Colon and rectal cancer treatment patterns and their associations with clinical, sociodemographic and lifestyle characteristics: analysis of the Australian 45 and Up Study cohort.

BACKGROUND: Colorectal cancer is the third most diagnosed cancer globally and the second leading cause of cancer death. We examined colon and rectal cancer treatment patterns in Australia. METHODS: From cancer registry records, we identified 1,236 and 542 people with incident colon and rectal cancer, respectively, diagnosed during 2006-2013 in the 45 and Up Study cohort (267,357 participants). Cancer treatment and deaths were determined via linkage to routinely collected data, including hospital and medical services records. For colon cancer, we examined treatment categories of "surgery only", "surgery plus chemotherapy", "other treatment" (i.e. other combinations of surgery/chemotherapy/radiotherapy), "no record of cancer-related treatment, died"; and, for rectal cancer, "surgery only", "surgery plus chemotherapy and/or radiotherapy", "other treatment", and "no record of cancer-related treatment, died". We analysed survival, time to first treatment, and characteristics associated with treatment receipt using competing risks regression. RESULTS: 86.4% and 86.5% of people with colon and rectal cancer, respectively, had a record of receiving any treatment ≤2 years post-diagnosis. Of those treated, 93.2% and 90.8% started treatment ≤2 months post-diagnosis, respectively. Characteristics significantly associated with treatment receipt were similar for colon and rectal cancer, with strongest associations for spread of disease and age at diagnosis (p<0.003). For colon cancer, the rate of "no record of cancer-related treatment, died" was higher for people with distant spread of disease (versus localised, subdistribution hazard ratio (SHR)=13.6, 95% confidence interval (CI):5.5-33.9), age ≥75 years (versus age 45-74, SHR=3.6, 95%CI:1.8-7.1), and visiting an emergency department ≤1 month pre-diagnosis (SHR=2.9, 95%CI:1.6-5.2). For rectal cancer, the rate of "surgery plus chemotherapy and/or radiotherapy" was higher for people with regional spread of disease (versus localised, SHR=5.2, 95%CI:3.6-7.7) and lower for people with poorer physical functioning (SHR=0.5, 95%CI:0.3-0.8) or no private health insurance (SHR=0.7, 95%CI:0.5-0.9). CONCLUSION: Before the COVID-19 pandemic, most people with colon or rectal cancer received treatment ≤2 months post-diagnosis, however, treatment patterns varied by spread of disease and age. This work can be used to inform future healthcare requirements, to estimate the impact of cancer control interventions to improve prevention and early diagnosis, and serve as a benchmark to assess treatment delays/disruptions during the pandemic. Future work should examine associations with clinical factors (e.g. performance status at diagnosis) and interdependencies between characteristics such as age, comorbidities, and emergency department visits.

Economic impact of using risk models for eligibility selection to the International lung screening Trial.

OBJECTIVES: Using risk models as eligibility criteria for lung screening can reduce race and sex-based disparities. We used data from the International Lung Screening Trial(ILST; NCT02871856) to compare the economic impact of using the PLCOm2012 risk model or the US Preventative Services' categorical age-smoking history-based criteria (USPSTF-2013). MATERIALS AND METHODS: The cost-effectiveness of using PLCOm2012 versus USPSTF-2013 was evaluated with a decision analytic model based on the ILST and other screening trials. The primary outcomes were costs in 2020 International Dollars ($), quality-adjusted life-years (QALY) and incremental net benefit (INB, in $ per QALY). Secondary outcomes were selection characteristics and cancer detection rates (CDR). RESULTS: Compared with the USPSTF-2013 criteria, the PLCOm2012 risk model resulted in $355 of cost savings per 0.2 QALYs gained (INB=$4294 at a willingness-to-pay threshold of $20 000/QALY (95 %CI: $4205-$4383). Using the risk model was more cost-effective in females at both a 1.5 % and 1.7 % 6-year risk threshold (INB=$6616 and $6112, respectively), compared with males ($5221 and $695). The PLCOm2012 model selected more females, more individuals with fewer years of formal education, and more people with other respiratory illnesses in the ILST. The CDR with the risk model was higher in females compared with the USPSTF-2013 criteria (Risk Ratio = 7.67, 95 % CI: 1.87-31.38). CONCLUSION: The PLCOm2012 model saved costs, increased QALYs and mitigated socioeconomic and sex-based disparities in access to screening.

Updated cost-effectiveness analysis of lung cancer screening for Australia, capturing differences in the health economic impact of NELSON and NLST outcomes.

BACKGROUND: A national, lung cancer screening programme is under consideration in Australia, and we assessed cost-effectiveness using updated data and assumptions. METHODS: We estimated the cost-effectiveness of lung screening by applying screening parameters and outcomes from either the National Lung Screening Trial (NLST) or the NEderlands-Leuvens Longkanker Screenings ONderzoek (NELSON) to Australian data on lung cancer risk, mortality, health-system costs, and smoking trends using a deterministic, multi-cohort model. Incremental cost-effectiveness ratios (ICERs) were calculated for a lifetime horizon. RESULTS: The ICER for lung screening compared to usual care in the NELSON-based scenario was AU$39,250 (95% CI $18,150-108,300) per quality-adjusted life year (QALY); lower than the NLST-based estimate (ICER = $76,300, 95% CI $41,750-236,500). In probabilistic sensitivity analyses, lung screening was cost-effective in 15%/60% of NELSON-like simulations, assuming a willingness-to-pay threshold of $30,000/$50,000 per QALY, respectively, compared to 0.5%/6.7% for the NLST. ICERs were most sensitive to assumptions regarding the screening-related lung cancer mortality benefit and duration of benefit over time. The cost of screening had a larger impact on ICERs than the cost of treatment, even after quadrupling the 2006-2016 healthcare costs of stage IV lung cancer. DISCUSSION: Lung screening could be cost-effective in Australia, contingent on translating trial-like lung cancer mortality benefits to the clinic.

Capacity of the 45 and Up Study to mobilise evidence-based improvements in cancer control: lung cancer case study.

OBJECTIVE: Over the 15 years since the 45 and Up Study (the Study) was established, researchers have harnessed its capacity for enabling rigorous, comprehensive investigation of cancer causes, care, and outcomes. For the first time in Australia, the entire cancer-control continuum could be investigated by linking questionnaire data with cancer registry notifications, hospital records, outpatient medical services and prescription medications at scale. Here, we use lung cancer as a case study to demonstrate the Study's potential to improve cancer control. METHOD: Narrative description. RESULTS: Between 2006-2013, approximately 1200 participants in the Study cohort who had no prior history of cancer were diagnosed with lung cancer, allowing the generation of novel, policy- and practice-relevant evidence for tobacco control, screening, and systems of care. The Study produced evidence on the continuing impact of smoking, including that 'light smoking' (1-5 cigarettes/day) is associated with nine times the risk of lung cancer compared to never-smoking; and that 54% of lung cancers could be avoided long-term if all Australians who smoked were to quit. The Study was used to validate a lung cancer screening risk prediction tool, correctly identifying 70% of the participants with a history of smoking who developed lung cancer within a 6-year period as 'high-risk'. Potential inequities in lung cancer care were identified using the Study cohort, including suboptimal levels of radiotherapy utilisation, below benchmark levels of systemic therapy for patients with metastatic disease, and high numbers of emergency department presentations prior to diagnosis. Participants with lung cancer reported poorer quality of life than those with almost any other cancer type, and about 50% reported severe physical functioning limitations. The Study also provided the infrastructure for the first comprehensive report on lung cancer health system costs. LESSONS LEARNT: As a statewide, population-based cohort, the Study provides reliable estimates of cancer risk, health services utilisation, and person-centred outcomes that can inform policy and practice decision making; and has provided the backbone for localising policy-relevant insights from international experience. We have found that the direct involvement of clinicians and policy makers in research design, and engagement with community networks, can yield tractable, policy-relevant, and ultimately impactful scientific insights.

Health utilities for participants in a population-based sample who meet eligibility criteria for lung cancer screening.

INTRODUCTION: Trial-based, risk-targeted lung cancer screening with low-dose computed tomography has been shown to reduce lung cancer mortality but implementation may depend on favourable cost-effectiveness evaluations where quality-adjusted life-years are a key metric. Baseline health utility values for a screening population at high risk of lung cancer are not likely to match age-specific population norms, and utilities derived from screening trials may not be representative of real-world screening populations. We estimated utility values for screening-eligible individuals in a population-based cohort study in Australia. METHODS: Cancer-free participants aged 50-80 years in the New South Wales 45 and Up Study completed the 12-Item Short Form Survey (2010-2011). Mean SF-6D utility values were calculated for 19,991 participants and compared across screening criteria defined by the US Preventive Services Task Force (USPSTF-2021/2013), NELSON trial eligibility, and the PLCOm2012 risk tool. RESULTS: Mean SF-6D utility values were comparable across screening criteria: USPSTF-2021, 0.772 (95%CI, 0.768-0.776); USPSTF-2013, 0.764 (95%CI, 0.759-0.770); NELSON, 0.768 (95%CI, 0.763-0.774), and were each lower than among ineligible participants (0.810-0.814). While there was a decline in utilities with increasing risk of lung cancer as measured with the PLCOm2012 risk tool, mean utility values for those with ≥ 1.51% 6-year risk did not differ to other criteria (0.772, 95%CI, 0.767-0.776). CONCLUSION: Risk criteria are necessary for the efficiency of lung cancer screening programs, but they select populations with lower mean health utilities than population norms. We provide baseline values that can be used in cost-effectiveness evaluations of risk-targeted lung cancer screening.

Large-Scale Population-Based Surveys Linked to Administrative Health Databases as a Source of Data on Health Utilities in Australia.

OBJECTIVES: Large-scale health surveys that contain quality-of-life instruments are a rich source of health utility data for health economic evaluations, especially when linked to routinely collected, administrative health databases. We derived health utility values for a wide range of health conditions using a large Australian cohort study linked to population-wide health databases. METHODS: Short-Form 6-Dimension utility values were calculated for 56 094 adults, aged 47+ years, in the New South Wales 45 and Up Study who completed the Social, Economic, and Environmental Factors survey (2010-2011). Mean utilities were summarized for major health conditions identified through self-report, hospital records, primary cancer notifications, and claims for government-subsidized prescription medicines and medical services. To identify unique associations between health conditions and utilities, beta regression was performed. Utility values were analyzed by time to death using linked death records. RESULTS: Mean Short-Form 6-Dimension utility was 0.810 (95% confidence interval [CI] 0.809-0.811), was age dependent, and was higher in men than women. Utilities for serious health conditions ranged from 0.685 (95% CI 0.652-0.718) for lung cancer to 0.800 (95% CI 0.787-0.812) for melanoma whereas disease-free respondents had a mean of 0.859 (95% CI 0.858-0.861). Most health conditions were independently associated with poorer quality of life. Utility values also declined by proximity to death where participants sampled 6 months before death had a mean score of 0.637 (95% CI 0.613-0.662). CONCLUSIONS: Our data offer a snapshot of the health status of an older Australian population and show that record linkage can enable comprehensive ascertainment of utility values for use in health economic modeling.

Applying utility values in cost-effectiveness analyses of lung cancer screening: A review of methods.

Lung cancer screening with low-dose computed tomography (LDCT) in high-risk populations has been shown in randomised controlled trials to lead to early diagnosis and reduced lung cancer mortality. However, investment into screening will largely depend on the outcomes of cost-effectiveness analyses that demonstrate acceptable costs for every quality-adjusted life year (QALY) gained. The methods used to apply utility values to measure QALYs can significantly impact the outcomes of cost-effectiveness analyses and if applied inaccurately can lead to unreliable estimates. We reviewed the use of utility values in 26 cost-effectiveness analyses of lung screening with LDCT conducted between 2005 and 2021, and found considerable variation in methods. Specifically, authors made different assumptions made relating to (i) baseline quality-of-life among screening participants, (ii) potential harms from screening, (iii) utilities and disutilities applied to lung cancer health states, and (iv) quality-of-life for lung cancer survivors. We discuss how each of these assumptions can influence incremental cost-effectiveness ratios. Key recommendations for future evaluations are (i) that modelling studies should justify the choice of baseline utilities, especially if patients are assumed to recover fully after curative treatment; (ii) the impact of false positive scans on quality-of-life should be modelled, at least in sensitivity analyses; (iii) modellers should justify assumptions relating to post-operative recovery, preferably based on knowledge of local practices; (iv) utilities applied to a lung cancer diagnosis should be appropriately sourced and calculated; and (v) adjustment for age-related declines in quality-of-life should be considered, especially for models that examine lifetime horizons.

Evaluating Prognostic Factors for Sex Differences in Lung Cancer Survival: Findings From a Large Australian Cohort.

INTRODUCTION: Women tend to survive a lung cancer diagnosis longer than men; however potential drivers of this sex-related disparity remain largely elusive. We quantified factors related to sex differences in lung cancer survival in a large prospective cohort in Australia. METHODS: Participants in the 45 and Up Study (recruited 2006-2009) diagnosed with incident lung cancer were followed up to December 2015. Prognostic factors were identified from questionnaire data linked with cancer registrations, hospital inpatient records, emergency department records, and reimbursement records for government-subsidized medical services and prescription medicines. Hazard ratios (HRs) and 95% confidence intervals (CIs) for lung cancer death for men versus women were estimated using Cox proportional hazard regression in relation to key prognostic factors alone and jointly. RESULTS: A total of 488 women and 642 men were diagnosed with having lung cancer. Women survived significantly longer (median 1.28 versus 0.77 y; HR for men = 1.43, 95% CI: 1.25-1.64, p < 0.0001). The survival disparity remained when each subgroup of major prognostic factors was evaluated separately, including histologic subtype, stage at diagnosis, treatment received, and smoking status. Multivariable analyses revealed that treatment-related factors explained half of the survival difference, followed by lifestyle and tumor characteristics (explaining 28%, 26%, respectively). After adjusting for all major known prognostic factors, the excess risk for men was reduced by more than 80% (HR = 1.06, 95% CI: 0.96-1.18, p = 0.26). CONCLUSIONS: The sex-related lung cancer survival disparity in this Australian cohort was largely accounted for by known prognostic factors, indicating an opportunity to explore sex differences in treatment preferences, options, and access.