Deferred prosecution agreements (DPAs) are a legal tool for the nontrial resolution of cases of corruption. Each DPA is accompanied by a Statement of Facts that provides detailed and publicly available textual records of the given cases, including summarized evidence of who was involved, what they committed, and with whom. These statements can be translated into networks amenable to social network analysis allowing an analysis of the structure and dynamics of each case. In this study, we show how to extract information about which actors were involved in a given case, the relations and interactions among these actors (e.g., communication or payments), and their relevant individual attributes (gender, affiliation, and sector) from five Statements of Fact. We code the extracted information manually with two independent coders and subsequently, we assess the inter-coder reliability. For assessing the coding reliability of nodes and attributes, we use a matching coefficient, whereas for assessing the coding reliability of ties, we construct a network from the coding of each coder and subsequently calculate the graph correlations of the two resulting networks. The coding of nodes and ties in the five extracted networks turns out to be highly reliable with only slightly lower coding reliability in the case of the largest network. The coding of attributes is highly reliable as well, although it is prone to missing data on actors’ gender. We conclude by discussing the flexibility of our data collection framework and its extension by including network dynamics and nonhuman actors (such as companies) in the network representation.

There is increasing interest in collecting morbidity data from general practice. We describe our experience from Northumberland MEDICS, one of the first morbidity data collection projects in the UK. All Northumberland practices were invited to participate. Data were initially collected every 3–6 months and included the prevalence of chronic diseases, disability in the over 75s, and recording of health markers, such as smoking status. Thirty-three out of 52 practices participated. There was marked variation in prevalences and recording of health markers between practices. Recorded prevalence of hypertension and diabetes increased steadily from 1994 to 1998. Outcomes, judged by the original objectives, were mixed. However, as the project evolved, evidence emerged that MEDICS was contributing to a culture in which the use of data from practice clinical systems to improve patient care has become a core objective. Key lessons from our experience include appreciating: the importance of data quality and minimising workload for practices; the difficulties practices face in recording morbidity data consistently and systematically; the limitations of GP morbidity data for health needs assessment and commissioning at district level; and the need to focus on providing useful and relevant data for individual practices. MEDICS now covers all 53 Northumberland practices. The project focuses on recording and analysing data to help practices improve structured patient care. Increasingly data collection in general practice in Northumberland is seen as a core activity with the current dataset linking closely with local priorities and reflecting national initiatives such as Clinical Governance and National Service Frameworks.