Presentation on theme: "Performance variation in managing chronic disease by Italian Family Medicine. A population study using health administrative data: The VALORE study Modesta."— Presentation transcript:
Performance variation in managing chronic disease by Italian Family Medicine. A population study using health administrative data: The VALORE study Modesta Visca Gotenborg, 4 September 2012 EFPC 4th biannual conference in Gothenburg "Crossing Borders in Primary Care"
Increasing diffusion of chronic disease Crucial role of Medical Primary Care Performance Measurements Huge amount availability of administrative data
Objective Definition of a methodology in order to : Evaluate the impact of specific organizational aspect of Family Medicine (group practice vs solo practice) regarding chronic disease management process indicators in six Italian regions.
Administrative data Study design 3 observational longitudinal retrospective studies + ad hoc survey The observation unit was GP: the set of GPs at 1°January 2008 having at least 4 patients detected as affected by the disease; having more than 300 patient on their list GPs adherence or not to a “traditional” group practice in the previous year Descriptive analyses Frequency and ditribution Average value and standard deviation GPs description of their practice typology Statistical analyses: Multilevel model : Level I unit : GPs Level II unit : Groups/Health Districts Analysis
Algorithms for case definition La banca dati MaCro delle malattie croniche in Toscana. Pubblicazione ARS \Toscana numero 48. Dicembre 2009.www.ars.toscana.it/c/document_library/get_file?uuid=65f497a2-bd99-4cc6-832bab37ebd72dfb&groupId=11864
Outcome variable: the average score of GP’s patients, the score of the patient as sum of the standards that were met during one year follow up (2008) Diabetes Creatinine: At least one record of glomerular filtration rate (GFR) or serum creatinine testing during the measurement year Glycated Hemoglobin: At least one record of A1c test during the measurement year Lipid profile: At least one record of lipid profile during the measurement year Eye exam: At least one record of eye exam during the measurement year Congestive heart failure Creatinine sodium and potassium: At least one record of serum creatinine testing and electrolyte (sodium and potassium) testing in the measurement year Lipid profile: At least one record of lipid profile during the measurement year ACE inhibitor: Prescription of ACE inhibitor/ARB (ATC codes: C09A, C09B, C09C, C09D) during the measurement year: at least two dispensings separated by at least 180 days Beta-blockers: Prescription of beta-blockers (ATC codes: C07AA, C07AB, C07AG, C07BB, C07CA, C07CB, C07CG C09A, C09B, C09C, C09D) during the measurement year: at least two dispensings separated by at least 180 days Ischemic heart disease Total cholesterol test: At least one record of cholesterol test during the measurement year ACE inhibitor: Prescription of ACE inhibitor/ARB (ATC codes: C09A, C09B, C09C, C09D) during the measurement year Antithrombotic Therapy: Prescription of anti-thrombotic therapy (ATC codes: B01A) during the measurement year: at least two dispensings separated by at least 180 days Misures of process outcome
RegionLombardy Emilia- RomagnaVenetoTuscanyMarcheSicilyTotal No. Selected Health Districts Total population 215,5411,151,546209,105704,09478,753311,7702,670,809 Selected population (≥16 years) (%) (1,948,662) Selected population as a % of regional population Residents per km No. of selected GPs ,082 Base group (%) Network group (%) Advanced group (%) Sex (% male) Age (average yrs)
Average number of recomandations followed by each GP (max 4): Diabetes Advanced group practice Base group practice Solo practice Network group practice
Average number of recomandations followed by each GP (max 3): Ischemic heart disease Solo practice Base group practice Network group practice Advanced group practice
Average number of recomandations followed by each GP (max 4): Congestive heart failure Solo practice Base group practice Network group practice Advanced group practice
Other variables Characteristics of the GP: GPs age and gender (indicator variable for female); Profile of assisted population: number, proportion aged 75+, average Charlson index; Profile of patients with the chronic condition under analysis: proportion aged 85+, proportion bearing the condition for 4+ years; average Charlons Index Socio-demographic: average population density (inhab/km2) of the municipality of residence of the patients with the chronic condition under analysis; Health district policy: indicator variable of financial incentives for the adherence to diabetes management recommendations (only for diabetes).
Multilevel analysis : Diabetes Ischaemic heart disease Congestive heart failure VariableCoeff95 % CICoeff95 % CICoeff95 % CI Constant term , , ,4.487 Female , ,0.051n.s.-- Age (effect x10 yrs) , , , Single vs group practice , , ,0.053 number (effect x100) , ,0.007n.s.-- Chronic patients (%)n.s.--n.s , Chronic patients 85+ y (%) , , , Charlson Indexn.s ,-0.011n.s.-- >4 yrs old diagnosis (%) ,0.005n.s.--n.s.-- Pay-for-participation ,0.147n.s.--n.s.-- Chronic patients (%)n.s.--n.s , Chronic patients 85+ y(%)* ,0.137n.s.--n.s.-- Charlson Index*n.s , , >4 yrs old diagnosis (%) , ,0.012n.s.-- GP in team (%)* , , ,0.008 Variance Level Variance Level GP level District level
Conclusions 15 In the selected Health Districts there appears to be no significant difference between the impact of traditional group practice and solo practice on chronic disease management The reorganization of primary care system required is wider and involves GPs who still lie at the core of the system, togheter with other professional forces, such as specialist, nurses, social worker, etc Success Prerequisites are Sustainable evidence-baced innovation and planning at local level Committment, from regional policy maker, local administrators and professionals Reproducibility of methodology for information collecting and standards for process measurement Motivational Mechanisms, since the economic constraints in terms di research and education (on the base of the change management in Primary Health Care principles).
Emilia-Romagna AGENAS Lombardia ARS Toscana Marche Univ. Cattolica Sacro Cuore – Roma Toscana Univ. di Cassino Sicilia Univ. Politecnica delle Marche Veneto Health Search - SIMG with the collaboration of Dipartimento di Statistica della Università di Firenze per le analisi statistiche Thank you for your attention VALORE PROJECT : Research Units