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Background Australian age-standardized incidence and death rates for liver cancer are lower than world averages, but increasing as in other economically advanced western countries. World Health Organization emphasizes the need to address sociodemographic disparities in cancer risk. A more detailed sociodemographic risk profiling was undertaken for liver cancer in New South Wales (NSW) by diagnostic stage, than possible with NSW Cancer Registry (NSWCR) alone, by incorporating linked data from the Australian Bureau of Statistics (ABS). The purpose was to inform targeting and monitoring of cancer services. Methods The ABS manages the Multi-Agency Data Integration Project (MADIP) which includes a wide range of health, educational, welfare, census, and employment data. These data were linked at person level to NSWCR liver cancer registrations for the period post 2016 census to December 2018. De-identified data were analyzed. Sex-specific age-adjusted odds ratios (95%CIs) of liver cancer were derived using logistic regression by age, country of birth, residential remoteness, proficiency in spoken English, household income, employment status, occupation type, educational attainment, sole person household, joblessness, socioeconomic status, disability status, multimorbidity, and other health-related factors, including GP consultations. These data complement the less detailed sociodemographic data available from the NSWCR, with alignment of numerators and population denominators for accurate risk assessment. Results Results indicate liver cancer disproportionately affects population members already experiencing excess social and health disadvantage. Examples where 95% confidence intervals of odds ratios of liver cancer were elevated included having poor English-speaking proficiency, limited education, housing authority tenancy, living in sole-person households, having disabilities, multiple medicated conditions, and being carers of people with a disability. Also, odds of liver cancer were higher in more remote regions outside major cities, and in males, with higher odds of more advanced cancer stages (degrees of spread) at diagnosis in more remote regions. Conclusions Linked data enabled more detailed risk profiling than previously possible. This will support the targeting of cancer services and benchmarking.

Background Cancer control initiatives are informed by quantifying the capacity to reduce cancer burden through effective interventions. Burden measures using health administrative data are a sustainable way to support monitoring and evaluating of outcomes among patients and populations. The Fraction of Life Years Lost After Diagnosis (FLYLAD) is one such burden measure. We use data on Aboriginal and non-Aboriginal South Australians from 1990 to 2010 to show how FLYLAD quantifies disparities in cancer burden: between populations; between sub-population cohorts where stage at diagnosis is available; and when follow-up is constrained to 24-months after diagnosis. Method FLYLAD cancer is the fraction of years of life expectancy lost due to cancer (YLL cancer ) to life expectancy years at risk at time of cancer diagnosis (LYAR) for each person. The Global Burden of Disease standard life table provides referent life expectancies. FLYLAD cancer was estimated for the population of cancer cases diagnosed in South Australia from 1990 to 2010. Cancer stage at diagnosis was also available for cancers diagnosed in Aboriginal people and a cohort of non-Aboriginal people matched by sex, year of birth, primary cancer site and year of diagnosis. Results Cancers diagnoses ( N  = 144,891) included 777 among Aboriginal people. Cancer burden described by FLYLAD cancer was higher among Aboriginal than non-Aboriginal (0.55, 95% CIs 0.52–0.59 versus 0.39, 95% CIs 0.39–0.40). Diagnoses at younger ages among Aboriginal people, 7 year higher LYAR (31.0, 95% CIs 30.0–32.0 versus 24.1, 95% CIs 24.1–24.2) and higher premature cancer mortality (YLL cancer  =  16.3, 95% CIs 15.1–17.5 versus YLL cancer  =  8.2, 95% CIs 8.2–8.3) influenced this. Disparities in cancer burden between the matched Aboriginal and non-Aboriginal cohorts manifested 24-months after diagnosis with FLYLAD cancer 0.44, 95% CIs 0.40–0.47 and 0.28, 95% CIs 0.25–0.31 respectively. Conclusion FLYLAD described disproportionately higher cancer burden among Aboriginal people in comparisons involving: all people diagnosed with cancer; the matched cohorts; and, within groups diagnosed with same staged disease. The extent of disparities were evident 24-months after diagnosis. This is evidence of Aboriginal peoples’ substantial capacity to benefit from cancer control initiatives, particularly those leading to earlier detection and treatment of cancers. FLYLAD’s use of readily available, person-level administrative records can help evaluate health care initiatives addressing this need.

Background Australian health surveys occasionally include health utility measures in describing health related quality of life (HRQoL) across the general population. However, the HRQoL of specific population groups, such as Aboriginal and Torres Strait Islander (respectfully referred to as Aboriginal), are poorly understood. Our analysis describes HRQoL utility among Aboriginal South Australians by examining the characteristics of respondents completing HRQoL questioning, the relationship between HRQoL and respondent characteristics, then considers reported HRQoL utility in the wider population context. Methods Population weighted and self-reported HRQoL was measured using SF-6D, as derived from the SF-12 version 2, in the South Australian Aboriginal Health Survey’s face to face interviews with 399 respondents aged 15 or more in 2010/11. Results Mean HRQoL utility was 0.77 (95% CIs 0.76–0.79) with marked variations by gender (females 0.03, 95% CIs 0.00–0.06 lower than males), age (with ages 55 or more 0.08, 95% CIs 0.02–0.14 lower than 15–35 years) and number of chronic health conditions (3 or more conditions 0.14, 95% CIs 0.09–0.19 lower than those with 0 conditions). A pattern of response to HRQoL questions was also evident. Response was less likely among respondents speaking Aboriginal languages at home, living in non-urban settings, and experiencing multiple chronic health conditions. Conclusions The SF-6D provides useful information on the HRQoL of Aboriginal South Australians. However, non-completion was pronounced among respondents speaking traditional languages and experiencing more chronic health conditions. Improved participation of vulnerable and health compromised respondents through culturally safe and relevant self-reporting HRQoL utility instruments is needed.

Background Australia’s Aboriginal and Torres Strait Islander women have poorer survival and twice the disease burden from breast cancer compared to other Australian women. These disparities are influenced, but not fully explained, by more diagnoses at later stages. Incorporating breast screening, hospital and out of hospital treatment and cancer registry records into a person-linked data system can improve our understanding of breast cancer outcomes. We focussed one such system on a population-based cohort of Aboriginal women in South Australia diagnosed with breast cancer and a matched cohort of non-Aboriginal women with breast cancer. We quantify Aboriginal and non-Aboriginal women’s contact with publicly funded screening mammograms; quantify exposure to a selection of cancer treatment modalities; then assess the relationship between screening, treatment and the subsequent risk of breast cancer death. Methods Breast cancers registered among Aboriginal women in South Australia in 1990–2010 ( N  = 77) were matched with a random selection of non-Aboriginal women by birth and diagnostic year, then linked to screening records, and treatment 2 months before and 13 months after diagnosis. Competing risk regression summarised associations of Aboriginality, breast screening, cancer stage and treatment with risk of breast cancer death. Results Aboriginal women were less likely to have breast screening (OR = 0.37, 95%CIs 0.19–0.73); systemic therapies (OR = 0.49, 95%CIs 0.24–0.97); and, surgical intervention (OR = 0.35, 95%CIs 0.15–0.83). Where surgery occurred, mastectomy was more common among Aboriginal women (OR = 2.58, 1.22–5.46). Each of these factors influenced the risk of cancer death, reported as sub-hazard ratios (SHR). Regional spread disease (SHR = 34.23 95%CIs 6.76–13.40) and distant spread (SHR = 49.67 95%CIs 6.79–363.51) carried more risk than localised disease (Reference SHR = 1). Breast screening reduced the risk (SHR = 0.07 95%CIs 0.01–0.83). So too did receipt of systemic therapy (SHR = 0.06 95%CIs 0.01–0.41) and surgical treatments (SHR = 0.17 95%CIs 0.04–0.74). In the presence of adjustment for these factors, Aboriginality did not further explain the risk of breast cancer death. Conclusion Under-exposure to screening and treatment of Aboriginal women with breast cancers in South Australia contributed to excess cancer deaths. Improved access, utilisation and quality of effective treatments is needed to improve survival after breast cancer diagnosis.

Background Comorbidity is known to increase risk of death in cancer patients, both Aboriginal and non-Aboriginal. The means of measuring comorbidity to assess risk of death has not been studied in any depth in Aboriginal patients in Australia. In this study, conventional and customized comorbidity indices were used to investigate effects of comorbidity on cancer survival by Aboriginal status and to determine whether comorbidity explains survival disparities. Methods A retrospective cohort study was undertaken using linked population-based South Australian Cancer Registry and hospital inpatient data for 777 Aboriginal people diagnosed with primary cancer between 1990 and 2010 and 777 randomly selected non-Aboriginal controls matched by sex, birth year, diagnosis year and tumour type. A customised comorbidity index was developed by examining associations of comorbid conditions with 1-year all-cause mortality within the Aboriginal and non-Aboriginal patient groups separately using Cox proportional hazard model, adjusting for age, stage, sex and primary site. The adjusted hazard ratios for comorbid conditions were used as weights for these conditions in index development. The comorbidity index score for combined analyses was the sum of the weights across the comorbid conditions for each case from the two groups. Results The two most prevalent comorbidities in the Aboriginal cohort were “uncomplicated” hypertension (13.5%) and diabetes without complications (10.8%), yet in non-Aboriginal people, the comorbidities were “uncomplicated” hypertension (7.1%) and chronic obstructive pulmonary disease (4.4%). Higher comorbidity scores were associated with higher all-cause and cancer-specific mortality. The new index showed minor improvements in predictive ability and model fit when compared with three common generic comparison indices. After accounting for the competing risk of other deaths, stage at diagnosis, socioeconomic status, area remoteness and comorbidity, the increased risk of cancer death in Aboriginal people remained. Conclusions Our new customised index performed at least as well, although not markedly better than the generic indices. We conclude that in broad terms, the generic indices are reasonably effective for adjusting for comorbidity when comparing survival outcomes by Aboriginal status. Irrespective of the index used, comorbidity has a negative impact on cancer-specific survival, but this does not fully explain the lower survival in Aboriginal patients.

Background Aboriginal and Torres Strait Islander Australians have poorer cancer outcomes than other Australians. Comparatively little is known of the type and amount of cancer treatment provided to Aboriginal and Torres Strait Islander people and the consequences for cancer survival. This study quantifies the influence of surgical, systemic and radiotherapy treatment on risk of cancer death among matched cohorts of cancer cases and, the comparative exposure of cohorts to these treatments. Methods Cancers registered among Aboriginal South Australians in 1990–2010 ( N  = 777) were matched with randomly selected non-Indigenous cases by sex, birth and diagnostic year, and primary site, then linked to administrative cancer treatment for the period from 2 months before to 13 months after diagnosis. Competing risk regression summarised associations of Indigenous status, geographic remoteness, comorbidities, cancer stage and treatment exposure with risk of cancer death. Results Fewer Aboriginal cases had localised disease at diagnosis (37.2% versus 50.2%) and they were less likely to: experience hospitalisation with cancer diagnosis, unadjusted odds ratio (UOR) = 0.76; 95%CI = 0.59–0.98; have surgery UOR = 0.65; 95%CI = 0.53–0.80; systemic therapies UOR = 0.64; 95%CI = 0.52–0.78; or radiotherapy, UOR = 0.76; 95%CI = 0.63–0.94. Localised disease carried lower risk of cancer death compared to advanced cases receiving surgery or systemic therapies, SHR = 0.34; 95%CI = 0.25–0.47 and SHR = 0.35; 95%CI = 0.25–0.48. Advanced disease and no treatment carried higher risk of cancer death, SHR = 1.82; 95%CI = 1.26–2.63. Conclusion The effects of treatment did not differ between Aboriginal and non-Indigenous cohorts. However, comparatively less exposure to surgical and systemic treatments among Aboriginal cancer cases further complicated the disadvantages associated with geographic remoteness, advanced stage of disease and co-morbid conditions at diagnosis and add to disparities in cancer death. System level responses to improving access, utilisation and quality of effective treatments are needed to improve survival after cancer diagnosis.

Background Lung cancer is a major cause of health loss internationally, and in Australia. Most of that loss is inequitably concentrated among vulnerable or disadvantaged people and amenable to prevention and earlier detection. In response, best practice lung cancer care considers peoples’ background, circumstances and care needs. Comprehensive, person level descriptions of demographic, health and discrete socio-economic disadvantage related factors are therefore required to inform best practice. We examine population wide correlations of demographic, health and socioeconomic characteristics with lung cancer diagnosis for use in cancer control programs, including screening. Methods A study of 5,504,777 (89.9%) adults living in New South Wales and participating in Australia’s Census in August 2016 with subsequent follow-up to the end of 2018. The Australian Bureau of Statistics’ (ABS) person-level integrated data asset linked census records with the NSW population cancer registry which includes primary site. Our study compared census participants who did not experience cancer in the follow-up period with those diagnosed with lung cancer, (n = 6160 and ICD10 C33-34). Outcomes are expressed as the adjusted relative odds (aOR) of incident lung cancer among adults in the community and measured using multi-variable logistic regression models. Validated ABS methods informed categorisation of social and economic variables. Results Multivariable comparison of those with lung cancer and those without a first cancer diagnosis (3276 lung cancers among 2,484,145 males; 2884 lung cancers among 2,944,148 females) showed associations with increasing age, varying ancestry, living alone (aOR = 1.30 95% CI 1.19–1.42 males; 1.24 95% CI 1.14–1.35 females), number of health conditions medicated, less than Year 12 education (aOR = 1.40 95% CI 1.30–1.51 males; 1.37 95% CI 1.27–1.48 females) and housing authority rental (aOR = 1.69 95% CI 1.48–1.94 males; 1.85 95% CI 1.63–2.11 females). Additional associations occurred among males with low income, disabilities before age 70, those unemployed and labouring occupations. As numbers of characteristics increased, so did the likelihood of lung cancer. Conclusion We provided a population wide description of characteristics relevant to lung cancer diagnosis. Deeper knowledge of these characteristics inform continuing development of lung cancer programs in prevention (e.g. tobacco control) and detection (e.g. lung cancer screening), then help prioritise targeted delivery of those programs.

Background The analysis describes trends in the levels and social distribution of total life expectancy and healthy life expectancy in South Australia from 1999 to 2008. Methods South Australian Burden of Disease series for the period 1999-2001 to 2006-2008 and across statistical local areas according to relative socioeconomic disadvantage were analyzed for changes in total life expectancy and healthy life expectancy by sex and area level disadvantage, with further decomposition of healthy life expectancy change by age, cause of death, and illness. Results Total life expectancy at birth increased in South Australia for both sexes (2.0 years [2.6%] among males; 1.5 years [1.8%] among females). Healthy life expectancy also increased (1.4 years [2.1%] among males; 1.2 years [1.5%] among females). Total life and healthy life expectancy gains were apparent in all socioeconomic groups, with the largest increases in areas of most and least disadvantage. While the least disadvantaged areas consistently had the best health outcomes, they also experienced the largest increase in the amount of life expectancy lived with disease and injury-related illness. Conclusions While overall gains in both total life and healthy life expectancy were apparent in South Australia, gains were greater for total life expectancy. Additionally, the proportion of expected life lived with disease and injury-related illness increased as disadvantage decreased. This expansion of morbidity occurred in both sexes and across all socio-economic groups. This analysis outlines the continuing improvements to population health outcomes within South Australia. It also highlights the challenge of reducing population morbidity so that gains to healthy life match those of total life expectancy.

Background This paper initially describes premature mortality by Aboriginality in South Australia during 1999 to 2006. It then examines how these outcomes vary across area level socio-economic disadvantage and geographic remoteness. Methods The retrospective, cross-sectional analysis uses estimated resident population by sex, age and small areas based on the 2006 Census, and Unit Record mortality data. Premature mortality outcomes are measured using years of life lost (YLL). Subsequent intrastate comparisons are based on indirect sex and age adjusted YLL results. A multivariate model uses area level socio-economic disadvantage rank, geographic remoteness, and an interaction between the two variables to predict premature mortality outcomes. Results Aboriginal people experienced 1.1% of total deaths but 2.2% of YLL and Aboriginal premature mortality rates were 2.65 times greater than the South Australian average. Premature mortality for Aboriginal and non-Aboriginal people increased significantly as area disadvantage increased. Among Aboriginal people though, a significant main effect for area remoteness was also observed, together with an interaction between disadvantage and remoteness. The synergistic effect shows the social gradient between area disadvantage and premature mortality increased as remoteness increased. Conclusions While confirming the gap in premature mortality rates between Aboriginal South Australians and the rest of the community, the study also found a heterogeneity of outcomes within the Aboriginal community underlie this difference. The results support the existence of relationship between area level socio-economic deprivation, remoteness and premature mortality in the midst of an affluent society. The study concludes that vertically equitable resourcing according to population need is an important response to the stark mortality gap and its exacerbation by area socio-economic position and remoteness.

PurposeOlder age, risks from pre-existing health conditions and socio-economic disadvantage are negatively related to the prospects of an early-stage cancer diagnosis. With older Aboriginal Australians having an elevated prevalence of these underlying factors, this study examines the potential for the mitigating effects of more frequent contact with general practitioners (GPs) in ensuring local-stage at diagnosis.MethodsWe compared the odds of local vs. more advanced stage at diagnosis of solid tumours according to GP contact, using linked registry and administrative data. Results were compared between Aboriginal (n = 4,084) and non-Aboriginal (n = 249,037) people aged 50 + years in New South Wales with a first diagnosis of cancer in 2003–2016.ResultsYounger age, male sex, having less area-based socio-economic disadvantage, and fewer comorbid conditions in the 12 months before diagnosis (0–2 vs. 3 +), were associated with local-stage in fully-adjusted structural models. The odds of local-stage with more frequent GP contact (14 + contacts per annum) also differed by Aboriginal status, with a higher adjusted odds ratio (aOR) of local-stage for frequent GP contact among Aboriginal people (aOR = 1.29; 95% CI 1.11–1.49) but not among non-Aboriginal people (aOR = 0.97; 95% CI 0.95–0.99).ConclusionOlder Aboriginal Australians diagnosed with cancer experience more comorbid conditions and more socioeconomic disadvantage than other Australians, which are negatively related to diagnosis at a local-cancer stage. More frequent GP contact may act to partly offset this among the Aboriginal population of NSW.