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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

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The Italian case: methods and case-studies


Silvia Francisci (ISS)

Anna Gigli (IRPPS-CNR)

Maura Mezzetti (Università di Roma Tor Vergata)

Francesco Giusti (Tuscany Cancer Registry)

Stefano Guzzinati (Veneto Cancer Registry)



  • Description of the situation in Italy
  • Aims and challenges
  • Methods for costs estimation
  • Data sources: needed Vs available
  • Two case-studies
  • Open issues


  • 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)




  • 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


  • 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

NobsT,1+ NuT,1+ LT,1

NT =

Continuing phase

NobsT,2+ NuT,2+ LT,2

Terminal phase

Before registration

Lost to follow-up


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

Transition probabilities

0 1 2

0 − p01 p02

1 − p11 p12

2 − − −

Initial 0 → Cont 1 → Term 2

p01(t)= Prob(yt= 1 | yt-1= 0)

Nobst,0 is estimated from an ad-hoc incidence function

Nobst,1 = Nobst-1,0x p01(t) + Nobst-1,1 x p11(t)

Nobst,2 = Nobst-1,0 x p02(t) + Nobst-1,1x 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


Nux= Nu1, x+ Nu2, x=> decomposition?


Unobserved prevalent cases: decomposition strategy

  • Hp 1: Nu1 and Nu2 same proportion as Nobs1 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 Nobs1 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 Nobs1 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/(NobsT,1+NobsT,2)}
  • LT,2=LT X {NobsT,2/(NobsT,1+NobsT,2)}

Cost estimate and decomposition

  • 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,...)







CT =





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:

  • Regional Health System
    • Hospital Discharge Cards(HDC/SDO)
    • Administrative source
    • Clinical and cost information(based on DRG system)
  • Cancer Registries
    • Incidence and follow-up data
    • Surveillance


    • Demographic and clinical information

Data Available:

the Italian Cancer Registries

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

No homogeneous life span: 30 registries from 1976 to 2010

Source: AIRTUM


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.


  • 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 description
Data description
  • Cancer 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
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
Record linkage (RL)
  • Deterministic RL of incident cases with Hospital discharges by unique identified number


  • 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


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



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 – Padua13%, Florence-Prato15%

Other neoplasm different than colorectal – Padua10%, Florence-Prato12%

distribution of subjects by phase of care
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







Complete path:

Padua 44%

Florence-Prato 47%


Final (terminal) phase (last year of life)

(date of death – date of discharge) < 1 year


Distribution over time (2000-2006) of hospital expenditure (€) of colorectal cancer patients diagnosed in years 2000-2001 for appropriate discharges



average patients expenditure padua
Average patients expenditure (€), Padua

% appropriate discharge by year

average expenditure by phase of care during the period 2000 06 for the 2000 01 incident cases
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

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


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)