Improved prediction of 5-year mortality by updating the chronic related score for risk profiling in the general population: lessons from the italian region of Lombardy.

Objective: The aim of this study was to improve the performance of the Chronic Related Score (CReSc) in predicting mortality and healthcare needs in the general population. Methods: A population-based study was conducted, including all beneficiaries of the Regional Health Service of Lombardy, Italy, aged 18 years or older in January 2015. Each individual was classified as exposed or unexposed to 69 candidate predictors measured before baseline, updated to include four mental health disorders. Conditions independently associated with 5-year mortality were selected using the Cox regression model on a random sample including 5.4 million citizens. The predictive performance of the obtained CReSc-2.0 was assessed on the remaining 2.7 million citizens through discrimination and calibration. Results: A total of 35 conditions significantly contributed to the CReSc-2.0, among which Alzheimer's and Parkinson's diseases, dementia, heart failure, active neoplasm, and kidney dialysis contributed the most to the score. Approximately 36% of citizens suffered from at least one condition. CReSc-2.0 discrimination performance was remarkable, with an area under the receiver operating characteristic curve of 0.83. Trends toward increasing short-term (1-year) and long-term (5-year) rates of mortality, hospital admission, hospital stay, and healthcare costs were observed as CReSc-2.0 increased. Conclusion: CReSC-2.0 represents an improved tool for stratifying populations according to healthcare needs.

Web-based surveillance of respiratory infection outbreaks: retrospective analysis of Italian COVID-19 epidemic waves using Google Trends.

Introduction: Large-scale diagnostic testing has been proven insufficient to promptly monitor the spread of the Coronavirus disease 2019. Electronic resources may provide better insight into the early detection of epidemics. We aimed to retrospectively explore whether the Google search volume has been useful in detecting Severe Acute Respiratory Syndrome Coronavirus outbreaks early compared to the swab-based surveillance system. Methods: The Google Trends website was used by applying the research to three Italian regions (Lombardy, Marche, and Sicily), covering 16 million Italian citizens. An autoregressive-moving-average model was fitted, and residual charts were plotted to detect outliers in weekly searches of five keywords. Signals that occurred during periods labelled as free from epidemics were used to measure Positive Predictive Values and False Negative Rates in anticipating the epidemic wave occurrence. Results: Signals from "fever," "cough," and "sore throat" showed better performance than those from "loss of smell" and "loss of taste." More than 80% of true epidemic waves were detected early by the occurrence of at least an outlier signal in Lombardy, although this implies a 20% false alarm signals. Performance was poorer for Sicily and Marche. Conclusion: Monitoring the volume of Google searches can be a valuable tool for early detection of respiratory infectious disease outbreaks, particularly in areas with high access to home internet. The inclusion of web-based syndromic keywords is promising as it could facilitate the containment of COVID-19 and perhaps other unknown infectious diseases in the future.

Early-life exposure to antibiotics and subsequent development of atopic dermatitis.

BACKGROUND: Antibiotic exposure may be associated with atopic dermatitis (AD). We assessed the risk of developing AD among children early exposed to antibiotics. RESEARCH DESIGN AND METHODS: From the Italian Pedianet database, children aged 0-14 years between 2004-2017 were enrolled from birth up to at least one year. Cox proportional-hazards models were fitted to estimate Hazard Ratios (HR) and 95% Confidence Intervals (CI) for the association between antibiotic exposure during the first year of life with incident AD. Exposure was also considered as a time-varying variable. RESULTS: 73,816 children were included in the final cohort, of which 34,202 had at least one antibiotic prescription. Incident AD was present in 8% of unexposed and exposed children. Early antibiotic exposure was not associated with any excess risk of AD compared to unexposed children (HR: 1.02, 95% CI: 0.97-1.07), and no dose-response effect was observed. In the time-varying analysis, antibiotic exposure was significantly associated with AD onset (1.12, 1.07-1.17). However, when taking into account the time-lag between exposure and outcome, risks progressively decreased, suggesting possible protopathic bias. CONCLUSION: These results are not suggestive of any significant association between exposure to antibiotics and subsequent AD onset and support the possible presence of protopathic bias.