Governments increasingly require policy documents to be evidence-based. This paper analyses the use of scientific evidence in such documents by reviewing reports from government-appointed committees in Norway to assess the committees' handling of questions of effect.This study uses the 'Index of Scientific Quality' (ISQ) to analyse all Norwegian official reports (NOUs) that were: (1) published by the Norwegian Ministry of Health and Care Services during 1994-1998 (N = 20); and (2) concerned with questions of effect either because these were included in the mandate or as a result of the committee's interpretation of the mandate. The ISQ is based on scientific criteria common in all research concerning questions of effect. The primary outcome measure is an ISQ score on a five-point scale.Three reports were excluded because their mandates, or the committees' interpretations of them, did not address questions of effect. For the remaining 17 NOUs in our study, overall ISQ scores were low for systematic literature search and for explicit validation of research. Two reports had an average score of three or higher, while scores for five other reports were not far behind. How committees assessed the relevant factors was often unclear.The reports' evaluations of health evidence in relation to questions of effect lacked transparency and, overall, showed little use of systematic processes. A systematic, explicit and transparent approach, following the standards laid down in the ISQ, may help generate the evidence-based decision-making that Norway, the UK, the EU and the WHO desire and seek. However, policy-makers may find the ISQ criteria for assessing the scientific quality of a report too narrow to adequately inform policy-making. Nearly 30 years ago, Lindblom and Cohen stated: '... in public policy making, many suppliers and users of social research are dissatisfied, the former because they are not listened to, the latter because they do not hear much they want to listen to' . Twenty A call for evidence-based policy-making is also a call for the use of scientific methods in data collection and in the validation of information. Whenever policies are discussed, the question is often simply, "What will work?" Accordingly, when making priorities and formulating healthcare policies, a key factor will be evidence of their effects. A transparent and explicit approach is essential for reliable, valid and rational decision-making.In interview studies, policy-makers state that three factors will facilitate their use of scientific evidence in policy decisions: high quality research; the inclusion of data on effectiveness; and summaries with clear recommendations ,15-18. IPrevious studies of health policy documents commissioned in the UK and elsewhere and by the WHO have investigated the consistency of suggested policies with evidence gathered in the field ,19-23. SIn 2002, the Norwegian government stated that it wished to adopt the evidence-based approach to policy-making. In NorwAll NOUs published by the Norwegian Department of Health and Care Services during 1994-1998 were collected and those that reported evidence of health effects were included in this study. Of the 20 NOUs published during the study period, three were excluded either because the mandates did not address questions of effect or because the committees did not interpret the mandates as doing so, resulting in a total of 17 analysed NOUs. A full list of titles appears in Table Each NOU included within the study was circulated between four researchers (see the acknowledgements at the end of the paper) until general agreement was reached regarding the relevant questions of effect in the mandate or the committee's interpretation of the mandate. The same researchers then worked in pairs to score all the reports using a predefined set of evaluation questions. Group discussions were then held to resolve opposing views. We also analysed the composition of each committee, i.e., the relative importance of the roles of researchers, policy-makers and representatives from interest organisations.The predefined questionnaire for evaluating the NOUs consisted of four main components table .The first component, concerning the mandate's description of the task, was included to clarify whether the committee was supposed to evaluate evidence of effect. The purpose of the second component, concerning the strategy used by the committee to gather research information, was to ascertain whether the intentions of the committee were research-based, how the committee proceeded to gather information, and the sources of its data.The third component concerned the use of evidence in the report's recommendations and summary. Research suggests that policy-makers often rely heavily on these aspects of reports, making an assessment of their scientific quality particularly important . The speThe fourth component concerned the quality of the scientific evidence. To assess the use of scientific evidence in the reports, we applied the Index of Scientific Quality (ISQ) ,26. The The index is based on a five-point scale, with a score of 5 representing the highest level of scientific quality. According to the ISQ criteria for documentation (question B), clear references to the evidence will score 4 or 5, while partly unclear or definitely unclear references to evidence will score 2 or 3, depending upon the degree of uncertainty. When references to evidence are potentially misleading, the ISQ score will be 1 or 2. Table Previously the ISQ has mainly been used by studies evaluating health reporting in newspapers. We know of no other studies that have used the ISQ to evaluate governmental reports. A few reservations are relevant regarding the use of the index in this context. Firstly, public policy formation is clearly much more complicated than the discussion here might suggest. In addition to scientific evidence, policy-makers need to weigh up different factors such as the values of stakeholders, budgets, cost-effectiveness, broader perspectives, political decisions and aims, other, perhaps more relevant, evidence than the existing evidence of effect, prudent practices, and commonsense understandings that are obvious, but not supported by research evidence. As a consequence, evidence of effect will only form one aspect of the decision-making process when evidence-based policies are made ,27. DiscSecondly, questions may be raised regarding the sensibility of the ISQ. A comprehensive sensibility analysis of the ISQ ,26 foundThirdly, a more contextual evaluation of the variations in scores, or treating each NOU as a separate case, could give important insights into the nature of the variations. An example of this problem arises in connection with one of the NOUs about the Norwegian cancer programme, where narrow questions of effect can only make a minor contribution to a broad mandate.Finally, the analysed reports are not the most recent ones, so they may not be representative for recent NOUs. And a move to promote evidence-based policy making in Norway appears to have been made in January 2004, with the formation of an institution called The Norwegian Knowledge Centre for the Health Services (NKCHS). The Centre is organised and funded by the Norwegian Directorate of Health. NKCHS is meant to be scientifically and professionally independent. The Centre has two overarching tasks: The first is to promote evidence-based policy making, by producing health technology assessment reports, systematic reviews, overviews and reports that provide policy makers with early warnings. The second task is to support health services at all levels to incorporate evidence into their practices.The NOUs published during 1994-1998 seem to be representative of the 19 NOUs published from 1999 to the present. There have been no significant changes in the formal framework for preparing these reports, although some minor revisions were implemented in 2005. Only a shallow investigation of the 19 NOUs published since 1999 is necessary to confirm that when committees interpret their mandates and design their reports, they do not utilize a systematic approach that is similar to the ISQ or familiar evidence-based approaches. Generally speaking it is still the government-appointed committee that determines the methods and procedures used to produce the report. Only one of the NOUs published after 1998 reported that the committee preparing the report asked the NKCHS for a review of the relevant research. The NKCHS responded by stating that such a report would take the Centre from 6 to 24 months to produce. The committee deemed this to be beyond the framework provided to it to complete its work.The themes of the reports varied from broad policy issues, such as the setting of healthcare priorities, to narrower issues such as the health consequences of particular stimuli . Some reports spanned a number of topics, such as those covering health and social services among the Sami population or public health services in local communities.Some mandates specified objectives relating to the organisation of care, e.g., 'How should care be organised to give the best medical and social outcomes for patients with multiple sclerosis?' Other mandates posed more concrete questions: 'Will dramatic experiences during war cause long-term psychiatric problems in soldiers?' 'What is the effect of directly observed treatment for patients suffering from tuberculosis?' The mandates were short, generally a half to one page in length. Most mandates required the committee to consider the following issues: need (15); the effectiveness of relevant interventions (16); and economic implications (15). None of the mandates specified the type or quality of data required. Six of the 17 mandates analysed required a discussion of relevant values, such as the preferences of patients, public considerations, or juridical or ethical consequences.Some committees consisted mainly of experts in the relevant field, while other committees consulted external experts and included their recommendations as appendices to their reports. Expert participation on the committees did not appear to influence the strategy used for information gathering. While all the reports but one referred to reliance upon research, none of the reports described the procedures or literature searches used to gather research information on the harm or efficacy of relevant interventions. Although one report (on electromagnetic fields) stated that it included all the relevant literature, it provided no information about how the literature search had been conducted. Another report (on complementary medicine) stated that various organisations had been asked to provide relevant publications, but did not state the type of literature requested.Component 3 in table The 17 NOUs were evaluated through the application of seven criteria to assess the quality of scientific evidence. This produced 119 evaluations distributed on a five-point scale. The scores for all 119 evaluations appear in Figure In general, evidence of harm and evidence of effectiveness were presented in combination with information about preferences and problems concerning access to, and availability of, health services. Fifty of the 119 evaluations (42 per cent) resulted in scores ranging between 3 and 5. Only 15 evaluations (13 per cent) resulted in scores exceeding 4.0. Sixty-nine of the 119 evaluations (58 per cent) only scored 1 to 2. In short, these results suggest that more than half of the evaluations identified insufficient descriptions and assessments of the quality of evidence of effect.Figure the specific population for whom the report was relevant. Question A produced an average score of 3.1, the only average score to exceed 3.0. While most reports were partly unclear about the population for whom they were relevant, many also provided important and relevant information. Question B asked whether the presented evidence relied on research and whether the reports referred properly to the research. The average score here was 2.3. Only two reports specified references for the most important information.Question A asked whether the report made clear the assessment of study quality. Only two reports provided details of study design and only one described how the committee had assessed the quality of the evidence. The average score here was 1.5. Question D concerned the reporting of the size of the effect and produced an average score of 2.1. Question E asked about the precision with which the effect had been measured and produced an average score of 1.6. Many reports described interventions as 'effective', but none reported the magnitude of the most significant effects. Questions C and E produced the lowest scores, both with an average slightly exceeding 1.5.Question C concerned consistency between studies). The average score for question F was 2.2. We identified discussions concerning more than one study in five reports. These were awarded scores of 3. The other 11 reports scored only 1 or 2 as they provided either potentially misleading information or no information at all about consistency between studies. As a general comment, committees that, for example, only identify a single research study relevant to their report should at least discuss whether this is the result of a low-quality literature search. In cases where there is only one study dealing with a given question of effect, an evidence-based evaluation should include an assessment of the robustness of the evidence.Only one report attained a score of 5 for its consideration of how the evidence presented related to other findings in the field  were above or just under 3.0. These reports generally provided important scientific information regarding questions of effect. Only one report had an average score exceeding 4.0, while one other report scored above 3.0. There was a wide range of average scores, since while one report failed to describe the effectiveness of the services suggested and therefore scored 1 on all questions, another received high scores all round.Systematic reviews of the outcomes of randomised controlled trials have probably been the key element in the development of evidence-based medicine. Systematic reviews assess search strategies and carry out critical appraisals. The ISQ is based upon the same principles as those underlying systematic reviews. Ministries, the government, and parliament rely on NOUs to provide the most comprehensive advice on subjects of national importance. Our analysis suggests that more than half of NOUs dealing with healthcare contain inadequate descriptions and assessments of the quality of the evidence of effect. We are not suggesting that NOUs provide the public with low-quality reports, merely that their scientific quality regarding questions of effect is low.Our study suggests that committees prioritise policy relevance, as the NOUs included in the study primarily focused on policy relevance and policy implications. In both respects we found the reports' use of scientific evidence to be generally mediocre. We identified a lack of clarity regarding both the reports' primary intended audience and the identification and assessment of the main consequences of the study. There was insufficient treatment of validity and precision in relation to hard science questions. This may arise from a belief among the authors of reports that policy-makers are uninterested in the science underlying committee recommendations. Committees may also not consider their time and resources best spent teaching policy-makers about scientific standards.It is impossible to be sure why committees give lower priority to hard science questions, since we have no data from interviews with committee members. We do know, however, from a systematic review of interview studies concerning health policy-makers' perceptions of their use of evidence, that policy-makers want summaries that make clear recommendations . The comThe fact that seven reports obtained an average ISQ score of below 2.0 suggests that committees give insufficient attention to the assessment of scientific evidence. In other words, policy-makers must look elsewhere for transparent descriptions of search strategies for literature, references to evidence gathered in the field, and assessments of the evidence of effect. Our raw data indicates that questions of effect escaped the committees' attention in three of the seven reports, making their ISQ scores of 1 or 2 hardly surprising. We have no information about why some of the committees largely avoided questions of effect.The mandates typically posed broad questions, rather than clear-cut questions of effect. Although NOUs were only included in our study if questions of effect were relevant to their mandates, these questions of effect were sometimes rather implicit and were always connected to broader issues and to a broader mandate. Since NOUs are intended to deal with questions of national importance, their mandates will naturally be broad. It is easy to imagine the broad mandate for the NOU report 'Care and knowledge: the Norwegian Cancer Programme', which deals with a variety of topics, such as: definitions of cancer; cancer as a national problem; the social consequences of cancer; care structures and processes for cancer sufferers; and the administrative and organisational consequences. Within the time span allotted for producing a report of this nature, it is very difficult for a committee to carry out a comprehensive review of questions of effect.When reviewing cancer treatments, it is also of course difficult to decide where to focus. Even if there is a decision to focus on breast cancer , it will still be necessary to select from the many different questions of effect in the treatment of breast cancer. Even a narrow focus on 10 different questions of effect with a protocol in the Cochrane library will still exclude more than 50 other protocol questions in the same database . Let us Politicians, scientists, bureaucrats, experts and lay people often have (quite legitimately) differing interests. If public governmental reports refer to evidence in an explicit and transparent way, readers will largely be able to make reliable judgments by comparing the pros and cons apparent from the evidence. Explicit and transparent references to supporting evidence will assist in the identification of bias rooted in special interests in the recommendations of governmental reports.This study shows that NOUs, in relation to their evaluation of health effects, lack transparency and, overall, show few signs of the use of systematic processes as far as scientific quality is concerned. A systematic, explicit and transparent approach - in fact, an approach that would generate a high ISQ score - may encourage the development of the evidence-based decision-making that Norway and other nations and international institutions desire and seek.Some people might object that applying the ISQ criteria would overly narrow the focus of reports and cause the omission of other kinds of information more relevant for policy-makers: instead of promoting democratic debate, application of the ISQ criteria might lead to meritocracy. However, such objections would be convincing only if reports scoring highly on the ISQ criteria were inherently difficult for politicians, bureaucrats, experts and lay people to understand. Instead of rejecting out of hand the possibility of writing understandable reports of high scientific quality, committees should focus on writing reports that are accessible to academics and non-academics alike. Committees that include experts as well as non-experts should make particular efforts in this regard. One possible solution would be to prepare separate reports for different audiences. The process of doing so might help clarify the role of scientists in such committees and protect researchers from having their results abused ,43.Research is a cumulative enterprise. Whether the ISQ is useful for policy-making, and whether a narrow focus on questions of effect helps policy-makers make wise decisions remains to be seen. The ISQ cannot and should not exclude politics from evidence-based policy-making. But if calls by governments for evidence-based policy-making are to be taken seriously, we need to develop and test evidence-based approaches such as those endorsed by the ISQ so that evidence-based policy may progress from aspiration to reality.EU: European Union; ISQ: Index of Scientific Quality; NKCHS: The Norwegian Knowledge Centre for the Health Services; NOU: Norwegian Official Reports (Norges Offentlige Utredninger); WHO: World Health Organisation.The author declares that he has no competing interests.The pre-publication history for this paper can be accessed here:


A major goal of computational studies of gene regulation is to accurately predict the expression of genes based on the cis-regulatory content of their promoters. The development of computational methods to decode the interactions among cis-regulatory elements has been slow, in part, because it is difficult to know, without extensive experimental validation, whether a particular method identifies the correct cis-regulatory interactions that underlie a given set of expression data. There is an urgent need for test expression data in which the interactions among cis-regulatory sites that produce the data are known. The ability to rapidly generate such data sets would facilitate the development and comparison of computational methods that predict gene expression patterns from promoter sequence.We developed a gene expression simulator which generates expression data using user-defined interactions between cis-regulatory sites. The simulator can incorporate additive, cooperative, competitive, and synergistic interactions between regulatory elements. Constraints on the spacing, distance, and orientation of regulatory elements and their interactions may also be defined and Gaussian noise can be added to the expression values. The simulator allows for a data transformation that simulates the sigmoid shape of expression levels from real promoters. We found good agreement between sets of simulated promoters and predicted regulatory modules from real expression data. We present several data sets that may be useful for testing new methodologies for predicting gene expression from promoter sequence.We developed a flexible gene expression simulator that rapidly generates large numbers of simulated promoters and their corresponding transcriptional output based on specified interactions between cis-regulatory sites. When appropriate rule sets are used, the data generated by our simulator faithfully reproduces experimentally derived data sets. We anticipate that using simulated gene expression data sets will facilitate the direct comparison of computational strategies to predict gene expression from promoter sequence. The source code is available online and as additional material. The test sets are available as additional material. Transcriptional regulation of genes is controlled largely through the concerted action of combinations of cis-regulatory sites in the promoters and surrounding regulatory DNA of genes. The interactions between cis-regulatory sites can be complex and may include synergistic , competiComputational approaches for determining the cis-regulatory code include multiple regression models , BayesiaSimulators are playing a useful role in reconstructing gene regulatory networks (GRN). A GRN models the regulatory connections between genes, as opposed to the interactions between cis-regulatory sites in a promoter. Because the true GRN of a cell is not known, artificially created GRNs are used to evaluate the accuracy of algorithms that attempt to determine network architecture and dynamics . GRN simRegulatory Logic Simulator), are available for download [We present a regulatory rule simulator that generates random promoters and produces expression data based on user-defined interactions between cis-elements. Whereas a GRN simulator attempts to create a web of genes connected in a biologically relevant manner, our simulator generates promoter regions and predicts the expression from those promoters. We also present test datasets, created by the simulator, which can be used to assess the performance of algorithms that attempt to determine underlying regulatory rules. The promoter generator and simulator, named ReLoS . A rule may simply specify the additive contribution of a particular regulatory element, or it may determine the parameters of an epistatic  interaction between elements. Promoters are parsed by each rule in the order in which the rules are specified. When a rule matches a promoter, however, that rule may specify a set of rules which should be skipped in the analysis of the matched promoter.Promoter processing by rules is delegated to the "analyzer". The analyzer is responsible for determining whether a rule will affect a promoter, based on the constraints specified for the rule. The analyzer is also responsible for specifying the effect of a rule on the expression of a promoter. Analyzers serve as the central point of extensibility in Relos. For each rule, it is possible to specify a custom analyzer. Relos comes with a regular expression analyzer, which modifies promoter expression if the regular expression is matched. Another analyzer allows user-defined mathematical functions to be used to determine rule outputs. For example, a Hill function ,17 mightReal expression data are bounded. At the lower bound, a cell cannot express less than zero copies of a gene. There is also an upper limit of detection in any experimental setup and to the levels of RNA that can be produced when a promoter is fully occupied by the transcriptional machinery and transcribing at the maximum rate. These constraints produce sigmoid expression patterns. For this reason, Relos allows users to sigmoidally transform the output data. Users may explicitly tell Relos to transform the data. In this case, Relos uses a sigmoid transformation centered on the average expression for the simulation (see methods). Using the simulation expression mean to center the transformation allows rule-sets to be compared in terms of the variation present in the parsed promoters. Simulations with large variance will show a spread of values between zero and one. Simulations with little variance will, when transformed, cluster around the value of 0.5. One consequence of the mean-dependent transformation is that it is impossible to generate a transformed dataset in which all expression is either "on" or "off" since datasets with very little variation will result in midline expression when transformed. Users may therefore specify a rule at the end of the pipeline employing a custom analyzer to transform the data. Relos comes with a SigmoidalTransform analyzer (see Methods) that can be used for this purpose, but users may also provide their own transformations. The SigmoidalTransform analyzer uses four parameters (see Methods) to adjust the shape and scale of the transformation. These parameters are independent of the simulation dataset and determine an absolute scale of expression onto which all rule-sets are mapped. By using a consistent set of parameters, users can compare rule-sets with regard to their strength of expression and compare variances according to where the mean lies in the absolute expression scale. Since this transformation does not depend on the dataset, the absolute scale is arbitrarily determined by the choice of parameters and users should be careful to use rules consistent with the scale determined by the parameters.In addition to rules, their analyzers and constraints, and transformation parameters, the XML simulation file contains other adjustable attributes for the simulation. For example, after the promoters have been interpreted using the current rule set, Gaussian noise is added by the simulator with a user defined standard deviation. Relos is also capable of generating random promoters based on user-defined properties, such as promoter length, cis-regulatory elements and their frequencies and outputting promoters in either fasta or Relos format. These synthetic promoters can be used directly by the simulator. For more details, see Methods.Examples of simulated datasets are shown in Figure x is the input and ϕ and n are parameters used to adjust the location and steepness of the transition. Hill functions have been used to model biological cooperativity in proteins such as Hemoglobin [x is the number of cooperative elements, n is 3, and ϕ is 5. Since the expression is a function of the number of A-elements, and the number of A-elements is distributed according to the Poisson distribution, the expression pattern should be a function of a Poisson distribution. As expected, the simulator output in Figure Where moglobin  and in cmoglobin . In FiguThe main motivation for creating the simulator was to synthesize expression datasets for which we know the underlying regulatory rules. These datasets will be necessary to compare the accuracy of different methods that infer cis-regulatory rules because there are no experimental datasets for which the true underlying relationships between cis-regulatory sites are known. We therefore created ten test datasets using different rule-sets. The test datasets vary in the number and types of rules and in the complexity of the rule-set. We have made the datasets and rule sets used to generate them , a sequence, and a frequency (expected occurrence). The sequence is only used for output purposes; all built-in rule processing is done on promoter elements.Relos supports two modes of promoter generation: exact length and expected length. In exact length mode, a cis-element is selected from the user-specified list of elements by a roulette wheel selection process. The selected element is added to the promoter starting from the position furthest upstream of the transcription start site. The element is added in a sense or anti-sense orientation with equal probability. Element selection and addition continues until the number of elements added equals the user-specified length. Relos does not insert spacer elements between cis-elements. Rather, all cis-elements are treated as spacer elements unless a rule is defined which uses the cis-element in a manner inconsistent with a spacer element (see Rule Specification below).In expected-length mode, the element frequencies are transformed by:Di is the transformed frequency of the i-th element, di is the non-transformed frequency for i-th element, E is the expected promoter length, and n is the number of elements. This results in a distribution of cis-elements that includes a "stop" pseudo-element with probably 1/E. The distribution sums to one and preserves the relative probabilities of the user-specified elements. Promoter elements are added as in the exact length procedure until the stop element is selected.Where Rules are specified in an XML-based format defined by the expression_rules.dtd document type definition file. Each rule is defined in terms of the cis-elements the rule uses, an optional custom analyzer to use in place of the default Relos analyzer, the "output" (the amount by which the rule will affect the current expression level for the promoter), and the "operation" (the way in which the output will affect the current expression). Rules may also define precluded rules. Precluded rules are those that are prevented from operating on a promoter should the precluding rule match. Rules using the default analyzer, or custom analyzers that rely on the default analyzer, may specify one or more conditions that determine whether a particular element on the promoter "matches" the rule. Conversely, these conditions may "exclude" elements on the promoter that should not match the rule.Conditions are comprised of the cis-element(s) to consider, the allowed position(s) and required orientation of the element(s), and zero or more contexts. Each context defines a cis-element that must appear in the promoter with the element under consideration in the condition. Contexts may include specification of the spacing between the two elements and the orientation of the "context" element.More details on rule specification can be found in additional file Relos uses a pipeline to perform rule by rule analysis of the promoters. Typically, promoters are moved through the pipeline in the order in which the rules appear in the simulation XML file. However, when a precluding rule matches a promoter, Relos prevents the precluded rules from operating on the matched promoter. Rules which define a custom analyzer delegate promoter analysis to the custom module. All other rules delegate promoter analysis to the default analyzer. The default analyzer determines the number of elements in a promoter that match the rule and multiplies the number of matches by the output amount to determine the magnitude of the effect on the current promoter expression. Promoter expression is then affected by this amount according to the operation defined for the current rule. Valid operations include add ; multiply ; exponentiate ; and replace . Matching is performed on a promoter element-wise basis. If the attributes and contexts of at least one condition and no exclusions match, an element will be considered a match. When no conditions or exclusions are specified, the element only needs to match one of the cis-elements specified by the rule.Once all promoters have been through the rule pipeline, a user-specified amount of noise is added to each promoter by replacing the current expression value with a random value X, where the probability of replacing the current expression value with X is given by the Gaussian distribution,μ is the current expression value, σ = μ*η, and η is the user defined level of noise. The Relos default sets the noise to be 5% of the current expression level.Where Relos will also transform the data to fit a sigmoidal curve if specified by the user. For each promoter, the transformed expression value is given by:VT is the transformed expression value of a particular promoter, V0 is the original expression for that promoter, and μT is the mean of the untransformed expression of all promoters in the simulation. An alternative method of transformation is provided by adding a transformation rule with a custom analyzer to the end of the pipeline. Relos provides an example of a transforming analyzer in the SigmoidalTransformAnalyzer, which transforms the data according to:Where VT is the transformed expression value, V0 is the original expression, α adjusts the slope of the curve at the inflection point, β adjusts the position of the inflection point, γ determines the expected midline expression, and ϕ scales the resulting transformation.Where More details on promoter analysis can be found in additional file Ten test-set simulations were run. Two hundred promoters, comprised of eight cis-elements selected from a pool of four possible elements (A-D), were generated for each simulation, except for test-set simulation ten. A noise level of 5% of the expression level was used. None of the datasets were subjected to upper or lower bound constraints. The first nine test-set simulation rule sets were comprised of: an additive activator, an activator with spacing and ordering constraints, two synergistic rule sets with spacing constraints, two cooperative rule sets, a dominant-negative competitive rule set, a dominant positive rule set, and a rule set with constraints on many elements and an enhancer. In the final test-set simulation, two hundred promoters were generated, each comprised of eight cis-elements selected from a pool of eight possible elements (A-H). The final simulation rule set consisted of multiple additive and non-additive effects, incorporating many of the non-additive effects encountered separately in other rule sets. For more details, see additional file S. cerevisiae. We simulated the "ribosome biogenesis", "peroxisome", "mitochondrion", "cell cycle", and "glycolysis" modules. These modules were chosen because they vary in size, expression outputs, and regulatory complexity. Promoters with no regulatory motifs were removed from the dataset, leaving 254 promoters. Tree regression [Beer and Tavazoie  classifigression  was perfWe used Relos to generate synthetic promoters based on the frequency of the motifs used in the above rule set. The frequency of each motif was determined in the 254 biological promoters as the number of times each motif occurred divided by the total number of motifs in these promoters. The frequencies of the remaining biological motifs not considered by the ruleset were conglomerated into a single "Spacer" motif  containing: the rulesets used to generate the test-set datasets (ASCII/xml); the datasets in both Relos and fasta format (ASCII); and histograms of each test-set to provide an overview of the data (PNG).Click here for fileFigure 2 Rule-sets. A compressed archive (zip) file containing the simulation files (ASCII/xml). used in the generation of figure Click here for fileRule Specification and Promoter Analysis. Detailed information on how to specify rules and how promoters are analyzed.Click here for fileSupplementary Table 1: Relos Dependencies. A listing of modules needed for Relos to run and where they can be obtained.Click here for fileRelos Source Code. A compressed archive (zip) containing the perl source for running Relos, the xml document-type definitions (DTD) which define simulation files, example simulation files, the README, and the source license (GPL).Click here for fileImage of modules from generated promoters. A "heat map" image showing the expression from the generated promoters. Promoters with only "Spacer" elements are not depicted.Click here for fileSample Ruleset for Biological Comparison. Ruleset used in one of the 255 "microarray" simulations.Click here for fileRuleset Used for Promoter Generation. The ruleset used to generate the 1000 promoters used in testing the biological relevance of Relos-generated promoters.Click here for file


We propose a technique based on independent component analysis (ICA) with constraints, applied to the rhythmic electroencephalographic (EEG) data recorded from a brain-computer interfacing (BCI) system. ICA is a technique that can decompose the recorded EEG into its underlying independent components and in BCI involving motor imagery, the aim is to isolate rhythmic activity over the sensorimotor cortex. We demonstrate that, through the technique of spectrally constrained ICA, we can learn a spatial filter suited to each individual EEG recording. This can effectively extract discriminatory information from two types of single-trial EEG data. Through the use of the ICA algorithm, the classification accuracy is improved by about 25%, on average, compared to the performance on the unpreprocessed data. This implies that this ICA technique can be reliably used to identify and extract BCI-related rhythmic activity underlying the recordings where a particular filter is learned for each subject. The high classification rate and low computational cost make it a promising algorithm for application to an online BCI system. The electroencephalogram (EEG) is a recording of thebrain's electrical activity and is one of the most important measurements usedto evaluate neurological disorders in the clinic and to investigate brainfunction in the laboratory. The recording is obtained by placing electrodes onthe scalp, generally according to the 10/20 electrode placement system depicts the range of lags . x ∈ [XP(i), is defined as a trial spectrum in ith channel bythe sum of the ordinates of the frequency bins (hd) within the proposed frequency band, that is,D denotes the number of frequency bins. After cICA decomposition, the EEG data are extractedinto the ICs. Thus, the power spectrum after cICA is defined as the sum of theweighted spectra of sources (ICs) within the μ band. So, forgiven source epochs, the power feature reflected in an individual channel isdefined ask denotes the number of sources. As this implementation of cICA assumes a square mixingmatrix, then the number of sources is the same as the number of measurementchannels, and ai, j is an element in the mixing matrix A. ak+1, j is a particularelement in the last row of A. Pic(j) denotes a trialspectrum in the jth IC source.The spectrum, r2 values which represent the proportion of the variance of spectral power values from the labelled training sets. By comparing to the averaged power spectracorresponding to two targets, this describes the relationship between power intensity and target labels. These parameters were slightly different due to differences in each individual recording. For example, in dataset IIa, twodiscriminative power bands roughly around 10–15 Hz and 23–28 Hz  in the chosen frequency band for the finalclassification. The threshold value is selected by minimizing the number oftrials misclassified in both classes from the training set for individualsubjects. In addition, as a comparison for the classification performance, wealso applied a more complex classifier, a support vector machine (SVM) which cor2 values across the topography maps in the previous section, a number of channels (between 1 and 3) around the left sensorimotor cortex were selected. The power on C3 was used in the threshold classifier and the power on C3, CP1, and CP5 for the SVMclassifier as the use of these power features was found to be able to achieve better classification accuracy in our study.The next procedure is to decide which power featurewill be suitable to use for the classification. Based on the distribution of Using the proposed method, the designed spatialfilters will be able to capture the relevant dynamics of the subject's brainstate more robustly. Furthermore, the resulting time series would haveoptimized the spectra which could result in better discrimination between twodifferent brain states. The results show that following this pre-processing,even a simple linear classifier can achieve superior classification accuracy.μ rhythmic band.Figures  and 7 deμ/β-rhythm data.Both of these datasets use the characteristic that changes in the amplitude ofsensorimotor rhythms over the right/left hemisphere act as the major control pattern.The difficulty here is to maximally and reliably identify two classes fromsingle-trial data. The proposed ICA technique using constraints has beendeveloped and applied to isolate and extract the power spectrum in the rhythmicband of interest. In order to demonstrate the performance of the proposed cICA,we only applied the power feature in the μ-rhythmfrequency band as the major classification pattern. The results, using a simplelinear classifier and an advanced SVM to classify the ICA processed data, showthat the classification accuracy has considerably increased over processing theraw data. After the basic analysis, the overall classification accuracy isimproved about 20% in dataset IIa and 30% in dataset IVa. As an additionalcomparison of classification performance to cICA in dataset IVa, we cited theresults of a method using DS features as well as an SVM in a publication. Thismethod also includes two steps for data pre-processing . The accuracy was about 3% more than the resultsof cICA with a linear classifier and 1% more than cICA with an SVM. If we use aset of well-tuned parameters to the proposed method, then the classificationwould be expected to reach a slightly better performance. Furthermore, the useof a linear classifier following a simple spatial filter as in our system isdesirable from a computational complexity perspective.Two datasets have been used to examine the performanceof the proposed algorithm. Dataset IVa concerns MI data, and dataset IIaregards the self-regulation of As this work is an application to single trialclassification, the sensitivity to artifacts in the EEG becomes a majorproblem. The LSDIAG ICA algorithm uses thecovariance of the trail data to estimate the covariance stack matrices whichare the essentials to calculate the unmixing matrix and hence the spatialfilter. The random selection of training trials with artifacts can causeserious changes to the final filter. Therefore, most methods require that the datashould be artifact-free, which can be achieved by several preprocessing stepssuch as filtering or manual artifact rejection. Here, instead of applying anypreprocessing methods before hand, we estimate the stack matrices by using theaveraged lagged covariance matrices from the data. The idea behind the processis that the influence of artifacts is reduced since the procedure of averagingthe covariance matrices acts as a filter which could balance and minimize therandom noise level. Moreover, the system includes a training phase used to tunethe proper unmixing matrix  using the proposed ICA. Once theunmixing matrix has been computed, it works as a spatial filter to remove theadditional artifacts by weighted spatial averaging the testing data trials andreturns the processed time series patterns. After filtering, the differentbrain activities in the form of power can be clearly extracted. It indicatesthat through the use of cICA, it is possible to track the rhythmic changes ofdifferent brain states in the EEG. These results show a clear improvement foruse in this kind of BCI system.In order to bring a BCI system to work outside oflaboratory conditions, several items need to be taken into account in futurework. The number of electrodes used in the system usually decides the cost ofhardware and the related difficulty of processing the ensuing data. Theapplication of ICA using fewer channels or even a single channel may be thesolution of the problem and is one area we are actively pursuing . Similar


The aim of the study was to assess the influence of annual volume and factors related to intensive care unit (ICU) organization on in-hospital mortality among patients admitted to the ICU with severe sepsis.A retrospective cohort study was conducted using the database of the Dutch National Intensive Care Evaluation (NICE) registry. Analyses were based on consecutive patients admitted between 1 January 2003 and 30 June 2005 who fulfilled criteria for severe sepsis within the first 24 hours of admission. A 13-item questionnaire was sent to all 32 ICUs across The Netherlands that participated in the NICE registry within this period in order to obtain information on ICU organization and staffing. The association between in-hospital mortality and factors related to ICU organization was investigated using logistic regression analysis, combined with generalized estimation equations to account for potential correlations of outcomes within ICUs. Correction for patient-related factors took place by including Simplified Acute Physiology Score II, age, sex and number of dysfunctioning organ systems in the analyses.P = 0.029). The presence of a medium care unit (MCU) as a step-down facility with intermediate care is associated with a higher in-hospital mortality (P = 0.013). For other items regarding ICU organization, no independent significant relationships with in-hospital mortality were found.Analyses based on 4,605 patients from 28 ICUs (questionnaire response rate 90.6%) revealed that a higher annual volume of severe sepsis patients is associated with a lower in-hospital mortality (A larger annual volume of patients with severe sepsis admitted to Dutch ICUs is associated with lower in-hospital mortality in this patient group. The presence of a MCU as a step-down facility is associated with greater in-hospital mortality. No other significant associations between in-hospital mortality and factors related to ICU organization were found. During the past decade monitoring the performance of health care providers has become common because of increased awareness of accountability and because of increased attention for optimizing quality of care and patient safety . This trThis report describes a study that made use of a national registry database to investigate the outcomes of patients admitted with severe sepsis to Dutch ICUs. These patients form an important and frequently encountered patient group in the ICU, which is known for its high mortality and consumption of resources -9. It isThis study was conducted to investigate whether variation in risk-adjusted hospital mortality in patients admitted with severe sepsis could be explained by differences in annual sepsis volume or ICU organization.The database of the Dutch National Intensive Care Evaluation (NICE) registry was used in this study. Since 1996, ICUs participating in the NICE registry have provided information on all admissions to those units, with the aim being to assess and compare the performance of the ICUs and to improve the quality of care. Per ICU admission variables are collected that describe patient characteristics, severity of illness during the first 24 hours of ICU admission, and the ICU and in-hospital mortality, and length of stay.Data collection takes place in a standardized manner according to strict definitions and is subject to stringent data quality checks . This haThe recorded variables are used to calculate probabilities of death for each patient using the Acute Physiology and Chronic Health Evaluation (APACHE) II score , the SimPatients were identified as being admitted with severe sepsis if they fulfilled the following criteria within the first 24 hours of ICU admission: confirmed infection with at least two modified Systemic Inflammatory Response Syndrome (SIRS) criteria  and at lA 13-item questionnaire was developed to obtain information on organizational factors in the ICUs. The questionnaire was developed by a medical informatician and a senior ICU physician. Subsequently, the questionnaire was tested by a panel of six senior ICU physicians involved in the NICE registry who judged the questions to be clear and unambiguous. The questionnaire is provided in Additional file Information was collected on the size of the ICU and the hospital , the numbers of intensivists and nurses, whether the ICUs had an open or closed format, at which shifts an intensive care physician was exclusively available to the ICU, and the staffing pattern . In previous studies ,17,18 thThe questionnaire was sent to the senior ICU physician responsible for the NICE registry in all ICUs participating in the registry during the study period.The relationship between volume and organizational factors and in-hospital mortality was assessed using logistic regression analyses.Before the analyses, the amount of staffing  and the number of ICU beds were used to calculate the 'number of intensivists per ICU bed' and the 'number of nurses per ICU bed'. The annual patient volume and the annual volume of patients admitted with severe sepsis were calculated based on these data. Variables that did not show sufficient variation (defined as > 90% of ICUs providing the same answer) were excluded from the regression analyses.P < 0.10 in the preceding analyses was included in a multivariate regression analysis, which was performed using a stepwise backward procedure .In the logistic regression analyses, the following modelling strategy was employed. First, to investigate the influence of patient-related factors on in-hospital mortality (which may serve as possible confounders when investigating ICU-related factors), logistic regression analysis was performed using age, sex, SAPS II score and number of dysfunctioning organ systems as covariates. In the remainder of the report this model is referred to as the 'case-mix correction' model. To measure discrimination and calibration of this model, the C index and the Hosmer-Lemeshow C statistic were calculated. Second, logistic regression analyses were performed using in-hospital mortality as the dependent variable and each of the variables related to volume or ICU organization as a covariate, together with the aforementioned possible patient-related confounding factors: age, sex, SAPS II score and number of dysfunctioning organ systems. Third, each covariate with Continuous covariates  were included in the models using the fractional polynomials method , which mThe analyses were performed using SPSS version 14.0  and SPLUS version 7.0 .During the period of study 32 ICUs were providing data to the NICE registry, thereby covering about one-third of all Dutch ICUs and more than half of all ICU beds in The Netherlands. Twenty-nine of the 32 ICUs returned the questionnaire (response rate 90.6%). One ICU did not register the variable 'confirmed infection', which impeded the selection of severe sepsis patients based on the definition given in Table Together, the responding ICUs admitted 57,765 patients within the study period, of which 23,995 (41.5%) were excluded from the analyses based on the SAPS II criteria, most of them because they were admitted after cardiac surgery. Of the remaining 33,770 patients, 4,605 (13.6%) fulfilled criteria for severe sepsis during the first 24 hours of admission. These patients were included in our study. Table P < 0.001, by Mann-Whitney U-test). Among the severe sepsis patients 1,153 (25.0%) died in the ICU and, in total, 1,599 patients (34.7%) died during hospitalization.The patients admitted with severe sepsis exhibited a higher SAPS II score, as compared with the other ICU patients . Figure The first regression analysis yielded a case-mix correction model in which age, SAPS II score and number of dysfunctioning organ systems were shown to be significantly related to in-hospital mortality. The sex of the patient was not significantly related to outcome, but was retained in the model for case-mix correction purposes. The C index for this model was 0.78 and the Hosmer-Lemeshow C statistic was 1.68  did not show sufficient variability to perform a regression analysis, because 96.4% of the ICUs provided a positive response on these variables. The annual number of patients admitted with severe sepsis and the number of intensivists per ICU bed exhibited significant relationships with hospital mortality . The covariate denoting the presence of a MCU as a step-down facility yielded a P value of 0.061, and was therefore also included in the multivariate analysis.Table The third regression analysis demonstrated significant associations of the annual number of patients admitted with severe sepsis and the availability of a MCU as a step-down unit with in-hospital mortality Table . AdmittiThe leverage analysis revealed that these findings were not attributable to the influence of individual ICUs. The confidence intervals of the parameters in the full model based on the leverage analysis were comparable to the intervals given in Table The ICUs participating within the NICE registry showed variation in RAMRs for patients admitted with severe sepsis. We studied factors related to annual volume and ICU organization for this patient group that might explain the variation in RAMRs and found that higher annual volume of patients admitted with severe sepsis was associated with a lower in-hospital mortality in this patient group. The presence of a MCU as a step-down unit increased the probability of in-hospital death for these patients.The influence of volume on outcome has been studied extensively in other clinical domains. A systematic review  found a Within the area of intensive care, volume-outcome studies have only recently begun to emerge. One study investigated the volume-outcome relation in medical ICU patients . It did A recent study  found a The results of volume-outcome studies that focused on specific surgical procedures have led to discussions on whether to assign specific procedures to high-volume centres exclusively ,30. HoweAlthough the volume-outcome effect is a major finding of the present study, the initial focus of the study was not only on the volume-outcome effect, but also on the influence of other organizational ICU characteristics on in-hospital mortality. In recent years several studies have been conducted to investigate the influence of factors related to the organization of the ICU on patient outcomes. Most of these studies were performed in a single ICU. A systematic review that evaluated 26 of these studies  found thper se is responsible for a higher mortality.The association we found between the availability of a MCU as a step-down unit in the hospital and the risk-adjusted in-hospital mortality is remarkable. Interestingly, no association between availability of a MCU as a step-down unit and hospital mortality was found when ICU mortality was used as an outcome (results not shown). The higher post-ICU mortality in hospitals with a MCU as step-down facility suggests that the changes in organization and staffing that accompany the presence of a MCU do not improve overall patient outcomes. It seems unlikely that transfer to a MCU as a step-down facility There are several possible explanations for our findings. First, it could be that ICUs without a MCU transfer their patients to another, better equipped hospital. This would shift the mortality burden from the ICU in a hospital without a MCU to an ICU in a hospital with a MCU. In our study, however, only 150 patients out of 4,605 were transferred to another ICU. When repeating the analyses excluding those patients, similar results were obtained (results not shown). Second, it could be that the presence of a MCU leads to premature patient discharge. Third, we cannot exclude the possibility that, despite our efforts, there are still differences in case-mix that are not taken into account, with the hospitals with a MCU including a patient population with a greater burden of disease. Finally, the availability of a MCU as a step down-unit may act as a confounder for another organizational aspect, possibly unrelated to the MCU, which we did not incorporate in our analyses. To our knowledge the influence of the presence of a MCU as a step-down unit on in-hospital mortality has not previously been specifically investigated. Given our findings, further investigations into the influence of a MCU on patient outcomes are required.There are some limitations to the present study that must be taken into account when interpreting the results. In the regression analyses we included the patient-specific factors age, sex, SAPS II score and number of dysfunctioning organ systems to account for potential differences in case-mix between ICUs. Although the model solely based on these patient-level factors had good discrimination and calibration, the fit of this model could have been further improved by including other factors .Furthermore, despite the high response rate, the statistical analyses are likely to be influenced by a lack of power. The lack of power might have obscured other possible effects of ICU organization on hospital mortality, which could have been found with a greater number of hospitals. The relatively small number of participating ICUs could have resulted in findings that were dominated by one particular ICU. Several steps were undertaken to reduce this potential problem. First, we did not include variables in the analyses that exhibited too little variation. Second, we used the statistical technique of generalized estimation equations, which compensates for potential correlation of outcomes within ICUs. Finally, the leverage analysis revealed that similar results were obtained based on the jack-knife estimates and that none of the findings were attributable to the influence of individual ICUs participating in the study.Another limitation of this study is the fact that the questionnaire was sent at the end of the period over which we collected patient data. Because ICU organization changes over time, this might have had a slight influence on the extent to which responses to the survey were representative of the entire study period. To reduce the potential effect of timing of the questionnaire, we used patient data from a 2.5-year period only.The study was conducted using data from a Dutch national registry, which – at the time of the study – covered about one-third of all Dutch ICUs and more than half of all ICU beds in The Netherlands. The results of our study might not be generalizable to other countries, however, because they may differ in general health care structure, incidence of severe sepsis and availability of treatment strategies. Furthermore, we focused on patients admitted with severe sepsis, and we did not take into account patients who developed severe sepsis while on the general ward or patients who developed severe sepsis after the first 24 hours of ICU stay. Our findings may not apply to those patient groups.The present study focused only on factors related to ICU organization and did not include treatment aspects. In future analyses, factors related to treatment strategies that are believed to reduce hospital mortality in severe sepsis patients (such as treatments mentioned in the Guidelines from the Surviving Sepsis Campaign ) and facFinally, in the present study we only focused on part of the treatment period in patients with severe sepsis, namely their stay in the ICU. However, several ICUs responding to the questionnaire indicated that, in their daily experience, outcomes in severe sepsis patients are also influenced by timely recognition of sepsis at the ward, adequate treatment at the emergency department  and appICUs in the Netherlands exhibit variation in RAMR among patients admitted with severe sepsis. A lower in-hospital mortality in this patient group is associated with a higher number of patients annually admitted with severe sepsis. The presence of a MCU as step-down facility is associated with greater in-hospital mortality. Other associations between in-hospital mortality and factors related to ICU organization were not identified. The volume-outcome effect found in this study must be confirmed by future studies before a change in the admission policy with regard to patients with severe sepsis can be considered.• ICUs in The Netherlands exhibit variation in RAMR among patients admitted with severe sepsis.• After adjustment for patient-related factors, a higher annual number of patients admitted with severe sepsis was associated with a lower in-hospital mortality for this patient group.• The presence of a MCU as a step-down facility was found to be associated with a higher in-hospital mortality.• The volume-outcome effect found in this study must be confirmed by future studies before a change in admission policy with regard to patients with severe sepsis can be considered.ICU = intensive care unit; MCU = medium care unit; NICE = National Intensive Care Evaluation; RAMR = risk-adjusted mortality rate; SAPS = Simplified Acute Physiology Score.During the period from 2002 to 2004 LP received an unrestricted educational grant from Eli Lilly Netherlands BV. The study described in this manuscript was not conducted under this grant, and Eli Lilly Netherlands BV has not been involved in any part of the present study. All other authors declare that they have no competing interests.LP designed the study, conducted the questionnaire, performed statistical analyses and drafted the manuscript. NdK was involved in the set-up of the study, and helped in interpreting the results and in drafting the manuscript. NP assisted in the statistical analyses, in interpreting the results and in drafting the manuscript. GJS was involved in the set-up of the NICE registry and helped in drafting the manuscript. PvdV was involved in the design of the study and in interpreting the results of the analyses. EdJ participated in the study design, and helped in the design of the questionnaire, in interpreting the results and in drafting the manuscript. All authors read and approved the final manuscript.A PDF file including the questionnaire for ICU organization characteristics.Click here for file


Malaria is re-emerging in most of the African highlands exposing the non immune population to deadly epidemics. A better understanding of the factors impacting transmission in the highlands is crucial to improve well targeted malaria control strategies.Anopheles density was the best predictor for high malaria prevalence. Then lower rainfall, no vector control, higher minimum temperature and houses near breeding sites were associated by order of importance to higher Anopheles density.A conceptual model of potential malaria risk factors in the highlands was built based on the available literature. Furthermore, the relative importance of these factors on malaria can be estimated through “classification and regression trees”, an unexploited statistical method in the malaria field. This CART method was used to analyse the malaria risk factors in the Burundi highlands. The results showed that Anopheles densities when rainfall is low may be able to predict epidemics. The conceptual model combined with the CART analysis is a decision support tool that could provide an important contribution toward the prevention and control of malaria by identifying major risk factors.In Burundi highlands monitoring  In recent decades, highland malaria has been a re-emerging problem in several African countries  Former reviews published in 1998, have already shown the complexity of factors influencing malaria in the highlands The CART method has been applied to the case of Burundi Based on a literature review, different risk factors for malaria in African highlands were identified and used to build a conceptual model. The main source of information was peer-reviewed scientific papers obtained through PubMed using the keywords “malaria” and “highland”. Both English and French papers, describing malaria potential risk factors, were used. The reported risk factors were classified according to their impact on vectors or on malaria. To determine the hierarchical importance of different risk factors identified in the conceptual model the Classification and Regression Trees (CART) were used on malaria data collected in the Burundi highlands.Anopheles were collected with the spray catch method and a blood slide of two randomly selected persons (≤9 and >9 years old) were taken . Of the 14,932 An.gambiae and An.funestus that were collected, 244 were found positive for the detection by ELISA of the P.falciparum circumsporozoite antigen (Wirtz). The intervention was evaluated on the basis of the reduction of the Anopheles density, infective bites by house by month and the prevalence of malaria infection. Information on location, housing construction , livestock, separate kitchen, vector control activities (net use and spraying), prior antimalarial treatment, sex and age was also collected. Altitude and distance to the marsh of the houses were registered with a hand held positioning system . Average monthly minimum and maximum temperatures and monthly rainfall, recorded at the Karuzi meteorological station, were obtained from the Institute of Geography of Burundi (IGEBU).A four year vector control programme based on one annual round of Indoor Residual Spraying (IRS) was carried out between 2002 and 2005 in the central highland province of Karuzi, and targeted the valleys where malaria transmission was the highest A verbal informed consent was obtained for the blood slides and mosquito collections. For children the consent was obtained from the parents. In case of refusal, other persons or the next household was asked for consent. Present procedure and the full study was approved by the Commission of Medical Ethics of the Prince Leopold Institute of Tropical Medicine Antwerp (Belgium) (ref number 04 26 4 461). At the time of the implementation of the study, the Institutional Ethical Committee was not functional in Burundi. However, the Ministry of Health signed an agreement for the vector control program and the study design and the national malaria control program (LMTC) offered close collaboration.Anopheles mosquitoes. CART models are useful tools to explore the interactions between a desired outcome and its determinants The non-parametric classification and regression tree (CART) models were used to explore the influence of the specified determinants on the level of malaria and on the CART expresses its result in the form of a decision tree, a different approach that the better known parametric techniques. Indeed, in classical regressions the linear combinations are the primary method of expressing the relationships between variables while in CART this does not need to be linear or additive and the possible interactions do not need to be pre-specified or of a particular multiplicative form. The decision tree resulting of CART is useful, as well as the resulting flexibility and the non-parametric form (no assumption upon the covariates). The CART has many advantages, but they are known as instable approach. Therefore in the present paper we used a 10-fold cross-validation as estimation method.The building of a classification tree begins with a root (parent) node, containing the entire set of observations, and then through a process of yes/no questions, generates descendant nodes. Beginning with the first node, CART finds the best possible variable to split the node into two child nodes. In order to identify the best splitting variable , the software checks all possible variables, as well as all possible values of the variable to be used to split the node. In choosing the best splitter, the program seeks to maximize the average “purity” of the two child nodes. The splitting is repeated along the child nodes until a terminal node is reached. Each terminal node is characterized by an average and a standard error , indicating the purity of the node. The node purity measure provides an indication of the relative homogeneity (the inverse of impurity) of cases in the terminal nodes. If all cases in each terminal node show identical values, then node impurity is minimal, homogeneity is maximal, and prediction is perfect (at least for the cases used in the computations). In this study, the Gini criterium and the interclass variance were used as a measure of “purity”.The one standard error rule was applied to select the best tree . A minimum terminal node size of 500 samples was selected to avoid too many splits, with few observations, that are difficult to explain.CART also provides a ranking based on the overall contribution of each variable in the construction of the tree. This ranking indicates the importance of each independent variable as a predictor. Importance, for a particular variable, is the sum across all nodes in the tree of the improvement scores between this variable and the best splitter at a particular node Anopheles density.The risk of a positive slide was analyzed using the survey logistic regression in Stata 9.2 , taking into account the study design. Negative binomial regressions were used for the analysis of Anopheles vector, the parasite and the human host and (3) human related factors such as socio-economic status, health access, migration, gender, control activities  and land use . The conceptual model of potential factors influencing either Anopheles  or the outcome of transmission  in the highland based on this review is presented in Due to the instability of transmission in the highlands small variations in environmental or human related factors can have dramatic consequences for malaria transmission due to the low immune status of the human population alThe ability to suppress malaria infection depends on immunity. It has been suggested by Bodker et The health status of the population can have an important impact on malaria infection. Malnutrition can weaken children's immunity and can increase the level of malaria morbidity and mortality In several countries, the resurgence of malaria has been largely attributed to the emergence and spread of drug-resistant parasites Finally, other human related factors, such as population migrations Mosquitos' longevity, man-vector contact and mosquito density determine the transmission capacity of a vector population. First, a reduction in the lifespan of the mosquitoes will reduce the sporozoite rate and hence the proportion of infective bites. Secondly, a reduction in the human/vector contact will decrease the proportion of blood meals taken on human hosts. Finally, a reduction of vector density by decreasing the number of adult or larvae will also reduce transmission intensity. Therefore, any factors that could have an impact on any of these components will influence malaria transmission. According to the MacDonald model An altitude around 1800–2000 meters is usually considered the upper limit at which malaria transmission occurs Rainfalls play a crucial role in malaria epidemiology by providing breeding sites for the aquatic stages of the mosquito's life cycle. In addition, rainfalls may increase the relative humidity; above 60%, adult mosquitoes longevity, and consequently the vectorial capacity, increases Anopheles gambiae and Anopheles funestus. Environmental management, on the other hand, can reduce the availability of the breeding sites and therefore decrease the vectors density population Anopheles survival and density, if the coverage is high In highland settings, malaria transmission is negatively correlated with the increasing breeding sites-house distance Household and socio-economic factors have also an impact on malaria transmission by influencing the human-vector contact. For example, more mosquitoes were found in poorly constructed houses as compared to good ones Anopheles prevalence. Predictive factors for malaria infection included in the CART analysis were Anopheles density, density of infected Anopheles, individual human characteristics (age and sex), housing condition, past treatment, and “survey” , housing conditions, vector control activities, and environmental parameters. Detailed information of the houses was available on the Burundi database. The characteristics of these houses were scored by size and type of construction: house size , type of wall , type of roof  and separate kitchen . The score of the houses' characteristics were combined and divided into four categories . Other factors presented in the conceptual model were not collected in our study and could not be included in the analysis.Based on the conceptual model , variabl“survey” . The varAnopheles density were the two most important factors. Housing conditions that influence the human-vector contact and infective mosquitoes were ranked third and sixth. In the corresponding classification tree, Anopheles density was the first splitter . Then, in this last group, malaria prevalence was lower in surveys 5 to 11 (year 2004 to 2007) compared to surveys 1 to 4 (year 2002 to 2003). For surveys 1 to 4, age was the best discriminator with a threshold at 38 years; for survey 5 to 11, malaria prevalence among people living in the poorest houses was higher (25.7%) than for people living in the better-constructed ones (16.8%). Each terminal node is categorised as 1 (positive) or 0 (negative) depending on whether the proportion of 1's exceeds the proportion of 1's in the population (30.3%). From all negative individuals, 62% (5486/8889) were properly classified as negative (0) and from all positive individuals, 64% (2484/3856) were properly classified as positive.The overall ranking of predictor variables for malaria infection is presented in splitter  with a hAnopheles density was more important than density of infected Anopheles for the malaria prevalence . This sub-node was further split by the variable “distance to the marshes”. The highest Anopheles density (2.4/house) was found in houses located within 500 metres from the marsh. Spraying was then the best discriminative variable for this node, Anopheles density being the highest (3.4/house) when houses were not in treated areas. In this group, current rainfall had an influence over the average Anopheles density. Monthly rainfalls higher than 96.2 mm were associated with lower Anopheles density (2.2/house). Monthly rainfalls in the preceding month (power: 99.9), net use (power: 83.1) and a lag minimum temperature of two months (power: 73.0) did not appeared as main splitters in the final tree whereas they were identified as important risk factors for Anopheles density as shown by the ranking of their discriminatory power . In the negative binomial regression the following variables were retained in the model: rain with one and two months time lag, spraying, net used, minimum temperature the previous month, distance to the marsh, altitude and presence of animals in the house.The logistic regression, showed the surveys, density of The conceptual model used in this study provides insight into a complex disease, malaria, by listing all the potential risk factors influencing the transmission capacity of the vectors and the malaria prevalence in the human population. Such complexity requires tools that enable to explore the hierarchical importance of these risk factors. CART is such a tool and has proven its adequacy and usefulness in other contexts, for example for bovine spongiform encephalopathy Variables of high importance in both CART and parametric analysis are almost the same, except for rain which is non significant in the logistic regression. This can probably be explained by high colinearity with other rain variables (with time lag of one and two months). A CART Analysis works with (non-predefined) interactions. This means that the only similarity between a parametric model and a CART model will be the first split. The variable “animals” was ranked last in CART, probably because it is important only at the first step in the CART tree  but will only contribute a small part to the final importance which is obtained by adding contributions at different splits in the tree.Anopheles density, hence vector density because 95% of the collected Anopheles mosquitoes in the study area were malaria vectors et alIn the Burundi highlands, Anopheles density on malaria prevalence. It is surprising that ITN-use was not associated with lower malaria prevalence while it affects the Anopheles density, a good predictor of malaria infection. This finding can be explained by the absence of any additional impact of ITNs on transmission once an almost full coverage of IRS has been achieved In the current study, important variations in prevalence between surveys were observed and cannot be explained by any of the factors included in this analysis. Some variables such as health access, health status, and migration were not collected during the surveys. Differences between the first four surveys (year 2002 and 2003) and the later ones could be attributed to the normal decline of malaria prevalence after the malaria epidemic of 2001 Anopheles density are in agreement with other studies, i.e. lower rainfall An. gambiae s.s. (Form S) An. arabiensis, however, it has been observed that keeping cattle in houses was a risk factor for malaria In the highlands of Burundi, the CART analysis shows that factors responsible for high Anopheles density. The attribution of malaria resurgence observed in recent decades in African highlands to climate change is, however, controversial. Whereas some studies have associated warming trends to the increase in malaria transmission et alAnopheles density, a powerful predictor of malaria infection. The long dry season preceding the epidemic of 2000 Climate  is an important determinant of malaria vectors in the highlands. An increase in temperature and a modification in the frequency or amount of rainfall would affect future transmission of malaria. Different biological and statistical models have assessed the potential geographical expansion of malaria ranging from small change in the next decades Anopheles density should be further evaluated and a threshold risk set up in different areas. If such rainfall variability could be assessed a few weeks in advance, the follow up of the residual Anopheles densities when rainfall is low could help in predicting or early detecting epidemics Monitoring the most important malaria risk factors will help to more adequately prevent and control increases in malaria. The forecasting or early detection of meteorological variability could give time for the implementation of control measures. For example, in the Burundi, a more careful monitoring of the impact of rainfall and temperature variability on Anopheles density is the most important factor determining malaria infections; any means aiming at reducing the Anopheles population will have an impact on malaria. In the past, successful malaria prevention programmes targeting larval habitats were implemented Anopheles density.It is commonly known that decrease in mosquito longevity will be more effective to limit transmission than reducing mosquito density In conclusion, the conceptual model of highland risk factors in combination with a CART analysis can be considered as a simple decision support tool to better understand malaria epidemiology in various high altitude settings. The ranking of risk factors will help to prioritize monitoring, prevention and control efforts to the most important identified factors.