1000 Days: The "WeCare Generation" Program-The Ultimate Model for Improving Human Mental Health and Economics: The Study Protocol.

INTRODUCTION: The COVID-19 pandemic stressed the necessity of a new resilience of the human population and health system. The "WeCare Generation" program is a new proposal of territorial intervention, with a new paradigm, on the diseases of the human body and mind. BACKGROUND: In recent decades, the independent strands of investigation on brain plasticity and early trauma consequences have demonstrated that traumatic experiences in the period from pregnancy to the age of 3 years have an enormous impact on an individual's future development, and both physical and mental health. Research shows that adverse child experiences (ACEs) are associated with a strong risk of conditions such as: harmful alcohol use, smoking, illicit drug use, high body-mass index, depression, anxiety, interpersonal violence, cancer, type 2 diabetes, cardiovascular diseases, stroke respiratory diseases and, as a consequence, to a high financial cost in Italy and also across Europe (1-9% GDP) and the USA (total annual costs estimated to be USD 581 billion in Europe and USD 748 billion in North America). All this suggests that an early intervention on that traumatized-slice of population leads to multiplied savings. METHODS: A multi-center, randomized, controlled trial was designed. The parents of the future neonatal population (from pregnancy to delivery) with trauma will be enrolled, and randomized to treatment, or control arm. The article describes in detail how the primary outpoint (cost to the national health system), and some secondary outpoints, will be collected. DISCUSSION: An overall rate of return on investment (ROI) statistically significant 13.0% per annum with an associated benefit/cost ratio (BCR) of 6.3 is expected as the primary outcome of the "WeCare Generation" program. Our proposed model predicts a new medical paradigm aiming to empower new generations, with a strong return on economy and health.

Algorithm for Individual Prediction of COVID-19-Related Hospitalization Based on Symptoms: Development and Implementation Study.

BACKGROUND: The COVID-19 pandemic has placed a huge strain on the health care system globally. The metropolitan area of Milan, Italy, was one of the regions most impacted by the COVID-19 pandemic worldwide. Risk prediction models developed by combining administrative databases and basic clinical data are needed to stratify individual patient risk for public health purposes. OBJECTIVE: This study aims to develop a stratification tool aimed at improving COVID-19 patient management and health care organization. METHODS: A predictive algorithm was developed and applied to 36,834 patients with COVID-19 in Italy between March 8 and the October 9, 2020, in order to foresee their risk of hospitalization. Exposures considered were age, sex, comorbidities, and symptoms associated with COVID-19 (eg, vomiting, cough, fever, diarrhea, myalgia, asthenia, headache, anosmia, ageusia, and dyspnea). The outcome was hospitalizations and emergency department admissions for COVID-19. Discrimination and calibration of the model were also assessed. RESULTS: The predictive model showed a good fit for predicting COVID-19 hospitalization (C-index 0.79) and a good overall prediction accuracy (Brier score 0.14). The model was well calibrated (intercept -0.0028, slope 0.9970). Based on these results, 118,804 patients diagnosed with COVID-19 from October 25 to December 11, 2020, were stratified into low, medium, and high risk for COVID-19 severity. Among the overall study population, 67,030 (56.42%) were classified as low-risk patients; 43,886 (36.94%), as medium-risk patients; and 7888 (6.64%), as high-risk patients. In all, 89.37% (106,179/118,804) of the overall study population was being assisted at home, 9% (10,695/118,804) was hospitalized, and 1.62% (1930/118,804) died. Among those assisted at home, most people (63,983/106,179, 60.26%) were classified as low risk, whereas only 3.63% (3858/106,179) were classified at high risk. According to ordinal logistic regression, the odds ratio (OR) of being hospitalized or dead was 5.0 (95% CI 4.6-5.4) among high-risk patients and 2.7 (95% CI 2.6-2.9) among medium-risk patients, as compared to low-risk patients. CONCLUSIONS: A simple monitoring system, based on primary care data sets linked to COVID-19 testing results, hospital admissions data, and death records may assist in the proper planning and allocation of patients and resources during the ongoing COVID-19 pandemic.

SARS-CoV-2 transmission in the Lombardy Region: the increase of household contagion and its implication for containment measures.

This study aimed at the identification of the settings linked to SARS-CoV-2 transmission through the analysis of clusters and small outbreaks detected by the Lombardy Region surveillance system during the second epidemic wave. Comparing the data before and after the introduction of restrictive measures (night curfew, partial closure of schools and businesses, smaart working), we observed a significant decrease of infections in workplaces, social gatherings, coffee shops, restaurants, and sports centers; contagion in schools decreased from 9.2% to 3.4%, in hospitals environments and nursing homes from 5% to 2%; domestic infections increased instead from 73.5% to 92.7%. These results suggest that containment measures have been effective in controlling virus circulation in the community but not at the household level and might inform future interventions, including the establishment of structures (Covid Hotels) for the isolation infected people. At the same time, they raise awareness on the risk of transmission among family members and during households social gatherings.