[Objectives, tools and methods for an epidemiological use of electronic health archives in various areas of Italy].

The availability of Electronic Health Archives (EHA) has increased remarkably over the last twenty years. As part of a joint project of the Italian Association of Epidemiology (AIE) and the Italian Association of Medical Statistics and Clinical Epidemiology (SISMEC), a workgroup of experts was set up in 2005 with the aim of comparing various experiences and of standardizing the procedures by which electronic sources can be integrated. In particular, the workgroup's aim was to estimate the frequency of certain major diseases using standard algorithms applied to EHA. This volume is published with the purpose of making available in a common publication the methods and the results obtained. The results from a multicentre study using a standard approach to probabilistic record-linkage procedures are also included in a specific chapter. Eleven Italian centres from five Italian regions with an overall population of 11,932,026 collected and treated more than 21,374,426 records (year 2003) from five electronic information sources: death certificates, hospital discharge records (including outpatient discharges), drug prescriptions, tax- exemptions, and pathology records in order to estimate the frequency of the following diseases: diabetes, ischemic heart diseases, acute myocardial infarction, stroke, asthma, chronic obstructive pulmonary disease, obstructive lung diseases. For each pathology a specific algorithm was developed and used by all centres for the identification of the prevalent/incident cases of the selected diseases. Standardized methods were used to estimate the rates. The results confirm the need for a common standard approach to produce estimates based on EHA, considering the variability of the quality and of the completeness of the archives, and the difficulties of standardizing record-linkage operations in the various centres. The main achievement of this work was the elimination of the variability due to the use of different algorithms to identify cases using EHA.