Empirical Evidence from a Panel of Italian Patients

MEDICAL COMPLIANCE AND HEALTH OUTCOME: EMPIRICAL EVIDENCE FROM A PANEL OF ITALIAN PATIENTS

Vincenzo Atella • CEIS Tor Vergata and Department of Economics • Università Tor Vergata - Roma Structure of the presentation

• Introducing the dataset

• Compliance: definition and determinants

• The effect of compliance on health outcomes: hospitalization and mortality

• Exploring the effetcs of a natural experiment on compliance and on health outcomes I

Introducing the dataset The data from the Treviso LHA

• The data include information on health care access for all patients registered in the LHA of Treviso: – from 1993 to 2002 for prescription drug utilization; – from 1996 to 2002 for specialists, diagnostic tests and hospitalization. • For each patient is then possible to reconstruct the precise history of health care accesses in the public sector. • For each physician registered with the Treviso LHA is possible reconstruct the prescription behaviour. • Data are “registry data” covering all the population living in that area. • Information are also available on the in-coming and out-coming flows of patients. This allows to know if and when a patient has moved away or if a patient has died. Criteria for selection of patients

• The study is limited to all those patients who have been prescribed at least one drug in the following ATC classes: – C01 – Cardiac Therapy – C02 - Anthypertensive –C03 -Diuretics – C07 - Beta-blocklers – C08 - Calcium Antagonists – C09 – Ace Inhibitors • Our database is then composed of all information concerning health care accesses by these patients selected above from 1996 until 2003. • Thanks to an anonimous identifier, patients have been structured according to an “un-balanced” panel. Variables available in the dataset

Servizio Farmaceutico Servizio specialistica Servizio ospedaliero 1. Anno di prescrizione 1. Medico prescrittore 1. Codice istituto 2. Mese di prescrizione 2. Codice sanitario individuale 2. Codice sanitario individuale 3. Giorno di prescrizione 3. Provincia e comune di residenza 3. Regime di ricovero 4. USLL dell’assistito 4. Data della prestazione 4. Data di ricovero 5. importo della degenza (DRG + altri 5. Sesso 5. Codice prestazione costi) 6. Data di nascita dell’assistito 6. Quantità 6. Tipo di ricovero 7. Numero tessera di esenzione 7. Posizione dell’utente verso ticket 7. Motivo del ricovero 8. Codice posizione ticket 8. Importo Ticket 8. Traumatismi o intossicazioni 9. Tipo ente prescrivente 9. Importo totale 9. Reparto di dimissione 10. Codice medico prescrivente 10. Data di dimissione o di morte 11. Numero pezzi 11. Modalità di dimissione 12. Numero accessi in caso di ricovero in 12. Codice ATC DH 13. Fascia di appartenenza 13. DRG 14. Prezzo unitario farmaco 14. Importo degenza 15. Importo ticket 16. Importo quota fissa 17. Nota CUF 18. descrizione ATC No. of patients selected and physicians per year

anno pazienti medici paz/med 1993 80,034 1,143 70.0 1994 71,662 1,128 63.5 1995 66,165 595 111.2 1996 82,645 441 187.4 1997 67,817 328 206.8 1998 72,138 323 223.3 1999 76,676 389 197.1 2000 77,117 857 89.9 2001 81,785 922 88.7 2002 84,672 1,056 80.2

Some results – Demografy 1: Number of observed patients by age and sex

Uomini 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 Total 20- 185 99 97 106 84 66 79 76 69 72 933 21-40 1.223 729 713 879 849 893 1.046 1.013 1.101 1.069 9.515 41-50 1.982 1.883 1.934 2.230 2.181 2.240 2.491 2.532 2.751 2.886 23.110 51-60 4.136 4.128 4.226 4.461 4.828 5.207 5.626 5.835 6.176 6.321 50.944 61-70 6.678 5.911 5.958 6.341 6.614 6.800 7.471 7.627 8.209 8.571 70.180 71-80 5.109 4.643 4.805 5.142 5.470 5.985 6.666 7.046 7.439 7.707 60.012 81+ 2.458 2.325 2.492 2.636 2.693 2.623 2.632 2.579 2.892 3.084 26.414 Total 21.771 19.718 20.225 21.795 22.719 23.814 26.011 26.708 28.637 29.710 241.108

Donne 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 Total 20- 278 108 69 101 77 74 74 69 63 54 967 21-40 2.179 858 872 987 997 1.091 1.200 1.158 1.196 1.187 11.725 41-50 2.492 1.954 1.905 2.162 2.207 2.280 2.433 2.451 2.624 2.666 23.174 51-60 5.783 4.759 4.753 5.090 5.251 5.540 5.892 5.973 6.387 6.426 55.854 61-70 9.539 7.594 7.677 7.910 8.222 8.382 8.880 9.033 9.527 9.807 86.571 71-80 8.412 7.265 7.420 7.966 8.609 9.385 10.529 10.947 11.363 11.441 93.337 81+ 6.365 6.040 6.275 6.731 6.772 6.715 6.709 6.578 7.093 7.482 66.760 Total 35.048 28.578 28.971 30.947 32.135 33.467 35.717 36.209 38.253 39.063 338.388 Some results – Demografy 2:

25.000

20.000 20- 21-40 15.000 41-50 51-60 10.000 61-70 71-80 5.000 81+

0 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 Some results – Demografy 3:

25.000

20.000 1995

15.000 1997 1999 2000 10.000 2001 2002 5.000

0 20- 21-40 41-50 51-60 61-70 71-80 81+ Some results - 4 : Percentage of drug prescriptions by patient age and year

1993 1994 1995 1996 .03 .02 .01 0

1997 1998 1999 2000 .03 .02 .01 Density 0

20 60 100 20 60 100

2001 2002 .03 .02 .01 0

20 60 100 20 60 100 età Graphs by anno prescrizione Some results - 5 Number of prescriptions by year and age class for patients enrolled in 1993

160.000

140.000

120.000 20- 21-40 100.000 41-50 80.000 51-60 61-70 60.000 71-80 40.000 81+

20.000

0 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 Some results - 6 Ratio between patients died and patients observed by year and age class

70,00

60,00

50,00 20- 21-40 40,00 41-50 51-60 61-70 30,00 71-80 81+ 20,00

10,00

0,00 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 Some results - 7 Percentage of hospitalization by patient age and year

1997 1998 1999 .03 .02 .01 0

2000 2001 2002 Density .03 .02 .01 0

20 60 100 20 60 100 20 60 100 età Graphs by Anno Some results - 8 Distribution of LOS by age class and percentile

p50 p75 p90 20- 3 9 21 21-40 4 8 17 41-50 5 10 21 51-60 6 12 24 61-70 7 14 25 71-80 9 15 26 81+ 9 15 24 Total 8 14 24 Some results - 9 Number of hospitalizations by age class and year

12.000

10.000

8.000 1997 1998 1999 6.000 2000 2001 2002 4.000

2.000

0 20- 21-40 41-50 51-60 61-70 71-80 81+ Health care hystory of patients: the interesting aspects of a panel structure – 1

No. of Specialist codsan Year DOB age sex prescriptions Hospitaliz LOS vis it s ations 45 anni 254597 1998 1953 45 F 3 4 2 41 254597 1999 1953 46 F 7 4 1 356 254597 2000 1953 47 F 12 4 1 355 254597 2001 1953 48 F 18 6 4 303 254597 2002 1953 49 F 95 2 3 278 262911 1998 1953 45 M 21 9 2 13 262911 1999 1953 46 M 15 6 1 2 262911 2000 1953 47 M 9 6 3 62 262911 2001 1953 48 M 19 10 9 33 262911 2002 1953 49 M 21 16 2 3 Health care hystory of patients: the interesting aspects of a panel structure – 1 – 2

Number of # of # of codsan year DOB agesex prescription specialist hospitaliza LOS s vis its tions 60 anni 29394 1998 1937 61 M 5 5 3 29 29394 1999 1937 62 M 7 2 2 31 29394 2000 1937 63 M 7 6 4 27 29394 2001 1937 64 M 11 6 4 87 29394 2002 1937 65 M 20 22 1 12 42190 1998 1937 61 F 14 10 1 9 42190 1999 1937 62 F 19 1 3 42 42190 2000 1937 63 F 27 5 5 28 42190 2001 1937 64 F 48 7 2 37 42190 2002 1937 65 F 45 13 1 3 Some sample statistics ALL PATIENTS IN THE SAMPLE

PATIENTS WITH AT LEAST ONE ACE PRESCRIPTION

sum | Freq. Percent Cum. ------+------Only ACE | 40,980 55.90 55.90 Multitherapy| 32,333 44.10 100.00 ------+------Total | 73,313 100.00 Distribution of patients by year of birth, sex and ATC codes

Distribution of patients by year of birth and sex men women .04 .03 .02 relative frequencies relative .01 0

1910 1920 1930 1940 1950 19601910 1920 1930 1940 1950 1960 year of birth ATC code: C09AA ATC code: others Comparison across samples

Distribution of patients by year of birth and sex men women .04 .03 .02 relative frequencies relative .01 0

1910 1920 1930 1940 1950 19601910 1920 1930 1940 1950 1960 year of birth Italy Treviso Treviso - our database HospitalizationHospitalization rates (cardiovasc.rates (cardiovascular DRG) by age and DRG) sex by .2 age and sex .15 .1 .05 0

40 50 60 70 80 90 Age

Men Women

Centered MA(3) smoothing Hospitalization rates (cardiovasc. DRG) by age and sex men women .2 Hospitalization rates (cardiovascular DRG) by age, sex

.15 and ATC code .1 .05 0

40 50 60 70 80 90 40 50 60 70 80 90 Age ATC code: C09AA ATC code: others Average length of stay (cardiovasc. DRG) by age and sex

Average14 LOS (cardiovascular DRG) by age and sex 12 10 8 6 4

40 50 60 70 80 90 Age

Men Women

Centered MA(3) smoothing Mortality rates by age and sex

.2 Mortality rates by age and sex .15 .1 .05 0

40 50 60 70 80 90 Age

Men Women

Centered MA(3) smoothing Mortality rates by age and sex men women .2

Mortality rates by age, sex and ATC code .1 0

40 50 60 70 80 90 40 50 60 70 80 90 Age ATC code: C09AA ATC code: others Mortality rates (all causes) by age categories and sex men women .1

Comparison of mortality rates for all causes across

.05 samples 0

35-44 45-54 55-64 65-74 75+ 35-44 45-54 55-64 65-74 75+ agecat Italy Treviso Treviso - our database

Compliance: definition and determinants Composition of ATC class C09AA by year

ATC code Chemical substance 1997 1998 1999 2000 2001 2002 Total C09AA01 Captopril 8.16 6.62 5.5 4.86 4.02 3.24 5.25 C09AA02 Enalapril 45.52 46.49 46.98 46.9 44.37 40.56 44.99 C09AA03 Lisinopril 5.66 5.34 5.77 6.63 6.92 6.89 6.25 C09AA04 Perindopril 10.95 10.26 10.46 10.35 11.04 13.84 11.23 C09AA05 Ramipril 8.16 10.08 10.09 11.15 13.75 16.35 11.84 C09AA06 Quinapril 6.51 6.15 5.67 5.81 5.53 4.56 5.65 C09AA07 Benazepril 1.77 1.67 1.4 1.05 0.81 0.97 1.25 C09AA08 Cilazapril 1.64 1.36 1.05 0.82 0.65 0.5 0.97 C09AA09 Fosinopril 9.18 9.04 9.17 8.3 7.69 6.36 8.2 C09AA10 Trandolapril 0.49 0.59 0.54 0.46 0.55 0.42 0.5 C09AA11 Spirapril 0.09 0.26 1.29 1.63 1.44 1.17 1.02 C09AA12 Delapril 1.24 1.08 1.03 1.15 0.89 0.84 1.03 C09AA13 Moexipril 0.64 1.04 1.06 0.88 0.89 0.82 0.89 C09AA15 Zofenopril 0 0 0 0 1.44 3.49 0.92 100 100 100 100 100 100 100 Source: OSMED

AverageBenazepril purchasedCaptopril daily dosesCilazapril (in mg) byDelapril AI .2 .1 .15 .08 .06 .15 .1 .1 .05 .04 .05 .02 .05 0 0 0 0

0 5 10 15 20 0 50 100 150 200 0 5 10 15 0 20 40 60 80

Enalapril Fosinopril Lisinopril Moexipril .1 .1 .08 .15 .06 .1 .05 .05 .04 .05 .02 0 0 0 0

0 20 40 60 0 10 20 30 40 0 20 40 60 0 10 20 30

Perindopril Quinapril Ramipril Spirapril Density .3 .3 .15 .3 .2 .2 .1 .2 .1 .1 .1 .05 0 0 0 0

0 5 10 0 10 20 30 40 0 5 10 15 0 5 10

Trandolapril Zofenopril .2 .6 .4 .1 .2 0 0

0 1 2 3 4 0 20 40 60 Average purchased daily doses (in mg) Compliance by active ingredient – 1

Benazepril Captopril Cilazapril Delapril 10 5 0

Enalapril Fosinopril Lisinopril Moexipril 10 5 0

Perindopril Quinapril Ramipril Spirapril 10 Density 5 0

0 .5 1 1.5 2 0 .5 1 1.5 2

Trandolapril Zofenopril 10 5 0

0 .5 1 1.5 2 0 .5 1 1.5 2 compliance DDDs and PDDs for ACE Inhibitors

ATC Active WHO code ingredient DDDs PDDs DDD/PDD C09AA01 Captopril 50 50 1 C09AA02 Enalapril 10 20 0.5 C09AA03 Lisinopril 10 20 0.5 C09AA04 Perindopril 4 4 1 C09AA05 Ramipril 2.5 5 0.5 C09AA06 Quinapril 15 15 1 C09AA07 Benazepril 7.5 10 0.75 C09AA08 Cilazapril 2.5 5 0.5 C09AA09 Fosinopril 15 15 1 C09AA10 Trandolapril 2 2 1 C09AA11 Spirapril 6 6 1 C09AA12 Delapril 30 30 1 C09AA13 Moexipril 15 15 1 C09AA15 Zofenopril 30 30 1 Compliance by active ingredient - 2

Benazepril Captopril Cilazapril Delapril 10 5 0

Enalapril Fosinopril Lisinopril Moexipril 10 5 0

Perindopril Quinapril Ramipril Spirapril 10 Density 5 0

0 .5 1 1.5 2 0 .5 1 1.5 2

Trandolapril Zofenopril 10 5 0

0 .5 1 1.5 2 0 .5 1 1.5 2 compliance Some formulas – 1

• A first indicator of compliance is obtained as ratio between “purchased” doses (PD) and DDD Ti ∑ PDijt (1) t=1 PDij ⋅Ti PDij cij = = = DDD j ⋅Ti DDD j ⋅Ti DDD j

• Compliance is then adjusted by Prescribed Daily Doses (PDD):

(2) (1) DDD j cij = cij ⋅ PDD j Some formulas – 2

• Given that a patient can purchase more than just one single active ingredient within the same class of ACE Inhibitor, it is necessary to compute the average compliance per patient over the reference period (year or month):

J D ⋅ PD ij J ∑ ij c = D ⋅ c (2) = j=1 i ∑ ij ij J j=1 ∑ Dij ⋅ PDD j j=1

•where

1 if active ingredient j is included in the patient i therapy Dij =  0 otherwise Histogram of compliance 4 1 2 .8 3 1.5 .6 1 density cumulated Total compliance .4 .5 .2 0 0

.1 .3 .5 .7 .9 1.1 1.3 1.5 1.7 1.9 .1 .3 .5 .7 .9 1.1 1.3 1.5 1.7 1.9

2 .2 .4 .6 .8 1 1.2 1.4 1.6 1.8 2 .2 .4 .6 .8 1 1.2 1.4 1.6 1.8 2 compliance compliance Density 1 0

0 .25 .5 .75 1 1.25 1.5 1.75 2 compliance Average hospitalization correction of compliance by age and sex .02

.015 Average hospitalization correction of compliance .01 .005 0

40 50 60 70 80 90 Age

Men Women

Centered MA(3) smoothing Average compliance by age and sex .6 .55 Average compliance by age and sex .5 .45 .4 40 50 60 70 80 90 Age

Men Women

Centered MA(3) smoothing Some sample statistics - once more ALL PATIENTS IN THE SAMPLE

PATIENTS WITH AT LEAST ONE ACE PRESCRIPTION

sum | Freq. Percent Cum. ------+------Only ACE | 40,980 55.90 55.90 Multitherapy| 32,333 44.10 100.00 ------+------Total | 73,313 100.00 Econometric results: the determinants of compliance

stage 1 stage 2 men women men women age1 -0.053 0.085 -0.056 * 0.031 age2 0.100 -0.105 0.114 ** -0.023 age3 -0.061 0.041 -0.073 ** -0.001 bc1020 0.152 0.115 0.055 0.059 bc2030 0.087 0.101 0.040 0.067 ** bc3040 0.035 0.048 0.034 * 0.049 ** bc5060 -0.076 0.049 -0.050 * -0.045 y1998 -1.681 ** -1.791 ** -0.030 ** -0.051 ** y1999 -1.941 ** -2.125 ** -0.052 ** -0.057 ** y2000 -2.130 ** -2.318 ** -0.026 ** -0.041 ** y2001 -2.228 ** -2.418 ** 0.023 ** -0.008 y2002 -2.289 ** -2.502 ** 0.062 ** 0.031 ** confez 0.197 ** 0.178 ** sexmed -0.015 -0.029 ** agemed -0.001 * -0.001 sp -0.418 ** -0.347 ** Constant 2.625 ** 2.655 ** 0.349 ** 0.329 ** Observations 74,093 85,307 53,094 58,230 pseudor2 0.074 0.084 sigma 0.445 0.443 Note: * significant at 10%; ** significant at 5%

Observed vs fitted probability of prescription by age and sex M F .7

.5 ACE Mono Therapy

.3 40 50 60 70 80 90 40 50 60 70 80 90 Age Observ Fitte

Observed vs fitted probability of prescription by age and sex M F .8 Observed vs fitted .7 probability of ACE Multi Therapy

.6 prescription by age and sex .5

40 50 60 70 80 90 40 50 60 70 80 90 Age Observed Fitted Observed vs fitted compliance by age and sex M F .65

.6 Observed vs fitted ACE Mono Therapy .55 compliance by age .5 and sex .45

40 50 60 70 80 90 40 50 60 70 80 90 Age Observed Fitted

Observed vs fitted compliance by age and sex M F .7 .6

ACE Multi Therapy .5 .4 40 50 60 70 80 90 40 50 60 70 80 90 Age Observed Fitted III

The effect of compliance on health outcomes: hospitalization and mortality Fitted probability of hospitalization by compliance and sex FittedM probability F .15 of hospitalization .1 by level of ACE Mono therapy - Cardiovascular DRGs

.05 compliance and

0 sex 0 .2 .4 .6 .8 1 1.2 1.4 1.6 1.8 2 0 .2 .4 .6 .8 1 1.2 1.4 1.6 1.8 2 compliance lower bound/upper bound Fitted Observed

Fitted probability of hospitalization by compliance and sex M F .4 .3 ACE Multi therapy -

.2 Cardiovascular DRGs .1

0 .2 .4 .6 .8 1 1.2 1.4 1.6 1.8 2 0 .2 .4 .6 .8 1 1.2 1.4 1.6 1.8 2 compliance lower bound/upper bound Fitted Observed Fitted probability of hospitalization by compliance and sex M F .06

.05 Fitted probability of

.04 hospitalization by ACE Mono therapy - .03 level of compliance Cardiovascular DRGs

.02 and sex – a closer .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 lookcompliance lower bound/upper bound Fitted Observed

Fitted probability of hospitalization by compliance and sex M F .15

ACE Pluri therapy -

.1 Cardiovascular DRGs .05 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 compliance lower bound/upper bound Fitted Observed Fitted mortality rates by compliance and sex M F .15

.1 Fitted mortality rates by level of compliance and ACE Mono therapy - .05 sex Cardiovascular DRGs 0 0 .2 .4 .6 .8 1 1.2 1.4 1.6 1.8 2 0 .2 .4 .6 .8 1 1.2 1.4 1.6 1.8 2 compliance lower bound/upper bound Fitted Observed

Fitted mortality rates by compliance and sex M F .15

.1 ACE Pluri therapy - Cardiovascular DRGs .05 0

0 .2 .4 .6 .8 1 1.2 1.4 1.6 1.8 2 0 .2 .4 .6 .8 1 1.2 1.4 1.6 1.8 2 compliance lower bound/upper bound Fitted Observed Fitted mortality rates by compliance and sex M F .08

.06 Fitted mortality rates

.04 by level of compliance ACE Mono therapy - and sex – a closer look Cardiovascular DRGs .02

.4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 compliance lower bound/upper bound Fitted Observed

Fitted mortality rates by compliance and sex M F .08

.06 ACE Pluri therapy - Cardiovascular DRGs .04 .02 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 compliance lower bound/upper bound Fitted Observed Fitted probability of hospitalization and fittedComparison mortality rates by compliance of and sex M F

.2 fitted probability.2 of .08 .08 hospitalization and .15 .15 .06 .06 mortality rates ACE Mono therapy -

.1 .1 Cardiovascular DRGs Hospitalization .04 .04 .02 .05 .02 .05 0 .2 .4 .6 .8 1 1.2 1.4 1.6 1.8 2 0 .2 .4 .6 .8 1 1.2 1.4 1.6 1.8 2 compliance Hospitalization Mortality

Fitted probability of hospitalization and fitted mortality rates by compliance and sex M F .1 .1 .2 .2

.08 .08 ACE Pluri therapy - Cardiovascular DRGs .15 .15 .06 .06 Hospitalization .04 .04 .1 .1 .02 .02 0 .2 .4 .6 .8 1 1.2 1.4 1.6 1.8 2 0 .2 .4 .6 .8 1 1.2 1.4 1.6 1.8 2 compliance Hospitalization Mortality Fitted probability of hospitalization and fitted mortality rates by compliance and sex M F .06 .06

.1 Comparison.1 of fitted

probability.05 of .05 .08 .08 hospitalization.04 and .04 ACE Mono therapy -

Hospitalization Cardiovascular DRGs

mortality .03 rates – a .03 .06 .06

closer.02 look .02 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 compliance Hospitalization Mortality

Fitted probability of hospitalization and fitted mortality rates by compliance and sex M F .14 .07 .14 .07

.06 .06 ACE Pluri therapy - .12 .12 Cardiovascular DRGs .05 .05 .1 .1 Hospitalization .04 .04 .08 .03 .08 .03 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 .4 .5 .6 .7 .8 .9 1 1.1 1.2 1.3 compliance Hospitalization Mortality IV

The effect of the prescription ticket on compliance and health outcomes Fitted level of compliance for both compliant and non- compliant groups

1.4 1.3 1.2 1.1 1 .9 .8 .7 .6 .5 .4

7 m7 11 m3 000m7 00m11 01m3 2000m12000m32000m52 2000m920 2001m120 2001m52001 2001m92001m 2002m12002 2002m52002m 2002m92002m11

non-compliant compliant Difference-in-difference coefficient (tc) for different thresholds. -.2 -.25 -.3 -.35 difference-in-difference coefficient (tc) coefficient difference-in-difference -.4 -.45

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 1.1 1.2 threshold Dif in Dif parameters

Variable Parameter Definition Estimates coeff F NC Const α µ1 0.3148 34657.7 C Const+c α +β µ1 0.8838 127251.9 NC Const+t α +α2 µ2 0.5243 23369.1 C Const+c+t+tc α+β1+α2+β2 µ2 0.8568 52666.4 CNC c β1 µµ11− 0.5689 35976.2 CNC c+tc β1+β2 µµ22− 0.3326 4303.7 NC t α2 ∆µ 0.2094 3616.1 C Const+tc α+β2 ∆µ 0.0785 184.2 CNC tc β2 ∆−∆µµ -0.2364 2044.2

Limits of the data set

• The data set does not include economic and socio-demografic characteristics such as: – Education; – Profession; – Sector of employment; – Presence of private insurance; – Personal and/or household income; – Household composition; What can we do to improve the informational content of these data

• Merge these information with other administrative registry available (i.e. fiscal data) • Collect information based on a sample survey Questionner - 1 CAPOFAMIGLIA PRIMO componente SECONDO componente TERZO componente QUARTO componente QUINTO componente 1 – Età ?? Anni ?? Anni ?? Anni ?? Anni ?? Anni ?? Anni ? Maschio ? Maschio ? Maschio ? Maschio ? Maschio ? Maschio 2 - Sesso ? Femmina ? Femmina ? Femmina ? Femmina ? Femmina ? Femmina 3 - Titolo di studio ? Nessuno ? Nessuno ? Nessuno ? Nessuno ? Nessuno ? Nessuno ? Licenza Elementare ? Licenza Elementare ? Licenza Elementare ? Licenza Elementare ? Licenza Elementare ? Licenza Elementare ? Licenza Media ? Licenza Media ? Licenza Media ? Licenza Media ? Licenza Media ? Licenza Media ? Lic/Dipl Scuola Sup. ? Lic/Dipl Scuola Sup. ? Lic/Dipl Scuola Sup. ? Lic/Dipl Scuola Sup. ? Lic/Dipl Scuola Sup. ? Lic/Dipl Scuola Sup. ? Laurea ? Laurea ? Laurea ? Laurea ? Laurea ? Laurea ? Altro:______? Altro:______? Altro:______? Altro:______? Altro:______? Altro:______? Non so ? Non so ? Non so ? Non so ? Non so ? Non so ? Non rispondo ? Non rispondo ? Non rispondo ? Non rispondo ? Non rispondo ? Non rispondo 4 – Condizione ? Occupato ? Occupato ? Occupato ? Occupato ? Occupato ? Occupato professionale ? Pensionato ? Pensionato ? Pensionato ? Pensionato ? Pensionato ? Pensionato ? Disoccupato ? Disoccupato ? Disoccupato ? Disoccupato ? Disoccupato ? Disoccupato ? Casalinga/o ? Casalinga/o ? Casalinga/o ? Casalinga/o ? Casalinga/o ? Casalinga/o ? Studente ? Studente ? Studente ? Studente ? Studente ? Studente ? Inabile al lavoro ? Inabile al lavoro ? Inabile al lavoro ? Inabile al lavoro ? Inabile al lavoro ? Inabile al lavoro ? Altro:______? Altro:______? Altro:______? Altro:______? Altro:______? Altro:______? Non so ? Non so ? Non so ? Non so ? Non so ? Non so ? Non rispondo ? Non rispondo ? Non rispondo ? Non rispondo ? Non rispondo ? Non rispondo 4.1 – Se lavora: ? meno di 1 anno ? meno di 1 anno ? meno di 1 anno ? meno di 1 anno ? meno di 1 anno ? meno di 1 anno Da quanti anni lavora ? 1 anno ? 1 anno ? 1 anno ? 1 anno ? 1 anno ? 1 anno ? 2 – 4 anni ? 2 – 4 anni ? 2 – 4 anni ? 2 – 4 anni ? 2 – 4 anni ? 2 – 4 anni ? 5 – 10 anni ? 5 – 10 anni ? 5 – 10 anni ? 5 – 10 anni ? 5 – 10 anni ? 5 – 10 anni ? più di 10 anni ? più di 10 anni ? più di 10 anni ? più di 10 anni ? più di 10 anni ? più di 10 anni ? Non so ? Non so ? Non so ? Non so ? Non so ? Non so ? Non rispondo ? Non rispondo ? Non rispondo ? Non rispondo ? Non rispondo ? Non rispondo 4.2 – Se in pensione: a) da quanto tempo? ?? ?? ?? ?? ?? ?? b) a che età ha iniziato a lavorare? ?? ?? ?? ?? ?? ?? c) ultimo lavoro svolto ______5 – Settore di ? Agricoltura ? Agricoltura ? Agricoltura ? Agricoltura ? Agricoltura ? Agricoltura occupazione ? Industria ? Industria ? Industria ? Industria ? Industria ? Industria ? Servizi ? Servizi ? Servizi ? Servizi ? Servizi ? Servizi ? Pubblica Amm/ne ? Pubblica Amm/ne ? Pubblica Amm/ne ? Pubblica Amm/ne ? Pubblica Amm/ne ? Pubblica Amm/ne ? Non so ? Non so ? Non so ? Non so ? Non so ? Non so ? Non rispondo ? Non rispondo ? Non rispondo ? Non rispondo ? Non rispondo ? Non rispondo 6 – Tipologia impresa ? Pubblico ? Pubblico ? Pubblico ? Pubblico ? Pubblico ? Pubblico ? Privato ? Privato ? Privato ? Privato ? Privato ? Privato ? Non so ? Non so ? Non so ? Non so ? Non so ? Non so ? Non rispondo ? Non rispondo ? Non rispondo ? Non rispondo ? Non rispondo ? Non rispondo

Questionner – 2

CAPOFAMIGLIA Primo componente Secondo componente Terzo componente Quarto componente Quinto componente 7 – Dimensione ? 0 – 5 ? 0 – 5 ? 0 – 5 ? 0 – 5 ? 0 – 5 ? 0 – 5 dell’impresa ? 6 – 15 ? 6 – 15 ? 6 – 15 ? 6 – 15 ? 6 – 15 ? 6 – 15 ? 15 – 50 ? 15 – 50 ? 15 – 50 ? 15 – 50 ? 15 – 50 ? 15 – 50 ? 50 – 100 ? 50 – 100 ? 50 – 100 ? 50 – 100 ? 50 – 100 ? 50 – 100 ? 100 – 250 ? 100 – 250 ? 100 – 250 ? 100 – 250 ? 100 – 250 ? 100 – 250 ? Oltre 250 ? Oltre 250 ? Oltre 250 ? Oltre 250 ? Oltre 250 ? Oltre 250 ? Non so ? Non so ? Non so ? Non so ? Non so ? Non so ? Non rispondo ? Non rispondo ? Non rispondo ? Non rispondo ? Non rispondo ? Non rispondo 8 – ? Si ? Si ? Si ? Si ? Si ? Si Ha un’assicurazione ? No ? No ? No ? No ? No ? No sanitaria integrativa? ? Non so ? Non so ? Non so ? Non so ? Non so ? Non so ? Non rispondo ? Non rispondo ? Non rispondo ? Non rispondo ? Non rispondo ? Non rispondo 9 – Se ne ha una, chi paga il costo ? il datore di lavoro ? il datore di lavoro ? il datore di lavoro ? il datore di lavoro ? il datore di lavoro ? il datore di lavoro dell’assicurazione? ? pagata personalmente ? pagata personalmente ? pagata personalmente ? pagata personalmente ? pagata personalmente ? pagata personalmente 10 – ? 0-1000 Euro ? 0-1000 Euro ? 0-1000 Euro ? 0-1000 Euro ? 0-1000 Euro ? 0-1000 Euro Quale è il suo reddito ? 1000-2000 Euro ? 1000-2000 Euro ? 1000-2000 Euro ? 1000-2000 Euro ? 1000-2000 Euro ? 1000-2000 Euro mensile netto e quello ? 2000-3000 Euro ? 2000-3000 Euro ? 2000-3000 Euro ? 2000-3000 Euro ? 2000-3000 Euro ? 2000-3000 Euro dei suoi familiari? ? più di 3000 Euro ? più di 3000 Euro ? più di 3000 Euro ? più di 3000 Euro ? più di 3000 Euro ? più di 3000 Euro ? Non so ? Non so ? Non so ? Non so ? Non so ? Non so ? Non rispondo ? Non rispondo ? Non rispondo ? Non rispondo ? Non rispondo ? Non rispondo 11 – ? Ottimo ? Ottimo ? Ottimo ? Ottimo ? Ottimo ? Ottimo Come considera il suo ? Buono ? Buono ? Buono ? Buono ? Buono ? Buono attuale stato di salute e ? Discreto ? Discreto ? Discreto ? Discreto ? Discreto ? Discreto quello dei suoi ? Cattivo ? Cattivo ? Cattivo ? Cattivo ? Cattivo ? Cattivo familiari? ? Pessimo ? Pessimo ? Pessimo ? Pessimo ? Pessimo ? Pessimo 12 – Soffre di malattie ? Si ? Si ? Si ? Si ? Si ? Si croniche? ? No ? No ? No ? No ? No ? No ? Non so ? Non so ? Non so ? Non so ? Non so ? Non so ? Non rispondo ? Non rispondo ? Non rispondo ? Non rispondo ? Non rispondo ? Non rispondo 13 – Quali? ? ______? ______? ______? ______? ______? ______? ______? ______? ______? ______? ______? ______? ______? ______? ______? ______? ______? ______? ______? ______? ______? ______? ______? ______

14 – ? Si ? Si ? Si ? Si ? Si ? Si Ha mai cambiato il suo ? No ? No ? No ? No ? No ? No medico di famiglia? ? Non so ? Non so ? Non so ? Non so ? Non so ? Non so ? Non rispondo ? Non rispondo ? Non rispondo ? Non rispondo ? Non rispondo ? Non rispondo 15 – ? Ottimo ? Ottimo ? Ottimo ? Ottimo ? Ottimo ? Ottimo Come valuta i servizi ? Buono ? Buono ? Buono ? Buono ? Buono ? Buono offerti dalla sua ASL? ? Discreto ? Discreto ? Discreto ? Discreto ? Discreto ? Discreto ? Cattivo ? Cattivo ? Cattivo ? Cattivo ? Cattivo ? Cattivo ? Pessimo ? Pessimo ? Pessimo ? Pessimo ? Pessimo ? Pessimo