Survival of indigenous and non-Indigenous Queenslanders after a diagnosis of lung cancer: a matched cohort study

Objective: To compare survival of Indigenous and non-Indigenous lung cancer patients and to investigate any corresponding differences in stage, treatment and comorbidities.Design and setting: Cohort study of 158 Indigenous and 152 non-Indigenous patients (frequency-matched on age, sex and rurality) diagnosed with lung cancer between 1996 and 2002 and treated in Queensland public hospitals.Main outcome measures: Survival after diagnosis of lung cancer; effects of stage at diagnosis, treatment, comorbidities and histological subtype on lung cancer-specific survival.Results: Survival of Indigenous lung cancer patients was significantly lower than that of non-Indigenous patients (median survival, 4.3 v 10.3 months; hazard ratio, 1.48; 95% CI, 1.14–1.92). Of 158 Indigenous patients, 72 (46%) received active treatment with chemotherapy, radiotherapy or surgery compared with 109 (72%) of the 152 non-Indigenous patients, and this treatment disparity remained after adjusting for histological subtype, stage at diagnosis, and comorbidities (adjusted risk ratio, 0.65; 95% CI, 0.53–0.73). The treatment disparity explained most of the survival deficit: the hazard ratio reduced to 1.10 (95% CI, 0.83–1.44) after inclusion of treatment variables in the proportional hazards survival model. The remaining survival deficit was explained by the higher prevalence of comorbidities among Indigenous cancer patients, mainly diabetes.Conclusion: Survival after a diagnosis of lung cancer is worse for Indigenous patients than for non-Indigenous patients, and differences in treatment between the two groups are mainly responsible

Cancer survival for Aboriginal and Torres Strait Islander Australians: a national study of survival rates and excess mortality

BackgroundNational cancer survival statistics are available for the total Australian population but not Indigenous Australians, although their cancer mortality rates are known to be higher than those of other Australians. We aimed to validate analysis methods and report cancer survival rates for Indigenous Australians as the basis for regular national reporting.MethodsWe used national cancer registrations data to calculate all-cancer and site-specific relative survival for Indigenous Australians (compared with non-Indigenous Australians) diagnosed in 2001-2005. Because of limited availability of Indigenous life tables, we validated and used cause-specific survival (rather than relative survival) for proportional hazards regression to analyze time trends and regional variation in all-cancer survival between 1991 and 2005.ResultsSurvival was lower for Indigenous than non-Indigenous Australians for all cancers combined and for many cancer sites. The excess mortality of Indigenous people with cancer was restricted to the first three years after diagnosis, and greatest in the first year. Survival was lower for rural and remote than urban residents; this disparity was much greater for Indigenous people. Survival improved between 1991 and 2005 for non-Indigenous people (mortality decreased by 28%), but to a much lesser extent for Indigenous people (11%) and only for those in remote areas; cancer survival did not improve for urban Indigenous residents.ConclusionsCancer survival is lower for Indigenous than other Australians, for all cancers combined and many individual cancer sites, although more accurate recording of Indigenous status by cancer registers is required before the extent of this disadvantage can be known with certainty. Cancer care for Indigenous Australians needs to be considerably improved; cancer diagnosis, treatment, and support services need to be redesigned specifically to be accessible and acceptable to Indigenous people

A study of head and neck cancer treatment and survival among indigenous and non-indigenous people in Queensland, Australia, 1998 to 2004

Background: Overall, Indigenous Australians with cancer are diagnosed with more advanced disease, receive less cancer treatment and have poorer cancer survival than non-Indigenous Australians. The prognosis for Indigenous people with specific cancers varies however, and their prognosis for cancers of the head and neck is largely unknown. We therefore have compared clinical characteristics, treatment and survival between Indigenous and non-Indigenous people diagnosed with head and neck cancer in Queensland, Australia. Methods: Rates were based on a cohort of Indigenous people (n = 67), treated in public hospitals between 1998 and 2004 and frequency-matched on age and location to non-Indigenous cases (n = 62) also treated in the public health system. Data were obtained from hospital records and the National Death Index. We used Pearson's Chi-squared analysis to compare categorical data (proportions) and Cox proportional hazard models to assess survival differences.Results: There were no significant differences in socioeconomic status, stage at diagnosis or number and severity of comorbidities between Indigenous and non-Indigenous patients, although Indigenous patients were more likely to have diabetes. Indigenous people were significantly less likely to receive any cancer treatment (75% vs. 95%, P = 0.005) and, when cancer stage, socioeconomic status, comorbidities and cancer treatment were taken into account, they experienced greater risk of death from head and neck cancer (HR 1.88, 1.10, 3.22) and from all other causes (HR 5.83, 95% CI 1.09, 31.04).Conclusion: These findings show for the first time that Indigenous Australians with head and neck cancer receive less cancer treatment and suggest survival disparity could be reduced if treatment uptake was improved. There is a need for a greater understanding of the reasons for such treatment and survival disparities, including the impact of the poorer overall health on cancer outcomes for Indigenous Australians

Bayesian versus frequentist statistical inference for investigating a one-off cancer cluster reported to a health department

Background. The problem of silent multiple comparisons is one of the most difficult statistical problems faced by scientists. It is a particular problem for investigating a one-off cancer cluster reported to a health department because any one of hundreds, or possibly thousands, of neighbourhoods, schools, or workplaces could have reported a cluster, which could have been for any one of several types of cancer or any one of several time periods. Methods. This paper contrasts the frequentist approach with a Bayesian approach for dealing with silent multiple comparisons in the context of a one-off cluster reported to a health department. Two published cluster investigations were re-analysed using the Dunn-Sidak method to adjust frequentist p-values and confidence intervals for silent multiple comparisons. Bayesian methods were based on the Gamma distribution. Results. Bayesian analysis with non-informative priors produced results similar to the frequentist analysis, and suggested that both clusters represented a statistical excess. In the frequentist framework, the statistical significance of both clusters was extremely sensitive to the number of silent multiple comparisons, which can only ever be a subjective "guesstimate". The Bayesian approach is also subjective: whether there is an apparent statistical excess depends on the specified prior. Conclusion. In cluster investigations, the frequentist approach is just as subjective as the Bayesian approach, but the Bayesian approach is less ambitious in that it treats the analysis as a synthesis of data and personal judgements (possibly poor ones), rather than objective reality. Bayesian analysis is (arguably) a useful tool to support complicated decision-making, because it makes the uncertainty associated with silent multiple comparisons explicit

Using routine inpatient data to identify patients at risk of hospital readmission

Background: A relatively small percentage of patients with chronic medical conditions account for a much larger percentage of inpatient costs. There is some evidence that case-management can improve health and quality-of-life and reduce the number of times these patients are readmitted. To assess whether a statistical algorithm, based on routine inpatient data, can be used to identify patients at risk of readmission and who would therefore benefit from case-management

A cancer geography paradox?:Poorer cancer outcomes with longer travelling times to healthcare facilities despite prompter diagnosis and treatment: a data-linkage study

This study was funded by Cancer Research UK (Grant number = C10673/A17593). The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review or approval of the manuscript; or the decision to submit the manuscript for publication. All authors are independent of Cancer Research UK.Peer reviewedPublisher PD

Ageing and healthcare costs in Australia: a case of policy-based evidence?

There have been dire predictions that population ageing will result in skyrocketing health costs. However, numerous studies have shown that the effect of population ageing on health expenditure is likely to be small and manageable. Pessimism about population ageing is popular in policy debates because it fits with ideological positions that favour growth in the private sector and seek to contain health expenditure in the public sector. It might also distract attention from the need to evaluate the appropriateness and effectiveness of current patterns of care. Pessimistic scenarios have stifled debate and limited the number of policy options considered. Policy making in Australia would be improved if we took a more realistic view of the effect of population ageing on health expenditure