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 N obs T,0 N obs T,1 + N u T,1 + L T,1 N obs T,2 + N u T,2 + L T,2 Lost to follow-up Before registration N T =

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 N obs from (T-K) to T.

Markov process Initial 0 → Cont 1 → Term 2 Transition probabilities − p 01 p 02 1 − p 11 p 12 2 − − − p 01 (t)= Prob(y t = 1 | y t-1 = 0) N obs t,0 is estimated from an ad-hoc incidence function N obs t,1 = N obs t-1,0 x p 01 (t) + N obs t-1,1 x p 11 (t) N obs t,2 = N obs t-1,0 x p 02 (t) + N obs t-1,1 x p 12 (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 R x = completeness index but N u x = N u 1, x + N u 2, x => decomposition?

Unobserved prevalent cases: decomposition strategy Hp 1: N u 1 and N u 2 same proportion as N obs 1 and N obs 2 of the first available diagnosis cohort unobserved have same survival as first observed cohort => need to isolate cohort Hp 2: N u 1 and N u 2 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: N u 1 and N u 2 same proportion as N obs 1 and N obs 2 wrt age at prevalence N u made of older patients diagnosed when they were younger (i.e. better prognosis) => overestimation of terminal patients Hp 4: N u 1 and N u 2 same proportion as N obs 1 and N obs 2 wrt age at diagnosis N u 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,…) L T,1 =L T X {N obs T,1 /(N obs T,1 +N obs T,2 )} L T,2 =L T X {N obs T,2 /(N obs T,1 +N obs T,2 )}

Cost estimate and decomposition Initial Phase Continuing Phase Terminal Phase C T,0 C T,1 C T,2 C T = 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 C T,x = N obs T,0,x x C T,0,x + (N obs T,1,x + N U T,1,x + L T,1,x ) x C T,1,x + (N obs T,2,x + N U T,2,j + L T,2,x ) x C T,2,x and then summing up by x  C T, 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 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 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 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 with 7,6 million of regional hospital discharges (H) for period  5,195 records for 607 incident cases Florence-Prato:  11,121 records for 2,115 colorectal incident cases in

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 colonoscopy, injection or infusion of cancer chemotherapeutic substance, 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%1/1% 18/23% 13/15% 19/16% 1/1%1/1% Complete path: Padua 44% Florence-Prato 47% Padua/Florence-Prato

Distribution over time ( ) of hospital expenditure (€) of colorectal cancer patients diagnosed in years for appropriate discharges Padua Florence-Prato

Average patients expenditure (€), Padua 66%84%79%73%64%59%49% % appropriate discharge by year

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 Padua Florence-Prato

Average expenditure (€) by phase of care during the period for the incident cases by type of discharge, Padua *every subject could contribute to more than one phase

Average expenditure (€) by stage at diagnosis (Dukes), Padua Distribution of subjects 17%20%18%8%22%14%

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

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

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

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

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

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)

Dataset: OECD Health Data Total expenditure on health, % of gross domestic product Total health expenditure per capita, US$ PPP Public expenditure on health, % total expenditure on health Public health expenditure per capita, US$ PPP Out-of-pocket expenditure on health, % of total expenditure on health Out-of-pocket expenditure on health, US$ PPP Pharmaceutical expenditure, % total expenditure on health Pharmaceutical expenditure per capita, US$ PPP