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1.  Survival disparities in Australia: an analysis of patterns of care and comorbidities among indigenous and non-indigenous cancer patients 
BMC Cancer  2014;14:517.
Background
Indigenous Australians have lower overall cancer survival which has not yet been fully explained. To address this knowledge deficit, we investigated the associations between comorbidities, cancer treatment and survival in Indigenous and non-Indigenous people in Queensland, Australia.
Methods
A cohort study of 956 Indigenous and 869 non-Indigenous patients diagnosed with cancer during 1998–2004, frequency-matched on age, sex, remoteness of residence and cancer type, and treated in Queensland public hospitals. Survival after cancer diagnosis, and effect of stage, treatment, and comorbidities on survival were examined using Cox proportional hazard models.
Results
Overall Indigenous people had more advanced cancer stage (p = 0.03), more comorbidities (p < 0.001), and received less cancer treatment (77% vs. 86%, p = 0.001). Among patients without comorbidities and social disadvantage, there was a lower uptake of treatment among Indigenous patients compared to non-Indigenous patients. For those who received treatment, time to commencement, duration and dose of treatment were comparable. Unadjusted cancer survival (HR = 1.30, 95% CI 1.15-1.48) and non-cancer survival (HR = 2.39, 95% CI 1.57-3.63) were lower in the Indigenous relative to non-Indigenous patients over the follow-up period. When adjusted for clinical factors, there was no difference in cancer-specific survival between the groups (HR = 1.10, 95% CI 0.96-1.27). One-year survival was lower for Indigenous people for all-causes of death (adjusted HR = 1.33, 95% CI 1.12-1.83).
Conclusion
In this study, Indigenous Australians received less cancer treatment, had more comorbidities and had more advanced cancer stage at diagnosis, factors which contribute to poorer cancer survival. Moreover, for patients with a more favourable distribution of such prognostic factors, Indigenous patients received less treatment overall relative to non-Indigenous patients. Personalised cancer care, which addresses the clinical, social and overall health requirements of Indigenous patients, may improve their cancer outcomes.
doi:10.1186/1471-2407-14-517
PMCID: PMC4223410  PMID: 25037075
Indigenous; Cancer; Diabetes; Comorbidity; Disparity; Cancer stage; Survival; Queensland
2.  Cancer survival for Aboriginal and Torres Strait Islander Australians: a national study of survival rates and excess mortality 
Background
National 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.
Methods
We 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.
Results
Survival 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.
Conclusions
Cancer 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.
doi:10.1186/1478-7954-12-1
PMCID: PMC3909914  PMID: 24479861
Cancer; Survival; Australia; Australian Aboriginal; Torres Strait Islander; Indigenous Australian; Relative survival; Cause-specific survival
3.  A hierarchical spatial modelling approach to investigate MRSA transmission in a tertiary hospital 
BMC Infectious Diseases  2013;13:449.
Background
Most hospitals have a hierarchical design with beds positioned within cubicles and cubicles positioned within wards. Transmission of MRSA may be facilitated by patient proximity and thus the spatial arrangements of beds, cubicles and wards could be important in understanding MRSA transmission risk. Identifying high-risk areas of transmission may be useful in the design of more effective, targeted MRSA interventions.
Methods
Retrospective data on numbers of multi-resistant and non-multiresistant MRSA acquisitions were collected for 52 weeks in 2007 in a tertiary hospital in Brisbane, Australia. A hierarchical Bayesian spatio-temporal modelling approach was used to investigate spatial correlation in the hierarchically arranged datasets. The spatial component of the model decomposes cubicle-level variation into a spatially structured component and a spatially unstructured component, thereby encapsulating the influence of unmeasured predictor variables that themselves are spatially clustered and/or random. A fixed effect for the presence of another patient with the same type of MRSA in the cubicles two weeks prior was included.
Results
The best-fitting model for non-multiresistant MRSA had an unstructured random effect but no spatially structured random effect. The best-fitting model for multiresistant MRSA incorporated both spatially structured and unstructured random effects. While between-cubicle variability in risk of MRSA acquisition within the hospital was significant, there was only weak evidence to suggest that MRSA is spatially clustered. Presence of another patient with the same type of MRSA in the cubicles two weeks prior was a significant predictor of both types of MRSA in all models.
Conclusions
We found weak evidence of clustering of MRSA acquisition within the hospital. The presence of an infected patient in the same cubicle two weeks prior may support the importance of environmental contamination as a source of MRSA transmission.
doi:10.1186/1471-2334-13-449
PMCID: PMC3854069  PMID: 24073821
MRSA; Staphylococcus aureus; Spatial model; Spatial clustering
4.  Indirect Estimation of the Comparative Treatment Effect in Pharmacogenomic Subgroups 
PLoS ONE  2013;8(8):e72256.
Evidence of clinical utility is a key issue in translating pharmacogenomics into clinical practice. Appropriately designed randomized controlled trials generally provide the most robust evidence of the clinical utility, but often only data from a pharmacogenomic association study are available. This paper details a method for reframing the results of pharmacogenomic association studies in terms of the comparative treatment effect for a pharmacogenomic subgroup to provide greater insight into the likely clinical utility of a pharmacogenomic marker, its’ likely cost effectiveness, and the value of undertaking the further (often expensive) research required for translation into clinical practice. The method is based on the law of total probability, which relates marginal and conditional probability. It takes as inputs: the prevalence of the pharmacogenomic marker in the patient group of interest, prognostic effect of the pharmacogenomic marker based on observational association studies, and the unstratified comparative treatment effect based on one or more conventional randomized controlled trials. The critical assumption is that of exchangeability across the included studies. The method is demonstrated using a case study of cytochrome P450 (CYP) 2C19 genotype and the anti-platelet agent clopidogrel. Indirect subgroup analysis provided insight into relationship between the clinical utility of genotyping CYP2C19 and the risk ratio of cardiovascular outcomes between CYP2C19 genotypes for individuals using clopidogrel. In this case study the indirect and direct estimates of the treatment effect for the cytochrome P450 2C19 subgroups were similar. In general, however, indirect estimates are likely to have substantially greater risk of bias than an equivalent direct estimate.
doi:10.1371/journal.pone.0072256
PMCID: PMC3754999  PMID: 24015225
5.  A study of head and neck cancer treatment and survival among indigenous and non-indigenous people in Queensland, Australia, 1998 to 2004 
BMC Cancer  2011;11:460.
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.
doi:10.1186/1471-2407-11-460
PMCID: PMC3213195  PMID: 22026423
6.  Analysing low‐risk patient populations allows better discrimination between high‐performing and low‐performing hospitals: a case study using inhospital mortality from acute myocardial infarction 
Quality & Safety in Health Care  2007;16(5):324-328.
Objective
To assess whether performance indicators based on administrative hospital data can be rendered more useful by stratifying them according to risk status of the patient.
Design
Retrospective analysis of 10 years of administrative hospital data for patients with acute myocardial infarction (AMI). Four risk groups defined by cross‐classifying patient age (<75 years, 75+ years) against the presence or otherwise of at least one risk condition that predicted short‐term AMI mortality.
Setting
17 public hospitals in Queensland, Australia, with more than 50 AMI admissions annually.
Participants
21 537 patients admitted through the emergency department and subsequently diagnosed as having AMI.
Main outcome measure
Systematic variation in standardised case fatality ratios. Systematic variation is the variation across hospitals after accounting for the Poisson variation in the number of deaths at each hospital. It was obtained from an empirical‐Bayes model. Case fatality ratios were standardised according to the age, sex and risk factor profile of the patient.
Results
Systematic variation decreased monotonically across the four risk groups as case fatality increased (likelihood ratio test: χ2 = 8.08, df = 1, p = 0.004). Systematic variation was largest and statistically significant (0.375; 95% CI 0.144 to 0.606) for low‐risk patients (<75 years with no risk conditions; case fatality rate = 2.0%) but was smallest (0.126; 0.039 to 0.212) for high‐risk patients (75+ years with at least one risk condition; case fatality rate = 24.3%).
Conclusion
Analysis of data from high‐risk patients with AMI provides little opportunity to identify better‐performing hospitals because there is relatively little variation across hospitals. In such patients, older age and comorbid illness are probably more important than quality of care in determining outcomes. In contrast, for low‐risk patients the systematic variation was large suggesting that outcomes for such patients are more sensitive to clinical error. Analysing data for low‐risk patients maximises our ability to identify best‐performing hospitals and learn from their processes and structures to effect system‐wide changes that will benefit all patients.
doi:10.1136/qshc.2006.018457
PMCID: PMC2464975  PMID: 17913771
7.  Detecting the start of an influenza outbreak using exponentially weighted moving average charts 
Background
Influenza viruses cause seasonal outbreaks in temperate climates, usually during winter and early spring, and are endemic in tropical climates. The severity and length of influenza outbreaks vary from year to year. Quick and reliable detection of the start of an outbreak is needed to promote public health measures.
Methods
We propose the use of an exponentially weighted moving average (EWMA) control chart of laboratory confirmed influenza counts to detect the start and end of influenza outbreaks.
Results
The chart is shown to provide timely signals in an example application with seven years of data from Victoria, Australia.
Conclusions
The EWMA control chart could be applied in other applications to quickly detect influenza outbreaks.
doi:10.1186/1472-6947-10-37
PMCID: PMC2909986  PMID: 20587013
8.  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.
Methods
Queensland database study of public-hospital patients, who had at least one emergency admission for a chronic medical condition (e.g., congestive heart failure, chronic obstructive pulmonary disease, diabetes or dementia) during 2005/2006. Multivariate logistic regression was used to develop an algorithm to predict readmission within 12 months. The performance of the algorithm was tested against recorded readmissions using sensitivity, specificity, and Likelihood Ratios (positive and negative).
Results
Several factors were identified that predicted readmission (i.e., age, co-morbidities, economic disadvantage, number of previous admissions). The discriminatory power of the model was modest as determined by area under the receiver operating characteristic (ROC) curve (c = 0.65). At a risk score threshold of 50, the algorithm identified only 44.7% (95% CI: 42.5%, 46.9%) of patients admitted with a reference condition who had an admission in the next 12 months; 37.5% (95% CI: 35.0%, 40.0%) of patients were flagged incorrectly (they did not have a subsequent admission).
Conclusion
A statistical algorithm based on Queensland hospital inpatient data, performed only moderately in identifying patients at risk of readmission. The main problem is that there are too many false negatives, which means that many patients who might benefit would not be offered case-management.
doi:10.1186/1472-6963-9-96
PMCID: PMC2700797  PMID: 19505342
9.  Utility of routine data sources for feedback on the quality of cancer care: an assessment based on clinical practice guidelines 
Background
Not all cancer patients receive state-of-the-art care and providing regular feedback to clinicians might reduce this problem. The purpose of this study was to assess the utility of various data sources in providing feedback on the quality of cancer care.
Methods
Published clinical practice guidelines were used to obtain a list of processes-of-care of interest to clinicians. These were assigned to one of four data categories according to their availability and the marginal cost of using them for feedback.
Results
Only 8 (3%) of 243 processes-of-care could be measured using population-based registry or administrative inpatient data (lowest cost). A further 119 (49%) could be measured using a core clinical registry, which contains information on important prognostic factors (e.g., clinical stage, physiological reserve, hormone-receptor status). Another 88 (36%) required an expanded clinical registry or medical record review; mainly because they concerned long-term management of disease progression (recurrences and metastases) and 28 (11.5%) required patient interview or audio-taping of consultations because they involved information sharing between clinician and patient.
Conclusion
The advantages of population-based cancer registries and administrative inpatient data are wide coverage and low cost. The disadvantage is that they currently contain information on only a few processes-of-care. In most jurisdictions, clinical cancer registries, which can be used to report on many more processes-of-care, do not cover smaller hospitals. If we are to provide feedback about all patients, not just those in larger academic hospitals with the most developed data systems, then we need to develop sustainable population-based data systems that capture information on prognostic factors at the time of initial diagnosis and information on management of disease progression.
doi:10.1186/1472-6963-9-84
PMCID: PMC2695440  PMID: 19473504
10.  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.
doi:10.1186/1471-2288-9-30
PMCID: PMC2694210  PMID: 19426561
11.  Provision of taped conversations with neonatologists to mothers of babies in intensive care: randomised controlled trial 
BMJ : British Medical Journal  2006;334(7583):28.
Objective To determine whether providing mothers of babies in neonatal intensive care units with audiotapes of their conversations with a neonatologist improves recall of information and psychological wellbeing.
Design Randomised, single blinded trial.
Setting Neonatal intensive care unit, North Queensland, Australia.
Participants 200 mothers of babies in a neonatal intensive care unit.
Interventions Mothers given (n=102) or not given (n=98) audiotapes of their conversations with a neonatologist.
Main outcome measures Recall of information, attitudes to and use of the tape, satisfaction with conversations, postnatal depression, anxiety, general health, and stress about parenting, at 10 days and four and 12 months.
Results 91% (n=93) of mothers in the tape group listened to the tape (once by day 10, twice by four months, and three times by 12 months; range 1-10). At 10 days and four months, mothers in the tape group recalled significantly more information about diagnosis, treatment, and outcome than mothers in the control group. At four months mothers in the tape group were 75% more likely to recall all of the information about treatment than mothers in the control group (59% v 34%; risk ratio 1.75, 95% confidence interval 1.27 to 2.4). Six mothers, all in the control group, could not recall their conversations. No statistically significant differences were found between the groups in satisfaction with conversations (10 days), postnatal depression and anxiety scores (10 days, four and 12 months), and stress about parenting (12 months).
Conclusion Providing the mothers of babies in neonatal intensive care units with audiotapes of conversations with a neonatologist enhanced their recall of information (up to four months). The taped conversations did not affect the mothers' wellbeing or satisfaction with the neonatologist.
Trial registration Australian Clinical Trials Registry 12606000478516.
doi:10.1136/bmj.39017.675648.BE
PMCID: PMC1764090  PMID: 17142256
12.  Reducing perinatal mortality among Indigenous babies in Queensland: should the first priority be better primary health care or better access to hospital care during confinement? 
Background
The perinatal mortality rate among Indigenous Australians is still double that of the rest of the community. The aim of our study was to estimate the extent to which increased risk of low birthweight and preterm birth among Indigenous babies in Queensland account for their continuing mortality excess. If a large proportion of excess deaths can be explained by the unfavourable birthweight and gestational age distribution of Indigenous babies, then that would suggest that priority should be given to implementing primary health care interventions to reduce the risk of low birthweight and preterm birth (eg, interventions to reduce maternal smoking or genitourinary infections). Conversely, if only a small proportion is explained by birthweight and gestational age, then other strategies might need to be considered such as improving access to high-quality hospital care around the time of confinement.
Methodology
Population-based, descriptive study of perinatal mortality rates among Indigenous and non-Indigenous babies, in Queensland, stratified by birthweight and gestational age.
Results
Indigenous babies are twice as likely to die as their non-Indigenous counterparts (rate ratio1998–2002: 2.01; 95%ci 1.77, 2.28). However, within separate strata of birth weight and gestational age, Indigenous and non-Indigenous rates are similar. The Mantel-Haenszel rate ratio adjusted for birth weight and gestational age was 1.13 (0.99, 1.28). This means that most of the excess mortality in Indigenous babies is largely due to their unfavourable birth weight and gestational-age distributions. If Indigenous babies had the same birth weight and gestational age distribution as their non-Indigenous counterparts, then the relative disparity would be reduced by 87% and 20 fewer Indigenous babies would die in Queensland each year.
Conclusion
Our results suggest that Indigenous mothers at high risk of poor outcome (for example those Indigenous mothers in preterm labour) have good access to high quality medical care around the time of confinement. The main reason Indigenous babies have a high risk of death is because they are born too early and too small. Thus, to reduce the relative excess of deaths among Indigenous babies, priority should be given to primary health care initiatives aimed at reducing the prevalence of low birth weight and preterm birth.
doi:10.1186/1743-8462-2-11
PMCID: PMC1168887  PMID: 15918912

Results 1-12 (12)