Impact of quality on cancer

estimators estimators

EPAAC WP9 Satellite Meeting

Ispra, 22-23 January 2014

� Cancer is one of the major public health problems in

Europe.

� Cancer surveillance built around population-based

cancer registries is an essential element of any cancer

3.45 million new cases

1.75 million deaths

€126 billion

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cancer registries is an essential element of any cancer

control programme.

� The reliability and utility of the information provided by

CRs depends on the quality of their data.

•Completeness Validity Comparability Timeliness

The extent to

which all the % cases with a

given

Comparability of

statistics generated The extent to

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incident cancers

occurring in the

population are

included in the CR

given

characteristic

which truly have

the attribute

for different

population groups

and over time is

essential to their

meaningful

interpretation

which data are

complete and

accurate

Bray F, Parkin DM. Evaluation of data quality in the cancer registry: Principles and methods. Part I: Comparability, validity and timeliness. EJC.2009; 45: 747-755

Parkin DM, Bray F. Evaluation of data quality in the cancer registry: Principles and methods Part II. Completeness. EJC. 2009; 45: 756-764

•Completeness

Semi-quantitative methods Quantitative methods

1. Historic data methods:

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1. Independent case ascertainment

2. Capture–recapture methods

3. Death certificate methods

� DCN/M:I method

� The ‘flow’ method

� Stability of incidence rates over time

� Comparison of incidence rates in

different populations

� Shape of age-specific curves

� Incidence of childhood cancers

2. Mortality / incidence ratio (M:I)

3. # of sources/notifications per case

4. Histological verification (HV)

•Validity

1. Re-abstracting and recoding samples of cases

2. Diagnostic criteria methods:

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� % HV

� % Death certificate only (DCO)

3. Missing or incomplete information � stage, follow-up,

etc.

4. Internal consistency methods

A systematic evaluation of data quality has been performed by IARC

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MV(%): % of cases microscopically verified

DCO (%): % of cases from a death certificate only

UB (%): % of unknown basis of diagnosis

MI (%): the ratio between the number of deaths and the number of cases registered during the same period

Source: Cancer Incidence in Five Continents, Volume IX

Some specific studies have been carried out to assess different

dimensions of data quality in CRs

M:I (2000–2004) versus 1-survival (based on cases in

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(based on cases in 1996–2000)

Completeness indicators

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DCN: death certificate notified

DCO: death certificate only

RS: cumulative 5-year relativesurvival

SE: standard error.

b: Weights from the International Cancer Survival

Standards were used for standardisation

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Pearson correlation coefficient = 0.37 (p<0.001)

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0% 50% 100%

Corpus Uteri (2%)

Bladder

Larynx

Non Hodgkin (2%)

Kidney (1%)

Vagina and vulva …

Colon

Relative survival

Internal consistency checks ���� survival impact

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Colon

Colon Rectum

All Sites

Nasal cavities …

Tongue

Ovary (1%)

Plasma cell

Stomach

Gallbladder

Lung

Pancreas

Correct

Errors/

Warnings

Source: M. Sant, Personal communication

Incomplete follow-up ���� survival impact

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� A systematic evaluation of data quality has been performed

by IARC to ensure comparability, completeness and validity

of CRs data.

� Several local studies have been carried out to assess

different dimensions of data quality in CRs.

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different dimensions of data quality in CRs.

� A few papers have analysed the impact of quality indicators

on cancer estimators (incidence and survival). Most of them

using %DCO and/or DCIs as quality indicators.

1. Data quality has an impact on cancer incidence and

survival.

2. In addition to the %DCO or %MV, other indicators such

as those related to internal consistency or incomplete

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information, can be obtained to assess the quality of

cancer data.

3. The identification of quality indicators and their cut points

which have a significant impact on survival and incidence

would provide the CRs useful information to manage their

efforts in improving data quality.

� To define reliable, standard and common quality

indicators for evaluating data quality in European

population-based cancer registries.

Working Group on Cancer Data Quality Checks

� To identify the quality indicator cut points which have a

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� To identify the quality indicator cut points which have a

significant impact on incidence and survival.

To estimate, on the basis of real data, the direction

and magnitude of incidence and survival bias

associated with quality indicators