The Italian case: methods and case-studies

Authors:

Silvia Francisci (ISS)

Anna Gigli (IRPPS-CNR)

Maura Mezzetti (Università di Roma Tor Vergata)

Francesco Giusti (Tuscany Cancer Registry)

Stefano Guzzinati (Veneto Cancer Registry)

Overview

Description of the situation in Italy

Aims and challenges

Methods for costs estimation

Data sources: needed Vs available

Two case-studies

Open issues

Background

Prevalent cases (in 2008): 1.8 mln

Total health expenditure (2008): €112 bln (7.1% of GDP)

Expenditure dedicated to cancer: €7.5 bln

(6.7% of health expenditure)

Growth trends both in terms of costs (more expensive treatments) and cases (population ageing, improving survival)

(Sources: ITAPREVAL, ISTAT, WHO)

Rationale

Develop a methodology suitable to the Italian context to:

• estimate present and future cancer costs

• evaluate different scenarios (screening, etc.)

• plan resources to be allocated to oncology

Major challenges• Create a dataset by merging information from

different sources

• Adapt existing methods and develop new ones

Methods Cancer survivors at current time T are assumed to be

distributed according to three disease phases: initial 0, continuing 1, terminal 2.

The following steps are required to derive the cancer burden profile, according to disease phases:

• Estimate and decompose observed survivors by phases

• Estimate and decompose unobserved survivors by phases

• Estimate the distribution of costs by phases

• Combine survivors (prevalent cases) and costs by phases

Decomposition of prevalent cases

Initial phase

Continuing phase

Terminal phase

NobsT,0

NobsT,1

+ NuT,1

+ LT,1

NobsT,2

+ NuT,2

+ LT,2

Lost to follow-up

Before registration

NT =

Observed prevalent cases

Markov process

Initial → Continuing → Terminal

Transition probabilities are estimated for the last year of available data (T-K) and then used to update Nobs from (T-K) to T.

Markov process

Initial 0 → Cont 1 → Term 2

Transition probabilities 0 1 2 0 − p

01 p

02

1 − p11

p12

2 − − −

p01

(t)= Prob(yt= 1 | y

t-1= 0)

Nobst,0

is estimated from an ad-hoc incidence function

Nobst,1

= Nobst-1,0

x p01

(t) + Nobst-1,1

x p11

(t)

Nobst,2

= Nobst-1,0

x p02

(t) + Nobst-1,1

x p12

(t)

These equations are reiterated from T- K to the current time T

Unobserved prevalent cases: estimation

Patients diagnosed before the registry activity and still alive at the current time t, are not directly observed and are estimated using the completeness index R, specific by tumour site, age, sex and length of CR (all these variables are included in vector x):

where Rx= completeness index but

Nux = Nu

1, x + Nu

2, x => decomposition?

1

1

x

obsx

ux R

N=N

Unobserved prevalent cases: decomposition strategy

• Hp 1: Nu1

and Nu2 same proportion as Nobs

1

and Nobs2

of the first available diagnosis

cohort unobserved have same survival as first observed cohort =>

need to isolate cohort

• Hp 2: Nu1

and Nu2 same proportion as cured

and non-cured cases (estimated from survival) proportion of cured estimated from more recent cases =>

overestimate of intermediate patients

Unobserved prevalent cases: decomposition strategy

• Hp 3: Nu1

and Nu2 same proportion as Nobs

1

and Nobs2 wrt age at prevalence

Nu made of older patients diagnosed when they were younger

(i.e. better prognosis) => overestimation of terminal patients

• Hp 4: Nu1

and Nu2 same proportion as Nobs

1

and Nobs2

wrt age at diagnosis Nu made of patients diagnosed in the past (i.e. worse

therapies) => underestimation of terminal patients

Which is the preferable hypothesis?

Lost of follow up

• Survival and distribution into disease phases of cases lost to follow-up is needed in order to adjust the observed prevalent cases

• Assume they survive and decompose like observed cases (homogeneously with respect to age, sex,…)

LT,1=LT X {NobsT,1/(Nobs

T,1+NobsT,2)}

LT,2=LT X {NobsT,2/(Nobs

T,1+NobsT,2)}

Cost estimate and decomposition

Initial Phase

Continuing Phase

Terminal Phase

CT,0

CT,1

CT,2

CT =

• The cost profile is a vector, with three components, according to the disease phases.

• Each component is derived by averaging the cost of cancer patients observed in a given phase of the disease.

• The average is specific by x = (cancer site, age, stage,...)

Estimate total current cost

The total current cost for a specific cancer is derived by multiplying prevalent cases by corresponding cost wrt disease phase:

Total CT,x

= NobsT,0,x

x CT,0,x

+

(NobsT,1,x

+ NUT,1,x

+ LT,1,x

) x CT,1,x

+

(NobsT,2,x

+ NUT,2,j

+ LT,2,x

) x CT,2,x

and then summing up by x CT, total

Data needed

Two different sources need to be combined and used:

Cancer Registries• Incidence and

follow-up data• Surveillance source• Demographic and

clinical information

Regional Health System • Hospital Discharge

Cards (HDC/SDO)• Administrative

source• Clinical and cost

information (based on DRG system)

Data Available: the Italian Cancer Registries

No homogeneous life span: 30 registries from 1976 to 2010

Source: AIRTUM

19 mln residents in CR's areas (34% population)

Data Available: the Italian Cancer Registries

No sample design

North 50%Centre 25%

South 18%

Source: AIRTUM

Data Available: Hospital Discharge Card

• Within the NHS every hospital must fill the HDCs, that will be centrally collected at regional level

• HDC contains demographic, clinical and cost related information for each individual hospital admission and discharge

• HDCs allow to identify each single patient disease history from first diagnosis to possible recovery or death.

Regionalization

• National Ministry of Health supervises and sets the minimum reimbursement price

• Regional independent public health systems (21). Each of them provides care to residents and sets the final reimbursement to be given to hospitals

Two case-studies

• Two cancer registries (Padua and Florence and Prato) have been analyzed

• Major data issues (availability and completeness of information, record linkage) will be presented for colorectal cancer patients in Veneto and Tuscany

Data descriptionCancer Registries:

Padua and Florence-Prato (high quality data)

Cancer site:

Colorectal cases (ICD-X C18-21)

Information collected:

site, morphology, stage, date of diagnosis, date of last follow up, vital status

Padua Local Health Unit: 380,000 inhabitants

Florence and Prato provinces: 1,200,000 inhabitants

Hospital discharges

Ordinary and day hospital (DH) discharges with information about date of discharge, diagnosis, procedures, DRG code

In Veneto CR 95% of colorectal incident cases in 1990-2005 have at least 1 hospital discharge with a diagnosis of tumour

Record linkage (RL)Deterministic RL of incident cases with Hospital

discharges by unique identified number

Padua:

- RL of 609 colorectal incident cases in 2000-2001 with 7,6 million of regional hospital discharges (H) for 2000-2006 period

5,195 records for 607 incident cases

Florence-Prato:

11,121 records for 2,115 colorectal incident cases in 2000-2001

APPROPRIATE DISCHARGES:

Every discharge is classified according a list of ICD9-CM codes about

disease and injuries (for example 153=malignant neoplasm of colon, 154=malignant neoplasm of rectum, rectosigmoid junction and anus, V58.1 chemotherapy)

procedures (for example 45.23 colonoscopy, 99.25 injection or infusion of cancer chemotherapeutic substance, 45.73 Open and other right hemicolectomy)

Padua: 74% of total discharges linked (3,828 records) is appropriate

Florence-Prato: 69% (7,715 records) is appropriate

Major NON-APPROPRIATE Discharges

Diseases Of The Circulatory System – Padua 23%, Florence-Prato 22%

Diseases Of The Digestive System – Padua 13%, Florence-Prato 15%

Other neoplasm different than colorectal – Padua 10%, Florence-Prato 12%

Distribution of subjects by phase of care

Initial phase (first 12 months after diagnosis)(date of discharge – date of diagnosis) < 1 year

Continuing (intermediate) phase

Final (terminal) phase (last year of life)(date of death – date of discharge) < 1 year

12/9%

37/35%1/1%

18/23%

13/15%

19/16%

1/1%

Complete path: Padua 44%Florence-Prato 47%

Padua/Florence-Prato

Distribution over time (2000-2006) of hospital expenditure (€) of colorectal cancer patients

diagnosed in years 2000-2001 for appropriate dischargesYear Discharges Expenditure

(€)N. of patients

in INITIAL phase

N. of patients in CONTINUING

phase

N. of patients in FINAL

phase

2000 721 2,721,737 231 36

2001 1438 4,836,668 349 49 118

2002 730 1,918,096 102 79 83

2003 397 867,395 75 622004 239 623,272 45 352005 187 464,572 42 272006 116 221,025 19 16Total 3828 11,652,765 682 309 377

Padua

Year Discharges Expenditure (€) N. of patients in INITIAL

phase

N. of patients in CONTINUING

phase

N. of patients in FINAL phase

2000 1862 10,022,688 776 236

2001 2729 14,684,233 970 137 368

2002 1339 6,341,692 272 265 249

2003 713 2,526,120 208 170

2004 486 1,750,252 130 108

2005 357 1,281,530 100 83

2006 229 817,016 64 54

Total 7715 37,423,531 2,001 921 1,268

Florence-Prato

Average patients expenditure (€), Padua

66% 84% 79% 73% 64% 59% 49%

% appropriate discharge by year

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

2000 2001 2002 2003 2004 2005 2006

€ Ordinary

DH

Average expenditure (€) by phase of care during the period 2000-’06 for the 2000-’01

incident cases

*every subject could contribute to more than one phase

Phase of care Discharges Subjects* Average expenditure

by subject (€)Initial (1 year since diagnosis) 1.939 488 13.107

Continuing 746 194 9.136

Final (1 year before death) 1.143 265 13.147

Total 3.828 947 12.305

Phase of care Discharges Subjects* Average expenditure by

subject (€)Initial (1 year since diagnosis) 3.722 1.546 12.847

Continuing 1.527 559 10.336Final (1 year before death) 2.466 977 12.062

Total 7.715 3.082 12.143

Padua

Florence-Prato

Average expenditure (€) by phase of care during the period 2000-2006 for the 2000-2001 incident cases by type

of discharge, Padua

*every subject could contribute to more than one phase

0

2.000

4.000

6.000

8.000

10.000

12.000

Initial Continuing Final

€ Ordinary

DH

Average expenditure (€) by stage at diagnosis (Dukes),

Padua

Distribution of subjects

13,323

17,314

22,652

25,64523,303

14,062

0

5,000

10,000

15,000

20,000

25,000

30,000

A B C1 C2 D missing

17% 20% 18% 8% 22% 14%

Average expenditure (€) by phase of care and age class,

Padua

0

5000

10000

15000

20000

25000

29-49 50-59 60-69 70-79 80-95

Initial

Continuing

Final

Average expenditure (€) by stage and age class,

Padua

05,000

10,00015,00020,00025,00030,00035,000

Loca

l

Regi

onal

/Dis

tant

Loca

l

Regi

on/D

ista

nt

Loca

l

Regi

onal

/Dis

tant

Loca

l

Regi

onal

/Dis

tant

Loca

l

Regi

onal

/Dis

tant

29-49 50-59 60-69 70-79 80-95

Average expenditure (€) by phase, age class and stage,

Padua

0

5000

10000

15000

20000

25000

30000

35000

40000

local reg/dist local reg/dist local reg/dist local reg/dist local reg/dist

<49 50- 60- 70- 80+

INITIAL CONTINUING FINAL

Average expenditure (€) by type of DRG and type of discharges, Padua

0

2,000

4,000

6,000

8,000

10,000

12,000

chemotherapy surgery medical not chemo

ordinary discharges Day Hospital

n.a.

n=282n=567

n=15

n=344

n=208

Average expenditure (€) by phase and vital status, Padua

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

INITIAL PHASE CONTINUING PHASE

FINAL PHASE

Alive

Deceased

Open issues

Projections: implementation, validation

Scenarios: screening, primary prevention, population ageing

Uncertainty: how to estimate

Data collection: how to improve

Integration of other data sources (e.g. drugs, out-of-hospital care)