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Background: Primary care physicians often lack resources and training to correctly diagnose and manage chronic insomnia disorder. Tools supporting chronic insomnia diagnosis and management could fill this critical gap. A survey was conducted to understand insomnia disorder diagnosis and treatment practices among primary care physicians, and to evaluate a diagnosis and treatment algorithm on its use, to identify ways to optimize it specifically for these providers. Methods: A panel of experts developed an algorithm for diagnosing and treating chronic insomnia disorder, based on current guidelines and experience in clinical practice. An online survey was conducted with primary care physicians from France, Germany, Italy, Spain, and the United Kingdom, who treat chronic insomnia patients, between January and February 2023. A sub-sample of participants provided open-ended feedback on the algorithm and gave suggestions for improvements. Results: Overall, 106 primary care physicians completed the survey. Half (52%, 55/106) reported they did not regularly screen for insomnia and half (51%, 54/106) felt they did not have enough time to address patients' needs in relation to insomnia or trouble sleeping. The majority (87%,92/106) agreed the algorithm would help diagnose chronic insomnia patients and 82% (87/106) agreed the algorithm would help improve their clinical practice in relation to managing chronic insomnia. Suggestions for improvements were making the algorithm easier to read and use. Conclusion: The algorithm developed for, and tested by, primary care physicians to diagnose and treat chronic insomnia disorder may offer significant benefits to providers and their patients through ensuring standardization of insomnia diagnosis and management. [ABSTRACT FROM AUTHOR]

This research aims to investigate how to protect workers from discrimination dictated by an algorithm in the contractual conditions. Article 15 of the Italian Workers' Statute declares invalid any agreement or act aimed at: dismissing a worker, discriminating him in the assignment of qualifications or tasks, transfers, disciplinary measures, or otherwise prejudicing him because of his affiliation or union activity, or his participation in a strike. These provisions shall also apply to pacts or acts for the purposes of political, religious, racial, language, sex, disability, age, sexual orientation, or belief. Our work intends to explore the risk of gender or age discrimination in the contractual terms for platform workers in Italy. How can workers' protections be preserved when decisions are made by an algorithm? The research is conducted with a multidisciplinary methodology. We first analyze both national and international literature and jurisprudence. Then, by means of probit models on INAPP PLUS 2021 survey data, we analyze contract characteristics, in particular the written form of the contract and the hourly minimum wage. Controlling for individual and job characteristics, we find evidence of discrimination according to gender and age of workers. We conclude with policy recommendations. [ABSTRACT FROM AUTHOR]

Introduction: Health administrative databases are widely used for the estimation of the prevalence of Parkinson's disease (PD). Few in general, and none used in Italy, have been validated by testing their diagnostic accuracy. The primary objective was to validate two algorithms for the identification of persons with PD using clinical diagnosis as the reference standard on an Italian sample of people with PD. The second objective was to estimate 10-year trends in PD prevalence in the Bologna Local Health Trust from 2010 to 2019. Methods: Two algorithms (index tests) applied to health administrative databases (hospital discharge, drug prescriptions, exemptions for medical costs) were validated against clinical diagnosis of PD by an expert neurologist (reference standard) in a cohort of consecutive outpatients. Sensitivity and specificity with relative 95% confidence intervals (CIs) were calculated. The prevalence of PD in a specific year was estimated as the ratio between the number of subjects fulfilling any criteria of the algorithm with better diagnostic accuracy and the total population in the same year (×1,000), stratified by age, sex, and district of residence. Results: The two algorithms showed high accuracy for identifying patients with PD: one with greater sensitivity of 94.2% (CI: 88.4–97.6) and the other with greater specificity of 98.1% (CI: 97.7–98.5). For the estimation of prevalence, we chose the most specific algorithm with the fewest total number of misclassified cases. We identified 3,798 people with PD as of December 31, 2019, corresponding to a prevalence of 4.3 per 1,000 inhabitants (CI: 4.2–4.4). Prevalence was higher in males (4.7, CI: 4.5–5.0) than females (3.8, CI: 3.7–4.0) and increased with age. The crude prevalence over time was slightly elevated as it followed a progressive aging of the population. When stratifying the prevalence for age groups, we did not observe a trend except in the 45–64 year category where we observed an increasing trend over time. Conclusion: Algorithms based on administrative data are accurate when detecting people with PD in the Italian public health system. In a large northern Italian population, increased prevalence of about 10% was observed in the decade 2010–2019 and is explained by increased life expectancy. These data may be useful in planning the allocation of health care resources for people with PD. [ABSTRACT FROM AUTHOR]

Background: Inflammatory bowel diseases (IBDs), Crohn's disease (CD) and ulcerative colitis (UC), are chronic diseases that have been increasingly treated with biological drugs in recent years. Newly developed coding algorithms for IBD identification using claims databases are needed to improve post-marketing surveillance of biological drugs. Objective: To test algorithms to identify CD and UC, as indication for use of biological drugs approved for IBD treatment, using a claims database. Methods: Data were extracted from the Caserta Local Health Unit database between 2015 and 2018. CD/UC diagnoses reported by specialists in electronic therapeutic plans (ETPs) were considered as gold standard. Five algorithms were developed based on ICD-9-CM codes as primary cause of hospital admissions, exemption from healthcare service co-payment codes and drugs dispensing with only indication for CD/UC. The accuracy was assessed by sensitivity (Se), specificity (Sp), positive (PPV) and negative predicted values (NPV) along with computation of the Youden Index and F-score. Results: In the study period, 1205 subjects received at least one biological drug dispensing approved for IBD and 134 (11.1%) received ≥ 1 ETP with IBD as use indication. Patients with CD and CU were 83 (61.9%) and 51 (38.1%), respectively. Sensitivity of the different algorithms ranged from 71.1% (95% CI: 60.1– 80.5) to 98.8 (95% CI: 93.5– 100.0) for CD and from 64.7% (95% CI: 50.1– 77.6) to 94.1 (95% CI: 83.8– 98.8) for UC, while specificity was always higher than 91%. The best CD algorithm was "Algorithm 3", based on hospital CD diagnosis code OR CD exemption code OR [IBD exemption code AND dispensing of non-biological drugs with only CD indication] (Se: 98.8%; Sp: 97.2%; PPV: 84.5%, NPV: 99.8%), achieving the highest diagnostic accuracy (Youden Index=0.960). The best UC algorithm was "Algorithm 3", based on specific hospital UC diagnosis code OR UC exemption code OR [IBD exemption code AND golimumab dispensing] OR dispensing of non-biological drugs with only UC indication (Se: 94.1%; Sp: 91.6%; PPV: 50.0%; NPV: 99.4%), and achieving the highest diagnostic accuracy (Youden Index=0.857). Conclusion: In a population-based claims database, newly coding algorithms including diagnostic and exemption codes plus specific drug dispensing yielded highly accurate identification of CD and UC as distinct indication for biological drug use. [ABSTRACT FROM AUTHOR]

Aim: Treatment algorithms define lines of glucose lowering medications (GLM) for the management of type 2 diabetes (T2D), but whether therapeutic trajectories are associated with major adverse cardiovascular events (MACE) is unclear. We explored whether the temporal resolution of GLM usage discriminates patients who experienced a 4P-MACE (heart failure, myocardial infarction, stroke, death for all causes). Methods: We used an administrative database (Veneto region, North-East Italy, 2011–2018) and implemented recurrent neural networks (RNN) with outcome-specific attention maps. The model input included age, sex, diabetes duration, and a matrix of GLM pattern before the 4P-MACE or censoring. Model output was discrimination, reported as area under receiver characteristic curve (AUROC). Attention maps were produced to show medications whose time-resolved trajectories were the most important for discrimination. Results: The analysis was conducted on 147,135 patients for training and model selection and on 10,000 patients for validation. Collected data spanned a period of ~ 6 years. The RNN model efficiently discriminated temporal patterns of GLM ending in a 4P-MACE vs. those ending in an event-free censoring with an AUROC of 0.911 (95% C.I. 0.904–0.919). This excellent performance was significantly better than that of other models not incorporating time-resolved GLM trajectories: (i) a logistic regression on the bag-of-words encoding all GLM ever taken by the patient (AUROC 0.754; 95% C.I. 0.743–0.765); (ii) a model including the sequence of GLM without temporal relationships (AUROC 0.749; 95% C.I. 0.737–0.761); (iii) a RNN model with the same construction rules but including a time-inverted or randomised order of GLM. Attention maps identified the time-resolved pattern of most common first-line (metformin), second-line (sulphonylureas) GLM, and insulin (glargine) as those determining discrimination capacity. Conclusions: The time-resolved pattern of GLM use identified patients with subsequent cardiovascular events better than the mere list or sequence of prescribed GLM. Thus, a patient's therapeutic trajectory could determine disease outcomes. [ABSTRACT FROM AUTHOR]

Introduction: The identification of dementia cases through routinely collected health data represents an easily accessible and inexpensive method to estimate the prevalence of dementia. In Italy, a project aimed at the validation of an algorithm was conducted. Methods: The project included cases (patients with dementia or mild cognitive impairment [MCI]) recruited in centers for cognitive disorders and dementias and controls recruited in outpatient units of geriatrics and neurology. The algorithm based on pharmaceutical prescriptions, hospital discharge records, residential long-term care records, and information on exemption from health-care co-payment, was applied to the validation population. Results: The main analysis was conducted on 1110 cases and 1114 controls. The sensitivity, specificity, and positive and negative predictive values in discerning cases of dementia were 74.5%, 96.0%, 94.9%, and 79.1%, respectively, whereas in detecting cases of MCI these values were 29.7%, 97.5%, 92.2%, and 58.1%, respectively. The variables associated with misclassification of cases were also identified. Discussion: This study provided a validated algorithm, based on administrative data, which can be used to identify caseswith dementia and, with lower sensitivity, also early onset dementia but not cases withMCI. [ABSTRACT FROM AUTHOR]

Background: The aim of this Italian multicenter study was to evaluate the diagnostic performance of a minimally invasive method for the detection of oral squamous cell carcinoma (OSCC) based on 13‐gene DNA methylation analysis in oral brushing samples. Methods: Oral brushing specimens were collected in 11 oral medicine centers across Italy. Twenty brushing specimens were collected by each center, 10 from patients with OSCC, and 10 from healthy volunteers. DNA methylation analysis was performed in blindness, and each sample was determined as positive or negative based on a predefined cutoff value. Results: DNA amplification failed in 4 of 220 (1.8%) samples. Of the specimens derived from patients with OSCC, 93.6% (103/110) were detected as positive, and 84.9% (90/106) of the samples from healthy volunteers were negative. Conclusion: These data confirmed the diagnostic performance of our novel procedure in a large cohort of brushing specimens collected from 11 different centers and analyzed in blindness. [ABSTRACT FROM AUTHOR]

Objectives: to estimate Amyotrophic Lateral Sclerosis (ALS) incidence and prevalence in three Italian Regions (Lazio, Tuscany, and Umbria), using health administrative databases., Design: retrospective population-based study., Setting and Participants: ALS patients residing in Lazio, Umbria, and Tuscany were identified through an algorithm based on three different administrative databases: hospital discharge records, exemptions from health care co-payment, and emergency departments (study period 2014-2019). Crude, age- and gender-specific prevalence were calculated on 31.12.2019 and incidence rates of ALS were standardised by region, year, and gender between 2014-2019. Using a clinical dataset available in the Lazio Region, the proportion of individuals residing in the region correctly identified as ALS cases by the algorithm were calculated., Main Outcomes Measures: prevalence and incidence rates., Results: a total of 1,031 ALS patients (>=18 years) were identified: 408 cases in Tuscany, 546 in Lazio, and 77 in Umbria. ALS standardised prevalence (per 100,000) was similar among regions: 12.31 in Tuscany, 11.52 in Lazio, and 9.90 in Umbria. The 5-year crude rates were higher in men, and in people aged 65-79 years. Among 310 patients included in the clinical dataset, 263 (84.8%) were correctly identified by the algorithm based on health administrative databases., Conclusions: ALS prevalence and incidence in three Central Italy Regions are rather similar, but slightly higher than those previously reported. This finding is plausible, given that previous results relate to at least ten years ago and evidenced increasing trends. Overall, the results of this paper encourage the use of administrative data to produce occurrence estimates, useful to both epidemiological surveillance and research and healthcare policies.

Objective: To validate the performance of a first-trimester simple risk score based on the ASPRE trial algorithm for pre-eclampsia.Design: Multicentre retrospective cohort analysis.Setting: Four Italian hospitals.Population: Unselected nulliparous women at 11-13 weeks of gestation from January 2014 through to January 2018.Methods: Model performance was evaluated based on discrimination and calibration.Main Outcome Measures: Delivery before 37 weeks of gestation with a diagnosis of pre-eclampsia.Results: Based on 73 preterm pre-eclampsia cases and 7546 controls (including 101 cases of late pre-eclampsia), the area under the receiver operating characteristics curve was 0.659 (95% CI 0.579-0.726). The sensitivity was 32.9% (95% CI 22.1-43.7) at a false-positive rate of 8.8%. The positive likelihood ratio was 3.74 (95% CI 2.67-5.23), the positive predictive value was 3.49% (95% CI 2.12-4.86%) and the negative predictive value was 99.3% (95% CI 99.1-99.5%). The sensitivity and positive likelihood ratio were approximately 40% lower than in the original study. The calibration analysis showed a good agreement between observed and expected risks (P = 0.037). Comparison with the Fetal Medicine Foundation (FMF) algorithm yielded a difference in the area under the curve of 0.084 (P = 0.007).Conclusions: In our Italian population, the simple risk score had a lower performance than expected for the prediction of preterm pre-eclampsia in nulliparous women. The FMF algorithm applied to the same data set resulted in a better prediction.Tweetable Abstract: Simple risk score predicts preterm pre-eclampsia in Italy. [ABSTRACT FROM AUTHOR]

More than 750 000 women in Italy are surviving a diagnosis of breast cancer. A large body of literature tells us which characteristics impact the most on their prognosis. However, the prediction of each disease course and then the establishment of a therapeutic plan and follow‐up tailored to the patient is still very complicated. In order to address this issue, a multidisciplinary approach has become widely accepted, while the Multigene Signature Panels and the Nottingham Prognostic Index are still discussed options. The current technological resources permit to gather many data for each patient. Machine Learning (ML) allows us to draw on these data, to discover their mutual relations and to esteem the prognosis for the new instances. This study provides a primary evaluation of the application of ML to predict breast cancer prognosis. We analyzed 1021 patients who underwent surgery for breast cancer in our Institute and we included 610 of them. Three outcomes were chosen: cancer recurrence (both loco‐regional and systemic) and death from the disease within 32 months. We developed two types of ML models for every outcome (Artificial Neural Network and Support Vector Machine). Each ML algorithm was tested in accuracy (=95.29%‐96.86%), sensitivity (=0.35‐0.64), specificity (=0.97‐0.99), and AUC (=0.804‐0.916). These models might become an additional resource to evaluate the prognosis of breast cancer patients in our daily clinical practice. Before that, we should increase their sensitivity, according to literature, by considering a wider population sample with a longer period of follow‐up. However, specificity, accuracy, minimal additional costs, and reproducibility are already encouraging. [ABSTRACT FROM AUTHOR]

Copyright of IDP: Revista de Internet, Derecho y Politica is the property of Universitat Oberta de Catalunya and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of IDP: Revista de Internet, Derecho y Politica is the property of Universitat Oberta de Catalunya and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives: To generate and validate algorithms for the identification of individuals with dementia in the community setting, by the interrogation of administrative records, an inexpensive and already available source of data.Methods: We collected and anonymized information on demented individuals 65 years of age or older from ten general practitioners (GPs) in the district of Brianza (Northern Italy) and compared this with the administrative data of the local health protection agency (Agenzia per la Tutela della Salute). Indicators of the disease in the administrative database (diagnosis of dementia in the hospital discharge records; use of cholinesterase inhibitors/memantine; neuropsychological tests; brain CT/MRI; outpatient neurological visits) were used separately and in different combinations to generate algorithms for the detection of patients with dementia.Results: When used individually, indicators of dementia showed good specificity, but low sensitivity. By their combination, we generated different algorithms: I-therapy with ChEI/memantine or diagnosis of dementia at discharge or neuropsychological tests (specificity 97.9%, sensitivity 52.5%); II-therapy with ChEI/memantine or diagnosis of dementia at discharge or neuropsychological tests or brain CT/MRI or neurological visit (sensitivity 90.8%, specificity 70.6%); III-therapy with ChEI/memantine or diagnosis of dementia at discharge or neuropsychological tests or brain CT/MRIMRI and neurological visit (specificity 89.3%, sensitivity 73.3%).Conclusions: These results show that algorithms obtained from administrative data are not sufficiently accurate in classifying patients with dementia, whichever combination of variables is used for the identification of the disease. Studies in large patient cohorts are needed to develop further strategies for identifying patients with dementia in the community setting. [ABSTRACT FROM AUTHOR]

The Prevention Technician in the Environment and Workplaces (PTEW) is a health professional who works in the identification, assessment, and management of risk in living and working places. The PTEW implements specific corrective actions at reducing exposure levels to chemicals such as formaldehyde. The aim of this report was to update the formaldehyde risk assessment document (RAD). The risk assessment process was divided into three steps as follows: (1) preliminary data collection, (2) an on-site visit to identify the use patterns and process, and (3) application of the algorithm to calculate the exposure levels of healthcare workers. In addition, with the introduction of closed-circuit systems, 23 devices were evaluated to identify possible airborne dispersion of formaldehyde. The algorithm was applied in 31 hospital units and the results allowed us to classify the staff in two levels of exposure for each hospital unit; healthcare workers were classified as "exposed" or "potentially exposed." Most of the HCWs are categorized as potentially exposed, and only workers working in laboratories are considered to be exposed. The results showed that devices must be used properly according to the user manual. To increase the level of worker safety, we have proposed to introduce closed-circuit safe handling systems and keeping the duration and intensity of exposure at the lowest possible levels according to the "ALARA" principle. The assignment of the Italian PTEW is to achieve excellence in the levels of health and safety of patients and hospital workers by pursuing a shared mission: improving the quality of public health., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Pattavina, Wachocka, Tuti, Boninti, Santi, Grossi and Laurenti.)

Review of the literature on the reconstruction of the rainfall responsible for slope failures reveals that criteria for the identification of rainfall events are lacking or somewhat subjective. To overcome this problem, we developed an algorithm for the objective and reproducible reconstruction of rainfall events and of rainfall conditions responsible for landslides. The algorithm consists of three distinct modules for (i) the reconstruction of distinct rainfall events, in terms of duration ( D, in h) and cumulated event rainfall ( E, in mm), (ii) the identification of multiple ED rainfall conditions responsible for the documented landslides, and (iii) the definition of critical rainfall thresholds for possible landslide occurrences. The algorithm uses pre-defined parameters to account for different seasonal and climatic settings. We applied the algorithm in Sicily, southern Italy, using rainfall measurements obtained from a network of 169 rain gauges, and information on 229 rainfall-induced landslides occurred between July 2002 and December 2012. The algorithm identified 29,270 rainfall events and reconstructed 472 ED rainfall conditions as possible triggers of the observed landslides. The algorithm exploited the multiple rainfall conditions to define objective and reproducible empirical rainfall thresholds for the possible initiation of landslide in Sicily. The calculated thresholds may be implemented in an operational early warning system for shallow landslide forecasting. [ABSTRACT FROM AUTHOR]

Unlabelled: Cervical lymphadenopathy is a common disorder in children due to a wide spectrum of disorders. On the basis of a complete history and physical examination, paediatricians have to select, among the vast majority of children with a benign self-limiting condition, those at risk for other, more complex, diseases requiring laboratory tests, imaging and, finally, tissue sampling. At the same time, they should avoid expensive and invasive examinations when unnecessary. The Italian Society of Preventive and Social Pediatrics, jointly with the Italian Society of Pediatric Infectious Diseases, the Italian Society of Pediatric Otorhinolaryngology, and other Scientific Societies, issued a National Consensus document, based on the most recent literature findings, including an algorithm for the management of cervical lymphadenopathy in children.Methods: The Consensus Conference method was used, following the Italian National Plan Guidelines. Relevant publications in English were identified through a systematic review of MEDLINE and the Cochrane Database of Systematic Reviews from their inception through March 21, 2014.Results: Basing on literature results, an algorithm was developed, including several possible clinical scenarios. Situations requiring a watchful waiting strategy, those requiring an empiric antibiotic therapy, and those necessitating a prompt diagnostic workup, considering the risk for a severe underling disease, have been identified.Conclusion: The present algorithm is a practice tool for the management of pediatric cervical lymphadenopathy in the hospital and the ambulatory settings. A multidisciplinary approach is paramount. Further studies are required for its validation in the clinical field. [ABSTRACT FROM AUTHOR]

In Italy, rainfall-induced landslides are recurrent phenomena that cause societal and economic damage. Thus, assessing the rainfall conditions responsible for landslides is important and may contribute to reducing risk. The prediction of rainfall-induced landslides relies primarily on empirical rainfall thresholds. However, the thresholds are affected by uncertainties that limit their use in operational warning systems. A source of uncertainty lies in the characterization of the rainfall events responsible for landslides. Objective criteria for the definition of rainfall events are lacking. To overcome the problem, we propose an algorithm that reconstructs the rainfall events, identifies the rainfall conditions that have resulted in landslides, and measures the duration and the cumulated rainfall for the events. The algorithm is independent from the local settings and uses a reduced set of parameters to account for different physical settings and operational conditions. We tested the algorithm in Sicily, Italy, with rainfall and landslide information between January 2002 and December 2012. The rainfall conditions responsible for landslides identified by the algorithm were compared against results obtained manually. The algorithm was proven capable of accurately reconstructing most (87.7 %) of the rainfall events. For each landslide, the algorithm identified a variable number of rainfall conditions responsible for the failures, which are equally likely triggers of the landslide. This opens the possibility of evaluating the uncertainty introduced by different criteria to determine the rainfall events responsible for landslides. Use of the algorithm shall contribute to reducing the uncertainty in the definition of landslide-triggering rainfall events, to compiling large catalogues of rainfall events with landslides and to determining reliable rainfall thresholds for possible landslide occurrence. [ABSTRACT FROM AUTHOR]

Aims: We evaluated de-intensification of basal-bolus insulin (BBI) after initiation of a GLP-1 receptor agonist (GLP-1RA) under routine care.Research Design and Methods: This retrospective, multicenter study conducted at outpatient clinics in North-East Italy collected data on patients with T2D on BBI who initiated a GLP-1RA. Patients were divided according to whether they de-intensified BBI at the end of observation by stopping prandial insulin.Results: We included 425 patients with mean age of 61.3 years and 13 years of diabetes duration. Baseline HbA1c was 8.6% and BMI was 35.5 kg/m2. After 14 months. 58.6% of patients de-intensified BBI after initiating GLP-1RA: they were younger, had a shorter disease duration, lower HbA1c and insulin dose, and less frequent microangiopathy than those who continued BBI. A probability estimation based on these variables was validated in an independent cohort of 40 patients. Body weight improved in both groups, but HbA1c and fasting plasma glucose significantly declined only among patients who de-intensified BBI. Patients who de-intensified BBI and persisted on GLP-1RA at the last observation (80.7%) had greater HbA1c reductions.Conclusion: Under routine care, GLP-1RA initiation frequently allowed discontinuing BBI, especially among patients with shorter disease duration, lower insulin requirement, and better glucose control. [ABSTRACT FROM AUTHOR]

A Landsat time series has been recognized as a viable source of information for monitoring and assessing forest disturbances and for continuous reporting on forest dynamics. This study focused on developing automated procedures for detecting disturbances in Mediterranean coppice forests which are characterized by rapid regrowth after a cut. Specifically, new methods specific to Mediterranean coppice forests are needed for mapping clearcut disturbances over time and for estimating related indicators in the context of Sustainable Forest Management and Biodiversity International monitoring frameworks. The aim of this work was to develop a new change detection algorithm for mapping clearcut disturbances in Mediterranean coppice forests with Landsat time series (LTS) using a short time window. Accuracy for the new algorithm, characterized as the Two Thresholds Method (TTM), was evaluated using an independent clearcut reference dataset over a temporal period of the 13 years between 2001 and 2013. TTM was also evaluated against two benchmark approaches: (i) LandTrendr, and (ii) the forest loss category of the Global Forest Change Map. Overall Accuracy for LandTrendr and TTM were greater than 0.94. Meanwhile, smaller accuracies were always obtained for the GFC. In particular, Producer's Accuracy ranged between 0.45 and 0.84 for TTM and between 0.49 and 0.83 for LT, while for the GFC, PA ranged between 0 and 0.38. User's Accuracy ranged between 0.86 and 0.96 for TTM and between 0.73 and 0.91 for LT, while for the GFC UA ranged between 0.19 and 1.00. Moreover, to illustrate the utility of TTM for mapping clearcut disturbances in Mediterranean coppice forests, we applied TTM to a Landsat scene that covered almost the entirety of the Tuscany region in Italy. [ABSTRACT FROM AUTHOR]

The current systematic review presented and discussed the most recent studies on acute cough in pediatric age. After that, the Italian Society of Pediatric Allergy and Immunology elaborated a comprehensive algorithm to guide the primary care approach to pediatric patients, such as infants, children, and adolescents, with acute cough. An acute cough is usually consequent to upper respiratory tract infections and is self-resolving within a few weeks. However, an acute cough may be bothersome, and therefore remedies are requested, mainly by the parents. An acute cough may significantly affect the quality of life of patients and their family.Several algorithms for the management of acute cough have been adopted and validated in clinical practice; however, unlike the latter, we developed an algorithm focused on pediatric age, and, also, in accordance to the Italian National Health System, which regularly follows the child from birth to all lifelong. Based on our findings, infants from 6 months, children, and adolescents with acute cough without cough pointers can be safely managed using well-known medications, preferably non-sedative agents, such as levodropropizine and/or natural compounds, including honey, glycerol, and herb-derived components.

The current systematic review presented and discussed the most recent studies on pediatric chronic cough. In addition, the Italian Society of Pediatric Allergy and Immunology elaborated a comprehensive algorithm to guide the primary care approach to a pediatric patient with chronic cough.Several algorithms on chronic cough management have been adopted and validated in clinical practice; however, unlike the latter, we developed an algorithm focused on pediatric age, from birth until adulthood. Based on our findings, children and adolescents with chronic cough without cough pointers can be safely managed, initially using the watchful waiting approach and, successively, starting empirical treatment based on cough characteristics. Unlike other algorithms that suggest laboratory and instrumental investigations as a first step, this review highlighted the importance of a "wait and see" approach, consisting of parental reassurance and close clinical observation, also due to inter-professional collaboration and communication between general practitioners and specialists that guarantee better patient management, appropriate prescription behavior, and improved patient outcome. Moreover, the neonatal screening program provided by the Italian National Health System, which intercepts several diseases precociously, allowing to treat them in a very early stage, helps and supports a "wait and see" approach.Conversely, in the presence of cough pointers or persistence of cough, the patient should be tested and treated by the specialist. Further investigations and treatments will be based on cough etiology, aiming to intercept the underlying disease, prevent potentially irreversible tissue damage, and improve the general health of patients affected by chronic cough, as well as the quality of life of patients and their family.

Background: Confirmed COVID-19 cases have been registered in more than 200 countries, and as of July 28, 2020, over 16 million cases have been reported to the World Health Organization. This study was conducted during the epidemic peak of COVID-19 in Italy. The early identification of individuals with suspected COVID-19 is critical in immediately quarantining such individuals. Although surveys are widely used for identifying COVID-19 cases, outcomes, and associated risks, no validated epidemiological tool exists for surveying SARS-CoV-2 infection in the general population., Objective: We evaluated the capability of self-reported symptoms in discriminating COVID-19 to identify individuals who need to undergo instrumental measurements. We defined and validated a method for identifying a cutoff score., Methods: Our study is phase II of the EPICOVID19 Italian national survey, which launched in April 2020 and included a convenience sample of 201,121 adults who completed the EPICOVID19 questionnaire. The Phase II questionnaire, which focused on the results of nasopharyngeal swab (NPS) and serological tests, was mailed to all subjects who previously underwent NPS tests., Results: Of 2703 subjects who completed the Phase II questionnaire, 694 (25.7%) were NPS positive. Of the 472 subjects who underwent the immunoglobulin G (IgG) test and 421 who underwent the immunoglobulin M test, 22.9% (108/472) and 11.6% (49/421) tested positive, respectively. Compared to NPS-negative subjects, NPS-positive subjects had a higher incidence of fever (421/694, 60.7% vs 391/2009, 19.5%; P<.001), loss of taste and smell (365/694, 52.6% vs 239/2009, 11.9%; P<.001), and cough (352/694, 50.7% vs 580/2009, 28.9%; P<.001). With regard to subjects who underwent serological tests, IgG-positive subjects had a higher incidence of fever (65/108, 60.2% vs 43/364, 11.8%; P<.001) and pain in muscles/bones/joints (73/108, 67.6% vs 71/364, 19.5%; P<.001) than IgG-negative subjects. An analysis of self-reported COVID-19 symptom items revealed a 1-factor solution, the EPICOVID19 diagnostic scale. The following optimal scores were identified: 1.03 for respiratory problems, 1.07 for chest pain, 0.97 for loss of taste and smell 0.97, and 1.05 for tachycardia (ie, heart palpitations). These were the most important symptoms. For adults aged 18-84 years, the cutoff score was 2.56 (sensitivity: 76.56%; specificity: 68.24%) for NPS-positive subjects and 2.59 (sensitivity: 80.37%; specificity: 80.17%) for IgG-positive subjects. For subjects aged ≥60 years, the cutoff score was 1.28, and accuracy based on the presence of IgG antibodies improved (sensitivity: 88.00%; specificity: 89.58%)., Conclusions: We developed a short diagnostic scale to detect subjects with symptoms that were potentially associated with COVID-19 from a wide population. Our results support the potential of self-reported symptoms in identifying individuals who require immediate clinical evaluations. Although these results come from the Italian pandemic period, this short diagnostic scale could be optimized and tested as a screening tool for future similar pandemics., (©Luca Bastiani, Loredana Fortunato, Stefania Pieroni, Fabrizio Bianchi, Fulvio Adorni, Federica Prinelli, Andrea Giacomelli, Gabriele Pagani, Stefania Maggi, Caterina Trevisan, Marianna Noale, Nithiya Jesuthasan, Aleksandra Sojic, Carla Pettenati, Massimo Andreoni, Raffaele Antonelli Incalzi, Massimo Galli, Sabrina Molinaro. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 06.01.2021.)

Background: Administrative healthcare databases are widespread and are often standardized with regard to their content and data coding, thus they can be used also as data sources for surveillance and epidemiological research. Chronic dialysis requires patients to frequently access hospital and clinic services, causing a heavy burden to healthcare providers. This also means that these patients are routinely tracked on administrative databases, yet very few case definitions for their identification are currently available. The aim of this study was to develop two algorithms derived from administrative data for identifying incident chronic dialysis patients and test their validity compared to the reference standard of the regional dialysis registry., Methods: The algorithms are based on data retrieved from hospital discharge records (HDR) and ambulatory specialty visits (ASV) to identify incident chronic dialysis patients in an Italian region. Subjects are included if they have at least one event in the HDR or ASV databases based on the ICD9-CM dialysis-related diagnosis or procedure codes in the study period. Exclusion criteria comprise non-residents, prevalent cases, or patients undergoing temporary dialysis, and are evaluated only on ASV data by the first algorithm, on both ASV and HDR data by the second algorithm. We validated the algorithms against the Emilia-Romagna regional dialysis registry by searching for incident patients in 2014 and performed sensitivity analyses by modifying the criteria to define temporary dialysis., Results: Algorithm 1 identified 680 patients and algorithm 2 identified 676 initiating dialysis in 2014, compared to 625 patients included in the regional dialysis registry. Sensitivity for the two algorithms was respectively 90.8 and 88.4%, positive predictive value 84.0 and 82.0%, and percentage agreement was 77.4 and 74.1%., Conclusions: Algorithms relying on retrieval of administrative records have high sensitivity and positive predictive value for the identification of incident chronic dialysis patients. Algorithm 1, which showed the higher accuracy and has a simpler case definition, can be used in place of regional dialysis registries when they are not present or sufficiently developed in a region, or to improve the accuracy and timeliness of existing registries.

Purpose: Recent studies from national registries have described changing patterns in epidemiology of acromegaly. Our retrospective study used administrative databases to estimate prevalence and incidence of acromegaly in the Piedmont Region, Italy., Methods: This study was conducted in Piedmont between 2012 and 2016 on administrative health databases for inpatients and outpatients of any age. Enrollees were included if claims suggestive of acromegaly were identified in at least two of the following databases: Drug Claims Registry, Hospital Information System, Co-payment Exemption Registry and Outpatient Specialist Service Information System., Results: 369 individuals (M = 146, F = 223) met our criteria. Overall incidence was 5.3 per million person years (95% CI 4.2-6.7), and prevalence was 83 cases per million inhabitants (95% CI 75-92). Mean age was 50.9 years. Both incidence and prevalence were slightly higher among women (rate ratio 1.08, prevalence ratio 1.43). Age-specific incidence was similar between sexes up to 39 years and diverged thereafter, with an increasing trend recorded among men. Prevalence was higher in women aged 40-79 years, and increased continuously up to 79 years in both sexes., Conclusions: This is the first population-based study conducted in Italy to estimate incidence and prevalence of acromegaly and results show a higher prevalence than previously reported. Although our algorithm requires proper validation, it constitutes a promising tool to describe the epidemiology of acromegaly.

Background: Relapse is frequently considered an outcome measure of disease activity in relapsing-remitting multiple sclerosis (MS). The objectives of this study were to identify relapse episodes in patients with MS in the Lazio region using health administrative databases and to evaluate the validity of the algorithm using patients enrolled at MS treatment centers., Methods: MS cases were identified in the period between January 1, 2006 and December 31, 2009 using data from regional Health Information Systems (HIS). An algorithm based on HIS was used to identify relapse episodes, and patients recruited at MS centers were used to validate the algorithm. Positive and negative predictive values (PPV, NPV) and the Cohen's kappa coefficient were calculated., Results: The overall MS population identified through HIS consisted of 6,094 patients, of whom 67.1% were female and the mean age was 41.5. Among the MS patients identified by the algorithm, 2,242 attended the centers and 3,852 did not. The PPV was 58.9%, the NPV was 76.3%, and the kappa was 0.36., Conclusions: The proposed algorithm based on health administrative databases does not seem to be able to reliably detect relapses; however, it may be a helpful tool to detect healthcare utilization, and therefore to identify the worsening condition of a patient's health., (© 2017 S. Karger AG, Basel.)

Toxigenic cyanobacteria are one of the main health risks associated with water resources worldwide, as their toxins can affect humans and fauna exposed via drinking water, aquaculture and recreation. Microscopy monitoring of cyanobacteria in water bodies and massive growth systems is a routine operation for cell abundance and growth estimation. Here we present ACQUA (Automated Cyanobacterial Quantification Algorithm), a new fully automated image analysis method designed for filamentous genera in Bright field microscopy. A pre-processing algorithm has been developed to highlight filaments of interest from background signals due to other phytoplankton and dust. A spline-fitting algorithm has been designed to recombine interrupted and crossing filaments in order to perform accurate morphometric analysis and to extract the surface pattern information of highlighted objects. In addition, 17 specific pattern indicators have been developed and used as input data for a machine-learning algorithm dedicated to the recognition between five widespread toxic or potentially toxic filamentous genera in freshwater: Aphanizomenon, Cylindrospermopsis, Dolichospermum, Limnothrix and Planktothrix. The method was validated using freshwater samples from three Italian volcanic lakes comparing automated vs. manual results. ACQUA proved to be a fast and accurate tool to rapidly assess freshwater quality and to characterize cyanobacterial assemblages in aquatic environments., (Copyright © 2016 Elsevier B.V. All rights reserved.)

The purpose of this article is to outline the current approach to patients affected by metastasis to the long bones and to present a clinical and surgical algorithm available for clinicians and for future research. A modern approach to patients affected by long bone metastasis in fact requires a multidisciplinary contest where oncologists, radiotherapists, surgeons and physical therapists cooperate with a shared vision, in order to provide the best possible integrated treatments available. The authors of this article constitute the Bone Metastasis Study Group of the Italian Orthopaedic Society (SIOT): a national group of orthopedic tumor surgeons who are dedicated to studying the approach, techniques and outcomes of surgery for metastatic tumours of the musculoskeletal system.