Children’s independent mobility (CIM) is an important contributor to physical activity and health in children. However, in the last 20 years CIM has significantly decreased. To develop effective intervention programs to promote CIM, the impact of the environment on CIM must be identified. This review seeks to provide an overview of sex/gender-specific socio-ecological correlates of CIM.A systematic literature search of five databases  was conducted with a priori defined eligibility criteria and identified 1838 potential articles published between January 1990 and November 2017. Two independent reviewers screened the literature and identified and rated methodological quality of the studies. Related factors of CIM were summarized separately for CIM license  and CIM destination (destinations to which a child travels independently), and separately for boys and girls using a semi-quantitative method.Twenty-seven peer-reviewed journal articles were identified which examined the relationship between the social and physical environment and CIM. Only seven studies reported results divided by sex/gender. Most associations between the environment and CIM were found in the expected direction (positive or negative) or not associated at all. The social environment seemed to be more influential for ensuring CIM than the physical environment. Neighborhood safety, fear of crime and stranger, parental support, and perception of traffic were important social environmental factors influencing CIM, while car ownership, distance, and neighborhood design were relevant physical environmental attributes. Few studies examined sex/gender-related environmental correlates of independent mobility, and those findings were inconsistent.The findings of this systematic review serve as suggestions for intervention programs to increase CIM and to identify future directions in research. To establish a robust comprehension of the impact of the social and physical environment on CIM, further sex/gender-sensitive studies using comparable measurements for CIM and environmental correlates are needed.The online version of this article (10.1186/s12942-018-0145-9) contains supplementary material, which is available to authorized users. Physical activity is associated with numerous health benefits , 2. HoweChildren’s independent mobility (CIM) defined as “the freedom of children to travel around their neighborhood or city without adult supervision”  is one iIn addition to these health outcomes, CIM is associated with cognitive and motor development as well as social competencies of children –12. RissHowever, CIM has significantly decreased over the past 20 years , 16. In Socio-ecological models postulate multiple environmental influences on health behavior . ChildreAccounting for sex/gender differences with regard to independent mobility is important. Gender theories postulate that such differences are due to socially determined gender roles; gender-typed patterns of behavior occur based on socialization processes , 29. GenHealth promotion programs are increasingly designed based on the socio-ecological perspective, thus identifying various levels of contextual influences on children’s independent mobility is required , 38. NevThis systematic review was conducted and is reported based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines .The literature search was conducted on 7 November 2017 using the databases PubMed, Medline, Scopus, PsycInfo and Web of Science Core Collection. The search strategy included a combination of terms for independent mobility (“independent mobil*”), correlates (environment* OR neighborhood OR family OR families OR home OR parent* OR mother* OR father* OR sibling* OR urban* OR park*) and children (kids* OR child* OR girl* OR boy*). Additional articles were sought by reviewing reference lists of included full text articles and citations of full text articles using the Web of Science “Citation Network” statistics of each study.Studies were deemed eligible if they met all following inclusion criteria: (1) subjects of the study were healthy children (age 3–12 years or the average age was in this range); (2) at least one association between CIM and an environmental  correlate was examined; (3) an appropriate study design was used ; (4) the study employed a quantitative design; (5) the study was published in a peer-reviewed journal, written in English or German language; (6) the study was published after 1990, because in that year Hillman et al.  introducIf the study examined attitudes towards independent mobility instead of CIM itself, it was excluded. Studies referring to active commuting to school or active travel were only included if they clearly defined whether children travelled independently.Social and physical correlates were selected based on the socio-ecological model of Sallis et al. . CorrelaThe study selection occurred in three steps compromising (1) title-screening, (2) abstract-screening, and (3) full-text-screening by two independent researchers . Studies were included or excluded depending on the eligibility criteria. During each step of the screening process, all references that could not be conclusively excluded were kept for further screening in the next step. Disagreement between the two reviewers on final inclusion was resolved by discussion with a third researcher (AKR). The selection process was documented using the reference management software EndNote X7 .The following data was extracted from each article: author(s); year of publication; country; study design; sample description ; definition, measurement, and instrument of CIM; type, measurement, and instruments of examined correlates; and main study results on the relationship between social and physical environmental factors and CIM  using 11 a priori defined quality criteria based on existing quality assessments published by Downes et al.  and UijtDue to the heterogeneity of social and physical environmental correlates and outcome measures of CIM a meta-analysis of the selected studies was considered inappropriate. The results of all selected studies were analyzed using a semi-quantitative method. In addition to associations between the social and physical environment and independent mobility in children in general, associations were considered separately for both, girls and boys, with appropriate studies. Bivariate associations between CIM and environmental correlates (19 studies) and multivariate regression models (20 studies) were considered separately as various socio-demographic, social, and physical environmental correlates were integrated into multiple regression models. As no study evaluated CIM time and only three CIM range, the results were only analyzed separately for CIM destination and CIM license.The strength of evidence was adapted from previously published scoring systems , 43, 47.As the publications of Foster et al. , VillanuA total of 1838 potentially relevant articles (2165 including duplicates) were identified by the database search and screened based on title and abstract. Next, the full texts of 59 studies were retrieved for detailed review. As 34 studies were excluded due to inappropriate age range, aim of study, study design or for multiple reasons, 25 studies identified by the database screenings have been included in this systematic review. Two additional relevant publication were identified by backward reference tracking, yielding a total of 27 papers included in this systematic review  were collected and an umbrella term was used  yielding a total sum of 23 social environmental correlates . In total eight correlates regarding the parental perceived neighborhood environment and social cultural environment were reviewed Table . PerceivSimilar to CIM destination less significant associations existed for physical environmental attributes and CIM license; only three of nine variables demonstrated significant associations. As shown in Table The results of the multivariate regression models of 12 studies, including these models in addition to univariate associations, and of eight studies solely presenting multivariate regression models were also analyzed in this systematic review Table . These rDue to a limited number of studies reporting results separately for boys and girls and the heterogeneity of correlates, no evidence can be found for sex/gender-specific correlates of independent mobility. Mobility licenses were positively associated with CIM for boys and girls. Two further correlates analyzed in two different studies , 52 wereThe aim of this systematic review was to identify social and physical environmental correlates of independent mobility in children with a special focus on sex/gender. Associations of CIM destination and license and social environment were consistently positive, except for fear of strangers and crime. Car ownership and urban setting showed consistently negative associations with CIM License and CIM destination, respectively. However, five physical environmental attributes  were positively associated with CIM destination. Differences in correlates of independent mobility in boys and girls were found solely for the physical environment, with neighborhood design and bike ownership influencing girls’ independent mobility and associations of destination accessibility and car ownership with boys’ independent mobility.CIM is related to physical activity, e.g., if children are walking or cycling without adult accompaniment. However, correlates of CIM seem to be slightly different to correlates of physical activity . CIM tenPrevious reviews of the physical environment and children’s travel behavior reported inconsistent results and non-occurring associations for several physical attributes as well , 65. ForThe two constructs “parental perception of traffic” and “traffic (objective)” supported the conclusion that social environmental attributes determine CIM more than those of the physical environment: traffic perception was consistently associated with CIM; inconsistent associations were reported for objectively measured traffic. Nevertheless, physical environmental correlates with inconsistent results  need to be addressed in future research as some studies have shown that activity-friendly environments can promote active travel behavior .Results of multivariate regression models of studies included in this review underline the differing relevance of the social and physical environment for CIM. Age, sex/gender and parental perceived neighborhood environment tend to be represented as significant correlates of CIM , 61, 62.Only three studies focused on parental physical activity and parents’ and friends’ encouragement for walking, but the results were inconsistent , 59, 63.In physical activity, sport participation and CIM sex/gender differences are consistently reported, with girls tending to be less active or rather mobile than boys , 12, 72.Thus, this systematic review was the first to investigate sex/gender-related correlates of CIM for the development of effective intervention programs. Sex/gender differences with regard to the extent of independent mobility are consistently reported as being lower for girls than for boys , 34, 58.In this systematic review all studies with a quantitative design were included, irrespective of whether they were cross-sectional analyses or longitudinal analyses. Thus, the objective was to identify correlates of CIM because the number of high quality longitudinal studies was very limited. However to identify causal relationships between the environment and CIM, more longitudinal studies are needed.According to Sharmin and Kamruzzaman , in the Apart from similarities between CIM license and CIM destination, some studies , 61 repoCIM range was analyzed in merely three studies and CIM time not at all. Time and range may be insufficient indicators for CIM or simply rarely used at this stage. Using GPS as an objective measure of CIM could provide a more comprehensive understanding of CIM time and CIM range as indicator of CIM .Based on the results of the methodological quality assessment, the lack of a standardized definition and measurement of independent mobility and environmental correlates limited the comparison of the included studies. In fact, a wide variety of measurements were used to determine CIM. Although results were separated for CIM license and CIM destination, measurements differed for child and parental report, with no study employing objective measures. To compare studies Bates and Stone  recommenAdditionally, a wide selection of social and physical environmental correlates was used in the studies to evaluate the relationship between the environment and CIM. Thus, some correlates appeared only in one study, which provided only limited evidence. Furthermore, terminological differences were observed between researchers. For example, “stranger danger” was paraphrased as “trust in strangers” , “worrieThe lack of generalizability of study results is caused by insufficient response rates in almost all studies. Acceptable response rates should to be at least 80% . The resTo analyze the influence of the methodological quality of studies on strength of evidence and the associations evaluated, differences in the results between high and low quality studies were considered. However, no systematic differences were found for positive, negative and non-existing associations in high and low quality studies. Thus, the methodological quality has no potential influence on inconsistent associations.A strength of this review is the systematic search of relevant primary studies employing several search engines and a comprehensive list of search terms. Furthermore, the reference lists of all studies included were reviewed for additional sources. Another strength is that two independent reviewers  systematically screened relevant articles in three steps. Additionally, to evaluate the risk of bias a quality assessment was developed based on existing criteria lists for cross-sectional studies focusing on the methodological quality of the studies included. All results (bivariate and multivariate associations) were included in this systematic review, but analyzed separately as the multivariate associations integrated different correlates and, thus, were not directly comparable with the bivariate associations. Nevertheless, including both statistical methods helps to provide a further understanding of the relationship between socio-demographic characteristics, the social environment, and the physical environment with CIM. Stratifications of results by CIM definition provided additional information on the relationship between the environment and CIM.A first limitation of this systematic review is the lack of evidence for causal relationships between the environment and CIM, because only two studies contained a longitudinal study design. Secondly, associations were only considered by significance and direction, not by effect size. Thirdly, conclusions may be heavily influenced by the low methodological quality of the studies and by individual findings of single studies, because many conclusions were based only on the result of one study. Additionally, no association demonstrated strong evidence, i.e., associations with at least four studies of high quality reporting the significant association in the expected direction. The fourth limitation is that only English- and German-language articles were considered for this review. Finally, as few studies reported sex/gender-specific results, sex/gender-related correlates could not be analyzed in detail.This systematic review provides an overview of social and environmental correlates of independent mobility in children and highlights important research gaps.Based on the socio-ecological perspective, this systematic review pointed out important social and physical environmental correlates which could be considered when developing intervention programs to halt the decline of CIM, especially in girls. Overall, the synthesis of existing studies revealed that neighborhood safety, fear of crime and strangers, parental support and perception of traffic are significant social correlates. Furthermore, car ownership, distance, and neighborhood design belong to physical environmental attributes, which influence CIM. To possibly address factors, such as neighborhood safety or fear of crime, which are limitedly modifiable and more determined by political decisions concerning the domestic security, intervention programs should focus on the interaction between social and physical environment . PromotiAdditionally, this systematic review identified future directions of research and suggests that the influence of the environment on CIM has not yet been fully understood. One important aspect is the implementation of longitudinal studies focusing on children’s independent mobility to get insights into causal relationships of the social and physical environment and CIM. Additionally, to foster a robust understanding of the impact of the social and physical environment on CIM, more studies employing comparable measuring standards for CIM and environmental predictors are needed.Furthermore, this systematic review showed that sex/gender-related correlates are limitedly evaluated in literature until now. Promoting independent mobility, especially in girls, could additionally promote their physical activity and thus, contributes to healthy development in children . To consAdditional file 1. Characteristics of studies included on association of CIM and the social and physical environment The table shows the extracted data separated for all studies included, consisting of author(s); year of publication; country; study design; sample description ; definition, measurement, and instrument of CIM; type, measurement, and instruments of examined correlates; and main study results on the relationship between social and physical environmental factors and CIM.Additional file 2. Quality Assessment for all studies included. The table shows the methodological quality rating of each criteria and the total quality score for all included studies.


Patients with functional gastrointestinal tract who are unable to meet their nutritional requirements may benefit from the use of enteral nutrition via feeding tubes which could be nasogastric, percutaneous endoscopic gastrostomy and jejunostomy. Although enteral tube feeding has been shown to promote nutritional status, improve wound healing, and enhance patients’ quality of life (QoL), evidence of tube and feed complications and reduced QoL has also been reported. Despite the increasing prevalence of patients on enteral tube feeding, no systematic review examining the role of enteral tube feeding on patients’ QoL appears to have been published. Aim: The aim of this systematic review is to evaluate the effect of enteral tube feeding on patients’ QoL. Method: Three databases  plus Google Scholar were searched for relevant articles based on the Population, Intervention, Comparator, Outcomes (PICO) framework. The review was in line with preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines and involved the use of synonyms and medical subject headings. In addition, search terms were combined using Boolean operators (AND/OR) and all the articles retrieved were exported to EndNote for de-duplication. Results: Fourteen articles which met the criteria were included and three distinct areas were identified: the effect of early versus late enteral tube feeding on QoL; the QoL of patients on gastrostomy versus standard care, and the effect of enteral tube feeding on QoL. Overall, nine studies reported improvement in the QoL of patients on enteral tube feeding, while five studies demonstrated either no significant difference or reduction in QoL. Some factors which may have influenced these outcomes are differences in types of gastrostomy tubes, enteral feeding methods , and patients’ medical conditions, as well as the generic and/or type of QoL measuring instrument used. Conclusion: Most reviewed studies suggest that enteral tube feeding is effective in improving patients’ QoL. The use of enteral tube feeding-specific QoL measuring instruments is recommended for future research, and improved management strategies including use of mobile enteral feeding pumps should further enhance patients’ QoL. More studies on the effect of delivery systems/enteral feeding pumps on QoL are needed as research in this area is limited. There is evidence of increasing prevalence of patients on enteral tube feeding in the UK and around the world ,2 and thSometimes, enteral tube feeding is needed to support patients with human immuno-deficiency virus (HIV), those who fail to thrive and individuals with learning and intellectual disability and may involve patients in their own homes, residential care, nursing homes, acute hospitals including intensive care units ,11,12. TThe nature of enteral feeding systems may be implicated in these problems. These include the feed and enteral feeding tube which is usually placed or inserted in the patient . Other eQoL has been defined as the way in which illness, pain, reduced motor activity and unease may influence daily behavior, social activities, psychological well–being and other aspects of an individual’s life . TherefoIt remains unclear whether these QoL measuring instruments can identify the effect of the various enteral feeding tubes such as nasogastric (NGT), nasojejunal (NJ), percutaneous endoscopic gastrostomy (PEG), radiologically inserted gastrostomy (RIG) and percutaneous endoscopic jejunostomy (PEJ) which require different procedures for placement in the respective anatomical sites on QoL ,6,21. InOther potential problems include the enteral feeding pump that delivers the feed and to which patients could be connected for many hours ,22. In aThe approaches to enteral nutrition provisions in hospitals and in community settings may also affect patients’ QoL as there are variations in the provision of enteral tube feeding in the UK and globally ,6. For eGiven the role of enteral tube feeding in terms of its importance in prolonging life and reducing length of hospital stay in patients with different health conditions and the range of challenges that have been highlighted, it is not surprising that while some studies have noted the merits of enteral nutrition provisions, other articles have drawn our attention to its demerits and the negative effects on patients’ QoL. However, despite the studies conducted in this area, it would appear that there is no systematic review that has attempted to explore the role of enteral tube feeding on patients’ QoL.Aim:The aim of this systematic review is to evaluate the effect of enteral tube feeding on patients’ QoL.Research question:Do enteral tube feeding provisions impact on patients’ QoL?This systematic review has been conducted in line with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) .Due to the nature of the area being reviewed , a range of study designs including randomized controlled trials, cross-sectional studies, prospective cohort studies, uncontrolled clinical trial and retrospective reviews were included in this review.Participants were patients on enteral tube feeding involving different enteral feeding tubes and various types of enteral feed. The studies included evaluated the effect of various gastrostomy tube placements, the timing of tube placement, HEN, and compared enteral feed and standard care on QoL.The outcome of interest was the QoL of patients based on the use of different QoL measuring scales. The tools used in the studies selected included the EuroQoL 5D (EQ5D) index and EQ5D VAS , Short-FThe search strategy for this review was based on the Population, Intervention, Comparator, Outcomes (PICO) framework . The seaSearches were conducted in the three databases from the date of inception to 27 December 2018. The following were the inclusion criteria: studies involving patients on only enteral tube feeding irrespective of the medical condition and type of enteral feeding tube; studies involving patients older than 18 years; studies involving patients’ QoL.The exclusion criteria were: studies involving patients on home parenteral nutrition; patients on both home parenteral and enteral tube feeding; patients on oral nutritional supplements; studies involving children aged below 18 years; studies involving family members and/or healthcare professionals; letters; studies comparing different feeding tubes; studies based only on medical conditions and gender of patients; studies with abstract only and insufficient information/data.The quality of each study included in this review was evaluated by using the critical appraisal skills program (CASP)  tool.Data was extracted from the studies selected by one researcher (O.O.) and cross checked by three other researchers  .Three studies each were conducted in the United Kingdom, France and China. Furthermore, Ireland, Australia, Iran, Germany and New Zealand each had one study . These sThe effect of early versus late enteral tube placement/feeding on QoLQoL of patients on gastrostomy compared with standard careThe effect of enteral tube feeding on QoLBased on the objectives, the interventions and outcomes of the studies, the following three areas were identified from the review;Two studies ,29 evalup = 0.83) following gastrostomy insertion. In fact, Rogers et al. (2007) reported that patients with PEGs (at 34 months) reported significant deficits in all UW-QoL domains compared to non-PEG or PEG-removed (at 7 months) patients and reported a much poorer QoL.Five studies ,31,32,33p > 0.05) in SF-36 scores at the time of tube placement and 1, 6, 12 months follow-up, except for physical function score. However, the PEG-Qu assessment showed at 6 and 12 months, 71% and 75% of patients respectively expressed a positive overall effect of gastrostomy on their QoL. In addition, Hossein et al. [p < 0.005) 6 months after PEG placement in the QoL index scores. There is also evidence that prophylactic gastrostomy can improve post-treatment QoL for unresectable head and neck cancer patients, after adjusting for other potential predictive QoL factors [Bannerman et al.  found non et al.  demonstr factors  based onSeven studies ,38,39,40Schneider et al.  reportedEvaluation of the selected studies on the impact of enteral tube feeding on QoL resulted in the emergence of 3 distinct areas  with different outcomes observed in each area. Overall, nine ,38,39,40The differences observed in the outcomes of the studies across the three areas outlined above could be due to a range of factors. These possible factors and the implications for research and practice will be discussed.These factors may include the types of gastrostomy feeding tubes, the various chronic conditions requiring enteral tube feeding, the enteral feeding methods, the time spent by patients being connected to enteral feed/pump and the different clinical settings ,22,27,38There were a range of enteral feeding tubes such as NGT, NJ, PEG and RIG, used by the patients in the selected studies. These gastrostomy tubes vary in their indications, method of tube placement, anatomical sites, complications, and care and management ,42,43. TSimilarly, the pathophysiology of the long-term conditions requiring enteral tube feeding such as stroke, Crohn’s disease and cancers are different ,44, and The differences observed with respect to the impact of enteral tube feeding on QoL is underscored by the fact that although this method of feeding has significant advantages, it is not perfect . There iIt would appear that some of the studies included in this review recognized the limitations of using generic QoL measurement tools for evaluating patients on enteral tube feeding by including PEG specific questionnaires to complement these tools. For example, Rogers et al.  used theStrategies for ameliorating some of the challenges of enteral tube feeding and improving patients’ QoL should be promoted. These approaches could involve the development of technology such as improved mobile enteral feeding pump with less noise, development of HEN services in the community to provide specialist enteral nutrition services and support patients to reduce the risk of feed, tube and pump complications and thus improve their QoL.The use of generic QoL tools/questionnaires to evaluate the impact of enteral tube feeding on patients’ QoL presents a significant limitation. However, this has been discussed extensively in this review to raise awareness among researchers and recommendations for future research have been suggested.Most studies in this review suggest that enteral tube feeding is effective in improving patients’ QoL. The varying outcomes of the effect of enteral tube feeding on QoL across the three areas may be partly explained by differences in types of gastrostomy tubes, enteral feeding methods , and patients’ medical conditions, as well as the type of QoL measuring instrument used.However, the use of an enteral nutrition specific QoL measuring tool which does not discriminate in terms of the type of enteral feeding tube and patients’ condition and has been validated is recommended for evaluating the impact of enteral tube feeding on patients’ QoL. In addition, improved management strategies including the use of mobile enteral feeding pumps should further enhance patients’ QoL. More studies on the effect of delivery systems/enteral feeding pumps on QoL are needed as research in this area is limited.


The nosology and epidemiology of Autism has undergone transformation following consolidation of once disparate disorders under the umbrella diagnostic, autism spectrum disorders. Despite this re-conceptualization, research initiatives, including the NIMH’s Research Domain Criteria and Precision Medicine, highlight the need to bridge psychiatric and psychological classification methodologies with biomedical techniques. Combining traditional bibliometric co-word techniques, with tenets of graph theory and network analysis, this article provides an objective thematic review of research between 1994 and 2015 to consider evolution and focus. Results illustrate growth in Autism research since 2006, with nascent focus on physiology. However, modularity and citation analytics demonstrate dominance of subjective psychological or psychiatric constructs, which may impede progress in the identification and stratification of biomarkers as endorsed by new research initiatives. There are in fact two things, science and opinion; the former begets knowledge, the latter ignorance”“-Hippocrates.Figure 1A). However, the amalgamation of formerly heterogeneous disorders under a single diagnostic term was, and arguably continues to be, contentious . Similarly, the NIMH’s RDoC initiative  is an umbrella term encompassing the diagnoses of autism, Asperger’s syndrome (AS) and pervasive developmental disorder – not otherwise specified (PDD-NOS) . Proposetentious . Clinicatentious . In additentious —endorsesa priori to target the dimensions of interest), highlighting the now prominent role of biological and physiological processes in Autism focused research  examination of broad trends in research proliferation, journal focus, and citation analytics through objective time-series analytics, (2) comprehensive empirical co-keyword analysis to delineate self-emerging areas of research convergence via modularity statistics.This article seeks to empirically explore and examine whether broad trends toward a biological and physiological research focus of ASD have continued in light of the consolidation of disorders under the ASD umbrella and recent health initiatives, when no a priori user-specified heuristics for core elements, such as the number of concepts or keywords that constitute a theme and the number of themes to be identified, artificially constrains, and shapes subsequent clustering. This article departs from this approach, and instead, objectively profiles the evolution and diversity of Autism research with a focus on self-emerging patterns of term association (rather than a priori enforced heuristics). Modularity techniques and graph theory visualization provide a comprehensive, accessible overview of self-identified trends in research focus. Network modularity—indicative of research convergence—was self-identified via keyword co-occurrence . However, to enable discussion within the precision medicine context, trends were a posteriori contextualized using the broad thematic categorizations of Psychological , Physiological  or Interdisciplinary. This application of statistics before heuristics marks a departure from traditional hand coding and text-processing techniques. Underpinned by empirical, self-emergent trends as identified across the broad Autism corpus, these emerging patterns enable discussion as to the past, current and future direction of Autism research, within a broader context of new health initiatives.Acting as an article ‘tag,’ keywords provide a unique and condensed method to visualize areas of research focus , and fac1.A systematic literature search was completed using the core collection of ‘Web of Science.’ Literature with the Topic Field, ‘Autism’ (to reflect pre-2013 term consolidation) was identified and refined based on the following criteria: published between 1994 and 2015 (the year of DSM-IV publication to time of data collection), classified as an Original Research article, published in English and within the United States of America only. This search resulted in a corpus of 17,620 original research articlesunique dictionary of 6242 stemmed keywords for corpus analysis. Tailor-made author software (MATLAB 2017a) generated matrices of database keyword occurrence (frequency) for this unique corpus dictionary, while preserving underlying indexing. A total of 4482 keywords were subsequently removed from the unique corpus due to low occurrence (occurrences less than the median frequency of the database: <6). This refinement resulted in a profile of 1760 unique stemmed keywords (ranging from 7 to 3847 occurrences), which were used for the analytics outlined below. The maximal and minimally occurring keywords across the corpus were also isolated—defined as the 5th and 95th percentile—across the trimmed 1760 keyword frequency distribution, and empirically tracked across the corpus to demonstrate the evolution of research topics .Bibliometric information was extracted for each article, including author(s), affiliation(s), publication title, journal, attributed keywords and cited references. This information was formatted and processed within R software  using fuself-emerged as a critical landmark signaling the field was poised for transformative change – see section “Trend Analysis: Growth of the Research Field and Thematic Focus” below). Results for the ‘Full Corpus’ and both ‘Decades’ are presented below, while additional analysis of each landmark year can be found in the Supplementary Material.Data was analyzed across the entire corpus, before being split into smaller ‘Decade’ collections to examine any shift in publication focus (Decade 1: 1994–2004 inclusive and Decade 2: 2005–2015 inclusive). In addition, key years were isolated for individual profiling, namely; 1994 as the start year aligned with the publication of DSM-IV, 2015 as the most recent completed year of research, and 2006 which Figure 2A inset). The local maxima and minima across the resulting slope trajectory were further extracted, and the first maximal positive difference between local maxima and minima was identified—i.e., the first moment of maximal increase in the slope of the line reflective of an increase in publication rate.Research proliferation was initially profiled according to the number of articles identified using the outlined criteria per year. To enable an empirical assessment of the year of inflection , this data was examined as a time-series trajectory. Specifically, the overarching trend line was subject to cubic spline interpolation—a form of polynomial interpolation—to achieve higher levels of accuracy, from which the derivative of each year/number of publications was extracted, and used to calculate the slope of the trend line of publications . This machine learning clustering algorithm employed minimal squared Euclidean distance to identify groups of closely related observations by randomly inserting placeholders into the data set (user specified N), and identifying neighboring clusters. While user enforced N specification is required for K-means clustering, this was applied only to identify the most frequently occurring journals, and was empirically justified via systematic exploration of the data settings. For example, if N = 2, N = 3, and N = 4 all resulted in similar group profiles, N = 3 was adopted. Thus, an N was applied when all neighboring values resulted in similar group profile for the isolation of the demarcated ‘tail,’ representative of the most frequent publications.To further contextualize trends of research proliferation in light of the precision medicine platform, the journal of publication was considered, with broad categorization according to three disciplinary silos: Psychological, Physiological focus, or Interdisciplinary. This categorization was performed per year of analysis, with the 100 most frequently occurring journals per year extracted .official description—extracted from the relevant online presence, and crosschecked (where possible) with Thomson Reuters Journal classification. Terms of focus such as Genetic, Immunological, and Neurobiological were deemed as Physiological, while those such as Education, Intervention, and Mental Health were deemed as Psychological or Clinical Psychiatric in nature. Journal descriptions that explicitly stated interdisciplinary research, or referred to terminology associated with both Physiological and Psychological terms were deemed Interdisciplinary. Authors crosschecked all classification results. While this method involves heuristics, it is applied to enable discussion of research scope and does not impact subsequent modularity techniques employed for keyword analysis. To control for variability in the number of frequently occurring journals per year, the percentage of categorization was calculated. A line of best fit was then empirically applied to the time-series of each categorization distribution, with the slope calculated to allow further trend interpretation and identification of self-emerging trends .The most frequent publications were subsequently classified according to the Journals’ The references that researchers cite provide a foundation for further discussion or support, and are therefore indicative of research focus or procedures. As such, the above process was repeated for the most frequently cited articles  across the corpus. To enable a broader discussion in light of the precision medicine platform, these were again broadly classified as Psychological, Physiological, or Interdisciplinary in nature. Note, the full reference for each of the top cited articles is provided for reader clarity.keyword i alongside keyword j within a single publication. The matrix entry (weight) is thus the number of instances keywords occurred together within a single publication across the corpus or years of analysis.Acting as an accessible summary of research focus, article keywords were profiled to provide a quantifiable overview of research trajectory, with keyword co-occurrence within a single publication providing insight into broad research themes and convergence. Keyword frequency and indexing were therefore utilized to create weighted keyword co-occurrence matrices across all levels of analysis  through tailor-made author software (MATLAB 2017a)—with each non-zero entry indicating co-occurrence of Figure 3A). Graph theory connectivity analytics were subsequently utilized to examine the resulting keyword networks and facilitate identification of self-emerging corpus modules . Specifically, machine learning modularity analytics drawn from computational neural networks  is equal to the product of the number of keywords (N) identified within the module times the sum of keyword occurrences  across the module. Where, I denotes the module ranking index, and index j runs through the keywords included within the module, and index j runs across the size N of the module. Thus, modules with high levels of modular interconnectivity, spanning a large range of keywords, are prioritized.Module ranking  within a module , Physiological, or Interdisciplinary. While a broad level categorization, the full list of central keywords extracted for each module, and the subsequent thematic coding, is available for the reader in Supplementary Section 2.Self-clustered modules of co-occurring keywords were thematically coded to visualize the evolution of the research corpus and enable discussion within the broader landscape of precision medicine. To empirically guide this process, the internal importance of each keyword within each module was assessed via eigenvector centrality, as defined by a module . The resTo maximize visualization of the data, modules were graphically represented in a concentric ring configuration, with central rings reflecting higher ‘ranking modules’ (as derived via Eq. 1), and each keyword represented as a network node. Modular thematic assignment  was coded through node color, while node size was indicative of the internal module importance . To enable comparison across all modules at the level of analysis , all values of Eigenvector Centrality were normalized via Eq. 2.Equation 2: Normalization of ParametersPn represents the normalized parameter value, Po is the original parameter value, and Pmax and Pmin are the maximum and minimum parameter values derived from each level of analysis.External and Internal Connectivity of self-clustering module keywords were calculated according to Eqs 3 and 4, respectively. The modular external connectivity provided a summative metric of the total external links associated with a module. This provides a summary of the links present between keywords within the module  and external keywords in neighboring modules , with higher levels of module external connectivity indicating a high number of external connections established to neighboring modules.The Equation 3: Modular External ConnectivityKeyword i and Keyword j as denoted by ije, where the number of keywords within the module is denoted by N.In contrast, the internal connectivity metric provides a summary of the number of internal links within a module, such as the number of links between Equation 4: Modular Internal ConnectivityModular internal and external connectivity co-occurrence metrics were normalized across the level of examination according to Eq. 2, and visualized to consider the role and prominence of modules across the corpus. Specifically, these normalized connectivity metrics for each module were plotted on a strategic diagram , and vieth percentile of NEM were isolated, and associated co-keyword frequency metrics extracted, and systematically ranked. Associated partner keywords  were subsequently identified. From this, an isolated co-occurrence matrix was created and visualized via a color-coded matrix, allowing the identification of core points of modular and thus thematic ‘cross talk.’As outlined, levels of external connectivity were profiled for each keyword across the self-emerging modules – with this metric reflective of the number of connections that a keyword establishes with those in neighboring modules. These external connections can be formed with neighboring modules of either the same (intra-thematic) or alternative (inter-thematic) thematic coding. Connections forming across themes (inter-thematic) act as junctures or points of ‘cross talk’ that tie domains together. To this end, levels of external connectivity were normalized (via Eq. 2) across each level of analysis to provide a normalized external metric (NEM). Module keywords with a normalized external connectivity level above the 95network hubs. Network hubs were defined as maximally occurring keywords across a corpus. Such terms may mask nuanced connections with less prominent keywords across or within thematic modules, and as such were removed to probe the stability of the self-emerging networks. To this end, keyword frequency was profiled across levels of the corpus, with those keywords constituting the top demarcated group  see Supplementary Figure 1) removed from the corpus. Again, this user specified value of N = 3 was identified upon systematic exploration of settings—N = 2, N = 3, N = 4 resulted in similar group profiles at the full corpus level, therefore N = 3 was adopted for consistency across all levels.The resilience and stability of the corpus and self-clustering modules was examined via removal of Through this method and across all levels of corpus analysis , the following keywords were empirically identified using k-means clustering as network hubs  and thus removed from the corpus: Asperger, Autism/Autistic, Child , and Spectrum. Self-clustering modularity metrics were again processed and visualized, as derived from this refined corpus, allowing consideration of the stability of internal structures and thematic re-organization or prominence.Supplementary Section 1 for full list). This more in-depth consideration of research focus was refined to systematically profile, and individually track, a core group of 12-prominent (frequency based) keywords. This individual tracking was completed after removal of network hubs (as outlined in Section “Probing Network Stability”), and the amalgamation of related or interchangeable terms to minimize redundancy.Maximal  and minimal nodes were tracked across the research corpus to consider changes in prevalence via time-series analytics , illustrates a strong trend of research proliferation—with an average publication growth rate of 14.74%. This trend analysis was further quantified to isolate the year of research expansion—a ‘critical point’ in the evolution of Autism research. Defined as the first point of maximal difference between local minima and maxima , 2006 was identified as the inflection year . However, despite this general increase in Autism publications across the timeframe, self-clustering analytics demonstrate a simultaneous reduction in the number of modules self-emerging per year , indicative of a convergence of research discourse.A total of 17,620 articles were isolated through the systematic search outlined above, of which 13,513 had database-attributed keywords used for thematic trend analysis. Examination of the number of publications per year . However, despite the downward trend in publications within psychological journals, results indicate that the most frequently cited articles across the research corpus retain a psychological stance . This would imply that psychological terminology and constructs continue to play a role in emerging Interdisciplinary and Physiological research—a trend that continues across both decades of analysis, see Supplementary Tables 1–3. Thus, despite promising trends for a shift toward physiological axes of Autism, potentially reflecting neuroscientific and genetic efforts to delineate quantifiable biological markers, psychological and psychiatric terms, and related methodologies, appear to dominate and influence the foundations of research across the corpus.Classification of journal focus by broad disciplinary silos  demonstrates a potential underlying shift in research focus. A clear downward trend in research published within Psychological journals can be identified, along with a growing prominence of research published within Interdisciplinary focused journals. Interestingly, despite being characterized as a psychological construct, the trend for Physiological research  has remained relatively consistent across the timeframe . The resulting self-clustered modules were ranked according to Eq. 1, and the underlying connectivity metrics of each were extracted for thematic coding. The below provides a brief overview of this analysis for the complete networks.self-emerging clustering networks through modularity analytics, which were broadly categorized into two discrete thematic silos: Psychological and Physiological . Despite being comprised of fewer individual nodes , the Psychological module displays prominence across the corpus, with notably higher levels of internal and external connectivity relative to both the corpus normalized median, and the associated metrics of the neighboring Physiological module . This would imply a well-established field of Psychological research that acts as a ‘driving’ force within the broader context of Autism discourse. While this trend persists across the Decade level of analysis, results also demonstrate the growing momentum of Physiological research in Decade 2, with an increase in sub-themes.Examination of the Full Corpus (1994–2015) identified two Figure 3C). Further, examination of junctures of thematic cross talk between modules highlights the prominence of high-level descriptor terminology, such as Autism and Children, in creating research interconnectivity across the broader arena . As illustrated in the color-matrix of external connectivity, both Psychological and Physiological networks display restricted thematic interconnectivity, with primary junctures largely isolated to such high-level group descriptors located in each module. Initial interpretation may infer inappropriate terminology clustering . However, high-level descriptor terms identified within Physiologically themed modules are, by virtue of the network algorithm, maximally connected to internal modular Physiological co-keywords. Results thus indicate the role of these network hubs in the consolidation of the broader research arena—providing junctures to create cohesiveness between Psychological and Physiological terminology and research—as well as their role in consolidating each individual research arena.Specifically, Decade 1 analytics reveal the clustering of modules deemed Physiological according to research focus on Genetic/Genomic, and Cerebellar/Neurological factors, which are expanded further in Decade 2 via the inclusion of Maternal or Prenatal factors associated with Autism. However, despite this increase in Physiological thematic focus, modular interconnectivity continues to demonstrate the prominence of Psychological modules across both Decades (Supplementary Table 4), and a notable drop in the corpus median across all levels of analysis—allowing expansion of the module networks.Modularity co-keyword analytics demonstrate the influence of Psychological research, while also indicating a move toward Physiological themes. However, the persistent trend for prominent interconnectivity located in high-level descriptor terminology questions the quality and level of interconnectivity between thematic areas, and the dominance of Psychological clusters. Specifically, the dominance of descriptor terminology may serve to mask underlying nuanced connections between keywords and modules, and thus impact modular self-clustering. Removal of these network hubs resulted in an intuitive reduction of the range of internal and external connectivity . In particular, the removal of core network hubs—all of which were deemed as high-level descriptive terminology—resulted in the dissolution of the single Physiological module, into three distinct networks all with a Physiological thematic trend : M2 Genetics and Mouse Models, M3 Genetics and DNA, and M4 Neuroscience or Neurological Factors. Levels of internal and external connectivity across these refined modules demonstrate the continued dominance of the Psychological module in terms of both internal cohesiveness and external connections to the neighboring Physiological modules .Removal of network hubs resulted in an expansion of modules across the Full Corpus from a discrete Psychological and Physiological thematic cluster to four distinct modules . However, the somewhat descriptive term, ‘Brain,’ is identified as the main area of thematic overlap driving connectivity factors between modules . While Physiological modules (M2–M4) display lower levels of overall external connectivity , prominent nuanced junctures are created . Interestingly, however, the more ‘restricted’ module (M4) demonstrates the highest levels of connectivity  in relation to the more nuanced M2—perhaps a by-product of the dominance of the descriptor Psychological terms to which it forms junctures —again illustrating the importance of such Psychological research themes. In sum, removal of core network hubs results in notable fragmentation in research thematic cohesiveness, which specifically targets the Physiological strand of academic research.When examined considering levels of modularity cross talk, the Psychological module appears to make a range of connections with external Physiological term landmarks including: Removal of core hubs continues to impact self-clustering modularity during both Decade 1 (1994–2004) and Decade 2 (1995–2005).Figure 3B)—increasing the number of modules from two to four . Sub-group thematic trends demonstrate specific Physiological sub-modules related to Genetic (M1) and Neurological (M3) research—mirroring those present at the complete (hub terminology included) Decade 1 level—with two additional thematic clusters now identified focused on Pharmaceutical topics: MMR Vaccination  and the administration of Risperdal (M7). Viewing these sub-cluster thematic trends within the context of corpus analytics further demonstrate this evolution. Specifically, while Psychological constructs remain largely dominant, Physiological modules M1 and M6 display high levels of internal and external connectivity  during Decade 1, inferring dominance in the wider research domain across this period. As such, results indicate that network hub descriptor terms play a key role in the cohesiveness of Physiological research clusters during Decade 1, and their removal reveals a nuanced pattern of emerging strength in this area of research.Similar to results at the Full Corpus level, the removal of descriptor terminology in Decade 1 fragments the Physiological thematic network . As a result, this descriptor terminology is now core to the cohesiveness of the dominant Psychological modules—rather than Physiological modules in contrast to Decade 1. Combined this may imply greater refinement and consolidation of Physiological research, and a parallel dissolution of Psychological research. Indeed, corpus analytics illustrate that the tenuous strength of Physiological modules in Decade 1 has solidified—leading to a new dominance of Physiological research in Decade 2, with higher levels of external cohesiveness driving the broader research arena . In contrast, the growing dissolution and insularity of Psychological research is highlighted via the dominance and impact of higher-level group terminology (network hubs) during Decade 2, and the lower levels of external connectivity indicative of a weakened research area. However, it should be noted that despite the insularity of the Psychological module, and comparable growth in Physiological dominance, lower levels of internal connectivity of the Physiological module infers a need for further consolidation of this research field. This may be reflective of the disparate nature of Physiological constructs and research to date, and may be a contributing factor in the slow emergence of the physiological Autism research domain.In contrast to the trend of Physiological fragmentation of Decade 1, the removal of network hub descriptor terminology across Decade 2 has a consolidation effect on Physiological modules—now causing fragmentation within the Psychological module . Detailed examination of Psychological modules that retain dominance across each level of analysis  further infers a level of intra-thematic insularity inflating metrics, with such clusters subsuming terms such as Behavior, Language and Intervention, yet showing minimal external thematic cross talk .Examination of areas of thematic cross talk upon removal of high-level descriptor terminology also illustrates a more nuanced level of interconnectivity. New junctures or thematic intersections now include terms such as Brain, Fragile, Disorder, Prevalence, Mental, Infant, Diagnosis, Individual, and Behavior  and related minimal nodes (<5th percentile) were automatically isolated and tracked across the timeline of the corpus . A sub-cluster was subsequently identified to represent core research topics that are prevalent across the corpus.The growing prevalence of Physiological research, as examined via co-keyword coherence metrics, raises questions as to the specific research focus of the field. As such, the maximally occurring nodes from across the corpus .These core research topics emerged by trimming the corpus maximal keyword list of high-level descriptor terminology, as outlined above, and then amalgamating associated terminology to minimize redundancy. Specifically, Mental Retardation (now a disputed term) was amalgamated with Intelligence Quotient (IQ) and Intelligence to form an overarching ‘Intelligence’ area. Similarly, Diagnosis was combined with Assessment, Identification and reference to the DSM  to form ‘Diagnosis and Assessment.’ Finally, Language and Communication were collapsed, as were Intervention and Therapy, and Gene and Genome. A total of 12 prominent keywords were subsequently identified through the application of the K-means clustering algorithm for systematic profiling and tracking, namely: Behavior, Prevalence, Intelligence , Brain, Individual, Gene, Diagnosis, Infant, Development, Social, Intervention and Therapy, and Language and Communication , with Infant identified as the maximally declining prominent keyword across the corpus. Such trends support a broadening scope of Autism research, with a gradual departure from a strong psychological focus and isolated fields of inquiry, such as Language and Communication, toward a more refined examination of outcome metrics. However, in all instances, it should be noted that despite such decline in prominence, these terms remain within the group of a priori application of heuristics can result in user-specified criteria that artificially shape results, raising questions of the validity of such an approach. This is often illustrated in user imposed heuristics such as prior manual hand coding to cherry pick thematic modules that are to be delineated across a corpus , or were applied a posteriori, driven by empirical self-emerging clusters. This use of empirically informed, data-driven methods to define user heuristics ensured minimal subjectivity.Traditionally, analytical text review techniques employ a range of heuristics that can limit the ability to systematically quantify the inherent dynamics of research trends. While heuristics are often required to enable meaningful discussion and interpretation of data, the order of application is critical. The a corpus , pre-spea corpus . These mThis self-evolving heuristic free principle of modularity analytics to identify research focus, coupled with the empirical examination of publication trends using time-series analytics, demonstrated a trend of research proliferation—in line with increased public awareness and incidence rates of Autism —and resudevelop and implement a strategic plan to conduct and support autism spectrum disorder research’ (full Act available here: https://www.congress.gov/bill/109th-congress/senate-bill/843). With an increase in United States funding, it is perhaps unsurprising that 2006 marked the beginning of increased research interest within the American research community. Furthermore, this four-step program explicitly included promoting research into the development and validation of reliable screening tools—perhaps a driving factor in the growth and persistence of core research themes, such as Diagnosis and Assessment, Prevalence and Intervention—as illustrated in the individualized examination and tracking of core research topics.Empirical examination of the rate of publications across the broad Autism field facilitated automatic identification of 2006 as the year of critical change—the inflection point—where research demonstrated an accelerated rate of change. While in line with previous results inferring the importance of 2005–2006 as derived via parsimonious methods, such as hand counting and subjective interpretation see , this quFigure 1), questioning the ability for these observational, behavioral based tools to be integrated into the broader context of computational psychiatry and precision medicine. So what role do such clinical tools play in the broader research arena? With a characteristic symptomatology profile and diagnostic procedure strongly—and historically—entrenched within the Psychological field, this broad application may imply the field of academic Autism enquiry draws on such metrics and constructs to inform and guide underlying research questions, outcomes and methodologies. Further, the continued prevalence of Psychological constructs and tools, despite a general trend toward growing Interdisciplinary and Physiological research, poses the possibility that Autism research continues to adopt these dominant behavioral paradigms to provide behavioral markers on which to map physiological correlates and substrates. Indeed, psychological and psychiatric metrics such as the DSM and ADOS are often the first step in Autism clinical research—arguably of any nature—providing the pre-defining label criteria to stratify participants by diagnostic terminology. It is this stratification of participants by broad and arbitrary thresholds from DSM criteria, using subjective clinical observational techniques that negate the physiological axes of development  in line with previous trend assessments  demonstrates the continued dominance of psychological constructs and terminology across the research domain. This is reflected in the internal and external cohesion of these thematic clusters across levels of analysis. However, more nuanced examination relative to the decade evolution reveals a gradual prevalence and consolidation of physiologically focused research—with initially disparate terms of Decade 1 (1994–2004) covering sub-themes such as Genetic, Neurological, Pharmaceutical and Prenatal axes of Autism, converging by Decade 2 (2005–2015).Further novel metrics facilitated examination of the implicit struggle between Psychological and Physiological defining thematic constructs associated with Autism by consideration of inter- and intra-thematic keyword interconnectivity. These metrics of interconnectivity repeatedly demonstrated the latent role of Psychological terminology in consolidating early research (Decade 1). Specifically, broad group descriptor terminology acts as a cohesive landmark or juncture between early disparate Physiological themes, particularly during Decade 1 (1994–2005). It is not until Decade 2 (2005–2015) that this consolidator role of Psychological terminology in Physiological research is minimized—with the removal of descriptor terminology having a minimal impact on Physiological modularity. Such results are illustrative of the growing cohesiveness of this emerging research arena, with internal and external connectivity metrics now identified at more nuanced levels. By Decade 2 this high-level network hub descriptor terminology provides cohesion to Psychological modules—illustrative of growing insularity (a decrease in inter-thematic trends), dissolution (fragmentation), and reduced dominance as profiled in the corpus strategic diagram.4. These new initiatives are paired with a fast-growing wave of open access repositories aimed at examining signals from the nervous systems .This implicit struggle in terminology and focus inferred by both self-clustering modularity analytics and supported by broader research categorization and citation analytics, again points to the innate struggle currently evidenced in the Autism clinical and research domain. This evolving physiological thematic dominance may reflect mounting questions raised over the role of broad DSM criteria in light of recent health initiatives, such as the RDoC  and CompThe present work constrained the search criteria to the United States owing to the fundamental differences that exist in health system policies and legislation between the United States, Europe and the rest of the world. Such differences shape the type of research, public policies and legislations that drive diagnostics and treatment criteria in Autism and related neurodevelopmental conditions. As such, extending the search to the scientific literature derived from research in other countries would have posed the additional challenge of considering different policies across different nations. Our work aimed at first gaining an understanding of the landscape of autism research in the United States, and then begin the process of evaluating how the outcome of this search would compare with the search that would include scientific literature from other nations, i.e., with different policies and diagnostic criteria. In this sense, the results reported here may serve as a baseline to measure departure in research from other countries with different insurance coverage policies and medical systems, relative to the United States case. We reasoned that perhaps taking this quantitative approach to express and characterize the prevalence of subjective observational criteria may alert the United States system of the unmet needs of scientists working on autism and in this way, begin to explore new horizons by considering approaches in other nations.5, provides a comprehensive encyclopedia of both psychological and physiological conditions. Updated yearly, the entry for Autism can be found under section F80–F89, under ‘Disorders of Psychological Development’ of the Mental and Behavioral Disorders: Diagnostic Criteria6. This block section, covering Autism and additional syndromes of development, is prefaced with the following statement:Outside the United States, taking a more holistic approach, i.e., one that is inclusive of both mental and physical states of the person, is not new. The ICD-10 , the corstrongly related to biological maturation of the central nervous system; … affected include language, visuo-spatial skills, and motor coordination…’ (http://apps.who.int/classifications/icd10/browse/2016/en#/F80-F89). Emphasis added by Author.‘The disorders included in this block have in common: (a) onset invariably during infancy or childhood; (b) impairment or delay in development of functions that are While this preface is in reference to a broader set of developmental disorders than Autism alone, it sits in contrast to the DSM, published and approved by the 7, is currently authorized for use to treat Autism in children, Schizophrenia and manic episodes in those that suffer from Bipolar disorder  may be at the future juncture between physiological and psychological Autism research. However, current results that infer the dominance of Psychological constructs and tools driving the research arena raise fundamental questions as to the design, scope and precision of primary psychological methods—and their impact on Physiological assessment. While the current approach of co-keyword analysis limits the extent of this interpretation, it points toward this far-reaching and consistent psychological impact. As such, future studies of refined full-text analysis focused on methodologies may reveal a more nuanced understanding of the application of such tools and psychological methods across the Autism arena. Indeed, such investigation is warranted as current Psychological behavioral  based inventories lack the precision (sensitivity and specificity), and adequate statistical framework  to enable appropriate translation into the tenets of precision psychiatry . The impFuture work utilizing similar types of analyses as those used in the present work, but using instead the peer-reviewed scientific literature from nations that provide universal medical insurance coverage, will give us a better sense of how differences in public health policy influence and restrict the type of autism-science we can afford to do in the United States.CW conceived study, designed and created the analytical methods, implemented all code for analyses, processed all data, wrote and edited the paper. ET conceived study framework and designed platform for analyses, guided study, edited and revised paper. Both authors approved the last version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The global financial crisis that originated in developed countries in 2008 has been spreading to the developing world, middle and low-income countries, and threatening years of progress on poverty reduction. Research community worldwide has agreed about challenges in studying the impact of economic crises on health, concerning the uniqueness of each crisis and investigating the lag effects, policies feedback and contradictory trends which made results ambiguous . SpecifiAdverse effects are seen to be concentrated among people experiencing or at risk of poverty, social exclusion, unemployment, and poor health and can be masked in the aggregate analysis, by improvements for other population . The advper capita, unemployment rate, total health expenditure as a percentage of GDP and total health expenditure (PPP$) per capita.The study aims were to analyze trends in socioeconomic and health status indicators and to identify a possible association between economic recession and health of the Serbian population. The time trends analysis of socio-economic and health status indicators of the Serbian population (without data for Kosovo and Metohija), from 2000 to 2013, or 2012, was performed Four socio-economic indicators were included: real gross domestic product (GDP) expressed in purchasing power parity (PPP$) Morbidity indicators included are incidence rate (per 100000 inhabitants) of HIV, AIDS, TBC, cancer, cervix uteri cancer and female breast cancer. Mortality indicators included infant mortality rate and standardized cause-specific mortality rates (SDR) per 100000 inhabitants for diseases of circulatory system, cerebrovascular diseases, malignant diseases, diabetes mellitus, infections, and parasitic diseases and suicide. All indicators were taken from the European Health for All database.per capita PPP$, the unemployment rate and total health expenditure (PPP$) per capita, demonstrating the onset of the global crisis on Serbian economy. The impact of the recession on health has raised significant concerns since 2008 with contradictory evidence about relationships between the current crisis and mortality and morbidity of the population. Our results indicate that most health indicators changed trends, although there was no statistically significant identification in 2008. In 2009 SDR for malignant and cerebrovascular diseases, showed a statistically significant decreasing trend (Joinpoint regression analysis software was used to identify changes in trends of all indicators. The year 2008 was identified as a joinpoint for GDP ng trend .The possible explanation for variation could be the fact that various national health programs and strategies targeting to strengthen health promotion and prevention, implemented during the onset of the crisis, potentially contributed that some trends of health indicators of the population are not consistent with results of other studies . However


In order for governments to plan health and social care strategies to help people maintain independence, evidence is required to show how risk factors are associated with progression in dependency. We use a transparent measure of dependency, based on help needed with activities of daily living, incontinence and cognitive impairment, categorised as: high (24-hour care); medium (daily care); low (less than daily) and independent, then characterise changes over ten years (age 85-95) using the Newcastle 85+ Study while exploring how eight disease groups, multimorbidity and impairments interact to increase care needs. Stroke and diabetes confer an increased risk of low-level dependency. Complex multimorbidity, or three or more falls engendered the greatest risk of transitions to substantial dependency. There should be a focus on prevention of, and appropriate and efficient service provision for those with complex multimorbidity with emphasis on stroke, diabetes and falls, to maintain the independence of older people


Over the past 10 years, interest in plant biostimulants (PBs) has been on the rise compelled by the growing interest of scientists, extension specialists, private industry, and growers in integrating these products in the array of environmentally friendly tools that secure improved crop performance and yield stability. Based on the new EU regulation PBs are defined through claimed agronomic effects, such as improvement of nutrient use efficiency, tolerance to abiotic stressors and crop quality. This definition entails diverse organic and inorganic substances and/or microorganisms such as humic acids, protein hydrolysates, seaweed extracts, mycorrhizal fungi, and N-fixing bacteria. The current mini-review provides an overview of the direct (stimulatory on C and N metabolism) and indirect (enhancing nutrient uptake and modulating root morphology) mechanisms by which microbial and non-microbial PBs improve nutrient efficiency, plant performance, and physiological status, resilience to environmental stressors and stimulate plant microbiomes. The scientific advances underlying synergistic and additive effects of microbial and non-microbial PBs are compiled and discussed for the first time. The review identifies several perspectives for future research between the scientific community and private industry to design and develop a second generation of PBs products (biostimulant 2.0) with specific biostimulatory action to render agriculture more sustainable and resilient. According to the United Nations estimates, the global population will expand from approximately 7.5 billion presently to more than 9.7 billion by 2050, compelling modern agriculture to become increasingly more efficient by producing more food in an eco-friendly and sustainable way. An innovative technology addressing these important challenges involves the development of novel plant biostimulants (PBs) and effective methods for their application . The glowhat they are not. The term “biostimulant” was first proposed by materials that, in minute quantities, promote plant growth.” The PBs referred to in the former article were humic substances and macro-algal extracts whose stimulatory effect on plants was essentially suggesting a hormonal mode of action.Plant biostimulants are natural compounds that trigger physiological and molecular processes modulating crop yield and quality, though their primary function is neither to supply nutrients (fertilizers) nor to protect plants against soilborne or foliar pests and pathogens (Plant Protection Products) . Therefo1. From a regulatory point of view, there is no agreement globally over the definition of PBs and many EU and non-EU countries lack a specific legal framework (and the plant rhizosphere or phyllosphere: nutrient use efficiency (NUE), tolerance to abiotic stress, crop quality, availability of confined nutrients in the soil and rhizosphere, humification and degradation of organic compounds in the soil”  and/or micro-organisms whose function is to stimulate natural processes that enhance nutrient uptake, nutrient use efficiency, tolerance to abiotic stress, and crop quality” [European Biostimulant Industry Council (EBIC)] ramework . Recentlramework . Under tmore isn’t always better. The identification of synergistic properties among PBs based on reasonable scientific hypotheses and sound experimental approaches , rather than a try-and-see approach, can be pivotal for developing novel and target-specific biostimulant products able to increase NUE and improve plant resilience to environmental stressors.Significant advancements have been made in studying and unraveling the mode of action(s) of PBs, thanks to the omics science, in particular genomic and transcriptomic tools, as well as through high-throughput phenotyping technologies . In addiTaking this background into consideration, limited published data is available concerning the interaction between microbial and non-microbial PBs. The current mini-review article examines the mode of actions/mechanisms by which microbial and non-microbial PBs affect nutrient uptake efficiency, plant performance, and tolerance for abiotic stressors. Subsequently, the scientific advances addressing the synergistic and additive effects among microbial and non-microbial PBs are reviewed and discussed. Finally, the current mini-review identifies the challenges ahead and the future direction of research to develop and exploit a second generation of effective biostimulants rendering agriculture more sustainable and resilient.Based on the latest draft of the +-ATPase activity, and (v) stimulating nitrate assimilation through the upregulation of the target enzymes  involved in this process , and (iv) rhizosphere effects (increased activity of rhizobacteria and mycorrhizae) , phytohormones, and hormone-like compounds . Brown melf-life . The benrrhizae) . AlthougAzospirillum, Azotobacter, and Rhizobium spp. as well as mycorrhizal fungi are highly considered as promising means not only to secure yield stability under low-input conditions  but also as a tool to solving some environmental constraints  of strains belonging to the genera straints . In factstraints  demonstrThe phytostimulation effect of PGPR and mycorrhizal fungi under both optimal and suboptimal conditions could be attributed to several direct and indirect mechanisms including: (i) improved uptake and translocation of nutrients including N and P and micronutrients , (ii) more vigorous root system apparatus  especially in crops having a taproot system  or a shallow root apparatus , (iii) improved water relations and photosynthetic capacity, (iv) stronger antioxidative defense system, (v) regulation of plant hormones , (vi) promotion of nutrient transporters  activity, and (vii) production of enzymes (phosphatases) and/or excretion of low-  and high-molecular weight organic compounds (mucilage and proteins) in the rhizosphere .The agricultural sector’s pursuit of decreased reliance on organic and inorganic fertilizers by improving NUE and mitigating the negative impact of environmental stress factors and soil degradation  on crop growth and productivity. Biostimulants have the capacity to improve NUE and reduce abiotic stress on crops, and these are claims supporting their placing on the market. In fact, Trichoderma spp. through mycoparasitism on the mycorrhizal fungi mycelium  to the roots 2 days after transplanting resulted in a significant increase in onion root dry weight and leaf carotenoids by 43.9 and 12.1% (for HA) and by 29.6 and 57.1% , respectively, whereas the application of both HA and mycorrhizal fungi induced a synergistic effect with the highest accumulation of the two parameters measured (106.7 and 123.6%)  and HA spray applications at 30-day intervals was more effective in enhancing root biomass and chlorophyll biosynthesis than either application alone  with weekly substrate drench applications of a plant-derived PH was more effective than microbial or non-microbial biostimulant applications alone in improving crop productivity  Figure . The pree Figure . Dippingmultifaceted properties. Significant advancements have been made in studying the physiological and biochemical mechanisms of PBs owing to the “omics” sciences and recently to the high-throughput phenotyping technologies. Nonetheless, additional research is required for confronting a number of open questions, such as: (1) which molecular mechanisms underlie the observed biostimulatory action? (2) what is the optimal method, time, rate of application and phenological stage for improving plant performance and resilience to stress and to what extend the plant species/cultivar, environment and management practices applied may affect these effects? (3) how effectively can the PBs modulate the microbial population quantitatively and qualitatively when applied as foliar spray, substrate drench or seed treatment? (4) how long do the PBs effects persist subsequently to their foliar application and how do factors such as leaf cuticle morphology and stomatal aperture interact with the different components of PBs and the target species in impacting leaf permeability and thus the efficacy of the product? and (5) what are the physiological and molecular mechanisms behind the synergistic properties among PBs and how can they be accounted for in developing novel and specific biostimulant products? While presently there seem to be more questions than answers, the findings of the few researchers that have attempted to unravel the complex biostimulation action behind PBs, particularly the synergistic properties, suggest that additional investment in research interaction between the scientific community and the private industry is required in order to develop a second generation of PBs products (biostimulant 2.0) with specific biostimulation action.The use of PBs in agriculture has greatly increased in the last 10 years, mostly due to their YR and GC had the original idea of Synergistic Biostimulatory Action and were both involved in writing the article.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.