Machine learning and XAI approaches highlight the strong connection between O 3 and N O 2 pollutants and Alzheimer's disease.

Alzheimer's disease (AD) is the most common type of dementia with millions of affected patients worldwide. Currently, there is still no cure and AD is often diagnosed long time after onset because there is no clear diagnosis. Thus, it is essential to study the physiology and pathogenesis of AD, investigating the risk factors that could be strongly connected to the disease onset. Despite AD, like other complex diseases, is the result of the combination of several factors, there is emerging agreement that environmental pollution should play a pivotal role in the causes of disease. In this work, we implemented an Artificial Intelligence model to predict AD mortality, expressed as Standardized Mortality Ratio, at Italian provincial level over 5 years. We employed a set of publicly available variables concerning pollution, health, society and economy to feed a Random Forest algorithm. Using methods based on eXplainable Artificial Intelligence (XAI) we found that air pollution (mainly O 3 and N O 2 ) contribute the most to AD mortality prediction. These results could help to shed light on the etiology of Alzheimer's disease and to confirm the urgent need to further investigate the relationship between the environment and the disease.

Pharmacoeconomic Evaluation of Costs of Myelomeningocele and Meningocele Treatment and Screening.

Background: The prevention of myelomeningocele (MMC) and meningocele (MC) is a public health concern. A systematic review on economic factors associated with MMC and MC can help the policy makers to evaluate the cost-effectiveness of screening and treatment. To our knowledge, this is the first systematic review to provide up-to date pharmacoeconomic evidence of all economic studies present in literature on different aspects of MMC and MC. Methods: We searched in the National Health Service Economic Evaluation Database (NHSEED), PubMed, Cost-effectiveness Analysis Registry (CEA Registry), Centre for Reviews and Dissemination (CRD), Health Technology Assessment Database (HTAD), Cochrane Library, and Econlit. The PRISMA guidelines were followed in the search and evaluation of literature. Only articles in English not limited by the year of publication that fulfilled the eligibility criteria were included in this systematic review. Results: Nineteen papers were included in the study. The studies were very heterogeneous and reported a comparison of the costs between prenatal versus postnatal repair, the cost of fetoscopic approach versus open surgery, the cost of ventriculoperitoneal shunting (VPS) versus endoscopic third ventriculostomy (ETV), and ETV with choroid plexus cauterization (ETV/CPC), the cost of hospitalization, and the cost of diagnosis for MMC. Conclusion: The results of this study can help in implementing new policies in different countries to assist MC and MMC patients with the cost of treatment and screening.

A Dementia mortality rates dataset in Italy (2012-2019).

Dementia is on the rise in the world population and has been defined by the World Health Organization as a global public health priority. In Italy, according to demographic projections, in 2051 there will be 280 elderly people for every 100 young people, with an increase in all age-related chronic diseases, including dementia. Currently the total number of patients with dementia is estimated to be over 1 million (mainly with Alzheimer's disease (AD) and Parkinson's disease (PD)). In-depth studies of the etiology and physiology of dementia are complicated due to the complexity of these diseases and their long duration. In this work we present a dataset on mortality rates (in the form of Standardized Mortality Ratios, SMR) for AD e PD in Italy at provincial level over a period of 8 years (2012-2019). Access to long-term, spatially detailed and ready-to-use data could favor both health monitoring and the research of new treatments and new drugs as well as innovative methodologies for early diagnosis of dementia.

Forecasting Model Based on Lifestyle Risk and Health Factors to Predict COVID-19 Severity.

The COVID-19 pandemic has now spread worldwide, becoming a real global health emergency. The main goal of this work is to present a framework for studying the impact of COVID-19 on Italian territory during the first year of the pandemic. Our study was based on different kinds of health features and lifestyle risk factors and exploited the capabilities of machine learning techniques. Furthermore, we verified through our model how these factors influenced the severity of the pandemics. Using publicly available datasets provided by the Italian Civil Protection, Italian Ministry of Health and Italian National Statistical Institute, we cross-validated the regression performance of a Random Forest model over 21 Italian regions. The robustness of the predictions was assessed by comparison with two other state-of-the-art regression tools. Our results showed that the proposed models reached a good agreement with data. We found that the features strongly associated with the severity of COVID-19 in Italy are the people aged over 65 flu vaccinated (24.6%) together with individual lifestyle behaviors. These findings could shed more light on the clinical and physiological aspects of the disease.