Eurasian beavers have been released throughout Europe since the 1900s, with most introductions following the international reintroduction guidelines. Conversely, unauthorised releases have occurred in several countries, e.g., in Spain, Belgium, Scotland, and Central Italy (Tuscany and Umbria regions). In March 2023, other unequivocal presence signs were also detected in southern regions (Molise and Campania), confirming the local presence of beavers. Moreover, two more occurrence sites were recorded in Central Italy (Abruzzi). Despite representing a native species, unauthorised releases of beavers raise management problems, emphasizing the importance of tight monitoring to clarify the actual distribution of this large rodent in these areas.Castor fiber was once present in the Palearctic, ranging from the western Iberian Peninsula to northwestern China. In the Middle Ages, this rodent underwent a severe decline in population due to habitat loss, hunting for fur and meat, and the demand for castoreum. At the beginning of 1900, the range of the Eurasian beaver was limited to scattered refugia in Eurasia. Since 1920, legal protection, reintroduction events, and natural spread triggered the recovery of the species in most of its original range. In March 2021, the presence of the Eurasian beaver in Central Italy (Tuscany and Umbria regions) was confirmed through camera trapping after the detection of unequivocal signs of presence . Recordings are located about 550 km south of the known range of this species, thus suggesting that the presence of beavers in Tuscany and Umbria might be due to a local unauthorised reintroduction. In this work, we also reported the presence of beavers in the Abruzzi region and in Southern Italy (Molise-Campania regions), over 380 km in a straight line south to the southernmost record of beaver presence in Central Italy.The Eurasian beaver  Castor fiber Linnaeus, 1758, once inhabited a significant portion of the Palearctic, ranging from the western Iberian Peninsula to northwestern China, throughout several riparian habitat types in forests, tundra, and steppe [The Eurasian beaver d steppe ,3. At thd steppe ,5.C. fiber occurred in the Early Pleistocene in most Italian regions, between the Alpine chain and the Volturno River in Molise . Reintroduction programs that occurred in neighbouring countries (Austria and Switzerland) between the 1970s and the 1990s have triggered a widespread recolonisation [Salari et al.  summarisnisation ,8. In thnisation . Accordinisation . The actnisation ,13. Beavnisation ,15,16) nnisation . Besidesnisation ,15,16. Inisation ,18. An uLutra lutra, represent another important source of data about the presence of beavers.In 2022–2023, the research project “Rivers with Beavers” was conducted with the aim of monitoring distribution and population size, as well as the potential impacts and genetics of Eurasian beavers in Central Italy. After the first recorded sighting in Central Italy, a widespread media campaign was triggered with over 30 newspaper/tabloid magazine articles, which in turn led to an increase of recorded sightings, which were sent to the research team of “Rivers with Beavers” or the project’s Facebook page. Citizen science initiatives involve common citizens in data collection, always followed by the validation by experts. Projects involving common citizens are increasing worldwide and may store a huge amount of data on species occurrences, thus providing scientists with paramount information to develop strategies for wildlife conservation and management ,20. MoreIn this study, we report the results of four records of beaver sightings obtained through citizen science and a camera trap photo of a beaver, confirming the presence of the Eurasian beaver in Southern Italy, over 350 km from the nearest known sighting previously recorded in Italy.https://www.facebook.com/riverswithbeavers/; accessed on 17 April 2023) has over 1260 followers in Italy, which may have increased citizen awareness on beaver occurrence and behaviour. Therefore, new presence data were collected on the Facebook page through a citizen science approach, based on photos of individual beavers and/or signs of presence. Records confirmed by photos were followed up on by direct field investigations to confirm beaver presence. In particular, we traveled on foot a total of 17.05 km by retracing the same riverine areas 2–3 times (once every three months per area), in the early morning and with wet soil to increase footprint detection, in all rivers where the species was reported to be present.We collected beaver occurrences through the Facebook page of the project “Rivers with Beavers” (see Acknowledgements) between January 2022 and April 2023. This Facebook page includes photos of signs of presence , as well as camera-trapping data showing diagnostic features of Eurasian beavers. The Facebook page (available at: Lutra lutra survey along rivers recently occupied by this species [If beaver signs of presence were detected, we followed  for data species .Between January 2022 and March 2023, we received reliable photos of beaver signs of presence, which were reported to be shot in three areas of Italy:In Areas 1–2 in In Area 3 in In Area 4 in The beaver data in the Abruzzo region (Aterno river, Areas 5 and 6 of Salix spp. and black poplars Populus nigra L. along the Volturno River between Campania and Molise (Evidence from Piedmont and the surroundings of Rome was not authenticated, i.e., no sign of beaver presence was detected in any of these locations, at the coordinates reported by the citizen scientist, or in their surroundings. Furthermore, no beaver was recorded by camera traps in these areas. Conversely, the presence of adult and juvenile beavers was immediately confirmed by different sizes of incisive signs and debarking on stems of willows d Molise . Signs od Molise .The beaver population occurring in the Volturno River corresponds to the southernmost population of this species in the world. However, this area is suitable for beaver presence , and it The Eurasian beaver population is rapidly expanding in Europe following legal protection, habitat recovery, and reintroduction events ,25,26. TBeavers are widely recognized as “ecosystem engineers”, and their presence in river habitats can be easily detected by non-specialists, given their conspicuous gnawing behaviour and construction of dams and lodges . MoreoveA positive attitude towards a species may increase the success of citizen- science initiatives, providing a remarkable increase in its distribution data. Indeed, the intensive media campaign that followed the detection of beavers in central Italy triggered an interest towards this species in Italy, which would have brought an improvement of knowledge on its presence.In our work, citizen science has played a pivotal role in detecting the southernmost occurrence of beavers in Eurasia. In central and southern Italy, it would be important to concentrate on how to manage the species. Given the distance between the central-southern Italian populations of the Eurasian beaver and the rest of the distribution of the species, it is most likely that the Italian nuclei derive from an unauthorised release. A genetic analysis comparing individuals from Central Italy with those from neighbouring countries cf.  would alIn conclusion, our work reveals new Italian hotspots of beaver presence, suggesting the existence of multiple sites of potentially unauthorized releases across the Italian peninsula. Moreover, the discovery in the Volturno River represents the southernmost worldwide suitable habitat for reproductive beavers in Europe. Further monitoring programs should be required to better define the distribution of this large rodent in suitable areas of southern Italian regions.


Internet-based intervention platforms may improve access to mental health care for women with perinatal depression (PND). Though the majority of platforms in the market lack an evidence base, a small number of them are supported by research evidence.This study aims to assess the current status of internet-based PND intervention platforms supported by published evidence, understand the reasons behind the disappearance of any of these previously accessible platforms, examine adjustments made by those active platforms between research trials and market implementation, and evaluate their current quality.A cohort of internet-based PND intervention platforms was first identified by systematic searches in multiple academic databases from database inception until March 26, 2021. We searched on the World Wide Web and the iOS and Android app stores to assess which of these were available in the marketplace between April and May 2021. The basic characteristics of all platforms were collected. For inaccessible platforms, inquiries were made via email to the authors of publications to determine the reasons for their unavailability. We compared the intervention-related information of accessible platforms in the marketplace with that reported in original publications and conducted quality assessments using the App Evaluation Model of the American Psychiatric Association. Fisher exact tests were used to compare the functional characteristics in publications of available and unavailable platforms and to investigate potential associations between functional adjustments or quality indices and platform survival time.P=.04). No significant differences were found for functional adjustments or other quality indices.Out of 35 platforms supported by research evidence, only 19 (54%) were still accessible in the marketplace. The main reason for platforms disappearing was the termination of research projects. No statistically significant differences were found in functional characteristics between available and unavailable platforms. A total of 18 (95%) platforms adapted their core functions from what was reported in related publications. The adjustments included changes to intervention methods , target population , human resources for intervention support , mood assessment and monitoring , communication modality , and platform type . Quality issues across platforms included low frequency of update, lack of crisis management mechanism, poor user interactivity, and weak evidence base or absence of citation of supporting evidence. Platforms that survived longer than 10 years had a higher tendency to use external resources from third parties compared to those that survived less than 10 years (Internet-based platforms supported by evidence were not effectively translated into real-world practice. It is unclear if adjustments to accessible platforms made during actual operation may undermine the proven validity of the original research. Future research to explore the reasons behind the success of the implementation of evidence-based platforms in the marketplace is warranted. Perinatal depression (PND) is one of the most common complications of childbirth , with a One problem in this burgeoning area is that the vast majority of platforms available in the marketplace are not supported by any scientific evidence of their effectiveness. Larsen and colleagues  assessedIn this study, we aimed to (1) identify a cohort of internet-based platforms (websites or apps) for which at least some research evidence was found in the literature; (2) track down their current statuses in the marketplace; and (3) understand the reasons behind the disappearance of previously accessible platforms. We also aimed to examine any adjustments made by these platforms to the functionality reported in the research evidence and assess the quality of currently accessible platforms against the App Evaluation Model of the American Psychiatric Association (APA’s model) .The study cohort consisted of internet-based PND intervention platforms supported by research evidence. In this study, we defined “platform” as an internet-based technology that offers a variety of mental health interventions or services to users, accessible through mobile apps or websites on smartphones, tablets, or computers. We excluded text messaging interventions that use SMS text messaging or technology-enabled services accessed through smartwatches or virtual reality headsets from our definition. We searched PubMed, Web of Science, Cochrane Library, Embase, CNKI, Wanfang, and VIP databases using “perinatal,” “depression,” “internet,” “intervention,” and their extended terms as keywords, from database inception until March 26, 2021 , and Google Play store (Android) between April and May 2021 to identify the corresponding platforms in the marketplace. The current status (accessible or not) of the platform was recorded. For platforms currently accessible, 2 researchers then independently collected market-related data, including platform type (website or app), estimated operating time (period between the launch of the platform and data extraction in this study), and language. For platforms that were no longer accessible at the time of follow-up, emails were addressed to the authors of the related papers, inquiring about the reason for platform inaccessibility. Our approach to contacting authors is the same as previously outlined.The data collected in the literature and marketplace were analyzed using descriptive statistics, calculating the percentages, averages, and ranges to describe the characteristics of the platforms. We used Fisher exact tests to assess the functional differences between available and unavailable platforms, as well as potential associations between functional adjustments or quality indices and platform survival time. The statistical significance was set at α=.05. All statistical tests were conducted using IBM SPSS Statistics (version 25.0).For platforms currently accessible, we assessed the functional adjustments by comparing intervention-related information in real-world operations with the original publications, including platform type, intervention methods, target population, human resources to support interventions, communication modality, and mood assessment and monitoring functions. The quality of accessible platforms was evaluated based on the APA’s model, which was developed by the American Psychiatric Association to assist clinicians and individuals in reviewing the risks and benefits of app use. APA’s model comprises the following 5 levels of evaluation: access and background, privacy and security, clinical foundation, usability, and data integration toward the therapeutic goal. Each level contains a series of objective indicators that address the critical standards for app evaluation as identified by mobile health leaders . The APAAfter excluding duplicates (n=4594), a total of 12,097 publications were retrieved from academic databases. A cohort of 35 platforms derived from 53 original research papers was established following the literature screening. A detailed platform database can be seen in Of the 35 platforms supported by published literature, only 19 were currently accessible, with an average estimated operating time of 12 years. The authors of 7 of the 16 inaccessible platforms provided the precise out-of-service time, from which we calculated an average estimated operating time of 2.1 years. The basic characteristics of 35 platforms can be found in The authors of 10 of the 16 inaccessible platforms replied to our emails and confirmed the reasons for unavailability were the termination of the research project (n=9) and a technology incompatibility issue (n=1). A total of 18 of the 19 still-accessible platforms adjusted their actual functions in the market in comparison to those reported in the publications . Eight pDetailed results of the quality assessment of all accessible platforms following the APA’s model can be seen in All platforms had specific developers, including private companies (n=5), academic organizations (n=5), and medical health providers (n=3). In addition, there were also 6 platforms with multiple types of developers, 5 of which were led by academic organizations in collaboration with government, private companies, and medical health providers respectively, and the remaining 1 was led by a private company in cooperation with individuals that had living experience. Eleven of the 19 platforms were exclusively accessible through websites; 5 were only delivered with apps; and 3 offered both. Two of the 8 platforms offering apps were exclusive to either Android or iOS devices, while 6 were available on both devices. Only 6 platforms have been updated within 6 months. Twelve of the 19 platforms were entirely free, while the remaining 7 charged, mostly on a weekly, monthly, or quarterly basis, according to the services, with an average weekly cost of US $14.17. Two platforms would charge extra for personalized services on top of the base rate.The majority of platforms disclosed user-related specifications; 14 platforms included a privacy policy, and 13 provided terms of use. A total of 16 platforms claimed that they collect, use, or transmit users’ data. In terms of resource use, 13 platforms used third-party resources. Of the 19 platforms, 8 had crisis management mechanisms, of which 5 were passive, that is, they offered information such as helplines to be used when necessary. Three of them adopted active management procedures, that is, the platforms would proactively send out alerts and offer assistance to users in need.Only 8 platforms provided research-based references. All platforms had explicit intervention methods, with health education (n=17) being the most commonly used, followed by mindfulness meditation (n=6), CBT (n=5), social support (n=5), and mood diary (n=3), respectively. A total of 13 platforms provided users with human support during the intervention, 6 of which were given by professionals , 5 by nonprofessionals, and 2 by a combination of both sorts. Ten platforms offered users feedback. There were 10 platforms that assessed users’ mood, but only 2 of them specified the method of assessment . Regarding platform engagement styles, 4 platforms used only image-text presentation mode; 14 platforms integrated audio, video, animation, and other forms; and only 1 platform employed artificial intelligence technology, albeit ineffectively.Ten platforms offered data export options to users. Six platforms provided users in need with referral information, 4 of which offered offline visits and 2 web-based visits. However, only 2 platforms were capable of directly integrating user data into existing health care systems.P=.04). However, we did not find any statistically significant differences in platform survival time for other quality indices. More information can be found in We investigated the association between quality indices and platform survival time and found that platforms lasting over 10 years had a significantly higher tendency to use external resources from third parties than those lasting less than 10 years . But in contrast to previous studies that only took snapshots of market available apps, this study provided a longitudinal perspective by focusing on a cohort of internet-based platforms supported by peer-reviewed literature and tracking their real-world status. This study did not examine subjective indicators of quality, such as ease of use. However, we have conducted a comprehensive, objective quality assessment of all platforms based on the APA’s model to obtain a complete understanding of the quality of internet-based platforms.Internet-based intervention platforms for PND are in a period of rapid development, but this study shows that many evidence-based platforms have poor sustainability in the marketplace. Despite a variety of functionality adjustments in actual operation, most platforms still had quality concerns that were not discussed in the research design phase. The ultimate goal of intervention platform development is to be implemented in the real world. It is clear that feasibility or effectiveness studies within the research context alone are insufficient for this goal. Further research from the perspective of implementation science would be required to evaluate the long-term effectiveness of the platform in a real-world setting, identify the barriers and facilitators of the implementation, and inform platform design in the future.


Cranberries have known anti-inflammatory properties, which extend their benefits in the context of several chronic diseases. These benefits highly rely on the polyphenol profile of cranberries, one of few foods rich in A-type proanthocyanidin (PAC). A-type PAC comprises flavan-3-ol subunits with an additional interflavan ether bond in the conformational structure of the molecule, separating them from the more commonly found B-type PAC. PACs with a degree of polymerization higher than three are known to reach the colon intact, where they can be catabolyzed by the gut microbiota and biotransformed into lower molecular weight organic acids that are available for host absorption. Gut microbiota-derived metabolites have garnered much attention in the past decade as mediators of the health effects of parent compounds. Though, the mechanisms underlying this phenomenon remain underexplored. In this review, we highlight emerging evidence that postulates that polyphenols, including ones derived from cranberries, and their metabolites could exert anti-inflammatory effects by modulating host microRNAs. Our review first describes the chemical structure of cranberry PACs and a pathway for how they are biotransformed by the gut microbiota. We then provide a brief overview of the benefits of microbial metabolites of cranberry in the intestinal tract, at homeostasis and in inflammatory conditions. Finally, we discuss the role of microRNAs in intestinal health and in response to cranberry PAC and how they could be used as targets for the maintenance of intestinal homeostasis. Most of this research is pre-clinical and we recognize that conducting clinical trials in this context has been hampered by the lack of reliable biomarkers. Our review discusses the use of miRNA as biomarkers in this context. Vaccinium macrocarpon) is the most consumed species of cranberry among the four known types in North America . MiRNA bacteria ]. More rbacteria ], celiacbacteria ], and otbacteria ].in vitro and in vivo models  containing or not 5% polyphenol-rich wine grape seed flour (GSF)  . MiR-129ur (GSF) . The stuWith regard to cranberry, some miRNAs that were found to respond to PACs, have previously been proposed as fecal or serum biomarkers of gastrointestinal diseases. For example, miR-1260b and miR-542-5p were previously found to be upregulated in CRC and other cancers and suggested to be used as predictive markers –139. IntAT, ZL, and EC conceived the review and drafted the manuscript. AT, ZL and BL-I conducted the literature search and constructed the tables. AT and BL-I created the figures. EC supervised this work. All authors edited, revised, read, and approved the final manuscript.


Background: Statistical methods are a cornerstone of research in clinical psychology and are used in clinical trials and reviews to determine the best available evidence. The most widespread statistical framework, frequentist statistics, is often misunderstood and misused. Even when properly applied, this framework can lead to erroneous conclusions and unnecessarily prolonged trials. The implications for clinical psychology are difficulties in interpreting best available evidence and unnecessarily costly and burdensome research. An alternative framework, Bayesian statistics, is proposed as a solution to several issues with current practice.Methods: Statistical tests of primary outcome measures were extracted from 272 studies, which were cited in 11 recent reviews in the Evidence-based updates series in the Journal of Clinical Child and Adolescent Psychology. The extracted tests were examined regarding relevant features and re-analyzed using Bayes Factors.Results: When statistical tests were significant, the majority (98%) of re-analyzed tests agreed with such claims. When statistical tests were nonsignificant almost half (43%) of re-analyzed tests disagreed with such claims. Equally important for clinical research, an average of 13% fewer participants per study would have been required if the studies had used Bayes Factors.Conclusions: Bayes Factors offer benefits for research in clinical psychology through intuitive interpretations, and less costly trials. Statistical methods are a cornerstone of research in clinical psychology and play an important role when assessing the evidence base of treatments. Such methods are intended to rigorously test posited hypotheses and inform researchers and clinicians whether a treatment is effective or not, why it is effective, and how to improve treatment. In clinical psychology, the importance of appropriate use of statistical methods has been formalized into criteria, which are necessary to evaluate the evidence base for a treatment . Throughfrequentist statistical methods, typically thep-value and confidence intervals (p = .07) may be taken to indicate a lack of effect, when they more appropriately should be taken to indicate an uncertainty about the existence of an effect. This in turn may lead to premature discontinuation of further research and recommendations for practice that overlook potentially effective — but as yet uncertain — interventions.However, researchers in the field of clinical psychology over-rely on a single set of methods, despite certain limitations of these. The vast majority of research within this filed is namely based on thetervals . Moreovep-values have been misreported in the field of psychology in general and how Bayesian methods can be beneficial for evaluating evidence-based treatments in an adult population  to investigate how the current practice of frequentist statistics affects the conclusions that are reached in this field of research and how the field can benefit from adopting Bayes Factors in place of or in addition to existing methods. The overall aim is to assess the clinical consequences of employing Bayes Factors versusp-values in research.A promising solution to these issues is Bayesian statistics . These mp-value developed byH0. IfH0 is true, thep-value will vary between 0 and 1, with equal probability of any value , that the treatment is effective.To enter a discussion of the clinical consequences of statistical choices, preliminary clarification of frequentist and Bayesian methods is needed. The frequentist methods most widely used in clinical psychology today have their origin in thep-value method. They proposed a decision rule for concluding that the treatment works or not, after conductingseveral independent studies (H0), and which if there is a treatment effect (H1). One then decides on acceptable values for α and β. The measure α is the error rate, which is the proportion of times the researcher will be wrong in concluding the treatmentdoes work if the experiment was repeated indefinitely. The measure β is the proportion of times the researcher would be wrong in concluding the treatmentdoes not work if the experiment was repeated indefinitely. Based on the values ofH0,H1, α, and β, one computes a sample size. If the researcher has followed this method carefully, she can choose to conclude that the treatment does work if thep-value is lower than the set α-level or choose to say the treatment does not work if thep-value is above the set α-level  is an essential aspect of this approach as it formalizes how the data inform us. TheBF indicates the relative strength of evidence in the data for two competing theories  There is evidence of a treatment effect, (b) There is evidence of no treatment effect, and (c) There is equal evidence for there being an effect and there not being an effect , which states that the conditional relationship between two variables A and B is defined by P(A|B) = [P(B|A)*P(A)]/P(B). Theheories . When no effect . There ap-value is equally distributed between 0 and 1 when there is no treatment effect  is treated differently by the frequentist and Bayesian methods. Because the to 25% . To prevrformed . Thus, f0H is true and then proceeds to describe the observed data with probability statements. The question asked is “what is the probability of observing these data, if there is no effect?”. In Bayesian statistics, one assigns probability statements to the hypothesis not the data. The question asked here is “What is the probability that there is a treatment effect when we have observed these data?”. Because of this difference in the view of the data, Bayesian analysis can be performed as many times as you want without jeopardizing the reliability of the results. You can stop sampling when results are convincing either way. Bayesian methods are affected by the how convincing the data are of a hypothesis and how much evidence one requires to make a satisfactory conclusion  between 2017 and 2019. From these we extracted the statistical tests on primary outcome measures, and reanalyzed these usingBFs. The subfield of clinical child and adolescent psychology was selected for two reasons. First, within this subfield there is an inherent power imbalance between adult researchers and non-adult clients. Thus, it is of particular importance to follow the ethical imperative to not burden clients through research. Second, the evidence-base updates series in JCCAP has highly transparent reporting standards, allowing for easy identification of individual studies and primary outcomes within studies. We investigated two primary hypotheses:1.The conclusions reached in current practice would have been reached with a smaller sample size if data had been monitored using Bayes Factors.2.Current research practice indicates an effect or lack of effect when there is no evidence in the data for such conclusions.We have described some major benefits of the Bayesian statistics, with emphasis onThe study is a cross-sectional analysis of a strategic sample of articles that laid the foundation for the evidence base in child and adolescent clinical psychology between 2017–2019. The sample was selected to investigate what statistical analyses make up the evidence base in clinical child and adolescent psychology. The definition of evidence base was articles contained within the evidence base update series of Journal of Child and Adolescent Clinical Psychology. Thus it is important to note that our sample does not contain the entire evidence base for child and adolescent psychology. Rather it contains those articles that we believe have had substantial influence as evidence base.d , the summary statistics that required the least assumptions about the data were preferred over those that required more assumptions. That is, means and standard deviations were preferred overt-values, which were preferred overF-values, which were preferred over regression models, and so on. A subset of 29 (11% of included) articles was independently assessed by the first and third authors, with complete agreement on the description and interpretation ofp-values, and which effects were primary. For the effect-size measure andn associated with it, there was high inter-rater reliability (Cronbach’s α = 0.91). Disagreement between authors was handled through discussion.Measures associated with primary outcomes within each study were extracted by hand. These included the effect size measure, how authors interpreted theAn outcome was defined as primary if described as such within the article. If such information was not available, the definition was based on whether the evidence base update article, in which it was included, had treated it as a primary outcome. If this was not available, it was based on whether other studies within the same field had defined the present outcome as primary.p < .05 was used.Assessment of whether a test was treated as significant or not was based on authors’ reports. If such reports were not available, tests were supposed to be treated as significant based on the described cut-offs for significance elsewhere in the article. If this was not available, the cut-off ofp-value was coded as either indicating an effect of treatment, no effect of treatment, or a negative effect of treatment . If authors described a significant treatment effect or described that the treatment was better or outperformed the control condition, this was taken as indicating a treatment effect. If authors reported no differences, not significantly different, or simply did not describe any outcome measure, this was taken as indicating equality. If authors described a treatment as worse than, or having a negative effect, or control groups being superior, this was taken to indicate a negative effect of treatment.The authors' interpretation of then associated with a test was extracted for participants in the treatment condition and the control condition. If Intent-To-Treat analyses were available, these were used and then associated with these was extracted.Thet-statistics (81% of summary statistics). For outcomes for which binomial proportions were available (13% of summary statistics), or computable, Bayes factors were calculated using the method described by2-statistics (5% of summary statistics), the method described byR2 values were available (0.4%), the method described byd and interpreted ast-statistics and Bayes factors were calculated based on this.Calculation of Bayes factors from summary statistics was conducted in R  using thBF) or credibility interval. TheBF describes the hypothesis of an effect (H1) over the hypothesis of no effect (H0) unless otherwise specified. The credibility interval (CI) describes the interval where the parameter is with 95% probability, with values closer to the centre being more probable. For all analyses, priors were specified with the intent that conclusions should be primarily influenced by observed data. For continuous outcomes the prior distribution was Cauchy distribution with a location parameter of 0 and a scale of 0.7. For categorical outcomes the prior was a beta distribution with parameter a = 1 and b = 1.In the presented analyses, the inferential statistic is either represented as the Bayes factor  achieving a sample size that would have 95% power to achieve aBF > 3 if the effect was present. In performing these simulations, we assumed a non-informative prior. The first stopping rule (BF > 9) was chosen based on preliminary testing suggesting this stopping rule would result in a false-positive rate below .05, which we believe would be reasonable to researchers accustomed to using α = .05. The second stopping rule  was chosen as a futility limit, based on our belief that researchers would not sample indefinitely if the data gave them no indication of an effect and they would expect to see an indication if there was an effect.To analyze the expected requiredge in R , to simuCategorization of Bayes factorsp-values andBF we divided theBFs into eight categories based on recommendations in common use . The aim of this focus is to assess how results would have been interpreted differently usingBFs both in terms of direction of conclusion and whether evidence was strong enough to draw a conclusion.To exemplify the difference between inference based onM = 96.5,Mdn = 65, min = 6, max = 832), and 2431 statistical tests were extracted, with a mean of 9.4 statistical tests on primary outcome measures per study . The mean observed power of the studies was 0.77 . Of the 2431 statistical tests, 171 showed indication of mismatchedp-values and thus 2260 were used for further analysis. SeeIn the 11 evidence base updates assessed, 309 articles were described. Of these, we were unable to obtain 5 of the original articles. Additionally, 12 articles did not report summary statistics that allowed for the recalculation of Bayes factors. Finally, 5 articles did not include a control group or comparison condition (i.e. pre-treatment measures or benchmark) and were thus not selected for further evaluation. In the remaining 287 articles, 24 appeared in more than one evidence base update. In the final 263 articles, 272 studies were included for analysis, with 26170 participants  fewer participants per control group compared to what was observed. The average relative decrease in sample size was 12.7% . Across studies, 1656  fewer participants would have been required if Bayes Factors had been applied. To understand the implications of a smaller required sample, a subset of studies that reported on suicide attempts (k studies = 17) were further analyzed. Within the Bayesian replication, using power equal to the described studies, an estimated 67 individuals who attempted suicide would not have been placed in a control group due to fewer required participants. Overall, these findings support the hypothesis that smaller sample sizes would be required if monitoring data using Bayes Factors.Our first hypothesis was that conclusions reached in current practice would have been reached with a smaller sample size if data had been monitored using Bayes Factors. To assess this, simulated replications of each study were conducted based on the median observed effect size. The observed sample size and that required by Bayesian methods are shown inBFs were calculated on the basis of summary statistics and compared to the claims made in the articles. Results shown inH1 the majority ofBFs were in agreement with that claim. Of thep-values deemed significant, only 1.8% (n = 21) were evidence in the direction ofH0 (BF10 < 1), whereas 3.3% (n = 39) were “not worth a bare mention”. In the case of findings claimed to be support forH0, theBFs did not uniformly support such claims. Of thep-values deemed nonsignificant 43.6% (n = 470) were evidence in the direction ofH1 (BF10 > 1) and 50% (n = 539) were “not worth a bare mention”. Among the tests that were reported as nonsignificant, but theBF indicated evidence in the direction ofH1, 61.3% (n = 301) had used correction methods to lower the α-level. These findings indicate that when research indicates the presence of an effect there is evidence in the data for such a claim. However, these findings also suggest that when research indicates a lack of an effect the evidence in the data does not generally support such a claim.Our second hypothesis was that current research practice indicates an effect or lack of effect when there is no evidence in the data for such claims. To assess this hypothesis,BFs in relation to what the authors of articles argued the test to indicate. When authors had argued for the existence of a treatment effect 94.6% of tests indicated support for this statement (BF > 3), whereas 4.3% indicated not much evidence (BF: 0.33–3) and 1.9% indicated evidence for lack of an effect (BF < 0.33). When authors had argued that a test indicated a lack of difference or an equality 23.4% of tests supported this statement, whereas 50.5% indicated not much evidence and 26.1% indicated evidence for the existence of an effect. When authors had argued that a test indicated superiority of the control group condition 71.4% of tests supported this statement, whereas 23.8% indicated not much evidence and 4.8% indicated evidence for the lack of superiority of control group condition.To expand on these findingsp-values were reported correctly, 55% of the tests did not strongly support the hypothesis claimed by thep-value when reanalyzed with Bayes Factors. Unfortunately, 22% of tests supported the opposite hypothesis of that stated by thep-value. Of clinical importance when statistical tests were used to argue for a lack of effect, only 23% of tests were in agreement with this, with slightly more (27%) indicating the existence of an effect.The purpose of this paper was to investigate how the widespread use of frequentist statistics affects research in clinical psychology, and what benefits the field may have from incorporating Bayesian methods, such as Bayes Factors. 11 recent evidence base updates were selected, and 2431 tests were extracted from 272 included studies. Sample size estimation with Bayesian methods using data-monitoring indicated that we would expect a decrease in the expected sample size per study of 12.7% due to being able to monitor the data while it was gathered. Among tests in whichn is particularly poignant in the case when negative outcomes may befall participants in control group conditions. In the current study, it was found that 67 individuals who attempted suicide and were placed in a control group would not have been placed in a control group if Bayes Factors were applied. Although this does not directly support the statement that they would not have attempted suicide if Bayes Factors had been applied, one cannot preclude that allocation to the control instead of the treatment group contributed to demoralization and suicidality.The results indicate that the required sample size in studies could have been considerably reduced had Bayes Factors been adopted. This is due to the ability of these methods to allow for continuous monitoring of data and altering of the trial as it is in progress. These aims are difficult to attain with corresponding frequentist methods, which require a large and predefined sample size to allow for multiple comparisons . The ethp-values described as significant indicated evidence in favor ofH1 when reanalyzed withBF, with 96% (n = 1132) having aBF above 3. Of greater concern is the finding that only 23% (n = 244) of the nonsignificant findings showed evidence in the direction ofH0 (BF10 < 0.33), with 27% (n = 296) showing evidence in favor ofH1 (BF10 > 3) and 50% (n = 539) showing not much evidence at all (BF10 0.33-3). These findings are surprising, and are the opposite of what is usually raised as a critique of frequentist methods  the Bayesian interpretation:H1 is more probable than0H if we observe this data. Thus, Bayesian methods cannot be said to be more true than frequentist methods and conclusions in both frameworks depend on thresholds chosen. However, a distinct benefit of Bayesian methods from a clinical perspective is the ability to withhold judgement by allowing to reach the conclusion of no conclusion .Whether we are using frequentist or Bayesian methods, we want to make correct conclusions. However, Bayesian methods and frequentist methods may reach different conclusions, due to different assumptions and thresholds. This may at first glance seem confusing and leave researchers asking what to believe – thearadox” , but do p-values andBFs based on summary statistics regardless of whether these were supplementary  or of primary concern  •BFSpeedDatashare.csv •DataKey.docx (Contains data key for variables in BayesFactorOutcomeDatashare.csv and BFSpeedDatashare.csv)•Supplement.docx This project contains the following underlying data:Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication).Data are available under the terms of the I do not consider that the authors have completely answered my criticisms. Unless I have misunderstood, they are only considering cases where there is a true effect. Thus, they cannot provide an estimate of false positive rates. That being so it is difficult to evaluate the performance of any method proposed. Some extremely bad methods would classify true positive studies as positive at an even higher rate. I am prepared to approve the study, however, on the grounds that what it has found and demonstrated may be of interest to those who wish to undertake a proper investigation of Bayes factors. Such an investigation would have to include the performance of Bayes factors under the null. I reject, however, the authors' conclusions. Bayes Factors may or may not "offer benefits for research in clinical psychology". However, this study does not demonstrate that this must be so.Is the work clearly and accurately presented and does it cite the current literature?PartlyIf applicable, is the statistical analysis and its interpretation appropriate?PartlyAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?PartlyAre the conclusions drawn adequately supported by the results?NoAre sufficient details of methods and analysis provided to allow replication by others?PartlyReviewer Expertise:Biostatistics and statistical methodologyI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. ,,. This immediately raises the question, "how have the authors come to their conclusion?". I am not sure what the answer is. I note, however, that the circumstances under which frequentist tests "give significance too easily", as noted by Casella and Berger, is where there is a lump of probability on H0. See Stephen Senn: The pathetic P-value (Guest Post) | Error Statistics Philosophy for a discussion and also.The strangest thing about this study is that it claims the opposite of the usual Bayesian criticism of frequentist approaches. The usual criticism is that frequentist approaches give significance too easily. See, for example,1 is true. They state that they chose to "simulate 10,000 repeated studies based on the median observed effect size of the articles". But if the median observed effect size is not zero, then H0 is not true. However, the whole motivation of the proposal by Jeffreys of Bayes Factors in 1939 in hisTheory of Probability was to be able to prove that scientific laws were true. This in turn required that a lump of probability should be placed on the null hypothesis. It is the fact that the probability under the alternative hypothesis is widely spread that causes some values under H1 to be less well supported (in terms of likelihood) than is the value under H1. It is this that leads to the Jeffreys-Lindley paradox,. The authors mention this but don't seem to appreciate its relevance for their examination.  Thus, the simulation carried out by the authors does not reflect what Bayes factors were created to deal with. Now, in the simulation, it seems to me that the authors have simulated from the case where H It is unclear to me whether this is the origin of the discrepancy. Nevertheless, it seems to me that the set-up the authors have used is confusing. To judge classification methods one needs a gold standard. This is usually provided by simulators by assumption and by varying such assumptions, the performance of two systems may be compared. In any case, to use either Bayes factors or P-values to make a decision to either accept or reject a hypothesis also requires the use of thresholds. Varying these will lead to different error rates. It is not clear to me that this aspect is properly addressed in this paper. in 1900.  It was Fisher who gave them their modern interpretation, at least as early as 1925. The authors have Neyman and Pearson in 1933 improving on Fisher's approach of 1934, which would require a time machine. I am also unhappy with the discussion of the history of statistics. P-values were in use before Fisher. For example, by PearsonIs the work clearly and accurately presented and does it cite the current literature?PartlyIf applicable, is the statistical analysis and its interpretation appropriate?PartlyAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?PartlyAre the conclusions drawn adequately supported by the results?NoAre sufficient details of methods and analysis provided to allow replication by others?PartlyReviewer Expertise:Biostatistics and statistical methodologyI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Thank you for your time and insightful remarks. We agree that our article may have been unclear on our goal, and especially in the discussion, we did not emphasize three critical aspects that you raised: 1. These findings are unexpected  2. The procedures are not applied in the conventional manner (we sample where H1 is assumed true) 3. All findings are contingent on thresholds (as are ours). In our new manuscript, we emphasize that our findings are unusual in the literature criticizing frequentism. The findings, however, are not surprising (at least in our profession), and it is crucial to highlight that clinical psychology research are often underpowered. We have also highlighted that our goal was not to see if Bayesian methods were more "right" than frequentist approaches, and that they were utilized in a particular case where we assumed that the initial findings were valid. Our goal was to see whether we could reach conclusions faster if the original study was correct. The paper's main point is that Bayesian approaches provide pragmatic advantages that are especially useful in clinical psychology. In the discussion, we sought to explain this point. In accordance with the aforementioned, we endeavored to emphasize that all conclusions are dependent on thresholds and that the benefits of Bayesian approaches are not better thresholds but rather more intuitive interpretation, which leads to more reasonable conclusions in the event of null results. We understand that our introduction to statistical methods is somewhat lacking. Our intent is that it should be readily available to researchers within the field of clinical psychology and therefore we wish to keep the background for the methods short. However, we have highlighted that this is an abridged history. If you would prefer, we could also include references to more in-depth discussions on the history of both Bayesian and frequentist methods. We appreciated your remark about time machines, and while Fisher caused innovation in many areas, I do not recall him being involved with time travel. The Fisher reference has been updated to the 1925 edition. I think that the researchers make a strong case for using Bayesian methods in clinical research. Few of the positives might profit from a bit stronger argumentation/illustration. The point that Bayesian methods lend themselves to at more adaptable research strategy is mentioned. This is actually a quite strong argument. Frequentist methods force a discipline on clinical research that makes such research more difficult to do. More doable research will enhance the pace of empirical testing and improving existing methods, and increase the likelihood of clinical research focusing on novel ideas. Bayesian methods will strengthen explorative and innovative clinical research. The burden of doing frequentist research is so heavy, that it primes researchers and clinicians to bet on "safe" and well established methods. In short - it is worth emphasizing that adapting Bayesian methods will increase innovation in clinical treatment and research. Secondly. And this point is implicit in the paper, but could be spelled out in a bit more words. Frequentist methods work wonderfully in a classical experimental setting where the focus is on a distinct part of a system, and the measurements are both fairly exact and reliable. None of this is possible in clinical research. The phenomena we observe are fussy, the measurements we use are inherently imprecise, and no amount of experimental control can master the dynamic processes and changes characteristic of phenomena in clinical research. Here Bayesian methods promise a better and more suitable approach. The final comment, also on something the authors mention. Frequentist methods increase the likelihood of false negatives. Bayesian methods decrease the likelihood of false negatives. The only real alternative to adopting the pragmatic approach of Bayesian methods, is to focus on more and more power. And that approach comes with a multitude of pitfalls. In short. In my view this paper makes an important contribution to the debate about how to approach and evaluate clinical research.  A bit stronger focus on the interaction between design and methods (the specific contribution of Bayesian methods) would strengthen the paper. On the other hand, maybe this part of the discussion merits a paper in its own right.Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Research in occupational health, clinical research focusing on diagnosis and treatment in everyday life, and philosophy/method of science.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Thank you for your time and encouragement. You have wonderfully captured the essence of this paper. In response to your concerns and those of Stephen Senn, we attempted to highlight the pragmatic merits of Bayesian methods.


Format for Formulary Submissions, Version 4.0, is the next iteration of the Format, which was first released in 2000. Version 4.0, developed by pharmacists from health plan, manufacturer, and academic perspectives, provides updated recommendations on acquiring and evaluating clinical and economic evidence to inform formulary and medical policy decisions. It also includes new guidance related to emerging special topic considerations such as biosimilars, specialty pharmacy products, and companion diagnostic tests. Version 4.0 has been modified to improve the usability of the Format, with clarifying guidance related to logistical considerations such as a recommended time frame for implementation of Version 4.0, as well as dossier updates and ongoing communication between manufacturers and health care decision makers. The Format should be used as a framework for ongoing evidence-based dialogue between manufacturers and payers. The evolving health care landscape will require new levels of collaboration and communication among key stakeholders to successfully navigate the challenges of this new environment. The Format provides a framework to support these critical interactions related to product value by facilitating an evidence-based, transparent approach.Managed care pharmacists are increasingly presented with complex considerations related to prescription drug formulary management. As prescription drug spending soars, and new effective, but expensive drugs rush to the market, pharmacists and other health care decision makers must evaluate a myriad of important clinical and economic considerations in determining the relative value and, subsequently, the appropriate placement of a product within a formulary. The AMCP  As such, traditional pharmacy roles have evolved to incorporate complex considerations related to evaluating and balancing important attributes such as treatment benefit and risks, cost-effectiveness, and affordability.The role of the pharmacist in managed care settings has evolved considerably in recent years. This is in part because of a flood of new, effective, and often expensive interventions that require careful analysis for developing and managing a viable pharmacy benefit. The financial impact of these new products is considerable, contributing in part to a 12.2% increase in national prescription drug spending in 2014—the largest increase since 2002.4 These initiatives serve to provide guidance to health care decision makers (HCDMs) and patients regarding important considerations related to assessing the value of health technologies. While these initiatives represent a positive step to encourage a more focused dialogue regarding the value of health technologies, they also highlight, as noted by Neumann and Cohen (2015), that “value is an elusive target, and there’s no consensus about what dimensions should be taken into account.”3This enhanced focus on deciphering the inherent value of new products is reinforced by the recent proliferation of initiatives from a number of health care organizations to develop value frameworks with the objective of providing a more rigorous and comprehensive assessment of value when considering the adoption of new health technologies, including new pharmaceutical products.Format for Formulary Submissions has served as a benchmark to guide drug manufacturers regarding important payer evidence requirements for evaluating new technologies for formulary and medical policy consideration. The Format serves to improve the timeliness, scope, quality, and relevance of clinical and economic information provided by manufacturers to HCDMs, as well as to streamline the evidence acquisition and review process for decision makers. Given that value is in the eye of the beholder, the Format does not prescribe a specific formula to calculate the overall value of a health technology. Rather, the Format endeavors to provide guidance on the key clinical and economic evidence requirements that serve as the fundamental components of determining value. Thus, one of the intended benefits of the Format is to improve the credibility and transparency of the process, which is aligned with the core values of the Academy of Managed Care Pharmacy (AMCP).Since its initial release in 2000, the AMCP Format as a top priority in 2015. The Committee solicited input from AMCP members and revisited content considerations noted since the previous update (Version 3.1), which was published in 2012.5 Based on this input and a detailed review of relevant developments in the payer evidence landscape, the Committee moved forward with a full revision, forming 3 work groups composed of committee members and external contributors to address updated considerations regarding clinical evidence, economic evidence, and key logistical considerations related to the Format. A draft of the Format, Version 4.0, was released for public comment in December 2015. The Committee carefully considered the input received and made revisions where appropriate. Subsequently, the final draft was reviewed and approved by the AMCP Board of Directors in February 2016.Given the evolving health care landscape and enhanced focus on discussions of value, the Format Executive Committee, composed of AMCP members representing a mix of health plan, manufacturer, and academic perspectives, identified a full revision to the Format, Version 4.0, we have endeavored to address evolving considerations in the health care environment, including considerations related to biosimilars, medical devices, comparative effectiveness research, and companion diagnostic tests, to name a few. Guidance on logistical matters related to updating dossiers, the challenge of providing preapproval evidence, and ongoing communication between manufacturers and HCDMs is provided as well. Some critical updates to the Format include the following:With the release of the Format for drugs, tests, and devices; biosimilars; and assessing heterogeneity of treatment effect.Detailed and clarified guidance for handling of contemporary content topics such as comparative effectiveness research; use of the Inclusion of guidance related to evidence requirements for companion diagnostic tests.Comprehensive guidance added for specialty pharmaceutical product information, including handling and distribution requirements and restrictions; appropriate settings; supportive care services; and medical benefit considerations, such as coding.Format with biosimilar products, including comparative data requirements relative to reference products.Specific guidance related to using the Restructured Section 3.0  and Section 5.0 , with additional guidance provided on recommended evidence components for each section.Format to more closely align with updated best practices published by the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) and Society for Medical Decision Making (SMDM) Modeling Good Research Practices Task Force for economic models.6Revision of the economic evidence section of the Incorporating new guidance on budget impact models as unique deliverables in addition to cost-effectiveness analyses as a component of the overall economic evidence dataset.Guidance on modeling considerations for biosimilars and specialty products.Updated guidance on updating dossiers and handling dossier requests before U.S. Food and Drug Administration approval.Format, Version 4.0.Implementation time frame for adoption of the Format, with the provision of links to publications or PubMed where available.Revision of many references throughout the Format, our primary objective was to provide updated guidance to HCDMs and manufacturers that enhanced the clarity, transparency, and usability of the Format, while reinforcing the role of the Format in facilitating ongoing scientific dialogue between payers and manufacturers. Additionally, we wanted to ensure that this new version addressed important new developments in the marketplace. One challenge in providing this guidance is the recognition that our audience is comprised primarily of 2 disparate groups: dossier developers, such as manufacturers, medical writers, and consulting groups, and dossier consumers, who are primarily HCDMs involved in evidence assessment and formulary management. We have attempted to target an appropriate middle ground in developing this updated version. By outlining and describing the key evidence components that are relevant to a formulary or medical policy decision, we hope to reinforce a multifaceted approach to evaluating new technologies, while leaving the final value determination to the ultimate decision maker. On the other end of the spectrum, we have developed specific guidance to dossier producers regarding the content and format of a dossier, while relying on existing standards related to the more technical considerations of value evidence generation and communication. With this approach, we hope that the Format will meet the needs of a broad range of perspectives, while providing clear direction to related resources that may offer additional relevant technical detail as required.In developing this new version of the Format to be viewed as a vehicle to enhance evidence-based communication between manufacturers and HCDMs. Substantial, ongoing communication throughout the evaluation process is a critical success factor in promoting credible, transparent discussions related to product value. We encourage HCDMs to use the unsolicited request process as a means to engage manufacturers in these important conversations, and we encourage manufacturers to provide clinical and economic evidence in a consistent, transparent framework as represented by the Format. We view the Format as offering a clear, shared vision of the requirements to facilitate the collaboration necessary between HCDMs and manufacturers to support appropriate and evidence-based product evaluation.Ultimately, we want the 7 HCDMs are encouraged to revisit and appropriately revise formulary decisions based on the availability of new and compelling evidence. As such, ongoing communication between manufacturers and HCDMs as the value evidence evolves is a critical component to the process.It is important to emphasize that the scope and context of communications between manufacturers and HCDMs should evolve over the product lifecycle as new evidence becomes available. While launch-timed dossiers may rely to a greater extent on modeled projections based on clinical trial evidence and reasonable assumptions related to market dynamics and product uptake, new evidence describing the actual use and effect of the product in a real-world setting should be developed and used to inform formulary management or medical policy criteria across the product lifecycle. Ongoing generation of real-world evidence serves the important purpose of further defining and validating claims related to product value.Format, we appreciate the input provided by our committee members and external contributors and, particularly, the outstanding feedback we received during the open comment period by HCDMs, manufacturers, and other interested parties. This feedback provided useful confirmation of key issues and helped us identify areas that required additional work. We hope the result is an updated version of the Format that provides enhanced clarity, transparency, and usability to HCDMs and manufacturers alike. The Format should stimulate relevant, evidence-based, ongoing communication among the key stakeholders in the process and support the development of informed, rational formulary decision making.In revising the


Orientia. Though cases have been documented, a comprehensive body of evidence has not previously been compiled to give an overview of scrub typhus in Indonesia. This study aimed to address this key knowledge gap by mapping and ranking geographic areas based on existing data on the presence or absence of the pathogen in humans, vectors, and host animals.Scrub typhus is a potentially fatal acute febrile illness caused by bacteria in the genus Orientia tsutsugamushi presence has been identified on all the main islands . About two thirds of the data points were collected before 1946. South Sumatra and Biak had the strongest evidence for sustaining infectious vectors. There was only one laboratory confirmed case in a human identified but 2,780 probable cases were documented. The most common vector was Leptotrombidium deliense.We performed searches on local and international electronic databases, websites, libraries, and collections including Embase, Medline, and Scopus to gather relevant evidence (including grey literature). After extracting data on the presence and absence of the pathogen and its vectors, we ranked the evidence based on the certainty for the presence of human infection risk. The country was divided into subnational units, and each were assigned a score based on the evidence available for that unit. We presented this in an evidence map. O. tsutsugamushi is widespread in Indonesia and likely to be causing significant morbidity and mortality. There is an urgent need to increase surveillance to better understand the burden of the disease across the archipelago and to inform national empirical fever treatment guidelines.Our review highlights the concerning lack of data on scrub typhus in Indonesia, the fourth most populous country in the world. The presence of seropositive samples, infected vectors and rodents confirm  Orientia and spread via the bite of Trombiculid mites. While we know that scrub typhus is present in Indonesia, there is very limited information on how widespread the problem is. To address this problem, this study aimed to create a map of geographic areas that are at risk of scrub typhus by analysing existing data on human, mites, and other animals. By doing so, we hope to provide a better understanding of the extent and distribution of scrub typhus in Indonesia. Evidence of Orientia tsutsugamushi, has been reported from all the five main islands of Indonesia. South Sumatra and Biak had the strongest evidence. However, there was no evidence of presence or absence in about two thirds of the regions and most of the data points were collected before 1946. While the available data suggest that scrub typhus is prevalent in Indonesia, up-to-date information is lacking. Therefore, it is crucial to increase scrub typhus surveillance to improve our understanding of its burden and distribution, and eventually inform treatment strategies.Scrub typhus is a febrile illness common in the Asia Pacific area. It is caused by bacteria in the genus  Orientia. In South and Southeast Asia, scrub typhus is one of the leading causes of non-malarial fever ..65]. We The study years range from 1908 to 2018. Most data points  were collected before 1946. All probable and possible human cases data points were derived from studies done prior to 1955 Figs .There were 354 data points extracted . These dA 1997 article reported an acute scrub typhus case confirmed by more than four-fold rise in ELISA IgG titre between acute and convalescent samples . This paNorth Sumatra had the highest number of probable case data points  . There wThere were 25 data points and 158 cases diagnosed with the Weil-Felix test. Aceh had the highest number of data points  and possible cases diagnosed with the Weil-Felix test . North Sumatra had the highest number of data points  and possible cases diagnosed clinically .There were 11 subnational units with seroprevalence data. Lampung and East Java had the most data points . The median seroprevalence was 6% (ranging from 0.36% in Jakarta to 20% in Lampung)   each in Jakarta, North Sulawesi, and East Borneo. One data point recorded infection in solation . Two othR (qPCR)  64]..L. areniRattus tanezumi was the most commonly found positive . Most of the data points used ELISA as the diagnostic method , followed by IFA , isolation , and Weil-Felix test .There were 33 data points for infection in other hosts. Lampung had the highest number of data points , followed by Jakarta with five data points (15.2%). All documented infected hosts were rodents . These data points spanned 1922 to 2011.L. deliense  followed by L. fletcheri  (Trombiculae). These genus-only data points were not included in the main analysis since we could not be sure if they were confirmed vectors of scrub typhus.The most commonly found vector species was = 10/74) . In threSouth Sumatra and Biak had the highest score for evidence for the presence of scrub typhus disease in humans with 23 points. Among subnational units with evidence, South Borneo and Bintan+ had the lowest scores with 5 points–indicating a lack of evidence . The medThe darker the colour the stronger the evidence for presence of scrub typhus disease in humans. Low scores indicate a lack of evidence (rather than confirmed absence of this disease). Considering the range and distribution of the scores, ‘pretty breaks’ i.e. classification using round numbers as class limits, with interval of five  were selected for visualisation , 68. TheO. tsutsugamushi infections across the Indonesian archipelago from Aceh, Sumatra in the West to Papua in the East and on all the largest islands. Within the archipelago however, we found a lack of evidence for either presence or absence of scrub typhus in two thirds of the subnational units. Further, there was a lack of large-scale epidemiological surveys that would allow areas of disease absence to be identified or infection prevalence to be estimated. South Sumatra and Biak had the best evidence available for sustaining infective vectors, but vector and pathogen surveys have not been conducted or published in many areas. This is particularly important because the widespread nature of the evidence that we did identify indicates that a risk of infection could be present in many more areas of Indonesia. Only one confirmed acute case of scrub typhus using robust diagnostics was documented, showing that current data on human disease is lacking [L. deliense was the most common vector. We have also identified O. tsutsugamushi infection in Leptotrombidium sp. All non-human infected hosts were rodents. Almost two thirds of data points were collected before 1946.To the best of our knowledge, this is the first attempt to systematically map the evidence for the risk of scrub typhus transmission in Indonesia. Despite a lack of human cases diagnosed with gold standard testing, we found strong evidence for the presence of  lacking . HoweverOrientia tsutsugamushi” or the term ‘tsutsugamushi’ in Latin script in the text and therefore can be captured by these search terms. To minimise the possibility of missing any relevant articles, in addition to searching in 13 international databases, we also searched the websites of three Indonesian databases, Indonesia Ministry of Health networks, three Dutch journals and three Dutch museums.Because the search was performed in English and Dutch only, there could be data in other languages not captured. However, this is unlikely, as publications usually mention “One limitation was that this study was conducted at the level of subnational regions spanning relatively large areas and a range of environments including urban and rural. The scores of each subnational unit are not necessarily reflective of the evidence available in each area within the region. This is important to keep in mind when interpreting the results, given that the distribution of scrub typhus is often described as patchy in the form of ‘mite islands’ .The scores assigned to each subnational area reflect the strength of evidence for the presence of an infection risk. The scores, however, do not reflect the volume of data collected nor do they provide a value for the likelihood of infection. For areas with no evidence of presence or absence, the likelihood of sustaining infectious reservoirs is unknown. The lack of evidence for an absence of cases or infectious vectors highlights where surveys are most needed.Almost two thirds of the data points were collected before 1946 and may no longer be representative of the current situation due to changes in land use and ecosystems ,72. In aThis evidence mapping underlines the absence of evidence in about two thirds of subnational units in Indonesia. The lack of data points on confirmed human cases could be due to the limited availability of accurate and accessible diagnostic modalities and the low awareness of medical practitioners. Scrub typhus and other rickettsial diseases are not mentioned in the Indonesian list of medical graduates’ competencies . There iO. tsutsugamushi presence e.g. Riau in east Sumatra and Central Borneo, could potentially bear an unidentified scrub typhus burden. These areas should be targeted in future studies to investigate if scrub typhus is an important current public health concern.North Sumatra has the highest number of data points and evidence for sustaining infected vectors, however, the data points on human cases were more than 50 years old. Although the presence of infected vectors has been confirmed with qPCR in East Borneo and North Sulawesi, we have not found any reported human scrub typhus infection or any study attempting to look for it. Regions adjoining areas with evidence of It is possible that the incidence of scrub typhus in Indonesia is low or absent in certain areas. However, given the widespread presence of infective vectors and seropositive participants, the presence of scrub typhus infection in nations sharing land borders with Indonesia e.g. Papua New Guinea and Malaysia ,77, and To better understand the patchiness of infection risk within an area, species distribution models incorporating ecological parameters to predict presence in areas without data points would help fill the knowledge gaps . This coO. tsutsugamushi represents a widespread infection risk in Indonesia. In some locations, the presence of vectors that can sustain infection is well documented. However, evidence mapping has also identified vast areas where data are lacking and highlights the urgent need to improve our understanding of scrub typhus across the Indonesian archipelago to inform empirical treatment guidelines for febrile patients and guide public health strategies.The available data supports a reasonable conclusion that S1 AppendixThe full search strategy including search terms and databases searched.(DOCX)Click here for additional data file.S2 AppendixAn Excel file containing spreadsheets with full dataset and data dictionary.(XLSX)Click here for additional data file.S3 AppendixA Word document with additional information on methods and results: (1) Section A. Ranking and scores of evidence based on the likelihood of sustaining infectious vectors and recency; (2) Section B. Criteria of vectorship; (3) Fig A. The scoring algorithm applied to score each subnational unit; (4) Fig B. The number of data points collection ending in each period; (5) Fig C. The distribution of data points across the subnational units in Indonesia; (6) Table A. The distribution of data points with seropositive participants across subnational units; (7) Table B. The number of data points for each non-human host species; (8) Table C. The distribution of data points on vector presence across subnational units; (9) Table D. Scores and number of data points in each subnational unit.(DOCX)Click here for additional data file.