Quantifying Queensland patients with cancer health service usage and costs: study protocol

DAta Linkage to Enhance Cancer Care (DaLECC): Protocol of a Large Australian Data Linkage Study

Cancer is a leading cause of global morbidity and mortality, accounting for 250 Disability-Adjusted Life Years and 10 million deaths in 2019. Minimising unwarranted variation and ensuring appropriate cost-effective treatment across primary and tertiary care to improve health outcomes is a key health priority. There are few studies that have used linked data to explore healthcare utilisation prior to diagnosis in addition to post-diagnosis patterns of care. This protocol outlines the aims of the DaLECC project and key methodological features of the linked dataset. The primary aim of this project is to explore predictors of variations in pre- and post-cancer diagnosis care, and to explore the economic and health impact of any variation. The cohort of patients includes all South Australian residents diagnosed with cancer between 2011 and 2020, who were recorded on the South Australian Cancer Registry. These cancer registry records are being linked with state and national healthcare databases to capture health service utilisation and costs for a minimum of one-year prior to diagnosis and to a maximum of 10 years post-diagnosis. Healthcare utilisation includes state databases for inpatient separations and emergency department presentations and national databases for Medicare services and pharmaceuticals. Our results will identify barriers to timely receipt of care, estimate the impact of variations in the use of health care, and provide evidence to support interventions to improve health outcomes to inform national and local decisions to enhance the access and uptake of health care services.

Quantifying the Costs to Different Funders over Five-Years for Women Diagnosed with Breast Cancer in Queensland, Australia: A Data Linkage Study

Individuals diagnosed with breast cancer have the highest rates of survival among all cancer types. Due to high survival, the costs of breast cancer to different healthcare funders are of interest. This study aimed to describe the cost to public hospital and private health funders and individuals due to hospital and emergency department (ED) admissions, as well Medicare items and pharmaceuticals over five years for Queensland women with breast cancer. We used a linked administrative dataset, CancerCostMod, limited to Queensland female breast cancer diagnoses between July 2011 and June 2013 aged 18 years or over who survived for 5 years (n = 5383). Each record was linked to Queensland Health Admitted Patient Data Collection, Emergency Department Information Systems, Medicare Benefits Schedule, and Pharmaceutical Benefits Scheme records between July 2011 and June 2018. Total costs for different healthcare funders as a result of breast cancer diagnoses were reported, with high costs and service use identified in the first six months following a breast cancer diagnosis. After the first six months post-diagnosis, the financial burdens incurred by different healthcare funders for breast cancer diagnoses in Queensland remain steady over a long period. Recommendations for reducing long term costs are discussed.

Quantifying the hospital and emergency department costs for women diagnosed with breast cancer in Queensland

PURPOSE

With increasing rates of cancer survival due to advances in screening and treatment options, the costs of breast cancer diagnoses are attracting interest. However, limited research has explored the costs to the Australian healthcare system associated with breast cancer. We aimed to describe the cost to hospital funders for hospital episodes and emergency department (ED) presentations for Queensland women with breast cancer, and whether costs varied by demographic characteristics.

METHODS

We used a linked administrative dataset, CancerCostMod, limited to all breast cancer diagnoses aged 18 years or over in Queensland between July 2011 and June 2015 (n = 13,285). Each record was linked to Queensland Health Admitted Patient Data Collection and Emergency Department Information Systems records between July 2011 and June 2018. The cost of hospital episodes and ED presentations were determined, with mean costs per patient modelled using generalised linear models with a gamma distribution and log link function.

RESULTS

The total cost to the Queensland healthcare system from hospital episodes for female breast cancer was AUD$309 million and AUD$12.6 million for ED presentations during the first 3 years following diagnosis. High levels of costs and service use were identified in the first 6 months following diagnosis. Some significant differences in cost of hospital and ED episodes were identified based on demographic characteristics, with Indigenous women and those from lower socioeconomic backgrounds having higher costs.

CONCLUSION

Hospitalisation costs for breast cancer in Queensland exert a high burden on the healthcare system. Costs are higher for women during the first 6 months from diagnosis and for Indigenous women, as well as those with underlying comorbidities and lower socioeconomic position.

Optimising patient care in medical radiation services through health economics: an introduction

The role of health economics in optimising patient care in medical radiation clinical settings is of increasing importance in ensuring efficient and effective service delivery. This commentary introduces health economics to medical radiation professionals by outlining the main analysis types utilised, highlighted by examples in the literature. The purpose is to provide an over-arching framework and starting point for incorporating health economics into medical radiation research study protocols.

Predictors of health care use in Australian cancer patients

Objective

The purpose of this study is to measure health care utilization in Australian cancer patients based on their demographic, geographic and socioeconomic backgrounds.

Method

A total of 13,609 participants (aged 15 and over) from 7,230 households were interviewed as part of Wave 13 of the national Household, Income and Labour Dynamics in Australia (HILDA) survey. Five hundred and seventeen participants indicated a current cancer diagnosis with 90% of those receiving active treatment at the time of interview. Independent sample t-tests, Pearson Chi-sq tests, Kruskal‒Wallis H test, binary logistic regression and a zero-inflated Poisson regression were used to examine inequality in health care use.

Results

Demographic and sociocultural factors such as advancing age, gender, low income, low education status, rurality, no private health insurance, increased psychological distress and less access to specialist care are associated with lower health care utilization among cancer patients. However, models of care such as general practitioner-led cancer care is preferable in younger individuals with cancer, while accessing specialist care is associated with lower rates of hospitalization and higher levels of psychological distress increases hospital length of stay.

Conclusions

The findings of lower health care utilization by those cancer patients with characteristics of disadvantage have implications for policy development and intervention design. Broadly, policies targeting structural social inequities are likely to increase health care utilization among the most affected/disadvantaged populations. Further investigation is needed to identify potential links between health care utilization and cancer outcomes as a step toward targeted interventions for improving outcomes in the adversely affected groups.

The patient co-payment and opportunity costs of accessing healthcare for Indigenous Australians with cancer: A whole of population data linkage study

AIM

To quantify the direct out-of-pocket patient co-payments and time opportunity costs (length of hospital stay) incurred by Indigenous and non-Indigenous persons diagnosed with cancer during the first year postdiagnosis.

METHODS

CancerCostMod was used, which is a model of cancer costs based upon a whole-of-population data linkage. The base population was a census of all persons diagnosed with cancer in Queensland, Australia between 1 July 2011 and 30 June 2012 (n = 25,553). Individual records were linked to corresponding Queensland Health Admitted Patient Data Collection, Emergency Data Information System, Medicare Benefits Schedule, and Pharmaceutical Benefits Scheme records between 1 July 2011 and 30 June 2015. Queensland data were weighted to be representative of the Australian population (approximately 123,900 Australians, 1.7% Indigenous Australians).

RESULTS

After adjusting for age, sex, rurality, area-based deprivation, and cancer group, Indigenous Australians accrued significantly less in postdiagnosis patient co-payments at 0-6 months (61% less) and 7-12 months (63% less). Indigenous Australians also had significantly fewer postdiagnosis hospitalizations at 0-6 months (21% fewer) and 7-12 months (27% fewer).

CONCLUSION

There is growing concern regarding the financial burden of cancer to the patient. The time spent away from family and their community may also have an important time opportunity cost, which may affect a person's decision to undertake or continue treatment. This is the first study in Australia to identify the financial cost of co-payments for Indigenous people with cancer, as well as the number and length of hospitalizations as drivers of time opportunity costs.

Long-term out of pocket expenditure of people with cancer: comparing health service cost and use for indigenous and non-indigenous people with cancer in Australia

Background

Indigenous Australians diagnosed with cancer have poorer survival compared to non-Indigenous Australians. We aim to: 1) identify differences by Indigenous status in out-of-pocket expenditure for the first three-years post-diagnosis; 2) identify differences in the quantity and cost of healthcare services accessed; and 3) estimate the number of additional services required if access was equal between Indigenous and non-Indigenous people with cancer.

Methods

We used CancerCostMod, a model using linked administrative data. The base population was all persons diagnosed with cancer in Queensland, Australia (01JUL2011 to 30JUN2012) (n = 25,553). Each individual record was then linked to their Admitted Patient Data Collection, Emergency Data Information System, Medicare Benefits Schedule (MBS), and Pharmaceutical Benefits Scheme (PBS) records (01JUL2011 to 30JUN2015). We then weighted the population to be representative of the Australian population (approximately 123,900 Australians, 1.7% Indigenous Australians). The patient co-payment charged for each MBS service and PBS prescription was summed for each month from date of diagnosis to 36-months post-diagnosis. We then limited our model to MBS items to identify the quantity and type of healthcare services accessed during the first three-years.

Results

On average Indigenous people with cancer had less than half the out-of-pocket expenditure for each 12-month period (0–12 months: mean $401 Indigenous vs $1074 non-Indigenous; 13–24 months: mean $200 vs $484; and 25–36 months: mean $181 vs $441). A stepwise generalised linear model of out-of-pocket expenditure found that Indigenous status was a significant predictor of out of pocket expenditure. We found that Indigenous people with cancer on average accessed 236 services per person, however, this would increase to 309 services per person if Indigenous people had the same rate of service use as non-Indigenous people.

Conclusions

Indigenous people with cancer had lower out-of-pocket expenditure, but also accessed fewer Medicare services compared to their non-Indigenous counterparts. Indigenous people with cancer were less likely to access specialist attendances, pathology tests, and diagnostic imaging through MBS, and more likely to access primary health care, such as services provided by general practitioners.

CancerCostMod: a model of the healthcare expenditure, patient resource use, and patient co-payment costs for Australian cancer patients

Although cancer survival in general has improved in Australia over the past 30 years, Indigenous Australians, socioeconomically disadvantaged persons, and people living in remote areas still experience poorer health outcomes. This paper aims to describe the development of CancerCostMod, and to present the healthcare expenditure and patient co-payments for the first 12-months post-diagnosis. The base population is a census of all cancer diagnoses (excluding non-melanoma skin cancer) in Queensland, Australia between 1 July 2011 and 30 June 2012 (N = 25,553). Each individual record was linked to their Queensland Health Admitted Patient Data Collection, Emergency Department Information System, Medicare Benefits Schedule, and Pharmaceutical Benefits Scheme records from 1 July 2011 to 30 June 2015. Indigenous status was recorded for 87% of participants in our base population. Multiple imputation was used to assign Indigenous status to records where Indigenous status was missing. This base population was then weighted, using a programmed SAS macro (GREGWT) to be representative of the Australian population. We adopted a national healthcare perspective to estimate the cost of cancer for hospital episodes, ED presentations, primary healthcare, and prescription pharmaceuticals. We also adopted an individual perspective, to estimate the primary healthcare and prescription pharmaceutical patient co-payments. Once weighted, our sample represents approximately 123,900 Australians (1.7% Indigenous Australians). The total healthcare system cost of all cancers during the first 12-months post diagnosis was $4.8 billion, [corrected] and patient co-payments costs were $127 million. After adjusting for sex, age at diagnosis, Indigenous status, rurality, socioeconomic status, and broad cancer type, significant differences in costs were observed for population groups of interest within the first year post-diagnosis. This paper provides a more recent national estimate of the cost of cancer, and addresses current research gaps by highlighting the distribution of healthcare and individual costs by Indigenous status, rurality, and socioeconomic status.

Labour force participation and the cost of lost productivity due to cancer in Australia

Background

In Australia, 40% of people diagnosed with cancer will be of working age (25–64 years). A cancer diagnosis may lead to temporary or permanent changes in a person’s labour force participation, which has an economic impact on both the individual and the economy. However, little is known about this economic impact of cancer due to lost productivity in Australia. This paper aims to determine the labour force participation characteristics of people with cancer, to estimate the indirect cost due to lost productivity, and to identify any inequality in the distribution of labour force absence in Australia.

Methods

This study used national cross-sectional data from the 2015 Survey of Disability, Ageing and Carers, conducted by the Australian Bureau of Statistics (ABS). The ABS weighted each component of the survey to ensure the sample represented the population distribution of Australia. The analysis was limited to people aged 25–64 years. Participants were assigned to one of three health condition groups: ‘no health condition’, ‘cancer’, and ‘any other long-term health condition’. A series of logistic regression models were constructed to determine the association between health condition and labour force participation.

Results

A total of 34,393 participants surveyed were aged 25–64 years, representing approximately 12,387,800 Australians. Almost half (46%) of people with cancer were not in the labour force, resulting in a reduction of $1.7 billion to the Australian gross domestic product (GDP). Amongst those in the labour force, people with no health condition were 3.00 times more likely to be employed full-time compared to people with cancer (95% CI 1.96–4.57), after adjusting for age, sex, educational attainment and rurality. Amongst those with cancer, people without a tertiary qualification were 3.73 times more likely to be out of the labour force (95% CI 1.97–7.07).

Conclusions

This paper is the first in Australia to estimate the national labour force participation rates of people with cancer. People with cancer were less likely to be in the labour force, resulting in a reduction in Australia’s GDP. Cancer survivors, especially those without a tertiary qualification may benefit from support to return to work after a diagnosis.