Verbal autopsy (VA) is used to estimate the causes of death in areas with incomplete vital registration systems. The King and Lu method (KL) for direct estimation of cause-specific mortality fractions (CSMFs) from VA studies is an analysis technique that estimates CSMFs in a population without predicting individual-level cause of death as an intermediate step. In previous studies, KL has shown promise as an alternative to physician-certified verbal autopsy (PCVA). However, it has previously been impossible to validate KL with a large dataset of VAs for which the underlying cause of death is known to meet rigorous clinical diagnostic criteria.We applied the KL method to adult, child, and neonatal VA datasets from the Population Health Metrics Research Consortium gold standard verbal autopsy validation study, a multisite sample of 12,542 VAs where gold standard cause of death was established using strict clinical diagnostic criteria. To emulate real-world populations with varying CSMFs, we evaluated the KL estimations for 500 different test datasets of varying cause distribution. We assessed the quality of these estimates in terms of CSMF accuracy as well as linear regression and compared this with the results of PCVA.KL performance is similar to PCVA in terms of CSMF accuracy, attaining values of 0.669, 0.698, and 0.795 for adult, child, and neonatal age groups, respectively, when health care experience (HCE) items were included. We found that the length of the cause list has a dramatic effect on KL estimation quality, with CSMF accuracy decreasing substantially as the length of the cause list increases. We found that KL is not reliant on HCE the way PCVA is, and without HCE, KL outperforms PCVA for all age groups.Like all computer methods for VA analysis, KL is faster and cheaper than PCVA. Since it is a direct estimation technique, though, it does not produce individual-level predictions. KL estimates are of similar quality to PCVA and slightly better in most cases. Compared to other recently developed methods, however, KL would only be the preferred technique when the cause list is short and individual-level predictions are not needed. In settings where a non-negligible proportion of the population dies outside of the hospital system, verbal autopsies (VAs) are emerging as a vital tool for understanding the population-level patterns of cause-specific mortality fractions (CSMFs). By combining this with robust information on levels of age-specific all-cause mortality , it is possible to estimate age- and cause-specific mortality rates. Most population-level estimates derived from VAs are created in two phases, by first assigning a cause or several causes to each death and then calculating CSMFs from the number of deaths or partial deaths assigned to each cause. Direct estimation is an alternative approach that produces population-level estimates of CSMFs directly from the VAs without the intermediate stage that requires assigning deaths to each VA. The direct estimation method proposed by King and Lu  is designed to capture complex patterns of interdependence between various signs and symptoms in the VA instrument ,2. This The KL method is based on the following matrix expression:P(S) is the distribution of symptom profiles in the test dataset, P(S|D) is the distribution of symptom profiles for each cause of death , and P(D) is the distribution of causes of death in the test dataset. A symptom profile is a combination of k different symptoms. Each symptom is dichotomous, so k symptoms yield 2k symptom profiles. P(S) and P(S|D) are calculated by tabulation. For a symptom profile s0, P(S = s0) is calculated by counting the fraction of VAs to be analyzed that endorse symptom profile s0. For a symptom profile s0 and cause j, P(S = s0|D = j) is calculated by counting the fraction of VAs in the "training set" with disease j as the cause of death that endorses symptom profile s0. Quadratic programming or least squares approaches may be used to solve this equation. King and Lu reported that the expected value of CSMFs estimated by their direct estimation method in repeated samples yields plausible CSMFs in a simulation study using data for 13 adult causes of death in China and 11 causes of child death in Tanzania. King and Lu [Where g and Lu .Despite the impressive results with small errors in CSMFs reported by King and Lu, there are several outstanding issues that need to be understood before widespread adoption of the method. First, King and Lu report in repeated experiments the expected value of the CSMF produced by their method compared to the true CSMFs using test and train datasets. They do not report a metric of the average error in CSMFs across repeated experiments, leaving it unclear how well the method will work in a given real-world application. Second, in all of the cases that they report, the CSMF composition of the train and test datasets are either identical or very close to each other. The performance of the KL method when the CSMF composition of the training set is different than the test dataset has not been established. Third, the validation data reported by King and Lu pertain to relatively short cause lists of length 11 and 13, respectively. The performance of the KL method for the longer cause lists desired in most VA studies has not yet been established. Fourth, until recently  there haIn this paper we present the results of a validation study of the KL method, using a large dataset with a realistically diverse cause list collected in the Population Health Metrics Research Consortium (PHMRC) gold standard verbal autopsy validation study . The stuWe use the PHMRC gold standard VA dataset to undertake three distinct analyses to understand the performance of the KL method in different settings. Details of the methods used for establishing the gold standard cause of death and for the collection of the VA data are reported elsewhere in detail . The PHMFigure Over the course of the PHMRC gold standard VA validation study, it became clear that metrics for gauging the quality of VA methods are quite subtle and are not standardized between research efforts. The complex issues are described fully by Murray et al. , who alswith HCE) and one version excluding recall of health care experience (without HCE).Murray et al.  showed tnSymp), and the total number of draws of different subsets (n.subset). For these main results, we used settings of 10 symptoms and 400 iterations.In the first test, we apply the KL software to the 500 pairs of training and test datasets for each of the three age groups. We assess the performance of the KL method by reporting median CSMF accuracy and the relationship between the estimated CSMFs and true CSMFs by cause. The KL method requires the user to select two parameters: the number of symptoms to be subset from all symptoms  The deaths were sampled from the original 25% test and 75% train pool datasets described above. We performed 500 iterations for each n. By the nature of this test, the number of deaths in the train and test datasets do not vary as the number of causes are altered. This provides a direct assessment of performance strictly as a function of the number of causes.To evaluate the dependence of the method's CSMF accuracy on the number of causes in the cause list, we performed the following experiment. For The technique described for the experiments above generates test and training sets that have independently random CSMFs. We suspected that the KL performance in previous studies has been exaggerated because the CSMF compositions of test and train datasets have been similar. To investigate this hypothesis, we conducted an additional analysis using training and test sets generated by sampling deaths from training and test pools uniformly at random (with replacement). In contrast to previous experiments in which the CSMFs of the test and train datasets are independent, the test and train datasets in this case both have CSMF combinations similar to those of the original pool. The same metrics are used for this assessment.n.subset of KL method, which specifies the total number of draws of different subsets of symptoms, is set to 400. Through our experiments we saw no significant variation in the CSMF estimation accuracy by changing the symptom cluster size when n.subset is large enough (greater than 200). Figure k. Hence, theoretically k = 6 is the smallest allowed. In addition, since some symptom profiles never appear in the data, k = 8 is the smallest nSymp we could use for all adult, child, and neonate datasets.)CSMF accuracy of KL for adult, child, and neonatal VA analysis was found to be largely independent of using different sized symptom clusters and including or excluding HCE Table . For allAs shown in Table The relationship between estimated and true CSMFs for each cause in adults, children, and neonates are shown in Additional file We found that in adult VA, the KL method is most effective in predicting CSMF for maternal causes and causes that are due to injuries, such as drowning. In child VA, measles, malaria, bite of venomous animal, and violent death were most accurately predicted. For neonatal VA, stillbirth and preterm delivery cause group were best. In contrast, KL performs poorly in predicting stomach cancer and other noncommunicable disease in adults, other digestive disease and other infectious disease in children, and pneumonia in neonates.As shown in Table Figures As shown in Figure We found that KL is extremely sensitive to the level of similarity between cause composition in the train and test datasets. We observed that if both test and train sets are randomly sampled with the same cause composition, KL estimation will yield dramatically higher CSMF accuracy. For example, for adult VAs with HCE when the test and train set have the same CSMF, the median CSMF accuracy is 0.947 , which is 0.28 points higher than the accuracy of KL for redistributed test sets and within 0.05 of the maximum possible accuracy.In this first large-scale validation of the KL method for direct CSMF estimation compared to gold standard cause of death assignment, we found that the method performs about as well as PCVA in terms of CSMF accuracy. Compared with some new methods ,11,12, KOur findings contradict several previous claims about details of the method. First, we found that varying symptom cluster size from eight to 18 made essentially no difference to the results. Second, KL does well in estimating CSMFs for causes such as road traffic accidents and drowning for which there are sensitive and specific symptoms. These are the same causes on which physicians also perform well. Our experiments show that, similarly to individual-level cause assignment techniques, KL is inaccurate in finding CSMFs for causes with weak symptom presence. Where there is not a clear set of sensitive and specific symptoms, the KL method tends to yield CSMF estimates that are biased towards the cause fraction in the training dataset rather than the test dataset. This tendency of the KL method to project the training dataset CSMF onto the test dataset is confirmed by the experiment in which we found that KL accuracy was exaggerated when the training and test datasets have identical CSMF compositions.One clear advantage of KL compared to PCVA is in the tests in which household recall of health care experience is excluded from physician review and the KL method. Thus, in settings where populations are expected to have little exposure to health care, the KL approach should be preferred to PCVA. This finding, however, must be tempered with the comparison to other methods  that all have better performance than KL in the absence of household recall of health care experience.The relatively disappointing performance of KL compared to published claims will surprise some readers. The key explanation is the number of causes included in our study for adults and children. Our finding that the KL method's accuracy dramatically decreases as the number of causes increases explains why KL has performed well in previous validation studies . These A central assumption of the KL approach is that, conditional on the cause of death, the symptom profiles of reference deaths, usually from hospitals, are the same as community deaths. The data in the PHMRC study was collected from deaths that met stringent gold standard diagnostic criteria, and most of these necessarily occur within the hospital system (community deaths simply cannot meet the diagnostic criteria for many causes). As a result, this validation study cannot directly investigate the importance of this assumption to the KL method. However, by excluding HCE variables from the study, we have emulated this setting and found little change to our results.Our validation of the KL method for direct estimation of CSMF from VA data collected in the PHMRC study showed that KL performs at about the same level as PCVA for adults, slightly better for children, and much better for neonates. Since it is a direct method, it does not yield cause of death assignments for individual deaths. We also found that KL performance is sensitive to the number of causes on the cause list, and as the number of causes under consideration increases, the quality of KL estimation decreases precipitously. This degradation is especially relevant when using VA to understand population-level patterns of adult mortality, in which the accuracy of KL becomes comparable to PCVA. Thus we judge KL to be a reasonable approach for neonatal VA and other settings with very short cause lists, but not as useful in its current form for adult or child VA. For adults and children, other methods, such as the Simplified Symptom Pattern, Random Forest, and Tariff, have better CSMF accuracy and also provide individual death cause assignment.CSMF: cause-specific mortality fraction; KL: King and Lu cause-specific mortality fraction direct estimation method; PCVA: physician-certified verbal autopsy; PHMRC: Population Health Metrics Research Consortium; RMSE: root mean squared error; HCE: health care experience; VA: verbal autopsyThe authors declare that they have no competing interests.AV performed analyses and helped write the manuscript. SLJ and JKB helped in data preparation and preliminary studies. CJLM designed the study and drafted the manuscript. ADF contributed in the study design, edited the manuscript, and approved the final version. ADF accepts full responsibility for the work and the conduct of the study, had access to the data, and controlled the decision to publish. All authors have read and approved the final manuscript.Slope, intercept, and RMSE from linear regression of estimated versus true CSMFs, by age group and cause with and without HCE.Click here for file


A survey was conducted to identify perceptions of cancer and its treatment among the general public, patients, and care givers in six countries. The purpose of the research—the PACE Cancer Perceptions Index (2012)—was to share public perspective on the progress of cancer care and treatment with stakeholders who make decisions about cancer innovation and access to treatments in order to allow patient-centric decisions. The results revealed that although understanding of cancer is increasing, a number of misconceptions persist. Although most respondents recognise that progress has been made in cancer treatment and a majority express satisfaction with this progress, they nevertheless want more investment and faster access to new cancer treatments. In particular, there was clear agreement that more collaboration was needed across countries and between the various stakeholders within countries. In addition, a clear majority of respondents does not think that their country invests sufficient funds in cancer research, and there is concern that progress may be jeopardised by the current difficult economic times. Forty years after the US government declared a ‘War on Cancer’, and following decades of massive investment in cancer research and treatment throughout much of the world, it is an appropriate time to consider what can be done to improve the rate of progress against this disease. In 2012, Lilly Oncology formed a global network dedicated to encouraging policy changes that can accelerate research and development (R&D) in the cancer field and improve patient access to the most effective treatments, which is called PACE (for Patient Access to Cancer Care Excellence); this network includes stakeholders from diverse sectors involved in cancer R&D, care delivery, policy, and patient advocacy operating under a transparent action plan —aIt is hoped that the insights from the PACE Cancer Perceptions Index will play a role in formalising the voice of the public on these and other cancer-related issues while also providing insights to be explored in follow-on public-opinion research.The primary goal of the survey was to provide useful data that help inform and educate stakeholders about public perceptions and expectations of cancer care/innovation in order to make patient-centric decisions. In total, 4,341 individuals, including the general population , cancer patients (663), and caregivers (669) were polled from 28 August to 4 October 2012. The general public samples are projectable to the adult populations of the countries but patient and caregiver samples were convenience samples intended to provide comparisons of patterns across the group.Survey participants were from six countries: the United States, France, Germany, Italy, Japan, and the United Kingdom. These countries were selected for their significant influence in shaping broader policy models in cancer care. The survey was carried out by GfK, one of the leading research companies with extensive experience of research of this nature.The general population surveys were achieved using random digital dial computer-assisted telephone probability techniques in each country. The sample comprised approximately 70% landline call respondents and 30% cell phone respondents in each country in order to account for the fact that some households only have cell phones. Samples of 500 respondents were targeted in each country. All interviews were conducted from London, and all interviewers were native speakers of the language of the country being called. Aside from questions to screen and elicit demographic information, the survey consisted of 26 questions. For the most part, the same questions were asked of all sample groups. However, in certain cases, the question was adapted for the patient and caregiver sample groups, for example, in relation to experience of clinical trials.With the exception of the United States, in each field country, approximately 100 cancer patients and 100 caregivers were interviewed using an online survey substantially similar to the one administered to the general public. In the United States, approximately 150 patients and 150 caregivers were interviewed. The sample source for these surveys was primarily uSamp; in Germany, Survey Sampling Inc. (SSI) sample was also used. In Japan, four sample providers were used: uSamp, AIP, Toluna, and SSI. An overview of the sampling scheme is shown in The (weighted) mean age of the general population was 46 years, and in the patient and caregiver populations, it was 48 and 38 years, respectively. In this respect, it should be noted that patient samples were considerably younger than overall cancer patient populations, and their views may not therefore be reflective of older patients. The gender ratio of male to females was 49:51 in the general population versus 45:55 in the patient groups, and 50:50 for caregivers.The general population data in all countries were weighted in order to balance the sample to national demographic statistics including region. Weighting data, while beneficial in better reflecting the sample population, does increase the margin of sampling error and in turn reduces the effective base upon which statistical tests are performed. The effective bases and margins of sampling error for the weight results for each country are shown in Initially, the respondents were asked about their perspective on the fight against cancer and their satisfaction with the progress that has been made over the past 20 years. Nearly six in ten (57%) public respondents claimed to be very or somewhat satisfied with the progress made with highest satisfaction levels being reported in the United Kingdom; significantly higher levels of satisfaction were also expressed by patients and caregivers, 78% and 63%, respectively, said they were very or somewhat satisfied with the progress (5% risk level).p ≤ 0.05). Nevertheless, when asked about their greatest concerns if they were personally to receive a diagnosis of cancer, 67% of public respondents said the impact on family and friends, 66% said death as a result of cancer, and 65% said paying for treatment.Moreover, nearly half (48%) of the general population disagreed with the statement ‘regardless of treatment, a cancer diagnosis will ultimately result in death’. There were, however, significant differences between countries in relation to this, with public respondents in the United States being most optimistic (65% strongly disagree or disagree with the statement) and those in Japan the least (only 36% strongly disagree or disagree) (p ≤ 0.05). Among patients across countries, information about the financial impact of cancer tops the list of unmet needs (55%), although navigating treatment options and emotional impact were not far behind.In terms of the cancer treatment available to them, only 31% of general public responders were very or extremely confident in the cancer care provided by their respective health care systems although patients were much more likely than the general public—54% versus 31%—to be at least very confident that they received the best treatment . However, although perception of the importance of the pharmaceutical industry was high, six in ten in all three groups thought those pharmaceutical companies were in general more motivated to treat cancer rather than cure cancer.p ≤ 0.05).Furthermore, 62% of public respondents—reaching 70% in the United Kingdom—expressed concern that progress will be slowed because of the poor economy . However, from the patient group, few have participated in a clinical trial; inconvenience, concern about safety risks, and additional costs  work against participation.p ≤ 0.05).Survey participants were also asked about their attitudes to personalised medicine. As an introduction, it was explained that personalised medicine was an evolving approach to medicine that is being used in the treatment of cancer and other diseases. It went on to explain that it involved collecting biological information from the patient to help predict which treatment was likely to work, not work, or to cause side effects for that patient. Respondents were then asked whether they had heard about personalised medicine already; 34% of the general population said they had, and 64% said they had not. Awareness was highest in the United States (48%) and patients were more likely to have heard about personalised medicine but even so awareness levels remained quite low at 41% for all countries . The percentage in the United States is 48%.One of the concerns about personalised medicine is around privacy relating to the sharing of medical records. Asked about health information technology, large majorities—more than eight out of ten respondents in all three groups said they would be happy to share their own medical records to help themselves and other patients; still, sizable proportions—44% of the general population—report concerns about potential misuse of data, with Italian public respondents expressing the most concern (53%) and Japanese public respondents expressing the least concern (35%)  places a high value on this extra year . In the United States, 24% believe an extra year of life is worth as much as US$200,000 plus.However, there is consensus on who should pay for life-prolonging treatments—72% say public payers/insurers. Furthermore, strong majorities of respondents want patients and families (78%), along with physicians (41%), to decide on these treatment options.The findings from the PACE Cancer Perceptions Index provide interesting information about the public perception of cancer and serves as a useful starting point to better understand the public perspective. As a developer of cancer therapies, Lilly believes that bringing the perspectives of the public and patients to stakeholders, including policy makers and health care managers, into cancer care discussions will add a critical new dimension to ongoing discussions about access to treatments and the future of cancer care.As with any research, the limitations of the methods must of course be taken into account. There are several caveats that limit interpretation of the data including the significance of language, the known inconsistencies between stated intentions and behaviours and the preferred reliance on behaviour as a more credible indicator of preferences, the lack of consensus about cost computations across countries, and potential biases as a result of leading questions.Nevertheless, these findings do provide useful information on the patient perspective, and the voice of the patient can and should influence the dialog between groups with different agendas .The results indicate that while the public is aware of the magnitude of cancer innovation that has taken place, there is clear frustration that progress is not fast enough and concern that future progress may be slowed as a result of the challenging economic environment. Respondents see cancer as a clear health priority and want greater investment in tackling the disease and faster availability of medicines.In particular, the findings suggest more should be done to facilitate patient participation in clinical trials. This was one of the areas in particular where findings are hard to reconcile. For example, while the survey showed that, in all countries, the majority of respondents are willing to participate in clinical trials, the fact remains that only 2%–3% actually enrols . If low More insight is also needed to understand the value the public is prepared to place on treatment. In order to understand fair price and reimbursement, the factors that contribute to the calculation need to be clear. The survey showed that people continue to underestimate the costs of medicine development. Understanding patients’ attitude to value will require better understanding on their part of the true costs and efforts involved in developing medicines through education as well as additional future efforts to develop a clearer understanding of patients’ views.Regrettably, the survey indicates the public’s continued mistrust of the pharmaceutical industry, and more work is needed on the part of industry to work in a more transparent way and provide greater information to address trust concerns.In short, knowing patients and keeping their interests at the centre of all efforts is critical in these economically challenging times. This survey provides some useful information that can help stakeholders understand the public perspective and identifies areas where follow-on surveys can continue to discover and communicate public perceptions about topics related to cancer in general, cancer medicine/treatment development and innovation, research funding and sources, roles of different players in cancer medicine development, personalised medicine, health information technology, importance of clinical trials, and willingness to pay for innovative treatment options.


There is extensive evidence for the interaction of metabolic enzymes with the eukaryotic cytoskeleton. The significance of these interactions is far from clear.In the cytoskeletal integrative sensor hypothesis presented here, the cytoskeleton senses and integrates the general metabolic activity of the cell. This activity depends on the binding to the cytoskeleton of enzymes and, depending on the nature of the enzyme, this binding may occur if the enzyme is either active or inactive but not both. This enzyme-binding is further proposed to stabilize microtubules and microfilaments and to alter rates of GTP and ATP hydrolysis and their levels.Evidence consistent with the cytoskeletal integrative sensor hypothesis is presented in the case of glycolysis. Several testable predictions are made. There should be a relationship between post-translational modifications of tubulin and of actin and their interaction with metabolic enzymes. Different conditions of cytoskeletal dynamics and enzyme-cytoskeleton binding should reveal significant differences in local and perhaps global levels and ratios of ATP and GTP. The different functions of moonlighting enzymes should depend on cytoskeletal binding.The physical and chemical effects arising from metabolic sensing by the cytoskeleton would have major consequences on cell shape, dynamics and cell cycle progression. The hypothesis provides a framework that helps the significance of the enzyme-decorated cytoskeleton be determined. The eukaryotic cytoskeleton is a system of protein filaments within the cell that confers structural integrity, exerts force, responds to stimuli, drives the cell cycle, performs mechanotransduction and produces motion. Structural integrity results from the cytoskeleton being a robust meshwork extending throughout the cytoplasm. Responses to stimuli result from the cytoskeleton being a highly dynamic structure, in which filaments associate with or dissociate from one another, slide, grow and shrink, and provide tracks for motor proteins and their cargoes. Changes in cell shape and volume result from interaction of the cytoskeleton with membrane-bound receptors, which include growth factor receptors.A large and growing body of evidence also attests to the interaction of the cytoskeleton with a variety of metabolic enzymes  and refenot performing their task [One evident possibility is that metabolism and signaling are tightly linked to the ultrastructure and dynamics of the cytoskeleton. The concept of “functioning-dependent structure” (FDS) was developed to describe either those structures that only form when their constituents are performing a task (and that disappear when that these constituents cease performing the task) or the inverse, namely those structures that only form when the constituents are eir task . A metabeir task . Modelineir task . A relateir task . The coneir task . Such moBacillus subtilis, for example, the association of the glucosyltransferase, UgtP, with the tubulin-like FtsZ couples cell division to nutrient availability [enzoskeleton[Under strong evolutionary pressures, bacteria, which are highly structured, have had the time and the numbers to find the solutions to many problems. One of these solutions is metabolic sensing. In lability . Here weoskeleton – could alias hyperstructures[It might be argued that sensing is done by individual proteins (or other macromolecules) rather than by macromolecular assemblies tructures. It mightructures. In the tructures. We havetructures. If thentructures. In thistructures. The dynThe binding to microtubules and to actin filaments of enzymes responsible for catalyzing different metabolic pathways allows the cytoskeleton to sense and integrate metabolic activity Figure . This seThe organization of the cytoskeleton is affected by specific proteins such as Microtubule-Associated Proteins or MAPs. In neurons, for example, MAPs bind strongly to MTs to make them relatively stable (in the sense that they can be isolated) whilst in higher plant cells, MAPs play a major role in the various dynamic transformations undergone by the MT network . The cytThe levels of nucleotides such as ATP and GTP affect the binding by the cytoskeleton of enzymes; reciprocally, the ultrastructure of the cytoskeleton affects the rate and extent of the hydrolysis of these nucleotides as catalyzed by either the cytoskeleton itself and/or the cytoskeleton-associated enzymes.Since the cytoskeleton and GTP and ATP are central players in cell architecture and in related physiological functions, stabilization of the cytoskeleton and regulation of nucleotide levels then allow the enzyme-associated cytoskeleton to act as a sensor that helps determine the phenotype of the cell.Saccharomyces cerevisiae, in response to altered metabolic conditions, moreover, association and dissociation of enzyme foci can be controlled by availability of specific metabolites, leading to the suggestion that metabolite-specific, reversible protein assemblies are common [S. cerevisiae also undergoes a major change as cells go from a quiescent state to growth. In the quiescent state, actin is in the form of immobile bodies; on resumption of metabolic activity following refeeding, actin again forms a dynamic network [Macromolecular interactions, compartmentation, channeling and ambiquity have a long history (for references see ). It nowe common ,18. Some network . The tub network . This un network .Xenopus laevis and keratocytes [An extensive body of literature attests to the interactions of metabolic enzymes with microfilaments of actin and with microtubules. Interactions with microtubular proteins have been observed for the glycolytic kinases, hexokinase (HK), phosphofructokinase (PFK) and pyruvate kinase (PK) as well as aldolase (for references see ). These atocytes . In planatocytes .Interactions between MTs and enzymes can be extended to interactions between MT motor proteins and docking proteins that connect the motor proteins to their cargoes. Identification of the docking proteins has shown that they are often moonlighting and have other functions in, for example, signaling and metabolism (for references see ). These Arabidopsis thaliana[Finally, dynamic interactions between microfilaments and MTs, which have been proposed to play a major role in neuronal growth cones , have re thaliana. We cann thaliana and (2)  thaliana. That sa thaliana.The essence of our interpretation of the following results is that if the binding by the cytoskeleton of an enzyme increases the probability of catalysis (via for example its affinity for its substrate), reciprocally, the cytoskeleton might well have a higher probability of associating with an enzyme that is active in catalysis. In other words, if being bound to a cytoskeletal filament confers a conformation on an enzyme that allows it to bind its substrate then the activation of the free enzyme by substrate might promote the binding to this enzyme to the filament.Microtubule binding to glycolytic enzymes alters the catalytic and regulatory properties of these enzymes (see Table one in ). Such bin vitro and probably in vivo[Results on the interaction of microfilaments with PFK, GAPDH and aldolase can also be interpreted as consistent with such interaction depending on the state of the enzyme. Binding to filamentous actin is known to activate PFK. Recently, it has been shown that insulin signaling increases the association of PFK with actin filaments and it was suggested that this association plays a role in the stimulation of glycolysis by insulin . In seruy in vivo.+/K+-ATPase and the ERK1/2 signaling pathway induce formation of clusters containing not only glycogen synthase and distorted MTs but also glycogen [Interactions between metabolites and the cytoskeleton are closely related to those between enzymes and the cytoskeleton and, in terms of metabolic sensing, are even more direct. It has been found, for example, that cardiac steroids acting via the Naglycogen .Energy dissipation is critical for the dynamic non-equilibrium behavior of microfilaments and microtubules as when ATP and GTP associated with actin monomers and tubulin dimers, respectively, are irreversibly hydrolyzed into ADP and GDP . How migDrosophila[What then of the inverse – is there evidence that cytoskeletal dynamics affects the levels of the nucleoside triphosphates? It has been suspected that the dynamics of actin are implicated in the regulation of ATP levels  and thatrosophila).A. thaliana, hexokinase1 interacts with actin; given that a normal functioning actin cytoskeleton is required for hexokinase1 to play its role in glucose signaling, it has been suggested that this enzyme “might alter F-actin dynamics and thereby influence the formation and/or stabilization of cytoskeleton-bound polysomes” [There is abundant evidence that, in addition to the metabolic enzymes, other proteins binding to the actin and tubulin cytoskeletons alter their dynamics. In the case of actin, the unregulated polymerization of actin filaments is inhibited in cells by actin monomer-binding proteins such as profilin and Tbeta4 . Nucleatlysomes” ,64.It was proposed in 1951 that the cytoskeleton might serve as a cellular ‘nervous system’ . More reThere are intriguing relationships between growth factor receptors, cytoskeletal organization, changes in cell shape, the glycolytic flux and the onset of S-phase. Shape changes are central to the cell cycle, which entails major reorganizations of both microfilaments and MTs. In the case of microfilaments, it should be noted that some actin exists in the form of a perinuclear actin cap and associated focal adhesions that shape the nucleus during interphase and that mediate mechanotransduction, motility, polarization and differentiation . ExternaAn important consequence of cytoskeletal sensing is on transcription. When the F-actin:G-actin levels are low, G-actin binds to MAL and prevents MAL activating the SRF transcription factor whilst when the F-actin:G-actin levels are high, MAL is free to activate SRF. This has led to the question being raised as to whether “altered F-actin:G-actin ratios arising during guidance responses mediated by UNC-115/ablim or other cytoskeletal regulators in the growth cone influence gene expression during guidance” .Where do isoforms fit in? In the integrative sensing hypothesis, cytoskeletal binding to different isoforms should have different functional consequences. MTs bind PK, an isoform of which, pyruvate kinase M2, is a major regulator of the glycolytic flux in tumor cells; this has led to the suggestion that M2-PK is a metabolic sensor which regulates cell proliferation, cell growth and apoptotic cell death in a glucose supply-dependent manner . EnolaseThe following predictions and corollaries (in the case of isoforms) of the hypothesis could readily be tested experimentally or by simulation and modeling:Isoform differences. The numerous isoforms of actin and tubulin occurring within the same cell should differ in their capacity to bind enzymes and, reciprocally, the isoforms of enzymes should differ in their affinities for microfilaments and microtubules. Some in vitro evidence exists already for this insofar as the dissociated form of the M (muscle) isoform of PFK binds to MT but the C (brain) isoform, which is a stable tetramer, does not [1/ does not .Metabolism to cytoskeletal dynamics and back to metabolism. The state of the metabolism should influence, via post-translational modifications to the cytoskeleton-binding enzymes and to actin and tubulin themselves, the nature of the cytoskeleton so as to determine the binding and activity of enzymes; reciprocally, this binding should then determine metabolic activity. The prediction is that there would be a relationship between alterations to the sites on tubulin and actin that undergo post-translational modification and cytoskeletal interaction with metabolic enzymes.2/ Cytoskeletal dynamics and ATP/GTP levels. The changes in efficiency of ATP and GTP hydrolysis that result from the changes in the ultrastructure of the cytoskeleton (as described in 1/ and 2/) should be sufficient to cause changes in metabolism and signaling that affect the phenotype. Measurement  of the intracellular levels of ATP and GTP under different conditions of cytoskeletal dynamics and enzyme-cytoskeleton binding should reveal whether this is sufficient to significantly affect local and perhaps global levels and ratios of ATP and GTP and related nucleotides. In some cases, it is conceivable that changes in the level and ratios of nucleotides might be masked by, for instance, an up-regulation of the activity of mitochondria (which could itself be tested for).3/ Signaling gradients. Non-homogeneous spatio-temporal distributions of metabolites with a potential for signaling (as well as the distribution of the enzymes themselves) should be generated by functioning-dependent association or disassociation of enzymes from cytoskeletal filaments. Such distributions could be revealed by simulation with stochastic automata such as HSIM [13C-glycogen and Secondary Ion Mass Spectrometry [4/  as HSIM  which hatrometry .Cytoskeletal relationships with moonlighters. Moonlighting enzymes [5/  enzymes ,84 shoulGene expression. In the case of isoforms, gene expression is clearly involved during myoblast differentiation, when cytoskeletal reorganization and, in particular, the regulation of microtubule dynamics, lead to sarcomere formation (see above). This complex process requires ATP, which is supplied by glycolysis, and the association of different isoforms of enolase to the microtubule system [OCAS transcriptional coactivator complex [6/ e system ; signifie system . In the  complex .Cell shape, volume and the cell cycle. Alterations to metabolic enzymes (or to growth factor receptors) that result in their adopting either an active conformation in the presence of substrate or an inactive conformation in the presence of substrate should alter cytoskeletal dynamics so as to advance or retard, respectively, the onset of S-phase. Such alterations should also affect shape and volume, consistent with a cytoskeleton-mediated sensing of metabolism.7/ The cytoskeletal integrative sensor hypothesis is an attempt to answer the question of how cells sense and integrate a wide diversity of chemical and physical information so as to converge onto a few outputs and generate a coherent phenotype. This hypothesis is in itself insufficient but it can be combined with other, complementary, hypotheses to give an integrated picture of cell functioning.One of these hypotheses is based on the possibility of ion condensation on the cytoskeleton . PositivA second relevant, integrative hypothesis involves the mechano-transduction properties of the cytoskeleton and associated structures such as focal adhesion complexes ,100 whicA third integrative hypothesis of relevance is that of the cell as being a set of functioning-dependent hyperstructures in which, first, the functioning of an enzyme determines whether or not that enzyme binds to related enzymes and, second, enzymes that are free (i.e. unbound) are degraded preferentially . There iMT: Microtubule; FDS: Functioning-dependent structure; PFK: Phosphofructokinase; GAPDH: Glyceraldehyde-3-phosphate dehydrogenase; TPPP: Tau and tubulin polymerization-promoting protein.All authors contributed equally to the ideas and to the writing of the manuscript. All authors read and approved the final manuscript.


Health systems evidence can enhance policymaking and strengthen national health systems. In the Middle East, limited research exists on the use of evidence in the policymaking process. This multi-country study explored policymakers’ views and practices regarding the use of health systems evidence in health policymaking in 10 eastern Mediterranean countries, including factors that influence health policymaking and barriers and facilitators to the use of evidence.This study utilized a survey adapted and customized from a similar tool developed in Canada. Health policymakers from 10 countries  were surveyed. Descriptive and bi-variate analyses were performed for quantitative questions and thematic analysis was done for qualitative questions.A total of 237 policymakers completed the survey (56.3% response rate). Governing parties, limited funding for the health sector and donor organizations exerted a strong influence on policymaking processes. Most (88.5%) policymakers reported requesting evidence and 43.1% reported collaborating with researchers. Overall, 40.1% reported that research evidence is not delivered at the right time. Lack of an explicit budget for evidence-informed health policymaking (55.3%), lack of an administrative structure for supporting evidence-informed health policymaking processes (52.6%), and limited value given to research (35.9%) all limited the use of research evidence. Barriers to the use of evidence included lack of research targeting health policy, lack of funding and investments, and political forces. Facilitators included availability of health research and research institutions, qualified researchers, research funding, and easy access to information.Health policymakers in several countries recognize the importance of using health systems evidence. Study findings are important in light of changes unfolding in some Arab countries and can help undertake an analysis of underlying transformations and their respective health policy implications including the way evidence will be used in policy decisions. Health systems evidence can enhance policymaking and strengthen national health systems by identifying priorities, providing broader choice of policy options, informing policy formulation and implementation, and setting the stage for evaluating the outcomes of policies -3.Bamako Call to Action, issued at the Global Ministerial Forum on Research for Health in November 2008, urged national governments and international funders to promote knowledge translation (KT) and exchange through the application of effective and safe interventions, evidence- informed policies, policy- informed research, and effective dissemination of research results for improving health systems performance  at the educational level to control the high number of physicians due to political reasons” and another policymaker from Pakistan stated that “population age and sex structure data is not being used for developing human resources strategies”. Furthermore, respondents frequently reported that certain policies on chronic disease screening, prevention and treatment were formulated without using available evidence (9.7%). Respondents also frequently indicated that the establishment of new healthcare centers was not based on feasibility studies and needs assessment. For instance, a policymaker from Jordan reported that “[the decision to] establish new health centers was influenced mainly by politicians” Table .In response to this open- ended question, respondents most commonly indicated that evidence was needed on how to locally apply programs related to chronic diseases (9%) and accreditation (9%). Policymakers also indicated that evidence is needed to substantiate national health strategic plans, as a policymaker from Yemen stated “review and evaluation of the health system based on [evidence from] country specific health indicators [is needed]”. Another policymaker from Palestine stated that “evaluation of financial policies and the allocation of resources, and their impact on the health of the population [is needed]”. They also reported that evidence was needed to formulate policies on the distribution of healthcare workers. A policymaker from Jordan stated that more local evidence is needed for the formulation of “a policy that requires hospitals to apply accreditation standards to improve their health care services” and another policymaker from Lebanon stated that “[locally applicable] patient satisfaction surveys [are needed] for accreditation” Table .In response to this question, respondents most frequently reported lack of funding and investments in priority health research and in implementing evidence from research in policy 16%) and lack of policy relevant research (14%) as barriers to evidence- informed policies. Other commonly mentioned barriers are over- riding political forces, corruption, and weak administrative structure of policymaking, as well as lack of trained policymakers in accessing and using evidence , easy access to information (11%), availability of research funding (9%), support of NGOs and international organizations (9%). Other frequently mentioned facilitators are availability of research centers, policymakers’ belief in the importance of health systems evidence, availability of qualified health systems researchers, communication and networking between policymakers and researchers, wide dissemination of research, and qualified policymakers Table .Respondents most frequently suggested building the capacity of policymakers in locating proper information and assessing the quality of research, its cost effectiveness and local applicability (13%). Increasing funding and investments in health research, improving dissemination and translation of research, and conducting relevant health systems research to inform policy decisions were also commonly mentioned suggestions for improving the use of research in policy. Other commonly mentioned suggestions include establishing evidence- to- policy decision support units to support policymakers in using research in policy, conducting sensitization and awareness workshops on evidence- informed policymaking, and enhancing contact and exchange between policymakers and researchers through meetings and conferences and setting easy and effective methods for communication and networking Table .Policymakers frequently listed the need for training on how to develop health policies and use evidence in policymaking. Additional training areas include; training on policy analysis, health economics, setting policy-relevant research priorities, strategic planning, conducting operational health systems research, budgeting and financial management, and general management and leadership.There is very limited empirical work on evidence- informed health policy and KT across multiple countries in the Middle East region. By exploring policymakers’ views and practices regarding the use of health systems evidence in 10 countries from the Middle East and assessing contextual influences in health policymaking, this study is an effort to provide essential knowledge that can be used to design, promote and sustain effective KT strategies that would strengthen health policymaking in the region.Policymaking is a complex process and our findings show that a myriad of factors influence health policymaking and often compete with health systems evidence. Our findings show that factors such as values of governing parties, corruption, and weak administrative structure are among the many factors that play a major role in the health policymaking process in the region. Most countries in the region fare poorly with corruption and democracy indices, reflecting increased possibility for policy formulation based on personal preference of government actors rather than evidence -32. LiteThe influence of private providers, insurers, and professional associations was more pronounced in some countries more than in others. For example, private health providers and professional associations were more influential in Lebanon, where they are the main providers of health services . HoweverFindings show that limited health funding also competes with the use of research in the region. Resource constraints, in terms of both human and fiscal resources, were found to limit research utilization in LMICs . MoreoveOur study shows that donor organizations exert a strong influence on health policymaking processes in the region. They can facilitate the use of evidence in developing countries; however, they may dictate their agendas on recipient countries from the region and hinder evidence- informed policies ,38. It iGiven the diversity of the policymakers who were surveyed in this study and the different contexts in which they worked, it is striking to find that common influencing factors were identified. Policymakers outside the government play an important role in the policymaking process and may influence the policymaking process. This may be due to the fact that policymakers outside the government are often more affected by decisions made and thus they try their best to influence the policymaking process to influence the decisions being made to their advantage . This beOur findings show that policymakers recognize the benefits of integrating health systems evidence in the policymaking process. Policymakers from the region use globally produced evidence in formulating health policies such as vaccination schedules and accreditation strategies. However, policymakers did not use evidence when other contextual forces including politics competed with evidence such as in the reported examples related to establishing new healthcare centers and determining health human resources strategies. This means that even when relevant research exists, it can be overlooked due to other factors and constraints that could be more relevant for the decision making process and that different contexts can lead to different utilization of the same evidence. While global knowledge on certain areas is available, study findings also show that there is a need for locally produced context- specific evidence to formulate policies on the distribution of healthcare workers as well as the application of chronic diseases and accreditation programs. As literature indicates, contextualizing evidence for effective policymaking is a key challenge for health systems .Findings from this study corroborate those previously reported from semi-structured interviews conducted with health care managers and policymakers from Canada and the UK  and a su, and 83 policymakers from six countries [Our study has three main strengths: (1) it is among the very few studies (if not the first) to explore the views and practices of policymakers in 10 countries in the EMR on the use of health systems evidence including contextual influences in policymaking; (2) we sampled a very diverse group of policymakers, including governmental entities, professional associations, private sector and civil society representatives, donor agencies across the ten countries; and (3) we used combined quantitative and qualitative research data analysis methods. Our study has a few limitations. It presents results on the use of evidence in policy based on the perception of a purposefully selected sample of policymakers. Hence, findings might not be representative of all policymakers in the region and results may not be generalizable across the region. This study was not intended to include a representative sample since it is difficult to obtain given the mix of policymakers and the diversity of contexts in which health policy decision- making occurs. It is worth noting that there was a variable response between different categories of policymakers within each country whereby the largest group of respondents in most countries were policymakers affiliated with the MOH. Moreover, there was a variable response rate across countries (range: 9–42 respondents per country). The varying degree in responses in some countries may be explained by the fact that more positions in the sampling frame are filled in some countries compared to others. This may be due to larger health systems including reform initiatives, larger populations, etc. For example, in Pakistan 13 positions were filled for national NGOs compared to two in most countries. In Yemen, eight positions were filled by managers of pharmacists’ associations compared to one in other countries. Despite this, we achieved an average of 23.7 respondents. Furthermore, given the nature of policymakers’ work , the response rate of 56.3% obtained in this study is considered acceptable. The response rate is also comparable to response rates on surveying policymakers, stakeholders, and researchers from the region: researchers from the region: 56% , 64.6% , and 83 ountries .In addition to the above, it was difficult to discuss the different contextual factors that determine country specific results within the data available. This may be due to the fact that context mapping has not yet been conducted in the region to identify specific factors and indicators that reflect use of evidence in policymaking. Country specific factors may have had a major role in determining the findings in the qualitative survey component and the section on comparing responses by study country. Despite the fact that such factors are important, discussing them is beyond the context of this specific paper. Discussing the context-specific issues that govern how policies are made and how evidence is used should be given justice by making them the focus of future country case studies that include additional data on the policymaking environment of study countries.Another limitation of this study is the social desirability bias inherent with self- reported questionnaires. Respondents may have provided the answers they considered desirable by the investigators. Responses may present either true beliefs or perceptions of what respondents thought researchers wanted to hear, or a combination of both. However, it can be safely assumed that the results are not overly inflated because of the positive nature of almost all the questions. Furthermore, self- reports of current behavior provide clear information on where improvements should be implemented .In order to strengthen the use of evidence in policies, efforts should be targeted to promote a supportive climate for evidence- informed policies, produce, package, and disseminate evidence, facilitate users’ pull, and promote exchange between researchers and policymakers . Based oPolicymakers from the region suggested increasing health research funding to improve evidence- informed health policymaking. Improving research infrastructure and funding is important to generate policy- relevant evidence for policymaking . SeveralStudy findings show that there is a need to enhance the production of policy- relevant research and to improve the ways with which researchers present and disseminate evidence to policymakers. In order to increase the relevance of research to policy, it is important that policymakers identify and communicate gaps in knowledge and policy priorities for research to researchers . FurtherPolicymakers from the region also indicated that the available research was not delivered at the right time and that it lacked information on its quality and local applicability and actionable messages. Literature also shows that timeliness and relevance of research and the inclusion of a summary with clear recommendations facilitate the use of evidence in policymaking ,10,19. TIt was discussed earlier that policymakers’ lack of training and awareness on using evidence was a major barrier to using evidence in health policymaking in the region. It is worth noting that there are no established training and education programs to date that target policymakers in the Middle East region. As such, many policymakers might not have the skills and the scientific, professional or policymaking background. Building the capacity of policymakers in the region to locate the proper information and assess the quality of research, its cost effectiveness and local applicability was suggested by respondents to improve evidence- informed policymaking. Literature shows that lack of policymakers’ skills and expertise decreased the prospects for research use and that training can affect their decision making and the quality of decisions taken . Various. Two- way communication and exchange can facilitate a mutual understanding of policy questions and the kind of knowledge that is needed to answer these questions. It can also inform researchers on the decision making process and on what policymakers consider as timely, relevant, or good quality research [Findings clearly indicate that there is a need to improve communication and exchange between researchers and health policymakers in the region. In fact, less than half of the respondents (43.1%) indicated having close collaborative relationships with researchersresearch . Policy research ,9,10,19.Literature also suggests that the interaction between researchers and policymakers and the existence of an accountable “receptor” function in the government would favor the use of health services research in policymaking .Researchers and policymakers in the region should create more opportunities for interaction. Moreover, research funders need to emphasize that researchers should work in partnership with policymakers to ask and answer relevant policy concerns.Health policymakers in several Middle Eastern countries recognize the importance of using health systems evidence in health policymaking. Our findings show that there are no negative attitudes by policymakers toward research evidence, its use, and benefits in the policymaking process. Strengthening health policymaking by using effective KT strategies requires serious efforts by both policymakers and researchers. This study identified barriers and facilitators for the use of health systems evidence in policymaking and it is hoped that the findings would prompt policymakers and researchers to start a dialogue and cooperative relationships.Our study shows that fostering evidence- informed policymaking requires a clear understanding of the national contexts in which policy decisions are made. The process through which evidence from health systems research is utilized in policymaking is complex and multidimensional. To make significant impact on policy decisions, one must reflect a deeper understanding of the context in which these decisions are made.Future studies from the region should also focus on investigating how policymakers “use” or “not use” health systems evidence in relation to certain policy issues, and how evidence interacts with context. In addition, future research should focus on assessing policymaking organization capacity to acquire, assess, adapt and apply research evidence. Future studies could also focus on exploring the link between the use of evidence and health status indicators in a given country.Lastly, our study findings are important and timely in light of the changes that are unfolding in some Arab countries. Findings can provide some insights in setting the agendas for the new regimes as they strive to strengthen health systems and policy. Also, they can provide a baseline to undertake an analysis of the underlying transformations and their respective health policy implications including the way evidence will be used in the future into policy decisions. Study findings can also help inform and direct future plans and activities for the EVIPNet EMR and Middle East and North Africa (MENA) Health Policy Forum, in addition to being useful for countries that host or are planning to host KT platforms in the region.AUB, American University of Beirut; EMR, East Mediterranean region; EVIPNet EMR, Evidence Informed Policy Network- Eastern Mediterranean Region; IRB, Institutional review board; KT, Knowledge Translation; LMIC, Low and Middle Income Countries; MENA, Middle East and North Africa; MOH, Ministry of Health; NGO, Non-Governmental Organizations; SPSS, Statistical Package for Social Sciences; WHO, World Health Organization.The authors have no competing interests to declare.All authors meet criteria for authorship. All authors approved this version of the article for publication. FJ contributed to the conception, design, and interpretation of the data as well as to drafting and critically revising the article. JNL contributed to the conception, design and analysis of the study as well as critically revising the article. NA and DJ contributed to acquisition of data, analysis, interpretation as well as drafting and critically revising the article. WA assisted in study design in addition to customization and finalization of the tool. SR contributed to interpretation of data, and critically revising the article.This study was jointly funded by the Global Health Research Initiative and the World Health Organization – Eastern Mediterranean Regional Office. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1472-6963/12/200/prepub


Neuromuscular diseases are a group of pathologies characterized by the progressive loss of muscular strength, atrophy or hypertrophy, fatigue, muscle pain and degeneration of the muscles and the nerves controlling them . Perceived isolation and health related quality of life are affected in the majority of cases due to the illness chronicity. Internet, and in this way, the use of chat and videoconferencing programs, is an alternative option to mitigate the mentioned variables.The aim of the study is to assess the effectiveness of teleassistance on reducing isolation and improving health related quality of life in adults with neuromuscular diseases.http://neuromusculares.deusto.es/. An exhaustive preliminary analysis of this pre-post assessment is necessary in order to know if the psychosocial programme is effective and if it could be a helpful tool for this type of population.The sample is composed of 60 participants randomly selected and affected by different neuromuscular diseases . Thirty patients were assigned to the experimental group, which participated in the chat and videoconferencing sessions, and the other thirty to the control group, which did not participate. The inclusion criteria for both groups were: medical confirmed diagnosis (CIE-10) of one of the diseases mentioned above, age ≥18 years, agreeing to participate in the study by signing an informed consent, and finally, ability to manipulate a computer . The exclusion criteria for both groups were to have a psychiatric disorder (DSM-IV-TR), head trauma or severe visual limitations. All the patients were recruited from neuromuscular disorders associations and Hospitals of The Basque Country. Effectiveness were assessed by a pre-post design in which questionnaires and interviews were administrated . The online support entails different activities developed during three months in once a week sessions: a) Group videoconference sessions with a Psychologist, b) Individual videoconference sessions with a Neurologist, and c) Forum discussion groups about biopsychosocial issues. The psychologist counseling consists on a psychosocial program about general topics such as illness information, emotional reactions to the disease, the most frequently automatic thoughts, etc. A web site was developed to carry out the intervention: Preliminary results will be presented in order to confirm if teleassistance is an effective alternative way of advising people with neuromuscular disorders.


The stuff of adventure in literature, their twists and turns wreak havoc in the science laboratory and the art studio alike. Frans Snyders, old master and founder of Baroque still life with animals, understood the unexpected and its power to surprise and used it to animate his work. A thriving arts market in 17As he expressed the exuberance of his age in lifeless objects made dynamic and relevant, Snyders elevated the genre. His compositions often contained live animals as well as carcasses at the butcher shop or game fresh from the hunt. He so loved this part of the work that he abandoned still lifes altogether to become one of the first animaliers. He collected specimens of local and exotic animals to observe their behavior and physical characteristics and improve his specialized portrayal of them.Snyders was born in Antwerp, center of the arts during the Counter Reformation and playground of such luminaries as the Brueghel family of painters, Peter Paul Rubens, and Anthony van Dyck. A student of Pieter Brueghel the Younger, Snyders managed to convey the local culture not just with accuracy but with humor and commentary in compositions later considered best of the genre. He joined the Antwerp painters Guild of St. Luke and, like other serious artists of his time, visited Italy to study the masters. He was competent and prolific and attracted royal patronage and great popularity at home and abroad.He enjoyed the friendship of the best artists of his day rather than being overshadowed by them. Rubens, whose star eclipsed all those around him, admired his skills and commissioned him to paint animals and still life elements in many of his works. Once, when a patron could not tell their work apart, Rubens consented that no one could depict dead animals better than Snyders, though when it came to live ones, he, Rubens, was the best. Van Dyck painted several portraits of Snyders, who was related to the Connelis de Vos family of painters by marriage and counted among his students and associates Jan Fyt, a still life master in his own right.Still Life with Fighting Monkeys, on this month’s cover, contains many of the artist’s finest features.Animal still life—hunting or market scenes, butcher stalls, kitchen pantries―were transformed from static displays to vibrant collections of specimens shown to best advantage, colored with symbolism, and injected with humanity and interest. In his more than 50-year career, Snyders developed and refined his skills, leaving behind many paintings and drawings, hundreds of which survive. Middle-class folks were not allowed to hunt in Snyders’ Flanders, only the nobility. And though this painting does not show large trophy game, the row of colorful finches secured on a willow branch and small birds lying pathetically on the edge of the table hint at the status of this household―also home of such exotic pets as the mischievous monkeys in the center of the action. In the artist’s style, what might have been a sedate tabletop scene is enlivened by altercating primates, themselves joined by hostile feline intruders.Having toppled the basket, upsetting the fruit and scattering the arrangement, the monkeys tugged nervously on the branch of rainier cherries already manhandled and jutting off to nowhere. The finch display collapsed in a heap, china overturned and worse, and two angry cats ready to pounce from opposite ends complete the picture. Snyders’ skills shine in the fur of the live animals and the texture of the game birds, which far from rigid or damaged by the hunt are soft and languid as they rest on their backs human-like. The fruit is plump and enticing, even rolled to the edge of the cloth. Small branches with crinkly leaves add to the natural feel of the original arrangement.Monkeys were frequent visitors in Flemish paintings of this era, often linked to excess and greed, their troublesome anthropomorphic features mimicking the foolish aspects of human behavior. Shameless and unruly, they invite symbolism in this scene: the best choreographed arrangements could be instantly ruined by the slightest intrusion. This not only in still life painting but anywhere the law of unintended consequences applies, and no less in public health, where each day nature’s basket is toppled by unexpected ecologic, social, and biologic paws.In this issue of Emerging Infectious Diseases alone, diverse offerings from around the world attest to the immense influence of the monkey’s paw, particularly when another creature inflames an already dangerous situation. Such is the case with influenza. In the past century, three pandemics swept the globe in which viruses from birds likely played a role. A new strain, influenza A virus (H5N1), spread through bird populations across Asia, Africa, and Europe, infecting domesticated birds, including ducks and chickens, and long-range migratory birds. Its first recorded appearance in humans was in Hong Kong in 1997.Each time a new element of uncertainty is thrown into the mix, what will come out and how it will behave become more difficult to predict. Snyders knew this when he painted the fighting monkeys and the cats in his still life in Flanders. But the monkey’s paw in pandemic influenza remains to be seen.


There are several types of tobacco control interventions/policies which can change future smoking exposure. The most basic intervention types are 1) smoking cessation interventions 2) preventing smoking initiation and 3) implementation of a nationwide policy affecting quitters and starters simultaneously. The possibility for dynamic quantification of such different interventions is key for comparing the timing and size of their effects.We developed a software tool, DYNAMO-HIA, which allows for a quantitative comparison of the health impact of different policy scenarios. We illustrate the outcomes of the tool for the three typical types of tobacco control interventions if these were applied in the Netherlands. The tool was used to model the effects of different types of smoking interventions on future smoking prevalence and on health outcomes, comparing these three scenarios with the business-as-usual scenario. The necessary data input was obtained from the DYNAMO-HIA database which was assembled as part of this project. All smoking interventions will be effective in the long run. The population-wide strategy will be most effective in both the short and long term. The smoking cessation scenario will be second-most effective in the short run, though in the long run the smoking initiation scenario will become almost as effective. Interventions aimed at preventing the initiation of smoking need a long time horizon to become manifest in terms of health effects. The outcomes strongly depend on the groups targeted by the intervention.We calculated how much more effective the population-wide strategy is, in both the short and long term, compared to quit smoking interventions and measures aimed at preventing the initiation of smoking. By allowing a great variety of user-specified choices, the DYNAMO-HIA tool is a powerful instrument by which the consequences of different tobacco control policies and interventions can be assessed. After half a century of tobacco control policy, a vast range of interventions has been proposed, evaluated and implemented with varying degrees of success, though none of these have turned out to be fully effective in the worldwide eradication of tobacco consumption as a deadly habit Interventions differ in terms of their effectiveness and their ability to reach different population groups. All vary in terms of efforts and implementation costs We developed a software tool, DYNAMO-HIA, which allowed us to make a quantitative comparison of the health impact of different policy scenarios over time, by comparing the policy scenario with the “business-as-usual” scenario, i.e. no change as compared to the current situation. The tool has been described in more detail elsewhere DYNAMO-HIA is a recently-developed Markov type, multi-state simulation software. It was developed to allow researchers and policy makers in the field of Health Impact Assessment (HIA) to 1) quantify the development of risk factor exposure over time and to 2) estimate the impact of these changes in risk factor exposure on disease prevalence, mortality and on summary measures of population health. DYNAMO-HIA is a dynamic tool that synthesizes data according to the causal epidemiological pathway, linking risk factor exposure through relative risks (RRs) of incidence of associated diseases and death, to prevalence of diseases, mortality and summary measures of population health, and allowing to take into account relative risks by “time since quitting smoking” and age, as well as competing risks. Following the epidemiological causal chain implies that the model uses relative risks by risk factor class, i.e. incidence in exposed risk factor classes are a multiple of the incidence in the non-exposed. A change in risk factor exposure due to the policy or intervention thus changes disease incidence and in turn disease prevalence and mortality. The effect of the risk factor change on mortality through diseases not included in the model, i.e. other-cause mortality, is taken into account by additionally using the relative risk on total, i.e. all-cause, mortality. Other mortality is derived from total mortality and disease specific mortality, assuming additive mortality In order to isolate the effects of the intervention DYNAMO-HIA always compares one or more intervention scenarios which result in a modified risk factor prevalence and/or modified transition rates, with the reference or business-as-usual scenario.www.dynamo-hia.eu, includes input data on risk factor prevalence, relative risks, and IPM information for nine diseases for a large set of EU member states. The diseases included in the model are diabetes, ischemic heart disease, stroke, lung cancer, oral cancer, esophageal cancer, colorectal cancer, breast cancer and COPD. The risk factors include the body mass index (BMI), alcohol and tobacco consumption. Time since quitting smoking is taken into account by including prevalence and relative risks in former smokers by time since quitting. The model provides output on summary measures of population health such as life expectancy with and without disease, mortality, survival as well as disease and risk factor prevalence by age and sex. The effect of an intervention or policy on future risk factor exposure and future health is assessed by comparing one or more scenarios with a specific intervention or policy change to a scenario without any intervention, so business-as-usual. The effects of the intervention or policy on the risk factor prevalence in the first year and/or on transitions between risk factor states (i.e. smoking (re) start and quit rates) are given by the user. The risk factor prevalence in future years is an outcome of the model. The theoretical specifications of the model have been described elsewhere DYNAMO-HIA requires input such as 1) demographic data, including population numbers, numbers of future newborns and all-cause mortality, and 2) epidemiological information on incidence, prevalence and mortality (IPM) for relevant diseases, risk factor exposure, as well as relative risks linking exposure to disease and to all-cause mortality, all by age and sex. The present version of the DYNAMO-HIA software package, which is publicly available at: We evaluated the effects of three intervention scenarios, each reflecting one of the three basic types of tobacco control: 1) interventions to increase quitting, 2) interventions to reduce smoking initiation and 3) policies reducing population-wide smoking. Interventions to increase the quit rate among smokers are usually targeted towards adults and include measures such as counseling and personal or grouped pharmacological and/or psychological therapy. Interventions to decrease or prevent smoking initiation usually target adolescents and are often school-based interventions. Nationwide policy measures for population-wide smoking reduction, such as the use of tobacco price taxation, affect quitting and starting simultaneously.Each intervention scenario is characterized by a change in smoking prevalence in the first year, i.e. just after the intervention or policy, and/or by changed (re)start and quit rates, as compared to the reference scenario. In addition, the proportion of the target population that will effectively be reached by the intervention characterizes the intervention scenario. We modeled both a maximum scenario, which gives a better impression of the varying effects over age and time for maximally effective interventions, versus a more realistic scenario version. To quantify the order of magnitude of the change in smoking prevalence and/or (re)start and quit rates in the target population, we evaluated systematic reviews/meta-analyses, and where necessary, primary articles of intervention studies, based on a PubMed literature search.www.dynamo-hia.eu). Smoking prevalence, i.e. the percentage of smokers and the percentage of non-smokers for ages 10 to 15 was derived from Stivoro's Jeugdmonitor (Youth monitor) The reference scenario starts from the current prevalence of never, former and current smokers by age and sex, and from current transition probabilities between the risk factor states over the life course. The current prevalence e.g. specifies what percentage of those presently 20 years old are never, former or current smokers and the current transition rates, i.e. (re)start and quit rates of smoking, specify how many of the currently 20 years old never smokers will remain never smokers when they are 21, 22 etc. years old, and how many start smoking when they are 21, 22 etc. years old. The current prevalence and transition rates relate to the business-as-usual situation, that is, a situation with smoking control measures that are already in place, but without the specific intervention. Dutch baseline prevalence of smokers, former smokers and never smokers and smoking (re)start and quit rates used here are included as supporting information . The DYNRelative risks from smoking categories to diseases and all-cause mortality used in this analysis as well as an overview of the age-specific disease prevalence at baseline are also included as supporting information .For the first scenario, the “smoking cessation intervention”, we chose an odds ratio (OR) of 2.0 reflecting that the ORs quantifying the effects of interventions on cessation rates varied from 1.4 to 2.2 among persons aged 18 years and over to which these interventions are usually targeted For the second scenario, the “smoking initiation intervention”, we assumed a 50% decrease in the smoking initiation rate for those at school ages 10–18 in the maximum scenario, and a 20% reduction in the realistic scenario version, reflecting that the literature showed mixed results varying from no effects to a significant reduction in start rates For the third scenario, the “population-wide smoking control policy”, we almost doubled the price of tobacco products. That is, we chose to use a 95% increase in the price of tobacco in the maximum scenario, which reflects the price adjustment if the Netherlands was to increase the price of tobacco to match the price of tobacco in Ireland, which currently has the highest tobacco price in the EU The effect of the price increase on smoking is based on a price elasticity, which measures the average proportional reduction in demand when the price of a commodity increases. We used a price elasticity of smoking prevalence of −0.4 for persons aged 21 and over and of −0.7 for persons up to age 20, who usually show greater responsiveness In the maximum scenarios, we assumed that 100% of the target population will be reached by the interventions. However, given that the size of the population that will be reached by an intervention is likely to be smaller, and is likely to differ by type of intervention, we assumed a lower reach for the smoking cessation intervention and the smoking initiation intervention in the realistic scenario versions. Considering that approximately 40% of smokers are willing to give up smoking in the coming year We compared the changing patterns of smoking prevalence and health impact of each of these scenarios with the reference scenario over time, using DYNAMO-HIA.The smoking cessation intervention will cause an initially strong decrease in the prevalence of current smokers, mirrored by an increase in the prevalence of former smokers as compared to the reference scenario . By defiThe smoking initiation intervention also causes a decrease in the prevalence of current smokers as compared to the reference scenario, but it is smaller than in the smoking cessation scenario. In the short-term this decrease is mirrored by a similar increase in the prevalence of never smokers and no change in the prevalence of former smokers . The oveCompared to the reference scenario, the population-wide smoking control policy causes an immediate decrease in the prevalence of current smokers, reflecting that the price increase is assumed to affect behavior virtually immediately. Evidently, this decrease is initially accompanied by a higher prevalence of former and never smokers, as in the model adults were assumed to quit and adolescents not to take up smoking in response to the price increase. Further, in the longer run the prevalence of former smokers becomes lower than in the reference scenario , reflectThe set of realistic scenario versions for each of the three types of interventions/policy models smaller effects on smoking exposure. This either reflects less dramatic interventions , smaller effects of the interventions on the persons who participate (e.g. 20% reduction in start rates as compared to 50%) and/or a smaller percentage of the target population that participates (reach). This revealed similar patterns of smoking prevalence, though being less pronounced  and dampening the reductions of prevalence of smoking-related diseases. This goes in line with estimates of  life expectancy, also calculated by the DYNAMO tool, which increase over the projection period, and where the improvement is bigger between 2010 and 2035 than between 2035 and 2060.The differences in timing can be explained by the fact that these different types of interventions/policies target different exposure groups (current smokers vs. never/non smokers vs. entire population), and hence different age groups. For example, since the vast majority of smokers starts smoking before adulthood, interventions preventing persons from taking up the habit mainly target and affect these younger age groups. Cessation interventions, on the other hand, will mainly affect the adult population. Nationwide policy measures impacting population-wide smoking behavior such as a tax on tobacco affect both the young and the old. Age strongly affects the risks of the onset of chronic diseases, both associated and unassociated with smoking, and of death. Smoking-related diseases, such as COPD, lung cancer or IHD only start taking a substantial toll in adulthood and early old age, and within the smoking related diseases, the timing of the effects differs, partly because of variation in the incidence rates by age. Additionally, differences in timing can be explained by the effects of the interventions on mortality and hence on “surplus” aging caused by the intervention.The dynamic modeling tool DYNAMO-HIA, with its ability to quantify the effects of interventions or policies on future risk factor prevalence and in turn on population health is a powerful instrument when the consequences of different tobacco control policies and interventions are to be assessed. Our findings not only show the different general patterns which interventions can produce but also illustrate how important it is that participation, i.e. the reach of an intervention, is as high as possible. Only then will interventions produce the desired effects on the population level. Such differences are illustrated well by our comparison of the maximum and the realistic scenario versions for the cessation and initiation interventions. This goes in line with the findings of other simulation models. Using the SIMSMOKE model it was shown that there is only a visible population effect of individual interventions if as many smokers as possible attempt to quit and as many of them also make use of the array of available quitting support tools The present study modeled each intervention one by one. A combination of several interventions and policies affecting different target groups and covering different time horizons will yield better tobacco control outcomes than the implementation of just one intervention quantified in this study. However, in the situation of more interventions the effect of one intervention will depend on the effect of the second intervention, and vise versa. For instance, a smoking initiation intervention that is successful in preventing adolescents from taking up smoking will reduce the potential effects of a smoking cessation intervention among adults. On the other hand, the population-wide intervention might be more effective if those who are motivated to quit because of a price increase are reached by smoking cessation interventions. These interdependencies depend on the effectiveness and reach of the interventions involved, as well as on the demographic and epidemiological context. Given our purpose to disentangle and illustrate the effects of three types of interventions, we did not model combinations of intervention types.Some limitations of our analyses must be considered. While we were aiming at a realistic model, a model always remains only a simplified version of reality, here being a demonstration of stylized scenarios. Much more work can still be put into the development of actual and more elaborate scenarios. This is a simulation analysis synthesizing existing data and evidence on disease epidemiology, smoking exposure, effects of smoking exposure on diseases and effects of smoking interventions on smoking exposure, all by age and sex. DYNAMO-HIA compares the effects of interventions/policies, i.e. it quantifies a reference scenario and one or more intervention scenarios with a modified risk factor exposure. The goal is not to project future population health as such. For projecting future population health, accurate information on incidence, prevalence and excess mortality data (IPM) of the diseases included in the model are needed, while in reality those data are embedded with uncertainty. This is partly because of the presence of past trends which are not exactly known. For the DYNAMO-HIA database it was decided to include trend-free IPM data partly estimated using the DisModII software Additionally, smoking prevalence and quit and (re)start rates may be biased. Classifications of smoking exposure differ between adolescents and the adult population, as do the data sources. While for older ages non-smokers in the POLS study A second limitation relates to the translation of the effects of interventions, as reported in the literature, into parameters of a dynamic tool such as DYNAMO-HIA. For example, if intervention studies report a reduction in the prevalence of smokers, additional assumptions are needed about the origin of the reduction: less initiation of smoking, increasing the prevalence of never smokers vs. more quitting, increasing the prevalence of former smokers. This translation was needed for the population-wide smoking control policy. We made our choices explicit in the paper. Assuming that most persons start smoking up to the age of 20, it is around this age when most uncertainty exists on whether the reduction in smokers reflects less starting or more quitting. Given that future health outcomes do not differ between former and never smokers at these ages, we do not expect that this affected our estimates. For older ages, mainly the expected effect of the price increase on restart rates is embedded with uncertainty. We expected that a price increase would also reduce the likelihood that future former smokers take up smoking again, and assumed a similar decrease in restart rates as in initial start rates. But other quantifications of the effect on restart rates may be equally defendable, and might yield different changes in future smoking exposure and health. At the most extreme, assuming no change in the restart rates would imply that the effect of this intervention on smoking prevalence at adult and older ages would be virtually absent during part of the projection period. While we do not consider this a plausible scenario, it indicates that future intervention studies should also evaluate the long-term effects on future smoking behavior.A third limitation of our study is that the comparisons of the size of the effects partly depend on the exact quantification of each intervention scenario. Given that evidence from current evaluation studies is insufficient to set all the parameters in DYNAMO-HIA or any other modeling tool with certainty, this cannot be avoided. Next to a set of maximum scenarios we presented a set of realistic scenario versions, which indicated that the general patterns remained unchanged, only showing the lesser effects due to smaller effect size and reach. Due to the model's linear behavior all specific interventions or smoking control policies with effectiveness and reach specifications in-between these two versions will produce results between the realistic and maximum variant.The research presented here shows the general patterns of three types of smoking interventions and illustrates the general use of DYNAMO-HIA. For each of the three types of interventions a wide range of smoking control or prevention services with varying effectiveness and reach can be chosen. The effect of each specific intervention will depend on its exact specifications and the current risk factor exposure and demographic and epidemiological context, which may differ from the Dutch situation. In particular, in populations with a different smoking pattern, reflecting a different stage in the smoking epidemic and/or a different mix of smoking control policies, the room for gains that can potentially be realized by different types of interventions may differ. DYNAMO-HIA can be used to quantify these effects as it easily allows for taking such factors into account.The key strength of our study relates to using a dynamic multistate model that distinguishes separate smoking states in order to model the effects of the different interventions/policies.Smoking affects a large range of diseases, each to a different extent and the RRs associated with smoking also depend on age and sex. Further, the different smoking-related diseases have different epidemiological patterns . The health effects of different types of interventions depend on the effects of the intervention on future smoking exposure at different ages. Future smoking exposure, in turn, does not only depend on prevalence at baseline, and (re)start and quit rates, but also on selective mortality, as smoking is strongly associated with mortality. Using DYNAMO-HIA allowed us to assess how an intervention affects smoking exposure in future years and in different age groups, taking into account selective mortality, and to substitute morbidity and mortality in the extra years persons are alive because of the lower mortality due to the intervention. Other models like SIMSMOKE Also, DYNAMO-HIA allowed us to take into account the effect of smoking on various diseases, each having different excess risks, which vary by age. The effect of smoking exposure on death was accounted for through the effect of smoking on incidence of the nine included diseases and through the RR of total mortality, which allows DYNAMO-HIA to take into account the effect of smoking on mortality through diseases not included in the model. Technical advantages of our software also include the fact that it requires relatively modest data input resulting in rich model output, while being freely accessible through a website.The DYNAMO-HIA model showed that all smoking interventions will be effective in the long run, the population-wide strategy being most effective in both the short and long term. The quit smoking scenario evidently will be second-most effective in the short run, though in the long run the smoking initiation scenario will be almost as effective as the smoking cessation scenario. Even if interventions aimed at preventing the initiation of smoking take a long time to become manifest in terms of health effects, they need to be part of tobacco control measures as they keep in check the numbers of new smokers. A combination of interventions and policies with different time horizons reinforcing each other would be most optimal on the way to smoking eradication.The dynamic modeling tool DYNAMO-HIA, with its ability to quantify information on the long term impact of interventions on population health, is a powerful instrument when the consequences of different tobacco control policies and interventions are to be assessed. We can directly compare the differences in the timing as well as in the relative sizes of the effects of the scenarios.Appendix S1Baseline smoking prevalence and transition probabilities in the Netherlands.(DOC)Click here for additional data file.Appendix S2Relative risks from smoking to disease/mortality and disease prevalence at baseline.(DOC)Click here for additional data file.Figure S1Smoking prevalence over time; Effects of each scenario in the Netherlands .(TIF)Click here for additional data file.Figure S2Smoking prevalence by age over time; Effects of each scenario in the Netherlands .(TIF)Click here for additional data file.Figure S3Difference in the number of deaths; Effects of each scenario in the Netherlands .(TIF)Click here for additional data file.Table S1Effects of scenarios on point prevalence of diseases in the Netherlands .(DOC)Click here for additional data file.