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Supplementary Methods. Table S1. Attrition table showing patients with recorded diagnoses of NAFLD or NASH and matched unexposed controls. Table S2. Descriptive characteristics of coded NAFLD or NASH patients and matched unexposed cohorts in SIDIAP and THIN. Table S3. Number of patients with data available in coded NAFLD/NASH and matched unexposed cohorts. Table S4. Incidence rate of liver outcomes in four primary care databases. Table S5. Descriptive characteristics of coded NAFLD/NASH patients and matched non-NAFLD/NASH in a sample of patients with and without BMI data available, all datasets combined. Table S6. Median and interquartile range (in years) for time to event in coded NAFLD and matched non-NAFLD who experience a cirrhosis or hepatocellular carcinoma event during follow-up. Figure S1. Subgroup analysis of the association between coded NAFLD/NASH and incident (A) cirrhosis and (B) hepatocellular carcinoma events by medical history and demographics. Figure S2. Hazard ratio (HR) for cirrhosis (A) adjusted for age and smoking in all patients and (B) adjusted for age and smoking in patients with BMI. Figure S3. Hazard ratio (HR) for HCC (A) adjusted for age and smoking in all patients and (B) adjusted for age and smoking in patients with BMI. Figure S4. Fib-4 Association with (A) cirrhosis or (B) HCC. Figure S5. Risk of coded NASH in patients with coded NAFLD. (DOCX 540 kb)

Table S1. Characteristics of the primary-care databases included in the study. Figure S1. Identification of NAFLD patients in the IPCI database. Table S2. List of codes for the identification of NAFLD and description in ICD9CM, ICD10, Read Codes and SNOMEDCT US terminologies. Table S3. Number and proportion of patients with individual patient characteristic data available. Table S4. Point prevalence (95% CI) of NAFLD and NASH (per 100 persons) on 1 January of each calendar year in four primary-care databases, and pooled across databases. Table S5. One-year period prevalence (95% CI) of NAFLD (per 100 persons) on 1 January of each calendar year in four primary-care databases. Table S6. Point prevalence (95% CI) of NAFLD (per 1000 persons) by age categories and gender on 1 January 2015 in four primary-care databases. Table S7. Incidence estimates (95% CI) of NAFLD (per 1000 person-years) by calendar year in four primary-care databases, and pooled estimates across databases. Table S8. Incidence estimates (95% CI) of NAFLD (per 1000 person-years) in 2015 by age categories and gender in four primary-care databases. Figure S2. Pooled a prevalence (per 100 persons) and b incidence (per 1000 person-years) regressed over calendar year by meta-regression. Figure S3. Distribution of entry date for patients in the four databases. (DOCX 203 kb)

Table S1. Characteristics of the primary-care databases included in the study. Figure S1. Identification of NAFLD patients in the IPCI database. Table S2. List of codes for the identification of NAFLD and description in ICD9CM, ICD10, Read Codes and SNOMEDCT US terminologies. Table S3. Number and proportion of patients with individual patient characteristic data available. Table S4. Point prevalence (95% CI) of NAFLD and NASH (per 100 persons) on 1 January of each calendar year in four primary-care databases, and pooled across databases. Table S5. One-year period prevalence (95% CI) of NAFLD (per 100 persons) on 1 January of each calendar year in four primary-care databases. Table S6. Point prevalence (95% CI) of NAFLD (per 1000 persons) by age categories and gender on 1 January 2015 in four primary-care databases. Table S7. Incidence estimates (95% CI) of NAFLD (per 1000 person-years) by calendar year in four primary-care databases, and pooled estimates across databases. Table S8. Incidence estimates (95% CI) of NAFLD (per 1000 person-years) in 2015 by age categories and gender in four primary-care databases. Figure S2. Pooled a prevalence (per 100 persons) and b incidence (per 1000 person-years) regressed over calendar year by meta-regression. Figure S3. Distribution of entry date for patients in the four databases. (DOCX 203 kb)

Supplementary Methods. Table S1. Attrition table showing patients with recorded diagnoses of NAFLD or NASH and matched unexposed controls. Table S2. Descriptive characteristics of coded NAFLD or NASH patients and matched unexposed cohorts in SIDIAP and THIN. Table S3. Number of patients with data available in coded NAFLD/NASH and matched unexposed cohorts. Table S4. Incidence rate of liver outcomes in four primary care databases. Table S5. Descriptive characteristics of coded NAFLD/NASH patients and matched non-NAFLD/NASH in a sample of patients with and without BMI data available, all datasets combined. Table S6. Median and interquartile range (in years) for time to event in coded NAFLD and matched non-NAFLD who experience a cirrhosis or hepatocellular carcinoma event during follow-up. Figure S1. Subgroup analysis of the association between coded NAFLD/NASH and incident (A) cirrhosis and (B) hepatocellular carcinoma events by medical history and demographics. Figure S2. Hazard ratio (HR) for cirrhosis (A) adjusted for age and smoking in all patients and (B) adjusted for age and smoking in patients with BMI. Figure S3. Hazard ratio (HR) for HCC (A) adjusted for age and smoking in all patients and (B) adjusted for age and smoking in patients with BMI. Figure S4. Fib-4 Association with (A) cirrhosis or (B) HCC. Figure S5. Risk of coded NASH in patients with coded NAFLD. (DOCX 540 kb)

Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory disorder of the lungs associated with progressive disability. Although general practitioners (GPs) should play an important role in the COPD management, critical issues have been documented in the primary care setting. The aim of this study was to evaluate the effectiveness of an educational program for the improvement of the COPD management in a Sicilian general practice setting. The effectiveness of the program, was evaluated by comparing 15 quality-of-care indicators developed from data extracted by 33 GPs, at baseline vs. 12 and 24 months, and compared with data from a national primary care database (HSD). Moreover, data on COPD-related and all-cause hospitalizations over time of COPD patients, was measured. Overall, 1,465 patients (3.2%) had a registered diagnosis of COPD at baseline vs. 1,395 (3.0%) and 1,388 (3.0%) over time (vs. 3.0% in HSD). COPD patients with one spirometry registered increased from 59.7% at baseline to 73.0% after 2 years (vs. 64.8% in HSD). Instead, some quality of care indicators where not modified such as proportion of COPD patients treated with ICS in monotherapy that was almost stable during the study period: 9.6% (baseline) vs. 9.9% (after 2 years), vs. 7.7% in HSD. COPD-related and all-cause hospitalizations of patients affected by COPD decreased during the two observation years (from 6.9% vs. 4.0%; from 23.0% vs. 18.9%, respectively). Our study showed that educational program involving specialists, clinical pharmacologists and GPs based on training events and clinical audit may contribute to partly improve both diagnostic and therapeutic management of COPD in primary care setting, despite this effect may vary across GPs and indicators of COPD quality of care., Chronic lung disease: Education improves disease management An education program for doctors covering chronic lung disease diagnosis and management improves elements of patient care in Italy. Gianluca Trifirò at the University of Messina, Italy, and co-workers followed a cohort of 33 family doctors and 1,465 patients with chronic obstructive pulmonary disease (COPD) in Sicilian primary care for two years, comparing quality of care for patients before and after implementing the education program. The training, led by lung specialists and clinical pharmacologists, updated doctors with the latest information on COPD diagnosis, medication and disease management, and trained them in spirometry test interpretation. The team assessed the value of the program using quality-of-care indicators and asked each doctor to re-evaluate patients’ COPD diagnosis. Their results showed improvement in some areas, including spirometry testing, alongside a reduction in hospital admissions over the two-year period.

BACKGROUND: Antipsychotic (AP) drugs are commonly used to manage the behavioural symptoms of dementia. Nevertheless, international (i.e. the European Medicines Agency in Europe) and national (i.e. the Medicines and Healthcare products Regulatory Agency in the UK and the Italian Drug Agency) regulatory agencies issued safety warnings against AP use in dementia in 2004 and 2009.OBJECTIVE: The aim of this study is to investigate the short- and long-term impact of safety warnings on the use of APs in UK and Italian persons with dementia using two nationwide databases: The Health Improvement Network (THIN) from the UK and the Health Search Database-Cegedim-Strategic Data-Longitudinal Patient Database (HSD-CSD-LPD) from Italy.METHODS: We calculated the overall quarterly prevalence of AP use by class and by individual drug in persons with dementia aged ≥65 years and used generalized linear models to explore the effect of the safety warnings.RESULTS: We identified 58,497 and 10,857 individuals aged ≥65 years with dementia from the THIN and HSD-CSD-LPD databases, respectively, over the period 2000-2012. After the 2004 warnings, the use of atypical APs decreased, whereas the use of conventional APs increased, in Italy and the UK until 2009. However, the trend for APs individually showed that the use of risperidone/olanzapine decreased, whereas the use of quetiapine increased in both countries. After the 2009 warnings (until 2012), the use of atypical and conventional APs decreased in the UK (from 11 to 9 and 5 to 3 %, respectively), but such use increased in Italy (from 11 to 18 and 9 to 14 %, respectively).CONCLUSION: The 2004 warnings led to a reduction in the use of olanzapine and risperidone and increased the use of quetiapine/conventional APs in both countries. From 2009, the use of APs decreased in persons with dementia in the UK but not in Italy. Possible reasons for the difference in AP use between the two countries include a more proactive approach towards reducing the use of APs in the UK than in Italy.