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 : GPsLevel 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)

DiabetesCreatinine: At least one record of glomerular filtration rate (GFR) or serum creatinine testing during the measurement yearGlycated Hemoglobin: At least one record of A1c test during the measurement year Lipid profile: At least one record of lipid profile during the measurement yearEye exam: At least one record of eye exam during the measurement year  Congestive heart failureCreatinine sodium and potassium: At least one record of serum creatinine testing and electrolyte (sodium and potassium) testing in the measurement yearLipid 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 daysBeta-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

Region LombardyEmilia-

Romagna Veneto Tuscany Marche Sicily Total

No. Selected Health Districts 2 10 2 3 1 3 21

Total population 215,541 1,151,546 209,105 704,094 78,753 311,770 2,670,809

Selected population (≥16 years) (%) 77.72 70.18 82.64 70.48 94.63 77.91

73.47(1,948,662)

Selected population as a % of regional population

8.6 41.5 8.9 25.5 3.4 12.5 7.9

Residents per km2 314.2 203.8 1372.0 458.0 172.2 193.2 264.5

No. of selected GPs 140 772 174 664 75 257 2,082

Base group (%) 15.7 3.1 43.1 30.3 - 15.6 17.4

Network group (%) 35.0 43.0 24.7 - 20.0 42.0 25.1

Advanced group (%) 31.4 26.2 24.7 27.9 41.3 17.9 26.5

Sex (% male) 75.4 68.1 59.0 69.7 66.7 75.9 69.0

Age (average yrs) 53.1 53.5 53.6 54.7 51.8 52.1 54.1

Chronic disease patients Variabile Totale

N 164,267

Age 71.9

N Female 77,421 (47.1%)

Citizenship

Italian 151,603 (92.3%)

Advanced developing countries 425 (0.3%)

High pressure migration countries 2,737 (1.7%)

NA 9,502 (5.8%)

Diabetes mellitus 94,645 (57.6%)

Heart failure 25,240 (15.4%)

Ischeamic heart disease (IHD) 79,849 (48.6%)

concomitant chronic conditions

diabetes only 73,895 (45.0%)

IHD only 51,455 (31.3%)

diabetes IHD 13,677 (8.3%)

hearth failure IHD 9,992 (6.1%)

hearth failure only 8,175 (5.0%)

hearth failure diabetes IHD 4,725 (2.9%)

hearth failure diabetes 2,348 (1.4%)

Unadjusted Prevalences (N. chronic disease patients x 1.000 patients)

Ischemic heart disease Prevalence

Diabetes Prevalence Congestive heart failure Prevalence

4.9%

1.4%

4.1%

Average number of recomandations followed by each GP (max 4): Diabetes

0 1 2 3Media numero di indicatori

Gruppo

Rete

Ass semplice

Singolo

Advanced group practice

Base group practice

Solo practice

Network group practice

Average number of recomandations followed by each GP (max 3): Ischemic heart disease

.5 1 1.5 2 2.5Media numero di indicatori

Gruppo

Rete

Ass semplice

SingoloSolo practice

Base group practice

Network group practice

Advanced group practice

Average number of recomandations followed by each GP (max 4): Congestive heart failure

0 1 2 3 4Media numero di indicatori

Gruppo

Rete

Ass semplice

SingoloSolo

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

Variable Coeff 95 % CI Coeff 95 % CI Coeff 95 % CI

Constant term -2.728 -5.011,-0.445 2.977 0.420,5.535 3.116 1.744,4.487

Female 0.058 0.022,0.094 0.026 0.002,0.051 n.s. --

Age (effect x10 yrs) -0.092-0.123,-0.061 -0.027

-0.048,-0.005 -0.038 -0.072,-0.005

Single vs group practice 0.022 -0.015,0.059 0.040 0.015,0.065 0.013 -0.027,0.053

number (effect x100) 0.009 0.004,0.014 0.004 0.000,0.007 n.s. --

Chronic patients (%) n.s. -- n.s. -- -0.060 -0.091,-0.028

Chronic patients 85+ y (%) -0.004-0.007,-0.001 -0.008 -0.010,0.007 -0.006 -0.007,-0.005

Charlson Index n.s. -- -0.075-0.140,-0.011 n.s. --

>4 yrs old diagnosis (%) 0.003 0.001,0.005 n.s. -- n.s. --

Pay-for-participation 0.085 0.022,0.147 n.s. -- n.s. --

Chronic patients (%) n.s. -- n.s. -- -0.301 -0.491,-0.110

Chronic patients 85+ y(%)* 0.073 0.010,0.137 n.s. -- n.s. --

Charlson Index* n.s. -- -0.825 -1.651,0.001 -0.887 -1.500,-0.273

>4 yrs old diagnosis (%) 0.043 0.009,0.077 -0.011 -0.034,0.012 n.s. --

GP in team (%)* 0.016 0.006,0.026 0.001 -0.008,0.009 0.002 -0.004,0.008

Variance Level 1 0.051 0.109 0.077

Variance Level 2 0.949 0.786 0.947

GPlevel

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