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This systematic literature review aimed to provide an overview of the characteristics and methods used in studies applying the Disability-Adjusted Life Years (DALY) concept for infectious diseases within European Union (EU)/European Economic Area (EEA)/European Free Trade Association (EFTA) countries and the United Kingdom. Electronic databases and grey literature were searched for articles reporting the assessment of DALY and its components. We considered studies in which researchers performed DALY calculations using primary epidemiological data input sources. We screened 3,053 studies of which 2,948 were excluded and 105 studies met our inclusion criteria. Of these studies, 22 were multi-country and 83 were single-country studies, of which 46 were from the Netherlands. Food- and water-borne diseases were the most frequently studied infectious diseases. Between 2015 and 2022, the number of burden of infectious disease studies was 1.6 times higher compared to that published between 2000 and 2014. Almost all studies (97%) estimated DALYs based on the incidence- and pathogen-based approach and without social weighting functions; however, there was less methodological consensus with regards to the disability weights and life tables that were applied. The number of burden of infectious disease studies undertaken across Europe has increased over time. Development and use of guidelines will promote performing burden of infectious disease studies and facilitate comparability of the results., peer-reviewed

Background The capacity to use data linkage and artificial intelligence to estimate and predict health indicators varies across European countries. However, the estimation of health indicators from linked administrative data is challenging due to several reasons such as variability in data sources and data collection methods resulting in reduced interoperability at various levels and timeliness, availability of a large number of variables, lack of skills and capacity to link and analyze big data. The main objective of this study is to develop the methodological guidelines calculating population-based health indicators to guide European countries using linked data and/or machine learning (ML) techniques with new methods. Method We have performed the following step-wise approach systematically to develop the methodological guidelines: i. Scientific literature review, ii. Identification of inspiring examples from European countries, and iii. Developing the checklist of guidelines contents. Results We have developed the methodological guidelines, which provide a systematic approach for studies using linked data and/or ML-techniques to produce population-based health indicators. These guidelines include a detailed checklist of the following items: rationale and objective of the study (i.e., research question), study design, linked data sources, study population/sample size, study outcomes, data preparation, data analysis (i.e., statistical techniques, sensitivity analysis and potential issues during data analysis) and study limitations. Conclusions This is the first study to develop the methodological guidelines for studies focused on population health using linked data and/or machine learning techniques. These guidelines would support researchers to adopt and develop a systematic approach for high-quality research methods. There is a need for high-quality research methodologies using more linked data and ML-techniques to develop a structured cross-disciplinary approach for improving the population health information and thereby the population health.

Introduction: There is growing evidence that the impact of COVID-19 crisis may be stronger for individuals with multimorbidity, frailty and lower socioeconomic status. Existing reviews focus on few, mainly short-term effects of COVID-19 illness and patients with single chronic disease. Information is also largely missing for population representative samples.Applying population-based approach, the systematic reviews will have two objectives: (1) to evaluate the aetiological roles of frailty, multimorbidity and socioeconomic status on SARS-CoV-2 infection probability, hospitalisation, intensive care unit (ICU) admission, mechanical ventilation and COVID-19 related mortality among general population and (2) to investigate the prognostic roles of frailty, multimorbidity and socioeconomic characteristics on the risk of hospitalisation, ICU admission, mechanical ventilation, COVID-19 mortality, functioning, quality of life, disability, mental health and work absence. Methods and analysis: For this ongoing work, four databases were searched: PubMed, Embase, WHO COVID-19 Global literature on coronavirus disease and PsycINFO, for the period between January 2020 and April 7 2021. Peer-reviewed published literature in English and all types of population-based studies will be considered. Studies using standard tools to assess multimorbidity such as disease count, comorbidity indices or disease combinations will be retained, as well as studies with standard scales and scores for frailty or measurement of a socioeconomic gradient. Initial search included 10 139 articles, 411 for full-text reading. Results will be summarised by risk factor, objective and outcome. The feasibility of meta-analysis will be determined by the findings and will aim to better understand uncertainties of the results. Quality of studies will be assessed using standardised scales. Ethics and dissemination: The study will be based on published evidence, and it is exempt from the ethical approval. This work is part of the Population Health Information Research Infrastructure (PHIRI) project. Dissemination of the results will imply conference presentation, submission for scientific publication and PHIRI project report. These systematic reviews are part of PHIRI project (available from: https://cordis.europa.eu/project/id/101018317/fr). PHIRI is funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement no: 101018317. The protocol was developed in collaboration with all listed institutions. The funding body was not involved in the design of the study; it will not be involved in the collection, analysis and interpretation of data or in writing the manuscript. The content of this publication will represent the views of the authors only and not of the European Commission or any other body of the European Union. The European Commission does not take any responsibility for use that may be made of the information it will contain. Sí

Background The use of machine learning techniques is increasing in healthcare which allows to estimate and predict health outcomes from large administrative data sets more efficiently. The main objective of this study was to develop a generic machine learning (ML) algorithm to estimate the incidence of diabetes based on the number of reimbursements over the last 2 years. Methods We selected a final data set from a population-based epidemiological cohort (i.e., CONSTANCES) linked with French National Health Database (i.e., SNDS). To develop this algorithm, we adopted a supervised ML approach. Following steps were performed: i. selection of final data set, ii. target definition, iii. Coding variables for a given window of time, iv. split final data into training and test data sets, v. variables selection, vi. training model, vii. Validation of model with test data set and viii. Selection of the model. We used the area under the receiver operating characteristic curve (AUC) to select the best algorithm. Results The final data set used to develop the algorithm included 44,659 participants from CONSTANCES. Out of 3468 variables from SNDS linked to CONSTANCES cohort were coded, 23 variables were selected to train different algorithms. The final algorithm to estimate the incidence of diabetes was a Linear Discriminant Analysis model based on number of reimbursements of selected variables related to biological tests, drugs, medical acts and hospitalization without a procedure over the last 2 years. This algorithm has a sensitivity of 62%, a specificity of 67% and an accuracy of 67% [95% CI: 0.66–0.68]. Conclusions Supervised ML is an innovative tool for the development of new methods to exploit large health administrative databases. In context of InfAct project, we have developed and applied the first time a generic ML-algorithm to estimate the incidence of diabetes for public health surveillance. The ML-algorithm we have developed, has a moderate performance. The next step is to apply this algorithm on SNDS to estimate the incidence of type 2 diabetes cases. More research is needed to apply various MLTs to estimate the incidence of various health conditions.

Background The recent Austrian Primary Care Act established new primary health care units (PHCUs) and obliged them to draw up a “care strategy” specifying their focal care tasks and objectives and emphasizing the health care needs of the population in their catchment area with its specific local health and epidemiological profile. The main purpose of these care strategies is thus to ensure that care-providers meet the local needs, but they also provide a rationale for evaluation and organizational development. To assist new PHCUs in establishing care strategies it was necessary to develop a method for automatically generating comprehensive local case studies for any freely definable location in Austria. Results We designed an interactive report generator capable of producing location-specific regional health care profiles for a PHCU located in any of Austria’s 2122 municipalities and of calculating the radius of its catchment area (defined by different levels of maximum car-travelling times). The reports so generated, called “regional health care profiles for primary health care” (RHCPs/PHC), are in comprehensive PDF report format. The core of each report is a set of 35 indicators, classified under five health and health service domains. The reports include an introductory text, definitions, a map, a graphic and tabular presentation of all indicator values, including information on local, supra-regional and national value distribution, a ranking, and numbers of service providers (e.g. pharmacies, surgeries, nursing homes) located within the catchment area. Conclusions The RHCPs/PHC support primary health care planning, efforts to improve care-effectiveness, and strategic organizational development by providing comprehensive information on the health of the population, the utilization of health services and the health care structures within the catchment area. In addition to revealing the scope and nature of the health care needed, they also provide information on what public health approaches are necessary. RHCPs/PHC for different locations have already been distributed to numerous stakeholders and primary health care providers in Austria.

Background The InfAct (Information for Action) project is a European Commission Joint Action on Health Information which has promoted the potential role of burden of disease (BoD) approaches to improve the current European Union-Health Information System (EU-HIS). It has done so by raising awareness of the concept, the methods used to calculate estimates and their potential implications and uses in policymaking. The BoD approach is a systematic and scientific effort to quantify and compare the magnitude of health loss due to different diseases, injuries, and risk factors with estimates produced by demographic characteristics and geographies for specific points in time. Not all countries have the resources to undertake such work, and may therefore start with a more restricted objective, e.g., a limited number of diseases, or the use of simple measures of population health such as disease prevalence or life expectancy. The main objective to develop these recommendations was to facilitate those countries planning to start a national burden of disease study. Results These recommendations could be considered as minimum requirements for those countries planning to start a BoD study and includes following elements: (1) Define the objectives of a burden of disease study within the context of your country, (2) Identify, communicate and secure the benefits of performing national burden of disease studies, (3) Secure access to the minimum required data sources, (4) Ensure the minimum required capacity and capability is available to carry out burden of disease study, (5) Establish a clear governance structure for the burden of disease study and stakeholder engagement/involvement, (6) Choose the appropriate methodological approaches and (7) Knowledge translation. These were guided by the results from our survey performed to identify the needs of European countries for BoD studies, a narrative overview from four European countries (Belgium, Germany, The Netherlands and Scotland) and the summary of a comparative study of country health profiles with national health statistics. Conclusions These recommendations as minimum requirements would facilitate efforts by those European countries who intend to perform national BoD studies.

Background The use of artificial intelligence is increasing to estimate and predict health outcomes from large data sets. The main objectives were to develop two algorithms using machine learning techniques to identify new cases of diabetes (case study I) and to classify type 1 and type 2 (case study II) in France. Methods We selected the training data set from a cohort study linked with French national Health database (i.e., SNDS). Two final datasets were used to achieve each objective. A supervised machine learning method including eight following steps was developed: the selection of the data set, case definition, coding and standardization of variables, split data into training and test data sets, variable selection, training, validation and selection of the model. We planned to apply the trained models on the SNDS to estimate the incidence of diabetes and the prevalence of type 1/2 diabetes. Results For the case study I, 23/3468 and for case study II, 14/3481 SNDS variables were selected based on an optimal balance between variance explained and using the ReliefExp algorithm. We trained four models using different classification algorithms on the training data set. The Linear Discriminant Analysis model performed best in both case studies. The models were assessed on the test datasets and achieved a specificity of 67% and a sensitivity of 62% in case study I, and a specificity of 97 % and sensitivity of 100% in case study II. The case study II model was applied to the SNDS and estimated the prevalence of type 1 diabetes in 2016 in France of 0.3% and for type 2, 4.4%. The case study model I was not applied to the SNDS. Conclusions The case study II model to estimate the prevalence of type 1/2 diabetes has good performance and will be used in routine surveillance. The case study I model to identify new cases of diabetes showed a poor performance due to missing necessary information on determinants of diabetes and will need to be improved for further research.

BackgroundThe availability of data generated from different sources is increasing with the possibility to link these data sources with each other. However, linked administrative data can be complex to use and may require advanced expertise and skills in statistical analysis. The main objectives of this study were to describe the current use of data linkage at the individual level and artificial intelligence (AI) in routine public health activities, to identify the related estimated health indicators (i.e., outcome and intervention indicators) and health determinants of non-communicable diseases and the obstacles to linking different data sources.MethodWe performed a survey across European countries to explore the current practices applied by national institutes of public health, health information and statistics for innovative use of data sources (i.e., the use of data linkage and/or AI).ResultsThe use of data linkage and AI at national institutes of public health, health information and statistics in Europe varies. The majority of European countries use data linkage in routine by applying a deterministic method or a combination of two types of linkages (i.e., deterministic & probabilistic) for public health surveillance and research purposes. The use of AI to estimate health indicators is not frequent at national institutes of public health, health information and statistics. Using linked data, 46 health outcome indicators, 34 health determinants and 23 health intervention indicators were estimated in routine. The complex data regulation laws, lack of human resources, skills and problems with data governance, were reported by European countries as obstacles to routine data linkage for public health surveillance and research.ConclusionsOur results highlight that the majority of European countries have integrated data linkage in their routine public health activities but only a few use AI. A sustainable national health information system and a robust data governance framework allowing to link different data sources are essential to support evidence-informed health policy development. Building analytical capacity and raising awareness of the added value of data linkage in national institutes is necessary for improving the use of linked data in order to improve the quality of public health surveillance and monitoring activities.

Background: The studies on unknown diabetes are usually based on small samples of population. Information from the entire population can be accessible through health insurance databases like the French National Health Data System (SNDS) comprising data on reimbursement of out-of-hospital dispensed health care and from public and private hospitals from the 66 million people living in France. The objective of our study was to develop an algorithm to identify unknown diabetes cases in the SNDS using artificial intelligence (AI). Methods: Data from the Constances cohort were used to develop the algorithm since individual information recorded in self-administered questionnaire, medical examination and biological tests is linked with data from the SNDS. We applied a supervised machine learning method, involving eight steps. First, the reference database was selected (n=44,185 participants), after excluding known diabetes cases. Then, the unknown diabetes cases - fasting blood glucose ≥7 mmol/L- were identified as target positive (n = 655). The following steps were: codification of the SNDS variables, split the referenced database into training and testing databases, selection of variables and training, validation and selection of algorithms. Results: Among the 3471 variables coded, 12 variables were selected based on their ability to discriminate the target: unknown diabetes cases versus no diabetes. The final algorithm is a logistic regression model based on the 5 most discriminating variables: age, sex and number of out-of-hospital reimbursements in the previous year for blood lipid profile tests, general practitioner consultations and blood glucose tests. The specificity, sensitivity and precision of the algorithm were 70%, 71% and 69%, respectively. Conclusion: AI opens many perspectives in terms of diabetes prevention. Thus, unknown diabetes cases could be ascertained for development and evaluation of prevention policies at a national, regional or local level. Disclosure S. Fuentes: None. R. Hrzic: None. S. Kab: Employee; Spouse/Partner; Sanofi. R. Haneef: None. S. Fosse-Edorh: None. E. Cosson: None.

Background The availability of data generated from different sources is increasing with the possibility to link these data sources together. However, linked administrative data can be complex to use and may require advanced expertise and skills in statistical analysis. The main objectives of this study were to describe the current use of data linkage at the individual level and the artificial intelligence (AI) in routine public health activities, and to identify the related health outcome and intervention indicators and determinants of health for non-communicable diseases. Method We performed a survey across European countries to explore the current practices applied by national institutes of public health and health information and statistics for innovative use of data sources (i.e., the use of data linkage and/or the AI). Results The use of data linkage and the AI at national institutes of public health and health information and statistics in Europe varies. The majority of European countries use data linkage in routine by applying a deterministic method or a combination of two types of linkages (i.e., deterministic & probabilistic) for public health surveillance and research purposes. The use of AI to estimate health indicators is not frequent at national institutes of public health and health information and statistics. Using linked data, 46 health outcome indicators related to seven health conditions, 34 indicators related to determinants and 23 to health interventions were estimated in routine. Complex data regulation laws, lack of human resources, skills and problems with data governance, were reported by European countries as obstacles to link different data sources in routine for public health surveillance and research. Conclusions Our results highlight that the majority of European countries have integrated data linkage in routine public health activities but a few use the AI. A sustainable national health information system and a robust data governance framework allowing to link different data sources are essential to support evidence-informed health policy development process. Building analytical capacity and awareness of the added value of data linkage in national institutes is necessary for improving the utilization of linked data in order to improve the monitoring of public health activities.

Background Health information systems both at the national and international level play a key role in ensuring that timely and reliable evidence is used for operational and strategic decision making inside and outside the health sector. The availability of data generated from different sources is increasing with the possibility to link these data sources together. However, more efficient data generation processes are required to use data collected for different purposes initially, as well as advanced statistical techniques to generate comparable and timely health information. The main objective is to explore the innovative use of health information for better public health policy across the Member States. Methods As part of InfAct, we have conducted as survey among EU-MS to describe the innovative use of data sources. We are collecting inspiring examples on the innovative use of health information based on national or European data networks involved with health policy-making at national, regional or local level. We are further developing generic methods to estimate health indicators using machine learning techniques and mathematical modelling. Results These approaches will generate a roadmap on the innovative use of health information across Member States, enlarge the existing list of health indicators estimated from linked data and/or advanced statistical techniques, inform on the implications of these indicators in health policy with inspiring examples from Member States, and provide methodological guidelines for using linked data and advanced statistics to estimate health indicators, and composite outcome measures. Conclusions This work will highlight the gaps in the innovative use of data sources, and improve the comparability of health indicators and the capacity of EU-Member states to apply innovation for increased relevance and timeliness of health information for public health policy-making.

Background New metrics have been developed to assess the impact of research and provide an indication of online media attention and data dissemination. We aimed to describe online media attention of articles evaluating cancer treatments and identify the factors associated with high online media attention. Methods We systematically searched MEDLINE via PubMed on March 1, 2015 for articles published during the first 6 months of 2014 in oncology and medical journals with a diverse range of impact factors, from 3.9 to 54.4, and selected a sample of articles evaluating a cancer treatment regardless of study design. Altmetric Explorer was used to identify online media attention of selected articles. The primary outcome was media attention an article received online as measured by Altmetric score (i.e., number of mentions in online news outlets, science blogs and social media). Regression analysis was performed to investigate the factors associated with high media attention, and regression coefficients represent the logarithm of ratio of mean (RoM) values of Altmetric score per unit change in the covariate. Results Among 792 articles, 218 (27.5%) received no online media attention (Altmetric score = 0). The median [Q1–Q3] Altmetric score was 2.0 [0.0–8.0], range 0.0–428.0. On multivariate analysis, factors associated with high Altmetric score were presence of a press release (RoM = 10.14, 95%CI [4.91–20.96]), open access to the article (RoM = 1.48, 95%CI [1.02–2.16]), and journal impact factor (RoM = 1.10, 95%CI [1.07–1.12]. As compared with observational studies, systematic reviews were not associated with high Altmetric score (RoM = 1.46, 95%CI [0.74–2.86]; P = 0.27), nor were RCTs (RoM = 0.65, 95%CI [0.41–1.02]; P = 0.059) and phase I/II non-RCTs (RoM = 0.58, 95%CI [0.33–1.05]; P = 0.07). The articles with abstract conclusions favouring study treatments were not associated with high Altmetric score (RoM = 0.97, 95%CI [0.60–1.58]; P = 0.91). Conclusions Most important factors associated with high online media attention were the presence of a press release and the journal impact factor. There was no evidence that study design with high level of evidence and type of abstract conclusion were associated with high online media attention. Electronic supplementary material The online version of this article (doi:10.1186/s41073-017-0033-z) contains supplementary material, which is available to authorized users.

Background News stories represent an important source of information. We aimed to evaluate the impact of “spin” (i.e., misrepresentation of study results) in health news stories reporting studies of pharmacologic treatments on patients’/caregivers’ interpretation of treatment benefit. Methods We conducted three two-arm, parallel-group, Internet-based randomized trials (RCTs) comparing the interpretation of news stories reported with or without spin. Each RCT considered news stories reporting a different type of study: (1) pre-clinical study, (2) phase I/II non-RCT, and (3) phase III/IV RCT. For each type of study, we identified news stories reported with spin that had earned mention in the press. Two versions of the news stories were used: the version with spin and a version rewritten without spin. Participants were patients/caregivers involved in Inspire, a large online community of more than one million patients/caregivers. The primary outcome was participants’ interpretation assessed by one specific question “What do you think is the probability that ‘treatment X’ would be beneficial to patients?” (scale, 0 [very unlikely] to 10 [very likely]). Results For each RCT, 300 participants were randomly assigned to assess a news story with spin (n = 150) or without spin (n = 150), and 900 participants assessed a news story. Participants were more likely to consider that the treatment would be beneficial to patients when the news story was reported with spin. The mean (SD) score for the primary outcome for abstracts reported with and without spin for pre-clinical studies was 7.5 (2.2) versus 5.8 (2.8) (mean difference [95% CI] 1.7 [1.0–2.3], p

ObjectiveTo explore how the results from the 2014 dual antiplatelet therapy (DAPT) trial were disseminated to the scientific community and online media.DesignA a systematic review of scholarly and public attention surrounding the DAPT study.SettingsData were collected from the ISI Web of Knowledge, Google Scholar, PubMed Commons, EurekAlert, the DAPT study website (www.daptstudy.org) and theNew England Journal of Medicinewebsite (for scholarly attention) and Altmetric Explorer, Snap Bird, YouTube (for public attention) citing DAPT study results appearing from 16 November 2014 to 10 June 2015.ParticipantsNo participants were involved in this study.Main outcome measureProportion of contents highlighting the increased risk of mortality and critical to the author’s interpretation of the results.ResultsWe identified 425 items reported by seven sources; 164 (39%) disseminated the authors’ interpretation via an electronic link or a reference, with no additional text. Among 81 items (19 %), the message favoured prolonged treatment and consequently overstated the article conclusions. Among 119 items (28 %), the text was uncertain about the benefit of prolonged treatment but was reported with no or inappropriate mention of increased risk of mortality. Only 34 items (8 %) were uncertain about the benefit of prolonged treatment and mentioned increased risk of mortality. In all, 27 items (6 %) did not favour prolonged treatment, and only 12 of these (3 %) clearly raised some concerns about the reporting of increased risk of death.ConclusionDissemination of the DAPT study results to the scientific community and on different media sources rarely criticised the interpretation of the study results.

Introduction We aim to compare the interpretation of health news items reported with or without spin. ‘Spin’ is defined as a misrepresentation of study results, regardless of motive (intentionally or unintentionally) that overemphasises the beneficial effects of the intervention and overstates safety compared with that shown by the results. Methods and analysis We have planned a series of 16 randomised controlled trials (RCTs) to perform a prospective meta-analysis. We will select a sample of health news items reporting the results of four types of study designs, evaluating the effect of pharmacological treatment and containing the highest amount of spin in the headline and text. News items reporting four types of studies will be included: (1) preclinical studies; (2) phase I/II (non-randomised) trials; (3) RCTs and (4) observational studies. We will rewrite the selected news items and remove the spin. The original news and rewritten news will be appraised by four types of populations: (1) French-speaking patients; (2) French-speaking general public; (3) English-speaking patients and (4) English-speaking general public. Each RCT will explore the interpretation of news items reporting one of the four study designs by each type of population and will include a sample size of 300 participants. The primary outcome will be participants’ interpretation of the benefit of treatment after reading the news items: (What do you think is the probability that treatment X would be beneficial to patients? (scale, 0 (very unlikely) to 10 (very likely)). This study will evaluate the impact of spin on the interpretation of health news reporting results of studies by patients and the general public. Ethics and dissemination This study has obtained ethics approval from the Institutional Review Board of the Institut national de la santé et de la recherche médicale (INSERM) (registration no: IRB00003888). The description of all the steps and the results of this prospective meta-analysis will be available online and will be disseminated as a published article. On the completion of this study, the results will be sent to all participants. PROSPERO registration number CRD42017058941.

Background Observational studies are essential for assessing safety. The aims of this study were to evaluate whether results of observational studies evaluating an intervention with safety outcome(s) registered at ClinicalTrials.gov were published and, if not, whether they were available through posting on ClinicalTrials.gov or the sponsor website. Methods We identified a cohort of observational studies with safety outcome(s) registered on ClinicalTrials.gov after October 1, 2007, and completed between October 1, 2007, and December 31, 2011. We systematically searched PubMed for a publication, as well as ClinicalTrials.gov and the sponsor website for results. The main outcomes were the time to the first publication in journals and to the first public availability of the study results (i.e. published or posted on ClinicalTrials.gov or the sponsor website). For all studies with results publicly available, we evaluated the completeness of reporting (i.e. reported with the number of events per arm) of safety outcomes. Results We identified 489 studies; 334 (68 %) were partially or completely funded by industry. Results for only 189 (39 %, i.e. 65 % of the total target number of participants) were published at least 30 months after the study completion. When searching other data sources, we obtained the results for 53 % (n = 158; i.e. 93 % of the total target number of participants) of unpublished studies; 31 % (n = 94) were posted on ClinicalTrials.gov and 21 % (n = 64) on the sponsor website. As compared with non-industry-funded studies, industry-funded study results were less likely to be published but not less likely to be publicly available. Of the 242 studies with a primary outcome recorded as a safety issue, all these outcomes were adequately reported in 86 % (114/133) when available in a publication, 91 % (62/68) when available on ClinicalTrials.gov, and 80 % (33/41) when available on the sponsor website. Conclusions Only 39 % of observational studies evaluating an intervention with safety outcome(s) registered at ClinicalTrials.gov had their results published at least 30 months after study completion. The registration of these observational studies allowed searching other sources (results posted at ClinicalTrials.gov and sponsor website) and obtaining results for half of unpublished studies and 93 % of the total target number of participants. Electronic supplementary material The online version of this article (doi:10.1186/s12916-016-0551-4) contains supplementary material, which is available to authorized users.

Background Spin represents specific reporting strategies, either intentional or unintentional, to convince the reader that the beneficial effect of the experimental intervention in terms of efficacy and safety is greater than that shown by the results. The objectives of this study were to 1) develop a classification of spin specific to non-randomized studies assessing an intervention and 2) estimate the prevalence of spin in abstracts of reports of such studies. Methods In a first step, we developed a specific classification of spin for non-randomized studies by a literature review and pilot study. In a second step, 2 researchers trained in the field of methodology evaluated the prevalence of spin in the abstract of all non-randomized studies assessing an intervention published in the BioMed Central Medical Series journals between January 1, 2011 and December 31, 2013. All disagreements were resolved by consensus. We also determined whether the level of spin in abstract conclusions was high (spin reported without uncertainty or recommendations for further trials), moderate (spin reported with some uncertainty or recommendations for further trials) or low (spin reported with uncertainty and recommendations for further trials). Results Among the 128 assessed articles assessed, 107 (84 %) had at least one example of spin in their abstract. The most prevalent strategy of spin was the use of causal language, identified in 68 (53 %) abstracts. Other frequent strategies were linguistic spin, inadequate implications for clinical practice, and lack of focus on harm, identified in 33 (26 %), 25 (20 %), and 34 (27 %) abstracts respectively. Abstract conclusions of 61 (48 %) articles featured a high level of spin. Conclusion Abstract of reports of non-randomized studies assessing an intervention frequently includes spin. Efforts to reduce the prevalence of spin in abstract for such studies are needed. Electronic supplementary material The online version of this article (doi:10.1186/s12874-015-0079-x) contains supplementary material, which is available to authorized users.

Objective Spin is defined as a specific way of reporting, whether intentional or unintentional, to emphasize that the beneficial effect of the intervention is greater than that shown by the results. Our aim was to describe the spin identified, deleted or added by peer reviewers in reports of non-randomized studies assessing a therapeutic intervention and to evaluate the prevalence of spin in the abstract conclusions that peer reviewers failed to identify. Design Systematic review and retrospective before–after study. Sample All primary reports of non-randomized studies assessing a therapeutic intervention (n=128) published in BioMed Central Medical Series journals between January 1, 2011 and December 31, 2013. Main Outcome Measures Number and type of spin examples identified, deleted or added by peer reviewers in the whole manuscript. Number of reports with spin in abstract conclusions not detected by peer reviewers. The level of spin (i.e., no, low, moderate and high level of spin) in the abstract conclusions before and after the peer review. Results For 70 (55%) submitted manuscripts, peer reviewers identified at least one example of spin. Of 123 unique examples of spin identified by peer reviewers, 82 (67%) were completely deleted by the authors, 20 (16%) partially deleted and 21 (17%) not removed in the final published article. For19 articles (15%), peer reviewers requested adding some spin, and for 11 (9%), the spin was added by the authors. Peer reviewers failed to identify spin in abstract conclusions of 97 (76%) reports. The most prevalent example of spin in the abstract conclusions related to the use of causal language, was identified in 64 (50.0%) published abstract conclusions both before and after the peer review. The level of spin in the abstract conclusion increased in 8 articles (6%), decreased in 15 (12%), and remained unchanged in 105 (82%). Conclusion Peer reviewers identified many examples of spin in submitted manuscripts. However, their influence on change of spin in the conclusion was low. This is now added in the conclusion

Agnes Dechartres and colleagues searched ClinicalTrials.gov for completed drug RCTs with results reported and then searched for corresponding studies in PubMed to evaluate timeliness and completeness of reporting. Please see later in the article for the Editors' Summary, Background The US Food and Drug Administration Amendments Act requires results from clinical trials of Food and Drug Administration–approved drugs to be posted at ClinicalTrials.gov within 1 y after trial completion. We compared the timing and completeness of results of drug trials posted at ClinicalTrials.gov and published in journals. Methods and Findings We searched ClinicalTrials.gov on March 27, 2012, for randomized controlled trials of drugs with posted results. For a random sample of these trials, we searched PubMed for corresponding publications. Data were extracted independently from ClinicalTrials.gov and from the published articles for trials with results both posted and published. We assessed the time to first public posting or publishing of results and compared the completeness of results posted at ClinicalTrials.gov versus published in journal articles. Completeness was defined as the reporting of all key elements, according to three experts, for the flow of participants, efficacy results, adverse events, and serious adverse events (e.g., for adverse events, reporting of the number of adverse events per arm, without restriction to statistically significant differences between arms for all randomized patients or for those who received at least one treatment dose). From the 600 trials with results posted at ClinicalTrials.gov, we randomly sampled 50% (n = 297) had no corresponding published article. For trials with both posted and published results (n = 202), the median time between primary completion date and first results publicly posted was 19 mo (first quartile = 14, third quartile = 30 mo), and the median time between primary completion date and journal publication was 21 mo (first quartile = 14, third quartile = 28 mo). Reporting was significantly more complete at ClinicalTrials.gov than in the published article for the flow of participants (64% versus 48% of trials, p, Editors' Summary Background When patients consult a doctor, they expect to be recommended what their doctor believes is the most effective treatment with the fewest adverse effects. To determine which treatment to recommend, clinicians rely on sources that include research studies. Among studies, the best evidence is generally agreed to come from systematic reviews and randomized controlled clinical trials (RCTs), studies that test the efficacy and safety of medical interventions by comparing clinical outcomes in groups of patients randomly chosen to receive different interventions. Decision-making based on the best available evidence is called evidence-based medicine. However, evidence-based medicine can only guide clinicians if trial results are published in a timely and complete manner. Unfortunately, underreporting of trials is common. For example, an RCT in which a new drug performs better than existing drugs is more likely to be published than one in which the new drug performs badly or has unwanted adverse effects (publication bias). There can also be a delay in publishing the results of negative trials (time-lag bias) or a failure to publish complete results for all the prespecified outcomes of a trial (reporting bias). All three types of bias threaten informed medical decision-making and the health of patients. Why Was This Study Done? One initiative that aims to prevent these biases was included in the 2007 US Food and Drug Administration Amendments Act (FDAAA). The Food and Drug Administration (FDA) is responsible for approving drugs and devices that are marketed in the US. The FDAAA requires that results from clinical trials of FDA-approved drugs and devices conducted in the United States be made publicly available at ClinicalTrials.gov within one year of trial completion. ClinicalTrials.gov—a web-based registry that includes US and international clinical trials—was established in 2000 in response to the 1997 FDA Modernization Act, which required mandatory registration of trial titles and designs and of the conditions and interventions under study. The FDAAA expanded these mandatory requirements by requiring researchers studying FDA-approved drugs and devices to report additional information such as the baseline characteristics of the participants in each arm of the trial and the results of primary and secondary outcome measures (the effects of the intervention on predefined clinical measurements) and their statistical significance (an indication of whether differences in outcomes might have happened by chance). Researchers of other trials registered in ClinicalTrials.gov are welcome to post trial results as well. Here, the researchers compare the timing and completeness (i.e., whether all relevant information was fully reported) of results of drug trials posted at ClinicalTrials.gov with those published in medical journals. What Did the Researchers Do and Find? The researchers searched ClinicalTrials.gov for reports of completed phase III and IV (late-stage) RCTs of drugs with posted results. For a random sample of 600 eligible trials, they searched PubMed (a database of biomedical publications) for corresponding publications. Only 50% of trials with results posted at ClinicalTrials.gov had a matching published article. For 202 trials with both posted and published results, the researchers compared the timing and completeness of the results posted at ClinicalTrials.gov and of results reported in the corresponding journal publication. The median time between the study completion date and the first results being publicly posted at ClinicalTrials.gov was 19 months, whereas the time between completion and publication in a journal was 21 months. The flow of participants through trials was completely reported in 64% of the ClinicalTrials.gov postings but in only 48% of the corresponding publications. Results for the primary outcome measure were completely reported in 79% and 69% of the ClinicalTrials.gov postings and corresponding publications, respectively. Finally, adverse events were completely reported in 73% of the ClinicalTrials.gov postings but in only 45% of the corresponding publications, and serious adverse events were reported in 99% and 63% of the ClinicalTrials.gov postings and corresponding publications, respectively. What Do These Findings Mean? These findings suggest that the reporting of trial results is significantly more complete at ClinicalTrials.gov than in published journal articles reporting the main trial results. Certain aspects of this study may affect the accuracy of this conclusion. For example, the researchers compared the results posted at ClinicalTrials.gov only with the results in the publication that described the primary outcome of each trial, even though some trials had multiple publications. Importantly, these findings suggest that, to enable patients and physicians to make informed treatment decisions, experts undertaking assessments of drugs should consider seeking efficacy and safety data posted at ClinicalTrials.gov, both for trials whose results are not published yet and for trials whose results are published. Moreover, they suggest that the use of templates to guide standardized reporting of trial results in journals and broader mandatory posting of results may help to improve the reporting and transparency of clinical trials and, consequently, the evidence available to inform treatment of patients. Additional Information Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001566. Wikipedia has pages on evidence-based medicine and on publication bias (note: Wikipedia is a free online encyclopedia that anyone can edit; available in several languages) The US Food and Drug Administration provides information about drug approval in the US for consumers and health-care professionals, plus detailed information on the 2007 Food and Drug Administration Amendments Act ClinicalTrials.gov provides information about the US National Institutes of Health clinical trial registry, including background information about clinical trials, and a fact sheet detailing the requirements of the 2007 Food and Drug Administration Amendments Act PLOS Medicine recently launched a Reporting Guidelines Collection, an open access collection of reporting guidelines, commentary, and related research on guidelines from across PLOS journals that aims to help advance the efficiency, effectiveness, and equitability of the dissemination of biomedical information; a 2008 PLOS Medicine editorial discusses the 2007 Food and Drug Administration Amendments Act

Introduction Une loi americaine impose que les resultats d’essais cliniques de medicaments approuves par la Food and Drug Administration (FDA) soient rapportes dans ClinicalTrials.gov au plus tard un an apres la fin de l’essai. Notre objectif principal etait de comparer la precision des resultats rapportes dans ClinicalTrials.gov et dans les publications. Methodes Nous avons recherche en mars 2012 tous les essais controles randomises de medicaments ayant des resultats enregistres dans ClinicalTrials.gov. Pour un echantillon aleatoire de ces essais ( n = 600), nous avons recherche dans PubMed les publications correspondantes. Les donnees ont ete extraites independamment dans ClinicalTrials.gov et dans l’article publie pour les essais avec les resultats a la fois enregistres et publies. Nous avons compare la precision des informations concernant le flux de participants, les resultats d’efficacite, les evenements indesirables et les evenements indesirables graves entre ClinicalTrials.gov et les publications. Resultats La moitie des essais pour lesquels des resultats etaient postes dans ClinicalTrials.gov n’etait pas encore publiee au moment de notre recherche. Pour les essais avec les resultats rapportes dans ClinicalTrials.gov et publies ( n = 202), le flux des participants, les resultats d’efficacite, les evenements indesirables ainsi que les evenements indesirables graves etaient significativement mieux rapportes dans ClinicalTrials.gov que dans la publication. Discussion Nos resultats soulignent la necessite de rechercher systematiquement ClinicalTrials.gov pour identifier des essais non publies mais egalement pour evaluer les resultats des essais publies. Les resultats d’efficacite, les evenements indesirables particulierement les graves, etaient mieux rapportes dans ClinicalTrials.gov que dans la publication.

Background Several generic methods have been proposed to estimate transmission parameters during an outbreak, especially the reproduction number. However, as of today, no dedicated software exists that implements these methods and allow comparisons. Results A review of generic methods used to estimate transmissibility parameters during outbreaks was carried out. Most methods used the epidemic curve and the generation time distribution. Two categories of methods were available: those estimating the initial reproduction number, and those estimating a time dependent reproduction number. We implemented five methods as an R library, developed sensitivity analysis tools for each method and provided numerical illustrations of their use. A comparison of the performance of the different methods on simulated datasets is reported. Conclusions This software package allows a standardized and extensible approach to the estimation of the reproduction number and generation interval distribution from epidemic curves.

Background The European Union (EU) faces many health-related challenges. Accurate and timely data on mortality and morbidity from diseases and injuries and their trends over time are essential for health planning and priority setting. Methods We use the GBD 2019 study estimates and 95% uncertainty intervals for the whole EU and each country to evaluate age-standardized death, YLL, YLD and DALY rates for Level 2 causes. We evaluate trends by comparing estimates for the year 2019 with those for the year 2010. Results In 2019, the age-standardized death and DALY rates in the EU were 465.8 and 20 251.0 per 100 000 inhabitants, respectively. Between 2010 and 2019, there were significant decreases for age-standardized, death and YLL rates across EU countries. However, YLD rates remained mainly unchanged, with increases in the Netherlands and the ‘. The largest decreases in age-standardized DALY rates were observed for HIV/AIDS and sexually transmitted diseases and transport injuries (both -19%). On the other hand, only diabetes and kidney diseases showed a significant increase for age-standardized DALY rates in the EU (3.5%). Mental disorders showed an increasing age-standardized YLD rate, as well. Conclusions Overall, there was a visible trend of improvement in the health status in the EU with substantial differences between countries. EU health policy makers need to tackle incidence of diseases and injuries, with specific attention to causes such as mental disorders. There are many opportunities for mutual learning among otherwise similar countries with different patterns of disease.

Background In addition to information on mortality and morbidity from diseases and injuries, it is important to identify the attributable burden of risk factors to allow for health planning and prioritization. Methods For the whole EU and each country, using estimates and 95% uncertainty intervals from the GBD 2019 study, we report attributable (all-cause and by level 2 risk factors) age-standardized death and DALY rates, as well as summary exposure values (SEV). We evaluate trends by comparing estimates for the year 2019 with those for the year 2010. Results Age-standardized death and DALY rates attributable to risk factors declined by 10.7% (95%UI 13.8%-7.6%) and 9.1% (95%UI 12.0%-6.3%), between 2010 and 2019 in the EU. While there was a decreasing trend for both age-standardized death and DALY rates for almost all risk factors, some showed an increasing trend on SEV, including low physical activity and intimate partner violence. Conclusions Despite the improvement of health metrics attributable to risk factors, several modifiable behavioral and metabolic risk factors remain unchanged over the years. It is crucial to ensure a swift implementation of evidence-based policies and interventions in EU member states to achieve the targets of the Sustainable Development Goals.