As much as 10-15% of new mothers experience depression postpartum. An Internet-based intervention (Mamma Mia) was developed with the primary aims of preventing depressive symptoms and enhancing subjective well-being among pregnant and postpartum women. A secondary aim of Mamma Mia was to ease the transition of becoming a mother by providing knowledge, techniques, and support during pregnancy and after birth.The aim of the paper is to provide a systematic and comprehensive description of the intervention rationale and the development of Mamma Mia.For this purpose, we used the intervention mapping (IM) protocol as descriptive tool, which consists of the following 6 steps: (1) a needs assessment, (2) definition of change objectives, (3) selection of theoretical methods and practical strategies, (4) development of program components, (5) planning adoption and implementation, and (6) planning evaluation.Mamma Mia is a fully automated Internet intervention available for computers, tablets, and smartphones, intended for individual use by the mother. It starts in gestational week 18-24 and lasts up to when the baby becomes 6 months old. This intervention applies a tunneled design to guide the woman through the program in a step-by-step fashion in accordance with the psychological preparations of becoming a mother. The intervention is delivered by email and interactive websites, combining text, pictures, prerecorded audio files, and user input. It targets risk and protective factors for postpartum depression such as prepartum and postpartum attachment, couple satisfaction, social support, and subjective well-being, as identified in the needs assessment. The plan is to implement Mamma Mia directly to users and as part of ordinary services at well-baby clinics, and to evaluate the effectiveness of Mamma Mia in a randomized controlled trial and assess users’ experiences with the program.The IM of Mamma Mia has made clear the rationale for the intervention, and linked theories and empirical evidence to the contents and materials of the program. This meets the recent calls for intervention descriptions and may inform future studies, development of interventions, and systematic reviews. The postpartum period represents a vulnerable time where the woman is at increased risk of mental disorders . BetweenPsychological treatments for PPD are effective ; howeverInternet interventions may be feasible in preventing and treating PPD with its potential for high reach. In fact, many pregnant women use the Internet to search for pregnancy-related information such as fetal development or childbirth . Trend dreplication of studies. In other words, the reporting of interventions and conduct of intervention studies generally fail to contribute toward a cumulative science of Internet interventions.Recent studies have demonstrated the acceptability and feasibility of Internet interventions for PPD -20. ResuThe aim of this paper was therefore to provide a systematic and comprehensive review of an Internet intervention for the prevention of postpartum depressive symptoms and enhancement of subjective well-being. While previous research has mainly focused on the treatment of PPD, this intervention has a strong preventive focus considering the potential for social savings (see the earlier discussion). We used the intervention mapping (IM) protocol, which outlines the path from recognition of a need or problem to the identification of a solution . The endIM is a tool that systematizes and integrates theory, empirical evidence, and information collected from the target population when designing health promotion programs. It makes the development of interventions transparent and provides an explicit report of all the decisions and considerations throughout the intervention process. There are 6 fundamental steps in the IM process: (1) the conduction of a needs assessment or problem analysis; (2) the definition of proximal program objectives based on scientific analyses of a given health problem and its predictors; (3) the selection of theory-based intervention methods and practical strategies to change (determinants of) health-related behaviors; (4) the production of program components, design, and production; (5) the anticipation of program adoption, implementation, and sustainability; and (6) the anticipation of process and effect evaluation. Each step comprises several tasks and the completion of 1 task guides the completion of a subsequent task. Although IM is presented as a series of steps, Bartholomew and colleagues  look at The IM framework greatly influenced the development of Mamma Mia, which makes a post hoc analysis both feasible and informative. Ideally, the IM protocol is used a priori in intervention development. In this study, however, the IM procedure was applied in a post hoc manner. Previous studies illustrate, though, that a retrospective IM-based analysis can also be a useful tool for post hoc description of interventions ,28. SpecA thorough exploration of the health problem, referred to as the needs assessment, is an inherent part of the IM framework. The result of a needs assessment illustrates how prevalent the problem is and what factors are associated with it. In this study, the health problem is PPD, and the challenge is that many women who experience depressive symptoms receive no counseling or support. An exploration of the literature suggests that many women report to be unfamiliar with symptoms of PPD and do not realize that they may be suffering from depression . SymptomAs in any preventive intervention, it is important to target risk and protective factors that may influence the onset and development of PPD. However, most studies have typically emphasized and identified risk factors that are hard or even impossible to modify such as a previous history of depression, negative life events, and certain demographic characteristics . Thus, aA recent and large Norwegian population-based study showed that relationship satisfaction protects against emotional distress during pregnancy , whereasFinally, parental insensitivity, which refers to disengagement, intrusiveness, or noncontingent responding, to the infant’s cues is associated with PPD ,41. IncrThe second step in IM is about defining the overall goals of an intervention and the performance and change objectives, which, in turn, specify how the overall goals can be achieved. To arrive at these objectives, the IM protocol suggests a procedure in which the overall goals are broken down into subgoals  and correlates of subgoals are identified . Change objectives are then constructed to target the determinants of the performance objectives. In short, change objectives are essentially what the user has to change or learn to attain the performance objectives.The intervention (Mamma Mia) was designed as a universal preventive measure that could be offered to all pregnant women with the primary goals to 1) prevent the onset or development of depression and (2) enhance subjective well-being during the prenatal and postnatal period . A secondary goal was to ease the transition of becoming a mother by providing knowledge, techniques, and support during pregnancy and after birth. The reason for starting in the second trimester is that expectant mothers can then be reached as early as possible, they have a reduced risk of spontaneous miscarriage, all women attend ultrasound at this time  and start forming prenatal attachment to their baby, and because the prevalence of prenatal depression is as common as postpartum. Six months after birth is the end point of the intervention as postpartum depressive symptoms tend to fade around this time . Th. Th75]. The intervention is used individually by the mother with health professionals, partners, or other actors only involved indirectly through the mother, but with no direct access to the program. For access to each session, users receive an email with a unique link. By clicking on the link, users are directed to a sequence of websites that are unique for that particular session. In the prenatal phase, emails with sessions are scheduled on a weekly basis, while sessions are scheduled 3 times a week in the active postpartum phase . This is followed by a low-intensity phase with weekly and eventually monthly sessions. The intervention starts in gestational weeks 18-24 and lasts until the baby is about 6 months. In total, the intervention consists of 44 sessions over a period of 11.5 months. Each session is designed to take about 10 minutes and must be completed before users can access the next session. This is done to ensure that relevant information has been reviewed and to create continuity and a narrative in the program. If a mother discontinues a session, she is required to complete the previous session before she can move on to the current session. If the mother is on schedule, the session will end by directing her to the intervention home page. On average, a typical website during a session consists of 80-100 words and a typical user may have 10-15 pages per session . For a demonstration, see .A session may utilize various functionality and interactivity to engage users, but typically consists of psychoeducational information, interactive tasks, and cognitive or behavioral homework assignments , which, importantly, will de facto turn Mamma Mia into a guided Internet intervention. This currently ongoing work will be offered as supplementary training at the Centre for Child and Adolescent Mental Health  in 2016.The feasibility of the intervention was demonstrated during piloting . Most paAt the time of this writing, there is an ongoing randomized controlled trial (RCT), which aims to test the effectiveness of Mamma Mia delivered in addition to usual care . The coIn parallel to the RCT, we are interviewing participants who have received the Mamma Mia intervention. The purpose is to assure the quality of the program and contribute strategically to its quality improvement to avoid that the intervention becomes obsolete given today’s rapid technological advances. To this end, we currently use the modified strengths, weaknesses, opportunities, and threats (SWOT) format. The SWOT format does not impose any specific assumptions or themes on the interview nor specify any particular types of answers. Yet, the SWOT format provides a certain structure to the interview and participants’ reflections along 2 dimensions .Finally, it becomes important to assess the supplementary training for midwifes and PHNs and implementation of Mamma Mia at well-baby clinics. The supplementary training is considered an important way of strengthening the implementation by making midwifes and PHNs familiar with Mamma Mia and associating it with knowledge or themes, which, according to the national guidelines, midwifes and PHNs should cover during consultations with pregnant and postpartum women and their partners. The supplementary training is also designed so that midwives and PHNs work actively with several of the organizational drivers to embed Mamma Mia into their local well-baby clinic and secure managerial and organizational commitment to the program, as part of the course requirements. This will hopefully help Mamma Mia to become an integrated part of the ordinary health services, and to ensure the dissemination and sustainability of the program.The aim of this paper was to provide a comprehensive overview of Mamma Mia and its development using the IM protocol. There are 2 overarching aims in IM: first, it serves as a tool in developing an intervention in a systematic manner. It links the phases of the intervention development to theory and empirical evidence, and thus ensures that decision making is based on sound logic. Second, the IM protocol makes the process of intervention development explicit and transparent. As such, the paper describes a fully automated Internet intervention (Mamma Mia) that is available on personal computers, tablets, and smartphones, and is also platform independent . The program consists of 3 phases: the first phase, which starts in the 2nd trimester, consists of 11 sessions. It starts in the gestational week 18-24, and ends in the gestational week 40 . The active phase starts when the infant is 2-3 weeks old, and lasts for 6 weeks, with 3 sessions/week. The final phase is the follow-up phase, which consists of 10 sessions spread over an 18-week period. In total, the program consists of 44 sessions disseminated over a period of 11.5 months.Users of the program progress through the intervention in a tunneled sequence with content centered around topics concerning coping with the transition to parenthood and signs of sadness or depression, engaging with the baby and one’s partner, and engaging in self-caring behaviors. The tunneled design is in accordance with the preparation and psychological reorganization of becoming a mother , and thThe adoption and implementation planning was largely in line with what is prescribed by the IM approach. Potential adopters and implementers of the intervention were included in the development, adoption, and implementation processes from the outset , as recowhy and how they work) and extend the evaluation of an intervention to, for example, other populations or settings . Third, it may assist educational institutions and other agencies in developing training and implementation strategies for the intervention. Finally, health personnel may more easily determine whether interventions such as Mamma Mia may be an appropriate intervention for their practice and users. Therefore, we argue that this paper illustrates why such post hoc descriptions are useful and comply with the call for transparency and thorough descriptions of interventions that had been noted by numerous authors ..why and Systematic and comprehensive descriptions of interventions are necessary to ensure that we can describe aspects of interventions that are generalizable and identify determinants used to achieve change and how the use of theory can be translated into practical strategies. This is necessary to promote and deepen our understanding of the development of complex interventions and move on from general remarks and conclusions such as “...more extensive use of theory was associated with increases in effect size...”  to a higBased on the IM protocol, this paper has 1 key limitation: it was applied in a post hoc manner. Hence, the development process described in this paper deviates somewhat from the IM protocol. Nevertheless, it is important to note that the IM framework influenced the development of Mamma Mia, which makes a post hoc analysis both feasible and informative. For instance, a long-term perspective in this project made it possible for us to perform studies on the determinants of PPD in Norwegian mothers ,34,104, In a sense, this may also be one of the strengths of applying IM analysis post hoc and highlights why it is better to perform IM after the development of an intervention, rather than never. For example, dropout rates in Internet interventions tend to be high ,106. ThuIn the development of any type of intervention, intervention designers translate theory into practice. However, there are many steps during development where the theoretical methods believed to be effective can be “lost in translation.” By applying the IM protocol to Mamma Mia, we have described the rationale and contents of the intervention, and opened up the black box for intervention designers, researchers, educational institutions, and health personnel working with perinatal mental health. The IM analysis of Mamma Mia helps to make clear decisions regarding intervention development from theory, empirical findings, and practical strategies that may contribute to its overall effectiveness. It also makes transparent some of the potential pitfalls such as its duration and lack of segmentation that may jeopardize intervention effectiveness. Overall, this paper adds to the plea for systematic reporting of intervention protocols that document the design and development of interventions, and accumulation of knowledge about interventions in the field of eHealth.


Human pathogen richness and prevalence vary widely across the globe, yet we know little about whether global patterns found in other taxa also predict diversity in this important group of organisms. This study (a) assesses the relative importance of temperature, precipitation, habitat diversity, and population density on the global distributions of human pathogens and (b) evaluates the species-area predictions of island biogeography for human pathogen distributions on oceanic islands.Historical data were used in order to minimize the influence of differential access to modern health care on pathogen prevalence. The database includes coded data  for a worldwide sample of 186 non-industrial cultures, including 37 on islands. Prevalence levels for 10 pathogens were combined into a pathogen prevalence index, and OLS regression was used to model the environmental determinants of the prevalence index and number of pathogens.Pathogens  showed the expected latitudinal gradient, but predictors varied by latitude. Pathogens increased with temperature in high-latitude zones, while mean annual precipitation was a more important predictor in low-latitude zones. Other environmental factors associated with more pathogens included seasonal dry extremes, frost-free climates, and human population density outside the tropics. Islands showed the expected species-area relationship for all but the smallest islands, and the relationship was not mediated by habitat diversity. Although geographic distributions of free-living and parasitic taxa typically have different determinants, these data show that variables that influence the distribution of free-living organisms also shape the global distribution of human pathogens. Understanding the cause of these distributions is potentially important, since geographical variation in human pathogens has an important influence on global disparities in human welfare. Geographic variation in infectious disease has played a major role in determining history's political and demographic winners and losers The dataset is unusual in being historical and in taking as units of observation the local pathogen and environmental conditions prevailing at 186 mostly small-scale non-industrial societies around the globe . The data are specific to these locations, which are for the most part not near major population centers and transportation hubs. While the use of historical pathogen data poses obvious limitations in accuracy and precision, it has the potential to give a clearer picture of the role of the physical environment, since influential moderators  played a smaller role than they do today. The more that global disease patterns rest on differential access to vaccines and antibiotics, good sanitation, and clean water, the more difficult it becomes to isolate the effect of climate and other biogeographical variables in a global analysis. The dataset also has the advantage, when compared to national data such as GIDEON, of being spatially focused and on a consistent scale. Finally, a number of relevant cultural variables have been coded for the SCCS, including several that are likely to affect pathogen abundance and diversity. The present study, therefore, complements global biogeographical pathogen analyses that have used modern datasets Species richness is greater at lower latitudes across a wide range of taxa, and there are reasons why we might expect this to hold for human pathogens also: parasite richness is strongly correlated with host species richness in area-based studies The reason for latitudinal gradients in species richness remains a subject of debate Species richness on islands is also shaped by island size and isolation. The MacArthur & Wilson Finally, the SCCS also allows us to include in the models aspects of human demography and culture likely to affect pathogen growth and transmission. Host population density is a strong predictor of parasite species richness across a wide range of host taxa The analyses begin by looking at the environmental variables that affect pathogen diversity and prevalence globally. The sample is then divided into tropical and non-tropical regions, and the relative importance of these environmental factors in the two regions is compared. Finally, a set of analyses was performed on the island locations only, in order to evaluate predictions about the effects of island area, isolation, and habitat diversity.http://www.worldcultures.org) functioning as a repository. This paper uses both existing coded data for the SCCS and a newly-developed set of SCCS pathogen codes.The analyses use data from the Standard cross-cultural sample (SCCS) of 186 non-industrial cultures see . Each SCThe new pathogen codes are described briefly below and in Cashdan & Steele The pathogen data for the new codes were derived from historical sources, chiefly global maps published in the mid-twentieth century. The codes reflect the prevalence levels of 8 pathogens: malaria, dengue, filariae, typhus, trypanosomes, leishmanias, schistosomes, and plague. Most of these pathogens include several related species, due to limitations of the source material. Prevalence levels were taken primarily from isolines on the epidemiological maps, and coded as 1  =  absent, 2  =  rare, 3  =  sporadic or moderate prevalence, and 4  =  epidemic or high prevalence. The prevalence levels of the different pathogens were combined, as described below, to form a pathogen prevalence index.The coding procedure followed that used by Murray & Schaller Because of limitations in the source material, both codes are biased toward pathogens that are transmitted through arthropod and other vectors. A few of these also have non-human hosts. The prevalence of such diseases is likely to be strongly shaped by the geographic distribution of the vectors that transmit them and the species that host them. This bias has the disadvantage that a number of important diseases  are omitted. It also has the advantage that the geographic patterning of this sample of diseases will be less affected by international travel and by socioeconomic and public health measures than are diseases spread via droplet and oral-fecal transmission.Another limitation is that the historical data do not contain information on variation in sampling effort, and less was known about pathogens in remote areas like tropical Africa than in more economically developed parts of the world. Since the sources nonetheless indicate more pathogens in these tropical regions, particularly in central Africa, the effect of this bias is likely to be conservative. Sampling bias is likely to be most problematic in studying the influence of island area, since pathogens on very small islands might have been estimated from better-known larger islands in the vicinity. The implications of this potential bias is discussed in the results section on island analyses.Energy measures included in this study were mean annual temperature, number of frost-free months, and within-year measures of temperature extremes http://islands.unep.ch, supplemented in a few cases by other sources. A few islands were so small that the 100 km radius used to calculate pathogens extended beyond the island border. In these cases, if there was another island within that radius, the area of that island was added to the focal island.Island area and various measures of isolation were obtained from the UNEP (United Nations Environment Programme) Island Directory at There are two parts to the analysis. The first uses the full sample of 186 locations to build a global model of significant environmental predictors of pathogen prevalence  and richness (using number of pathogens). The global model was built incrementally, beginning with a model of physical environmental variables  followed by a separate model of three related cultural environmental variables . The significant predictors from the two models were then combined into a single global model. In each case, analysis began with single-factor regressions followed by multivariate models, and variables that were individually significant but did not contribute independently to the multivariate models were dropped. The model was then applied separately to tropical and non-tropical regions, because the strength of these predictors was hypothesized to differ by latitude. Because the aim of the global model was to compare the relative effects of the different predictors, multiple regressions report standardized (beta) coefficients.The second part of the analysis uses only the subset of 37 island locations in order to test the specific hypotheses that pathogen number and prevalence are associated with island size and habitat diversity.In conducting the regressions, island area and some climate variables were transformed with a natural log transform prior to regression in order to make relationships linear and improve residual distributions. Where necessary, a constant was added before the log transform in order to make the minimum value 1.0. Mean annual temperature was negatively skewed, so those data were also reflected about zero before the log transform and then reflected back so as to restore the original order.The ordinal variables were handled in different ways, depending on the nature of the variable. Road quality was dichotomized into societies where only footpaths were present, originally coded 1  and as a way to evaluate how vulnerable the model was to the particular pathogens chosen (since the codes differed somewhat in the diseases coded). Another check was done to see whether the dependent and independent variables were associated only because they varied similarly across space, which would be indicated if there was spatial autocorrelation in the residuals. For each pair of points, the (squared) difference between the residuals and the actual geographic distance was calculated, to see whether the two values were correlated. This was done at various scales of distance down to 200 km. The societies in the sample are geographically dispersed (stratified both by geographic region and language group) so spatial autocorrelation at smaller scales cannot be assessed.SAS was used for all analyses.The first part of the analysis builds a global model using the full dataset, first by considering the physical environment, then the cultural environment, and finally both together in a single model. The sample is then divided into high and low latitude zones, to see how the relative importance of these variables differ by latitude. The final analyses are restricted to the island locations, in order to test specific predictions from island biogeography.The upper graph in Precipitation shows a more complicated relationship to pathogens, because two variables have independent effects: (a) mean annual precipitation and (b) the amount of precipitation in the dryest part of the year. As will be shown below, these two precipitation variables affect different kinds of pathogens. Mean annual precipitation showed a modest  has an adjusted It has been suggested The effects of temperature, mean precipitation, and dryness differ for the different pathogens, and the patterning appears to reflect the ecology of the vector more than the type of pathogen. The mosquito-borne pathogens are a variable lot, including malaria (protozoans), dengue (virus), and filariae (nematodes), but all were worse in hot wet climates. Typhus (rickettsia) leishmanias (protozoans), and schistosomes (flukes) were all worse in areas with dry months, perhaps because of greater aggregation of vectors and hosts during drought. Bivariate correlations between the various pathogen groups and environmental predictors are summarized in Pathogen distributions are affected by cultural as well as physical environmental factors. This section examines the effect of three cultural variables  on pathogen distributions.Better roads can be expected to broaden the geographic reach of pathogens by facilitating the movement of people, and of insect vectors transported inadvertently in the goods they carry. The mean pathogen prevalence index was higher in societies with roads (.29) as opposed to footpaths (−.14), These variables are correlated, since all are associated with greater social complexity. Examination of outliers in the climate analysis underscores the importance of considering the cultural as well as physical environment. For example, there was a highly influential point in the temperature and frost model. This point represents the Teda, a nomadic group in Chad with an unusually low pathogen score, given their local temperature and rainfall. None of the physical environmental factors in the dataset explain the discrepancy adequately, but their comparatively low pathogens are consistent with their very low density and high mobility at the time and place of their SCCS ethnographic description.The effects of the physical and cultural environment on disease are not independent, and so the final global analysis considers the variables in a single model. In a combined model with the physical environmental variables, residential mobility is no longer a significant predictor and is dropped from the final model. However, the earlier result suggests that the effect of density on pathogens in this model may be due both to its direct effects and to indirect effects resulting from associated decreased mobility.Taken together, the results indicate that there are more pathogens and pathogen types on the mainland than on islands, and that pathogens increase with mean temperature, population density, and a frost-free climate. The relationship with precipitation is more complex, peaking at intermediate levels of mean annual precipitation but also increasing in seasonally dry climates. The combined model explains 58% of the variance in both number of pathogens and the pathogen prevalence index. The regression statistics of this model are in As a check, the final model was run against each of the two databases from which the combined pathogen score was derived. Low Another check was done to see whether the relationship between the independent and dependent variables in this model was due to spatial autocorrelation . Where this is the case, there will be spatial autocorrelation in the residuals. In order to evaluate this, the squared difference between the residuals of each pair of points was plotted against their great circle geographic distance, using both the full sample and subsets at increasingly smaller scales . Visual inspection indicated that the relationship was flat . The prediction is supported: controlling for distance from the equator, the partial correlation of log island area with pathogens is As Theory predicts that pathogens will also decrease with distance from the mainland. The relationship is weak in this dataset, and its independent effect is hard to evaluate since the smallest islands are also farthest from the mainland. Getting a good measure of isolation is difficult, since it involves not just distance to the nearest continent but to nearby islands that could be links to sources of greater diversity. Various distance measures were used to try to capture this, but none showed more than a weak correlation with pathogens, or remained significant when island area was also included in the model. However, this could reflect measurement difficulties rather than relative importance. It is also possible that some of the area effect reflects the greater isolation of many of the smaller islands.Many of the variables that influence the distribution of free-living taxa also predict the number and prevalence index of human pathogens in this dataset. Pathogens increased with mean annual temperature and, controlling for mean temperature, in climates that remained free of frost throughout the year. The effect of temperature was highly significant, but only outside the tropics. Within the tropics, mean annual precipitation was a more important predictor, and was associated most strongly with mosquito-borne diseases . Extreme seasonal dryness was also associated with more pathogens, especially typhus, leishmaniasis, and schistosomiasis. Finally, pathogens were worse in areas with high population density. Pathogens were also more numerous on the mainland than on islands, and on large as opposed to small islands. Most of the predictions derived from species diversity patterns in other taxa were supported, and are discussed in turn below, beginning with the island results.The classic model of island biogeography The lower pathogen load on islands is consistent with Curtin's Most of the pathogens in this dataset are vector-borne, and this is likely to enhance the influence of island area and isolation since two species, pathogen and vector, need to be present at the same time Both the pathogen prevalence index and number of pathogens show a strong latitudinal gradient in these data, with more pathogens closer to the equator. This could reflect vector ecology, since malaria (protozoans), dengue (virus) and filariae (nematodes) are taxonomically diverse, yet all are transmitted by mosquitos and all are most prevalent in hot wet climates where mosquitos are abundant. Typhus, leishmanias, and schistosomes, on the other hand, are transmitted by other vectors and intermediates  and had different environmental correlates. The latitudinal gradient in human pathogens may also reflect the ecology of alternate hosts. Rodents, an important reservoir for many human diseases, host more viral parasites at lower latitudes, perhaps because of latitudinal gradients in their own viral vectors Although a latitudinal gradient in species richness exists across a wide range of free-living taxa, the cause of this pattern remains a topic of debate Pathogen prevalence levels increased with temperature in 7 of the 10 pathogen groups, including the mosquito-borne pathogens, which are known to be highly temperature sensitive Precipitation had a more complex relationship with the pathogen prevalence index, because mean yearly precipitation and extreme seasonal dryness had different effects and affected different pathogens. Mean annual precipitation had a curvilinear relationship with both the prevalence index and pathogen number, peaking at intermediate values and declining in very high precipitation areas. The association between pathogens and mean annual precipitation was especially strong in the tropics and for mosquito-borne pathogens; perhaps the decline in very wet areas is associated with mosquito larvae being washed out due to heavy rains and flooding.Areas with little or no precipitation during the dryest part of the year (lowest precipitation in dryest month) also had more pathogens. Seasonal dryness affected a different group of pathogens than did mean temperature and precipitation. The greatest effect was on typhus. Typhus is transmitted by fleas and lice and can become worse in crowded conditions with poor sanitation, and when drought causes rodents (and the fleas they carry) to move near human habitation in search of water. An analysis of tree-rings in pre-industrial Central Mexico found that a significant drought occurred during the first year of all 22 large typhus outbreaks studied Guernier et al. The relative importance of temperature and water as predictors of species richness has been shown to vary with latitude, with temperature being more important at high latitudes and availability of water more important at low latitudes, where energy is already abundant Cross-national differences in pathogen prevalence today have been shown to reflect differential access to disease prevention measures more than environmental variables, although pathogen richness still shows the latitudinal gradient found with other taxa Use of historical data has limitations as well as advantages, due primarily to lack of precision in the historical source material: pathogen distributions were for the most part not available at the species level, and prevalence was assessed by an index based on ordinal scales for each pathogen, rather than by direct counts of infected individuals. Limitations of the source material also bias the pathogen sample toward vector-borne pathogens, which are less global than other pathogens The results of this study show strong support for several theoretical and empirical findings in geographical ecology, and show that they explain human pathogen distributions on a global level. The results offer insights into past and present patterns of infectious disease, and provide information relevant to the likely effects of global warming on pathogens sensitive to temperature, frost, and seasonal dry extremes.Datafile S1New Pathogen Codes for the Standard Cross-Cultural Sample: Codes and Guidance.(PDF)Click here for additional data file.Datafile S2New Pathogen Codes for the Standard Cross-Cultural Sample: Data.(CSV)Click here for additional data file.


This occurred several months after the first and second Twenty-five years have passed since the first lung transplant was performed at the Instituto do Coração (INCOR) do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo. On October 16In parallel, pulmonologists and surgeons sought training at other lung transplant centers and research in the field was initiated at our laboratory in the Medical School. Because bronchial healing remained a problem in clinical cases, it was chosen as a starting point for research. Using animal models of bronchial anastomosis and lung transplant as well as an established methodology for the evaluation of airway mucous clearance, the effect of immunosuppressive drugs was tested. Cyclosporin 3th 2000, a single right lung transplant was performed on a 32-year-old male with silicosis. The post-operative period was uneventful and the patient is still alive fifteen years later. The main problems concerning intraoperative care, post-operative care and immunosuppression seemed to be solved. However, in the following three years we performed ten more procedures, of which only two patients survived for more than one year. Although only two patients of this period are still alive, the low survival rate indicated that there was still something lacking. At that time, the current knowledge and infrastructure were considered sufficient, but it was clear that unless a dedicated team was established, we could not proceed in this field.On August 9In 2003, a multi-professional and multi-disciplinary team was formed that included pulmonologists, surgeons, infectologists, nurses, physiotherapists, nutritionists, social workers and psychologists. We established a routine of morning rounds in ICU and on the wards; assessment meetings; scientific meetings and journal clubs. Based on candidate selection criteria established by the International Society for Heart and Lung Transplantation, patients considered for LTx were assessed and visited by all members of the lung transplant team. Inclusion on a waiting list was possible only upon the agreement of all members of the staff.Since then, almost 250 procedures have been performed, including some landmark procedures: the first bilateral procedure in 2003, the first pediatric transplant in 2006, the first split transplant in 2011 and the first transplant using the ex vivo lung perfusion (EVLP) technique in 2012. However, some problems persisted. Initially, simple preoperative monitoring and patient positioning were problematic and each transplant presented individualized challenges, illustrating the difficulties inherent in establishing routines in the operating room.However, over time, the program has achieved consistently satisfactory results and survival in line with international rates . For disex vivo perfusion Improving the quality of available donor lungs seemed to be the best solution for increasing the number of transplants. Thus, the first report on the reconditioning of initially rejected donor lungs by Two important issues remaining are the training of personnel and international recognition. All staff members were trained in our institution and completed the training in transplant centers in North America or Europe. The Ministry of Health has certified InCor for medical residency in pulmonary and surgical transplantation, as well as two years of training in a clinical or surgical fellowship in lung transplantation certified by the Ministry of Education. We are internationally recognized as a part of the ISHLT registry and we actively participate in international congresses as members of the society and through the submission of scientific papers.Institutional support is fundamental for the maintenance of staff and we were supported without fail throughout the formation of the transplant group. Although the number of transplants performed every month remains small, the need of these patients for care is intense. Thus, maintaining a professional team on a full-time basis will only be feasible with professional development and salary adjustments. In 2013, the Transplant Center Unit of InCor was created, which relies on a 24/7 nursing team fully dedicated to procuring thoracic organs and mediating and enhancing the work of the transplant teams, resulting in the realization of 98 transplants in 2014, including adult and pediatric heart transplants and lung transplants.The road traveled thus far has enabled the formation of an accomplished group and national leadership in this area. It is essential that the foundations of our group are the same as those of universities: care, teaching and research. We believe that we have achieved these goals, but much remains to be accomplished in this field.


These approaches are compared in the context of their reconfigurability: more specifically, we investigate the trade-off between the cost and the effectiveness of competing strategies aimed at adapting to changes in the sensing environment. To support this work, we introduce three new metrics: a cost/efficiency measure, a performance measure, and a resource consumption measure. The results of our experiments show that ad hoc clusters adapt more readily to changes in the sensing environment, but this higher level of adaptability is at the cost of overall efficiency.In this paper, we investigate alternative distributed clustering techniques for wireless sensor node tracking in an industrial environment. The research builds on extant work on wireless sensor node clustering by reporting on: (1) the development of a novel distributed management approach for tracking mobile nodes in an industrial wireless sensor network; and (2) an objective comparison of alternative cluster management approaches for wireless sensor networks. To perform this comparison, we focus on two main clustering approaches proposed in the literature: pre-defined clusters and  Since its introduction in the 1980s and 1990s for military defense applications, wireless sensor network (WSN) technology has expanded in scope to include a wide range of civilian applications such as infrastructure security, environmental and habitat monitoring, industrial sensing, and traffic control . For exaIndustrial wireless sensor networks (WSNs) have become a research trend due to the advances in processing power for micro-computers and reduced battery consumption of embedded battery powered devices. General WSNs are composed of wireless sensor nodes, which are small with limited processing and computing resources and are inexpensive compared to traditional sensors. Sensor nodes are used to sense, measure and gather information from the environment and transmit the data to a user or data acquisition system.Hardware and software technologies are currently available to provide sophisticated monitoring, control, and diagnostics of industrial systems at lower costs than ever before. There are, however, a number of implementation challenges when these new technologies are applied in industrial environments. Given the distributed nature of many of these systems, management and coordination are key challenges. Reliability and robustness given harsh industrial conditions, which can arguably require higher reliability requirements than other commercial systems, is another key implementation issue.Given the harsh, uncertain, and dynamic conditions present on the factory floor, WSNs applied in this domain must be capable of rapidly adapting to change in order to support the flexibility and responsiveness required of the system as a whole. More specifically, the WSN should be capable of allowing sensor nodes to be added and/or removed from the system on-the-fly to support changes in the configuration of the shop floor, and also be capable of handling signal loss that occurs during the tracking process as a result of blockage and/or noise from machinery.i.e., it should have “… the ability to repeatedly change and rearrange (its components) in a cost effective way” 20].In order to provide an objective comparison of alternative distributed cluster formation techniques, we chose to simulate three clustering techniques  that appi.e., are composed of multiple node types) and use range-free localization . As well, all three techniques use bidding-based protocols to recruit nodes to form clusters.It should be noted that all three techniques shown in The first two techniques result in pre-defined clusters of Anchor Nodes that are centered on a Sink Node cluster head. For the Static technique, the cluster formation process is initiated by Sink Nodes using the well-established Contract Net Protocol (CNP) for distributed communication and control . In contThe main difference between the static and the dynamic techniques is their ability to adapt to change during operation. Typically, a cluster formation technique is regarded as dynamic when it includes regular (periodic or event-driven) cluster head re-election or cluster reorganization procedures, either to effectively react to network topology changes and adjust appropriately the cluster topology, or simply aiming at the appropriate rotation of the cluster head role among the nodes to gain energy efficiency. Given that our WSN is heterogeneous with Sink Nodes serving as cluster heads, cluster head re-election does not occur. However, the dynamic technique differs from the static technique in its ability to reorganize its clusters to facilitate Mobile Node tracking. As its name implies, clusters formed by the static technique are fixed; in contrast, clusters formed by the dynamic technique can adapt by borrowing Anchor Nodes from adjacent clusters as Mobile Nodes approach cluster boundaries. The intention is to approximate the more dynamic pre-defined clustering approaches of ,15 with O(n), where n represent the number of nodes.The bidding-based protocols used by both the static and the dynamic techniques result in clusters where each Anchor Node is a member of the cluster whose mean (the Sink Node or “cluster head”) is located closer to the Anchor Node than any other. This process results in a distributed implementation of the k-means algorithm that has the advantage of addressing the computational complexity of the k-means problem : by distThe resulting network topology is a set of clusters in the form of Voronoi cells . For boti.e., rectangles, crosses, diamonds) and the Voronoi boundaries have been identified by lines. When an obstacle is introduced in the lower figure , the clusters reconfigure in response to the resulting signal blockage. As can be seen in The bidding-based protocols used for cluster formation also provide the static and dynamic techniques with the ability to adapt to the shop floor environment. For example, the sensor network shown in ad hoc clustering techniques described in the previous section. For this technique, Sink Nodes again serve the role of cluster heads, but Anchor Nodes are temporary members of clusters that are formed for each Mobile Node tracking task. In order to determine which Sink Node is to serve as cluster head for a given tracking task, Sink Nodes initiate Vickrey auctions amongst themselves. The winning Sink Node then manages the tracking task by recruiting Anchor Nodes into an ad hoc cluster. This cluster remains active until either the Sink Node does not have enough resources  to track the event , or the Sink Node decides to no longer track the event. At this point all the participating wireless nodes are dismissed and they will no longer have any commitment to the ad hoc cluster .The third technique shown in In this section, we compare the distributed cluster formation techniques described in A reconfigurable system is generally understood to be a system that can be adapted to meet new or changed requirements. However, as in natural systems , this adaptability is typically at the cost of efficiency. In order to quantify this trade-off in a distributed WSN, we propose the three basic metrics described in this subsection.The first metric, a cost/efficiency related criterion, measures the load distribution among Anchor Nodes. In a computer network, load distribution or load balancing is used to distribute workloads across multiple computing resources, such as computers, central processing units (Sink Nodes), or wireless nodes. Load distribution  aims to optimize resource use, maximize throughput, minimize response time, and avoid overload of any of the resources.i.e.,N is the number of Anchor Nodes and In this research, we are interested in measuring how efficient the three clustering approaches are at distributing the load among Anchor Nodes. To measure load distribution among Anchor Nodes, we propose utilizing the statistical variance of the number of times each anchor is employed to estimate a distance to a Mobile Node. The variance explains how the workloads are distributed among Anchor Nodes by the cluster. In other words, the lesser the variance the more distributed the workloads among wireless nodes, The second metric is concerned with the performance of the distributed cluster formation techniques, and more specifically, their ability to adapt to the movement of Mobile Nodes through the tracking zone. Since the WSN investigated in this research is composed of stationary sensor nodes (Sink Nodes and Anchor Nodes), this form of adaptability involves “virtual mobility” of these stationary tracking nodes. In other words, as a Mobile Node traverses the tracking zone, the clusters adapt to the tracking task by assigning Anchor Nodes with the closest proximity to the Mobile Node tracking task.i.e.,i to estimating anchor j at quantized time t, T is the WSN/WAN operation period, N is the number of Mobile Nodes in the wireless network, and M is the number of distance estimating Anchor Nodes.As a metric to measure how effective a given cluster formation is and to compare the performance of the cluster formation techniques in deploying a proper set of Anchor Nodes, we propose using the average distance each Mobile Node is at any point in time from the estimating Anchor Nodes during the course of WSN operation, Wireless nodes’ battery and communication bandwidth are two main resources of a WSN every distributed system must sparingly use.One key restriction on the wireless networks is the limited communication band width, which limits the number of message that can be communicated at a time among a cluster of wireless nodes located at close vicinity with respect to each other. Hence, it, in turn, limits the rate of data transferred among the wireless nodes.Transmission and receipt of messages also play a significant role in the battery life of a wireless node. In fact, wireless communication—in terms of transmission and receipt of messages—is the major energy consumer during the system operation . This isThus, the rate of messages communicated among wireless nodes in a cluster of WSN plays a very crucial role in the practicality of the clustering technique, hence the tracking technique, both in terms of available bandwidth and energy consumption of wireless nodes.i, at a given time, the accuracy of the location estimation, accf, is measured by the Euclidean distance between the estimated location of the Mobile Node  and its actual location  at any point in time during the course of simulation, i.e.,Given the actual location of a Mobile Node, A simulator is used to examine the proposed cluster formation techniques in dynamic and obstructed environments typical of industrial environments. Selecting a set of parameters of interest for further studies, statistical design of experiments is also used to analyze the sensitivity of these parameters to changes and fluctuations.In this paper, we take into consideration the following ambient and medium related factors as well as a selection of design factors for further investigations into their impact on the WSN clustering techniques: number of Sink Nodes, number of Anchor Nodes, signal blockage level, and cluster formation techniques. To study the adaptability of the cluster formation techniques, we limit the effect of ambient conditions to different levels of signal blockage. In a previous study, we tested the effect of other ambient conditions such as varying temperature on the wireless sensor networks, and concluded that air humidity, pressure and temperature have measurable but negligible impact on the wireless sensor networks .Given the factors introduced in This design has 24 base runs; with only four replications for every treatment and two additional replications for every run of E2 and E3 levels, the total number of experiments is 160.To reduce the effects of external factors—either in the form of noise or disturbance—randomization of the running order of the treatments are widely suggested . In thisA JADE (Java Agent DEvelopment framework ) based sNormal probability and histogram plots were used to examine the normal distribution of the obtained response data. For all sets of experiments under each of two criteria, to an acceptable level of approximation, the obtained response data follow a normal distribution. Moreover, for all sets of experiments under each of two criteria, the residual plots do not illustrate any patterns of anomaly.ad hoc technique recruits only those Anchor Nodes required for tracking Mobile Nodes, and shares Anchor Nodes between cluster heads (Sink Nodes) as can be seen by the overlapping Anchor Nodes in this figure. The dynamic and static techniques, shown in Before reporting on the reconfigurability and performance related measures, it is interesting to graphically compare the three cluster formation techniques. As shown in In the remainder of this section, we summarize the results for the four experimental factors in i.e., as would be expected, larger numbers of Anchor Nodes and lower levels of signal blockage allow the workload to be distributed more evenly among nodes. The results for the main effects of the load distribution parameter are summarized in As noted in The main effect plot shown in ad hoc technique for this metric. A brief inspection of i.e., the larger number of Anchor Nodes shown in the pre-defined figures  in As shown in  figures  should rFor the purposes of this study, effectiveness is a measure of the average distance between a Mobile Node and the Anchor Nodes that track it: the lower the average distance, the more effective the cluster formation technique. For this metric, both the number of Anchor Nodes and the cluster formation technique are significant. More specifically, the greater the number of Anchor Nodes, the shorter the distance between Mobile Nodes and tracking Anchor Nodes (due to a higher concentration of Anchor Nodes in the network). This result is shown in the main effect plot for effectiveness in ad hoc technique results in the closest spacing between Mobile Nodes and their tracking Anchor Nodes. This result is supported by As shown in i.e., as message rate increases, processor load and battery consumption increases. For this measure, all of the experimental factors described in As noted in i.e., as blockage increases, signal paths are limited, and consequently, message exchange decreases.The results for the first two factors are a direct consequence of increasing the number of participants in the bidding process. More specifically, as the number of nodes (Sink Nodes and Anchor Nodes) increase, more CNP and Vickrey auction messages are sent. The third factor, signal blockage, impacts the number of messages received: ad hoc approach results in the highest message rate, and as a result, the greatest consumption of network resources. This heavy resource consumption is exacerbated by the load distribution and effectiveness results described previously. More specifically, in addition to resulting in a higher number of messages per second to support its protocol, the ad hoc technique involves fewer Anchor Nodes in these communications. Given the direct link between communications and battery life, the ad hoc technique will result in a much shorter mean time between failure of sensor nodes, than the two pre-defined clustering techniques.ad hoc technique’s ability to freely form clusters results in the best tracking accuracy.etc.) as Mobile Nodes reach the boundary of the tracking cluster since they only deploy Anchor Nodes within their cluster; in contrast, a very adaptable technique like the ad hoc technique does not have any restrictions concerning Anchor Node commitments to clusters when choosing among available Anchor Nodes.These results are a consequence of the quality of service issues that occur as Mobile Nodes reach the boundaries of sensor node clusters. For example, fixed clustering techniques like the static technique can result in deteriorating conditions  and ad hoc approach resulted in better cluster formation effectiveness; in other words, the ad hoc approach appears to be more adaptable to the Mobile Node tracking task than the pre-defined clustering approaches. This higher effectiveness of the ad hoc approach is at the cost of poorer load distribution though. As a result, the trade-off for higher adaptability in this case is the potential for an unbalanced depletion of resources  and, correspondingly, unpredictable performance of the overall network.All three of the clustering techniques showed that they are capable of reconfiguring their structures to adapt to changing shop floor and Mobile Node tracking conditions. However, as would be expected given the basic designs of the three techniques, the In this paper, we merely focused on distributed dynamic cluster formation techniques as a solution to the reconfigurability and adaptability required of a tracking system to position/localize mobile wireless sensor nodes in factory environments. However, distributed dynamic clusters are only one component of such a tracking system. Details of network architectures and protocols as well as the multi-agent system to manage such a tracking system are to be reported in our future papers.Our current work in this area is focused on developing a multi-agent cluster management system to encourage maintenance by reclaiming and replacing nodes over time. For this work, we are investigating the use of homogeneous wireless sensor networks that provide minimal setup and changeover effort due to their hardware indifferences.


Electroencephalography (EEG) is a longstanding technique to measure electrical brain activity and thereby an indirect measure of synaptic activity. Synaptic dysfunction accompanies Alzheimer’s disease (AD) and EEG can be regarded as a potentially useful biomarker in this disease. Lately, emerging analysis techniques of time series have become available for EEG, such as functional connectivity and network analysis, which have increased the possibilities for use in AD clinical trials. In this review, we report the EEG changes in the course of AD, including slowing of the EEG oscillations, decreased functional connectivity in the higher-frequency bands, and decline in optimal functional network organization. We discuss the use of EEG in clinical trials and provide directions for future research. Alzheimer’s disease (AD) is a degenerative disease characterized by pathological changes at the neuronal and synaptic level that eventually result in dementia at the subject level. The present therapeutic strategies have a modest and only symptomatic effect. It therefore remains essential to develop and test new interventional strategies.Biomarkers can play a role as outcome measures in clinical trials. Classically, outcome measures in clinical trials in AD have focused on cognitive performance and behavioral outcome . HoweverSeveral investigational modalities are available that potentially can serve as biomarkers in AD clinical trials. Magnetic resonance imaging, functional magnetic resonance imaging (fMRI), cerebrospinal fluid analysis and positron emission tomography each assess different brain properties and are valuable for characterizing AD processes . HoweverEyes-closed task-free  electroencephalography (EEG) measures oscillatory electrical brain activity and captures dynamical processes at the macroscopic scale. This technique has potential use in phase III trials because of its favorable investigational properties: it is relatively inexpensive, widely available, stable over time, and patient friendly. Additionally, recent developments, such as the application of network theory, increased the possibilities for EEG signal analysis of contributing to the understanding the mode of action of interventions in AD – in particular, in relation to (synaptic) connectivity.This paper aims to reassess the potential role of eyes-closed task-free EEG in future AD clinical trials. We will report and discuss the results of EEG analyses in AD and in past clinical AD trials. We shall also review recent advances in EEG signal analysis and provide directions for their use in future trials.EEG measures the fluctuating electrical field surrounding neurons. The EEG signal results from the sum of all transmembrane currents picked up by the EEG electrodes, where the most important contribution comes from synaptic activity. Other currents include fast action potentials, calcium spikes, intrinsic voltage-dependent currents, ligand-gate ion fluxes, gap junctions, neuron–glia interactions, and ephaptic effects . TypicalAD is pathologically characterized by widespread neuronal cell loss and depositions of amyloid and tau aggregates, but previous studies have also suggested a link between AD and synaptic dysfunction . The synRecent technical advances have increased the possibilities for EEG analysis. Digital EEG files usually have a relatively small size, which allows fast electronic exchange between recording centers; for example, for central analysis in multicenter trials. In addition, most parameter settings are adjustable to the analytical needs at any time after the recording. Furthermore, with increasing computational power, classic frequency analysis as well as advanced and complex studies of the EEG time series that add information on the organization of brain function can be performed.Computerized frequency analysis allows the objective quantification of the different frequency components of the complex digital EEG signal and is at the basis of many different types of advanced EEG analysis. This analysis has been widely applied in the context of AD.EEG oscillations are often divided into several frequency bands (bins of adjacent frequencies) – such as delta band 0.5 to 4 Hz, theta band 4 to 8 Hz, alpha band 8 to 13 Hz , beta band 13 to 30 Hz, and gamma band 30 to 70 Hz – and analyzed according to these bands. Care should be taken when interpreting the results of the gamma band, and to a lesser extent also the beta band, since EEG activity above 20 Hz, and especially activity above 30 Hz, is merely activity of the skull muscles rather than of the cerebral cortex [The absolute and relative quantities of each frequency band within the total EEG signal are indicative of normal or pathological conditions and can be used to detect group differences. The dominant frequency, which is seen as a peak in the frequency spectrum, typically lies in the alpha frequency range in healthy awake adults. The analysis of these frequencies is one of the most widely available digital analysis techniques considering that EEG recording and reviewing systems are increasingly equipped with built-in tools for this purpose. In addition, frequency analysis is computationally fast and, since it is one of the most used analytical methods for EEG, results can be weighed against previous research. On the other hand, while the quantity of activity at a certain frequency measured at an EEG electrode is related to the amount of local synchronization of the underlying neuronal population, there is limited information on the phase relationship and synchronization between distant brain regions.The most consistent EEG finding in AD is diffuse slowing of oscillations, displayed as an increased proportion of theta and delta activity and reduced peak frequency ,7. LongiThe slowing of EEG oscillations has been used as a marker for intervention effects in clinical trials, but the results have varied. Administration during several weeks to months of the approved AD treatments was not found to have a consistent effect on EEG. However, the trials were not double-blind controlled and therefore definite conclusions with respect to the properties of EEG as a marker in clinical trials cannot be drawn from these studies –28. Two The methodological differences and suboptimal trial designs of most intervention studies render the results difficult to interpret. Most studies are relatively old (before 2001) and do not meet the current standards on clinical trial design and reporting, such as a description of the randomization, the use of blinding, the description and statistical handling of dropouts, and the trial registration number. Furthermore, the number of participants is generally modest, with patient groups smaller than 20. These trial properties should prompt caution when interpreting and comparing the results.Overall, the studies show that it is feasible to implement EEG in clinical trials and there are indications that peak frequency is sensitive to an intervention effect. Due to the insufficient quality of most clinical trial designs, most of the currently available EEG results are difficult to appreciate, but future studies that have implemented the present standards for clinical trials could resolve this issue.Lately, the field of EEG analysis has advanced considerably and analysis methods beyond the classic frequency analysis have become available for AD research. One of these relatively new analyses is functional connectivity . Brain As an example, Figure The strengths of the network approach in AD studies include the implementation of a theoretical framework and powerful mathematical models to study the determinants of network disruption in AD. The mechanisms that change the macroscopic functional organization in AD might contribute to our understanding of the clinical signs as well as the pathophysiological processes in this disease. In addition, the theoretical framework of network analysis is invariant of the modality used and can be applied to any study that consists of measurement points with a relationship between these points. This facilitates the integration of results of brain mapping techniques, including time series , structural studies (magnetic resonance imaging) and metabolic and perfusion studies .Indeed, the EEG-based functional brain networks of AD patients have been found to differ from those of healthy individuals. In one study, a change from the optimal small-world network configuration into a disorganized, more random network type was found in the higher-frequency bands, although volume conduction effects were included in this study . In the Although a general pattern of AD-related network changes is emerging from the literature, methodological differences between studies influence the results, reducing the comparability across studies ,54. FuncAn alternative approach is the construction of weighted networks, in which the exact values of the connectivity matrix are preserved without the need for thresholding. Nevertheless, functional connectivity also influences weighted network measures to a great extent. A partial solution to this problem is normalizing the weighted networks. This can be done by presenting the network measures as proportions of measures of a set of random networks with equal connectivity strength, derived from shuffling the functional connectivity matrix. However, it has become clear that the resulting random networks are not really random and still contain a great deal of structure. In addition, the methodological differences in the computation of functional connectivity also influence network results, since the functional connectivity values are used for the computation of functional networks. The use of methods that do not require arbitrary parameter settings but uniquely follow from the connectivity matrix can resolve some of these problems.One such approach is the minimum spanning tree . Figure Although a longitudinal MEG study found network changes to be correlated with the decline in cognitive performance in Parkinson’s disease, the longitudinal behavior of network changes in AD has not so far been studied. It can be conceived that the changes do not relate linearly to disease progression, as is the case for other modalities such as hippocampal atrophy and memory scores, and that the yield of EEG network measures in clinical trials for AD depends on the AD severity. To what extent network measures suffer from floor or ceiling effects, which could make them less sensitive in earlier or later disease stages respectively, is currently insufficiently known. Longitudinal studies, as well as measuring an intervention effect in different AD stages, will help to establish the value of this type of marker.This review presents an overview of eyes-closed task-free EEG analysis methods that are potentially useful in AD clinical trials and discusses past intervention studies implementing EEG in AD. Studies using conventional methods show a converging pattern of change in AD. This pattern of slowing of EEG oscillatory activity may serve as a benchmark in intervention studies. In addition, newer analysis methods, such as functional connectivity and network analysis, have been applied to EEG in AD and, when implemented in clinical trials, can contribute to the understanding the mode of action of the interventions. For example, an intervention-related preservation of local connectivity that is distributed across the brain regions according to the hub distribution can indicate that the intervention is especially effective in hub regions and thereby in the brain regions with the highest electrical activity and the highest metabolic demand. In addition, different intervention effects on long-distance and short-distance connectivity indicate what systems might be involved. However, it is presently too early to apply functional connectivity and network analyses for these hypotheses and more knowledge on the behavior of the network and connectivity measures is needed to fully understand their meaning.The value of EEG in clinical trials will improve when it is implemented in high-quality randomized clinical trials. So far, most controlled trials using EEG are very small and do not report enough details on the trial design and trial execution to assess the risk of bias. Furthermore, most studies are relatively old and not designed according to the current standards. However, with advances in designing and reporting of randomized controlled trials, future studies can address these problems. We recommend that the quality standards which apply to the performance of clinical trials should also be implemented in the EEG part of future studies.In addition, knowledge of the influence of the type of EEG analysis can improve comparison of results between studies. No universal method exists at each analysis level  and each method influences the outcome. Consequently, studies that use different analysis techniques are difficult to compare. When test characteristics are taken into account, however, differences in results might explain differences between studies and might even give additional information on the mode of action. The strategy of using multiple measures and searching for a consistent direction of connectivity changes in AD across the methods has the advantage of increasing knowledge on the behavior, such as sensitivity, specificity, availability, and applicability, of the different measures relative to each other. An alternative approach would be to further subdivide functional connectivity into categories based on the characteristics of the synchronization measures, such as a subdivision into short-distance and long-distance biased connectivity. In this way, it may be possible to interpret the results of different connectivity measures in addition to, rather than instead of, each other. In parallel, there is a need for the development of EEG analysis techniques that do not depend upon the often arbitrary choice of one or more parameters. The availability of an EEG analysis method that has good discriminative properties and which renders results without a single choice in parameter settings could be valuable for reaching consensus.At this moment, clinical measures are the only accepted endpoints for pivotal phase III intervention studies in AD . It is aIn conclusion, EEG in clinical trials in AD is a feasible and potentially useful addition to cognitive endpoint measures in AD trials. Recent developments in EEG signal analysis, such as network analysis, present promising methods to provide frameworks for the testing of hypotheses on the mode of action of interventions. When the quality of the clinical trial design and analysis meets the current standards, the role of EEG in clinical trials for AD can be fully appreciated.


This article presents an extensive comparative review of the emergence and application of geodemographics in both the United States and United Kingdom, situating them as an extension of earlier empirically driven models of urban socio-spatial structure. The empirical and theoretical basis for this generalization technique is also considered. Findings demonstrate critical differences in both the application and development of geodemographics between the United States and United Kingdom resulting from their diverging histories, variable data economies, and availability of academic or free classifications. Finally, current methodological research is reviewed, linking this discussion prospectively to the changing spatial data economy in both the United States and United Kingdom. Geodemographic classifications organize areas into categories sharing similarities across multiple socioeconomic attributes. These classifications have either a national extent or localized focus  and are built to describe the generalities of places or to examine the geography of specific domains of interest . Within a geodemographic typology, each cluster is identified from a distinctive collection of attributes; for example, wealthy neighborhoods, where most households comprise older individuals living within apartments. Clusters are typically named by the classification builder  and are accompanied by rich media descriptions. An example geodemographic classification for the United States is shown in Geodemographic classifications tend to be highly dimensional, typically including anywhere from a dozen to several hundred empirically derived characteristics. Using many variables to construct a set of meaningful categories can be a complex process, and there is an extensive literature on both the selection of input variables and the analytical methods for creating classifications. The literature reflecting on the use of geodemographic systems, however, is less well developed. As such, in this article we take an international and comparative perspective, specifically considering the evolution of geodemographics in the United States and the United Kingdom. In spite of similar origins in the mid-1970s and early historical parallels, geodemographic classifications are used quite differently between the two countries. In this article we consider the historical evolution of geodemographic classifications in the United States and the United Kingdom, the current geodemographic market, and the near-term prospects for geodemographic classification given recent changes and pressures on national statistical systems.Geodemographic analysis has its origins in the work of human ecologists in the 1920s and 1930s, and its history includes the large body of work in social area analysis and factorial ecology see  and, as Geodemographic classification emerged later  as a metBy the mid-1970s, commercial interest in the potential of geodemographic analysis had emerged on both sides of the Atlantic. Within the United Kingdom, the national census ward–level classification produced by Over time, both the U.S. and UK markets have expanded and diversified, with those national classifications existing as of 2012 summarized in In both countries there is a diverse set of geodemographics, with the United Kingdom having a greater variety of classifications supplied by small-to medium-sized enterprises (SMEs). Classifications in the United States included a maximum of two hierarchical levels, whereas in the United Kingdom, a number of classifications included a third level. In both countries, however, a limited number of providers advertise additional microlevel versions of their classifications that enable the appending of client-specific data and reclustering of the taxonomic units into new bespoke segmentations. The addition of hierarchies provides greater flexibility in how classifications can be used. More aggregate groupings enable the profiling of data with restricted numbers of cases and can reduce the standard errors of some variables that are imprecise at finer levels of aggregation (bringing them within usable limits); however, the coarser groupings improve attribute resolution at the expense of geographic resolution and reduce the utility of a classification for identifying interesting patterns.Differences in cluster frequency between commercial and noncommercial providers likely relate to the types of data available to commercial organizations , thus enabling areas to be profiled over a greater number of dimensions and increasing the possibility that additional groupings of similarity might emerge from a cluster analysis. The units used to build the classifications in the United States were most prevalently census block group level , which are much more aggregate than those typically used in the United Kingdom . A caveat is that the data used to build geodemographics can be drawn from a variety of scales; however, the units detailed in http://worldclusters.org/?pageid=22), but this does not appear within the peer-reviewed literature. A further U.S. geodemographic example  appeared in the context of health as an eight-cluster segmentation of counties for comparison of mortality disparities  than in the United States (thirty articles), these results might not reflect the use of geodemographic methods. The results emphasize that general-purpose geodemographic classifications are more widely used within the United Kingdom. However, in an attempt to capture American literature that has exploited geodemographic methods, but is not explicitly labeled as such, we conducted further searches using the various permutations of the following terms: neighborhood classification, k-means, census, and typology. These broader search terms uncovered eleven additional articles. The between-country differences do, however, require semantic distinction, chiefly between geodemographic methods and geodemographic systems. A geodemographic system is considered here as comprising a general-purpose classification designed by one party for multiple users and uses, whereas a geodemographic method would focus on the creation of bespoke multivariate typology of geolocated individuals or administrative units for a specific case study area. It should also be noted, however, that there is often overlap between the application of methods and the use of a system and, as such, they cannot be considered to be entirely mutually exclusive.The previous section considered the diversity of the U.S. and UK general-purpose classification market, which is now extended to summarize the range and extent of publications that apply geodemographic classifications within the United States and United Kingdom. The results are summarized in census classification returned 646,000 records in Google Scholar (accessed 20 December 2012).The themes highlighted in This comparative literature search illustrates greater publication activity in the United Kingdom over the United States, but the reasons for this disparity are not entirely clear. A second striking result is that there is such brevity to the academic application areas, with health appearing most prevalently, which could also in part be a function of the number of researchers within this field, and the types of analyses typically conducted. Furthermore, it was identified that geodemographics were applied in a number of different ways, with no particular U.S. or UK bias. These included use for survey sample design, as descriptive measures, as a surrogate for socioeconomic status, as control variables within regression analysis, as a set of levels within multilevel models, as both origin constrains and calibration criteria within spatial interaction models, and, finally, as a measure of small area deprivation.Geodemographic models can be considered idiographic, providing descriptive characterization of multiple geographical areas; with their operationalization based on the principle that socio-spatial structure is highly correlated with behaviors, attitudes, and preferences. In this way, geodemographic classifications are “theory-free,” as they do not hypothesize a priori about the role of large-scale social mechanisms or individual-level theoretical constructs.Contemporary research, however, has linked geodemographic classifications to the “spatialization of class” . SocioloDuring the mid-1990s, a general body of critique examined the social implications of geographical information systems GIS;  and inclNon-commercial systems’ lack of “theory” raises some complications for validation, as without a theory of socio-spatial stratification it remains unclear what attributes these systems should be measured against. Is a good classification one that accurately describes real divisions in society or one that fits a large demographic data set? Are there differences between these two criteria? The most interesting and nuanced considerations of geodemographics grapple with these theoretical questions, for example, by drawing analogies between geodemographics and Bourdieu’s concepts of habitus and field . Webber It is clear that there is no grand unifying theoretical framework for geodemographics, and this might pose some problems for validation. However, theory does not have to be seen as exogenous to the classification process, though. Going forward, geodemographic classifications face substantial challenges, with institutional shifts in both the United States and United Kingdom changing the nature and availability of the data on which these systems have historically relied. A key input to almost all geodemographic classifications remains the decennial census of the population. This data set has the most extensive coverage of all input data and is collected as a marker of population growth and characteristic change. In both countries, though, these data are not necessary guaranteed to be collected into the future given the growing costs associated with their collection, and additionally, the increasing volume of transactional data that are collected for other purposes, which arguably could provide an effective surrogate .http://www.scotland.gov.uk/Publications/2012/03/3260). There has been significant interest from the central government in the capitalization of current and future open data resources. For example, in 2012, through the Technology Strategy Board (the UK government’s innovation agency), £10 million of financial backing was provided to establish the Open Data Institute (http://www.theodi.org) with the aim of stimulating new innovations using open data. The extent to which open data will supplement future censuses in the United Kingdom is not yet known, but such substantial government support within an era of austerity should indicate that these developments are being considered seriously.In the United Kingdom, the Office for National Statistics (ONS) has launched the “Beyond 2011” consultation that aims to assess alternative options for producing demographic data that are currently demanded by end users of the census in England and Wales. The underlying motivation for this review is a concern over the rising costs of delivery aligned with tighter fiscal constraints and, additionally, the issue of whether the decennial interval is mismatched to demand for more detailed and frequent statistics. The first consultation concluded in January 2012 and was designed to gather end user views on the range of possible alternatives. These results will feed into a second consultation that is taking place in 2013, with final recommendations made during 2014. As such, it is not yet clear what the exact format of the next UK Census will include; however, the ONS has made strong indications that this will be different from a traditional census. Integral to this review will be consideration of the role and availability of administrative data released by the UK government under “open data” licenses. Of particular importance to the future of the UK Census will be the extent to which any disparate open data might be integrated through data linkage within a national information framework . A consuIn the mid-1990s a decision was taken in the United States to split the “long” and “short” form of the U.S. decennial census into separate surveys. The short form of the U.S. Census is constitutionally mandated and is used to apportion each state’s membership in the House of Representatives proportional to their population. The long form asked questions not strictly related to apportionment, including topics such as housing, income, education, commuting, and other variables of considerable value for general policymaking and social science. After more than a decade of testing the long form of the decennial census, in 2010, this was subsumed into a new survey called the American Community Survey (ACS). Whereas the decennial census is constitutionally mandated, the ACS is not and, as a result, it is under constant threat of losing funding. Indeed, during the summer of 2012 the U.S. House of Representatives voted to remove all appropriations for the ACS on the basis that it was an invasion of privacy and an infringement on individual liberty. Although the summer 2012 vote appears to have been largely symbolic, similar debates have occurred in Canada , suggestIn the context of the United States and United Kingdom, these developments will drive a series of challenges for the geodemographics industry. First, in the future, it might not be possible to rely on a base set of data covering demographic characteristics with high spatial resolution. In a U.S. context, the ACS provides estimates down to the block-group level; however, these estimates are so inefficient that they are virtually unusable at this scale (due to wide margins of error). In general, the ACS trades data quality for data frequency, providing annual estimates (based on a five-year moving average) for most U.S. census tracts and block groups. As such, this constant stream of uncertain demographic data raises some novel challenges for geodemographics in the future, and to our knowledge no classification methods account for uncertainty (standard errors) within their input data. In the United Kingdom, basic small-area population estimate data are provided as experimental statistics from the ONS, but they are of lower spatial resolution than those available from the census. Other UK national surveys exist that have potential in this area; however, as yet, these are not routinely used for small area estimation, and indeed it is doubtful that current sample sizes would create usable estimates at higher resolution geographies. For academic classifications using only census data, this could result in them having to be created at much coarser resolutions, limiting potential for geographical comparison. In both the United States and the United Kingdom, it is likely that the commercial sector will be less sensitive to these changes given that they have a lengthy history of compiling large consumer dynamics databases. These databases are populated using a variety of private or commercially sourced data such as credit checking histories, product registrations, private surveys, and public domain data. Although it is impossible to validate the accuracy and completeness of such data given their commercial sensitivity, it is also important to note that these sorts of data linkages have also not been implemented in any uniform way within the public sector in either country. There are, however, isolated cases where linked data from multiple administrative sources have enabled unique insights to be generated; for example, in the area of access to university  and in eIn this article we have drawn together an extensive literature on the U.S. and UK origins of geodemographics, outlining how the technique emerged as an extension of earlier attempts to understand urban population structure through social area analysis. Although the origins and development of geodemographics in the United States and United Kingdom have evolved in close parallel, a key difference between the regions is that there has been a reduced prevalence of free or academically produced models in the United States.An evaluation of the structure of contemporary general-purpose geodemographic classifications revealed that there is a similar level of classification diversity between the United Kingdom and the United States; however, more classifications were supplied in the United Kingdom by small- to medium-sized enterprises. Typologies within the United Kingdom often included three hierarchical levels, whereas in the United States, all classifications studied were limited with two, although in both regions, some classifications were also available with a further and higher level of disaggregation designed to be used when creating bespoke classifications. Finally, the scale of the public domain data typically used to build U.S. geodemographics was much coarser than the UK; additionally, the U.S. classifications had on average greater numbers of clusters at their finest level of disaggregation.Overall the United Kingdom was shown to have more published academic applications of geodemographics than the United States, and on examining the areas from which these were drawn, it was found that the most prevalent domain of use was health; however, in both regions, applications spanned a wealth of different areas. When exploring how geodemographics were being used within these application areas there was a diversity of analytical procedures ranging from exploratory and descriptive uses through to integration in more complex explanatory models.Like social area analysis and factorial ecology preceding it, geodemographics were also initiated with a lack of theory underpinning the empirical models. A fundamental theoretical question for geodemographic classification is therefore whether and to what extent differences in observed demographic characteristics align with meaningful “real” divisions in society. Validation is a continuing problem for nonacademic models, but we argue that geodemographics have utility and provide useful descriptors of context, even if they lack a firm theoretical grounding. It is entirely possible that more careful construction and broader use of geodemographic classification in the academy could support the development of a more robust theory of socio-spatial structure.In the future, the creation of geodemographics in both the United States and United Kingdom is likely to be more complex as the scale and extent of large surveys such as the census comes under increasing fiscal constraint. This is both a challenge and an opportunity for the public and academic sector. In particular, as individual variables become less precise, multidimensional contextual descriptors might be an effective surrogate for detailed variable-by-variable descriptions. In addition, the assembly of rich linked transactional data from the public and private sectors might be used to improve the discriminatory power of geodemographic systems, in spite of imprecise small-area data from public surveys. Similar systems are already in operation within the private sector in the form of consumer dynamics databases, and we argue that it would be very pertinent for knowledge exchange to occur between the sectors.