Evaluating Data Quality in the Cancer Registry
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Transcript of Evaluating Data Quality in the Cancer Registry
Evaluating data quality in the Cancer Registry
Freddie Bray
Deputy Head, Section of Cancer Information
IARC
Dharmais Cancer Hospital · Jakarta · November 2010
Cancer Incidence in Five Continents: Vol 1 (1966) Introduction
Reliable cancer registries:
• Those able to amass information (diagnostic and personal) on virtually all cases of cancer among patients genuinely resident within a defined catchment area during a prescribed period of time;
• able to supplement this with death certificate data for patients not seen in hospital
• having an adequate system for eliminating duplicate entries for the same person
• and good population data - by sex and by 5-year age groups and, if relevant, by race/language
Data quality and its evaluation
• Evaluation of data quality in the cancer registry: Principles and methods.
• Part I: Comparability, validity and timeliness (Bray & Parkin)• Part II: Completeness (Parkin & Bray)• Eur J Cancer (2009) 45: 747-77, 756-64• Update of 1994 IARC Technical Report• Application to Cancer Registry of Norway:• Larsen et al (2009) Eur J Cancer 45:1218-31
• Standards and guidelines for cancer registration in Europe: The ENCR recommendations, vol 1. Lyon: IARC (2003).
Data quality and its evaluation
Conclusion:
“This review indicated that the routines in place at the Cancer Registry of Norway yield comparable data that can be considered reasonably accurate, close-to-completion and timely, and serves as a justification for our policy of reporting annual incidence one year after the close of registration.”
Data quality and its evaluation
Four “classical” dimensions of quality:
• Comparability
• Validity
• Completeness
• Timeliness
1. Comparability2. Completeness3. Validity4. Timeliness
Special Issue: Data Quality at the Cancer Registry of Norway
http://www.kreftregisteret.no
Data quality and its evaluation
Four “classical” dimensions of quality:
• Comparability
• Validity
• Completeness
• Timeliness
Data quality and its evaluation
Comparability
• Ensuring comparable standards of reporting and classification across registries and within registries over time;
• Reporting of routines, standards and practices in place and, especially, dates in changes of practice;
• Where standards within a registry differ from “accepted” practice, requirement to provide means of conversion from one to other.
Data quality and its evaluation
Comparability
• Classification and coding systems
• Definition of incidence date
• Handling of multiple primaries
• Incidental diagnosis (basis)
• Screening and testing
• Imaging
• Autopsy diagnosis (basis)
• Handling of death certificate information
Data quality and its evaluation
Bray and Parkin (2009) EJC 45:747
Data quality and its evaluation
Larsen et al (2009) EJC 45:1218
Note: Rates are age-adjusted to the 1970 U.S. standard. Rates from 1973-1987 are
based on data from the 9 standard registries. Data from San Jose and Los
Angeles are included in the rate calculation for 1988-1995.
Data quality and its evaluation
Four “classical” dimensions of quality:
• Comparability
• Validity
• Completeness
• Timeliness
Data quality and its evaluation
Validity
• Accuracy of reporting
• Do cases reported to have a specific characteristic truly have that characteristic
• Depends on
• Accuracy of source information
• Registry “skill” in abstracting, coding and reporting
Data quality and its evaluation
Validity – assessment procedures:
• Diagnostic criteria methods
• Histologic/microscopic verification (% HV/MV)
• Death certificate only (% DCO)
• Missing information (e.g. % PSU)
• Internal consistency checks (QC)
• Re-abstracting and recoding (QA)
Data quality and its evaluation
Microscopic verification (% MV)
• Varies by cancer site (and age);
• Depends on pathology/cytology service
• 100% not always best;
• Are statistical tests to compare % MV of a registry against standard, other registries or itself at different time.
Data quality and its evaluation
Larsen et al (2009) EJC 45:1218
Data quality and its evaluation
Death certificate only (% DCO)• Varies by cancer site (and age);
• Depends on clinical service;
• Associated with reduction in validity (especially site and diagnosis date) and increase in missing information;
• Other validity issues around “Death certificate notified (DCN)” or “Death certificate initiatied (DCI)”.
Data quality and its evaluation
Larsen et al (2009) EJC 45:1218
Data quality and its evaluation
Bray and Parkin (2009) EJC 45:747
Data quality and its evaluation
Missing information (e.g. % PSU)• Varies by cancer site and age;
• Varies by data item (e.g. stage);
• Depends on both registry and clinical record practice;
• Care required in codes used to define “primary site uncertain” (not just “Unknown primary site ICD-10 C80);
• Low % MV associated with high “PSU”.
Data quality and its evaluation
Larsen et al (2009) EJC 45:1218
Data quality and its evaluation
Internal consistency checks (QC)• Invalid (or unlikely) codes or combinations of codes
or sequences of dates;
• Can be operationalised within software (including during data entry);
• IARC has developed such tools (IARC-CHECK) within IARCcrgTools which can be downloaded from IACR website: www.iacr.com.fr
• Checks applied should be reported along with results.
Data quality and its evaluation
Re-abstracting and recoding (QA)
• Expensive and time consuming;
• Can be operationalised on sample basis;
• Can make use of other ad-hoc studies;
• Requires approaches to correct identified problems prospectively and retrospectively.
Data quality and its evaluation
Four “classical” dimensions of quality:
• Comparability
• Validity
• Completeness
• Timeliness
Data quality and its evaluation
Completeness:
• The extent to which all of the incident cancers occurring in a target population are included in the registry database;
• Key defining criterion for population basis to registration;
• No perfect assessment tool
Data quality and its evaluation
Completeness assessment:
• Methods based on comparisons and inspection;
• Methods based on independent assessment.
• Ad-hoc planned or incidental studies
• Use of multiple (independent) sources of notification especially death certificates
Data quality and its evaluation
Completeness assessment:
• Methods based on comparisons and inspection;
• Compare rates over time and/or with similar populations;
• Inspect age-incidence curves;
• Stability of childhood cancer rates.
Data quality and its evaluation
Completeness assessment:• Methods based on independent assessment
• Ad-hoc planned or incidental studies(comments as for validity)
• M/I ratios
• Capture-recapture methods
• The DC and M/I methodAjiki et al (1998) Nippon KEZ 45:1011
• The Flow method (also measures timeliness)Bullard et al (2000) B.J.Cancer 82:111
Read Parkin & Bray
(2009) for details
Data quality and its evaluation
Completeness assessment:• M/I ratios;• Number of incident cases during defined time period;• Number of deaths during the same time period;• Assumption that mortality data from a source
independent of cancer registration;• Should analyse by cancer site and by age group;• Absolute values depend on survival rates and quality
of both registration and death certification;• Not robust to (usually rare) short-term changes in
incidence or survival.
Data quality and its evaluation
Larsen et al (2009) EJC 45:1218
Data quality and its evaluation
Four “classical” dimensions of quality:
• Comparability
• Validity
• Completeness
• Timeliness
Data quality and its evaluation
Timeliness:
• Speed with which registry can collect, process and make available data at a given standard of completeness and quality;
• Often pressure to increase timeliness at expense of other quality indicators;
• Some registries (e.g. SEER) publish at a given time point and make estimates of under reporting;
• 12-24 months after year end represents current “standard”.
Data quality indicators CI5 vol. 9Breast cancer (f)
Registry No. MV% DCO% M/I%
Brazil
Sao Paulo
22598 82.2 4.6 22.8
SEER (14) 237378 98.5 0.6 21.3
Norway 12521 98.4 0.3 29.4
UK
Scotland
17137 96.4 0.3 32.9
Japan
Osaka
11103 90.1 2.5 30.5
Data quality indicators CI5 vol. 9Lung cancer (m)
Registry No. MV% DCO% M/I%
Brazil
Sao Paulo
6525 66.9 13.8 72.8
SEER (14) 123409 89.8 1.8 80.7
Norway 6516 87.4 1.0 88.6
UK
Scotland
12969 74.9 0.9 88.3
Japan
Osaka
16759 73.1 19.3 83.7
Data quality and its evaluation
• Cancer registration is a worldwide activity and leads the way in global surveillance for non communicable diseases;
• The benefit of population based registration to cancer control programs and to epidemiological research can be realised only to the extent that data are of a comparable, high quality standard;
• Reporting on data quality in a registry is as important as reporting analyses of the data.