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This study was funded by the Compagnia di San Paolo, the Cariplo Foundation and the European Commission (grant number 801520 HP-JA-2017, Innovative Partnership for Action Against Cancer, iPAAC Joint Action). The sources of the funding played no role in designing the study, collecting, analyzing, or interpreting the data, writing the report, or deciding whether or not to submit the article for publication. This research was (partially) funded by Italian Ministry of Health "Ricerca Corrente" funds. Tyrosine kinase inhibitors (TKIs) have been improving the prognosis of patients with chronic myeloid leukemia (CML), but there are still large differences in survival among European countries. This raises questions on the added value of results from population-based studies, which use real-world data, compared to results of randomized controlled trials (RCTs) involving patients with CML. There are also questions about the extent of the findings on RCTs effectiveness for patients in the general population. We compare survival data extracted from our previous systematic review and meta-analysis of CML RCTs with the latest updated population-based survival data of EUROCARE-6, the widest collaborative study on cancer survival in Europe. The EUROCARE-6 CML survival estimated in patients (15-64 years) diagnosed in 2000-2006 vs. 2007-2013 revealed that the prognostic improvement highlighted by RCTs was confirmed in real-world settings, too. The study shows, evaluating for the first time all European regions, that the optimal outcome figures obtained in controlled settings for CML are also achievable (and indeed achieved) in real-world settings with prompt introduction of TKIs in daily clinical practice. However, some differences still persist, particularly in Eastern European countries, where overall survival values are lower than elsewhere, probably due to a delayed introduction of TKIs. Our results suggest an insufficient adoption of adequate protocols in daily clinical practice in those countries where CML survival values remain lower in real life than the values obtained in RCTs. New high-resolution population-based studies may help to identify failures in the clinical pathways followed there.

Non-Hodgkin lymphoma (NHL), multiple myeloma and chronic lymphocytic leukemia are possibly related to environmental and/or occupational exposure. The primary objective of this study was to develop a questionnaire for screening patients with these blood disorders who might benefit from a specialized consultation for possible recognition of the disease as an occupational disease. The study included 205 subjects (male gender, 67.3%, mean age, 60 years, NHL, 78.5%). The questionnaire performed very satisfactorily in identifying the exposures most frequently retained by experts for their potential involvement in the occurrence of NHL. Its sensitivity and specificity in relation to the final expertise were 96% and 96% for trichloroethylene, 85% and 82% for benzene, 78% and 87% for solvents other than trichloroethylene and dichloromethane, 87% and 95% for pesticides, respectively. Overall, 15% of the subjects were invited to ask National Social Insurance for compensation as occupational disease. These declarations concerned exposure to pesticides (64%), solvents (trichloroethylene: 29%, benzene: 18%, other than chlorinated solvents: 18%) and sometimes multiple exposures. In conclusion, this questionnaire appears as a useful tool to identify NHL patients for a specialized consultation, in order to ask for compensation for occupational disease.

BACKGROUND: there has been a long-standing, consistent use worldwide of Healthcare Administrative Databases (HADs) for epidemiological purposes, especially to identify acute and chronic health conditions. These databases are able to reflect health-related conditions at a population level through disease-specific case-identification algorithms that combine information coded in multiple HADs. In Italy, in the past 10 years, HAD-based case-identification algorithms have experienced a constant increase, with a significant extension of the spectrum of identifiable diseases. Besides estimating incidence and/or prevalence of diseases, these algorithms have been used to enroll cohorts, monitor quality of care, assess the effect of environmental exposure, and identify health outcomes in analytic studies. Despite the rapid increase in the use of case-identification algorithms, information on their accuracy and misclassification rate is currently unavailable for most conditions. OBJECTIVES: to define a protocol to systematically review algorithms used in Italy in the past 10 years for the identification of several chronic and acute diseases, providing an accessible overview to future users in the Italian and international context. METHODS: PubMed will be searched for original research articles, published between 2007 and 2017, in Italian or English. The search string consists of a combination of free text and MeSH terms with a common part on HADs and a disease-specific part. All identified papers will be screened for eligibility by two independent reviewers. All articles that used/defined an algorithm for the identification of each disease of interest using Italian HADs will be included. Algorithms with exclusive use of death certificates, pathology register, general practitioner or pediatrician data will be excluded. Pertinent papers will be classified according to the objective for which the algorithm was used, and only articles that used algorithms with "primary objectives" (I disease occurrence; II population/cohort selection; III outcome identification) will be considered for algorithm extraction. The HADs used (hospital discharge records, drug prescriptions, etc.), ICD-9 and ICD-10 codes, ATC classification of drugs, follow-back periods, and age ranges applied by the algorithms will be collected. Further information on specific accuracy measures from external validations, sensitivity analyses, and the contribution of each source will be recorded. This protocol will be applied for 16 different systematic reviews concerning eighteen diseases (Hypothyroidism, Hyperthyroidism, Diabetes mellitus, Type 1 diabetes mellitus, Acute myocardial infarction, Ischemic heart disease, Stroke, Hypertension, Heart failure, Congenital heart anomalies, Parkinson's disease, Multiple sclerosis, Epilepsy, Chronic obstructive pulmonary disease, Asthma, Inflammatory bowel disease, Celiac disease, Chronic kidney failure). CONCLUSION: this protocol defines a standardized approach to extensively examine and compare all experiences of case identification algorithms in Italy, on the 18 abovementioned diseases. The methodology proposed may be applied to other systematic reviews concerning diseases not included in this project, as well as other settings, including international ones. Considering the increasing availability of healthcare data, developing standard criteria to describe and update characteristics of published algorithms would be of great use to enhance awareness in the choice of algorithms and provide a greater comparability of results.

INTRODUCTION: Antineoplastic drugs (AD) are potentially carcinogenic and/or reprotoxic molecules. Healthcare professionals are increasingly exposed to these drugs and can be potentially contaminated by them. Internal contamination of professionals is a key concern for occupational physicians in the assessment and management of occupational risks in healthcare settings. Objectives of this study are to report AD internal contamination rate in nursing staff and to identify factors associated with internal contamination. METHODS AND ANALYSIS: This trial will be conducted in two French hospital centres: University Hospital of Bordeaux and IUCT-Oncopole of Toulouse. The target population is nurses practicing in one of the fifteen selected care departments where at least one of the five studied AD is handled (5-fluorouracil, cyclophosphamide, doxorubicin, ifosfamide, methotrexate). The trial will be conducted with the following steps: (1) development of analytical methods to quantify AD urine biomarkers, (2) study of the workplace and organization around AD in each care department (transport and handling, professional practices, personal and collective protection equipments available) (3) development of a self-questionnaire detailing professional activities during the day of inclusion, (4) nurses inclusion (urine samples and self-questionnaire collection), (5) urine assays, (6) data analysis. ETHICS AND DISSEMINATION: The study protocol has been approved by the French Advisory Committee on the Treatment of Information in Health Research (CCTIRS) and by the French Data Protection Authority (CNIL). Following the opinion of the Regional Committee for the Protection of Persons, this study is outside the scope of the provisions governing biomedical research and routine care (n degrees 2014/87). The results will be submitted to peer-reviewed journals and reported at suitable national and international meetings. TRIAL REGISTRATION NUMBER: NCT03137641.

Background: The monitoring of caloric intake is an important challenge for the maintenance of individual and public health. The instruments used so far for dietary monitoring (eg, food frequency questionnaires, food diaries, and telephone interviews) are inexpensive and easy to implement but show important inaccuracies. Alternative methods based on wearable devices and wrist accelerometers have been proposed, yet they have limited accuracy in predicting caloric intake because analytics are usually not well suited to manage the massive sets of data generated from these types of devices. Objective: This study aims to develop an algorithm using recent advances in machine learning methodology, which provides a precise and stable estimate of caloric intake. Methods: The study will capture four individual eating activities outside the home over 2 months. Twenty healthy Italian adults will be recruited from the University of Padova in Padova, Italy, with email, flyers, and website announcements. The eligibility requirements include age 18 to 66 years and no eating disorder history. Each participant will be randomized to one of two menus to be eaten on weekdays in a predefined cafeteria in Padova (northeastern Italy). Flows of raw data will be accessed and downloaded from the wearable devices given to study participants and associated with anthropometric and demographic characteristics of the user (with their written permission). These massive data flows will provide a detailed picture of real-life conditions and will be analyzed through an up-to-date machine learning approach with the aim to accurately predict the caloric contribution of individual eating activities. Gold standard evaluation of the energy content of eaten foods will be obtained using calorimetric assessments made at the Laboratory of Dietetics and Nutraceutical Research of the University of Padova. Results: The study will last 14 months from July 2017 with a final report by November 2018. Data collection will occur from October to December 2017. From this study, we expect to obtain a series of relevant data that, opportunely filtered, could allow the construction of a prototype algorithm able to estimate caloric intake through the recognition of food type and the number of bites. The algorithm should work in real time, be embedded in a wearable device, and able to match bite-related movements and the corresponding caloric intake with high accuracy. Conclusions: Building an automatic calculation method for caloric intake, independent on the black-box processing of the wearable devices marketed so far, has great potential both for clinical nutrition (eg, for assessing cardiovascular compliance or for the prevention of coronary heart disease through proper dietary control) and public health nutrition as a low-cost monitoring tool for eating habits of different segments of the population. International Registered Report Identifier (IRRID): DERR1-10.2196/12116

International audience

In randomized controlled trials (RCTs), sodium-glucose co-transporter-2 (SGLT2) inhibitors have been shown to confer glycaemic and extra-glycaemic benefits. The DARWIN-T2D (DApagliflozin Real World evIdeNce in Type 2 Diabetes) study was a multicentre retrospective study designed to evaluate the baseline characteristics of patients receiving dapagliflozin vs those receiving selected comparators (dipeptidyl peptidase-4 inhibitors, gliclazide, or glucagon-like peptide-1 receptor agonists), and drug effectiveness in routine clinical practice. From a population of 281 217, the analysis included 17 285 patients initiating dapagliflozin or comparator glucose-lowering medications (GLMs), 6751 of whom had a follow-up examination. At baseline, participants starting dapagliflozin were younger, had a longer disease duration, higher glycated haemoglobin (HbA1c) concentration, and a more complex history of previous GLM use, but the clinical profile of patients receiving dapagliflozin changed during the study period. Dapagliflozin reduced HbA1c by 0.7%, body weight by 2.7 kg, and systolic blood pressure by 3.0 mm Hg. Effects of comparator GLMs were also within the expected range, based on RCTs. This real-world study shows an initial channelling of dapagliflozin to difficult-to-treat patients. Nonetheless, dapagliflozin provided significant benefits with regard to glucose control, body weight and blood pressure that were in line with findings from RCTs.