The feasibility of the suggested maximum penalized likelihood approach is demonstrated by simulation.We further illustrate the approach using two real data applications, modelling (i) how sales data depend on advertising spending and (ii) how energy price in Spain depends on the Euro/Dollar exchange rate.
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    Structured Additive Regression Models: An R Interface to BayesX 

    Umlauf, Nikolaus; Adler, Daniel; Kneib, Thomas; Lang, Stefan; Zeileis, Achim
    Journal of Statistical Software 2015; 63(21)
    Structured additive regression (STAR) models provide a exible framework for modeling possible nonlinear e ects of covariates: They contain the well established frameworks of generalized linear models and generalized additive models as special cases but also allow a wider class of e ects, e.g., for geographical or spatio-temporal data, allowing for speci - cation of complex and realistic models. BayesX is standalone software package providing software for tting general class of STAR models. Based on a comprehensive open-source regression toolbox written in C++, BayesX uses Bayesian inference for estimating STAR models based on Markov chain Monte Carlo simulation techniques, a mixed model representation of STAR models, or stepwise regression techniques combining penalized least squares estimation with model selection. BayesX not only covers models for responses from univariate exponential families, but also models from less-standard regression situations such as models for multi-categorical responses with either ordered or unordered categories, continuous time survival data, or continuous time multi-state models. This paper presents a new fully interactive R interface to BayesX: the R package R2BayesX. With the new package, STAR models can be conveniently speci ed using R's formula language (with some extended terms), tted using the BayesX binary, represented in R with objects of suitable classes, and nally printed/summarized/plotted. This makes BayesX much more accessible to users familiar with R and adds extensive graphics capabilities for visualizing tted STAR models. Furthermore, R2BayesX complements the already impressive capabilities for semiparametric regression in R by a comprehensive toolbox comprising in particular more complex response types and alternative inferential procedures such as simulation-based Bayesian inference.
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    Laboratory experiments in innovation research: a methodological overview and a review of the current literature 

    Brüggemann, Julia; Bizer, Kilian
    Journal of Innovation and Entrepreneurship 2016; 5(1)
    Innovation research has developed a broad set of methodological approaches in recent decades. In this paper, we propose laboratory experiments as a fruitful methodological addition to the existing methods in innovation research. Therefore, we provide an overview of the existing methods, discuss the advantages and limitations of laboratory experiments, and review experimental studies dealing with different fields of innovation policy, namely intellectual property rights, financial instruments, payment schemes, and R&D competition. These studies show that laboratory experiments can fruitfully complement the established methods in innovation research and provide novel empirical evidence by creating and analyzing counterfactual situations.
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