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Additional file 1:eTable 1. Therapeutic categories and exposure for single chronic disease. eFigure 1. Provincial deprivation index map. eFigure 2. Medicine consumption rate (DDD per capita) for lipid-lowering agent in adults (aged ≥ 18 years) by province, standardized: (A) by age only; (B) by age and deprivation tertile. eFigure 3. Medicine consumption rate (DDD per capita) for antiosteoporotic drugs in adults (aged ≥ 18 years) by province, standardized: (A) by age only; (B) by age and deprivation tertile. eFigure 4. Medicine consumption rate (DDD per capita) for drugs for obstructive airway diseases in adults (aged ≥ 18 years) by province†, standardized: (A) by age only; (B) by age and deprivation tertile. eFigure 5. Adherence and persistence to treatment at 12 months (%) in adults (≥ 18 years) by province adjusted by age.

BACKGROUND: there has been a long-standing, consistent use worldwide of Healthcare Administrative Databases (HADs) for epidemiological purposes, especially to identify acute and chronic health conditions. These databases are able to reflect health-related conditions at a population level through disease-specific case-identification algorithms that combine information coded in multiple HADs. In Italy, in the past 10 years, HAD-based case-identification algorithms have experienced a constant increase, with a significant extension of the spectrum of identifiable diseases. Besides estimating incidence and/or prevalence of diseases, these algorithms have been used to enroll cohorts, monitor quality of care, assess the effect of environmental exposure, and identify health outcomes in analytic studies. Despite the rapid increase in the use of case-identification algorithms, information on their accuracy and misclassification rate is currently unavailable for most conditions. OBJECTIVES: to define a protocol to systematically review algorithms used in Italy in the past 10 years for the identification of several chronic and acute diseases, providing an accessible overview to future users in the Italian and international context. METHODS: PubMed will be searched for original research articles, published between 2007 and 2017, in Italian or English. The search string consists of a combination of free text and MeSH terms with a common part on HADs and a disease-specific part. All identified papers will be screened for eligibility by two independent reviewers. All articles that used/defined an algorithm for the identification of each disease of interest using Italian HADs will be included. Algorithms with exclusive use of death certificates, pathology register, general practitioner or pediatrician data will be excluded. Pertinent papers will be classified according to the objective for which the algorithm was used, and only articles that used algorithms with "primary objectives" (I disease occurrence; II population/cohort selection; III outcome identification) will be considered for algorithm extraction. The HADs used (hospital discharge records, drug prescriptions, etc.), ICD-9 and ICD-10 codes, ATC classification of drugs, follow-back periods, and age ranges applied by the algorithms will be collected. Further information on specific accuracy measures from external validations, sensitivity analyses, and the contribution of each source will be recorded. This protocol will be applied for 16 different systematic reviews concerning eighteen diseases (Hypothyroidism, Hyperthyroidism, Diabetes mellitus, Type 1 diabetes mellitus, Acute myocardial infarction, Ischemic heart disease, Stroke, Hypertension, Heart failure, Congenital heart anomalies, Parkinson's disease, Multiple sclerosis, Epilepsy, Chronic obstructive pulmonary disease, Asthma, Inflammatory bowel disease, Celiac disease, Chronic kidney failure). CONCLUSION: this protocol defines a standardized approach to extensively examine and compare all experiences of case identification algorithms in Italy, on the 18 abovementioned diseases. The methodology proposed may be applied to other systematic reviews concerning diseases not included in this project, as well as other settings, including international ones. Considering the increasing availability of healthcare data, developing standard criteria to describe and update characteristics of published algorithms would be of great use to enhance awareness in the choice of algorithms and provide a greater comparability of results.

The purpose of the project is to pilot an innovative intervention aiming to enroll the local actors in the responsibility to reduce inequalities in the epidemic of diabetes in the city. In the next few years, all the actors of the diabetes pathway (mostly the health professionals but also the local non-health actors) should analyse and recalibrate their organization and functioning, revising primary care system in the light of the paradigm of pro-active medicine At first, a community of practice of health professionals involved in diabetes is being convened with the mandate to revise the process of generation, recognition and treatment of diabetes in order to identify the mechanisms potentially able to create inequalities. At the same time a community of practice of local non-health actors is being convened to react to the analysis of mechanisms of the community of practices of health professionals, with the mandate to interpret the data and enrich the diagnosis on mechanisms, relevance and responsibility. The two communities of practice will elaborate a plan of innovation involving the responsibility of the institutions and actors that are playing a role in the mechanisms generating diabetes inequalities, setting targets, assigning responsibilities, orienting resources. The Turin Longitudinal Study will serve as a tool for monitoring the process and assessing results and impact of this innovation. The expected results concern two types of effects, which are monitored. Firstly, improving the care system and collaboration among different services should have an impact on the accessibility and quality of care. These improvements will be introduced concerning specific problems that emerge from the analysis. Secondly, social capital should increase thus increasing the resources available to people, especially the most fragile and isolated ones. The results of this pilot on diabetes will be replicated for the other chronic diseases of the National Plan for Chronic Disease.

Objective To examine the potential role of 2 early-life socioeconomic indicators, parental education, and crowding index, on risk of type 1 diabetes (T1DM) in patients up to age 29 years to test heterogeneity by age at onset according to the hygiene hypothesis. Study design The study base was 330 950 individuals born from 1967 to 2006 who resided in the city of Turin at any time between 1984 and 2007. Data on their early life socioeconomic position were derived from the Turin Longitudinal Study; 414 incident cases of T1DM up to age 29 years were derived from the Turin T1DM registry. Results Socioeconomic indicators had opposing effects on risk of T1DM in different age at onset subgroups. In a Poisson regression model that included both socioeconomic indicators, there was a 3-fold greater risk of T1DM (relative risk 2.91, 95% CI 0.99-8.56) in children age 0-3 years at diagnosis living in crowded houses. In the 4- to 14-year subgroup, a low parental educational level had a protective effect (relative risk 0.50, 95% CI 0.29-0.84), and the effect of crowding nearly disappeared. In the 15- to 29-year subgroup, neither crowding nor parental educational level was clearly associated with the incidence of T1DM. Conclusions We provide evidence of heterogeneity by age at onset of the association between early-life socioeconomic indicators and the risk of T1DM. This finding is consistent with the hypothesis that infectious agents in the perinatal period may increase the risk, whereas in the following years they may become protective factors (hygiene hypothesis).