This study aimed to validate methods for identifying cases of invasive breast cancer using administrative health data instead of cancer registry data. The study compared cases identified in hospital, prescription, medical services, and survey data to a cancer registry as the gold standard. The study found some individual data sources like hospital diagnoses had high predictive value and sensitivity. Combining data sources improved identification, with breast surgery plus radiotherapy or prescriptions identifying 92% of registry cases with 97% predictive value. The study concluded administrative data can accurately identify breast cancer cases for research when registry data is unavailable.

1. Ascertaining cases of invasive breast
cancer in the 45 and Up Study:
a validation study.
Kemp A, Preen DB, Saunders C, Holman CDJ, Bulsara M,
Rogers K, Roughead EE.

2. Background
• Routinely-collected and self-reported health data are
increasingly used to identify health status and service use
• State-based cancer registries are considered the ‘gold
standard’ for identifying breast cancer cases for research
purposes
o However researchers conducting survey-based studies or
working with other datasets (e.g. hospital data) may need to
identify cases without linkage to a cancer registry
o ‘Temporary’ access problems with cancer registrations in
New South Wales (NSW)

3. Background continued
• Larger program of work examining use of prescription
hormone therapies for invasive breast cancer in Australian
practice (e.g. tamoxifen)
• Planned to identify cases of invasive breast cancer through
the NSW Cancer Registry
o Power calculations based on cases diagnosed 2003-2009
• We received all the datasets needed for the study except
Cancer Registry records for 2009
• Can we ascertain 2009 cases of invasive
breast cancer using information from the
other available datasets?

4. Objective
• To determine the accuracy of hospital and outpatient
services, prescription claims, and self-report for breast
cancer in identifying cases of invasive breast cancer on the
NSW Cancer Registry

5. Data sources
• NSW Cancer Registry (gold standard)
o date of diagnosis for all invasive breast cancers in NSW
between 1 July 2004 to 31st December 2008
• 45 and Up Study baseline data
o self-reported demographic and clinical information
• NSW Admitted Patients Data Collection
o hospital separations for all public and private hospitals
• Pharmaceutical Benefits Scheme
o claims for government-subsidised dispensed prescription
medicines
• Medicare Benefits Schedule
o claims for government-subsidised outpatient procedures
and procedures in private hospitals

6. Breast cancer ‘flags’
• Hospital diagnosis of invasive breast cancer
o ≤ 6 months of diagnosis
• Breast cancer surgery
o ≤12 months of diagnosis
o mastectomy or lumpectomy
• Prescriptions dispensed
o ≤18 months of diagnosis
o tamoxifen, toremifene, anastrazole, exemestane, letrozole,
goserelin, trastubumab, lapatinib
• Breast radiotherapy
o ≤18 months of diagnosis
• Self-reported diagnosis breast cancer and age at diagnosis
o within 12 months of date of diagnosis
o analysis restricted to self-reports before January 2006

7. Analyses
• Cases of invasive breast cancer recorded on the Cancer
Registry during the study period were compared with flagged
(suspected) cases
• Comparison included
o flags from individual datasets (e.g. hospital diagnosis)
o clinically meaningful combinations of flags (e.g. hospital
diagnosis and a dispensed medicine for breast cancer)
• For each flag/s we calculated:
o positive predictive value (PPV)
o sensitivity
o Specificity
• We sought flags with high PPV (>90%)
and within that, the highest specificity

8. Results
• Of 143,010 women in the 45 and Up Study, 2661 (1.9%) had
a recorded invasive breast cancer on the Cancer Registry
during the study period
o 681 occurred between 1 July 2004 and 31st December
2005 (this subgroup was compared against self-reported
breast cancer)

9. Results from individual datasets
PPV Sensitivity Specificity
45 and Up Study baseline survey
Self-reported diagnosis 40.9% 73.0% 99.5%
(12 month window)
Hospital data
Inpatient primary diagnosis 80.3% 85.2% 99.7%
Lumpectomy 99.3% 61.3% 99.9%
Mastectomy 99.2% 35.2% 99.9%
Lumpectomy OR mastectomy 99.2% 86.3% 99.9%

10. Results from individual datasets
PPV Sensitivity Specificity
45 and Up Study baseline survey
Self-reported diagnosis 40.9% 73.0% 99.5%
(12 month window)
Self-reported diagnosis 72% of the ‘false positives’ had a record
on the Cancer Registry for an earlier
period
Hospital data
Inpatient primary diagnosis 80.3% 85.2% 99.7%
Lumpectomy 99.3% 61.3% 99.9%
Mastectomy 99.2% 35.2% 99.9%
Lumpectomy OR mastectomy 99.2% 86.3% 99.9%

11. Results from individual datasets
PPV Sensitivity Specificity
45 and Up Study baseline survey
Self-reported diagnosis 40.9% 73.0% 99.5%
(12 month window)
Self-reported diagnosis 72% of the ‘false positives’ had a record
on the Cancer Registry for an earlier
period
Hospital data
Inpatient primary diagnosis 80.3% 85.2% 99.7%
Lumpectomy 99.3% 61.3% 99.9%
Mastectomy 99.2% 35.2% 99.9%
Lumpectomy OR mastectomy 99.2% 86.3% 99.9%

12. Results from individual datasets
PPV Sensitivity Specificity
Pharmaceutical Benefits Scheme
Any dispensed medicine 88.5% 68.5% 99.9%
Medicare Benefits Schedule
Breast radiotherapy 95.8% 57.9% 99.9%

13. Results from 45 and Up Study,
MBS and PBS data package
PPV Sensitivity Specificity
Breast radiotherapy AND dispensed 95.8% 41.1% 99.9%
medicine
Breast radiotherapy OR dispensed 89.9% 85.3% 99.9%
medicine
Breast radiotherapy AND self-reported 70.2% 28.3% 99.9%
diagnosis
Breast radiotherapy AND dispensed 68.4% 19.4% 99.9%
medicine AND self-reported diagnosis
(Breast radiotherapy OR dispensed 67.8% 56.8% 99.9%
medicine) AND self-reported diagnosis
Breast radiotherapy OR dispensed 24.9% 94.1% 98.6%
medicine OR self-reported diagnosis

14. Results from 45 and Up Study,
MBS and PBS data package
PPV Sensitivity Specificity
Breast radiotherapy AND dispensed 95.8% 41.1% 99.9%
medicine
Breast radiotherapy OR dispensed 89.9% 85.3% 99.9%
medicine
Breast radiotherapy AND self-reported 70.2% 28.3% 99.9%
diagnosis
Breast radiotherapy AND dispensed 68.4% 19.4% 99.9%
medicine AND self-reported diagnosis
(Breast radiotherapy OR dispensed 67.8% 56.8% 99.9%
medicine) AND self-reported diagnosis
Breast radiotherapy OR dispensed 24.9% 94.1% 98.6%
medicine OR self-reported diagnosis

15. Results from hospital, 45 and
Up Study, MBS and PBS
datasets
PPV Sensitivity Specificity
(Lumpectomy or mastectomy) AND 99.4% 56.7% 99.9%
diagnosis of invasive breast cancer
AND dispensed medicine
(Lumpectomy or mastectomy) AND 96.8% 91.6% 99.9%
(diagnosis of invasive breast cancer
OR breast radiotherapy)
(Lumpectomy or mastectomy) AND 91.2% 93.7% 99.9%
(diagnosis of invasive breast cancer
OR dispensed medicine)
(Lumpectomy or mastectomy) AND 90.8% 96.8% 99.9%
(diagnosis of invasive breast cancer
OR breast radiotherapy OR dispensed
medicine)

16. Results from hospital, 45 and
Up Study, MBS and PBS
datasets
PPV Sensitivity Specificity
(Lumpectomy or mastectomy) AND 99.4% 56.7% 99.9%
diagnosis of invasive breast cancer
AND dispensed medicine
(Lumpectomy or mastectomy) AND 96.8% 91.6% 99.9%
(diagnosis of invasive breast cancer
OR breast radiotherapy)
(Lumpectomy or mastectomy) AND 91.2% 93.7% 99.9%
(diagnosis of invasive breast cancer
OR dispensed medicine)
(Lumpectomy or mastectomy) AND 90.8% 96.8% 99.9%
(diagnosis of invasive breast cancer
OR breast radiotherapy OR dispensed
medicine)

17. Strengths and weaknesses
• Strengths
o Large, heterogeneous sample of women
o Complete capture for all public and private inpatient
diagnoses and surgeries, subsidised outpatient
procedures and medicines
• Weaknesses
o We could not identify how many false positives were DCIS
vs. not breast cancer at all
o The flags we have identified may not predict invasive
breast cancer as well in younger women
o Validity of the flags examined here may change over time
with shifts in health service use

18. Conclusions
• Invasive breast cancer can be accurately ascertained
through administrative datasets other than the Cancer
Registry
• The most useful flags will depend on the research question
and available datasets
• Self report with date restriction had moderate sensitivity and
low PPV, however specificity was very high
o Suitable for excluding cases of breast
cancer from a study sample

19. Conclusions
• We had access to 45 and Up Study baseline survey,
hospital, MBS, and PBS data and needed to identify a
sample
• The most useful flag or this purpose:
o (Lumpectomy or mastectomy) AND (primary diagnosis of
invasive breast cancer or breast radiotherapy)
o 97% of those identified are true positives
o 92% of cases on the Cancer Registry
are identified

20. Acknowledgements
• Participants of the 45 and Up Study
• The 45 and Up Study is managed by the Sax Institute in
collaboration with:
• Cancer Council New South Wales (major partner)
• National Heart Foundation of Australia (NSW Division)
• NSW Ministry of Health
• beyondblue: the national depression initiative
• Ageing, Disability and Home Care NSW Family and
Community Services
• Australian Red Cross Blood Service
• UnitingCare Ageing

21. Acknowledgements
• Staff at the Centre for Health Record Linkage
• Other data custodians:
o NSW Ministry of Health
o Commonwealth Department of Human Services
o NSW Cancer Institute
• The project was funded by:
o Cancer Australia
o National Breast Cancer Foundation

22. Questions