## 1 Introduction  
Frequently, the diagnoses that physicians find in examined patients refer to multiple diseases, possibly interdependently defined [1] and organized [2] based on symptoms (subjective data), signs (objective data, clinically evidenced) and diagnostic tools (objective data, instrumentally evidenced). Diagnoses with such outcomes identify a condition commonly called comorbidity [3].  
Comorbidity has become a major health and societal burden [4, 5]. The incidence of chronic/multi-morbid diseases in the presence of aging populations, is steadily growing [6]. For instance, 80% of the US health budget [7] is spent on patients with four or more diseases. Electronic Health Records (EHR) can provide useful comorbidity information [8], although they present a main inherent limitation:EHR outsourced by hospitals refer only to subjects with severe disease conditions that usually require specialized intervention. Conversely, General Practitioners (GP) have routinely access to a much larger population [9; 10], not necessarily suffering from an acute pathological condition. Hence, the EHR spectrum covered by GPs, say GP records or GPR, is valuable because complementary, wider and more geo-localized than hospitalized patient EHR (also considering that an overlap is determined by appointments with specialists administered in most cases by family doctors). In Italy, GPR report clinically relevant information on population health, including the prescriptions administered persistently and not only at hospital discharge time. Therefore, GP prescriptions (GPP) can be considered a specific subtype of GPR, and are routinely collected by Italian Health Authorities for administrative purposes. The scope of this study is to assess the validity of GPP as an instrument for the analysis of comorbidities. Since the study is currently expanding beyond Salerno's province, our preliminary results refer currently to Salerno's area.  
To properly address this question, we rely on: (a) Data from a specific geographic location regarding GPs and their patients, and (b) Analytic tools, in particular network analysis to examine the complex multidimensional relationships between variables of relevance to people's health. The mathematical aspects of Network Theory [11] are beyond the scope of our work. In the present research we have used it as a visual analytics tool which could be used to find unexpected connections, which may lead to research hypotheses. The context of diseases is rarely a straightforward consequence of a genetic abnormality [12], rather the result of interaction between molecular processes and environmental cues and assembling information at genome, metabolome, proteome, neuroendocrine and immune systems scales, plus social, environmental and healthcare variables [13; 14; 15]. Interactome-driven disease models [16] have been proposed: they are network representations which integrate all known phenotype and disease gene associations, showing the relevant importance of some genes. Similarly, correlations between the symptoms of diseases may be related to the number of shared genetic associations [17].  
An aspect to emphasize is that the approach proposed here can become very useful in light of the role of Big Data for the Public Health sector [18]. Similar to the systems epidemiology approaches commonly used for analysis, monitoring and forecasting of the effects of healthcare programs implementation (see for instance [18]), our goal is to approach comorbidity from GPP taking advantage of a few valid comorbidity indices [19; 20]. Among the first applications, Medicare claims data for acute cardiovascular hospitalization have been successfully used for understanding and measuring comorbid conditions [21]. Our approach is particularly but not exclusively focused on the diabetes group of patients, and represents a scalable and generalizable analytical proposal supporting the decision processes of policymakers in the Public Health sector.