Demographic, health, and prognostic characteristics of Australians with liver cancer: a cohort study of linked data in New South Wales for informing cancer control.

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.

The fraction of life years lost after diagnosis (FLYLAD): a person-centred measure of cancer burden.

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: FLYLADcancer is the fraction of years of life expectancy lost due to cancer (YLLcancer) 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. FLYLADcancer 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 FLYLADcancer 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 (YLLcancer = 16.3, 95% CIs 15.1-17.5 versus YLLcancer = 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 FLYLADcancer 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.

The effect of general practice contact on cancer stage at diagnosis in Aboriginal and non-Aboriginal residents of New South Wales.

PURPOSE: Older 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. METHODS: We 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. RESULTS: Younger 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). CONCLUSION: Older 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.

Cancer treatment and the risk of cancer death among Aboriginal and non-Aboriginal South Australians: analysis of a matched cohort study.

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.

Disparities in breast screening, stage at diagnosis, cancer treatment and the subsequent risk of cancer death: a retrospective, matched cohort of aboriginal and non-aboriginal women with breast cancer.

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.

How much emergency department use by vulnerable populations is potentially preventable?: A period prevalence study of linked public hospital data in South Australia.

OBJECTIVES: To quantify emergency department (ED) presentations by individuals within vulnerable populations compared with other adults and the extent to which these are potentially preventable. DESIGN: Period prevalence study from 2005-2006 to 2010-2011. SETTING: Person-linked, ED administrative records for public hospitals in South Australia. PARTICIPANTS: Adults aged 20 or more in South Australia's metropolitan area presenting to ED and categorised as Refugee and Asylum Seeker Countries of birth (RASC); Aboriginal; those aged 75 years or more; or All others. MAIN OUTCOME MEASURES: Unadjusted rates of ambulatory care sensitive condition (ACSC), general practitioner (GP)-type presentations and associated direct ED costs among mutually exclusive groups of individuals. RESULTS: Disparity between RASC and All others was greatest for GP-type presentations (423.7 and 240.1 persons per 1000 population, respectively) with excess costs of $A106 573 (95% CI $A98 775 to $A114 371) per 1000 population. Aboriginal had highest acute ACSC presenter rates (125.8 against 51.6 per 1000 population) with twice the risk of multiple presentations and $A108 701 (95% CI $A374 to $A123 029) per 1000 excess costs. Those aged 75 or more had highest chronic ACSC presenter rates (119.7vs21.1 per 1000), threefold risk of further presentations (incidence rate ratio 3.20, 95% CI 3.14 to 3.26) and excess cost of $A385 (95% CI $A178 160 to $A184 609) per 1000 population. CONCLUSIONS: Vulnerable groups had excess ED presentations for a range of issues potentially better addressed through primary and community healthcare. The observed differences suggest inequities in the uptake of effective primary and community care and represent a source of excess cost to the public hospital system.

Health related quality of life (HRQoL) among Aboriginal South Australians: a perspective using survey-based health utility estimates.

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.

Developing a comorbidity index for comparing cancer outcomes in Aboriginal and non-Aboriginal Australians.

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.

Aboriginal experiences of cancer and care coordination: Lessons from the Cancer Data and Aboriginal Disparities (CanDAD) narratives.

BACKGROUND: Aboriginal people with cancer experience worse outcomes than other Australians for a range of complex and interrelated reasons. A younger age at diagnosis, higher likelihood of more advanced cancer or cancer type with poorer prognosis, geographic isolation and cultural and language diversity mean that patient pathways are potentially more complex for Aboriginal people with cancer. In addition, variation in the quality and acceptability of care may influence cancer outcomes. OBJECTIVE: This study sought to understand how care coordination influences Aboriginal people's experiences of cancer treatment. METHODS: Interviews with 29 Aboriginal patients or cancer survivors, 11 carers and 22 service providers were carried out. Interviews were semi-structured and sought to elicit experiences of cancer and the health-care system. The manifest content of the cancer narratives was entered onto a cancer pathway mapping tool and underlying themes were identified inductively. RESULTS: The practice of cancer care coordination was found to address the needs of Aboriginal patients and their families/carers in 4 main areas: "navigating the health system"; "information and communication"; "things to manage at home"; and "cultural safety". CONCLUSIONS: The CanDAD findings indicate that, when the need for cancer care coordination is met, it facilitated continuity of care in a range of ways that may potentially improve cancer outcomes. However, the need remains unmet for many. Findings support the importance of dedicated care coordination to enable Aboriginal people to receive adequate and appropriate patient-centred care, so that the unacceptable disparity in cancer outcomes between Aboriginal and non-Aboriginal people can be addressed.

Comorbidities contribute to the risk of cancer death among Aboriginal and non-Aboriginal South Australians: Analysis of a matched cohort study.

BACKGROUND: Aboriginal Australians have poorer cancer survival than other Australians. Diagnoses at later stages and correlates of remote area living influence, but do not fully explain, these disparities. Little is known of the prevalence and influence of comorbid conditions experienced by Aboriginal people, including their effect on cancer survival. This study quantifies hospital recorded comorbidities using the Elixhauser Comorbidity Index (ECI), examines their influence on risk of cancer death, then considers effect variation by Aboriginality. METHODS: Cancers diagnosed among Aboriginal South Australians in 1990-2010 (N = 777) were matched with randomly selected non-Aboriginal cases by birth year, diagnostic year, sex, and primary site, then linked to administrative hospital records to the time of diagnosis. Competing risk regression summarised associations of Aboriginal status, stage, geographic attributes and comorbidities with risk of cancer death. RESULTS: A threshold of four or more ECI conditions was associated with increased risk of cancer death (sub-hazard ratio SHR 1.66, 95%CI 1.11-2.46). Alternatively, the presence of any one of a subset of ECI conditions was associated with similarly increased risk (SHR = 1.62, 95%CI 1.23-2.14). The observed effects did not differ between Aboriginal and matched non-Aboriginal cases. However, Aboriginal cases experienced three times higher exposure than non-Aboriginal to four or more ECI conditions (14.2% versus 4.5%) and greater exposure to the subset of ECI conditions (20.7% versus 8.0%). CONCLUSION: Comorbidities at diagnosis increased the risk of cancer death in addition to risks associated with Aboriginality, remoteness of residence and disease stage at diagnosis. The Aboriginal cohort experienced comparatively greater exposure to comorbidities which adds to disparities in cancer outcomes.

Sociodemographic variations in the amount, duration and cost of potentially preventable hospitalisation for chronic conditions among Aboriginal and non-Aboriginal Australians: a period prevalence study of linked public hospital data.

OBJECTIVES: To determine disparities in rates, length of stay (LOS) and hospital costs of potentially preventable hospitalisations (PPH) for selected chronic conditions among Aboriginal and non-Aboriginal South Australians (SA), then examine associations with area-level socioeconomic disadvantage and remoteness. SETTING: Period prevalence study using linked, administrative public hospital records. PARTICIPANTS: Participants included all SA residents in 2005-2006 to 2010-2011. Analysis focused on those individuals experiencing chronic PPH as defined by the Australian Institute of Health and Welfare. PRIMARY OUTCOME MEASURES: Number and rates (unadjusted, then adjusted for sex and age) of chronic PPH, total LOS and direct hospital costs by Aboriginality. RESULTS: Aboriginal SAs experienced higher risk of index chronic PPH compared with non-Aboriginals (11.5 and 6.2 per 1000 persons per year, respectively) and at younger ages (median age 48 vs 70 years). Once hospitalised, Aboriginal people experienced more chronic PPH events, longer total LOS with higher costs than non-Aboriginal people (2.6 vs 1.9 PPH per person; 11.7 vs 9.0 days LOS; at $A17 928 vs $A11 515, respectively). Compared with population average LOS, the standardised rate ratio of LOS among Aboriginal people increased by 0.03 (95% CI 0.00 to 0.07) as disadvantage rank increased and 1.04 (95% CI 0.63 to 1.44) as remoteness increased. Non-Aboriginal LOS also increased as disadvantage increased but at a lower rate (0.01 (95% CI 0.01 to 0.01)). Costs of Aboriginal chronic PPH increased by 0.02 (95% CI 0.00 to 0.06) for each increase in disadvantage and 1.18 (95% CI 0.80 to 1.55) for increased remoteness. Non-Aboriginal costs also increased as disadvantage increased but at lower rates (0.01 (95% CI 0.01 to 0.01)). CONCLUSION: Aboriginal people's heightened risk of chronic PPH resulted in more time in hospital and greater cost. Systematic disparities in chronic PPH by Aboriginality, area disadvantage and remoteness highlight the need for improved uptake of effective primary care. Routine, regional reporting will help monitor progress in meeting these population needs.

Disparities in cancer stage at diagnosis and survival of Aboriginal and non-Aboriginal South Australians.

BACKGROUND/AIM: This study tested the utility of retrospectively staging cancer registry data for comparing stage and stage-specific survivals of Aboriginal and non-Aboriginal people. Differences by area level factors were also explored. METHODS: This test dataset comprised 950 Aboriginal cases and all other cases recorded on the South Australian cancer registry with a 1977-2010 diagnosis. A sub-set of 777 Aboriginal cases diagnosed in 1990-2010 were matched with randomly selected non-Aboriginal cases by year of birth, diagnostic year, sex, and primary site of cancer. Competing risk regression summarised associations of Aboriginal status, stage, and geographic attributes with risk of cancer death. RESULTS: Aboriginal cases were 10 years younger at diagnosis, more likely to present in recent diagnostic years, to be resident of remote areas, and have primary cancer sites of head & neck, lung, liver and cervix. Risk of cancer death was associated in the matched analysis with more advanced stage at diagnosis. More Aboriginal than non-Aboriginal cases had distant metastases at diagnosis (31.3% vs 22.0, p<0.001). After adjusting for stage, remote-living Aboriginal residents had higher risks of cancer death than Aboriginal residents of metropolitan areas. Non-Aboriginal cases had the lowest risk of cancer death. CONCLUSION: Retrospective staging proved to be feasible using registry data. Results indicated more advanced stages for Aboriginal than matched non-Aboriginal cases. Aboriginal people had higher risks of cancer death, which persisted after adjusting for stage, and applied irrespective of remoteness of residence, with highest risk of death occurring among Aboriginal people from remote areas.

Cancer Data and Aboriginal Disparities (CanDAD)-developing an Advanced Cancer Data System for Aboriginal people in South Australia: a mixed methods research protocol.

INTRODUCTION: In Australia, Aboriginal and Torres Strait Islander People carry a greater burden of cancer-related mortality than non-Aboriginal Australians. The Cancer Data and Aboriginal Disparities Project aims to develop and test an integrated, comprehensive cancer monitoring and surveillance system capable of incorporating epidemiological and narrative data to address disparities and advocate for clinical system change. METHODS AND ANALYSIS: The Advanced Cancer Data System will integrate routinely collected unit record data from the South Australian Population Cancer Registry and a range of other data sources for a retrospective cohort of indigenous people with cancers diagnosed from 1990 to 2010. A randomly drawn non-Aboriginal cohort will be matched by primary cancer site, sex, age and year at diagnosis. Cross-tabulations and regression analyses will examine the extent to which demographic attributes, cancer stage and survival vary between the cohorts. Narratives from Aboriginal people with cancer, their families, carers and service providers will be collected and analysed using patient pathway mapping and thematic analysis. Statements from the narratives will structure both a concept mapping process of rating, sorting and prioritising issues, focusing on issues of importance and feasibility, and the development of a real-time Aboriginal Cancer Measure of Experience for ongoing linkage with epidemiological data in the Advanced Cancer Data System. Aboriginal Community engagement underpins this Project. ETHICS AND DISSEMINATION: The research has been approved by relevant local and national ethics committees. Findings will be disseminated in local and international peer-reviewed journals and conference presentations. In addition, the research will provide data for knowledge translation activities across the partner organisations and feed directly into the Statewide Cancer Control Plan. It will provide a mechanism for monitoring and evaluating the implementation of the recommendations in these documents.

Health-related quality of life (HRQoL) changes in South Australia: comparison of burden of disease morbidity and survey-based health utility estimates.

BACKGROUND: Global research shows a clear transition in health outcomes over the past two decades where improved survival was accompanied by lower health related quality of life (HRQoL) as measured by morbidity and disability. These trends suggest the need to better understand changes in population HRQoL. This paper compares two perspectives on population HRQoL change using burden of disease morbidity estimates from administrative data and self-reports from random and representative population surveys. METHODS: South Australian administrative data including inpatient hospital activity, cancer and communicable disease registrations were used within a Burden of Disease study framework to quantify morbidity as Prevalent Years of Life lived with Disease and injury related illness (PYLD) for 1999 to 2008. Self-reported HRQoL was measured using the Assessment of Quality of Life (AQoL) in face to face interviews with at least 3000 respondents in each of South Australia's Health Omnibus Surveys (HOS) in 1998, 2004 and 2008. RESULTS: Age specific PYLD rates for those aged 75 or more increased by 5.1%. HRQoL dis-utility in this age group also increased significantly and beyond the minimally important difference threshold. Underlying increased dis-utility were greater difficulties in independent living (particularly requiring help with household tasks) and psychological well-being (as influenced by pain, discomfort and difficulty sleeping). CONCLUSIONS: Consistent with increased quantity of life being accompanied by reduced HRQoL, the analysis indicates older people in South Australia experienced increased morbidity in the decade to 2008. The results warrant routine monitoring of health dis-utility at a population level and improvement to the supply and scope of administrative data.

Practice nurse involvement in general practice clinical care: policy and funding issues need resolution.

In Australia, primary care-based funding initiatives have been implemented to encourage general practices to employ practice nurses. The aim of this paper is to discuss limitations of the current funding and policy arrangements in enhancing the clinical role of practice nurses in the management of chronic conditions. This paper draws on the results of a real-world economic evaluation, the Primary Care Services Improvement Project (PCSIP). The PCSIP linked routinely collected clinical and resource use data to undertake a risk-adjusted cost-effectiveness analysis of increased practice nurse involvement in clinical-based activities for the management of diabetes and obesity. The findings of the PCSIP suggested that the active involvement of practice nurses in collaborative clinical-based activities is cost-effective, as well as addressing general practice workforce issues. Although primary healthcare organisations (e.g. Medicare Locals) can play a key role in supporting enhanced practice nurse roles, improvements to practice nurse funding models could further encourage more efficient use of an important resource.

Practice nurse involvement in primary care depression management: an observational cost-effectiveness analysis.

BACKGROUND: Most evidence on the effect of collaborative care for depression is derived in the selective environment of randomised controlled trials. In collaborative care, practice nurses may act as case managers. The Primary Care Services Improvement Project (PCSIP) aimed to assess the cost-effectiveness of alternative models of practice nurse involvement in a real world Australian setting. Previous analyses have demonstrated the value of high level practice nurse involvement in the management of diabetes and obesity. This paper reports on their value in the management of depression. METHODS: General practices were assigned to a low or high model of care based on observed levels of practice nurse involvement in clinical-based activities for the management of depression (i.e. percentage of depression patients seen, percentage of consultation time spent on clinical-based activities). Linked, routinely collected data was used to determine patient level depression outcomes (proportion of depression-free days) and health service usage costs. Standardised depression assessment tools were not routinely used, therefore a classification framework to determine the patient's depressive state was developed using proxy measures (e.g. symptoms, medications, referrals, hospitalisations and suicide attempts). Regression analyses of costs and depression outcomes were conducted, using propensity weighting to control for potential confounders. RESULTS: Capacity to determine depressive state using the classification framework was dependent upon the level of detail provided in medical records. While antidepressant medication prescriptions were a strong indicator of depressive state, they could not be relied upon as the sole measure. Propensity score weighted analyses of total depression-related costs and depression outcomes, found that the high level model of care cost more (95% CI: -$314.76 to $584) and resulted in 5% less depression-free days (95% CI: -0.15 to 0.05), compared to the low level model. However, this result was highly uncertain, as shown by the confidence intervals. CONCLUSIONS: Classification of patients' depressive state was feasible, but time consuming, using the classification framework proposed. Further validation of the framework is required. Unlike the analyses of diabetes and obesity management, no significant differences in the proportion of depression-free days or health service costs were found between the alternative levels of practice nurse involvement.

An equity-effectiveness framework linking health programs and healthy life expectancy.

South Australia's Strategic Plan includes a target to improve the population's healthy life expectancy. A common question among health policy and service planners is: 'How do health programs and services in the community relate to healthy life expectancy?' In response, this paper outlines an effectiveness and equity framework (EEF) for evaluating health interventions in applied settings. Using the example of coronary heart disease (CHD) management in general practice in South Australia, the EEF: (1) applies an internally consistent approach to accounting for population healthy life expectancy at state and smaller geographic levels; (2) estimates average population health gains from health programs, and gains across different socioeconomic subgroups within the community; (3) conducts economic evaluation by equating health gains against health system costs in population subgroups; (4) summarises relevant information about candidate intervention programs within a multi-criteria performance matrix for presentation to decision makers; (5) reassesses outcomes (and processes) following the implementation of a program and iteratively adds to the relevant knowledge and evidence base. The EEF offers a practical approach to selecting and evaluating intervention programs. The challenge is to develop system culture and data capture methods clearly focussed on linking health system activities to population health outcomes.

Healthy life gains in South Australia 1999-2008: analysis of a local Burden of Disease series.

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.

Aboriginal premature mortality within South Australia 1999-2006: a cross-sectional analysis of small area results.

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.

Recognising potential for preventing hospitalisation.

To identify the incidence and distribution of public hospital admissions in South Australia that could potentially be prevented with appropriate use of primary care services, analysis was completed of all public hospital separations from July 2006 to June 2008 in SA. This included those classified as potentially preventable using the Australian Institute of Health and Welfare criteria for selected potentially preventable hospitalisations (SPPH), by events and by individual, with statistical local area geocoding and allocation of relative socioeconomic disadvantage quintile. A total of 744 723 public hospital separations were recorded, of which 79 424 (10.7%) were classified as potentially preventable. Of these, 59% were for chronic conditions, and 29% were derived from the bottom socioeconomic status (SES) quintile. Individuals in the lowest SES quintile were 2.5 times more likely to be admitted for a potentially preventable condition than those from the top SES quintile. Older individuals, males, those in the most disadvantaged quintiles, non-metropolitan areas and Indigenous people were more likely to have more than one preventable admission.