Impact of quality on cancer estimators · Impact of quality on cancer estimators EPAAC WP9...
Transcript of Impact of quality on cancer estimators · Impact of quality on cancer estimators EPAAC WP9...
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