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Background: Aboriginal people are known to be under-recorded in routinely collected datasets in Australia. This study examined methods for enhancing the reporting of cancer incidence among Aboriginal people using linked data methodologies., Methods: Invasive cancers diagnosed in New South Wales (NSW), Australia, in 2010-2014 were identified from the NSW Cancer Registry (NSWCR). The NSWCR data were linked to the NSW Admitted Patient Data Collection, the NSW Emergency Department Data Collection and the Australian Coordinating Register Cause of Death Unit Record File. The following methods for enhancing the identification of Aboriginal people were used: 'ever-reported', 'reported on most recent record', 'weight of evidence' and 'multi-stage median'. The impact of these methods on the number of cancer cases and age-standardised cancer incidence rates (ASR) among Aboriginal people was explored., Results: Of the 204,948 cases of invasive cancer, 2703 (1.3%) were recorded as Aboriginal on the NSWCR. This increased with enhancement methods to 4184 (2.0%, 'ever'), 3257 (1.6%, 'most recent'), 3580 (1.7%, 'weight of evidence') and 3583 (1.7%, 'multi-stage median'). Enhancement was generally greater in relative terms for males, people aged 25-34 years, people with cancers of localised or unknown degree of spread, people living in urban areas and areas with less socio-economic disadvantage. All enhancement methods increased ASRs for Aboriginal people. The weight of evidence method increased the overall ASR by 42% for males (894.1 per 100,000, 95% CI 844.5-945.4) and 27% for females (642.7 per 100,000, 95% CI 607.9-678.7). Greatest relative increases were observed for melanoma and prostate cancer incidence (126 and 63%, respectively). ASRs for prostate and breast cancer increased from below to above the ASRs of non-Aboriginal people with enhancement of Aboriginal status., Conclusions: All data linkage methods increased the number of cancer cases and ASRs for Aboriginal people. Enhancement varied by demographic and cancer characteristics. We considered the weight of evidence method to be most suitable for population-level reporting of cancer incidence among Aboriginal people. The impact of enhancement on disparities in cancer outcomes between Aboriginal and non-Aboriginal people should be further examined.

Background: In contrast to aetiological associations, there is little empirical evidence for generalising health service use associations from cohort studies. We compared the health service use of cohort study participants diagnosed with bowel or lung cancer to the source population of people diagnosed with these cancers in New South Wales (NSW), Australia to assess the representativeness of health service use of the cohort study participants., Methods: Population-based cancer registry data for NSW residents aged ≥45 years at diagnosis of bowel or lung cancer were linked to the 45 and Up Study, a NSW population-based cohort study (N~ 267,000). We measured hospitalisation, emergency department (ED) attendance and all-cause survival, and risk factor associations with these outcomes using administrative data for cohort study participants and the source population. We assessed bias in prevalence and risk factor associations using ratios of relative frequency (RRF) and relative odds ratios (ROR), respectively., Results: People from major cities, non-English speaking countries and with comorbidites were under-represented among cohort study participants diagnosed with bowel (n = 1837) or lung (n = 969) cancer by 20-50%. Cohort study participants had similar hospitalisation and ED attendance compared with the source population. One-year survival after major surgical resection was similar, but cohort study participants had up to 25% higher post-diagnosis survival (lung cancer 3-year survival: RRF = 1.24, 95% confidence interval 1.12,1.37). Except for area-based socioeconomic position, risk factors associations with health service use measures and survival appeared relatively unbiased., Conclusions: Absolute measures of health service use and risk factor associations in a non-representative sample showed little evidence of bias. Non-comparability of risk factor measures of cohort study participants and non-participants, such as area-based socioeconomic position, may bias estimates of risk factor associations. Primary and outpatient care outcomes may be more vulnerable to bias.