Automatic risk evaluation in elderly patients based on Autonomic Nervous System assessment

Dysfunction of Autonomic Nervous System (ANS) is a typical feature of chronic heart failure and other cardiovascular disease. As a simple non-invasive technology, heart rate variability (HRV) analysis provides reliable information on autonomic modulation of heart rate. The aim of this thesis was to research and develop automatic methods based on ANS assessment for evaluation of risk in cardiac patients. Several features selection and machine learning algorithms have been combined to achieve the goals. Automatic assessment of disease severity in Congestive Heart Failure (CHF) patients: a completely automatic method, based on long-term HRV was proposed in order to automatically assess the severity of CHF, achieving a sensitivity rate of 93% and a specificity rate of 64% in discriminating severe versus mild patients. Automatic identification of hypertensive patients at high risk of vascular events: a completely automatic system was proposed in order to identify hypertensive patients at higher risk to develop vascular events in the 12 months following the electrocardiographic recordings, achieving a sensitivity rate of 71% and a specificity rate of 86% in identifying high-risk subjects among hypertensive patients. Automatic identification of hypertensive patients with history of fall: it was explored whether an automatic identification of fallers among hypertensive patients based on HRV was feasible. The results obtained in this thesis could have implications both in clinical practice and in clinical research. The system has been designed and developed in order to be clinically feasible. Moreover, since 5-minute ECG recording is inexpensive, easy to assess, and non-invasive, future research will focus on the clinical applicability of the system as a screening tool in non-specialized ambulatories, in order to identify high-risk patients to be shortlisted for more complex investigations.

Essays in Health Economics

In the first chapter we develop a theoretical model investigating food consumption and body weight with a novel assumption regarding human caloric expenditure (i.e. metabolism), in order to investigate why individuals can be rationally trapped in an excessive weight equilibrium and why they struggle to lose weight even when offered incentives for weight-loss. This assumption allows the theoretical model to have multiple equilibria and to provide an explanation for why losing weight is so difficult even in the presence of incentives, without relying on rational addiction, time-inconsistency preferences or bounded rationality. In addition to this result we are able to characterize under which circumstances a temporary incentive can create a persistent weight loss. In the second chapter we investigate the possible contributions that social norms and peer effects had on the spread of obesity. In recent literature peer effects and social norms have been characterized as important pathways for the biological and behavioral spread of body weight, along with decreased food prices and physical activity. We add to this literature by proposing a novel concept of social norm related to what we define as social distortion in weight perception. The theoretical model shows that, in equilibrium, the effect of an increase in peers' weight on i's weight is unrelated to health concerns while it is mainly associated with social concerns. Using regional data from England we prove that such social component is significant in influencing individual weight. In the last chapter we investigate the relationship between body weight and employment probability. Using a semi-parametric regression we show that men and women employment probability do not follow a linear relationship with body mass index (BMI) but rather an inverted U-shaped one, peaking at a BMI way over the clinical threshold for overweight.

Multi-Principal Agency and the Compliance with Professional Guidelines in Health Systems: Economic and Regulatory Perspectives

Amid the trend of rising health expenditure in developed economies, changing the healthcare delivery models is an important point of action for service regulators to contain this trend. Such a change is mostly induced by either financial incentives or regulatory tools issued by the regulators and targeting service providers and patients. This creates a tripartite interaction between service regulators, professionals, and patients that manifests a multi-principal agent relationship, in which professionals are agents to two principals: regulators and patients. This thesis is concerned with such a multi-principal agent relationship in healthcare and attempts to investigate the determinants of the (non-)compliance to regulatory tools in light of this tripartite relationship. In addition, the thesis provides insights into the different institutional, economic, and regulatory settings, which govern the multi-principal agent relationship in healthcare in different countries. Furthermore, the thesis provides and empirically tests a conceptual framework of the possible determinants of (non-)compliance by physicians to regulatory tools issued by the regulator. The main findings of the thesis are first, in a multi-principal agent setting, the utilization of financial incentives to align the objectives of professionals and the regulator is important but not the only solution. This finding is based on the heterogeneity in the financial incentives provided to professionals in different health markets, which does not provide a one-size-fits-all model of financial incentives to influence clinical decisions. Second, soft law tools as clinical practice guidelines (CPGs) are important tools to mitigate the problems of the multi-principal agent setting in health markets as they reduce information asymmetries while preserving the autonomy of professionals. Third, CPGs are complex and heterogeneous and so are the determinants of (non-)compliance to them. Fourth, CPGs work but under conditions. Factors such as intra-professional competition between service providers or practitioners might lead to non-compliance to CPGs – if CPGs are likely to reduce the professional’s utility. Finally, different degrees of soft law mandate have different effects on providers’ compliance. Generally, the stronger the mandate, the stronger the compliance, however, even with a strong mandate, drivers such as intra-professional competition and co-management of patients by different professionals affected the (non-)compliance.

Big data in health IoE in emergency situations: between the right to privacy and digital health innovation

The chapters of the thesis focus on a limited variety of selected themes in EU privacy and data protection law. Chapter 1 sets out the general introduction on the research topic. Chapter 2 touches upon the methodology used in the research. Chapter 3 conceptualises the basic notions from a legal standpoint. Chapter 4 examines the current regulatory regime applicable to digital health technologies, healthcare emergencies, privacy, and data protection. Chapter 5 provides case studies on the application deployed in the Covid-19 scenario, from the perspective of privacy and data protection. Chapter 6 addresses the post-Covid European regulatory initiatives on the subject matter, and its potential effects on privacy and data protection. Chapter 7 is the outcome of a six-month internship with a company in Italy and focuses on the protection of fundamental rights through common standardisation and certification, demonstrating that such standards can serve as supporting tools to guarantee the right to privacy and data protection in digital health technologies. The thesis concludes with the observation that finding and transposing European privacy and data protection standards into scenarios, such as public healthcare emergencies where digital health technologies are deployed, requires rapid coordination between the European Data Protection Authorities and the Member States guarantee that individual privacy and data protection rights are ensured.

Bright-field multiplex immunohistochemistry for tumor. Microenvironment evaluation in mucosal melanoma of the head and neck region: technical aspects, results, and correlation with clinicopathologic features and molecular landscape

Mucosal melanoma of the head and neck region (MM-H&N) is a rare disease, characterized by a poor prognosis and limited therapeutic strategies, especially regarding targeted therapy (lower rate of targetable mutations compared to cutaneous melanoma) and immunotherapy (lack of diagnostic tools able to predict the response). Meanwhile, bright-field multiplex immunohistochemistry (BF-mIHC) is emerging as a promising tool for characterizing tumor microenvironment (TME) and predicting response to immunotherapy in several tumors, including melanoma. This PhD project aims to develop a BF-mIHC protocol to evaluate the TME in MM-H&N, analyze the correlation between immune markers/immune profiles and MM-H&N features (clinicopathologic and molecular), and find new biomarkers useful for prognostic-therapeutic stratification of these patients. Specific aims are: (I) describe the clinicopathological features of MM-H&N; (II) analyze the molecular status of MM-H&N and correlate it with the clinicopathological features; (III) analyze the molecular status of multiple specimens from the same patient to verify whether molecular heterogeneity of MM-H&N could affect the results with relevant prognostic-therapeutic implications; (IV) develop a BF-mIHC protocol to study TME in MM-H&N; (V) analyze the correlation between immune markers/immune profiles and MM-H&N features (clinicopathologic and molecular) to test whether BF-mIHC could be a promising tool for prognostic-therapeutic characterization of these patients.

Essays in policy evaluation

This dissertation consists of three empirical studies that aim at providing new evidence in the field of public policy evaluation. In particular, the first two chapters focus on the effects of the European cohesion policy, while the third chapter assesses the effectiveness of Italian labour market incentives in reducing long-term unemployment. The first study analyses the effect of EU funds on life satisfaction across European regions , under the assumption that projects financed by structural funds in the fields of employment, education, health and environment may affect the overall quality of life in recipient regions. Using regional data from the European Social Survey in 2002-2006, it resorts to a regression discontinuity design, where the discontinuity is provided by the institutional framework of the policy. The second study aims at estimating the impact of large transfers from a centralized authority to a local administration on the incidence of white collar crimes. It merges a unique dataset on crimes committed in Italian municipalities between 2007 and 2011 with information on the disbursement of EU structural funds in 2007-2013 programming period, employing an instrumental variable estimation strategy that exploits the variation in the electoral cycle at local level. The third study analyses the impact of an Italian labour market policy that allowed firms to cut their labour costs on open-ended job contracts when hiring long-term unemployed workers. It takes advantage of a unique dataset that draws information from the unemployment lists in Veneto region and it resorts to a regression discontinuity approach to estimate the effect of the policy on the job finding rate of long-term unemployed workers.