2. Building a framework for integrated use of health information system and clinical data in epidemiology R. Di Domenicantonio Workshop Challenges for epidemiology in the context of National Health Service Rome, 15-16 October 2012

3. Case definition / identification C lassification of acute miocardical infarction from clinical findings AHA Scientific Statement. Case Definitions for Acute Coronary Heart Disease in Epidemiology and Clinical Research Studies. Circulation. 2003;108:2543-2549

4. Case definition / identification through Health Information System DEATH 2006-2009 410 AMI 411-414 CHD FIRST CORONARY EVENT IN HOSPITAL DEATH WITHOUT: 411-414 CHD HOSPITAL ADMISSION IN PAST 3 YEARS 410-AMI 412-OLD MYOCARDIAL INFARCTION HOSPITAL DISCHARGE 2006-2009 410 AMI DISCHARGED ALIVE WHITHIN 3 DAYS SECONDARY DIAGNOSIS: 412-OLD MYOCARDIAL INFARCTION HOSPITAL ADMISSION IN PAST 3 YEARS : 410-AMI 412-OLD MYOCARDIAL INFARCTION

5. Case-definition validation Barchielli et al. Hospital discharge data for assessing myocardial infarction events and trends, and effects of diagnosis validation according to MONICA and AHA criteria. J Epidemiol Community Health. 2012 May;66(5):462-7. A samples of patients admitted to hospital in 2003 validated according to the American Heart Association criteria

6. Source of case identification Incidence of Hypertension by deprivation Physician billing database Hospitalization database Aubé-Maurice J et al. Divergent associations between incident hypertension and deprivation based on different sources of case identification. Chronic Dis Inj Can. 2012 Jun;32(3):121-30.

7. Population registries Lazio Region population (from 2006) Georeference 3 mounth update SES indicator by census trait Mortality information system 6 mounth update Municipality of ROME registry office Municipality of ROME population (from 1997) death Anagraphic data Residence / address (actual, stand. coded) Census trait Life status Anagraphic data Residence / address (actual, stand. coded) Census trait Life status access, changes, delection in regional health service insured population 55 health district in 12 LHA collects 20 administrative district collect residence and anagraphic data INSURED RESIDENT Anagraphic data General practitioner data Residence (municipality - updated ?) Anagraphic data General practitioner data Residence (municipality - updated ?) Census trait Address1 DEP

8. Health / Population Information System Anagraphic data HIS Code (Anonimous) HIS Code (Anonimous) Tracking of health care contacts Tracking of health care contacts Population cohorts Population cohorts population registries population registries DRUG PRESC EMERGENCY EXEMPTIONS OUTPATIENT MORTALITY ADMISSION HOSPITAL Limits due to privacy legislation

9. Lazio Region Quintile of rate Rome Municipality Standardized Rate X 100.000 residents Incidence rate ratio (ref. Regional) Relative risk ROME Occurence:Incidence of first coronay event in Lazio Region, Years 2006-2009

10. Adherence to evidence-based drug therapy Cohort of AMI patients followed 12 months after hospitalization in Rome Kirchmayer U et Al. Socio-demographic differences in adherence to evidence-based drug therapy after hospital discharge from acute myocardial infarction: a population-based cohort study in Rome, Italy. J Clin Pharm Ther. 2012 Feb;37(1):37-44.

11. Susceptibility to exposure Faustini A, et Al. Short-term effects of air pollution in a cohort of patients with chronic obstructive pulmonary disease. Epidemiology. 2012 Nov;23(6):861-79 Association between air pollutants and mortality in the COPD cohort and the non-COPD population. Rome, years 2005–2009

12. Among population with diabetes: Rate of diabetes related complication by area of residence Composite measure: Diabetes Short-Term Complications (ketoacidosis, hyperosmolarity, coma) Diabetes Long-Term Complications (renal, eye, neurological, circulatory) Lower-extremity amputation Primary care outcome indicator Age, gender standardized Rate (X 1,000)

13. Health Information Systems (H.I.S.) Pros Extensive coverage Time Cost Integration of all patient’s data Homogenity (?) Cons Inaccuracy of information Inconsistency of coding over time Exclusion of people with no contact with health services Information reflects reimbursement policies from NHS

14. Integration of HIS and clinical data TWO APPROACHES: A priori case definition, based on literature review –Validation on Clinical data (only sensitivity) Population data (sensitivity & specificity ) Panel approach –Building of predictive models based on clinical data of subject with ascertained diagnosis –Validation (sensitivity & specificity)

15. Inflammatory bowel disease (IBD) Year of diagnosis Patients Sensitivity OverallHospitalExemptions Before 20061,98082.176.239.0 200610988.176.261.5 20079788.778.463.9 200810281.472.657.8 20097068.651.445.7 Total2,35882.275.442.1 Sensitivity of the case finding algorithm in respect to a panel of Crohn’s disease patients by year of diagnosis and source Panel data: Patients from gastroenterology wards of five hospitals Case def. / identification: Discharge (ICD9 555) Exemption register Years 2000-2009

16. Chronic obstructive pulmunary disease (COPD) Belleudi V. et al : Definition and validation of a predictive model to identify patients with Chronic Obstructive Pulmonary Disease (COPD) from administrative databases. Epidemiol Prev. 2012 May;36(3-4):162-71 General Practitioner Survey (question.) Predictive model A-priori Age class Prevalence % Lazio Region prevalence comparison between different case definition Predictive model: from health care consumption patterns of panels (2) of clincal ascertained patients A-priori case definion: from literature review, knowledge Case identification: Hospitalization Drug prescription year 2007

17. Comparison between GP and HIS for Diabetes and COPD Case definition: Diabetes –Discharges –Exemptions –Drug prescriptions COPD –predictive model COPD DIABETES GPHIS Prevalent patient identified in HIS and in 49 general practitioner data. Lazio Region, years 2006-2008

18. Is it possible to identify heart failure patients using administrative database? Development and validation of a predictive model using information from HIS and clinical data from population with and without disease (PREDICTOR study) PREDICTOR patients (HF +) (HF -) Ambulatory setting patients (HF +) Administrative data Control group subjects (HF -) Bootstrap HF associated factors Predictive model Cut off Internal validation External validation

19. Results Age, consumption of several drug (furosemide, diuretics, antianemic preparations), previous hospitalizations (HF, myocardial infarction, other heart diseases) are factor associated with COPD Model showed high specificity (96.8%) and low sensitivity (34.2%) (although higher than only hospitalization) More appropriate to select cohorts of severe patient than to estimate occurrence

20. Added value of clinical data Integration of clinical and HIS data is a useful approach to asses validity of case-defining and to develop innovative model to identify population affected by chronic disease Uncertainty / lack of information in administrative data invoke their utilization to: –Check accuracy of coding in claims to guarantee comparable case-definition in different epidemiologic studies –Gather addictional information to develop and validate severity of disease measure

21. Added value of clinical data Future challenge: MOH project ongoing ! –Objective: validate some commonly used case definitions, including clinical stage classification, with respect to information gathered by general practitioners Diabetes Coronary heart disease Hearth failure Hypertension –unselected, highly representative population of control

22. Building a framework for integrated use of health information system and clinical data in epidemiology R. Di Domenicantonio Workshop Challenges for epidemiology in the context of National Health Service Rome, 15-16 October 2012